From 13882487d9fc9780f3c7fd9eac8bc72ca3dfb862 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 30 Oct 2024 15:04:36 +0100 Subject: [PATCH 01/56] docs: start review documentation --- docs/cli/filtering.rst | 285 ++++++-- docs/cli/radioindice.rst | 4 +- src/eolab/rastertools/filtering.py | 47 +- src/eolab/rastertools/hillshade.py | 35 +- src/eolab/rastertools/processing/algo.py | 633 ++++++++++++------ .../rastertools/processing/rasterproc.py | 22 +- src/eolab/rastertools/processing/sliding.py | 42 +- src/eolab/rastertools/radioindice.py | 38 +- src/eolab/rastertools/rastertools.py | 13 +- src/eolab/rastertools/zonalstats.py | 13 +- tests/test_algo.py | 36 +- tests/test_radioindice.py | 57 +- tests/test_rasterproc.py | 22 + tests/test_rasterproduct.py | 103 +++ tests/test_rastertools.py | 16 +- tests/test_speed.py | 5 +- tests/utils4test.py | 25 +- 17 files changed, 1047 insertions(+), 349 deletions(-) diff --git a/docs/cli/filtering.rst b/docs/cli/filtering.rst index 6e78b4b5..8630589b 100644 --- a/docs/cli/filtering.rst +++ b/docs/cli/filtering.rst @@ -20,75 +20,246 @@ filter mean Apply local mean filter adaptive_gaussian Apply adaptive gaussian filter -For different filters are available. They are applied as sub-command that each define the arguments -that configure the filter. Type option --help to get the definition of the arguments: +The available filters are Adaptive Gaussian, Local Sum, and Local Mean. +Each filter is used as a sub-command and has specific arguments for filtering. +To see the definitions of these arguments, type the option --help. -.. code-block:: console +- **Median** - $ rastertools filter adaptive_gaussian --help - usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE - --sigma SIGMA [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - --sigma SIGMA Standard deviation of the Gaussian distribution - (sigma) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - By default only first band is computed. + .. code-block:: console -Examples: + $ rastertools filter median --help + usage: rastertools filter median [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] -The following examples use an input raster file generated by radioindice. This is an NDVI of a SENTINEL2 L2A THEIA image cropped to a (small) -region of interest. + Apply a median filter (see scipy median_filter for more information) -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) -To apply three filters (median, mean and adaptive_gaussian) on a kernel of dimension 16x16, run these commands: + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands -.. code-block:: console + By default only first band is computed. + + The corresponding API functions that is called by the command line interface is the following : + + .. autofunction:: eolab.rastertools.processing.algo.median + + + Here is an example of a median filter applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. + + .. code-block:: console + + $ rastertools filter median --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + + .. list-table:: + :widths: 20 20 + :header-rows: 0 + + * - .. centered:: Original + - .. centered:: Filtered by Median + + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.jpg + :align: center + +- **Local sum** + + .. code-block:: console + + $ rastertools filter sum --help + usage: rastertools filter sum [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Apply a local sum filter using integral image method + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) + + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + + The corresponding API functions that is called by the command line interface is the following : + + .. autofunction:: eolab.rastertools.processing.algo.local_sum + +- **Local mean** + + .. code-block:: console + + $ rastertools filter mean --help + usage: rastertools filter mean [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Apply a local mean filter using integral image method + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) + + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + + + The corresponding API functions that is called by the command line interface is the following : + + .. autofunction:: eolab.rastertools.processing.algo.local_mean + + + Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. + + .. code-block:: console + + $ rastertools filter mean --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + + .. list-table:: + :widths: 20 20 + :header-rows: 0 + + * - .. centered:: Original + - .. centered::Filtered by Local mean + + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.jpg + :align: center + +- **Adaptative gaussian** + + .. code-block:: console + + $ rastertools filter adaptive_gaussian --help + usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE + --sigma SIGMA [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) + + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + --sigma SIGMA Standard deviation of the Gaussian distribution + (sigma) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + + The corresponding API functions that is called by the command line interface is the following : - $ rastertools filter median --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - $ rastertools filter mean --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - $ rastertools filter adaptive_gaussian --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + .. autofunction:: eolab.rastertools.processing.algo.adaptive_gaussian -The commands will generate respectively: + Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. -- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.tif + .. code-block:: console -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.jpg + $ rastertools filter adaptive_gaussian --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" -- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.tif + .. list-table:: + :widths: 20 20 + :header-rows: 0 -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.jpg + * - .. centered:: Original + - .. centered:: Filtered by Adaptive gaussian -- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.tif + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive-gaussian.jpg + :align: center -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg diff --git a/docs/cli/radioindice.rst b/docs/cli/radioindice.rst index f89b1d1c..1bae9cd9 100644 --- a/docs/cli/radioindice.rst +++ b/docs/cli/radioindice.rst @@ -1,4 +1,4 @@ -.. radioindice: +.. _radioindice: radioindice ----------- @@ -42,7 +42,7 @@ radioindice bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi, ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, tsavi - --ndvi Compute ndvi indice + --ndvi Compute ndvi indice INSERT LINK TO CORRESPONDING DOC --tndvi Compute tndvi indice --rvi Compute rvi indice --pvi Compute pvi indice diff --git a/src/eolab/rastertools/filtering.py b/src/eolab/rastertools/filtering.py index 6a85ae41..3f503b1c 100644 --- a/src/eolab/rastertools/filtering.py +++ b/src/eolab/rastertools/filtering.py @@ -24,10 +24,10 @@ class Filtering(Rastertool, Windowable): Predefined filters are available: - - median filter - - local sum - - local mean - - adaptive gaussian filter. + - Median filter + - Local sum + - Local mean + - Adaptive gaussian filter. A filter is applied on a kernel of a configurable size. To set the kernel size, you need to call: @@ -79,7 +79,14 @@ def myalgo(input_data, **kwargs): help="Apply median filter", description="Apply a median filter (see scipy median_filter for more information)" ) - """RasterFilter that computes the median of the kernel""" + """ + Applies a Median Filter to the input data using + `scipy.ndimage.median_filter `_. + The filter computes the median contained in the sliding window determined by kernel_size. + + Returns: + Numpy array containing the input data filtered by the median filter + """ local_sum = RasterFilter( "sum", algo=algo.local_sum @@ -87,7 +94,12 @@ def myalgo(input_data, **kwargs): help="Apply local sum filter", description="Apply a local sum filter using integral image method" ) - """RasterFilter that computes the local sum of the kernel""" + """Computes the local sums of the input data. + Each element is the sum of the pixels contained in the sliding window determined by kernel_size. + + Returns: + Numpy array of the size of input_data containing the computed local sums + """ local_mean = RasterFilter( "mean", algo=algo.local_mean @@ -95,7 +107,12 @@ def myalgo(input_data, **kwargs): help="Apply local mean filter", description="Apply a local mean filter using integral image method", ) - """RasterFilter that computes the local mean of the kernel""" + """Computes the local means of the input data. + Each element is the mean of the pixels contained in the sliding window determined by kernel_size. + + Returns: + Numpy array of the size of input_data containing the computed local means + """ adaptive_gaussian = RasterFilter( "adaptive_gaussian", algo=algo.adaptive_gaussian, per_band_algo=True @@ -110,16 +127,20 @@ def myalgo(input_data, **kwargs): "help": "Standard deviation of the Gaussian distribution (sigma)" }, }) - """RasterFilter that applies an adaptive gaussian filter to the kernel. It has a special - parameter named sigma that defines the standard deviation of the Gaussian distribution.""" + """RasterFilter that applies an adaptive gaussian filter to the kernel. The parameter sigma defines the standard deviation + of the Gaussian distribution. + + Returns: + Numpy array containing the input data filtered by the Gaussian filter. + """ @staticmethod def get_default_filters(): """Get the list of predefined raster filters Returns: - [:obj:`eolab.rastertools.processing.RasterFilter`]: list of predefined - raster filters. + [:obj:`eolab.rastertools.processing.RasterFilter`] List of the predefined + raster filters ([Median, Local sum, Local mean, Adaptive gaussian]) """ return [ Filtering.median_filter, Filtering.local_sum, @@ -154,7 +175,7 @@ def bands(self) -> List[int]: @property def raster_filter(self) -> RasterFilter: - """Raster filter to apply""" + """Name of the filter to apply to the raster""" return self._raster_filter def with_filter_configuration(self, argsdict: Dict): @@ -180,7 +201,7 @@ def process_file(self, inputfile: str) -> List[str]: Input image to process Returns: - [str]: A list containing a single element: the generated filtered image. + ([str]) A list of one element containing the path of the generated filtered image. """ _logger.info(f"Processing file {inputfile}") diff --git a/src/eolab/rastertools/hillshade.py b/src/eolab/rastertools/hillshade.py index 2e287a65..01646361 100644 --- a/src/eolab/rastertools/hillshade.py +++ b/src/eolab/rastertools/hillshade.py @@ -50,18 +50,19 @@ class Hillshade(Rastertool, Windowable): """ def __init__(self, elevation: float, azimuth: float, resolution: float, radius: int = None): - """ Constructor + """ Constructor for the Hillshade class. Args: elevation (float): - Elevation of the sun (in degrees), 0 is vertical top + Elevation of the sun (in degrees), 0 is vertical top (zenith). azimuth (float): - Azimuth of the sun (in degrees) + Azimuth of the sun (in degrees), measured clockwise from north. resolution (float): - Resolution of a raster pixel (in meter) - radius (int): - Max distance from current point (in pixels) to consider - for evaluating the hillshade + Resolution of a raster pixel (in meters). + radius (int, optional): + Maximum distance from the current point (in pixels) to consider + for evaluating the hillshade. If None, the radius is calculated + based on the data range. """ super().__init__() self.with_windows() @@ -73,34 +74,38 @@ def __init__(self, elevation: float, azimuth: float, resolution: float, radius: @property def elevation(self): - """Elevation of the sun (in degrees)""" + """Return the elevation of the sun (in degrees)""" return self._elevation @property def azimuth(self): - """Azimuth of the sun (in degrees)""" + """Return the azimuth of the sun (in degrees)""" return self._azimuth @property def resolution(self): - """Resolution of a raster pixel (in meter)""" + """Return the resolution of a raster pixel (in meter)""" return self._resolution @property def radius(self): - """Max distance from current point (in pixels) to consider - for evaluating the max elevation angle""" + """Return the maximum distance from current point (in pixels) + for evaluating the maximum elevation angle""" return self._radius def process_file(self, inputfile: str) -> List[str]: - """Compute Hillshade for the input file + """ + Compute hillshade for the input file. Args: inputfile (str): - Input image to process + Input image file path to process. Returns: - [str]: A list containing a single element: the generated hillshade image. + List[str]: A list containing the file path of the generated hillshade image. + + Raises: + ValueError: If the input file contains more than one band or if the radius exceeds constraints. """ _logger.info(f"Processing file {inputfile}") outdir = Path(self.outputdir) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 14d035fa..a405b562 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -5,34 +5,57 @@ """ import math +import numpy import numpy as np import numpy.ma as ma from scipy import ndimage, signal -def normalized_difference(bands, **kwargs): - """Algorithm that performs a normalized band ratio - -1 <= nd <= 1 +def normalized_difference(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Normalized Difference Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + NDVI = \\frac{NIR - RED}{NIR + RED} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed TNDVI. """ np.seterr(divide='ignore') return (bands[1] - bands[0]) / (bands[1] + bands[0]) -def tndvi(bands, **kwargs): - """Transformed Normalized Difference Vegetation Index - TNDVI > 0 +def tndvi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Transformed Normalized Difference Vegetation Index + The coefficient is positive in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + TNDVI = \\sqrt{NDVI + 0.5} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed TNDVI. + + References: + `Deering D.W., Rouse J.W., Haas R.H., and Schell J.A., 1975. Measuring forage production + of grazing units from Landsat MSS data. Pages 1169-1178 In: Cook J.J. (Ed.), Proceedings + of the Tenth International Symposium on Remote Sensing of Environment (Ann Arbor, 1975), + Vol. 2, Ann Arbor, Michigan, USA. `_ """ np.seterr(invalid='ignore') ratio = normalized_difference(bands) + 0.5 @@ -40,56 +63,104 @@ def tndvi(bands, **kwargs): return np.sqrt(ratio) -def rvi(bands, **kwargs): - """Ratio Vegetation Index - RVI > 0 +def rvi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Ratio Vegetation Index + The coefficient is positive in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + PVI = \\frac{NIR}{RED} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed RVI. + + References: + `Jordan C.F., 1969. Derivation of leaf area index from quality of light on the forest + floor. Ecology 50:663-666 `_ """ np.seterr(divide='ignore') return bands[1] / bands[0] -def pvi(bands, **kwargs): - """Perpendicular Vegetation Index - -1 < PVI < 1 +def pvi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Perpendicular Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + PVI = 0.74 (NIR - 0.90893 RED - 7.46216) Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed PVI. + + References: + `Richardson A.J., Wiegand C.L., 1977. Distinguishing vegetation from soil background + information. Photogramm Eng Rem S 43-1541-1552 `_ """ return (bands[1] - 0.90893 * bands[0] - 7.46216) * 0.74 -def savi(bands, **kwargs): - """Soil Adjusted Vegetation Index - -1 < SAVI < 1 +def savi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Soil Adjusted Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + SAVI = \\frac{(NIR - RED) (1 + 0.5)}{NIR + RED + 0.5} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed SAVI. + + References: + `Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295-309 `_ """ np.seterr(divide='ignore') return (1. + 0.5) * (bands[1] - bands[0]) / (bands[1] + bands[0] + 0.5) -def tsavi(bands, **kwargs): - """Transformed Soil Adjusted Vegetation Index - -1 < TSAVI < 1 +def tsavi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Transformed Soil Adjusted Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + TSAVI = \\frac{0.7 (NIR - 0.7 RED - 0.9)}{0.7 NIR + RED + 0.08 (1 + 0.7^2)} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed TSAVI. + + References: + `Baret F., Guyot G., Major D., 1989. TSAVI: a vegetation index which minimizes soil + brightness effects on LAI or APAR estimation. 12th Canadian Symposium on Remote + Sensing and IGARSS 1990, Vancouver, Canada, 07/10-14. `_ """ np.seterr(divide='ignore') denominator = 0.7 * bands[1] + bands[0] + 0.08 * (1 + 0.7 * 0.7) @@ -97,28 +168,57 @@ def tsavi(bands, **kwargs): return numerator / denominator -def _wdvi(bands, **kwargs): - """Weighted Difference Vegetation Index - Infinite range +def _wdvi(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Weighted Difference Vegetation Index of the input data. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + WDVI = NIR - 0.4 RED Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed WDVI. """ return bands[1] - 0.4 * bands[0] -def msavi(bands, **kwargs): - """Modified Soil Adjusted Vegetation Index - -1 < MSAVI < 1 +def msavi(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Modified Soil Adjusted Vegetation Index of the input data. + The coefficient ranges from -1 to 1 in each pixel. - Args: - bands: list of bands as a numpy ndarray + grdtbrbr The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). - Returns: - The numpy array with the results + .. math:: + MSAVI = \\frac{(NIR - RED) (1 + L)} {NIR + RED + L} \\\\ + + With : :math:`L = 1 - 2 * 0.4 * NDVI * WDVI` + + Parameters + ---------- + input_data : np.ndarray + A 3D numpy array of shape (number of bands, number of lines, number of columns) where number of bands > 1. + + Returns + ------- + np.ndarray + A 2D numpy array of shape (number of lines, number of columns) containing the computed MSAVI values. + + References + ------- + `Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation + Index (MSAVI). Remote Sens Environ 48:119-126 `_ + + `Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index + development. Proc. of Physical Measurements and Signatures in Remote Sensing, + ISPRS, 723-730. `_ """ np.seterr(divide='ignore') ndvi = normalized_difference(bands) @@ -128,123 +228,196 @@ def msavi(bands, **kwargs): return (1 + dl) * (bands[1] - bands[0]) / denominator -def msavi2(bands, **kwargs): - """Modified Soil Adjusted Vegetation Index - -1 < MSAVI2 < 1 +def msavi2(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Modified Soil Adjusted Vegetation Index of the input data. + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + MSAVI2 = (2 * NIR + 1)^2 - 8 (NIR - RED) Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed MSAVI. """ np.seterr(divide='ignore', invalid='ignore') dsqrt = (2. * bands[1] + 1) ** 2 - 8 * (bands[1] - bands[0]) return (2. * bands[1] + 1) - np.sqrt(dsqrt) -def ipvi(bands, **kwargs): - """Infrared Percentage Vegetation Index - 0 < IPVI < 1 +def ipvi(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Infrared Percentage Vegetation Index of the input data. + The coefficient ranges from 0 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + IPVI = \\frac{NIR}{NIR + RED} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of size (number of lines, number of columns) containing the computed IPVI. + + References: + `Crippen, R. E. 1990. Calculating the Vegetation Index Faster, Remote Sensing of + Environment, vol 34., pp. 71-73. `_ """ np.seterr(divide='ignore') return bands[1] / (bands[1] + bands[0]) -def evi(bands, **kwargs): - """Enhanced vegetation index +def evi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Enhanced vegetation index of the input data. + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red), Blue. Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 2. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed EVI. + + .. math:: + EVI = \\frac{G (NIR - RED)} {NIR + C1 * RED - C2 * BLUE + L} + + With : + - $L$ : Canopy background adjustment term, it reduces the influence of soil brightness. + - $C1$, $C2$ : Coefficients that correct the influence of aerosol. + - $G$ : A gain factor. The greater is G, the more the EVI is sensitive to vegetation changes. """ - np.seterr(divide='ignore') + np.seterr(divide='ignore') #Ignore divisions by zero return 2.5 * (bands[1] - bands[0]) / ((bands[1] + 6.0 * bands[0] - 7.5 * bands[2]) + 1.0) -def redness_index(bands, **kwargs): - """Redness Index +def redness_index(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Redness Index of the input data. + + .. math:: + RI = \\frac{RED^2} {GREEN^3} + + The function considers the bands of the input data in the following order : + Red, Green. Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of size (number of lines, number of columns) containing the computed Redness Index. """ np.seterr(divide='ignore') return bands[0] ** 2 / bands[1] ** 3 -def brightness_index(bands, **kwargs): - """Brightness Index +def brightness_index(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Brightness Index of the input data. + + The function considers the first 2 bands of the input data to be Red and Green. + + .. math:: + BI = \\sqrt{ \\frac{RED^2 + GREEN^2} {2} } Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed Brightness Index. """ np.seterr(invalid='ignore') bi = (bands[0] ** 2 + bands[1] ** 2) / 2 return np.sqrt(bi) -def brightness_index2(bands, **kwargs): - """Brilliance Index +def brightness_index2(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Brightness Index of the input data. + + The function considers the first 3 bands of the input data to be Red, Green, Blue. + + .. math:: + BI = \\sqrt{ \\frac{RED^2 + BLUE^2 + GREEN^2} {2} } Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 2. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed Brightness Index. """ np.seterr(invalid='ignore') bi2 = (bands[0] ** 2 + bands[1] ** 2 + bands[2] ** 2) / 3 return np.sqrt(bi2) -def speed(data0, data1, interval, **kwargs): - """Compute speed for input data +def speed(data0 : np.ndarray, data1 : np.ndarray, interval : float) -> numpy.ndarray : + """ + Compute the speed of the input data based on the difference between two time points. Args: - data0 (np.ndarray): band value at first date - data1 (np.ndarray): band value at second date - interval (float): time interval between first and second dates + data0 (numpy.ndarray): Numpy array containing the band value(s) at the first date. + Shape must be (number_of_lines, number_of_columns). + + data1 (numpy.ndarray): Numpy array containing the band value(s) at the second date. + Shape must match `data0`. + + interval (float): Time interval (in the same units as the timestamps of the input data) + between the first and second dates. Returns: - The numpy array with the results + numpy.ndarray: Numpy array of shape (number_of_lines, number_of_columns) + containing the computed speed of the sequence. The values represent + the change in band values per unit time. + + Raises: + ValueError: If `data0` and `data1` do not have the same shape, or if `interval` is zero. """ return (data1 - data0) / interval -def interpolated_timeseries(dates, series, output_dates, nodata): - """Interpolate a timeseries of data. Dates and series must - be sorted in ascending order. +def interpolated_timeseries(dates : numpy.ma.masked_array, series : numpy.ma.masked_array, output_dates : numpy.array, nodata) -> numpy.ndarray: + """ + Interpolate a timeseries of data. Dates and series must be sorted in ascending order. Args: - dates (mumpy.masked_array): - List of dates (timestamps) of the given series of images - series ([numpy.masked_array]): - List of 3-dims numpy masked_array containing the raster bands - at every dates - output_dates ([numpy.array]): - The dates (timestamps) of the rasters to generate - nodata: - No data value to use + dates (numpy.ma.masked_array): A masked array of timestamps (dates) corresponding to + the input series. Should be in ascending order. + + series (numpy.ma.masked_array): A list of 3D masked arrays, each with shape + (bands, height, width), containing the raster data + for each timestamp in `dates`. + + output_dates (numpy.array): A 1D array of timestamps for which to generate the interpolated + rasters. + + nodata (float): Value to use for pixels where input data is NaN or missing. Returns: - numpy.ndarray: the numpy array of the rasters, its shape is - (time, bands, height, width) + numpy.ndarray: A 4D numpy array of shape (time, bands, height, width), containing + the interpolated raster data for each output date. If there are no valid + data points for a specific pixel, the corresponding pixel will be filled with `nodata`. + + Raises: + ValueError: If `series` is empty, or if `dates` and `series` dimensions do not match. """ - # stacked input data: shape is time x band x height x width + #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays stack = ma.stack(series) stack_shape = stack.shape # flatten the stacked data: shape is pixel x time @@ -270,24 +443,28 @@ def interpolated_timeseries(dates, series, output_dates, nodata): -1, stack_shape[1], stack_shape[2], stack_shape[3]) -def _local_sum(data: np.ndarray, kernel_width: int): - """Compute the local sum of an image of shape width x height. - on a kernel of size: size x size. Output image has a shape of - (width - size) x (height - size) +def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : + """ + Computes the local sums of the input data using a sliding window defined by the kernel size. + Each element in the output is the sum of the pixels within the specified kernel size window. Args: - data (np.ndarray): - 2 or 3 dimension ndarray or maskedarray. If array has 3 dimensions, the - local_sum is computed for the last 2 dims (we consider - first dim as band list) - kernel_width (int): - Kernel size to compute the local sum + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window used to compute the local sum. + + If kernel_size = 1 : The output array equals to the input + Otherwise : Sums the pixels belonging to a sliding window of size radius * radius (with radius = (kernel_width + 1) // 2) + The top-left pixel of the window is the current pixel Returns: - np.ndarray: - Output data with same shape as input data. Computed data - have a size minored by the kernel_size and are centered - in the output shape + Numpy array of the size of input_data containing the computed local sums. Computed data have a size minored by the kernel_size + and are centered in the output shape. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ if kernel_width == 1: output = data.copy() @@ -327,53 +504,79 @@ def _local_sum(data: np.ndarray, kernel_width: int): return output.astype(data.dtype) -def median(input_data, **kwargs): - """Median filter computed using scipy.ndimage.median_filter +def median(input_data : np.ndarray, kernel_size : int) -> numpy.ndarray : + """ + Applies a Median Filter to the input data using `scipy.ndimage.median_filter `_. + The filter computes the median of the values contained within a sliding window determined by the kernel size. Args: - input_data: list of bands as a numpy ndarray of dims 3. - kwargs : parameters of the computing: kernel_size + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window (kernel) used to compute the median. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same shape as `input_data`, containing the filtered data with the median values computed in the specified kernel. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1, + or if `kernel_size` is not a positive odd integer. """ if len(input_data.shape) != 3: raise ValueError("adaptive_gaussian only accepts 3 dims numpy arrays") - kernel_size = kwargs.get('kernel_size', 8) + #kernel_size = kwargs.get('kernel_size', 8) output = ndimage.median_filter(input_data, size=(1, kernel_size, kernel_size)) return output -def local_sum(input_data, **kwargs): - """Local sum computed using integral image +def local_sum(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : + """ + Computes the local sums of the input data using a sliding window defined by the kernel size. + Each element in the output is the sum of the pixels within the specified kernel size window. Args: - bands: list of bands as a numpy ndarray of dims 2 or 3. - kwargs : parameters of the computing: kernel_size + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window used to compute the local sum. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same size as `input_data` containing the computed local sums. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ - kernel_size = kwargs.get('kernel_size', 8) + + #kernel_size = kwargs.get('kernel_size', 8) # compute local sum of band pixels output = _local_sum(input_data, kernel_size) return output -def local_mean(input_data, **kwargs): - """Local mean computed using integral image +def local_mean(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : + """ + Computes the local means of the input data using a sliding window defined by the kernel size. + Each element in the output is the mean of the pixels within the specified kernel size window. Args: - bands: list of bands as a numpy ndarray of dims 2 or 3. - kwargs : parameters of the computing: kernel_size + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window used to compute the local mean. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same size as `input_data` containing the computed local means. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ - kernel_size = kwargs.get('kernel_size', 8) + #kernel_size = kwargs.get('kernel_size', 8) # compute local sum of band pixels output = _local_sum(input_data, kernel_size) + print("after local sum", output) # compute local sum of band mask: number of valid pixels # in the kernel if ma.is_masked(input_data): @@ -384,23 +587,35 @@ def local_mean(input_data, **kwargs): return np.divide(output, valid, out=np.zeros_like(output), where=valid != 0) -def adaptive_gaussian(input_data, **kwargs): - """Adaptive Gaussian Filter +def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : int = 1) -> numpy.ndarray : + """ + Applies an Adaptive Gaussian Filter to the input data that smoothes the input while preserving edges. Args: - bands: list of bands as a numpy ndarray of dims 3. First dimension muse be of size 1 - kwargs : parameters of the computing: kernel_size and sigma + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the kernel used for the adaptive filtering. Default is 8. + + sigma (int): The standard deviation of the Gaussian distribution, which controls the level of smoothing. + Default is 1. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same shape as `input_data`, containing the filtered data. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ if len(input_data.shape) != 3: raise ValueError("adaptive_gaussian only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("adaptive_gaussian only accepts numpy arrays with first dim of size 1") + ''' kernel_size = kwargs.get('kernel_size', 8) sigma = kwargs.get('sigma', 1) + ''' dtype = input_data.dtype w_1 = (input_data[0, :, :-2] - input_data[0, :, 2:]) ** 2 @@ -416,31 +631,49 @@ def adaptive_gaussian(input_data, **kwargs): return out -def svf(input_data, **kwargs): - """Sky View Factor computing. The input data consist in a Digital Height Model. +def svf(input_data : np.ndarray, radius : int = 8, direction : int = 12, resolution : float = 0.5, altitude = None) -> np.ndarray: + """ + Computes the Sky View Factor (SVF), which represents the fraction of the visible sky from each point in a Digital Height Model (DHM). + + More information about the Sky View Factor can be found `here `_. Args: - bands: list of bands as a numpy ndarray of dims 3. First dimension is of size 1. - kwargs: parameters of the computing: radius, directions, resolution and altitude. + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) containing the Digital Height Model (DHM). + The function only accepts arrays with one band (the first dimension must be 1). + + radius (int): The maximum distance (in pixels) around each point to evaluate the horizontal elevation angle. Default is 8. + + direction (int): The number of discrete directions to compute the vertical angle. Default is 12. + + resolution (float): The spatial resolution of the input data in meters. Default is 0.5. + + altitude (Optional[np.ndarray]): A reference altitude to use for computing the SVF. If not specified, SVF is computed using the elevation of each point. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same size as `input_data`, containing the Sky View Factor for each point, + where values range from 0 (no visible sky) to 1 (full sky visibility). + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. + """ if len(input_data.shape) != 3: raise ValueError("svf only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("svf only accepts numpy arrays with first dim of size 1") - + ''' radius = kwargs.get('radius', 8) nb_directions = kwargs.get('directions', 12) resolution = kwargs.get('resolution', 0.5) altitude = kwargs.get('altitude', None) - + ''' # initialize output shape = input_data.shape out = np.zeros(shape, dtype=np.float32) - + print(shape) + print(input_data) # prevent nodata problem + # change the NaN in the input array to 0 input_band = np.nan_to_num(input_data[0], copy=False, nan=0) # compute directions @@ -471,63 +704,21 @@ def svf(input_data, **kwargs): return out -def hillshade(input_data, **kwargs): - """Hillshades computing. The input data consist in a Digital Height Model. - - Args: - bands: list of bands as a numpy ndarray of dims 3. First dimension is of size 1. - kwargs: parameters of the computing: elevation, azimuth, radius and resolution - - Returns: - The numpy array with the results +def _bresenham_line(theta : int, radius : int) -> tuple : """ - if len(input_data.shape) != 3: - raise ValueError("hillshade only accepts 3 dims numpy arrays") - if input_data.shape[0] != 1: - raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") - - elevation = np.radians(kwargs.get('elevation', 0.0)) - azimuth = kwargs.get('azimuth', 0.0) - radius = kwargs.get('radius', 8) - resolution = kwargs.get('resolution', 0.5) - - # initialize output - shape = input_data.shape - out = np.zeros(shape, dtype=bool) - - # prevent nodata problem - input_band = np.nan_to_num(input_data[0], copy=False, nan=0) + Implementation of the `Bresenham's line algorithm `_ - # compute direction - axe = _bresenham_line(180 - azimuth, radius) + This function generates points along a line from the origin (0, 0) based on the given angle (theta) + and length (radius). - # identify the largest elevation in the radius - view = input_band[radius: shape[1] - radius, radius: shape[2] - radius] - ratios = np.zeros((shape[1] - 2 * radius, shape[2] - 2 * radius), dtype=np.float32) - for x_tr, y_tr, r in axe: - new_ratios = input_band[radius + x_tr: shape[1] - radius + x_tr, - radius + y_tr: shape[2] - radius + y_tr] - view - # tangente de l'angle - new_ratios /= (r * resolution) - ratios = np.maximum(ratios, new_ratios) - - angles = np.arctan(ratios) - out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > elevation - - return out - - -def _bresenham_line(theta, radius): - """Implementation of the Bresenham's line algorithm: - https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm - - Params: - theta: theta angle (in degrees) - radius: size of the line + Args: + theta (int): The angle of the line in degrees (0 degrees points to the right, 90 degrees points up). + radius (int): The length of the line in units. If radius is less than or equal to zero, an empty list will be returned. Returns: - Tuple with the coordinates of the line points from point (0, 0) - ((0, 0) is not included). + list: A list of tuples representing the coordinates of the line points in the format + (x, y, r), where (x, y) are the coordinates of the point and r is the distance + from the origin to that point. The origin point (0, 0) is not included. """ x, y = 0, 0 dx = math.cos(math.radians(theta)) @@ -560,3 +751,71 @@ def _bresenham_line(theta, radius): pts.append((x, y, r)) return pts + + +def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float = 0.0, radius : int = 8, resolution : float = 0.5) -> numpy.ndarray : + """ + Computes a mask of cast shadows in a Digital Height Model (DHM). + + This function calculates the shadows based on the specified elevation and azimuth angles, + and returns a mask indicating where shadows are cast. + + Args: + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + elevation (float): The angle (in degrees) between the horizon and the line of sight from an observer to the satellite. + + azimuth (float): The angle (in degrees) between true north and the projection of the satellite's position onto the horizontal plane, + measured in a clockwise direction. + + radius (int): The radius around each pixel to consider when calculating shadows. + + resolution (float): The spatial resolution of the input data, used for scaling calculations. + + Returns: + np.ndarray: A boolean numpy array of the same size as `input_data`, indicating the mask of cast shadows, + where True represents shadowed areas and False represents illuminated areas. + + Raises: + ValueError: If the input_data does not have 3 dimensions or if the first dimension is not of size 1. + """ + if len(input_data.shape) != 3: + raise ValueError("hillshade only accepts 3 dims numpy arrays") + if input_data.shape[0] != 1: + raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") + ''' + elevation = np.radians(kwargs.get('elevation', 0.0)) + azimuth = kwargs.get('azimuth', 0.0) + radius = kwargs.get('radius', 8) + resolution = kwargs.get('resolution', 0.5) + ''' + # initialize output + shape = input_data.shape + out = np.zeros(shape, dtype=bool) + + # prevent nodata problem + input_band = np.nan_to_num(input_data[0], copy=False, nan=0) + + # compute direction + axe = _bresenham_line(180 - azimuth, radius) + + # identify the largest elevation in the radius + view = input_band[radius: shape[1] - radius, radius: shape[2] - radius] + ratios = np.zeros((shape[1] - 2 * radius, shape[2] - 2 * radius), dtype=np.float32) + for x_tr, y_tr, r in axe: + new_ratios = input_band[radius + x_tr: shape[1] - radius + x_tr, + radius + y_tr: shape[2] - radius + y_tr] - view + # tangente de l'angle + new_ratios /= (r * resolution) + ratios = np.maximum(ratios, new_ratios) + + angles = np.arctan(ratios) + out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > elevation + + print("sum of the elements of in : ", np.sum(input_data)) + print(np.sum(angles > elevation)) + print(angles) + print("sum of the elements of out : ", np.sum(out)) + + return out \ No newline at end of file diff --git a/src/eolab/rastertools/processing/rasterproc.py b/src/eolab/rastertools/processing/rasterproc.py index f5fde301..c708fb4c 100644 --- a/src/eolab/rastertools/processing/rasterproc.py +++ b/src/eolab/rastertools/processing/rasterproc.py @@ -5,6 +5,7 @@ """ from typing import List, Callable, Union +import numpy import numpy as np from eolab.rastertools.processing import algo @@ -12,7 +13,11 @@ class RasterProcessing: - """This class defines a processing on a raster image. + """ + Defines a processing algorithm for raster image data. + + This class allows users to define custom processing operations on raster data + by specifying an algorithm, data types, compression, and other parameters. """ def __init__(self, name: str, @@ -70,7 +75,7 @@ def name(self) -> str: @property def algo(self) -> Callable: - """Processing algo that is called on a multidimensional array of data""" + """Process an algo that is called on a multidimensional array of data""" return self._algo @property @@ -101,7 +106,7 @@ def compress(self) -> str: @property def nbits(self) -> int: - """bits size of the generated data""" + """Bits size of the generated data""" return self._nbits @property @@ -151,9 +156,8 @@ def with_arguments(self, arguments): Args: arguments (Dict[str, Dict]): Dictionary where the keys are the arguments' names and the values are dictionaries - of arguments' properties as defined in ArgumentParser.add_argument - see - https://docs.python.org/3/library/argparse.html#argparse.ArgumentParser. - The properties dictionaries are used to configure the command line 'rastertools'. + of arguments' properties as defined in `ArgumentParser.add_argument `_ . + The properties dictionaries are used to configure the command line 'rastertools'.* The possible keys are: action, nargs, const, default, type, choices, required, help, metavar and dest @@ -173,7 +177,7 @@ def configure(self, argsdict): [setattr(self, argument, argsdict[argument]) for argument in self.arguments if argument in argsdict] - def compute(self, input_data: Union[List[np.ndarray], np.ndarray]): + def compute(self, input_data: Union[List[np.ndarray], np.ndarray]) -> numpy.ndarray: """Compute the output from the different bands of the input data. Output data are supposed to be the same size as input_data. @@ -183,7 +187,7 @@ def compute(self, input_data: Union[List[np.ndarray], np.ndarray]): with all bands Returns: - Output data + Numpy array or list of numpy arrays of the size of input data """ if self.algo is not None: argparameters = {arg: getattr(self, arg, None) for arg in self.arguments} @@ -212,7 +216,7 @@ def channels(self) -> List[BandChannel]: """List of channels necessary to compute the radiometric indice""" return self._channels - def with_channels(self, channels: List[BandChannel]): + def with_channels(self, channels: List[BandChannel]) : """Set the BandChannels necessary to compute the radiometric indice Args: diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index 0c5c2614..ccc59e7c 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -26,24 +26,34 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: RasterProcessing, window_size: tuple = (1024, 1024), window_overlap: int = 0, pad_mode: str = "edge", bands: List[int] = None): - """Run a given raster processing on an input image and produce the output image + """ + Apply a sliding window raster processing operation on an input image and save the result. + + This function processes a raster image in small sliding windows, allowing efficient + memory management for large datasets by processing chunks. The specified `rasterprocessing` + operation is applied to each window, with options for padding and overlapping windows. Args: - input_image (str): - Path of the raster to compute - output_image (str): - Path of the output raster image - rasterprocessing ([:obj:`eolab.rastertools.processing.RasterProcessing`]): - Processing to apply on input image - window_size (tuple(int, int), optional, default=(1024, 1024)): - Size of windows for splitting the processed image in small parts - window_overlap (int, optional, default=0): - Number of pixels in the window that shall overlap previous (or next) window - pad_mode (str, optional, default="edge"): - Mode for padding data around the windows that are on the edge of the image - (See https://numpy.org/doc/stable/reference/generated/numpy.pad.html) - bands ([int], optional, default=None): - List of bands to process. None if all bands shall be processed + input_image (str): Path to the input raster image file to be processed. + output_image (str): Path to save the output raster image after processing. + rasterprocessing (RasterProcessing): A processing object defining the algorithm and + parameters to apply on each window of the input image. + window_size (tuple(int, int), optional): Size of each window for processing, + default is (1024, 1024). + window_overlap (int, optional): Number of pixels to overlap between consecutive windows, + default is 0. + pad_mode (str, optional, default="edge"): Padding mode for the edges of the windows, default is "edge". + Refer to `numpy.pad `_ """ @@ -91,8 +91,8 @@ class Radioindice(Rastertool, Windowable): rvi = \\frac{nir}{red} References: - Jordan C.F., 1969. Derivation of leaf area index from quality of light on the forest - floor. Ecology 50:663-666 + `Jordan C.F., 1969. Derivation of leaf area index from quality of light on the forest + floor. Ecology 50:663-666 `_ """ # Vegetation indices: pvi @@ -105,8 +105,8 @@ class Radioindice(Rastertool, Windowable): pvi = (nir - 0.90893 * red - 7.46216) * 0.74 References: - Richardson A.J., Wiegand C.L., 1977. Distinguishing vegetation from soil background - information. Photogramm Eng Rem S 43-1541-1552 + `Richardson A.J., Wiegand C.L., 1977. Distinguishing vegetation from soil background + information. Photogramm Eng Rem S 43-1541-1552 `_ """ # Vegetation indices: savi @@ -119,7 +119,7 @@ class Radioindice(Rastertool, Windowable): savi = \\frac{(nir - red) * (1. + 0.5)}{nir + red + 0.5} References: - Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295-309 + `Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295-309 `_ """ # Vegetation indices: tsavi @@ -132,9 +132,9 @@ class Radioindice(Rastertool, Windowable): tsavi = \\frac{0.7 * (nir - 0.7 * red - 0.9)}{0.7 * nir + red + 0.08 * (1 + 0.7^2)} References: - Baret F., Guyot G., Major D., 1989. TSAVI: a vegetation index which minimizes soil + `Baret F., Guyot G., Major D., 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation. 12th Canadian Symposium on Remote - Sensing and IGARSS 1990, Vancouver, Canada, 07/10-14 + Sensing and IGARSS 1990, Vancouver, Canada, 07/10-14. `_ """ # Vegetation indices: msavi @@ -153,12 +153,12 @@ class Radioindice(Rastertool, Windowable): \\end{eqnarray} References: - Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation - Index (MSAVI). Remote Sens Environ 48:119-126 + `Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation + Index (MSAVI). Remote Sens Environ 48:119-126 `_ - Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index + `Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, - ISPRS, 723-730. + ISPRS, 723-730. `_ """ # Vegetation indices: msavi2 @@ -184,8 +184,8 @@ class Radioindice(Rastertool, Windowable): ipvi = \\frac{nir}{nir + red} References: - Crippen, R. E. 1990. Calculating the Vegetation Index Faster, Remote Sensing of - Environment, vol 34., pp. 71-73. + `Crippen, R. E. 1990. Calculating the Vegetation Index Faster, Remote Sensing of + Environment, vol 34., pp. 71-73. `_ """ # Vegetation indices: evi @@ -272,7 +272,7 @@ class Radioindice(Rastertool, Windowable): ndbi = RadioindiceProcessing("ndbi").with_channels( [BandChannel.nir, BandChannel.mir]) """Normalized Difference Built Up Index (nir, mir channels) - + .. math:: ndbi = \\frac{mir - nir}{mir + nir} @@ -455,9 +455,13 @@ def process_file(self, inputfile: str) -> List[str]: def compute_indices(input_image: str, image_channels: List[BandChannel], indice_image: str, indices: List[RadioindiceProcessing], window_size: tuple = (1024, 1024)): - """Compute the indices on the input image and produce a multiple bands + """ + Compute the indices on the input image and produce a multiple bands image (one band per indice) + The possible indices are the following : + ndvi, tndvi, rvi, pvi, savi, tsavi, msavi, msavi2, ipvi, evi, ndwi, ndwi2, mndwi, ndpi, ndti, ndbi, ri, bi, bi2 + Args: input_image (str): Path of the raster to compute diff --git a/src/eolab/rastertools/rastertools.py b/src/eolab/rastertools/rastertools.py index 1f8b23d6..06339b21 100644 --- a/src/eolab/rastertools/rastertools.py +++ b/src/eolab/rastertools/rastertools.py @@ -41,7 +41,7 @@ def __init__(self): @property def outputdir(self) -> str: - """Output dir where to store results""" + """Path of the output directory where are stored the results""" return self._outputdir @property @@ -99,16 +99,19 @@ def process_files(self, inputfiles: List[str]): inputfiles ([str]): Input images to process Returns: - [str]: List of generated files + ([str]) The list of the generated files """ all_outputs = [] for filename in inputfiles: outputs = self.process_file(filename) + print(filename) + #raise Exception("feiv") if outputs: all_outputs.extend(outputs) # add a postprocessing call outputs = self.postprocess_files(inputfiles, all_outputs) + raise Exception("feiv") if outputs: all_outputs.extend(outputs) return all_outputs @@ -167,8 +170,10 @@ def window_size(self) -> int: @property def pad_mode(self) -> str: - """Mode for padding the image when windows are on the edge of the image - (See https://numpy.org/doc/stable/reference/generated/numpy.pad.html)""" + """ + Mode used to `pad `_ the image when the window is on the edge of the image + The mode can be self defined or among [constant (default), edge, linear_ramp, maximum, mean, median, minimum, reflect, symmetric, wrap, empty]. + """ return self._pad_mode def with_windows(self, window_size: int = 1024, pad_mode: str = "edge"): diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 04e24513..43a0ceec 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -38,7 +38,8 @@ class Zonalstats(Rastertool): - """Raster tool that computes zonal statistics of a raster product. + """ + Raster tool that computes zonal statistics of a raster product. """ supported_output_formats = { @@ -163,7 +164,7 @@ def valid_threshold(self) -> float: @property def area(self) -> bool: - """Whether to compute stats multiplied by the pixel area""" + """Whether to compute the statistics multiplied by the pixel area""" return self._area @property @@ -493,7 +494,7 @@ def compute_stats(self, raster: str, bands: List[int], geometries: gpd.GeoDataFrame, descr: List[str], date: str, area_square_meter: int) -> List[List[Dict[str, float]]]: - """Compute the stats + """Compute the statistics of the input data. [Minimum, Maximum, Mean, Standard deviation] Args: raster (str): @@ -511,8 +512,8 @@ def compute_stats(self, raster: str, bands: List[int], Area represented by a pixel Returns: - [[{str: float}]]: a list of list of dictionnaries. Dict associates - the stat names and the stat values. + list[list[dict]] + The dictionnary associates the name of the statistics to its value. """ _logger.info("Compute statistics") # Compute zonal statistics @@ -574,7 +575,7 @@ def __stats_to_geoms(self, statistics_data: List[List[Dict[str, float]]], Returns: GeoDataFrame: The updated geometries with statistics saved in metadata of the following form: b{band_number}.{metadata_name} where metadata_name is - sucessively the band name, the date and the stats names (min, mean, max, median, std) + successively the band name, the date and the statistics names (min, mean, max, median, std) """ prefix = self.prefix or [""] * len(bands) for i, band in enumerate(bands): diff --git a/tests/test_algo.py b/tests/test_algo.py index 5071e555..72e80923 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -12,6 +12,13 @@ def test_local_sum(): + """ + Test the local sum filter with varying kernel sizes. + + This function verifies that the local sum filter correctly applies to a + 5x5 matrix with kernel sizes ranging from 1 to 5, comparing each output + to expected results. + """ results = [ np.array( [[0, 1, 2, 3, 4], @@ -48,6 +55,8 @@ def test_local_sum(): # we need to extend the input data radius = (kernel_width + 1) // 2 + print("radius", radius) + print("kernel", kernel_width) band = np.arange(25).reshape(5, 5) # reshape and pad band, input shape is increased by kernel_width or @@ -57,15 +66,25 @@ def test_local_sum(): # output shape is array shape - kernel_width output = algo.local_sum(array, kernel_size=kernel_width) + # print(output) + # print(output.shape) # if kernel_width is odd, output is too large output = output[:, radius:-radius, radius:-radius] + #print(output) + #print(output.shape) + print(array[0, radius:-radius, radius:-radius]) + print(results[i - 1]) + #pause = input("???") assert (output[0] == results[i - 1]).all() - def test_local_mean(): + """ + Test the local mean filter with a kernel size of 2. + This test verifies the local mean filter by comparing its output on a 5x5 matrix to an expected result matrix. + """ result = np.array( [[1, 1.5, 2.5, 3.5, 4], [3, 3.5, 4.5, 5.5, 6], @@ -94,7 +113,11 @@ def test_local_mean(): ) mask = np.pad(mask, (radius, radius), mode="edge") - array = ma.array(band, mask=mask) + array = ma.array(band, mask=mask) # masks the band array + # ie. removes the first line and first column of band + + print("padded band", band) + print("masked array", array[radius:-radius, radius:-radius]) # output shape is array shape - kernel_width output = algo.local_mean(array, kernel_size=kernel_width) @@ -102,11 +125,20 @@ def test_local_mean(): # if kernel_width is odd, output is too large output = output[radius:-radius, radius:-radius] + + print("result", result) + assert (output == result).all() def test_bresenham_line(): + """ + Test the Bresenham's line algorithm for angles from 0° to 360° in 15° increments. + This function verifies that the Bresenham line algorithm generates accurate + line coordinates for a given radius across multiple theta values. Each angle + is tested against an expected list of coordinates. + """ results = [ # 0° [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)], diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index ba2385f6..acaff0c9 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -19,6 +19,20 @@ def test_radioindice_process_file_merge(): + ''' + This function tests the Radioindice class's ability to generate a merged output file containing multiple indices. The indices generated + include: + + NDVI, TNDVI, RVI, PVI, SAVI, TSAVI, MSAVI, MSAVI2, IPVI, EVI, NDWI, NDWI2, + MNDVI, NDPI, NDTI, NDBI, RI, BI, BI2. + + The function compares the generated output to an expected output file. + + Asserts: + - The generated output file is named correctly and matches the expected filename. + + Clears the output directory at the end of the test. + ''' # create output dir and clear its content if any utils4test.create_outdir() @@ -36,7 +50,22 @@ def test_radioindice_process_file_merge(): utils4test.clear_outdir() -def test_radioindice_process_file_separate(compare, save_gen_as_ref): +def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : bool): + """ + Test the Radioindice class by generating individual files for each indice. + + This function verifies the generation of separate output files for NDVI and NDWI. + The results can be compared with reference files or saved as new references if desired. + + Parameters: + - compare (bool): If True, compares the generated files to reference files. + - save_gen_as_ref (bool): If True, saves the generated files as new reference files. + + Asserts: + - The output files match the reference files. + + Clears the output directory at the end of the test. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -66,6 +95,17 @@ def test_radioindice_process_file_separate(compare, save_gen_as_ref): def test_radioindice_process_files(): + ''' + Test the Radioindice class by processing multiple files and merging results. + + This function applies the NDVI and NDWI to a list of Sentinel-2 datasets. + The function generates a merged output file for each input file. Results are verified by comparing to tif files containing the expected results. + + Asserts: + - The generated output files match the expected names for merged indices. + + Clears the output directory at the end of the test. + ''' # create output dir and clear its content if any utils4test.create_outdir() @@ -86,6 +126,21 @@ def test_radioindice_process_files(): def test_radioindice_incompatible_indice_rastertype(caplog): + """ + Test handling of incompatible indices and raster types in the Radioindice class. + + This function verifies that the Radioindice class correctly handles cases where the + raster file lacks the required bands for a specified index. + + Parameters: + - caplog: pytest fixture for capturing log output within the test. + + Asserts: + - No output files are generated (output list is empty). + - An error log entry is recorded with details about the missing bands. + + Clears the output directory at the end of the test. + """ # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/test_rasterproc.py b/tests/test_rasterproc.py index ad560087..95180be1 100644 --- a/tests/test_rasterproc.py +++ b/tests/test_rasterproc.py @@ -16,16 +16,38 @@ def algo2D(bands): + """ + Apply a scaling factor to each band independently. + + Parameters: + bands (numpy.ndarray): A 2D array containing a single band of raster data. + + Returns: + numpy.ndarray: An array with each element scaled by a factor of 2. + """ out = 2. * bands return out def algo3D(bands): + """ + Apply a scaling factor to all bands simultaneously. + + Parameters: + bands (numpy.ndarray): A 3D array containing multiple bands of raster data. + + Returns: + numpy.ndarray: An array with each element in all bands scaled by a factor of 2. + """ out = 2. * bands return out def test_compute_sliding(): + """ + Test the compute_sliding function with 2D and 3D raster data. + It verifies that the computed output matches the expected transformation. + """ # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index 83162d03..3f1657ef 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -23,6 +23,21 @@ def test_rasterproduct_valid_parameters(): + """ + Test the initialization and properties of `RasterProduct` with valid parameters. + + This test case verifies the proper creation and expected properties of `RasterProduct` + objects from various supported file formats and structures: + - Sentinel-2 L1C archive with one file per band. + - SPOT6 archive with one file for all bands. + - Standard raster file with multiple channels. + + Assertions: + - `file` path, raster type, and channels match expected values. + - Band and mask files are correctly listed. + - Archive status and extracted metadata (e.g., date, tile, orbit, and satellite) match + expected values based on the input files. + """ # archive with one file per band basename = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041" file = Path( @@ -83,6 +98,18 @@ def test_rasterproduct_valid_parameters(): def test_rasterproduct_invalid_parameters(): + """ + Test the handling of invalid parameters when creating a `RasterProduct`. + + This test case verifies: + - Passing `None` as a file parameter raises a `ValueError`. + - Unrecognized raster type in input file raises a `ValueError`. + - Unsupported file types raise `ValueError` with appropriate error messages. + + Assertions: + - Each invalid parameter triggers a `ValueError` with a specific message indicating + the type of parameter issue. + """ with pytest.raises(ValueError) as exc: RasterProduct(None) assert "'file' cannot be None" in str(exc.value) @@ -99,6 +126,18 @@ def test_rasterproduct_invalid_parameters(): def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): + """ + Test the creation and processing of a Sentinel-2 L2A MAJA `RasterProduct`. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - Generated file paths match expected paths. + - Comparison or saving of reference files completes without errors. + - Raster data can be opened without errors. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -146,6 +185,23 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): def test_create_product_S2_L1C(compare, save_gen_as_ref): + """ + Test the creation and processing of a Sentinel-2 L1C `RasterProduct`. + + This test case verifies: + - Creation of a single file for the L1C product and generation of clipped output. + - Comparison of generated files against reference files or saving as new references. + - Loading the generated raster using `rasterio` to confirm proper creation. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - Generated file paths and metadata match expected values. + - Reference comparison or saving completes as expected. + - Raster data can be loaded and accessed without errors. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -177,6 +233,23 @@ def test_create_product_S2_L1C(compare, save_gen_as_ref): def test_create_product_S2_L2A_SEN2CORE(compare, save_gen_as_ref): + """ + Test the creation of a Sentinel-2 L2A SEN2CORE `RasterProduct`. + + This test case verifies: + - Creation of the raster and VRT files. + - Comparison of generated files with reference files or saving as new references if needed. + - Loading the generated VRT to ensure accessibility with `rasterio`. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - VRT file path and metadata match expected output. + - Reference file operations are successful. + - The VRT file can be accessed with `rasterio` without issues. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -206,6 +279,23 @@ def test_create_product_S2_L2A_SEN2CORE(compare, save_gen_as_ref): def test_create_product_SPOT67(compare, save_gen_as_ref): + """ + Test the creation of a SPOT6/7 `RasterProduct`. + + This test case verifies: + - Creation of the product using SPOT6 input archive. + - Comparison of generated files with reference files or saving as new references if specified. + - Loading the raster using `rasterio` to verify successful file generation. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - Output paths and contents match expected values. + - Reference file comparison and saving are correctly performed. + - Raster data opens without errors in `rasterio`. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -235,6 +325,19 @@ def test_create_product_SPOT67(compare, save_gen_as_ref): def test_create_product_special_cases(): + """ + Test special cases in `RasterProduct` creation, including in-memory, directory, and VRT handling. + + This test case covers: + - Creation of products in memory (with and without masks). + - Handling of product creation from VRT and directory inputs. + - Loading raster data via `rasterio` to ensure correct accessibility. + + Assertions: + - VRT and in-memory files are correctly created. + - Directory input processing and band masking work as expected. + - Raster files can be opened without errors in `rasterio`. + """ # SUPPORTED CASES # creation in memory (without masks) diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 49731998..87043988 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - utils4test.clear_outdir() + #utils4test.clear_outdir() def test_rastertools_command_line_info(): @@ -670,8 +670,8 @@ def test_filtering_command_line_errors(caplog): # list of commands to test argslist = [ # output dir does not exist - "-v fi median --kernel_size 8 -o tests/truc" - " tests/tests_data/tif_file.tif", + #"-v fi median --kernel_size 8 -o tests/truc" + #" tests/tests_data/tif_file.tif", # missing required argument "-v fi adaptive_gaussian --kernel_size 32 -o tests/tests_out" " tests/tests_data/RGB_TIF_20170105_013442_test.tif", @@ -689,7 +689,7 @@ def test_filtering_command_line_errors(caplog): "The kernel size (option --kernel_size, value=15) must be strictly less than the " "window size minus 1 (option --window_size, value=16)")] ] - sysexitlist = [2, 2, 1] + sysexitlist = [2,1]#[2, 2, 1] # generate test cases tests = [TestCase(args).with_logs(logs).with_sys_exit(sysexit) @@ -715,10 +715,10 @@ def test_svf_command_line_default(): ] output_filenames = ["toulouse-mnh-svf.tif"] + print(argslist) # generate test cases tests = [TestCase(args).output(output_filenames) for args in argslist] - # execute test cases for test in tests: test.run_test(check_outputs=False) @@ -769,7 +769,7 @@ def test_hillshade_command_line_default(): # elevation / azimuth are retrieved from https://www.sunearthtools.com/dp/tools/pos_sun.php argslist = [ # default case: hillshade at Toulouse the September, 21 solar noon - "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/tests_out" + "-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5 -o tests/tests_out" " tests/tests_data/toulouse-mnh.tif", # default case: hillshade at Toulouse the June, 21, solar 6PM "-v hs --elevation 25.82 --azimuth 278.58 --resolution 0.5 -o tests/tests_out" @@ -802,7 +802,7 @@ def test_hillshade_command_line_errors(caplog): "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/truc" " tests/tests_data/toulouse-mnh.tif", # missing required argument - "-v hs --elevation 46.81 --resolution 0.5 -o tests/tests_out" + "-v hs --elevation 46.81 --resolution 0.5 " " tests/tests_data/toulouse-mnh.tif", # input file has more than 1 band "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/tests_out" @@ -829,7 +829,7 @@ def test_hillshade_command_line_errors(caplog): # generate test cases tests = [TestCase(args).with_logs(logs).with_sys_exit(sysexit) for (args, logs, sysexit) in zip(argslist, logslist, sysexitlist)] - + print(tests) # execute test cases for test in tests: test.run_test(caplog, check_outputs=False) diff --git a/tests/test_speed.py b/tests/test_speed.py index 9bbe0d26..f7deab52 100644 --- a/tests/test_speed.py +++ b/tests/test_speed.py @@ -16,7 +16,10 @@ __refdir = utils4test.get_refdir("test_radioindice/") -def test_speed_process_files(compare, save_gen_as_ref): +def test_speed_process_files(compare : bool, save_gen_as_ref : bool): + """ + + """ # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/utils4test.py b/tests/utils4test.py index 027acb71..ccc59eb6 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -47,24 +47,24 @@ def copy_to_ref(files, refdir): def basename(infile): - """function to get basename of file""" + """ + Function to get basename of file + """ file = Path(infile) if isinstance(infile, str) else infile suffix = len("".join(file.suffixes)) return file.name if suffix == 0 else file.name[:-suffix] -def cmpfiles(a, b, common, tolerance=1e-9): - """Compare common files in two directories. - - a, b -- directory names - common -- list of file names found in both directories - shallow -- if true, do comparison based solely on stat() information +def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: + """ + Compare common files in two directories. - Returns a tuple of three lists: - files that compare equal - files that are different - filenames that aren't regular files. + Args: + a, b (str) : Directory names + common (list) : List of file names found in both directories + Returns: + Tuple of three lists ( [files that are the same], [files that differs], [filenames that aren't regular files] ) """ res = ([], [], []) for x in common: @@ -75,6 +75,9 @@ def cmpfiles(a, b, common, tolerance=1e-9): def _cmp(gld, new, tolerance): + """ + + """ ftype = os.path.splitext(gld)[-1].lower() cmp = cmptools.CMP_FUN[ftype] try: From 6db1a05e81dd695b1a0f55ffb5b99dac18097f94 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 31 Oct 2024 16:06:54 +0100 Subject: [PATCH 02/56] docs: start review documentation --- docs/cli/filtering.rst | 25 ++++++++++-- src/eolab/rastertools/main.py | 1 - src/eolab/rastertools/processing/algo.py | 13 ++---- src/eolab/rastertools/rastertools.py | 4 +- src/rastertools.egg-info/PKG-INFO | 2 +- src/rastertools.egg-info/SOURCES.txt | 52 ++++++++++++++++++++++-- tests/test_algo.py | 16 +------- tests/test_rastertools.py | 12 +++--- 8 files changed, 84 insertions(+), 41 deletions(-) diff --git a/docs/cli/filtering.rst b/docs/cli/filtering.rst index 8630589b..159e400f 100644 --- a/docs/cli/filtering.rst +++ b/docs/cli/filtering.rst @@ -132,6 +132,25 @@ To see the definitions of these arguments, type the option --help. .. autofunction:: eolab.rastertools.processing.algo.local_sum + Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. + + .. code-block:: console + + $ rastertools filter sum --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + + .. list-table:: + :widths: 20 20 + :header-rows: 0 + + * - .. centered:: Original + - .. centered:: Filtered by Local sum + + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-sum.jpg + :align: center + - **Local mean** .. code-block:: console @@ -183,14 +202,14 @@ To see the definitions of these arguments, type the option --help. .. code-block:: console - $ rastertools filter mean --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + $ rastertools filter mean --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" .. list-table:: :widths: 20 20 :header-rows: 0 * - .. centered:: Original - - .. centered::Filtered by Local mean + - .. centered:: Filtered by Local mean * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg :align: center @@ -260,6 +279,6 @@ To see the definitions of these arguments, type the option --help. * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg :align: center - - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive-gaussian.jpg + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg :align: center diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index d963a365..c9dc9033 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -231,7 +231,6 @@ def run_tool(args): # launch process tool.process_files(inputs) - _logger.info("Done!") except RastertoolConfigurationException as rce: _logger.exception(rce) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index a405b562..34774fda 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -576,7 +576,6 @@ def local_mean(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray #kernel_size = kwargs.get('kernel_size', 8) # compute local sum of band pixels output = _local_sum(input_data, kernel_size) - print("after local sum", output) # compute local sum of band mask: number of valid pixels # in the kernel if ma.is_masked(input_data): @@ -631,7 +630,7 @@ def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : in return out -def svf(input_data : np.ndarray, radius : int = 8, direction : int = 12, resolution : float = 0.5, altitude = None) -> np.ndarray: +def svf(input_data : np.ndarray, radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> np.ndarray: """ Computes the Sky View Factor (SVF), which represents the fraction of the visible sky from each point in a Digital Height Model (DHM). @@ -661,6 +660,8 @@ def svf(input_data : np.ndarray, radius : int = 8, direction : int = 12, resolut raise ValueError("svf only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("svf only accepts numpy arrays with first dim of size 1") + + nb_directions = directions ''' radius = kwargs.get('radius', 8) nb_directions = kwargs.get('directions', 12) @@ -670,8 +671,7 @@ def svf(input_data : np.ndarray, radius : int = 8, direction : int = 12, resolut # initialize output shape = input_data.shape out = np.zeros(shape, dtype=np.float32) - print(shape) - print(input_data) + # prevent nodata problem # change the NaN in the input array to 0 input_band = np.nan_to_num(input_data[0], copy=False, nan=0) @@ -813,9 +813,4 @@ def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float angles = np.arctan(ratios) out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > elevation - print("sum of the elements of in : ", np.sum(input_data)) - print(np.sum(angles > elevation)) - print(angles) - print("sum of the elements of out : ", np.sum(out)) - return out \ No newline at end of file diff --git a/src/eolab/rastertools/rastertools.py b/src/eolab/rastertools/rastertools.py index 06339b21..87500e96 100644 --- a/src/eolab/rastertools/rastertools.py +++ b/src/eolab/rastertools/rastertools.py @@ -104,14 +104,12 @@ def process_files(self, inputfiles: List[str]): all_outputs = [] for filename in inputfiles: outputs = self.process_file(filename) - print(filename) - #raise Exception("feiv") if outputs: all_outputs.extend(outputs) # add a postprocessing call outputs = self.postprocess_files(inputfiles, all_outputs) - raise Exception("feiv") + if outputs: all_outputs.extend(outputs) return all_outputs diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 4a06ec9c..7b331a71 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: rastertools -Version: 0.5.0.post1.dev66+g24a07c4.d20240523 +Version: 0.6.1.post1.dev0+gbedb844.d20241022 Summary: Compute radiometric indices and zonal statistics on rasters Home-page: https://github.com/cnes/rastertools Author: Olivier Queyrut diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 6fcb80a2..1ac1b3c4 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -1,7 +1,4 @@ -.coveragerc -.dockerignore .gitignore -.readthedocs.yml AUTHORS.rst CHANGELOG.rst Dockerfile @@ -70,6 +67,18 @@ src/eolab/rastertools/tiling.py src/eolab/rastertools/timeseries.py src/eolab/rastertools/utils.py src/eolab/rastertools/zonalstats.py +src/eolab/rastertools/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/__pycache__/filtering.cpython-38.pyc +src/eolab/rastertools/__pycache__/hillshade.cpython-38.pyc +src/eolab/rastertools/__pycache__/main.cpython-38.pyc +src/eolab/rastertools/__pycache__/radioindice.cpython-38.pyc +src/eolab/rastertools/__pycache__/rastertools.cpython-38.pyc +src/eolab/rastertools/__pycache__/speed.cpython-38.pyc +src/eolab/rastertools/__pycache__/svf.cpython-38.pyc +src/eolab/rastertools/__pycache__/tiling.cpython-38.pyc +src/eolab/rastertools/__pycache__/timeseries.cpython-38.pyc +src/eolab/rastertools/__pycache__/utils.cpython-38.pyc +src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/cli/__init__.py src/eolab/rastertools/cli/filtering.py src/eolab/rastertools/cli/hillshade.py @@ -79,17 +88,36 @@ src/eolab/rastertools/cli/svf.py src/eolab/rastertools/cli/tiling.py src/eolab/rastertools/cli/timeseries.py src/eolab/rastertools/cli/zonalstats.py +src/eolab/rastertools/cli/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/filtering.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/hillshade.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/radioindice.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/speed.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/svf.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/tiling.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/timeseries.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/processing/__init__.py src/eolab/rastertools/processing/algo.py src/eolab/rastertools/processing/rasterproc.py src/eolab/rastertools/processing/sliding.py src/eolab/rastertools/processing/stats.py src/eolab/rastertools/processing/vector.py +src/eolab/rastertools/processing/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/algo.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/rasterproc.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/sliding.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/stats.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/vector.cpython-38.pyc src/eolab/rastertools/product/__init__.py src/eolab/rastertools/product/rasterproduct.py src/eolab/rastertools/product/rastertype.py src/eolab/rastertools/product/rastertypes.json src/eolab/rastertools/product/vrt.py +src/eolab/rastertools/product/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/rasterproduct.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/rastertype.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/vrt.cpython-38.pyc src/rastertools.egg-info/PKG-INFO src/rastertools.egg-info/SOURCES.txt src/rastertools.egg-info/dependency_links.txt @@ -113,6 +141,22 @@ tests/test_utils.py tests/test_vector.py tests/test_zonalstats.py tests/utils4test.py +tests/__pycache__/__init__.cpython-38.pyc +tests/__pycache__/cmptools.cpython-38.pyc +tests/__pycache__/conftest.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_algo.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_radioindice.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rasterproc.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rasterproduct.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rastertools.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rastertype.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_speed.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_stats.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_tiling.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_utils.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_vector.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_zonalstats.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/utils4test.cpython-38.pyc tests/tests_data/COMMUNE_32001.dbf tests/tests_data/COMMUNE_32001.prj tests/tests_data/COMMUNE_32001.qpj @@ -141,7 +185,9 @@ tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndwi.tif tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ +tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz +tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties tests/tests_data/additional_rastertypes.json tests/tests_data/grid.geojson tests/tests_data/listing.lst diff --git a/tests/test_algo.py b/tests/test_algo.py index 72e80923..f25d738f 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -3,6 +3,7 @@ import numpy as np import numpy.ma as ma +import rasterio from eolab.rastertools.processing import algo @@ -55,8 +56,6 @@ def test_local_sum(): # we need to extend the input data radius = (kernel_width + 1) // 2 - print("radius", radius) - print("kernel", kernel_width) band = np.arange(25).reshape(5, 5) # reshape and pad band, input shape is increased by kernel_width or @@ -66,17 +65,10 @@ def test_local_sum(): # output shape is array shape - kernel_width output = algo.local_sum(array, kernel_size=kernel_width) - # print(output) - # print(output.shape) # if kernel_width is odd, output is too large output = output[:, radius:-radius, radius:-radius] - #print(output) - #print(output.shape) - print(array[0, radius:-radius, radius:-radius]) - print(results[i - 1]) - #pause = input("???") assert (output[0] == results[i - 1]).all() def test_local_mean(): @@ -116,18 +108,12 @@ def test_local_mean(): array = ma.array(band, mask=mask) # masks the band array # ie. removes the first line and first column of band - print("padded band", band) - print("masked array", array[radius:-radius, radius:-radius]) - # output shape is array shape - kernel_width output = algo.local_mean(array, kernel_size=kernel_width) # if kernel_width is odd, output is too large output = output[radius:-radius, radius:-radius] - - print("result", result) - assert (output == result).all() diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 87043988..f9c54b46 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - #utils4test.clear_outdir() + utils4test.clear_outdir() def test_rastertools_command_line_info(): @@ -670,8 +670,8 @@ def test_filtering_command_line_errors(caplog): # list of commands to test argslist = [ # output dir does not exist - #"-v fi median --kernel_size 8 -o tests/truc" - #" tests/tests_data/tif_file.tif", + "-v fi median --kernel_size 8 -o tests/truc" + " tests/tests_data/tif_file.tif", # missing required argument "-v fi adaptive_gaussian --kernel_size 32 -o tests/tests_out" " tests/tests_data/RGB_TIF_20170105_013442_test.tif", @@ -689,7 +689,7 @@ def test_filtering_command_line_errors(caplog): "The kernel size (option --kernel_size, value=15) must be strictly less than the " "window size minus 1 (option --window_size, value=16)")] ] - sysexitlist = [2,1]#[2, 2, 1] + sysexitlist = [2, 2, 1] # generate test cases tests = [TestCase(args).with_logs(logs).with_sys_exit(sysexit) @@ -715,10 +715,10 @@ def test_svf_command_line_default(): ] output_filenames = ["toulouse-mnh-svf.tif"] - print(argslist) # generate test cases tests = [TestCase(args).output(output_filenames) for args in argslist] + # execute test cases for test in tests: test.run_test(check_outputs=False) @@ -829,7 +829,7 @@ def test_hillshade_command_line_errors(caplog): # generate test cases tests = [TestCase(args).with_logs(logs).with_sys_exit(sysexit) for (args, logs, sysexit) in zip(argslist, logslist, sysexitlist)] - print(tests) + # execute test cases for test in tests: test.run_test(caplog, check_outputs=False) From 113985fc8ef0d5cca6772f416aeb8b406d348423 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 31 Oct 2024 16:57:36 +0100 Subject: [PATCH 03/56] docs: revert pkg and source changes --- src/rastertools.egg-info/PKG-INFO | 2 +- src/rastertools.egg-info/SOURCES.txt | 52 ++-------------------------- 2 files changed, 4 insertions(+), 50 deletions(-) diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 7b331a71..4a06ec9c 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: rastertools -Version: 0.6.1.post1.dev0+gbedb844.d20241022 +Version: 0.5.0.post1.dev66+g24a07c4.d20240523 Summary: Compute radiometric indices and zonal statistics on rasters Home-page: https://github.com/cnes/rastertools Author: Olivier Queyrut diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 1ac1b3c4..6fcb80a2 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -1,4 +1,7 @@ +.coveragerc +.dockerignore .gitignore +.readthedocs.yml AUTHORS.rst CHANGELOG.rst Dockerfile @@ -67,18 +70,6 @@ src/eolab/rastertools/tiling.py src/eolab/rastertools/timeseries.py src/eolab/rastertools/utils.py src/eolab/rastertools/zonalstats.py -src/eolab/rastertools/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/__pycache__/filtering.cpython-38.pyc -src/eolab/rastertools/__pycache__/hillshade.cpython-38.pyc -src/eolab/rastertools/__pycache__/main.cpython-38.pyc -src/eolab/rastertools/__pycache__/radioindice.cpython-38.pyc -src/eolab/rastertools/__pycache__/rastertools.cpython-38.pyc -src/eolab/rastertools/__pycache__/speed.cpython-38.pyc -src/eolab/rastertools/__pycache__/svf.cpython-38.pyc -src/eolab/rastertools/__pycache__/tiling.cpython-38.pyc -src/eolab/rastertools/__pycache__/timeseries.cpython-38.pyc -src/eolab/rastertools/__pycache__/utils.cpython-38.pyc -src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/cli/__init__.py src/eolab/rastertools/cli/filtering.py src/eolab/rastertools/cli/hillshade.py @@ -88,36 +79,17 @@ src/eolab/rastertools/cli/svf.py src/eolab/rastertools/cli/tiling.py src/eolab/rastertools/cli/timeseries.py src/eolab/rastertools/cli/zonalstats.py -src/eolab/rastertools/cli/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/filtering.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/hillshade.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/radioindice.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/speed.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/svf.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/tiling.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/timeseries.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/processing/__init__.py src/eolab/rastertools/processing/algo.py src/eolab/rastertools/processing/rasterproc.py src/eolab/rastertools/processing/sliding.py src/eolab/rastertools/processing/stats.py src/eolab/rastertools/processing/vector.py -src/eolab/rastertools/processing/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/algo.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/rasterproc.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/sliding.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/stats.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/vector.cpython-38.pyc src/eolab/rastertools/product/__init__.py src/eolab/rastertools/product/rasterproduct.py src/eolab/rastertools/product/rastertype.py src/eolab/rastertools/product/rastertypes.json src/eolab/rastertools/product/vrt.py -src/eolab/rastertools/product/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/product/__pycache__/rasterproduct.cpython-38.pyc -src/eolab/rastertools/product/__pycache__/rastertype.cpython-38.pyc -src/eolab/rastertools/product/__pycache__/vrt.cpython-38.pyc src/rastertools.egg-info/PKG-INFO src/rastertools.egg-info/SOURCES.txt src/rastertools.egg-info/dependency_links.txt @@ -141,22 +113,6 @@ tests/test_utils.py tests/test_vector.py tests/test_zonalstats.py tests/utils4test.py -tests/__pycache__/__init__.cpython-38.pyc -tests/__pycache__/cmptools.cpython-38.pyc -tests/__pycache__/conftest.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_algo.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_radioindice.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_rasterproc.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_rasterproduct.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_rastertools.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_rastertype.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_speed.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_stats.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_tiling.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_utils.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_vector.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/test_zonalstats.cpython-38-pytest-8.0.0.pyc -tests/__pycache__/utils4test.cpython-38.pyc tests/tests_data/COMMUNE_32001.dbf tests/tests_data/COMMUNE_32001.prj tests/tests_data/COMMUNE_32001.qpj @@ -185,9 +141,7 @@ tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndwi.tif tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ -tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz -tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties tests/tests_data/additional_rastertypes.json tests/tests_data/grid.geojson tests/tests_data/listing.lst From ce984abfc59db7f82f92f91b0dc73d397c90413d Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 22 Nov 2024 17:41:59 +0100 Subject: [PATCH 04/56] refactor: wip xarray interface refactor: checking that the outputs with xarray are the same than with np --- src/eolab/rastertools/main.py | 6 + src/eolab/rastertools/processing/algo.py | 80 ++++----- .../rastertools/processing/rasterproc.py | 1 + .../rastertools/product/rasterproduct.py | 32 +++- src/eolab/rastertools/rastertools.py | 2 +- src/eolab/rastertools/speed.py | 86 +++++---- src/eolab/rastertools/timeseries.py | 89 ++++++++-- src/eolab/rastertools/zonalstats.py | 8 +- tests/test_algo.py | 167 +++++++++++++++++- tests/test_rastertools.py | 1 + tests/utils4test.py | 11 ++ 11 files changed, 383 insertions(+), 100 deletions(-) diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index c9dc9033..ef43a86d 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -220,8 +220,11 @@ def run_tool(args): # call function corresponding to the subcommand if "func" in argsdict: try: + print("before tool = args.func(args)") # initialize the rastertool to execute + # initialize the rastertool to execut tool = args.func(args) + print("after tool = args.func(args)") # handle the input file of type "lst" inputs = _extract_files_from_list(args.inputs) @@ -229,8 +232,11 @@ def run_tool(args): # setup debug mode in which intermediate VRT files are stored to disk or not tool.with_vrt_stored(args.keep_vrt) + print("before tool.process_files(inputs)") # launch process tool.process_files(inputs) + print("after tool.process_files(inputs)") + _logger.info("Done!") except RastertoolConfigurationException as rce: _logger.exception(rce) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 34774fda..81afb4e7 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -4,14 +4,16 @@ Algorithms on raster data """ import math +from typing import Union import numpy import numpy as np import numpy.ma as ma +import xarray as xr from scipy import ndimage, signal -def normalized_difference(bands : np.ndarray) -> numpy.ndarray : +def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Normalized Difference Vegetation Index The coefficient ranges from -1 to 1 in each pixel. @@ -33,7 +35,7 @@ def normalized_difference(bands : np.ndarray) -> numpy.ndarray : return (bands[1] - bands[0]) / (bands[1] + bands[0]) -def tndvi(bands : np.ndarray) -> numpy.ndarray : +def tndvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Transformed Normalized Difference Vegetation Index The coefficient is positive in each pixel. @@ -59,11 +61,15 @@ def tndvi(bands : np.ndarray) -> numpy.ndarray : """ np.seterr(invalid='ignore') ratio = normalized_difference(bands) + 0.5 - ratio[ratio < 0] = 0 + if (isinstance(ratio, np.ndarray) or isinstance(ratio, np.ma.masked_array)): + ratio[ratio < 0] = 0 + else: + if not ratio.isnull().all(): + ratio = ratio.where(ratio >= 0, 0) return np.sqrt(ratio) -def rvi(bands : np.ndarray) -> numpy.ndarray : +def rvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Ratio Vegetation Index The coefficient is positive in each pixel. @@ -89,7 +95,7 @@ def rvi(bands : np.ndarray) -> numpy.ndarray : return bands[1] / bands[0] -def pvi(bands : np.ndarray) -> numpy.ndarray : +def pvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Perpendicular Vegetation Index The coefficient ranges from -1 to 1 in each pixel. @@ -114,7 +120,7 @@ def pvi(bands : np.ndarray) -> numpy.ndarray : return (bands[1] - 0.90893 * bands[0] - 7.46216) * 0.74 -def savi(bands : np.ndarray) -> numpy.ndarray : +def savi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Soil Adjusted Vegetation Index The coefficient ranges from -1 to 1 in each pixel. @@ -139,7 +145,7 @@ def savi(bands : np.ndarray) -> numpy.ndarray : return (1. + 0.5) * (bands[1] - bands[0]) / (bands[1] + bands[0] + 0.5) -def tsavi(bands : np.ndarray) -> numpy.ndarray : +def tsavi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Transformed Soil Adjusted Vegetation Index The coefficient ranges from -1 to 1 in each pixel. @@ -168,7 +174,7 @@ def tsavi(bands : np.ndarray) -> numpy.ndarray : return numerator / denominator -def _wdvi(bands : numpy.ndarray) -> numpy.ndarray : +def _wdvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Weighted Difference Vegetation Index of the input data. @@ -188,7 +194,7 @@ def _wdvi(bands : numpy.ndarray) -> numpy.ndarray : return bands[1] - 0.4 * bands[0] -def msavi(bands : numpy.ndarray) -> numpy.ndarray : +def msavi(bands : Union[np.ndarray, xr.DataArray] ) -> Union[np.ndarray, xr.DataArray] : """ Compute the Modified Soil Adjusted Vegetation Index of the input data. The coefficient ranges from -1 to 1 in each pixel. @@ -228,7 +234,7 @@ def msavi(bands : numpy.ndarray) -> numpy.ndarray : return (1 + dl) * (bands[1] - bands[0]) / denominator -def msavi2(bands : numpy.ndarray) -> numpy.ndarray : +def msavi2(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Modified Soil Adjusted Vegetation Index of the input data. The coefficient ranges from -1 to 1 in each pixel. @@ -251,7 +257,7 @@ def msavi2(bands : numpy.ndarray) -> numpy.ndarray : return (2. * bands[1] + 1) - np.sqrt(dsqrt) -def ipvi(bands : numpy.ndarray) -> numpy.ndarray : +def ipvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Infrared Percentage Vegetation Index of the input data. The coefficient ranges from 0 to 1 in each pixel. @@ -277,7 +283,7 @@ def ipvi(bands : numpy.ndarray) -> numpy.ndarray : return bands[1] / (bands[1] + bands[0]) -def evi(bands : np.ndarray) -> numpy.ndarray : +def evi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Enhanced vegetation index of the input data. The coefficient ranges from -1 to 1 in each pixel. @@ -304,7 +310,7 @@ def evi(bands : np.ndarray) -> numpy.ndarray : return 2.5 * (bands[1] - bands[0]) / ((bands[1] + 6.0 * bands[0] - 7.5 * bands[2]) + 1.0) -def redness_index(bands : np.ndarray) -> numpy.ndarray : +def redness_index(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Redness Index of the input data. @@ -325,7 +331,7 @@ def redness_index(bands : np.ndarray) -> numpy.ndarray : return bands[0] ** 2 / bands[1] ** 3 -def brightness_index(bands : np.ndarray) -> numpy.ndarray : +def brightness_index(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Brightness Index of the input data. @@ -346,7 +352,7 @@ def brightness_index(bands : np.ndarray) -> numpy.ndarray : return np.sqrt(bi) -def brightness_index2(bands : np.ndarray) -> numpy.ndarray : +def brightness_index2(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Brightness Index of the input data. @@ -367,7 +373,7 @@ def brightness_index2(bands : np.ndarray) -> numpy.ndarray : return np.sqrt(bi2) -def speed(data0 : np.ndarray, data1 : np.ndarray, interval : float) -> numpy.ndarray : +def speed(data0 : Union[np.ndarray, xr.DataArray] , data1 : np.ndarray, interval : float) -> Union[np.ndarray, xr.DataArray] : """ Compute the speed of the input data based on the difference between two time points. @@ -392,7 +398,8 @@ def speed(data0 : np.ndarray, data1 : np.ndarray, interval : float) -> numpy.nda return (data1 - data0) / interval -def interpolated_timeseries(dates : numpy.ma.masked_array, series : numpy.ma.masked_array, output_dates : numpy.array, nodata) -> numpy.ndarray: + +def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], series : Union[numpy.ma.masked_array, xr.DataArray], output_dates : numpy.array, nodata) -> Union[np.ndarray, xr.DataArray] : """ Interpolate a timeseries of data. Dates and series must be sorted in ascending order. @@ -443,7 +450,7 @@ def interpolated_timeseries(dates : numpy.ma.masked_array, series : numpy.ma.mas -1, stack_shape[1], stack_shape[2], stack_shape[3]) -def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : +def _local_sum(data : Union[numpy.ndarray, xr.DataArray], kernel_width: int) -> Union[numpy.ndarray, xr.DataArray] : """ Computes the local sums of the input data using a sliding window defined by the kernel size. Each element in the output is the sum of the pixels within the specified kernel size window. @@ -468,6 +475,8 @@ def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : """ if kernel_width == 1: output = data.copy() + if isinstance(data, xr.DataArray): + output = xr.DataArray(output) else: # special case: size = 1 ==> returns data if np.issubdtype(data.dtype, np.floating): @@ -488,6 +497,8 @@ def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : # compute local sum at each pixel from integral image output = np.zeros(data.shape, dtype=ii.dtype) + if isinstance(data, xr.DataArray): + output = xr.DataArray(output) posd = (kernel_width + 1) // 2 posf = kernel_width - posd if data.ndim == 3: @@ -504,7 +515,7 @@ def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : return output.astype(data.dtype) -def median(input_data : np.ndarray, kernel_size : int) -> numpy.ndarray : +def median(input_data : Union[numpy.ndarray, xr.DataArray], kernel_size : int) -> Union[numpy.ndarray, xr.DataArray] : """ Applies a Median Filter to the input data using `scipy.ndimage.median_filter `_. The filter computes the median of the values contained within a sliding window determined by the kernel size. @@ -530,7 +541,7 @@ def median(input_data : np.ndarray, kernel_size : int) -> numpy.ndarray : return output -def local_sum(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : +def local_sum(input_data : Union[numpy.ndarray, xr.DataArray], kernel_size : int = 8) -> Union[numpy.ndarray, xr.DataArray] : """ Computes the local sums of the input data using a sliding window defined by the kernel size. Each element in the output is the sum of the pixels within the specified kernel size window. @@ -555,7 +566,7 @@ def local_sum(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : return output -def local_mean(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : +def local_mean(input_data : Union[numpy.ndarray, xr.DataArray], kernel_size : int = 8) -> Union[numpy.ndarray, xr.DataArray] : """ Computes the local means of the input data using a sliding window defined by the kernel size. Each element in the output is the mean of the pixels within the specified kernel size window. @@ -586,7 +597,7 @@ def local_mean(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray return np.divide(output, valid, out=np.zeros_like(output), where=valid != 0) -def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : int = 1) -> numpy.ndarray : +def adaptive_gaussian(input_data : Union[numpy.ndarray, xr.DataArray], kernel_size : int = 8, sigma : int = 1) -> Union[numpy.ndarray, xr.DataArray] : """ Applies an Adaptive Gaussian Filter to the input data that smoothes the input while preserving edges. @@ -611,10 +622,6 @@ def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : in if input_data.shape[0] != 1: raise ValueError("adaptive_gaussian only accepts numpy arrays with first dim of size 1") - ''' - kernel_size = kwargs.get('kernel_size', 8) - sigma = kwargs.get('sigma', 1) - ''' dtype = input_data.dtype w_1 = (input_data[0, :, :-2] - input_data[0, :, 2:]) ** 2 @@ -630,7 +637,7 @@ def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : in return out -def svf(input_data : np.ndarray, radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> np.ndarray: +def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> Union[numpy.ndarray, xr.DataArray]: """ Computes the Sky View Factor (SVF), which represents the fraction of the visible sky from each point in a Digital Height Model (DHM). @@ -662,15 +669,11 @@ def svf(input_data : np.ndarray, radius : int = 8, directions : int = 12, resolu raise ValueError("svf only accepts numpy arrays with first dim of size 1") nb_directions = directions - ''' - radius = kwargs.get('radius', 8) - nb_directions = kwargs.get('directions', 12) - resolution = kwargs.get('resolution', 0.5) - altitude = kwargs.get('altitude', None) - ''' # initialize output shape = input_data.shape out = np.zeros(shape, dtype=np.float32) + if isinstance(input_data, xr.DataArray) : + out = xr.DataArray(out) # prevent nodata problem # change the NaN in the input array to 0 @@ -753,7 +756,7 @@ def _bresenham_line(theta : int, radius : int) -> tuple : return pts -def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float = 0.0, radius : int = 8, resolution : float = 0.5) -> numpy.ndarray : +def hillshade(input_data : Union[numpy.ndarray, xr.DataArray], elevation : float = 0.0, azimuth : float = 0.0, radius : int = 8, resolution : float = 0.5) -> Union[numpy.ndarray, xr.DataArray] : """ Computes a mask of cast shadows in a Digital Height Model (DHM). @@ -784,15 +787,12 @@ def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float raise ValueError("hillshade only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") - ''' - elevation = np.radians(kwargs.get('elevation', 0.0)) - azimuth = kwargs.get('azimuth', 0.0) - radius = kwargs.get('radius', 8) - resolution = kwargs.get('resolution', 0.5) - ''' + # initialize output shape = input_data.shape out = np.zeros(shape, dtype=bool) + if isinstance(input_data, xr.DataArray): + out = xr.DataArray(out) # prevent nodata problem input_band = np.nan_to_num(input_data[0], copy=False, nan=0) diff --git a/src/eolab/rastertools/processing/rasterproc.py b/src/eolab/rastertools/processing/rasterproc.py index c708fb4c..e1795024 100644 --- a/src/eolab/rastertools/processing/rasterproc.py +++ b/src/eolab/rastertools/processing/rasterproc.py @@ -177,6 +177,7 @@ def configure(self, argsdict): [setattr(self, argument, argsdict[argument]) for argument in self.arguments if argument in argsdict] + def compute(self, input_data: Union[List[np.ndarray], np.ndarray]) -> numpy.ndarray: """Compute the output from the different bands of the input data. Output data are supposed to be the same size as input_data. diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 573fe00b..15ed21ef 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -15,11 +15,13 @@ from osgeo import gdal import rasterio +from rioxarray import rioxarray from eolab.rastertools import utils from eolab.rastertools.product import RasterType from eolab.rastertools.product.vrt import add_masks_to_vrt, set_band_descriptions from eolab.rastertools.processing.vector import crop +import xarray as xr __author__ = "Olivier Queyrut" __copyright__ = "Copyright 2019, CNES" @@ -208,7 +210,14 @@ def open(self, masks: Union[str, List[str]] = "all", roi: Union[Path, str] = None): """Proxy method to rasterio.open(rasterproduct.get_raster(...))""" - return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) + truc = rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) + print(truc) + print(dir(truc)) + # truc = truc.compute() + print(truc) + # pause = input("la") + # return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) + return rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) def get_raster(self, bands: Union[str, List[str]] = "all", @@ -414,6 +423,27 @@ def __create_vrt(self, del ds return rasterfile + + def open_xarray(self, + bands: Union[str, List[str]] = "all", + masks: Union[str, List[str]] = "all", + roi: Union[Path, str] = None, + chunks: Union[int, Dict[str, int], Tuple[int]] = None): + """Proxy method to xarray.open_rasterio(rasterproduct.get_raster(...))""" + print(bands) + raster = self.get_raster(bands=bands, masks=masks, roi=roi, create_maskband=True) + print('hej') + ds = rioxarray.open_rasterio(raster, masked=True, chunks=chunks) + print('opened with open_rasterio') + # ds = xa.to_dataset(dim="band") + print('before rename') + + print(ds) + # self.channels = list(range(len(ds.band.values))) + # ds = ds.rename({b + 1: self.rastertype.get_band_id(self.channels[b]) + # for b in range(len(ds.band.values))}) + print('after rename') + return ds def __wrap(self, input_vrt: Path, roi: Path, uuid: str = "") -> Path: """Clip the image to the given ROI. diff --git a/src/eolab/rastertools/rastertools.py b/src/eolab/rastertools/rastertools.py index 87500e96..d9bda63d 100644 --- a/src/eolab/rastertools/rastertools.py +++ b/src/eolab/rastertools/rastertools.py @@ -107,7 +107,7 @@ def process_files(self, inputfiles: List[str]): if outputs: all_outputs.extend(outputs) - # add a postprocessing call + # add a postprocessing call of the corresponding class outputs = self.postprocess_files(inputfiles, all_outputs) if outputs: diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index b30862b3..a2e47671 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -129,50 +129,58 @@ def compute_speed(date0: datetime, date1: datetime, # open input images with product0.open() as src0, product1.open() as src1: - if src1.count != src0.count: - raise ValueError(f"Number of bands in images {product0} and {product1}" + if src1.shape[0] != src0.shape[0]: + raise ValueError(f"Number of bands in images {src1.shape[0]} and {src0.count()}" " are not the same") - if src1.width != src0.width or src1.height != src0.height: - raise ValueError(f"Images {product0} and {product1} have different sizes") - if src1.transform != src0.transform: - raise ValueError(f"Images {product0} and {product1} are not fully" - " geographically overlapping") + # if src1.width != src0.width or src1.height != src0.height: + # raise ValueError(f"Images {product0} and {product1} have different sizes") + # if src1.transform != src0.transform: + # raise ValueError(f"Images {product0} and {product1} are not fully" + # " geographically overlapping") - profile = src0.profile + # profile = src0.profile dtype = rasterio.float32 # set block size - blockysize = 1024 if src0.width > 1024 else utils.highest_power_of_2(src0.width) - blockxsize = 1024 if src0.height > 1024 else utils.highest_power_of_2(src0.height) + # blockysize = 1024 if src0.width > 1024 else utils.highest_power_of_2(src0.width) + # blockxsize = 1024 if src0.height > 1024 else utils.highest_power_of_2(src0.height) # check band index and handle all bands options (when bands is an empty list) - if bands is None or len(bands) == 0: - bands = src1.indexes - elif min(bands) < 1 or max(bands) > src1.count: - raise ValueError(f"Invalid bands, all values are not in range [1, {src1.count}]") + # if bands is None or len(bands) == 0: + # bands = src1.indexes + # elif min(bands) < 1 or max(bands) > src1.count: + # raise ValueError(f"Invalid bands, all values are not in range [1, {src1.count}]") + + # profile.update(driver="GTiff", + # blockxsize=blockysize, blockysize=blockxsize, tiled=True, + # dtype=dtype, count=len(bands)) + print(type(src0)) + result = algo.speed(src0, src1, interval).astype(dtype)#.filled(src0.nodata) + # result = result.compute() + + ##Create the file and compute + result.rio.to_raster("/home/ecadaux/zob.tif") + print(result) + print("!"*50) + # with rasterio.open(speed_image, "w", **profile) as dst: + # # Materialize a list of destination block windows + # windows = [window for ij, window in dst.block_windows()] + # + # # read_lock = threading.Lock() + # # write_lock = threading.Lock() + # # + # # def process(window): + # # """Read input rasters, compute speed and write output raster""" + # # with read_lock: + # # data0 = src0.read(bands, window=window, masked=True).astype(dtype) + # # data1 = src1.read(bands, window=window, masked=True).astype(dtype) + # # + # # # The computation can be performed concurrently + # # result = algo.speed(data0, data1, interval).astype(dtype).filled(src0.nodata) + # # + # # with write_lock: + # # dst.write(result, window=window) + # + # disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + # thread_map(process, windows, disable=disable, desc="speed") - profile.update(driver="GTiff", - blockxsize=blockysize, blockysize=blockxsize, tiled=True, - dtype=dtype, count=len(bands)) - - with rasterio.open(speed_image, "w", **profile) as dst: - # Materialize a list of destination block windows - windows = [window for ij, window in dst.block_windows()] - - read_lock = threading.Lock() - write_lock = threading.Lock() - - def process(window): - """Read input rasters, compute speed and write output raster""" - with read_lock: - data0 = src0.read(bands, window=window, masked=True).astype(dtype) - data1 = src1.read(bands, window=window, masked=True).astype(dtype) - - # The computation can be performed concurrently - result = algo.speed(data0, data1, interval).astype(dtype).filled(src0.nodata) - - with write_lock: - dst.write(result, window=window) - - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - thread_map(process, windows, disable=disable, desc="speed") diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index 626b984a..10670037 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -13,6 +13,7 @@ import multiprocessing import os from pathlib import Path +import xarray as xr from typing import Dict, List import numpy as np @@ -24,7 +25,6 @@ from eolab.rastertools.processing import algo from eolab.rastertools.product import RasterProduct - _logger = logging.getLogger(__name__) @@ -76,7 +76,7 @@ def bands(self) -> List[int]: """List of bands to process""" return self._bands - def postprocess_files(self, inputfiles: List[str], outputfiles: List[str]) -> List[str]: + def postprocess_files(self, inputfiles: List[str], outputfiles: List[str], xarray_vers : bool = False) -> List[str]: """Generates the timeseries from a list of inputfiles. Args: @@ -124,26 +124,34 @@ def postprocess_files(self, inputfiles: List[str], outputfiles: List[str]) -> Li # STEP 3: Generate timeseries # create the list of output files - outdir = Path(self.outputdir) - timeseries_images = [] + outdir = Path("/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out" + "/test_timeseries_xarray") #Path(self.outputdir) # # # # # # + + times_img_np = [] + times_img_xarray = [] for date in dates: - img_name = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries.tif" - timeseries_images.append(outdir.joinpath(img_name).as_posix()) + img_name_np = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries.tif" + times_img_np.append(outdir.joinpath(img_name_np).as_posix()) + if xarray_vers : + img_name_xarray = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries_xarray.tif" + times_img_xarray.append(outdir.joinpath(img_name_xarray).as_posix()) # compute the timeseries - compute_timeseries(products_per_date, timestamps, timeseries_images, - self.bands, self.window_size) + compute_timeseries(products_per_date, timestamps, times_img_np, + self.bands, self.window_size, xarray_vers) # free resources for product in products_per_date.values(): product.free_in_memory_vrts() - return timeseries_images + if xarray_vers: + return times_img_np, times_img_xarray + else : + return times_img_np def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries_dates: List[float], timeseries_images: List[str], - bands: List[int] = None, window_size: tuple = (1024, 1024)): + bands: List[int] = None, window_size: tuple = (1024, 1024), xarray_vers : bool = False): """Generate the timeseries Args: @@ -223,13 +231,28 @@ def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries max_workers = os.getenv("RASTERTOOLS_MAXWORKERS") if max_workers is not None: kwargs["max_workers"] = int(max_workers) - process_map(_interpolate, - repeat(products_dates), repeat(products_per_date), - repeat(timeseries_dates), repeat(timeseries_images), - windows, repeat(bands), - repeat(dtype), repeat(nodata), - repeat(write_lock), - **kwargs) + + + if not(xarray_vers) : + #Launch with np + print("np") + process_map(_interpolate, + repeat(products_dates), repeat(products_per_date), + repeat(timeseries_dates), repeat(timeseries_images), + windows, repeat(bands), + repeat(dtype), repeat(nodata), + repeat(write_lock), + **kwargs) + else: + # Launch with xarray + print("xarray") + process_map(_interpolate_xarray, + repeat(products_dates), repeat(products_per_date), + repeat(timeseries_dates), repeat(timeseries_images), + windows, repeat(bands), + repeat(dtype), repeat(nodata), + repeat(write_lock), + **kwargs) def _interpolate(products_dates, products_per_date, @@ -254,3 +277,35 @@ def _interpolate(products_dates, products_per_date, for i, img in enumerate(timeseries_images): with rasterio.open(img, mode="r+") as dst: dst.write(output[i].astype(dtype), window=window) + + +def _interpolate_xarray(products_dates, products_per_date, + timeseries_dates, timeseries_images, + window, bands, + dtype, nodata, + write_lock): + """Internal method that performs the interpolation for a specific window. + This method can be called safely by several processes thanks to the locks + that prevent from reading / writing files simultaneously. + """ + datas = list() + for date in products_dates: + product = products_per_date[date] + + src = product.open_xarray() + band_data = src.isel(band=slice(0, len(bands))) # Select the desired bands + + # Process the desired window + window_data = band_data.isel(x=slice(window.col_off, window.col_off + window.width), + y=slice(window.row_off, window.row_off + window.height)) + + # data = src.read(bands, window=window, masked=True) + datas.append(window_data) + + + output = algo.interpolated_timeseries(products_dates, datas, timeseries_dates, nodata) + + with write_lock: + for i, img in enumerate(timeseries_images): + with rasterio.open(img, mode="r+") as dst: + dst.write(output[i].astype(dtype), window=window) diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 43a0ceec..12601b68 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -124,7 +124,7 @@ def generated_stats_per_date(self): that contains the statistics for each inputfile's date: - keys are timestamps - - values are the statistics at the corresponding timestam + - values are the statistics at the corresponding timestamp Warning: When the timestamp of the input raster cannot be retrieved, the dictionary does not @@ -139,6 +139,12 @@ def generated_stats_per_date(self): @property def generated_stats(self): + + my_data = truc.compute() + result = min(my_data) + + result = min(my_data) + my_data = truc.compute() """The list of generated stats in the same order as the input files""" return self._generated_stats diff --git a/tests/test_algo.py b/tests/test_algo.py index f25d738f..a52df276 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -1,16 +1,166 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +from datetime import datetime, timedelta +from pathlib import Path import numpy as np import numpy.ma as ma import rasterio +import pytest +import xarray as xr +from eolab.rastertools import utils from eolab.rastertools.processing import algo +from eolab.rastertools.product import RasterProduct __author__ = "Olivier Queyrut" __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from eolab.rastertools.timeseries import compute_timeseries, Timeseries +from .utils4test import RastertoolsTestsData + +zero_np = np.zeros((1,100,100)) +zero_xarr = xr.DataArray(zero_np, dims=("x", "y", "z")) + +rd_np = np.random.randn(1,100, 100) +rd_xarr = xr.DataArray(rd_np, dims=("x", "y", "z")) + +rd2_np = np.random.randn(1,100, 100) +rd2_xarr = xr.DataArray(rd2_np, dims=("x", "y", "z")) + +#Both zeros arrays +zero_nptest = [zero_np, zero_np] +zero_xarrtest = [zero_xarr, zero_xarr] + +#One random array and a zero array +rd1_nptest = [zero_np, rd_np] +rd1_xarrtest = [zero_xarr, rd_xarr] + +#Two same random array +rd2_nptest = [rd_np, rd_np] +rd2_xarrtest = [rd_xarr, rd_xarr] + +#Two different random array +rd3_nptest = [rd_np, rd2_np] +rd3_xarrtest = [rd_xarr, rd2_xarr] + +@pytest.mark.parametrize("input_np, input_xarray, interval", + [(zero_nptest, zero_xarrtest, 0.0), + (zero_nptest, zero_xarrtest, 5.0), + (rd1_nptest, rd1_xarrtest, 5.0), + (rd2_nptest, rd2_xarrtest, -4.0), + (rd3_nptest, rd3_xarrtest, 1.0)]) + + +def test_speed_algo(input_np :list, input_xarray : list, interval : float): + """ + Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray + """ + data1_np, data2_np = input_np + data1_xarr, data2_xarr = input_xarray + speed_np = algo.speed(data1_np, data2_np, interval) + speed_xarr = algo.speed(data1_xarr, data2_xarr, interval) + + #Assert that the values of both arrays are the same + np.testing.assert_array_equal(speed_np, speed_xarr.values) + assert type(speed_xarr) == xr.DataArray + + +input_file1 = "tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" +input_file2 = "tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" +file_list = [input_file1, input_file2] + +@pytest.mark.parametrize("file_list, start_date, end_date, period, window_size", + [(file_list,"2018-09-26", "2018-11-07" , 20, (512,512))]) + + +def test_xarray_timeseries_algo(file_list : list, start_date :str, end_date: str, period : int, window_size : tuple): + """ + Test if the output of the timeseries algorithm obtained with numpy.ndarray are the same that with xarray.DataArray + """ + # outputdir = RastertoolsTestsData.tests_output_data_dir + "/test_timeseries_xarray" + bands = [1] + + start_date = datetime.strptime(start_date, "%Y-%m-%d") + end_date = datetime.strptime(end_date, "%Y-%m-%d") + + all_outputs = [] + for filename in file_list: + outputs = list(filename) + if outputs: + all_outputs.extend(outputs) + + # create the rastertool object + tools = Timeseries(start_date, end_date, period, bands) + out_imgs_np, out_imgs_xarray = tools.postprocess_files(file_list, all_outputs) + + + for product_id in range(len(out_imgs_np)) : + with rasterio.open(out_imgs_np[product_id]) as src_np: + times_np = src_np.read(bands, masked=True) + with rasterio.open(out_imgs_xarray[product_id]) as src_xarray: + times_xarray = src_xarray.read(bands, masked=True) + + + np.testing.assert_array_equal(times_np, times_xarray) + + +zero3d_np = np.zeros((3,3,3)) +zero3d_xarr = xr.DataArray(zero3d_np, dims=("x", "y", "z")) + +rd3d_np = np.random.randn(3,3,3) +rd3d_xarr = xr.DataArray(rd3d_np, dims=("x", "y", "z")) + +@pytest.mark.parametrize("input_np, input_xarray", [(zero3d_np,zero3d_xarr), + (rd3d_np,rd3d_xarr)]) + +def test_indices_algo(input_np :np.ndarray, input_xarray : xr.DataArray): + """ + Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray + """ + ind_func = [algo.normalized_difference, algo.rvi, algo.tndvi, algo.pvi, algo.savi, algo.tsavi, algo.msavi, algo.msavi2, algo.ipvi, + algo.evi, algo.redness_index, algo.brightness_index, algo.brightness_index2] + + for indic in ind_func : + ind_np = indic(input_np) + ind_xarr = indic(input_xarray) + #Assert that the values of both arrays are the same + np.testing.assert_array_equal(ind_np, ind_xarr.values) + assert type(ind_xarr) == xr.DataArray + + +@pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [50, 16, 0.5, None]), + (zero_np,zero_xarr, [50, 16, 0.5, 0]), + (rd_np,rd_xarr, [50, 16, 0.5, None]), + (rd_np,rd_xarr, [50, 16, 0.5, 0])]) + +def test_svf_algo(input_np : np.ndarray, input_xarray : xr.DataArray, params : list): + """ + Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray + """ + radius, directions, resolution, altitude = params + svf_np = algo.svf(input_np, radius, directions, resolution, altitude) + svf_xarr = algo.svf(input_xarray, radius, directions, resolution, altitude) + #Assert that the values of both arrays are the same + np.testing.assert_array_equal(svf_np, svf_xarr.values) + assert type(svf_xarr) == xr.DataArray + + +@pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [27.2, 82.64, 3, 0.5]), + (rd_np,rd_xarr, [27.2, 82.64, 3, 0.5])]) + +def test_hillshade_algo(input_np : np.ndarray, input_xarray : xr.DataArray, params : list): + """ + Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray + """ + elevation, azimuth , radius, resolution = params + hills_np = algo.hillshade(input_np, elevation, azimuth , radius, resolution) + hills_xarr = algo.hillshade(input_xarray, elevation, azimuth , radius, resolution) + #Assert that the values of both arrays are the same + np.testing.assert_array_equal(hills_np, hills_xarr.values) + assert type(hills_xarr) == xr.DataArray + def test_local_sum(): """ @@ -71,6 +221,21 @@ def test_local_sum(): assert (output[0] == results[i - 1]).all() + +@pytest.mark.parametrize("input_np, input_xarray, kernel_width", [(zero_np,zero_xarr, 5)]) + +def test_xarray_local_mean(input_np : np.ndarray, input_xarray : xr.DataArray, kernel_width : int): + """ + Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray + """ + mean_np = algo.local_mean(ma.array(input_np), kernel_width) + mean_xarr = algo.local_mean(input_xarray, kernel_width) + + #Assert that the values of both arrays are the same + np.testing.assert_array_equal(mean_np, mean_xarr.values) + assert type(mean_xarr) == xr.DataArray + + def test_local_mean(): """ Test the local mean filter with a kernel size of 2. @@ -105,7 +270,7 @@ def test_local_mean(): ) mask = np.pad(mask, (radius, radius), mode="edge") - array = ma.array(band, mask=mask) # masks the band array + array = ma.array(band, mask=mask) # masks the band array # ie. removes the first line and first column of band # output shape is array shape - kernel_width diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index f9c54b46..4fb50e87 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -399,6 +399,7 @@ def test_timeseries_command_line_errors(caplog): test.run_test(caplog, check_outputs=False) + def test_zonalstats_command_line_default(): # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/utils4test.py b/tests/utils4test.py index ccc59eb6..a315cbcf 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -2,6 +2,7 @@ # -*- coding: utf-8 -*- import os import shutil +from dataclasses import dataclass from pathlib import Path from . import cmptools @@ -16,6 +17,16 @@ __root_refdir = "tests/tests_refs/" +@dataclass +class RastertoolsTestsData: + + project_dir: Path = Path(__file__).parent.parent + tests_project_dir:str = str(project_dir) + tests_input_data_dir:str = str(project_dir / "tests" / "tests_data" ) + tests_output_data_dir:str = str(project_dir / "tests" / "tests_out") + tests_ref_data_dir:str = str(project_dir / "tests" / "tests_refs") + + def get_refdir(testname: str): return __root_refdir + testname From 875378ec9a1133bed787fdce5b69f814d0a68342 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 25 Nov 2024 11:22:12 +0100 Subject: [PATCH 05/56] refactor: wip xarray interface refactor: checking similarity for timeseries np and xarray --- .../rastertools/product/rasterproduct.py | 39 ++++----- src/eolab/rastertools/timeseries.py | 11 ++- src/eolab/rastertools/zonalstats.py | 11 +-- tests/test_algo.py | 85 +++++++++++-------- 4 files changed, 80 insertions(+), 66 deletions(-) diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 15ed21ef..3e104228 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -216,8 +216,23 @@ def open(self, # truc = truc.compute() print(truc) # pause = input("la") - # return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) - return rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) + return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) + # return rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) + + def open_xarray(self, + bands: Union[str, List[str]] = "all", + masks: Union[str, List[str]] = "all", + roi: Union[Path, str] = None, + chunks: Union[int, Dict[str, int], Tuple[int]] = None): + """Proxy method to xarray.open_rasterio(rasterproduct.get_raster(...))""" + raster = self.get_raster(bands=bands, masks=masks, roi=roi, create_maskband=True) + ds = rioxarray.open_rasterio(raster, masked=True, chunks=chunks) + # ds = xa.to_dataset(dim="band") + + # self.channels = list(range(len(ds.band.values))) + # ds = ds.rename({b + 1: self.rastertype.get_band_id(self.channels[b]) + # for b in range(len(ds.band.values))}) + return ds def get_raster(self, bands: Union[str, List[str]] = "all", @@ -424,26 +439,6 @@ def __create_vrt(self, return rasterfile - def open_xarray(self, - bands: Union[str, List[str]] = "all", - masks: Union[str, List[str]] = "all", - roi: Union[Path, str] = None, - chunks: Union[int, Dict[str, int], Tuple[int]] = None): - """Proxy method to xarray.open_rasterio(rasterproduct.get_raster(...))""" - print(bands) - raster = self.get_raster(bands=bands, masks=masks, roi=roi, create_maskband=True) - print('hej') - ds = rioxarray.open_rasterio(raster, masked=True, chunks=chunks) - print('opened with open_rasterio') - # ds = xa.to_dataset(dim="band") - print('before rename') - - print(ds) - # self.channels = list(range(len(ds.band.values))) - # ds = ds.rename({b + 1: self.rastertype.get_band_id(self.channels[b]) - # for b in range(len(ds.band.values))}) - print('after rename') - return ds def __wrap(self, input_vrt: Path, roi: Path, uuid: str = "") -> Path: """Clip the image to the given ROI. diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index 10670037..cd992bba 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -132,12 +132,15 @@ def postprocess_files(self, inputfiles: List[str], outputfiles: List[str], xarra img_name_np = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries.tif" times_img_np.append(outdir.joinpath(img_name_np).as_posix()) if xarray_vers : - img_name_xarray = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries_xarray.tif" + img_name_xarray = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries-xarray.tif" times_img_xarray.append(outdir.joinpath(img_name_xarray).as_posix()) # compute the timeseries compute_timeseries(products_per_date, timestamps, times_img_np, - self.bands, self.window_size, xarray_vers) + self.bands, self.window_size, xarray_vers = False) + if xarray_vers: + compute_timeseries(products_per_date, timestamps, times_img_xarray, + self.bands, self.window_size, xarray_vers = xarray_vers) # free resources for product in products_per_date.values(): @@ -166,6 +169,8 @@ def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries window_size (tuple(int, int), optional, default=(1024, 1024)): Size of windows for splitting the process in small parts """ + print('...'*50) + print(timeseries_images) with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): # open all input rasters @@ -212,6 +217,7 @@ def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries dtype = refprofile.get("dtype") nodata = refprofile.get("nodata") + print("file creation") # create empty output files with correct metadata for i, img in enumerate(timeseries_images): with rasterio.open(img, mode="w", **refprofile) as dst: @@ -246,6 +252,7 @@ def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries else: # Launch with xarray print("xarray") + process_map(_interpolate_xarray, repeat(products_dates), repeat(products_per_date), repeat(timeseries_dates), repeat(timeseries_images), diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 12601b68..fc899051 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -140,11 +140,12 @@ def generated_stats_per_date(self): @property def generated_stats(self): - my_data = truc.compute() - result = min(my_data) - - result = min(my_data) - my_data = truc.compute() + print("suppr") + # my_data = truc.compute() + # result = min(my_data) + # + # result = min(my_data) + # my_data = truc.compute() """The list of generated stats in the same order as the input files""" return self._generated_stats diff --git a/tests/test_algo.py b/tests/test_algo.py index a52df276..7a69d6d9 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -8,6 +8,7 @@ import rasterio import pytest import xarray as xr +from rioxarray import rioxarray from eolab.rastertools import utils from eolab.rastertools.processing import algo @@ -67,43 +68,53 @@ def test_speed_algo(input_np :list, input_xarray : list, interval : float): assert type(speed_xarr) == xr.DataArray -input_file1 = "tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" -input_file2 = "tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" -file_list = [input_file1, input_file2] - -@pytest.mark.parametrize("file_list, start_date, end_date, period, window_size", - [(file_list,"2018-09-26", "2018-11-07" , 20, (512,512))]) - - -def test_xarray_timeseries_algo(file_list : list, start_date :str, end_date: str, period : int, window_size : tuple): - """ - Test if the output of the timeseries algorithm obtained with numpy.ndarray are the same that with xarray.DataArray - """ - # outputdir = RastertoolsTestsData.tests_output_data_dir + "/test_timeseries_xarray" - bands = [1] - - start_date = datetime.strptime(start_date, "%Y-%m-%d") - end_date = datetime.strptime(end_date, "%Y-%m-%d") - - all_outputs = [] - for filename in file_list: - outputs = list(filename) - if outputs: - all_outputs.extend(outputs) - - # create the rastertool object - tools = Timeseries(start_date, end_date, period, bands) - out_imgs_np, out_imgs_xarray = tools.postprocess_files(file_list, all_outputs) - - - for product_id in range(len(out_imgs_np)) : - with rasterio.open(out_imgs_np[product_id]) as src_np: - times_np = src_np.read(bands, masked=True) - with rasterio.open(out_imgs_xarray[product_id]) as src_xarray: - times_xarray = src_xarray.read(bands, masked=True) - - - np.testing.assert_array_equal(times_np, times_xarray) +# input_file1 = "tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" +# input_file2 = "tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" +# file_list = [input_file1, input_file2] +# +# @pytest.mark.parametrize("file_list, start_date, end_date, period, window_size", +# [(file_list,"2018-09-26", "2018-11-07" , 20, (512,512))]) +# +# +# def test_xarray_timeseries_algo(file_list : list, start_date :str, end_date: str, period : int, window_size : tuple): +# """ +# Test if the output of the timeseries algorithm obtained with numpy.ndarray are the same that with xarray.DataArray +# Only nan values are different +# """ +# # outputdir = RastertoolsTestsData.tests_output_data_dir + "/test_timeseries_xarray" +# bands = [1] +# +# start_date = datetime.strptime(start_date, "%Y-%m-%d") +# end_date = datetime.strptime(end_date, "%Y-%m-%d") +# +# all_outputs = [] +# for filename in file_list: +# outputs = list(filename) +# if outputs: +# all_outputs.extend(outputs) +# +# # # create the rastertool object +# # tools = Timeseries(start_date, end_date, period, bands) +# # out_imgs_np, out_imgs_xarray = tools.postprocess_files(file_list, all_outputs, xarray_vers = True) +# # +# +# out_imgs_np = ['/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20180926-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif', '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif', '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181105-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif'] +# out_imgs_xarray = [ +# '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20180926-000000-685_L2A_T30TYP_D-ndvi-timeseries-xarray.tif', +# '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries-xarray.tif', +# '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181105-000000-685_L2A_T30TYP_D-ndvi-timeseries-xarray.tif'] +# +# +# for product_id in range(len(out_imgs_np)) : +# with rasterio.open(out_imgs_np[product_id]) as src_np: +# times_np = src_np.read(bands, masked=True) +# +# src_xarray = rioxarray.open_rasterio(out_imgs_xarray[product_id]) +# times_xarray = src_xarray.sel(band=bands).values.astype(np.float32) +# +# if isinstance(times_np, np.ma.MaskedArray): +# times_np = times_np.filled(np.nan) +# np.testing.assert_allclose(times_np, times_xarray, equal_nan=True) zero3d_np = np.zeros((3,3,3)) From 05189dc9b0f3eef358898131592f973874e38d6f Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 25 Nov 2024 18:09:58 +0100 Subject: [PATCH 06/56] refactor: xarray interface for timeseries and speed --- src/eolab/rastertools/processing/algo.py | 65 +++++++++++++++++- .../rastertools/product/rasterproduct.py | 4 +- src/eolab/rastertools/speed.py | 66 +++++-------------- tests/test_rastertools.py | 2 +- 4 files changed, 83 insertions(+), 54 deletions(-) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 81afb4e7..641a4c7f 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -10,6 +10,7 @@ import numpy as np import numpy.ma as ma import xarray as xr +from rasterio import rio from scipy import ndimage, signal @@ -399,7 +400,7 @@ def speed(data0 : Union[np.ndarray, xr.DataArray] , data1 : np.ndarray, interval -def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], series : Union[numpy.ma.masked_array, xr.DataArray], output_dates : numpy.array, nodata) -> Union[np.ndarray, xr.DataArray] : +def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], series : list, output_dates : numpy.array, nodata) -> Union[np.ndarray, xr.DataArray] : """ Interpolate a timeseries of data. Dates and series must be sorted in ascending order. @@ -426,6 +427,7 @@ def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], """ #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays stack = ma.stack(series) + type (stack) stack_shape = stack.shape # flatten the stacked data: shape is pixel x time pixel_series = stack.transpose((1, 2, 3, 0)).reshape( @@ -446,8 +448,65 @@ def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], output.append([default_val] * len(output_dates)) output = np.array(output) - return output.transpose(1, 0).reshape( - -1, stack_shape[1], stack_shape[2], stack_shape[3]) + output = output.transpose(1,0).reshape(-1, stack_shape[1], stack_shape[2], stack_shape[3]) + + if isinstance(series[0], xr.DataArray): + output = xr.DataArray(np.flip(output,2), dims = ['time', 'bands', 'y', 'x']) + return output + + +# def interpolated_timeseries_xarray(dates : xr.DataArray, series : list, output_dates : numpy.array, nodata) -> xr.DataArray : +# """ +# Interpolate a timeseries of data. Dates and series must be sorted in ascending order. +# +# Args: +# dates (numpy.ma.masked_array): A masked array of timestamps (dates) corresponding to +# the input series. Should be in ascending order. +# +# series (numpy.ma.masked_array): A list of 3D masked arrays, each with shape +# (bands, height, width), containing the raster data +# for each timestamp in `dates`. +# +# output_dates (numpy.array): A 1D array of timestamps for which to generate the interpolated +# rasters. +# +# nodata (float): Value to use for pixels where input data is NaN or missing. +# +# Returns: +# numpy.ndarray: A 4D numpy array of shape (time, bands, height, width), containing +# the interpolated raster data for each output date. If there are no valid +# data points for a specific pixel, the corresponding pixel will be filled with `nodata`. +# +# Raises: +# ValueError: If `series` is empty, or if `dates` and `series` dimensions do not match. +# """ +# #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays +# stack = xr.concat(series, dim="time") +# stack_shape = stack.shape +# +# # Flatten spatial dimensions into a single pixel axis (time x pixels) +# stack_flat = stack.stack(pixel=("x", "y", "band")) +# # Transpose to have shape (pixels x time) +# pixel_series = stack_flat.transpose("pixel","time") +# +# output = [] #xr.DataArray(rd2_np, dims=("x", "y", "z")) +# for serie in pixel_series: +# compressed = serie.compressed() +# if serie.count() > 1: +# output.append(np.interp( +# output_dates, +# dates.where(serie.mask).compressed(), +# compressed, +# compressed[0], +# compressed[-1])) +# else: +# default_val = serie.sum() if serie.count() > 0 else nodata +# output.append([default_val] * len(output_dates)) +# +# # output = np.array(output) +# +# return output.transpose(1, 0).reshape( +# -1, stack_shape[1], stack_shape[2], stack_shape[3]) def _local_sum(data : Union[numpy.ndarray, xr.DataArray], kernel_width: int) -> Union[numpy.ndarray, xr.DataArray] : diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 3e104228..8df3b3b1 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -214,7 +214,6 @@ def open(self, print(truc) print(dir(truc)) # truc = truc.compute() - print(truc) # pause = input("la") return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) # return rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) @@ -226,7 +225,8 @@ def open_xarray(self, chunks: Union[int, Dict[str, int], Tuple[int]] = None): """Proxy method to xarray.open_rasterio(rasterproduct.get_raster(...))""" raster = self.get_raster(bands=bands, masks=masks, roi=roi, create_maskband=True) - ds = rioxarray.open_rasterio(raster, masked=True, chunks=chunks) + ds = rioxarray.open_rasterio(raster, masked=True, chunks=True) + print(ds) # ds = xa.to_dataset(dim="band") # self.channels = list(range(len(ds.band.values))) diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index a2e47671..ad740052 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -128,59 +128,29 @@ def compute_speed(date0: datetime, date1: datetime, interval = (date1 - date0).total_seconds() # open input images - with product0.open() as src0, product1.open() as src1: + with product0.open_xarray() as src0, product1.open_xarray() as src1: + if src1.shape[0] != src0.shape[0]: - raise ValueError(f"Number of bands in images {src1.shape[0]} and {src0.count()}" + raise ValueError(f"Number of bands in images {src1.shape[0]} and {src1.shape[0]}" " are not the same") - # if src1.width != src0.width or src1.height != src0.height: - # raise ValueError(f"Images {product0} and {product1} have different sizes") - # if src1.transform != src0.transform: - # raise ValueError(f"Images {product0} and {product1} are not fully" - # " geographically overlapping") + if src1.shape[1] != src0.shape[1] or src1.shape[2] != src0.shape[2]: + raise ValueError(f"All images have not the same size") + if src1.rio.transform() != src0.rio.transform() : + raise ValueError(f"Images {product0} and {product1} are not fully" + " geographically overlapping") - # profile = src0.profile - dtype = rasterio.float32 + # check band index and handle all bands options (when bands is an empty list) + if bands is None or len(bands) == 0: + bands = src1["band"].values + elif min(bands) < 1 or max(bands) > src1.shape[0]: + raise ValueError(f"Invalid bands, all values are not in range [1, {src1.shape[0]}]") - # set block size - # blockysize = 1024 if src0.width > 1024 else utils.highest_power_of_2(src0.width) - # blockxsize = 1024 if src0.height > 1024 else utils.highest_power_of_2(src0.height) + dtype = rasterio.float32 + src0 = src0.isel(band=slice(0, len(bands))) + src1 = src1.isel(band=slice(0, len(bands))) - # check band index and handle all bands options (when bands is an empty list) - # if bands is None or len(bands) == 0: - # bands = src1.indexes - # elif min(bands) < 1 or max(bands) > src1.count: - # raise ValueError(f"Invalid bands, all values are not in range [1, {src1.count}]") - - # profile.update(driver="GTiff", - # blockxsize=blockysize, blockysize=blockxsize, tiled=True, - # dtype=dtype, count=len(bands)) - print(type(src0)) result = algo.speed(src0, src1, interval).astype(dtype)#.filled(src0.nodata) - # result = result.compute() - + print(result.dims) ##Create the file and compute - result.rio.to_raster("/home/ecadaux/zob.tif") - print(result) - print("!"*50) - # with rasterio.open(speed_image, "w", **profile) as dst: - # # Materialize a list of destination block windows - # windows = [window for ij, window in dst.block_windows()] - # - # # read_lock = threading.Lock() - # # write_lock = threading.Lock() - # # - # # def process(window): - # # """Read input rasters, compute speed and write output raster""" - # # with read_lock: - # # data0 = src0.read(bands, window=window, masked=True).astype(dtype) - # # data1 = src1.read(bands, window=window, masked=True).astype(dtype) - # # - # # # The computation can be performed concurrently - # # result = algo.speed(data0, data1, interval).astype(dtype).filled(src0.nodata) - # # - # # with write_lock: - # # dst.write(result, window=window) - # - # disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - # thread_map(process, windows, disable=disable, desc="speed") + result.rio.to_raster(speed_image) diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 4fb50e87..83294ef1 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - utils4test.clear_outdir() + # utils4test.clear_outdir() def test_rastertools_command_line_info(): From 3294fd631e90b72efcb8c9fce1e1d911c2399ad1 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 25 Nov 2024 18:10:15 +0100 Subject: [PATCH 07/56] refactor: xarray interface for timeseries and speed --- src/eolab/rastertools/timeseries.py | 156 +++++----------------------- 1 file changed, 27 insertions(+), 129 deletions(-) diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index cd992bba..755ddd5d 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -76,7 +76,7 @@ def bands(self) -> List[int]: """List of bands to process""" return self._bands - def postprocess_files(self, inputfiles: List[str], outputfiles: List[str], xarray_vers : bool = False) -> List[str]: + def postprocess_files(self, inputfiles: List[str], outputfiles: List[str]) -> List[str]: """Generates the timeseries from a list of inputfiles. Args: @@ -124,37 +124,27 @@ def postprocess_files(self, inputfiles: List[str], outputfiles: List[str], xarra # STEP 3: Generate timeseries # create the list of output files - outdir = Path("/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out" + "/test_timeseries_xarray") #Path(self.outputdir) # # # # # # + outdir = Path(self.outputdir) - times_img_np = [] - times_img_xarray = [] + times_img = [] for date in dates: - img_name_np = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries.tif" - times_img_np.append(outdir.joinpath(img_name_np).as_posix()) - if xarray_vers : - img_name_xarray = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries-xarray.tif" - times_img_xarray.append(outdir.joinpath(img_name_xarray).as_posix()) + img_name = f"{template_name.format(date.strftime(reftype.date_format))}-timeseries.tif" + times_img.append(outdir.joinpath(img_name).as_posix()) # compute the timeseries - compute_timeseries(products_per_date, timestamps, times_img_np, - self.bands, self.window_size, xarray_vers = False) - if xarray_vers: - compute_timeseries(products_per_date, timestamps, times_img_xarray, - self.bands, self.window_size, xarray_vers = xarray_vers) + compute_timeseries(products_per_date, timestamps, times_img, + self.bands) # free resources for product in products_per_date.values(): product.free_in_memory_vrts() - if xarray_vers: - return times_img_np, times_img_xarray - else : - return times_img_np + return times_img -def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries_dates: List[float], +def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries_dates: np.ndarray, timeseries_images: List[str], - bands: List[int] = None, window_size: tuple = (1024, 1024), xarray_vers : bool = False): + bands: List[int] = None): """Generate the timeseries Args: @@ -169,8 +159,6 @@ def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries window_size (tuple(int, int), optional, default=(1024, 1024)): Size of windows for splitting the process in small parts """ - print('...'*50) - print(timeseries_images) with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): # open all input rasters @@ -178,141 +166,51 @@ def compute_timeseries(products_per_date: Dict[float, RasterProduct], timeseries for i, date in enumerate(products_dates): product = products_per_date[date] - with product.open() as src: + with product.open_xarray() as src: # check if srcs have same size and are geographically overlapping if i == 0: - refcount = src.count - refindexes = src.indexes - refwidth = src.width - refheight = src.height - reftransform = src.transform - refprofile = src.profile - descriptions = src.descriptions + refcount = src.shape[0] + refindexes = src["band"].values + refwidth = src.shape[2] + refheight = src.shape[1] + reftransform = src.rio.transform() else: - if src.count != refcount: + if src.shape[0] != refcount: raise ValueError(f"All images have not the same number of bands") - if src.width != refwidth or src.height != refheight: + if src.shape[2] != refwidth or src.shape[1] != refheight : raise ValueError(f"All images have not the same size") - if src.transform != reftransform: + if src.rio.transform() != reftransform: raise ValueError(f"All images are not fully" " geographically overlapping") - # set block size - blockxsize, blockysize = window_size - if refwidth < blockxsize: - blockxsize = utils.highest_power_of_2(refwidth) - if refheight < blockysize: - blockysize = utils.highest_power_of_2(refheight) - # check band index and handle all bands options (when bands is an empty list) if bands is None or len(bands) == 0: bands = refindexes elif min(bands) < 1 or max(bands) > refcount: raise ValueError(f"Invalid bands, all values are not in range [1, {refcount}]") - # update the profile to use for opening output files - refprofile.update(driver="GTiff", - blockxsize=blockysize, blockysize=blockxsize, tiled=True, - count=len(bands)) - dtype = refprofile.get("dtype") - nodata = refprofile.get("nodata") - - print("file creation") - # create empty output files with correct metadata - for i, img in enumerate(timeseries_images): - with rasterio.open(img, mode="w", **refprofile) as dst: - if i == 0: - # with rasterio.open(timeseries_images[0], mode="w", **profile) as dst: - windows = [window for ij, window in dst.block_windows()] - for j, band in enumerate(bands, 1): - dst.set_band_description(j, descriptions[band - 1]) - - m = multiprocessing.Manager() - write_lock = m.Lock() - - kwargs = { - "total": len(windows), - "disable": os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - } - max_workers = os.getenv("RASTERTOOLS_MAXWORKERS") - if max_workers is not None: - kwargs["max_workers"] = int(max_workers) - - - if not(xarray_vers) : - #Launch with np - print("np") - process_map(_interpolate, - repeat(products_dates), repeat(products_per_date), - repeat(timeseries_dates), repeat(timeseries_images), - windows, repeat(bands), - repeat(dtype), repeat(nodata), - repeat(write_lock), - **kwargs) - else: - # Launch with xarray - print("xarray") - - process_map(_interpolate_xarray, - repeat(products_dates), repeat(products_per_date), - repeat(timeseries_dates), repeat(timeseries_images), - windows, repeat(bands), - repeat(dtype), repeat(nodata), - repeat(write_lock), - **kwargs) - - -def _interpolate(products_dates, products_per_date, - timeseries_dates, timeseries_images, - window, bands, - dtype, nodata, - write_lock): - """Internal method that performs the interpolation for a specific window. - This method can be called safely by several processes thanks to the locks - that prevent from reading / writing files simultaneously. - """ - datas = list() - for date in products_dates: - product = products_per_date[date] - with product.open() as src: - data = src.read(bands, window=window, masked=True) - datas.append(data) + nodata = src.rio.nodata - output = algo.interpolated_timeseries(products_dates, datas, timeseries_dates, nodata) + _interpolate_xarray(products_dates, products_per_date, + timeseries_dates, timeseries_images, bands, nodata) - with write_lock: - for i, img in enumerate(timeseries_images): - with rasterio.open(img, mode="r+") as dst: - dst.write(output[i].astype(dtype), window=window) def _interpolate_xarray(products_dates, products_per_date, - timeseries_dates, timeseries_images, - window, bands, - dtype, nodata, - write_lock): + timeseries_dates, timeseries_images, bands, nodata): """Internal method that performs the interpolation for a specific window. This method can be called safely by several processes thanks to the locks that prevent from reading / writing files simultaneously. """ datas = list() for date in products_dates: - product = products_per_date[date] - src = product.open_xarray() + src = products_per_date[date].open_xarray() band_data = src.isel(band=slice(0, len(bands))) # Select the desired bands - # Process the desired window - window_data = band_data.isel(x=slice(window.col_off, window.col_off + window.width), - y=slice(window.row_off, window.row_off + window.height)) - - # data = src.read(bands, window=window, masked=True) - datas.append(window_data) - + datas.append(band_data) output = algo.interpolated_timeseries(products_dates, datas, timeseries_dates, nodata) - with write_lock: - for i, img in enumerate(timeseries_images): - with rasterio.open(img, mode="r+") as dst: - dst.write(output[i].astype(dtype), window=window) + for i, img in enumerate(timeseries_images): + output[i].rio.to_raster(img) From fb2265f83f0fd6ae537731e4b1559c31767b4aa2 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 26 Nov 2024 15:19:14 +0100 Subject: [PATCH 08/56] refactor: wip xarray interface for filtering --- src/eolab/rastertools/filtering.py | 43 +++-- src/eolab/rastertools/processing/algo.py | 171 ++++++++++-------- .../rastertools/processing/rasterproc.py | 3 +- src/eolab/rastertools/processing/sliding.py | 6 +- .../rastertools/product/rasterproduct.py | 6 - src/eolab/rastertools/speed.py | 4 - src/eolab/rastertools/timeseries.py | 11 +- ...7-455_L2A_T30TYP_D_targz.TAR.GZ.properties | 2 - ...CE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties | 2 - 9 files changed, 141 insertions(+), 107 deletions(-) delete mode 100644 tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties delete mode 100644 tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties diff --git a/src/eolab/rastertools/filtering.py b/src/eolab/rastertools/filtering.py index 3f503b1c..52d48dda 100644 --- a/src/eolab/rastertools/filtering.py +++ b/src/eolab/rastertools/filtering.py @@ -8,6 +8,10 @@ import logging.config from typing import List, Dict from pathlib import Path +import multiprocessing + +import rasterio +from rioxarray import rioxarray from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable @@ -160,8 +164,6 @@ def __init__(self, raster_filter: RasterFilter, kernel_size: int, bands: List[in Set None if all bands shall be processed. """ super().__init__() - # initialize default windowing configuration - self.with_windows() # the raster filter processing self._raster_filter = raster_filter self._raster_filter.configure({"kernel_size": kernel_size}) @@ -205,13 +207,6 @@ def process_file(self, inputfile: str) -> List[str]: """ _logger.info(f"Processing file {inputfile}") - overlap = (self.raster_filter.kernel_size + 1) // 2 - if overlap >= min(self.window_size) / 2: - raise ValueError("The kernel size (option --kernel_size, " - f"value={self.raster_filter.kernel_size}) " - "must be strictly less than the window size minus 1 " - f"(option --window_size, value={min(self.window_size)})") - # STEP 1: Prepare the input image so that it can be processed with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: @@ -220,11 +215,29 @@ def process_file(self, inputfile: str) -> List[str]: output_image = outdir.joinpath( f"{utils.get_basename(inputfile)}-{self.raster_filter.name}.tif") - compute_sliding( - product.get_raster(), output_image, self.raster_filter, - window_size=self.window_size, - window_overlap=(self.raster_filter.kernel_size + 1) // 2, - pad_mode=self.pad_mode, - bands=self.bands) + input_image = product.get_raster() + rasterprocessing = self.raster_filter + + with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + print(product) + with rioxarray.open_rasterio(input_image, chunks=True) as src: + print(src) + # dtype and creation options of output data + dtype = rasterprocessing.dtype or rasterio.float32 + + # check band index and handle all bands options (when bands is an empty list) + bands = self.bands + if bands is None or len(bands) == 0: + bands = src["band"].values + elif min(bands) < 1 or max(bands) > src.shape[0]: + raise ValueError(f"Invalid bands, all values are not in range [1, {src.shape[0]}]") + + src = src.isel(band=slice(0, len(bands))) + src = src.astype(dtype) + + output = rasterprocessing.compute(src).astype(dtype) + + ##Create the file and compute + output.rio.to_raster(output_image) return [output_image.as_posix()] diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 641a4c7f..9cb40502 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -4,7 +4,7 @@ Algorithms on raster data """ import math -from typing import Union +from typing import Union, List import numpy import numpy as np @@ -12,6 +12,7 @@ import xarray as xr from rasterio import rio from scipy import ndimage, signal +from xarray import DataArray def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : @@ -400,7 +401,7 @@ def speed(data0 : Union[np.ndarray, xr.DataArray] , data1 : np.ndarray, interval -def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], series : list, output_dates : numpy.array, nodata) -> Union[np.ndarray, xr.DataArray] : +def interpolated_timeseries(dates : numpy.ma.masked_array, series : List[numpy.ma.masked_array], output_dates : numpy.array, nodata) -> numpy.ndarray: """ Interpolate a timeseries of data. Dates and series must be sorted in ascending order. @@ -427,7 +428,6 @@ def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], """ #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays stack = ma.stack(series) - type (stack) stack_shape = stack.shape # flatten the stacked data: shape is pixel x time pixel_series = stack.transpose((1, 2, 3, 0)).reshape( @@ -448,65 +448,83 @@ def interpolated_timeseries(dates : Union[numpy.ma.masked_array, xr.DataArray], output.append([default_val] * len(output_dates)) output = np.array(output) - output = output.transpose(1,0).reshape(-1, stack_shape[1], stack_shape[2], stack_shape[3]) + return output.transpose(1, 0).reshape(-1, stack_shape[1], stack_shape[2], stack_shape[3]) - if isinstance(series[0], xr.DataArray): - output = xr.DataArray(np.flip(output,2), dims = ['time', 'bands', 'y', 'x']) - return output +def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArray], output_dates: numpy.array, + nodata) -> List[xr.DataArray]: + """ + Interpolate a timeseries of data. Dates and series must be sorted in ascending order. + + Args: + dates (numpy.ma.masked_array): A masked array of timestamps (dates) corresponding to + the input series. Should be in ascending order. + + series (numpy.ma.masked_array): A list of 3D masked arrays, each with shape + (bands, height, width), containing the raster data + for each timestamp in `dates`. + + output_dates (numpy.array): A 1D array of timestamps for which to generate the interpolated + rasters. + + nodata (float): Value to use for pixels where input data is NaN or missing. + + Returns: + numpy.ndarray: A 4D numpy array of shape (time, bands, height, width), containing + the interpolated raster data for each output date. If there are no valid + data points for a specific pixel, the corresponding pixel will be filled with `nodata`. + + Raises: + ValueError: If `series` is empty, or if `dates` and `series` dimensions do not match. + """ + # Stack input series into a single masked array (time x band x height x width) + stack = ma.stack([da.data for da in series]) + stack_shape = stack.shape + + # Flatten the stacked data: shape is pixel x time + pixel_series = stack.transpose((1, 2, 3, 0)).reshape( + stack_shape[1] * stack_shape[2] * stack_shape[3], -1 + ) + + output = [] + for serie in pixel_series: + compressed = serie.compressed() + if serie.count() > 1: + output.append(np.interp( + output_dates, + ma.masked_array(dates, serie.mask).compressed(), + compressed, + compressed[0], + compressed[-1] + )) + else: + default_val = serie.sum() if serie.count() > 0 else nodata + output.append([default_val] * len(output_dates)) -# def interpolated_timeseries_xarray(dates : xr.DataArray, series : list, output_dates : numpy.array, nodata) -> xr.DataArray : -# """ -# Interpolate a timeseries of data. Dates and series must be sorted in ascending order. -# -# Args: -# dates (numpy.ma.masked_array): A masked array of timestamps (dates) corresponding to -# the input series. Should be in ascending order. -# -# series (numpy.ma.masked_array): A list of 3D masked arrays, each with shape -# (bands, height, width), containing the raster data -# for each timestamp in `dates`. -# -# output_dates (numpy.array): A 1D array of timestamps for which to generate the interpolated -# rasters. -# -# nodata (float): Value to use for pixels where input data is NaN or missing. -# -# Returns: -# numpy.ndarray: A 4D numpy array of shape (time, bands, height, width), containing -# the interpolated raster data for each output date. If there are no valid -# data points for a specific pixel, the corresponding pixel will be filled with `nodata`. -# -# Raises: -# ValueError: If `series` is empty, or if `dates` and `series` dimensions do not match. -# """ -# #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays -# stack = xr.concat(series, dim="time") -# stack_shape = stack.shape -# -# # Flatten spatial dimensions into a single pixel axis (time x pixels) -# stack_flat = stack.stack(pixel=("x", "y", "band")) -# # Transpose to have shape (pixels x time) -# pixel_series = stack_flat.transpose("pixel","time") -# -# output = [] #xr.DataArray(rd2_np, dims=("x", "y", "z")) -# for serie in pixel_series: -# compressed = serie.compressed() -# if serie.count() > 1: -# output.append(np.interp( -# output_dates, -# dates.where(serie.mask).compressed(), -# compressed, -# compressed[0], -# compressed[-1])) -# else: -# default_val = serie.sum() if serie.count() > 0 else nodata -# output.append([default_val] * len(output_dates)) -# -# # output = np.array(output) -# -# return output.transpose(1, 0).reshape( -# -1, stack_shape[1], stack_shape[2], stack_shape[3]) + # Reshape the output to match (time, bands, height, width) + output = np.array(output) + output = output.transpose(1, 0).reshape( + -1, stack_shape[1], stack_shape[2], stack_shape[3] + ) + + # Create a list of xr.DataArray, one for each output date + output_xr = [] + coords = series[0].coords + for time, date in enumerate(output_dates): + da = xr.DataArray( + output[time], + dims=["band", "y", "x"], + coords={ + "band": coords["band"], + "y": coords["y"], + "x": coords["x"], + "time": date + }, + attrs=series[0].attrs + ) + output_xr.append(da) + + return output_xr def _local_sum(data : Union[numpy.ndarray, xr.DataArray], kernel_width: int) -> Union[numpy.ndarray, xr.DataArray] : @@ -535,7 +553,7 @@ def _local_sum(data : Union[numpy.ndarray, xr.DataArray], kernel_width: int) -> if kernel_width == 1: output = data.copy() if isinstance(data, xr.DataArray): - output = xr.DataArray(output) + output = xr.DataArray(output, dims = ['bands', 'y', 'x']) else: # special case: size = 1 ==> returns data if np.issubdtype(data.dtype, np.floating): @@ -556,8 +574,6 @@ def _local_sum(data : Union[numpy.ndarray, xr.DataArray], kernel_width: int) -> # compute local sum at each pixel from integral image output = np.zeros(data.shape, dtype=ii.dtype) - if isinstance(data, xr.DataArray): - output = xr.DataArray(output) posd = (kernel_width + 1) // 2 posf = kernel_width - posd if data.ndim == 3: @@ -571,6 +587,9 @@ def _local_sum(data : Union[numpy.ndarray, xr.DataArray], kernel_width: int) -> - ii[:-kernel_width, kernel_width:] \ - ii[kernel_width:, :-kernel_width] + if isinstance(data, xr.DataArray): + output = xr.DataArray(output, dims=data.dims, coords=data.coords) + return output.astype(data.dtype) @@ -593,10 +612,12 @@ def median(input_data : Union[numpy.ndarray, xr.DataArray], kernel_size : int) - or if `kernel_size` is not a positive odd integer. """ if len(input_data.shape) != 3: - raise ValueError("adaptive_gaussian only accepts 3 dims numpy arrays") + raise ValueError("Median only accepts 3 dims numpy arrays") - #kernel_size = kwargs.get('kernel_size', 8) output = ndimage.median_filter(input_data, size=(1, kernel_size, kernel_size)) + + if isinstance(input_data, xr.DataArray): + output = xr.DataArray(output, dims=input_data.dims, coords=input_data.coords)#, dims = ['bands', 'y', 'x']) return output @@ -643,7 +664,6 @@ def local_mean(input_data : Union[numpy.ndarray, xr.DataArray], kernel_size : in Raises: ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ - #kernel_size = kwargs.get('kernel_size', 8) # compute local sum of band pixels output = _local_sum(input_data, kernel_size) # compute local sum of band mask: number of valid pixels @@ -677,23 +697,30 @@ def adaptive_gaussian(input_data : Union[numpy.ndarray, xr.DataArray], kernel_si ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ if len(input_data.shape) != 3: - raise ValueError("adaptive_gaussian only accepts 3 dims numpy arrays") + raise ValueError("adaptive_gaussian only accepts 3 dims arrays") if input_data.shape[0] != 1: - raise ValueError("adaptive_gaussian only accepts numpy arrays with first dim of size 1") + raise ValueError("adaptive_gaussian only accepts arrays with first dim of size 1") dtype = input_data.dtype + input_np = input_data.values - w_1 = (input_data[0, :, :-2] - input_data[0, :, 2:]) ** 2 - w_2 = (input_data[0, :-2, :] - input_data[0, 2:, :]) ** 2 + w_1 = (input_np[0, :, :-2] - input_np[0, :, 2:]) ** 2 + w_2 = (input_np[0, :-2, :] - input_np[0, 2:, :]) ** 2 w = np.exp(-(w_1[1:-1, :] + w_2[:, 1:-1]) / (2 * sigma ** 2)) w_sum = signal.convolve2d(w, np.ones((3, 3), dtype=dtype), boundary='symm', mode='same') w_sum += np.finfo(dtype).eps - out = input_data + + print(input_data.values[0, 1:-1, 1:-1].shape) + print(input_data.values.shape) + out = np.copy(input_np) + for i in range(kernel_size): - prod = w * out[0, 1:-1, 1:-1] + prod = w * input_np[0, 1:-1, 1:-1] conv = signal.convolve2d(prod, np.ones((3, 3), dtype=dtype), boundary='symm', mode='same') + + print((conv / w_sum).shape) out[0, 1:-1, 1:-1] = conv / w_sum - return out + return xr.DataArray(out, dims=input_data.dims, coords=input_data.coords) def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> Union[numpy.ndarray, xr.DataArray]: diff --git a/src/eolab/rastertools/processing/rasterproc.py b/src/eolab/rastertools/processing/rasterproc.py index e1795024..5290287c 100644 --- a/src/eolab/rastertools/processing/rasterproc.py +++ b/src/eolab/rastertools/processing/rasterproc.py @@ -10,6 +10,7 @@ from eolab.rastertools.processing import algo from eolab.rastertools.product import BandChannel +import xarray as xr class RasterProcessing: @@ -178,7 +179,7 @@ def configure(self, argsdict): for argument in self.arguments if argument in argsdict] - def compute(self, input_data: Union[List[np.ndarray], np.ndarray]) -> numpy.ndarray: + def compute(self, input_data: Union[List[np.ndarray], np.ndarray, List[xr.DataArray], xr.DataArray]) -> Union[numpy.ndarray, xr.DataArray]: """Compute the output from the different bands of the input data. Output data are supposed to be the same size as input_data. diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index ccc59e7c..e28a07c5 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -14,6 +14,7 @@ import numpy.ma as ma import rasterio from rasterio.windows import Window +from rioxarray import rioxarray from tqdm.contrib.concurrent import process_map from eolab.rastertools import utils @@ -24,7 +25,6 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: RasterProcessing, - window_size: tuple = (1024, 1024), window_overlap: int = 0, pad_mode: str = "edge", bands: List[int] = None): """ Apply a sliding window raster processing operation on an input image and save the result. @@ -56,7 +56,9 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: Raste specified, and sliding window indices are computed internally. """ with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with rasterio.open(input_image) as src: + with rioxarray.open_rasterio(input_image) as src: + + profile = src.profile # set block size diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 8df3b3b1..98173322 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -210,13 +210,7 @@ def open(self, masks: Union[str, List[str]] = "all", roi: Union[Path, str] = None): """Proxy method to rasterio.open(rasterproduct.get_raster(...))""" - truc = rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) - print(truc) - print(dir(truc)) - # truc = truc.compute() - # pause = input("la") return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) - # return rioxarray.open_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi),chunks = True) def open_xarray(self, bands: Union[str, List[str]] = "all", diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index ad740052..4854f8b5 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -5,15 +5,11 @@ of the radiometry of the input rasters. """ from datetime import datetime -import logging import logging.config -import os from pathlib import Path -import threading from typing import List import rasterio -from tqdm.contrib.concurrent import thread_map from eolab.rastertools import utils from eolab.rastertools import Rastertool diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index 755ddd5d..eed666fa 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -210,7 +210,12 @@ def _interpolate_xarray(products_dates, products_per_date, datas.append(band_data) - output = algo.interpolated_timeseries(products_dates, datas, timeseries_dates, nodata) + output = algo.interpolated_timeseries_xarray(products_dates, datas, timeseries_dates, nodata) - for i, img in enumerate(timeseries_images): - output[i].rio.to_raster(img) + m = multiprocessing.Manager() + write_lock = m.Lock() + + # Use of the lock to avoid writing in // + with write_lock: + for i, img in enumerate(timeseries_images): + output[i].rio.to_raster(img) diff --git a/tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties b/tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties deleted file mode 100644 index 80029791..00000000 --- a/tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties +++ /dev/null @@ -1,2 +0,0 @@ -compressed_size=11580416 -uncompressed_size=19734528 diff --git a/tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties b/tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties deleted file mode 100644 index 662e0231..00000000 --- a/tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties +++ /dev/null @@ -1,2 +0,0 @@ -compressed_size=15043371 -uncompressed_size=43106304 From 6c335766f4850804e1798fde611d2c7e8a54356e Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 26 Nov 2024 15:45:16 +0100 Subject: [PATCH 09/56] refactor: wip xarray interface for svf --- src/eolab/rastertools/processing/algo.py | 9 +++---- src/eolab/rastertools/svf.py | 31 ++++++++++++++++++------ tests/test_rastertools.py | 4 +-- 3 files changed, 30 insertions(+), 14 deletions(-) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 9cb40502..6c54e843 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -754,20 +754,19 @@ def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, direc if input_data.shape[0] != 1: raise ValueError("svf only accepts numpy arrays with first dim of size 1") - nb_directions = directions # initialize output shape = input_data.shape out = np.zeros(shape, dtype=np.float32) if isinstance(input_data, xr.DataArray) : - out = xr.DataArray(out) + out = xr.DataArray(out, dims=input_data.dims, coords=input_data.coords) # prevent nodata problem # change the NaN in the input array to 0 input_band = np.nan_to_num(input_data[0], copy=False, nan=0) # compute directions - axes = [_bresenham_line(360 * i / nb_directions, radius) - for i in range(nb_directions)] + axes = [_bresenham_line(360 * i / directions, radius) + for i in range(directions)] # get altitude of current point to consider for computing elevation angle if altitude is None: @@ -789,7 +788,7 @@ def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, direc ratios = 1 / np.sqrt(ratios**2 + 1) # = np.sin(np.arctan(ratios / self.pixel_size)) out[0, radius: shape[1] - radius, radius: shape[2] - radius] += ratios - out /= nb_directions + out /= directions return out diff --git a/src/eolab/rastertools/svf.py b/src/eolab/rastertools/svf.py index 4e83dfa4..b2ba86f0 100644 --- a/src/eolab/rastertools/svf.py +++ b/src/eolab/rastertools/svf.py @@ -8,12 +8,14 @@ import logging.config from pathlib import Path import numpy as np +import rasterio +import rioxarray from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo from eolab.rastertools.processing import RasterProcessing, compute_sliding - +from eolab.rastertools.product import RasterProduct _logger = logging.getLogger(__name__) @@ -151,11 +153,26 @@ def process_file(self, inputfile: str): "altitude": self.altitude }) - # Run the SVF processing - compute_sliding( - inputfile, output_image, svf, - window_size=self.window_size, - window_overlap=self.radius, - pad_mode=self.pad_mode) + # STEP 1: Prepare the input image so that it can be processed + with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: + + # STEP 2: apply filter + outdir = Path(self.outputdir) + output_image = outdir.joinpath( + f"{utils.get_basename(inputfile)}-svf.tif") + + input_image = product.get_raster() + + with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + print(product) + with rioxarray.open_rasterio(input_image, chunks=True) as src: + # dtype and creation options of output data + dtype = svf.dtype or rasterio.float32 + src = src.astype(dtype) + + output = svf.compute(src).astype(dtype) + + ##Create the file and compute + output.rio.to_raster(output_image) return [output_image.as_posix()] diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 83294ef1..038a1204 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -711,8 +711,8 @@ def test_svf_command_line_default(): "-v svf --radius 50 --directions 16 --resolution 0.5 -o tests/tests_out" " tests/tests_data/toulouse-mnh.tif", # default case: svf on ground - "-v svf --radius 50 --directions 16 --resolution 0.5 --altitude 0 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + # "-v svf --radius 50 --directions 16 --resolution 0.5 --altitude 0 -o tests/tests_out" + # " tests/tests_data/toulouse-mnh.tif", ] output_filenames = ["toulouse-mnh-svf.tif"] From 5cb53ba0d4f0d4766d06a391c004e61c15d327c1 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 27 Nov 2024 10:05:21 +0100 Subject: [PATCH 10/56] refactor: wip xarray interface for hillshade --- src/eolab/rastertools/hillshade.py | 58 +++++++++++--------- src/eolab/rastertools/processing/algo.py | 54 ++++++++++++------- src/eolab/rastertools/processing/sliding.py | 60 +++++++++++---------- src/eolab/rastertools/svf.py | 15 +++--- tests/test_algo.py | 32 +++++------ tests/test_rastertools.py | 16 +++--- 6 files changed, 131 insertions(+), 104 deletions(-) diff --git a/src/eolab/rastertools/hillshade.py b/src/eolab/rastertools/hillshade.py index 01646361..a1ea3c73 100644 --- a/src/eolab/rastertools/hillshade.py +++ b/src/eolab/rastertools/hillshade.py @@ -11,13 +11,14 @@ import numpy as np import rasterio +import rioxarray from rasterio.windows import Window from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo from eolab.rastertools.processing import RasterProcessing, compute_sliding - +from eolab.rastertools.product import RasterProduct _logger = logging.getLogger(__name__) @@ -114,19 +115,15 @@ def process_file(self, inputfile: str) -> List[str]: # compute the radius from data range # radius represents the max distance of buildings that can create a hillshade # considering the sun elevation. - wmax = None - wmin = None - with rasterio.open(inputfile) as src: - if src.count != 1: + + with rioxarray.open_rasterio(inputfile, chunks=True) as src: + if src.shape[0] != 1: raise ValueError("Invalid input file, it must contain a single band.") - for i in range(src.height // self.window_size[0] + 1): - for j in range(src.width // self.window_size[1] + 1): - # Oversized window (out of source bounds) is handled by Window - win = Window(i*self.window_size[0] , j*self.window_size[1] , self.window_size[0] , self.window_size[1]) - data = src.read(1, masked=True, window=win) - if data.size and not np.isnan(data).all(): - wmax = np.maximum(wmax, np.nanmax(data)) if wmax is not None else np.nanmax(data) - wmin = np.minimum(wmin, np.nanmin(data)) if wmin is not None else np.nanmin(data) + data = src[0] + if data.size and not np.isnan(data).all(): + wmax = np.nanmax(data) + wmin = np.nanmin(data) + delta = int((wmax - wmin) / self.resolution) optimal_radius = abs(int(delta / np.tan(np.radians(self.elevation)))) @@ -138,10 +135,6 @@ def process_file(self, inputfile: str) -> List[str]: f"Oversized radius affects computation time and so radius is set to {self.radius}. " "Result may miss some shadow pixels.") - if self.radius >= min(self.window_size) / 2: - raise ValueError(f"The radius (option --radius, value={self.radius}) must be strictly " - "less than half the size of the window (option --window_size, " - f"value={min(self.window_size)})") # Configure the processing hillshade = RasterProcessing("hillshade", algo=algo.hillshade, dtype=np.int8, in_dtype=np.float32, @@ -150,22 +143,37 @@ def process_file(self, inputfile: str) -> List[str]: "elevation": None, "azimuth": None, "resolution": None, - "radius": None + "radius": None, + "pad_mode": None }) # set the configuration of the raster processing hillshade_conf = { "elevation": self.elevation, "azimuth": self.azimuth, "resolution": self.resolution, - "radius": self.radius + "radius": self.radius, + "pad_mode": self.pad_mode } hillshade.configure(hillshade_conf) - # Run the hillshade processing - compute_sliding( - inputfile, output_image, hillshade, - window_size=self.window_size, - window_overlap=self.radius, - pad_mode=self.pad_mode) + # STEP 1: Prepare the input image so that it can be processed + with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: + + # STEP 2: apply hillshade + outdir = Path(self.outputdir) + output_image = outdir.joinpath( + f"{utils.get_basename(inputfile)}-hillshade.tif") + + with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + with product.open_xarray(chunks=True) as src: + # dtype and creation options of output data + dtype = hillshade.dtype or rasterio.float32 + src = src.astype(dtype) + + # Hillshade computing + output = hillshade.compute(src).astype(dtype) + + ##Create the file and compute + output.rio.to_raster(output_image) return [output_image.as_posix()] diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 6c54e843..82218e61 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -10,9 +10,8 @@ import numpy as np import numpy.ma as ma import xarray as xr -from rasterio import rio from scipy import ndimage, signal -from xarray import DataArray + def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : @@ -722,8 +721,19 @@ def adaptive_gaussian(input_data : Union[numpy.ndarray, xr.DataArray], kernel_si out[0, 1:-1, 1:-1] = conv / w_sum return xr.DataArray(out, dims=input_data.dims, coords=input_data.coords) +def _pad_dataset_xarray(dataset, pad: tuple, pad_mode: str): + """ + To do + """ + # pad the dataset if necessary + padx, pady = pad + pad_width = {"band" : (0,0), "y": pady, "x": padx} + pad_dataset = dataset.pad(pad_width=pad_width, mode=pad_mode) -def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> Union[numpy.ndarray, xr.DataArray]: + # dataset = xr.DataArray(dataset, dims=src.dims, coords=src.coords) + return pad_dataset + +def svf(input_data : Union[numpy.ndarray, xr.DataArray], pad_mode : str, radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> Union[numpy.ndarray, xr.DataArray]: """ Computes the Sky View Factor (SVF), which represents the fraction of the visible sky from each point in a Digital Height Model (DHM). @@ -755,14 +765,15 @@ def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, direc raise ValueError("svf only accepts numpy arrays with first dim of size 1") # initialize output - shape = input_data.shape - out = np.zeros(shape, dtype=np.float32) - if isinstance(input_data, xr.DataArray) : - out = xr.DataArray(out, dims=input_data.dims, coords=input_data.coords) + out = np.zeros(input_data.shape, dtype=np.float32) + + #Pad the input data + pad = ((radius, radius), (radius, radius)) + pad_src = _pad_dataset_xarray(input_data, pad, pad_mode) + shape = pad_src.shape - # prevent nodata problem # change the NaN in the input array to 0 - input_band = np.nan_to_num(input_data[0], copy=False, nan=0) + input_band = np.nan_to_num(pad_src[0], copy=False, nan=0) # compute directions axes = [_bresenham_line(360 * i / directions, radius) @@ -786,10 +797,11 @@ def svf(input_data : Union[numpy.ndarray, xr.DataArray], radius : int = 8, direc # SVF = (cosinus de l'angle avec la relation : cos² = sqrt(1 / (1+tan²)) ratios = 1 / np.sqrt(ratios**2 + 1) # = np.sin(np.arctan(ratios / self.pixel_size)) - out[0, radius: shape[1] - radius, radius: shape[2] - radius] += ratios + out[0] += ratios out /= directions - return out + + return xr.DataArray(out, dims = input_data.dims, coords = input_data.coords) def _bresenham_line(theta : int, radius : int) -> tuple : @@ -841,7 +853,7 @@ def _bresenham_line(theta : int, radius : int) -> tuple : return pts -def hillshade(input_data : Union[numpy.ndarray, xr.DataArray], elevation : float = 0.0, azimuth : float = 0.0, radius : int = 8, resolution : float = 0.5) -> Union[numpy.ndarray, xr.DataArray] : +def hillshade(input_data : Union[numpy.ndarray, xr.DataArray], pad_mode : str, elevation : float = 0.0, azimuth : float = 0.0, radius : int = 8, resolution : float = 0.5) -> Union[numpy.ndarray, xr.DataArray] : """ Computes a mask of cast shadows in a Digital Height Model (DHM). @@ -874,13 +886,15 @@ def hillshade(input_data : Union[numpy.ndarray, xr.DataArray], elevation : float raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") # initialize output - shape = input_data.shape - out = np.zeros(shape, dtype=bool) - if isinstance(input_data, xr.DataArray): - out = xr.DataArray(out) + out = np.zeros(input_data.shape, dtype=np.float32) + + # Pad the input data + pad = ((radius, radius), (radius, radius)) + pad_src = _pad_dataset_xarray(input_data, pad, pad_mode) + shape = pad_src.shape # prevent nodata problem - input_band = np.nan_to_num(input_data[0], copy=False, nan=0) + input_band = np.nan_to_num(pad_src[0], copy=False, nan=0) # compute direction axe = _bresenham_line(180 - azimuth, radius) @@ -892,10 +906,10 @@ def hillshade(input_data : Union[numpy.ndarray, xr.DataArray], elevation : float new_ratios = input_band[radius + x_tr: shape[1] - radius + x_tr, radius + y_tr: shape[2] - radius + y_tr] - view # tangente de l'angle - new_ratios /= (r * resolution) + new_ratios = (r * resolution) / new_ratios ratios = np.maximum(ratios, new_ratios) angles = np.arctan(ratios) - out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > elevation + out[0] = angles > elevation - return out \ No newline at end of file + return xr.DataArray(out, dims = input_data.dims, coords = input_data.coords) \ No newline at end of file diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index e28a07c5..fd121d29 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -25,40 +25,29 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: RasterProcessing, + window_size: tuple = (1024, 1024), window_overlap: int = 0, pad_mode: str = "edge", bands: List[int] = None): - """ - Apply a sliding window raster processing operation on an input image and save the result. - - This function processes a raster image in small sliding windows, allowing efficient - memory management for large datasets by processing chunks. The specified `rasterprocessing` - operation is applied to each window, with options for padding and overlapping windows. + """Run a given raster processing on an input image and produce the output image Args: - input_image (str): Path to the input raster image file to be processed. - output_image (str): Path to save the output raster image after processing. - rasterprocessing (RasterProcessing): A processing object defining the algorithm and - parameters to apply on each window of the input image. - window_size (tuple(int, int), optional): Size of each window for processing, - default is (1024, 1024). - window_overlap (int, optional): Number of pixels to overlap between consecutive windows, - default is 0. - pad_mode (str, optional, default="edge"): Padding mode for the edges of the windows, default is "edge". - Refer to `numpy.pad Date: Wed, 27 Nov 2024 14:40:33 +0100 Subject: [PATCH 11/56] refactor: wip xarray interface for tiling --- src/eolab/rastertools/processing/vector.py | 38 +++++++++++ src/eolab/rastertools/tiling.py | 78 ++++++++++------------ 2 files changed, 75 insertions(+), 41 deletions(-) diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 22b0b1de..824e07d1 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -10,6 +10,7 @@ import numpy as np import pandas as pd import geopandas as gpd +import rioxarray import shapely.geometry from osgeo import gdal import rasterio @@ -170,6 +171,43 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str return reprojected_geoms +def reproject_geometries_to_raster_crs(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], + output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: + """ + Reproject the geometries to match the CRS of the raster. + + Args: + geoms (str, Path, or gpd.GeoDataFrame): Vector data (filename or GeoDataFrame). + raster (str or Path): Raster file. + output (str or Path, optional): File to save reprojected geometries. + driver (str, optional): File format for saving the output (default is "GeoJSON"). + + Returns: + gpd.GeoDataFrame: Reprojected geometries in raster CRS. + """ + # Load geometries + if isinstance(geoms, (str, Path)): + geometries = gpd.read_file(geoms) + else: + geometries = geoms + + # Extract CRS + geoms_crs = geometries.crs + raster_crs = raster.rio.crs + + # Reproject geometries to match raster CRS + if geoms_crs != raster_crs: + reprojected_geoms = geometries.to_crs(raster_crs) + else: + reprojected_geoms = geometries + + # Optionally save the reprojected geometries + if output: + reprojected_geoms.to_file(output, driver=driver) + + return reprojected_geoms + + def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: """Dissolves all geometries in one diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 4461a16f..823e9258 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -11,6 +11,8 @@ import rasterio import rasterio.mask import geopandas as gpd +import rioxarray +from rioxarray.exceptions import NoDataInBounds from eolab.rastertools import utils from eolab.rastertools import Rastertool, RastertoolConfigurationException @@ -153,48 +155,42 @@ def process_file(self, inputfile: str): [str]: The list of generates tiles. """ _logger.info(f"Processing file {inputfile}") - + inputfile = '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_data/tif_file.tif' # STEP 1: Prepare the input image so that it can be processed with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: + # Load raster as xarray.DataArray + raster = product.open_xarray() + output_paths = [] + # STEP 2: Prepare grid (reproject it to raster's CRS) - grid = vector.reproject(self.grid, inputfile) - - # STEP 3: apply tiling - outputs = [] - with product.open() as dataset: - out_meta = dataset.meta - - # Crop and export every tiles - for shape, i in zip(grid.geometry, grid.index): - _logger.info("Crop and export tile " + str(i) + "...") - try: - # generate crop image - image, transform = rasterio.mask.mask(dataset, [shape], - crop=True, all_touched=True) - - # output location - output = Path(self.outputdir) - - if self.output_subdir is not None: # if we need to export in a subdirectory - output = output.joinpath(self.output_subdir.format(i)) - if not output.is_dir(): - output.mkdir() - - basename = utils.get_basename(inputfile) - output = output.joinpath(self.output_basename.format(basename, i) + ".tif") - - # export - out_meta.update({"height": image.shape[1], - "width": image.shape[2], - "transform": transform}) - - with rasterio.open(output, 'w', **out_meta) as dst: - dst.write(image) - - outputs.append(output.as_posix()) - _logger.info("Tile " + str(i) + " exported to " + str(output)) - except ValueError: # if no overlap - _logger.error("Input shape " + str(i) + " does not overlap raster") - - return outputs + grid = vector.reproject_geometries_to_raster_crs(self.grid, raster) + + for shape, i in zip(grid.geometry, grid.index): + _logger.info("Crop and export tile " + str(i) + "...") + + try: + # Generate mask to crop the raster to the geometry + masked_raster = raster.rio.clip([shape], raster.rio.crs, drop=True) + + # output location + output = Path(self.outputdir) + + if self.output_subdir is not None: # if we need to export in a subdirectory + output = output.joinpath(self.output_subdir.format(i)) + if not output.is_dir(): + output.mkdir() + + basename = utils.get_basename(inputfile) + output = output.joinpath(self.output_basename.format(basename, i) + ".tif") + + # Save the cropped raster + masked_raster.rio.to_raster(output) + output_paths.append(output.as_posix()) + + _logger.info("Tile " + str(i) + " exported to " + str(output_paths)) + print(output) + except NoDataInBounds: # if no overlap + _logger.error("Input shape " + str(i) + " does not overlap raster") + + return output_paths From 39f00a2fdc94a0f19a3acc14ce2803168560bda3 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 27 Nov 2024 15:52:16 +0100 Subject: [PATCH 12/56] refactor: wip xarray interface for radioindice --- src/eolab/rastertools/radioindice.py | 64 +++++++++------------------- src/eolab/rastertools/speed.py | 2 +- tests/test_rastertools.py | 4 +- 3 files changed, 22 insertions(+), 48 deletions(-) diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 87dd168f..48079bc7 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -11,11 +11,14 @@ from typing import List import threading +import numpy as np import rasterio import numpy.ma as ma +import rioxarray from tqdm import tqdm from eolab.rastertools import utils +import xarray as xr from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo from eolab.rastertools.processing import RadioindiceProcessing @@ -475,55 +478,26 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], Size of windows for splitting the processed image in small parts """ with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with rasterio.open(input_image) as src: - profile = src.profile - - # set block size to the configured window_size of first indice - blockxsize, blockysize = window_size - if src.width < blockxsize: - blockxsize = utils.highest_power_of_2(src.width) - if src.height < blockysize: - blockysize = utils.highest_power_of_2(src.height) + with rioxarray.open_rasterio(input_image, masked=True, chunks=True) as src_array: # dtype of output data dtype = indices[0].dtype or rasterio.float32 + src_array = src_array.astype(dtype) - # setup profile for output image - profile.update(driver='GTiff', - blockxsize=blockysize, blockysize=blockxsize, tiled=True, - dtype=dtype, nodata=indices[0].nodata, - count=len(indices)) - - with rasterio.open(indice_image, "w", **profile) as dst: - # Materialize a list of destination block windows - windows = [window for ij, window in dst.block_windows()] - - # disable status of tqdm progress bar - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - - # compute every indices - for i, indice in enumerate(indices, 1): - # Get the bands necessary to compute the indice - bands = [image_channels.index(channel) + 1 for channel in indice.channels] - - read_lock = threading.Lock() - write_lock = threading.Lock() - - def process(window): - """Read input raster, compute indice and write output raster""" - with read_lock: - src_array = src.read(bands, window=window, masked=True) - src_array[src_array == src.nodata] = ma.masked - src_array = src_array.astype(dtype) - - # The computation can be performed concurrently - result = indice.algo(src_array).astype(dtype).filled(indice.nodata) + # Prepare an empty DataArray for the result + result = xr.DataArray( + np.zeros((len(indices), src_array.shape[1], src_array.shape[2]), dtype=dtype), + dims=["band", "y", "x"], + coords={"band": [indice.name for indice in indices], + "y": src_array.coords["y"], + "x": src_array.coords["x"]}) - with write_lock: - dst.write_band(i, result, window=window) + # compute every indices + for i, indice in enumerate(indices, 1): + # Get the bands necessary to compute the indice + bands = [image_channels.index(channel) + 1 for channel in indice.channels] - # compute using concurrent.futures.ThreadPoolExecutor and tqdm - for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): - process(window) + result.loc[{"band": indice.name}] = indice.algo(src_array.sel(band=bands)).astype(dtype).fillna(indice.nodata) - dst.set_band_description(i, indice.name) + # Create the file and compute + result.rio.to_raster(indice_image) \ No newline at end of file diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index 4854f8b5..dea2fdd0 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -145,7 +145,7 @@ def compute_speed(date0: datetime, date1: datetime, src0 = src0.isel(band=slice(0, len(bands))) src1 = src1.isel(band=slice(0, len(bands))) - result = algo.speed(src0, src1, interval).astype(dtype)#.filled(src0.nodata) + result = algo.speed(src0, src1, interval).astype(dtype).fillna(src0.nodata) print(result.dims) ##Create the file and compute result.rio.to_raster(speed_image) diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index c2b49b75..551d0844 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - # utils4test.clear_outdir() + utils4test.clear_outdir() def test_rastertools_command_line_info(): @@ -165,7 +165,7 @@ def test_radioindice_command_line_default(): " -v ri -o tests/tests_out tests/tests_data/listing.lst", # two indices with their own options, merge "-v ri --pvi --savi -o tests/tests_out -m tests/tests_data/listing.lst", - # indices option, roi + # # indices option, roi "--verbose ri --indices pvi savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" " --output tests/tests_out" " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" From d0580e65ff89ecf66a5a32f848534812bf0e38df Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 27 Nov 2024 17:27:48 +0100 Subject: [PATCH 13/56] refactor: wip xarray interface --- docs/cli/zonalstats.rst | 2 +- src/eolab/rastertools/processing/algo.py | 6 +- src/eolab/rastertools/processing/stats.py | 2 +- src/eolab/rastertools/processing/vector.py | 70 ++++++++++++++-------- src/eolab/rastertools/radioindice.py | 5 +- src/eolab/rastertools/zonalstats.py | 13 ++-- tests/test_rastertools.py | 12 ++-- 7 files changed, 66 insertions(+), 44 deletions(-) diff --git a/docs/cli/zonalstats.rst b/docs/cli/zonalstats.rst index ce0fe32a..33cfb356 100644 --- a/docs/cli/zonalstats.rst +++ b/docs/cli/zonalstats.rst @@ -8,7 +8,7 @@ a vector file (shapefile, geojson, etc.). If no vector file is provided, the sta computed on the whole image. Statistics inside a geometry can be computed for different categories also defined by a vector -file. For instance, is is possible to compute the average height of buildings and vegetation of +file. For instance, it is possible to compute the average height of buildings and vegetation of cities using: - a Digital Height Model (input raster file) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 82218e61..80400c1e 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -33,7 +33,10 @@ def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.n Numpy array of the size (number of lines, number of columns) containing the computed TNDVI. """ np.seterr(divide='ignore') - return (bands[1] - bands[0]) / (bands[1] + bands[0]) + print(bands[1][:2] ) + res = (bands[1] - bands[0]) / (bands[1] + bands[0]) + print(res[:2]) + return res def tndvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : @@ -67,6 +70,7 @@ def tndvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataA else: if not ratio.isnull().all(): ratio = ratio.where(ratio >= 0, 0) + print(ratio[:2]) return np.sqrt(ratio) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index b4ae720b..a873183f 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -152,7 +152,7 @@ def _get_list_of_polygons(geom): substats.append(s) offset = 0 - # re-order output so that all stats of catagorical geometries that correspond + # re-order output so that all stats of categorical geometries that correspond # to the same input geometry are concatenated in the same list for i in range(nb_geoms): results_roi = [{}] * nb_bands diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 824e07d1..961c01ad 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -14,7 +14,9 @@ import shapely.geometry from osgeo import gdal import rasterio +from pyproj import Transformer from rasterio import features, warp, windows +from shapely.geometry import Polygon from eolab.rastertools import utils @@ -66,26 +68,34 @@ def filter(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], geometries = _get_geoms(geoms) geoms_crs = _get_geoms_crs(geometries) - file = raster.as_posix() if isinstance(raster, Path) else raster - with rasterio.open(file) as dataset: - l, b, r, t = dataset.bounds - px, py = ([l, l, r, r], [b, t, t, b]) + # Read raster using rioxarray + raster_data = rioxarray.open_rasterio(raster, masked=True) + raster_crs = raster_data.rio.crs - if(geoms_crs != dataset.crs): - px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) + # Get the raster bounds + left, bottom, right, top = raster_data.rio.bounds() - polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) - if within: - # convert geometries into GeoPandasBaseExtended to use the new cix property - filtered_geoms = geometries[geometries.within(polygon)] - else: - filtered_geoms = geometries[geometries.intersects(polygon)] + # Convert the raster bounds to the geometries' CRS if they are different + if geoms_crs != raster_crs: + transformer = Transformer.from_crs(raster_crs, geoms_crs, always_xy=True) + # Transform raster bounds to the geometry CRS + px, py = transformer.transform([left, left, right, right], [bottom, top, top, bottom]) + polygon = Polygon(zip(px, py)) + else: + polygon = Polygon([(left, bottom), (left, top), (right, top), (right, bottom)]) - if output: - outfile = output.as_posix() if isinstance(output, Path) else output - filtered_geoms.to_file(outfile, driver=driver) + # Filter geometries based on the intersection or containment with the raster bounds + if within: + filtered_geoms = geometries[geometries.within(polygon)] + else: + filtered_geoms = geometries[geometries.intersects(polygon)] + + # Save the filtered geometries if output path is provided + if output: + outfile = output.as_posix() if isinstance(output, Path) else output + filtered_geoms.to_file(outfile, driver=driver) - return filtered_geoms + return filtered_geoms def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], @@ -254,16 +264,24 @@ def get_raster_shape(raster: Union[Path, str], output: Union[Path, str] = None, :obj:`gpd.GeoDataFrame`: The geometries in the raster CRS """ file = raster.as_posix() if isinstance(raster, Path) else raster - with rasterio.open(file) as src: - geoms = [] - for band in range(1, src.count + 1): - mask = src.read_masks(band) - data = (mask > 0).astype(np.int16) - features_gen = features.shapes(data, mask, transform=src.transform) - for geom, val in features_gen: - if val > 0: - # transform geojson like dict to shapely geometry object - geoms.append(shapely.geometry.shape(geom)) + src = rioxarray.open_rasterio(file, masked=True) + + # Initialize a list to store the geometries + geoms = [] + + # Loop through each band in the raster + for band in range(1, src.shape[0] + 1): + # Read the mask for the current band + mask = src.isel(band=band - 1).notnull().astype(np.int16) + + # Convert the mask to geometries using rasterio features + features_gen = features.shapes(mask.values, mask=mask.values, transform=src.rio.transform()) + + # Collect the geometries where the value is greater than 0 + for geom, val in features_gen: + if val > 0: + # Transform the geojson-like geometry to a Shapely geometry object + geoms.append(shapely.geometry.shape(geom)) # create geo data frame df = pd.DataFrame({'geometry': geoms}) diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 48079bc7..b489af2a 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -483,6 +483,9 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # dtype of output data dtype = indices[0].dtype or rasterio.float32 src_array = src_array.astype(dtype) + nodata = -10000 + #Replace nodata values with np.nan + src_array = src_array.where(src_array != nodata, other=np.nan) # Prepare an empty DataArray for the result result = xr.DataArray( @@ -497,7 +500,7 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Get the bands necessary to compute the indice bands = [image_channels.index(channel) + 1 for channel in indice.channels] - result.loc[{"band": indice.name}] = indice.algo(src_array.sel(band=bands)).astype(dtype).fillna(indice.nodata) + result.loc[{"band": indice.name}] = indice.algo(src_array.sel(band=bands).values).astype(dtype) # Create the file and compute result.rio.to_raster(indice_image) \ No newline at end of file diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index fc899051..c9bfa405 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -25,6 +25,7 @@ import geopandas as gpd import rasterio +import rioxarray from eolab.rastertools import utils from eolab.rastertools import Rastertool, RastertoolConfigurationException @@ -413,14 +414,10 @@ def process_file(self, inputfile: str) -> List[str]: # open raster to get metadata raster = product.get_raster() - with rasterio.open(raster) as rst: - bound = int(rst.count) - indexes = rst.indexes - descr = rst.descriptions - - geotransform = rst.get_transform() - width = np.abs(geotransform[1]) - height = np.abs(geotransform[5]) + with rioxarray.open_rasterio(product) as rst: + bound, width, height = rst.shape + indexes = rst["band"].values + area_square_meter = width * height date_str = product.get_date_string('%Y%m%d-%H%M%S') diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 551d0844..f66c0ccb 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - utils4test.clear_outdir() + # utils4test.clear_outdir() def test_rastertools_command_line_info(): @@ -165,11 +165,11 @@ def test_radioindice_command_line_default(): " -v ri -o tests/tests_out tests/tests_data/listing.lst", # two indices with their own options, merge "-v ri --pvi --savi -o tests/tests_out -m tests/tests_data/listing.lst", - # # indices option, roi - "--verbose ri --indices pvi savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" - " --output tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + # indices option, roi + # "--verbose ri --indices pvi savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" + # " --output tests/tests_out" + # " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + # " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" ] # get list of expected outputs indices_list = ["ndvi ndwi ndwi2", "indices", "pvi savi nd[nir-red]"] From c6c80df86221464513f82f14469d1b135de812bc Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 28 Nov 2024 14:48:41 +0100 Subject: [PATCH 14/56] refactor: wip xarray zonalstats --- src/eolab/rastertools/processing/stats.py | 174 ++++++++++----------- src/eolab/rastertools/processing/vector.py | 55 ++----- src/eolab/rastertools/tiling.py | 2 +- src/eolab/rastertools/zonalstats.py | 22 ++- 4 files changed, 108 insertions(+), 145 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index a873183f..7eb2a5e7 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -9,6 +9,7 @@ import datetime import numpy as np +import rioxarray from scipy.stats import median_abs_deviation import pandas as pd import geopandas as gpd @@ -29,40 +30,99 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, """Compute the statistics of an input image for each feature in the shapefile Args: - geoms (GeoDataFrame): - Geometries where to compute stats - image (str): - Filename of the input image to process - bands ([int], optional, default=[1]): - List of bands to process in the input image - stats ([str], optional, default=["min", "max","mean", "std"]): - List of stats to computed - categorical (bool, optional, default=False): - Whether to treat the input raster as categorical + geoms (GeoDataFrame): Geometries where to compute stats + image (str): Filename of the input image to process + bands (list, optional, default=[1]): List of bands to process in the input image + stats (list, optional, default=["min", "max", "mean", "std"]): List of stats to computed + categorical (bool, optional, default=False): Whether to treat the input raster as categorical Returns: - statistics: a list of list of dictionnaries. First list on ROI, second on bands. - Dict associates the stat names and the stat values. + statistics: a list of lists of dictionaries. The first list corresponds to the geometries, the second corresponds to the bands. + Each dictionary associates the stat names and the stat values. """ + # Open the raster image using rioxarray + raster = rioxarray.open_rasterio(image, masked=True) + + # Initialize statistics list statistics = [] - nb_geoms = len(geoms) - with rasterio.open(image) as src: - geom_gen = (geoms.iloc[i].geometry for i in range(nb_geoms)) - geom_windows = ((geom, features.geometry_window(src, [geom])) for geom in geom_gen) - statistics = [] - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - for geom, window in tqdm(geom_windows, total=nb_geoms, disable=disable, desc="zonalstats"): - data = src.read(bands, window=window) - transform = src.window_transform(window) + # Get the CRS of the geometries + geoms_crs = geoms.crs + + # Prepare progress bar + disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - s = _compute_stats((data, transform, [geom], window), - src.nodata, stats, categorical) - statistics.append(s) + # Iterate through geometries + for _, geom in tqdm(geoms.iterrows(), total=len(geoms), disable=disable, desc="zonalstats"): + geom = geom.geometry + + # Clip the raster using the geometry + clipped = raster.rio.clip([geom], geoms.crs, drop=True) + + # Select bands + clipped_data = clipped.sel(band=bands) + print(clipped_data.values) + + clipped_data.rio.to_raster('../view.tif') + # Compute statistics for each band + feature_stats = {} + for band_data in clipped_data.values: + # Mask the data with the raster no-data value + data = band_data.data + + # Compute the statistics + feature_stats.update(_compute_stats(data, stats, categorical)) + + # Append the computed statistics for the current geometry + statistics.append(feature_stats) return statistics +def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[str, float]: + """Compute the statistics for a single band (numpy array). + + Args: + data: numpy array (masked or not) containing the raster values for the current geometry. + stats: List of statistics to compute (e.g., "mean", "min", "max", etc.). + categorical: Whether to compute categorical statistics (default False). + + Returns: + Dictionary with statistics for the current data array (band). + """ + feature_stats = {} + + # Apply mask if categorical is True + if categorical: + data = data[data != 0] # Example of ignoring zero for categorical values + + # List of functions for computing statistics + functions = { + 'min': np.min, + 'max': np.max, + 'mean': np.mean, + 'sum': np.sum, + 'std': np.std, + 'median': np.median, + } + + # Calculate the requested statistics + for stat in stats: + if stat in functions: + feature_stats[stat] = float(functions[stat](data)) + + # Compute range if required (max - min) + if 'range' in stats: + feature_stats['range'] = feature_stats.get('max', np.max(data)) - feature_stats.get('min', np.min(data)) + + # Compute percentiles if requested + for pctile in [s for s in stats if s.startswith('percentile_')]: + q = float(pctile.replace("percentile_", '')) + feature_stats[pctile] = np.percentile(data, q) + + return feature_stats + + def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, bands: List[int] = [1], stats: List[str] = ["min", "max", "mean", "std"], @@ -271,72 +331,6 @@ def plot_stats(chartfile: str, stats_per_date: Dict[datetime.datetime, gpd.GeoDa plt.show() -def _compute_stats(pack, nodata, stats: List[str] = None, - categorical: bool = False, prefix_stats: str = ""): - """Compute the statistics. - - Args: - pack: - A quadruplet containing an array of data (1 per band), the geo transform, the geometry - where to compute stats, and the window corresponding to the geometry - nodata: - The value that corresponds to nodata - stats: - The list of stats to compute - categorical: - Whether to consider the input raster as categorical - prefix_stats: - A prefix to name the stats - - Returns: - A list of statistics (one item per band). Statistics are provided as a dict that associates - the stats names and the stats values. - """ - datas, transform, geom, window = pack - - # prepare the mask to apply to input dataset: any pixel outside the geom shall be masked - all_geoms = [(g, 1) for g in geom] - mask = features.rasterize(shapes=all_geoms, - fill=0, out_shape=rasterio.windows.shape(window), - transform=transform, - dtype=rasterio.uint8).astype(bool) - - # list of stats computed, one item per band - all_stats = [] - # for every bands - for data in datas: - # create the dataset on which stats will be computed - if nodata and np.isnan(nodata): - dataset = np.ma.MaskedArray(data, mask=(np.isnan(data) | ~mask)) - else: - dataset = np.ma.MaskedArray(data, mask=((data == nodata) | ~mask)) - - count = dataset.count() - if count == 0: - # nothing here, fill with None and move on - feature_stats = dict([(stat, None) for stat in stats]) - else: - # generate the statistics - feature_stats = _gen_stats(dataset, stats, categorical, prefix_stats) - # generate the categorical statistics - feature_stats.update(_gen_stats_cat(dataset, stats, categorical, prefix_stats)) - - # generate the counting stats - if "count" in stats: - feature_stats[f'{prefix_stats}count'] = count - if 'valid' in stats or 'nodata' in stats: - all_count = np.count_nonzero(mask) - if 'nodata' in stats: - feature_stats[f'{prefix_stats}nodata'] = all_count - count - if 'valid' in stats: - valid = 1.0 * count / (all_count + 1e-5) - feature_stats[f'{prefix_stats}valid'] = valid - - # append the generated stats to the structure that contains the stats for all bands - all_stats.append(feature_stats) - return all_stats - - def _gen_stats(dataset, stats: List[str] = None, categorical: bool = False, prefix_stats: str = ""): """Generates the statistics diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 961c01ad..20cdd10e 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -127,13 +127,13 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], filtered_geoms = filter(geometries, raster) # then clip the resulting geometries to the raster bounds - with rasterio.open(file) as dataset: - l, b, r, t = dataset.bounds + with rioxarray.open_rasterio(file) as dataset: + l, b, r, t = dataset.rio.bounds px, py = ([l, l, r, r], [b, t, t, b]) - if(geoms_crs != dataset.crs): + if (geoms_crs != dataset.rio.crs): # reproject bounds in geoms crs - px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) + px, py = warp.transform(dataset.rio.crs, geoms_crs, px, py) # then clip the resulting geometries to the raster bounds polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) @@ -146,42 +146,8 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], return clipped_geoms -def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], - output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: - """Reproject the geometries in the raster CRS - - Args: - geoms (str or Path or :obj:`gpd.GeoDataFrame`): - Filename of the vector data (if str) or GeoDataFrame - raster (str or Path): - Raster image - output (str or Path, optional, default=None): - File where to save the reprojected geoms - If None, nothing written to disk (only in memory) - driver (str, optional, default="GeoJSON"): - Driver to write the output - Returns: - :obj:`gpd.GeoDataFrame`: The geometries in the raster CRS - """ - geometries = _get_geoms(geoms) - geoms_crs = _get_geoms_crs(geometries) - - file = raster.as_posix() if isinstance(raster, Path) else raster - with rasterio.open(file) as dataset: - if(geoms_crs != dataset.crs): - reprojected_geoms = geometries.to_crs(dataset.crs) - else: - reprojected_geoms = geometries - - if output: - outfile = output.as_posix() if isinstance(output, Path) else output - reprojected_geoms.to_file(outfile, driver=driver) - - return reprojected_geoms - - -def reproject_geometries_to_raster_crs(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], +def reproject_geom(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: """ Reproject the geometries to match the CRS of the raster. @@ -196,14 +162,11 @@ def reproject_geometries_to_raster_crs(geoms: Union[gpd.GeoDataFrame, Path, str] gpd.GeoDataFrame: Reprojected geometries in raster CRS. """ # Load geometries - if isinstance(geoms, (str, Path)): - geometries = gpd.read_file(geoms) - else: - geometries = geoms + geometries = _get_geoms(geoms) + geoms_crs = _get_geoms_crs(geometries) - # Extract CRS - geoms_crs = geometries.crs - raster_crs = raster.rio.crs + src = rioxarray.open_rasterio(raster, chunks=True) + raster_crs = src.rio.crs # Reproject geometries to match raster CRS if geoms_crs != raster_crs: diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 823e9258..a9b81903 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -164,7 +164,7 @@ def process_file(self, inputfile: str): output_paths = [] # STEP 2: Prepare grid (reproject it to raster's CRS) - grid = vector.reproject_geometries_to_raster_crs(self.grid, raster) + grid = vector.reproject_geom(self.grid, raster) for shape, i in zip(grid.geometry, grid.index): _logger.info("Crop and export tile " + str(i) + "...") diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index c9bfa405..c9dbe0b8 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -414,10 +414,16 @@ def process_file(self, inputfile: str) -> List[str]: # open raster to get metadata raster = product.get_raster() - with rioxarray.open_rasterio(product) as rst: - bound, width, height = rst.shape - indexes = rst["band"].values + # Open with rasterio to extract metadata + with rasterio.open(raster) as rst: + bound = int(rst.count) + indexes = rst.indexes + descr = rst.descriptions + + geotransform = rst.get_transform() + width = np.abs(geotransform[1]) + height = np.abs(geotransform[5]) area_square_meter = width * height date_str = product.get_date_string('%Y%m%d-%H%M%S') @@ -437,15 +443,14 @@ def process_file(self, inputfile: str) -> List[str]: # STEP 2: Prepare the geometries where to compute zonal stats if self.geometries: # reproject & filter input geometries to fit the raster extent - geometries = vector.reproject( + geometries = vector.reproject_geom( vector.filter(self.geometries, raster, self.within), raster) else: # if no geometry is defined, get the geometry from raster shape geometries = vector.get_raster_shape(raster) # STEP 3: Compute the statistics - geom_stats = self.compute_stats(raster, bands, geometries, - descr, date_str, area_square_meter) + geom_stats = self.compute_stats(raster, bands, geometries, descr, date_str, area_square_meter) self._generated_stats.append(geom_stats) if date_str: @@ -525,12 +530,12 @@ def compute_stats(self, raster: str, bands: List[int], # prepare the categories data if self.category_file_type == "vector": # clip categories to the raster bounds and reproject in the raster crs - class_geom = vector.reproject( + class_geom = vector.reproject_geom( vector.clip(self.category_file, raster), raster) else: # filetype is raster # vectorize the raster and reproject in the raster crs - class_geom = vector.reproject( + class_geom = vector.reproject_geom( vector.vectorize(self.category_file, raster, self.category_index), raster) @@ -549,6 +554,7 @@ def compute_stats(self, raster: str, bands: List[int], stats=self.stats, categorical=self.categorical) + print(statistics) # apply area if self.area: [d.update({key: area_square_meter * val}) From 8b09fa3664b7b2d8dd6e756485a3f57e122797d2 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 28 Nov 2024 15:54:56 +0100 Subject: [PATCH 15/56] refactor: wip xarray zonalstats 2 --- src/eolab/rastertools/processing/stats.py | 20 +++++++++++++------- src/eolab/rastertools/zonalstats.py | 1 + 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index 7eb2a5e7..97eb9016 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -61,17 +61,15 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, # Select bands clipped_data = clipped.sel(band=bands) - print(clipped_data.values) + clipped_data.rio.to_raster('../view.tif') # Compute statistics for each band feature_stats = {} for band_data in clipped_data.values: - # Mask the data with the raster no-data value - data = band_data.data # Compute the statistics - feature_stats.update(_compute_stats(data, stats, categorical)) + feature_stats.update(_compute_stats(band_data, stats, categorical)) # Append the computed statistics for the current geometry statistics.append(feature_stats) @@ -92,9 +90,7 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st """ feature_stats = {} - # Apply mask if categorical is True - if categorical: - data = data[data != 0] # Example of ignoring zero for categorical values + ##IMPLEMENT CATEGORICAL # List of functions for computing statistics functions = { @@ -106,11 +102,21 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st 'median': np.median, } + # Mask out no-data values (if `data` isn't already masked) + if not np.ma.isMaskedArray(data): + mask = np.isnan(data) # Create a mask for NaN values (no-data) + data = np.ma.masked_array(data, mask=mask) + + print(np.sum(data.mask)) + print(data.size) # Calculate the requested statistics for stat in stats: if stat in functions: feature_stats[stat] = float(functions[stat](data)) + # print(stat) + # print(float(functions[stat](data))) + # Compute range if required (max - min) if 'range' in stats: feature_stats['range'] = feature_stats.get('max', np.max(data)) - feature_stats.get('min', np.min(data)) diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index c9dbe0b8..03388fc2 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -555,6 +555,7 @@ def compute_stats(self, raster: str, bands: List[int], categorical=self.categorical) print(statistics) + print(len(statistics)) # apply area if self.area: [d.update({key: area_square_meter * val}) From ef694f4a1f3273ccb856780000c0000e896e1b78 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 28 Nov 2024 18:13:34 +0100 Subject: [PATCH 16/56] refactor: xarray for zonalstats --- src/eolab/rastertools/processing/stats.py | 134 ++++++++++++---------- src/eolab/rastertools/zonalstats.py | 9 +- tests/test_rastertools.py | 2 +- 3 files changed, 77 insertions(+), 68 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index 97eb9016..f9736cf4 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -46,9 +46,6 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, # Initialize statistics list statistics = [] - # Get the CRS of the geometries - geoms_crs = geoms.crs - # Prepare progress bar disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] @@ -72,7 +69,7 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, feature_stats.update(_compute_stats(band_data, stats, categorical)) # Append the computed statistics for the current geometry - statistics.append(feature_stats) + statistics.append([feature_stats]) return statistics @@ -107,16 +104,11 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st mask = np.isnan(data) # Create a mask for NaN values (no-data) data = np.ma.masked_array(data, mask=mask) - print(np.sum(data.mask)) - print(data.size) # Calculate the requested statistics for stat in stats: if stat in functions: feature_stats[stat] = float(functions[stat](data)) - # print(stat) - # print(float(functions[stat](data))) - # Compute range if required (max - min) if 'range' in stats: feature_stats['range'] = feature_stats.get('max', np.max(data)) - feature_stats.get('min', np.min(data)) @@ -126,6 +118,42 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st q = float(pctile.replace("percentile_", '')) feature_stats[pctile] = np.percentile(data, q) + feature_stats.update(_gen_stats_cat(data, stats, categorical)) + + return feature_stats + + +def _gen_stats_cat(dataset, stats: List[str] = None, + categorical: bool = False): + """Generates the statistics + + Args: + dataset: + The dataset (numpy MaskedArray) from which stats are computed + stats: + The stats to compute + categorical: + Whether to consider the input raster as categorical + prefix_stats: + A prefix to name the stats + + Returns: + The list of statistics for the input dataset as a dict that associates the + stats names and the stats values. + + """ + + # if categorical stats is requested, extract all unique values from the dataset + if categorical or 'majority' in stats or 'minority' in stats or 'unique' in stats: + keys, counts = np.unique(dataset.compressed(), return_counts=True) + # pixel_count is a dict that associates a unique value with the number + # of occurrences in the dataset + pixel_count = dict(zip([k.item() for k in keys], + [c.item() for c in counts])) + + # initialize the feature_stats dict + feature_stats = dict(pixel_count) if categorical else {} + return feature_stats @@ -133,8 +161,7 @@ def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, bands: List[int] = [1], stats: List[str] = ["min", "max", "mean", "std"], categories: gpd.GeoDataFrame = None, - category_index: str = 'Classe', - category_labels: Dict[str, str] = None): + category_index: str = 'Classe'): """Compute the statistics of an input image for each feature in the shapefile Args: @@ -175,58 +202,41 @@ def _get_list_of_polygons(geom): nb_geoms = len(geoms) nb_bands = len(bands) - with rasterio.open(image) as src: - # each input geometry is split following the categorical geometries. - # geom_by_class contains the list of categorical geometries (one list of categorical - # geometries per input geometry) - geom_gen = (geoms.iloc[[i]] for i in range(nb_geoms)) - geom_by_class = [filter_dissolve(roi, categories, id=category_index) - for roi in geom_gen] - - # Compute the number of categorical geometries for each input geometry - nb_class_roi = [geom_by_class.shape[0] for geom_by_class in geom_by_class] - - # compute stats prefix - index_list_roi = [str(el) - for geom_by_class in geom_by_class - for el in geom_by_class[category_index]] - - # change index_list_roi names if a dict is given - if category_labels: - index_list_roi = [category_labels[el] if el in category_labels else el - for el in index_list_roi] - - # Generator to creates windows associated with each category - # geom_window_gen is a generator whose elements are list of geometries per class and per roi - geom_windows = [(_get_list_of_polygons(geom), - features.geometry_window(src, _get_list_of_polygons(geom))) - for geom_by_class in geom_by_class - for geom in geom_by_class.geometry] - - substats = [] - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - for geom_window, stats_prefix in tqdm(zip(geom_windows, index_list_roi), - disable=disable, desc="zonalstats"): - """Read input raster and compute stats""" - geom, window = geom_window - - data = src.read(bands, window=window) - transform = src.window_transform(window) - - s = _compute_stats((data, transform, geom, window), - src.nodata, stats, False, stats_prefix) - substats.append(s) - - offset = 0 - # re-order output so that all stats of categorical geometries that correspond - # to the same input geometry are concatenated in the same list + # Open raster using rioxarray + with rioxarray.open_rasterio(image, masked=True) as src: + # Loop over geometries (ROIs) for i in range(nb_geoms): - results_roi = [{}] * nb_bands - [results_roi[u].update(substats[v + offset][u]) - for u in range(nb_bands) - for v in range(nb_class_roi[i])] - offset = offset + nb_class_roi[i] - statistics.append(results_roi) + roi_geom = geoms.iloc[[i]] # Select the current geometry + roi_statistics = [] + + # Clip the raster to the current geometry + roi_raster = src.rio.clip(roi_geom.geometry, all_touched=True, drop=True) + + # Handle categories if provided + if categories is not None: + category_geoms = filter_dissolve(roi_geom, categories, id=category_index) + + for _, cat_geom in category_geoms.iterrows(): + # Clip the raster to categorical geometry provided + cat_raster = roi_raster.rio.clip([cat_geom.geometry], all_touched=True, drop=True) + + # Compute stats for each band + cat_stats = [] + for band in bands: + band_data = cat_raster.sel(band=band) + cat_stats.append(_compute_stats(band_data.values, stats)) + + roi_statistics.append(cat_stats) + else: + # Compute stats for each band without categories + band_stats = [] + for band in bands: + band_data = roi_raster.sel(band=band) + band_stats.append(_compute_stats(band_data.values, stats)) + + roi_statistics.append(band_stats) + + statistics.append(roi_statistics) return statistics diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 03388fc2..a0e9e87e 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -428,9 +428,10 @@ def process_file(self, inputfile: str) -> List[str]: date_str = product.get_date_string('%Y%m%d-%H%M%S') + # check band index and handle all bands options (when bands is None) if self.bands is None or len(self.bands) == 0: - bands = indexes + bands = list(indexes) else: bands = self.bands if min(bands) < 1 or max(bands) > bound: @@ -545,8 +546,7 @@ def compute_stats(self, raster: str, bands: List[int], bands=bands, stats=self.stats, categories=class_geom, - category_index=self.category_index, - category_labels=self.category_labels) + category_index=self.category_index) else: statistics = compute_zonal_stats( geometries, raster, @@ -554,8 +554,7 @@ def compute_stats(self, raster: str, bands: List[int], stats=self.stats, categorical=self.categorical) - print(statistics) - print(len(statistics)) + # apply area if self.area: [d.update({key: area_square_meter * val}) diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index f66c0ccb..9af16757 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - # utils4test.clear_outdir() + utils4test.clear_outdir() def test_rastertools_command_line_info(): From 1536bdee2923a773202ae5a3775055e65dc36e18 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 29 Nov 2024 10:13:24 +0100 Subject: [PATCH 17/56] refactor: xarray for zonalstats --- src/eolab/rastertools/processing/stats.py | 12 ++++++++++ src/eolab/rastertools/processing/vector.py | 7 +++--- src/eolab/rastertools/speed.py | 2 +- src/eolab/rastertools/tiling.py | 2 +- src/eolab/rastertools/zonalstats.py | 8 +++---- tests/test_algo.py | 26 +++++++++++----------- tests/test_rastertools.py | 10 ++++----- 7 files changed, 40 insertions(+), 27 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index f9736cf4..dfac9113 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -118,6 +118,18 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st q = float(pctile.replace("percentile_", '')) feature_stats[pctile] = np.percentile(data, q) + count = data.count() + # generate the counting stats + if "count" in stats: + feature_stats[f'count'] = count + if 'valid' in stats or 'nodata' in stats: + all_count = np.count_nonzero(mask) + if 'nodata' in stats: + feature_stats[f'nodata'] = all_count - count + if 'valid' in stats: + valid = 1.0 * count / (all_count + 1e-5) + feature_stats[f'valid'] = valid + feature_stats.update(_gen_stats_cat(data, stats, categorical)) return feature_stats diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 20cdd10e..8d4dba12 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -147,7 +147,7 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], -def reproject_geom(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], +def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: """ Reproject the geometries to match the CRS of the raster. @@ -165,8 +165,9 @@ def reproject_geom(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path geometries = _get_geoms(geoms) geoms_crs = _get_geoms_crs(geometries) - src = rioxarray.open_rasterio(raster, chunks=True) - raster_crs = src.rio.crs + # Extract raster CRS using rasterio to avoid error caused by different dtypes in bands + with rasterio.open(raster) as src: + raster_crs = src.crs # Reproject geometries to match raster CRS if geoms_crs != raster_crs: diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index dea2fdd0..7770cffa 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -145,7 +145,7 @@ def compute_speed(date0: datetime, date1: datetime, src0 = src0.isel(band=slice(0, len(bands))) src1 = src1.isel(band=slice(0, len(bands))) - result = algo.speed(src0, src1, interval).astype(dtype).fillna(src0.nodata) + result = algo.speed(src0, src1, interval).astype(dtype)#.fillna(src0.nodata) print(result.dims) ##Create the file and compute result.rio.to_raster(speed_image) diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index a9b81903..6002d0b0 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -164,7 +164,7 @@ def process_file(self, inputfile: str): output_paths = [] # STEP 2: Prepare grid (reproject it to raster's CRS) - grid = vector.reproject_geom(self.grid, raster) + grid = vector.reproject(self.grid, raster) for shape, i in zip(grid.geometry, grid.index): _logger.info("Crop and export tile " + str(i) + "...") diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index a0e9e87e..5bdef95c 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -444,7 +444,7 @@ def process_file(self, inputfile: str) -> List[str]: # STEP 2: Prepare the geometries where to compute zonal stats if self.geometries: # reproject & filter input geometries to fit the raster extent - geometries = vector.reproject_geom( + geometries = vector.reproject( vector.filter(self.geometries, raster, self.within), raster) else: # if no geometry is defined, get the geometry from raster shape @@ -531,12 +531,12 @@ def compute_stats(self, raster: str, bands: List[int], # prepare the categories data if self.category_file_type == "vector": # clip categories to the raster bounds and reproject in the raster crs - class_geom = vector.reproject_geom( + class_geom = vector.reproject( vector.clip(self.category_file, raster), raster) else: # filetype is raster # vectorize the raster and reproject in the raster crs - class_geom = vector.reproject_geom( + class_geom = vector.reproject( vector.vectorize(self.category_file, raster, self.category_index), raster) @@ -554,7 +554,7 @@ def compute_stats(self, raster: str, bands: List[int], stats=self.stats, categorical=self.categorical) - + print(statistics) # apply area if self.area: [d.update({key: area_square_meter * val}) diff --git a/tests/test_algo.py b/tests/test_algo.py index f2e5ebe3..fb2b003e 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -158,19 +158,19 @@ def test_indices_algo(input_np :np.ndarray, input_xarray : xr.DataArray): # assert type(svf_xarr) == xr.DataArray -@pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [27.2, 82.64, 3, 0.5]), - (rd_np,rd_xarr, [27.2, 82.64, 3, 0.5])]) - -def test_hillshade_algo(input_np : np.ndarray, input_xarray : xr.DataArray, params : list): - """ - Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray - """ - elevation, azimuth , radius, resolution = params - hills_np = algo.hillshade(input_np, elevation, azimuth , radius, resolution) - hills_xarr = algo.hillshade(input_xarray, elevation, azimuth , radius, resolution) - #Assert that the values of both arrays are the same - np.testing.assert_array_equal(hills_np, hills_xarr.values) - assert type(hills_xarr) == xr.DataArray +# @pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [27.2, 82.64, 3, 0.5]), +# (rd_np,rd_xarr, [27.2, 82.64, 3, 0.5])]) +# +# def test_hillshade_algo(input_np : np.ndarray, input_xarray : xr.DataArray, params : list): +# """ +# Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray +# """ +# elevation, azimuth , radius, resolution = params +# hills_np = algo.hillshade(input_np, elevation, azimuth , radius, resolution) +# hills_xarr = algo.hillshade(input_xarray, elevation, azimuth , radius, resolution) +# #Assert that the values of both arrays are the same +# np.testing.assert_array_equal(hills_np, hills_xarr.values) +# assert type(hills_xarr) == xr.DataArray def test_local_sum(): diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 9af16757..c2b49b75 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - utils4test.clear_outdir() + # utils4test.clear_outdir() def test_rastertools_command_line_info(): @@ -166,10 +166,10 @@ def test_radioindice_command_line_default(): # two indices with their own options, merge "-v ri --pvi --savi -o tests/tests_out -m tests/tests_data/listing.lst", # indices option, roi - # "--verbose ri --indices pvi savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" - # " --output tests/tests_out" - # " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" - # " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + "--verbose ri --indices pvi savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" + " --output tests/tests_out" + " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" ] # get list of expected outputs indices_list = ["ndvi ndwi ndwi2", "indices", "pvi savi nd[nir-red]"] From 3e6cd9fca69cac750edc35bb985bd989163dbc8b Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 29 Nov 2024 11:42:51 +0100 Subject: [PATCH 18/56] refactor: debug --- src/eolab/rastertools/processing/stats.py | 43 ++++++++++++----------- src/eolab/rastertools/zonalstats.py | 4 +++ tests/test_rastertools.py | 2 +- 3 files changed, 27 insertions(+), 22 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index dfac9113..cce194a4 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -40,36 +40,37 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, statistics: a list of lists of dictionaries. The first list corresponds to the geometries, the second corresponds to the bands. Each dictionary associates the stat names and the stat values. """ - # Open the raster image using rioxarray - raster = rioxarray.open_rasterio(image, masked=True) + with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + # Open the raster image using rioxarray + raster = rioxarray.open_rasterio(image, masked=True) - # Initialize statistics list - statistics = [] + # Initialize statistics list + statistics = [] - # Prepare progress bar - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + # Prepare progress bar + disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - # Iterate through geometries - for _, geom in tqdm(geoms.iterrows(), total=len(geoms), disable=disable, desc="zonalstats"): - geom = geom.geometry + # Iterate through geometries + for _, geom in tqdm(geoms.iterrows(), total=len(geoms), disable=disable, desc="zonalstats"): + geom = geom.geometry - # Clip the raster using the geometry - clipped = raster.rio.clip([geom], geoms.crs, drop=True) + # Clip the raster using the geometry + clipped = raster.rio.clip([geom], geoms.crs, drop=True) - # Select bands - clipped_data = clipped.sel(band=bands) + # Select bands + clipped_data = clipped.sel(band=bands) - clipped_data.rio.to_raster('../view.tif') - # Compute statistics for each band - feature_stats = {} - for band_data in clipped_data.values: + clipped_data.rio.to_raster('../view.tif') + # Compute statistics for each band + feature_stats = {} + for band_data in clipped_data.values: - # Compute the statistics - feature_stats.update(_compute_stats(band_data, stats, categorical)) + # Compute the statistics + feature_stats.update(_compute_stats(band_data, stats, categorical)) - # Append the computed statistics for the current geometry - statistics.append([feature_stats]) + # Append the computed statistics for the current geometry + statistics.append([feature_stats]) return statistics diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 5bdef95c..06358d20 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -555,6 +555,7 @@ def compute_stats(self, raster: str, bands: List[int], categorical=self.categorical) print(statistics) + print(len(statistics)) # apply area if self.area: [d.update({key: area_square_meter * val}) @@ -588,6 +589,7 @@ def __stats_to_geoms(self, statistics_data: List[List[Dict[str, float]]], successively the band name, the date and the statistics names (min, mean, max, median, std) """ prefix = self.prefix or [""] * len(bands) + print(bands) for i, band in enumerate(bands): # add general metadata to geometries if descr and descr[i]: @@ -595,6 +597,8 @@ def __stats_to_geoms(self, statistics_data: List[List[Dict[str, float]]], if date: geometries[utils.get_metadata_name(band, prefix[i], "date")] = date + + # get all statistics names since additional statistics coming from categorical # option may have been computed stats = self.stats.copy() diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index c2b49b75..1176fe7f 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -125,7 +125,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec caplog.clear() # clear output dir - # utils4test.clear_outdir() + utils4test.clear_outdir() def test_rastertools_command_line_info(): From 8653d0274347b23b8dda103737955b4caae3a571 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 29 Nov 2024 13:55:19 +0100 Subject: [PATCH 19/56] refactor: debug tests --- src/eolab/rastertools/processing/stats.py | 12 ++++++------ src/eolab/rastertools/processing/vector.py | 4 ++-- src/eolab/rastertools/zonalstats.py | 4 ++-- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index cce194a4..6fc467cd 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -63,14 +63,14 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, clipped_data.rio.to_raster('../view.tif') # Compute statistics for each band - feature_stats = {} + band_stat = [] for band_data in clipped_data.values: - # Compute the statistics - feature_stats.update(_compute_stats(band_data, stats, categorical)) + feature_stats = _compute_stats(band_data, stats, categorical) + band_stat.append(feature_stats) - # Append the computed statistics for the current geometry - statistics.append([feature_stats]) + # Append the computed statistics for the current geometry + statistics.append(band_stat) return statistics @@ -132,7 +132,7 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st feature_stats[f'valid'] = valid feature_stats.update(_gen_stats_cat(data, stats, categorical)) - + print(feature_stats) return feature_stats diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 8d4dba12..8f979955 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -236,7 +236,7 @@ def get_raster_shape(raster: Union[Path, str], output: Union[Path, str] = None, # Loop through each band in the raster for band in range(1, src.shape[0] + 1): # Read the mask for the current band - mask = src.isel(band=band - 1).notnull().astype(np.int16) + mask = src.isel(band=band - 1).notnull().astype("uint8") # Convert the mask to geometries using rasterio features features_gen = features.shapes(mask.values, mask=mask.values, transform=src.rio.transform()) @@ -251,7 +251,7 @@ def get_raster_shape(raster: Union[Path, str], output: Union[Path, str] = None, df = pd.DataFrame({'geometry': geoms}) gdf = gpd.GeoDataFrame(df, geometry='geometry') # set crs - gdf.crs = src.crs + gdf.crs = src.rio.crs # dissolve all shapes per band in one shape gdf['COMMON'] = 0 raster_shape = gdf.dissolve(by='COMMON', as_index=False) diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 06358d20..a85d23bd 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -555,7 +555,7 @@ def compute_stats(self, raster: str, bands: List[int], categorical=self.categorical) print(statistics) - print(len(statistics)) + # print(len(statistics)) # apply area if self.area: [d.update({key: area_square_meter * val}) @@ -589,7 +589,7 @@ def __stats_to_geoms(self, statistics_data: List[List[Dict[str, float]]], successively the band name, the date and the statistics names (min, mean, max, median, std) """ prefix = self.prefix or [""] * len(bands) - print(bands) + for i, band in enumerate(bands): # add general metadata to geometries if descr and descr[i]: From c425e6b40681afc8d64def90de7e8f997f7d4974 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 29 Nov 2024 14:38:44 +0100 Subject: [PATCH 20/56] refactor: debug tests --- src/eolab/rastertools/processing/stats.py | 12 ++++++------ src/eolab/rastertools/processing/vector.py | 10 +++++----- src/eolab/rastertools/tiling.py | 2 +- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index 6fc467cd..b74963d8 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -120,19 +120,19 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st feature_stats[pctile] = np.percentile(data, q) count = data.count() + print() # generate the counting stats if "count" in stats: - feature_stats[f'count'] = count + feature_stats['count'] = count if 'valid' in stats or 'nodata' in stats: all_count = np.count_nonzero(mask) if 'nodata' in stats: - feature_stats[f'nodata'] = all_count - count + feature_stats['nodata'] = all_count - count if 'valid' in stats: valid = 1.0 * count / (all_count + 1e-5) - feature_stats[f'valid'] = valid + feature_stats['valid'] = valid feature_stats.update(_gen_stats_cat(data, stats, categorical)) - print(feature_stats) return feature_stats @@ -155,7 +155,7 @@ def _gen_stats_cat(dataset, stats: List[str] = None, stats names and the stats values. """ - + pixel_count = {} # if categorical stats is requested, extract all unique values from the dataset if categorical or 'majority' in stats or 'minority' in stats or 'unique' in stats: keys, counts = np.unique(dataset.compressed(), return_counts=True) @@ -213,7 +213,7 @@ def _get_list_of_polygons(geom): statistics = [] # Process geometries one by one nb_geoms = len(geoms) - nb_bands = len(bands) + print(nb_geoms) # Open raster using rioxarray with rioxarray.open_rasterio(image, masked=True) as src: diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 8f979955..f823b182 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -127,13 +127,13 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], filtered_geoms = filter(geometries, raster) # then clip the resulting geometries to the raster bounds - with rioxarray.open_rasterio(file) as dataset: - l, b, r, t = dataset.rio.bounds + with rasterio.open(file) as dataset: + l, b, r, t = dataset.bounds px, py = ([l, l, r, r], [b, t, t, b]) - if (geoms_crs != dataset.rio.crs): + if (geoms_crs != dataset.crs): # reproject bounds in geoms crs - px, py = warp.transform(dataset.rio.crs, geoms_crs, px, py) + px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) # then clip the resulting geometries to the raster bounds polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) @@ -143,7 +143,7 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], outfile = output.as_posix() if isinstance(output, Path) else output clipped_geoms.to_file(outfile, driver=driver) - return clipped_geoms + return clipped_geoms diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 6002d0b0..59fca439 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -164,7 +164,7 @@ def process_file(self, inputfile: str): output_paths = [] # STEP 2: Prepare grid (reproject it to raster's CRS) - grid = vector.reproject(self.grid, raster) + grid = vector.reproject(self.grid, product.get_raster()) for shape, i in zip(grid.geometry, grid.index): _logger.info("Crop and export tile " + str(i) + "...") From 2474b2aae1b2295a92efbbae432d8645a50d79f8 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 29 Nov 2024 15:37:40 +0100 Subject: [PATCH 21/56] refactor: debug tests --- src/eolab/rastertools/processing/stats.py | 50 ++++++++++++++--------- 1 file changed, 31 insertions(+), 19 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index b74963d8..47ab1d81 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -60,8 +60,6 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, # Select bands clipped_data = clipped.sel(band=bands) - - clipped_data.rio.to_raster('../view.tif') # Compute statistics for each band band_stat = [] for band_data in clipped_data.values: @@ -75,7 +73,7 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, return statistics -def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[str, float]: +def _compute_stats(data, stats: List[str], categorical: bool = False, prefix_stats: str = "") -> Dict[str, float]: """Compute the statistics for a single band (numpy array). Args: @@ -108,36 +106,37 @@ def _compute_stats(data, stats: List[str], categorical: bool = False) -> Dict[st # Calculate the requested statistics for stat in stats: if stat in functions: - feature_stats[stat] = float(functions[stat](data)) + feature_stats[f'{prefix_stats}{stat}'] = float(functions[stat](data)) # Compute range if required (max - min) if 'range' in stats: - feature_stats['range'] = feature_stats.get('max', np.max(data)) - feature_stats.get('min', np.min(data)) + feature_stats[f'{prefix_stats}range'] = feature_stats.get('max', np.max(data)) - feature_stats.get('min', np.min(data)) # Compute percentiles if requested for pctile in [s for s in stats if s.startswith('percentile_')]: q = float(pctile.replace("percentile_", '')) - feature_stats[pctile] = np.percentile(data, q) + feature_stats[f'{prefix_stats}{pctile}'] = np.percentile(data, q) + if 'mad' in stats: + feature_stats[f'{prefix_stats}mad'] = median_abs_deviation(data.compressed().flatten()) count = data.count() - print() # generate the counting stats if "count" in stats: - feature_stats['count'] = count + feature_stats[f'{prefix_stats}count'] = count if 'valid' in stats or 'nodata' in stats: all_count = np.count_nonzero(mask) if 'nodata' in stats: - feature_stats['nodata'] = all_count - count + feature_stats[f'{prefix_stats}nodata'] = all_count - count if 'valid' in stats: valid = 1.0 * count / (all_count + 1e-5) - feature_stats['valid'] = valid + feature_stats[f'{prefix_stats}valid'] = valid - feature_stats.update(_gen_stats_cat(data, stats, categorical)) + feature_stats.update(_gen_stats_cat(data, stats, categorical, prefix_stats)) return feature_stats def _gen_stats_cat(dataset, stats: List[str] = None, - categorical: bool = False): + categorical: bool = False, prefix_stats: str = ""): """Generates the statistics Args: @@ -167,6 +166,21 @@ def _gen_stats_cat(dataset, stats: List[str] = None, # initialize the feature_stats dict feature_stats = dict(pixel_count) if categorical else {} + def _key_assoc_val(d, func, exclude=None): + """return the key associated with the value returned by func + """ + vs = list(d.values()) + ks = list(d.keys()) + key = ks[vs.index(func(vs))] + return key + + if 'majority' in stats: + feature_stats[f'{prefix_stats}majority'] = float(_key_assoc_val(pixel_count, max)) + if 'minority' in stats: + feature_stats[f'{prefix_stats}minority'] = float(_key_assoc_val(pixel_count, min)) + if 'unique' in stats: + feature_stats[f'{prefix_stats}unique'] = len(list(pixel_count.keys())) + return feature_stats @@ -201,7 +215,6 @@ def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, """ def _get_list_of_polygons(geom): """Get the list of polygons from the geometry""" - polygons = None if geom.geom_type == 'MultiPolygon': polygons = list(geom.geoms) elif geom.geom_type == 'Polygon': @@ -229,23 +242,22 @@ def _get_list_of_polygons(geom): if categories is not None: category_geoms = filter_dissolve(roi_geom, categories, id=category_index) - for _, cat_geom in category_geoms.iterrows(): + prefix_stats = [str(cat[category_index]) for _, cat in category_geoms.iterrows()] + + for prefix, (_, cat_geom) in zip(prefix_stats, category_geoms.iterrows()): # Clip the raster to categorical geometry provided cat_raster = roi_raster.rio.clip([cat_geom.geometry], all_touched=True, drop=True) # Compute stats for each band - cat_stats = [] for band in bands: band_data = cat_raster.sel(band=band) - cat_stats.append(_compute_stats(band_data.values, stats)) - - roi_statistics.append(cat_stats) + roi_statistics.append(_compute_stats(band_data.values, stats, prefix)) else: # Compute stats for each band without categories band_stats = [] for band in bands: band_data = roi_raster.sel(band=band) - band_stats.append(_compute_stats(band_data.values, stats)) + band_stats.append(_compute_stats(band_data.values, stats, prefix_stats[0])) roi_statistics.append(band_stats) From 1dba34b9d44bf63cb3281c93e62e3296c649677f Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 3 Dec 2024 14:17:34 +0100 Subject: [PATCH 22/56] refactor: fixing test --compare --- src/eolab/rastertools/processing/algo.py | 1 - src/eolab/rastertools/radioindice.py | 27 ++++++++++++++++++++++-- tests/test_radioindice.py | 2 +- 3 files changed, 26 insertions(+), 4 deletions(-) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 80400c1e..0d192354 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -33,7 +33,6 @@ def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.n Numpy array of the size (number of lines, number of columns) containing the computed TNDVI. """ np.seterr(divide='ignore') - print(bands[1][:2] ) res = (bands[1] - bands[0]) / (bands[1] + bands[0]) print(res[:2]) return res diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index b489af2a..55370bac 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -11,6 +11,7 @@ from typing import List import threading +import numpy import numpy as np import rasterio import numpy.ma as ma @@ -407,7 +408,8 @@ def process_file(self, inputfile: str) -> List[str]: outdir = Path(self.outputdir) - # STEP 1: Prepare the input image so that it can be processed + + # Prepare the input image so that it can be processed with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: _logger.debug(f"Raster product is : {product}") @@ -484,6 +486,7 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], dtype = indices[0].dtype or rasterio.float32 src_array = src_array.astype(dtype) nodata = -10000 + crs = src_array.rio.crs #Replace nodata values with np.nan src_array = src_array.where(src_array != nodata, other=np.nan) @@ -500,7 +503,27 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Get the bands necessary to compute the indice bands = [image_channels.index(channel) + 1 for channel in indice.channels] + print(src_array.values[0,200,:100]) + input('vf') result.loc[{"band": indice.name}] = indice.algo(src_array.sel(band=bands).values).astype(dtype) # Create the file and compute - result.rio.to_raster(indice_image) \ No newline at end of file + result.rio.write_crs(crs, inplace=True) + result.rio.to_raster(indice_image) + + # Attach statistics to the raster using Rasterio + with rasterio.open(indice_image, "r+") as dataset: + for band_idx in range(1, dataset.count + 1): + band = dataset.read(band_idx, masked=True) + band = np.ma.masked_invalid(band) # Handle NaN values + + # Calculate statistics + stats = { + "STATISTICS_MINIMUM": float(np.nanmin(band)), + "STATISTICS_MAXIMUM": float(np.nanmax(band)), + "STATISTICS_MEAN": float(np.nanmean(band)), + "STATISTICS_STDDEV": float(np.nanstd(band)) + } + + # Write metadata to the band + dataset.update_tags(band_idx, **stats) \ No newline at end of file diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index acaff0c9..b66ea593 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -91,7 +91,7 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(gen_files, __refdir) - utils4test.clear_outdir() + # utils4test.clear_outdir() def test_radioindice_process_files(): From 4103e8dae6fac1fe5564e5bd0694724697582fff Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 4 Dec 2024 09:16:20 +0100 Subject: [PATCH 23/56] refactor: fixing test --compare --- setup.py | 50 ++++++- src/eolab/rastertools/processing/vector.py | 43 +++--- src/eolab/rastertools/radioindice.py | 11 +- src/eolab/rastertools/timeseries.py | 13 +- src/eolab/rastertools/utils.py | 92 ++++++++++++ src/eolab/rastertools/zonalstats.py | 2 +- src/rastertools.egg-info/PKG-INFO | 34 ++++- src/rastertools.egg-info/SOURCES.txt | 50 ++++++- src/rastertools.egg-info/entry_points.txt | 4 +- src/rastertools.egg-info/requires.txt | 29 +++- tests/test_algo.py | 160 ++++++++++----------- 11 files changed, 365 insertions(+), 123 deletions(-) diff --git a/setup.py b/setup.py index 3aafee77..e9c75eb4 100644 --- a/setup.py +++ b/setup.py @@ -7,11 +7,57 @@ PyScaffold helps you to put up the scaffold of your new Python project. Learn more under: https://pyscaffold.org/ """ -from setuptools import setup +from setuptools import setup, find_packages if __name__ == "__main__": try: - setup(use_scm_version={"version_scheme": "no-guess-dev"}) + setup(name='rastertools', + version="0.1.0", + description=u"Collection of tools for raster data", + long_description="", + classifiers=[], + keywords='', + author=u"Olivier Queyrut", + author_email="", + url="https://github.com/CNES/rastertools", + packages=find_packages(exclude=['tests']), + include_package_data=True, + zip_safe=False, + setup_requires = ["setuptools_scm"], + install_requires=[ + 'click', + 'rasterio==1.3.0', + 'pytest>=3.6', + 'pytest-cov', + 'geopandas==0.13', + 'python-dateutil==2.9.0', + 'kiwisolver==1.4.5', + 'fonttools==4.53.1', + 'matplotlib==3.7.3', + 'packaging==24.1', + 'Shapely==1.8.5.post1', + 'tomli==2.0.2', + 'Rtree==1.3.0', + 'fiona==1.8.21', + 'Pillow==9.2.0', + 'sphinx_rtd_theme==3.0.1', + 'pip==24.2', + 'dask', + 'rioxarray', + 'xarray==2022.11.0', + 'pyproj==3.4.0', + 'sphinx==7.1.2', + 'scipy==1.8', + 'pyscaffold', + 'gdal==3.5.0', + 'tqdm==4.66' + ], + entry_points=""" + [rasterio.rio_plugins] + rastertools=eolab.rastertools.main:rastertools + """, + python_requires='==3.8.13', + use_scm_version={"version_scheme": "no-guess-dev"}) except: # noqa print( "\n\nAn error occurred while building the project, " diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index f823b182..49d0b495 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -19,6 +19,7 @@ from shapely.geometry import Polygon from eolab.rastertools import utils +from eolab.rastertools.utils import vsimem_to_rasterio def _get_geoms(geoms: Union[gpd.GeoDataFrame, Path, str]) -> gpd.GeoDataFrame: @@ -165,9 +166,9 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str geometries = _get_geoms(geoms) geoms_crs = _get_geoms_crs(geometries) - # Extract raster CRS using rasterio to avoid error caused by different dtypes in bands - with rasterio.open(raster) as src: - raster_crs = src.crs + file = raster.as_posix() if isinstance(raster, Path) else raster + raster = vsimem_to_rasterio(file) + raster_crs = raster.crs # Reproject geometries to match raster CRS if geoms_crs != raster_crs: @@ -333,24 +334,24 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], geometries = reproject(dissolve(roi), pinput) geom_bounds = geometries.total_bounds - with rasterio.open(pinput) as raster: - rst_bounds = raster.bounds - bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), - math.floor(max(rst_bounds[1], geom_bounds[1])), - math.ceil(min(rst_bounds[2], geom_bounds[2])), - math.ceil(min(rst_bounds[3], geom_bounds[3]))) - geotransform = raster.get_transform() - width = np.abs(geotransform[1]) - height = np.abs(geotransform[5]) - - ds = gdal.Warp(destNameOrDestDS=poutput, - srcDSOrSrcDSTab=pinput, - outputBounds=bounds, targetAlignedPixels=True, - cutlineDSName=roi, - cropToCutline=False, - xRes=width, yRes=height, - format="VRT") - del ds + raster = vsimem_to_rasterio(pinput) + rst_bounds = raster.bounds + bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), + math.floor(max(rst_bounds[1], geom_bounds[1])), + math.ceil(min(rst_bounds[2], geom_bounds[2])), + math.ceil(min(rst_bounds[3], geom_bounds[3]))) + geotransform = raster.get_transform() + width = np.abs(geotransform[1]) + height = np.abs(geotransform[5]) + + ds = gdal.Warp(destNameOrDestDS=poutput, + srcDSOrSrcDSTab=pinput, + outputBounds=bounds, targetAlignedPixels=True, + cutlineDSName=roi, + cropToCutline=False, + xRes=width, yRes=height, + format="VRT") + del ds def vectorize(category_raster: Union[Path, str], raster: Union[Path, str], diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 55370bac..f20ef501 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -408,7 +408,7 @@ def process_file(self, inputfile: str) -> List[str]: outdir = Path(self.outputdir) - + print(inputfile) # Prepare the input image so that it can be processed with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: _logger.debug(f"Raster product is : {product}") @@ -480,10 +480,12 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], Size of windows for splitting the processed image in small parts """ with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with rioxarray.open_rasterio(input_image, masked=True, chunks=True) as src_array: + print(input_image) + with rioxarray.open_rasterio(input_image, masked=True, chunks=True, cache=False, lock = False) as src_array: # dtype of output data dtype = indices[0].dtype or rasterio.float32 + src_array.load() src_array = src_array.astype(dtype) nodata = -10000 crs = src_array.rio.crs @@ -503,13 +505,12 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Get the bands necessary to compute the indice bands = [image_channels.index(channel) + 1 for channel in indice.channels] - print(src_array.values[0,200,:100]) - input('vf') + result.loc[{"band": indice.name}] = indice.algo(src_array.sel(band=bands).values).astype(dtype) # Create the file and compute result.rio.write_crs(crs, inplace=True) - result.rio.to_raster(indice_image) + result.rio.to_raster(indice_image, nodata=-2.0, dtype=dtype) # Attach statistics to the raster using Rasterio with rasterio.open(indice_image, "r+") as dataset: diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index eed666fa..c2d29d2c 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -206,8 +206,9 @@ def _interpolate_xarray(products_dates, products_per_date, for date in products_dates: src = products_per_date[date].open_xarray() + dtype = src.dtype or rasterio.float32 band_data = src.isel(band=slice(0, len(bands))) # Select the desired bands - + crs = src.rio.crs datas.append(band_data) output = algo.interpolated_timeseries_xarray(products_dates, datas, timeseries_dates, nodata) @@ -218,4 +219,12 @@ def _interpolate_xarray(products_dates, products_per_date, # Use of the lock to avoid writing in // with write_lock: for i, img in enumerate(timeseries_images): - output[i].rio.to_raster(img) + # Remove unexpected keys + unwanted_keys = ['long_name', 'STATISTICS_APPROXIMATE', 'STATISTICS_MAXIMUM', + 'STATISTICS_MEAN', 'STATISTICS_MINIMUM', 'STATISTICS_STDDEV', + 'STATISTICS_VALID_PERCENT'] + output[i].attrs = {k: v for k, v in output[i].attrs.items() if k not in unwanted_keys} + + output[i].rio.write_crs(crs, inplace=True) + output[i].rio.write_nodata(-2.0, inplace=True) + output[i].rio.to_raster(img, nodata=-2.0, dtype=dtype) diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index 7dd9b145..974bb6a9 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -9,8 +9,100 @@ - ... """ import math +import tempfile from pathlib import Path +import rasterio +from osgeo import gdal + + +def vsimem_to_rasterio(vsimem_file:str, nodata=None) -> rasterio.io.DatasetReader: + """ + Converts a VSIMEM (in-memory) raster dataset to a Rasterio dataset with optional nodata masking. + + This function opens a raster dataset stored in VSIMEM (virtual file system in memory) + using GDAL, extracts its metadata and data, handles optional nodata values and masks, + and saves it to a temporary GeoTIFF file. It then reopens the file with Rasterio and + returns a Rasterio dataset reader. + + Parameters + ---------- + vsimem_file : str + The path to the VSIMEM file to be converted. This file should be an in-memory GDAL dataset. + + nodata : float, optional + A user-defined nodata value to override the nodata value in the GDAL dataset. + If not provided, the nodata value from the GDAL dataset is used (if available). + + Returns + ------- + rasterio.io.DatasetReader + A Rasterio dataset reader object corresponding to the temporary GeoTIFF created from the VSIMEM file. + + Notes + ----- + - The function assumes the dataset is in a format that is compatible with both GDAL and Rasterio. + - The created temporary file is not deleted automatically. It can be removed manually after use. + - The function reads all raster bands from the dataset, applies the optional nodata masking, + and writes the data to a new GeoTIFF file. + - If a nodata value is provided, the function will apply the mask based on that value to each band. + If no nodata value is set, no mask is applied. + """ + gdal_ds = gdal.Open(vsimem_file) + cols = gdal_ds.RasterXSize + rows = gdal_ds.RasterYSize + bands = gdal_ds.RasterCount + geo_transform = gdal_ds.GetGeoTransform() + projection = gdal_ds.GetProjection() + + gdal_dtype_to_numpy = { + gdal.GDT_Byte: "uint8", + gdal.GDT_UInt16: "uint16", + gdal.GDT_Int16: "int16", + gdal.GDT_UInt32: "uint32", + gdal.GDT_Int32: "int32", + gdal.GDT_Float32: "float32", + gdal.GDT_Float64: "float64", + } + dtype = gdal_dtype_to_numpy[gdal_ds.GetRasterBand(1).DataType] + + data = [gdal_ds.GetRasterBand(i + 1).ReadAsArray() for i in range(bands)] + + masks = [] + for i in range(bands): + band = gdal_ds.GetRasterBand(i + 1) + band_nodata = band.GetNoDataValue() + # Prioriser la valeur nodata de l'utilisateur + nodata_value = nodata if nodata is not None else band_nodata + if nodata_value is not None: + masks.append(data[i] == nodata_value) + else: + masks.append(None) + + with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmpfile: + temp_filename = tmpfile.name + + profile = { + "driver": "GTiff", + "height": rows, + "width": cols, + "count": bands, + "dtype": dtype, + "crs": projection, + "transform": rasterio.transform.Affine.from_gdal(*geo_transform), + "nodata": nodata_value, + } + + with rasterio.open(temp_filename, "w", **profile) as dst: + # Écrire les données + for i, band_data in enumerate(data, start=1): + dst.write(band_data, i) + # Si un masque est défini, l'écrire + if masks[i - 1] is not None: + dst.write_mask((~masks[i - 1]).astype("uint8") * 255) + + return rasterio.open(temp_filename) + def to_tuple(val): """Convert val as a tuple of two val""" diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index a85d23bd..51a48659 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -554,7 +554,7 @@ def compute_stats(self, raster: str, bands: List[int], stats=self.stats, categorical=self.categorical) - print(statistics) + # print(len(statistics)) # apply area if self.area: diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 4a06ec9c..b5e6c029 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -1,8 +1,8 @@ Metadata-Version: 2.1 Name: rastertools -Version: 0.5.0.post1.dev66+g24a07c4.d20240523 -Summary: Compute radiometric indices and zonal statistics on rasters -Home-page: https://github.com/cnes/rastertools +Version: 0.1.0 +Summary: Collection of tools for raster data +Home-page: https://github.com/CNES/rastertools Author: Olivier Queyrut Author-email: olivier.queyrut@cnes.fr License: apache v2 @@ -12,10 +12,36 @@ Project-URL: Issues, https://github.com/cnes/rastertools/issues Platform: any Classifier: Development Status :: 5 - Production/Stable Classifier: Programming Language :: Python +Requires-Python: ==3.8.13 Description-Content-Type: text/x-rst; charset=UTF-8 License-File: LICENSE.txt License-File: AUTHORS.rst -Requires-Dist: importlib-metadata; python_version < "3.8" +Requires-Dist: click +Requires-Dist: rasterio==1.3.0 +Requires-Dist: pytest>=3.6 +Requires-Dist: pytest-cov +Requires-Dist: geopandas==0.13 +Requires-Dist: python-dateutil==2.9.0 +Requires-Dist: kiwisolver==1.4.5 +Requires-Dist: fonttools==4.53.1 +Requires-Dist: matplotlib==3.7.3 +Requires-Dist: packaging==24.1 +Requires-Dist: Shapely==1.8.5.post1 +Requires-Dist: tomli==2.0.2 +Requires-Dist: Rtree==1.3.0 +Requires-Dist: fiona==1.8.21 +Requires-Dist: Pillow==9.2.0 +Requires-Dist: sphinx_rtd_theme==3.0.1 +Requires-Dist: pip==24.2 +Requires-Dist: dask +Requires-Dist: rioxarray +Requires-Dist: xarray==2022.11.0 +Requires-Dist: pyproj==3.4.0 +Requires-Dist: sphinx==7.1.2 +Requires-Dist: scipy==1.8 +Requires-Dist: pyscaffold +Requires-Dist: gdal==3.5.0 +Requires-Dist: tqdm==4.66 Provides-Extra: testing Requires-Dist: setuptools; extra == "testing" Requires-Dist: pytest; extra == "testing" diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 6fcb80a2..9fa6867b 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -1,7 +1,4 @@ -.coveragerc -.dockerignore .gitignore -.readthedocs.yml AUTHORS.rst CHANGELOG.rst Dockerfile @@ -70,6 +67,18 @@ src/eolab/rastertools/tiling.py src/eolab/rastertools/timeseries.py src/eolab/rastertools/utils.py src/eolab/rastertools/zonalstats.py +src/eolab/rastertools/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/__pycache__/filtering.cpython-38.pyc +src/eolab/rastertools/__pycache__/hillshade.cpython-38.pyc +src/eolab/rastertools/__pycache__/main.cpython-38.pyc +src/eolab/rastertools/__pycache__/radioindice.cpython-38.pyc +src/eolab/rastertools/__pycache__/rastertools.cpython-38.pyc +src/eolab/rastertools/__pycache__/speed.cpython-38.pyc +src/eolab/rastertools/__pycache__/svf.cpython-38.pyc +src/eolab/rastertools/__pycache__/tiling.cpython-38.pyc +src/eolab/rastertools/__pycache__/timeseries.cpython-38.pyc +src/eolab/rastertools/__pycache__/utils.cpython-38.pyc +src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/cli/__init__.py src/eolab/rastertools/cli/filtering.py src/eolab/rastertools/cli/hillshade.py @@ -79,17 +88,36 @@ src/eolab/rastertools/cli/svf.py src/eolab/rastertools/cli/tiling.py src/eolab/rastertools/cli/timeseries.py src/eolab/rastertools/cli/zonalstats.py +src/eolab/rastertools/cli/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/filtering.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/hillshade.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/radioindice.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/speed.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/svf.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/tiling.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/timeseries.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/processing/__init__.py src/eolab/rastertools/processing/algo.py src/eolab/rastertools/processing/rasterproc.py src/eolab/rastertools/processing/sliding.py src/eolab/rastertools/processing/stats.py src/eolab/rastertools/processing/vector.py +src/eolab/rastertools/processing/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/algo.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/rasterproc.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/sliding.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/stats.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/vector.cpython-38.pyc src/eolab/rastertools/product/__init__.py src/eolab/rastertools/product/rasterproduct.py src/eolab/rastertools/product/rastertype.py src/eolab/rastertools/product/rastertypes.json src/eolab/rastertools/product/vrt.py +src/eolab/rastertools/product/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/rasterproduct.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/rastertype.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/vrt.cpython-38.pyc src/rastertools.egg-info/PKG-INFO src/rastertools.egg-info/SOURCES.txt src/rastertools.egg-info/dependency_links.txt @@ -113,6 +141,22 @@ tests/test_utils.py tests/test_vector.py tests/test_zonalstats.py tests/utils4test.py +tests/__pycache__/__init__.cpython-38.pyc +tests/__pycache__/cmptools.cpython-38.pyc +tests/__pycache__/conftest.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_algo.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_radioindice.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rasterproc.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rasterproduct.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rastertools.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rastertype.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_speed.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_stats.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_tiling.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_utils.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_vector.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_zonalstats.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/utils4test.cpython-38.pyc tests/tests_data/COMMUNE_32001.dbf tests/tests_data/COMMUNE_32001.prj tests/tests_data/COMMUNE_32001.qpj diff --git a/src/rastertools.egg-info/entry_points.txt b/src/rastertools.egg-info/entry_points.txt index 4366a5f5..467c161c 100644 --- a/src/rastertools.egg-info/entry_points.txt +++ b/src/rastertools.egg-info/entry_points.txt @@ -1,2 +1,2 @@ -[console_scripts] -rastertools = eolab.rastertools.main:run +[rasterio.rio_plugins] +rastertools = eolab.rastertools.main:rastertools diff --git a/src/rastertools.egg-info/requires.txt b/src/rastertools.egg-info/requires.txt index a5ca8f7c..e7a80201 100644 --- a/src/rastertools.egg-info/requires.txt +++ b/src/rastertools.egg-info/requires.txt @@ -1,6 +1,29 @@ - -[:python_version < "3.8"] -importlib-metadata +click +rasterio==1.3.0 +pytest>=3.6 +pytest-cov +geopandas==0.13 +python-dateutil==2.9.0 +kiwisolver==1.4.5 +fonttools==4.53.1 +matplotlib==3.7.3 +packaging==24.1 +Shapely==1.8.5.post1 +tomli==2.0.2 +Rtree==1.3.0 +fiona==1.8.21 +Pillow==9.2.0 +sphinx_rtd_theme==3.0.1 +pip==24.2 +dask +rioxarray +xarray==2022.11.0 +pyproj==3.4.0 +sphinx==7.1.2 +scipy==1.8 +pyscaffold +gdal==3.5.0 +tqdm==4.66 [testing] setuptools diff --git a/tests/test_algo.py b/tests/test_algo.py index fb2b003e..001dd90d 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -21,51 +21,51 @@ from eolab.rastertools.timeseries import compute_timeseries, Timeseries from .utils4test import RastertoolsTestsData -zero_np = np.zeros((1,100,100)) -zero_xarr = xr.DataArray(zero_np, dims=("x", "y", "z")) - -rd_np = np.random.randn(1,100, 100) -rd_xarr = xr.DataArray(rd_np, dims=("x", "y", "z")) - -rd2_np = np.random.randn(1,100, 100) -rd2_xarr = xr.DataArray(rd2_np, dims=("x", "y", "z")) - -#Both zeros arrays -zero_nptest = [zero_np, zero_np] -zero_xarrtest = [zero_xarr, zero_xarr] - -#One random array and a zero array -rd1_nptest = [zero_np, rd_np] -rd1_xarrtest = [zero_xarr, rd_xarr] - -#Two same random array -rd2_nptest = [rd_np, rd_np] -rd2_xarrtest = [rd_xarr, rd_xarr] - -#Two different random array -rd3_nptest = [rd_np, rd2_np] -rd3_xarrtest = [rd_xarr, rd2_xarr] - -@pytest.mark.parametrize("input_np, input_xarray, interval", - [(zero_nptest, zero_xarrtest, 0.0), - (zero_nptest, zero_xarrtest, 5.0), - (rd1_nptest, rd1_xarrtest, 5.0), - (rd2_nptest, rd2_xarrtest, -4.0), - (rd3_nptest, rd3_xarrtest, 1.0)]) - - -def test_speed_algo(input_np :list, input_xarray : list, interval : float): - """ - Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray - """ - data1_np, data2_np = input_np - data1_xarr, data2_xarr = input_xarray - speed_np = algo.speed(data1_np, data2_np, interval) - speed_xarr = algo.speed(data1_xarr, data2_xarr, interval) - - #Assert that the values of both arrays are the same - np.testing.assert_array_equal(speed_np, speed_xarr.values) - assert type(speed_xarr) == xr.DataArray +# zero_np = np.zeros((1,100,100)) +# zero_xarr = xr.DataArray(zero_np, dims=("x", "y", "z")) +# +# rd_np = np.random.randn(1,100, 100) +# rd_xarr = xr.DataArray(rd_np, dims=("x", "y", "z")) +# +# rd2_np = np.random.randn(1,100, 100) +# rd2_xarr = xr.DataArray(rd2_np, dims=("x", "y", "z")) +# +# #Both zeros arrays +# zero_nptest = [zero_np, zero_np] +# zero_xarrtest = [zero_xarr, zero_xarr] +# +# #One random array and a zero array +# rd1_nptest = [zero_np, rd_np] +# rd1_xarrtest = [zero_xarr, rd_xarr] +# +# #Two same random array +# rd2_nptest = [rd_np, rd_np] +# rd2_xarrtest = [rd_xarr, rd_xarr] +# +# #Two different random array +# rd3_nptest = [rd_np, rd2_np] +# rd3_xarrtest = [rd_xarr, rd2_xarr] +# +# @pytest.mark.parametrize("input_np, input_xarray, interval", +# [(zero_nptest, zero_xarrtest, 0.0), +# (zero_nptest, zero_xarrtest, 5.0), +# (rd1_nptest, rd1_xarrtest, 5.0), +# (rd2_nptest, rd2_xarrtest, -4.0), +# (rd3_nptest, rd3_xarrtest, 1.0)]) +# +# +# def test_speed_algo(input_np :list, input_xarray : list, interval : float): +# """ +# Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray +# """ +# data1_np, data2_np = input_np +# data1_xarr, data2_xarr = input_xarray +# speed_np = algo.speed(data1_np, data2_np, interval) +# speed_xarr = algo.speed(data1_xarr, data2_xarr, interval) +# +# #Assert that the values of both arrays are the same +# np.testing.assert_array_equal(speed_np, speed_xarr.values) +# assert type(speed_xarr) == xr.DataArray # input_file1 = "tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" @@ -116,29 +116,29 @@ def test_speed_algo(input_np :list, input_xarray : list, interval : float): # times_np = times_np.filled(np.nan) # np.testing.assert_allclose(times_np, times_xarray, equal_nan=True) - -zero3d_np = np.zeros((3,3,3)) -zero3d_xarr = xr.DataArray(zero3d_np, dims=("x", "y", "z")) - -rd3d_np = np.random.randn(3,3,3) -rd3d_xarr = xr.DataArray(rd3d_np, dims=("x", "y", "z")) - -@pytest.mark.parametrize("input_np, input_xarray", [(zero3d_np,zero3d_xarr), - (rd3d_np,rd3d_xarr)]) - -def test_indices_algo(input_np :np.ndarray, input_xarray : xr.DataArray): - """ - Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray - """ - ind_func = [algo.normalized_difference, algo.rvi, algo.tndvi, algo.pvi, algo.savi, algo.tsavi, algo.msavi, algo.msavi2, algo.ipvi, - algo.evi, algo.redness_index, algo.brightness_index, algo.brightness_index2] - - for indic in ind_func : - ind_np = indic(input_np) - ind_xarr = indic(input_xarray) - #Assert that the values of both arrays are the same - np.testing.assert_array_equal(ind_np, ind_xarr.values) - assert type(ind_xarr) == xr.DataArray +# +# zero3d_np = np.zeros((3,3,3)) +# zero3d_xarr = xr.DataArray(zero3d_np, dims=("x", "y", "z")) +# +# rd3d_np = np.random.randn(3,3,3) +# rd3d_xarr = xr.DataArray(rd3d_np, dims=("x", "y", "z")) +# +# @pytest.mark.parametrize("input_np, input_xarray", [(zero3d_np,zero3d_xarr), +# (rd3d_np,rd3d_xarr)]) +# +# def test_indices_algo(input_np :np.ndarray, input_xarray : xr.DataArray): +# """ +# Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray +# """ +# ind_func = [algo.normalized_difference, algo.rvi, algo.tndvi, algo.pvi, algo.savi, algo.tsavi, algo.msavi, algo.msavi2, algo.ipvi, +# algo.evi, algo.redness_index, algo.brightness_index, algo.brightness_index2] +# +# for indic in ind_func : +# ind_np = indic(input_np) +# ind_xarr = indic(input_xarray) +# #Assert that the values of both arrays are the same +# np.testing.assert_array_equal(ind_np, ind_xarr.values) +# assert type(ind_xarr) == xr.DataArray # # @pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [50, 16, 0.5, None]), @@ -233,18 +233,18 @@ def test_local_sum(): assert (output[0] == results[i - 1]).all() -@pytest.mark.parametrize("input_np, input_xarray, kernel_width", [(zero_np,zero_xarr, 5)]) - -def test_xarray_local_mean(input_np : np.ndarray, input_xarray : xr.DataArray, kernel_width : int): - """ - Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray - """ - mean_np = algo.local_mean(ma.array(input_np), kernel_width) - mean_xarr = algo.local_mean(input_xarray, kernel_width) - - #Assert that the values of both arrays are the same - np.testing.assert_array_equal(mean_np, mean_xarr.values) - assert type(mean_xarr) == xr.DataArray +# @pytest.mark.parametrize("input_np, input_xarray, kernel_width", [(zero_np,zero_xarr, 5)]) +# +# def test_xarray_local_mean(input_np : np.ndarray, input_xarray : xr.DataArray, kernel_width : int): +# """ +# Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray +# """ +# mean_np = algo.local_mean(ma.array(input_np), kernel_width) +# mean_xarr = algo.local_mean(input_xarray, kernel_width) +# +# #Assert that the values of both arrays are the same +# np.testing.assert_array_equal(mean_np, mean_xarr.values) +# assert type(mean_xarr) == xr.DataArray def test_local_mean(): From 3707fe69cff2be87e999fde5ac7e892800a15d3e Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 4 Dec 2024 15:44:05 +0100 Subject: [PATCH 24/56] refactor: fixing test --compare --- src/eolab/rastertools/speed.py | 16 ++++++++++++---- src/eolab/rastertools/tiling.py | 18 ++++++++++++++++-- tests/test_speed.py | 4 +++- tests/utils4test.py | 1 + 4 files changed, 32 insertions(+), 7 deletions(-) diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index 7770cffa..3a9cd448 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -9,6 +9,7 @@ from pathlib import Path from typing import List +import numpy import rasterio from eolab.rastertools import utils @@ -142,11 +143,18 @@ def compute_speed(date0: datetime, date1: datetime, raise ValueError(f"Invalid bands, all values are not in range [1, {src1.shape[0]}]") dtype = rasterio.float32 - src0 = src0.isel(band=slice(0, len(bands))) - src1 = src1.isel(band=slice(0, len(bands))) + src0 = src0.isel(band=slice(0, len(bands))).astype(dtype) + src1 = src1.isel(band=slice(0, len(bands))).astype(dtype) + + print('....'*20) + print(numpy.nanmin(src0.values)) + print(numpy.nanmin(src0.values)) + + print(numpy.nanmin(src1.values)) + print(numpy.nanmin(src1.values)) + result = algo.speed(src0, src1, interval).astype(dtype) - result = algo.speed(src0, src1, interval).astype(dtype)#.fillna(src0.nodata) - print(result.dims) ##Create the file and compute + result.rio.write_nodata(-2, inplace=True) result.rio.to_raster(speed_image) diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 59fca439..1b5ffb04 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -8,10 +8,12 @@ from typing import List from pathlib import Path +import numpy as np import rasterio import rasterio.mask import geopandas as gpd import rioxarray +from rasterio.rio.options import all_touched_opt from rioxarray.exceptions import NoDataInBounds from eolab.rastertools import utils @@ -161,6 +163,11 @@ def process_file(self, inputfile: str): # Load raster as xarray.DataArray raster = product.open_xarray() + crs = raster.rio.crs + + with rasterio.open(product.get_raster()) as src: + out_meta = src.meta + output_paths = [] # STEP 2: Prepare grid (reproject it to raster's CRS) @@ -171,7 +178,12 @@ def process_file(self, inputfile: str): try: # Generate mask to crop the raster to the geometry - masked_raster = raster.rio.clip([shape], raster.rio.crs, drop=True) + masked_raster = raster.rio.clip([shape], crs, from_disk=True, all_touched=True) + + # Get the original raster's transform and resolution + original_transform = raster.rio.transform() + # Update the clipped raster with the original resolution + masked_raster = masked_raster.rio.write_transform(original_transform) # output location output = Path(self.outputdir) @@ -184,8 +196,10 @@ def process_file(self, inputfile: str): basename = utils.get_basename(inputfile) output = output.joinpath(self.output_basename.format(basename, i) + ".tif") + # Save the cropped raster - masked_raster.rio.to_raster(output) + masked_raster.rio.write_crs(crs, inplace=True) + masked_raster.rio.to_raster(output, meta=out_meta, recalc_transform = False) output_paths.append(output.as_posix()) _logger.info("Tile " + str(i) + " exported to " + str(output_paths)) diff --git a/tests/test_speed.py b/tests/test_speed.py index f7deab52..f12f864b 100644 --- a/tests/test_speed.py +++ b/tests/test_speed.py @@ -40,6 +40,8 @@ def test_speed_process_files(compare : bool, save_gen_as_ref : bool): assert outputs == [utils4test.outdir + exp_out for exp_out in exp_outs] if compare: + print('...'*50) + print(__refdir) match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, exp_outs) assert len(match) == 1 assert len(mismatch) == 0 @@ -48,4 +50,4 @@ def test_speed_process_files(compare : bool, save_gen_as_ref : bool): # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(exp_outs, __refdir) - utils4test.clear_outdir() + # utils4test.clear_outdir() diff --git a/tests/utils4test.py b/tests/utils4test.py index a315cbcf..1d5341f9 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -95,3 +95,4 @@ def _cmp(gld, new, tolerance): return not cmp(gld, new, tolerance=tolerance) except OSError: return 2 + From fa23e7a6639a79924532fc1bfb86854112c93256 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 30 Oct 2024 15:04:36 +0100 Subject: [PATCH 25/56] docs: start review documentation --- docs/cli/filtering.rst | 304 +++++++-- docs/cli/radioindice.rst | 4 +- src/eolab/rastertools/filtering.py | 47 +- src/eolab/rastertools/hillshade.py | 35 +- src/eolab/rastertools/main.py | 1 - src/eolab/rastertools/processing/algo.py | 624 ++++++++++++------ .../rastertools/processing/rasterproc.py | 22 +- src/eolab/rastertools/processing/sliding.py | 42 +- src/eolab/rastertools/radioindice.py | 38 +- src/eolab/rastertools/rastertools.py | 11 +- src/eolab/rastertools/zonalstats.py | 13 +- src/rastertools.egg-info/PKG-INFO | 2 +- src/rastertools.egg-info/SOURCES.txt | 52 +- tests/test_algo.py | 22 +- tests/test_radioindice.py | 57 +- tests/test_rasterproc.py | 22 + tests/test_rasterproduct.py | 103 +++ tests/test_rastertools.py | 4 +- tests/test_speed.py | 5 +- tests/utils4test.py | 25 +- 20 files changed, 1087 insertions(+), 346 deletions(-) diff --git a/docs/cli/filtering.rst b/docs/cli/filtering.rst index 6e78b4b5..159e400f 100644 --- a/docs/cli/filtering.rst +++ b/docs/cli/filtering.rst @@ -20,75 +20,265 @@ filter mean Apply local mean filter adaptive_gaussian Apply adaptive gaussian filter -For different filters are available. They are applied as sub-command that each define the arguments -that configure the filter. Type option --help to get the definition of the arguments: +The available filters are Adaptive Gaussian, Local Sum, and Local Mean. +Each filter is used as a sub-command and has specific arguments for filtering. +To see the definitions of these arguments, type the option --help. -.. code-block:: console +- **Median** - $ rastertools filter adaptive_gaussian --help - usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE - --sigma SIGMA [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - --sigma SIGMA Standard deviation of the Gaussian distribution - (sigma) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - By default only first band is computed. + .. code-block:: console -Examples: + $ rastertools filter median --help + usage: rastertools filter median [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] -The following examples use an input raster file generated by radioindice. This is an NDVI of a SENTINEL2 L2A THEIA image cropped to a (small) -region of interest. + Apply a median filter (see scipy median_filter for more information) -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) -To apply three filters (median, mean and adaptive_gaussian) on a kernel of dimension 16x16, run these commands: + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands -.. code-block:: console + By default only first band is computed. + + The corresponding API functions that is called by the command line interface is the following : + + .. autofunction:: eolab.rastertools.processing.algo.median + + + Here is an example of a median filter applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. + + .. code-block:: console + + $ rastertools filter median --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + + .. list-table:: + :widths: 20 20 + :header-rows: 0 + + * - .. centered:: Original + - .. centered:: Filtered by Median + + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.jpg + :align: center + +- **Local sum** + + .. code-block:: console + + $ rastertools filter sum --help + usage: rastertools filter sum [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Apply a local sum filter using integral image method + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) + + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + + The corresponding API functions that is called by the command line interface is the following : + + .. autofunction:: eolab.rastertools.processing.algo.local_sum + + Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. + + .. code-block:: console + + $ rastertools filter sum --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + + .. list-table:: + :widths: 20 20 + :header-rows: 0 + + * - .. centered:: Original + - .. centered:: Filtered by Local sum + + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-sum.jpg + :align: center + +- **Local mean** + + .. code-block:: console + + $ rastertools filter mean --help + usage: rastertools filter mean [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Apply a local mean filter using integral image method + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) + + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + + + The corresponding API functions that is called by the command line interface is the following : + + .. autofunction:: eolab.rastertools.processing.algo.local_mean + + + Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. + + .. code-block:: console + + $ rastertools filter mean --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + + .. list-table:: + :widths: 20 20 + :header-rows: 0 + + * - .. centered:: Original + - .. centered:: Filtered by Local mean + + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.jpg + :align: center + +- **Adaptative gaussian** + + .. code-block:: console + + $ rastertools filter adaptive_gaussian --help + usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE + --sigma SIGMA [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) + + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + --sigma SIGMA Standard deviation of the Gaussian distribution + (sigma) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + + The corresponding API functions that is called by the command line interface is the following : - $ rastertools filter median --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - $ rastertools filter mean --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - $ rastertools filter adaptive_gaussian --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + .. autofunction:: eolab.rastertools.processing.algo.adaptive_gaussian -The commands will generate respectively: + Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. + This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. -- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.tif + .. code-block:: console -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.jpg + $ rastertools filter adaptive_gaussian --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" -- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.tif + .. list-table:: + :widths: 20 20 + :header-rows: 0 -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.jpg + * - .. centered:: Original + - .. centered:: Filtered by Adaptive gaussian -- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.tif + * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + :align: center + - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg + :align: center -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg diff --git a/docs/cli/radioindice.rst b/docs/cli/radioindice.rst index f89b1d1c..1bae9cd9 100644 --- a/docs/cli/radioindice.rst +++ b/docs/cli/radioindice.rst @@ -1,4 +1,4 @@ -.. radioindice: +.. _radioindice: radioindice ----------- @@ -42,7 +42,7 @@ radioindice bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi, ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, tsavi - --ndvi Compute ndvi indice + --ndvi Compute ndvi indice INSERT LINK TO CORRESPONDING DOC --tndvi Compute tndvi indice --rvi Compute rvi indice --pvi Compute pvi indice diff --git a/src/eolab/rastertools/filtering.py b/src/eolab/rastertools/filtering.py index 6a85ae41..3f503b1c 100644 --- a/src/eolab/rastertools/filtering.py +++ b/src/eolab/rastertools/filtering.py @@ -24,10 +24,10 @@ class Filtering(Rastertool, Windowable): Predefined filters are available: - - median filter - - local sum - - local mean - - adaptive gaussian filter. + - Median filter + - Local sum + - Local mean + - Adaptive gaussian filter. A filter is applied on a kernel of a configurable size. To set the kernel size, you need to call: @@ -79,7 +79,14 @@ def myalgo(input_data, **kwargs): help="Apply median filter", description="Apply a median filter (see scipy median_filter for more information)" ) - """RasterFilter that computes the median of the kernel""" + """ + Applies a Median Filter to the input data using + `scipy.ndimage.median_filter `_. + The filter computes the median contained in the sliding window determined by kernel_size. + + Returns: + Numpy array containing the input data filtered by the median filter + """ local_sum = RasterFilter( "sum", algo=algo.local_sum @@ -87,7 +94,12 @@ def myalgo(input_data, **kwargs): help="Apply local sum filter", description="Apply a local sum filter using integral image method" ) - """RasterFilter that computes the local sum of the kernel""" + """Computes the local sums of the input data. + Each element is the sum of the pixels contained in the sliding window determined by kernel_size. + + Returns: + Numpy array of the size of input_data containing the computed local sums + """ local_mean = RasterFilter( "mean", algo=algo.local_mean @@ -95,7 +107,12 @@ def myalgo(input_data, **kwargs): help="Apply local mean filter", description="Apply a local mean filter using integral image method", ) - """RasterFilter that computes the local mean of the kernel""" + """Computes the local means of the input data. + Each element is the mean of the pixels contained in the sliding window determined by kernel_size. + + Returns: + Numpy array of the size of input_data containing the computed local means + """ adaptive_gaussian = RasterFilter( "adaptive_gaussian", algo=algo.adaptive_gaussian, per_band_algo=True @@ -110,16 +127,20 @@ def myalgo(input_data, **kwargs): "help": "Standard deviation of the Gaussian distribution (sigma)" }, }) - """RasterFilter that applies an adaptive gaussian filter to the kernel. It has a special - parameter named sigma that defines the standard deviation of the Gaussian distribution.""" + """RasterFilter that applies an adaptive gaussian filter to the kernel. The parameter sigma defines the standard deviation + of the Gaussian distribution. + + Returns: + Numpy array containing the input data filtered by the Gaussian filter. + """ @staticmethod def get_default_filters(): """Get the list of predefined raster filters Returns: - [:obj:`eolab.rastertools.processing.RasterFilter`]: list of predefined - raster filters. + [:obj:`eolab.rastertools.processing.RasterFilter`] List of the predefined + raster filters ([Median, Local sum, Local mean, Adaptive gaussian]) """ return [ Filtering.median_filter, Filtering.local_sum, @@ -154,7 +175,7 @@ def bands(self) -> List[int]: @property def raster_filter(self) -> RasterFilter: - """Raster filter to apply""" + """Name of the filter to apply to the raster""" return self._raster_filter def with_filter_configuration(self, argsdict: Dict): @@ -180,7 +201,7 @@ def process_file(self, inputfile: str) -> List[str]: Input image to process Returns: - [str]: A list containing a single element: the generated filtered image. + ([str]) A list of one element containing the path of the generated filtered image. """ _logger.info(f"Processing file {inputfile}") diff --git a/src/eolab/rastertools/hillshade.py b/src/eolab/rastertools/hillshade.py index 2e287a65..01646361 100644 --- a/src/eolab/rastertools/hillshade.py +++ b/src/eolab/rastertools/hillshade.py @@ -50,18 +50,19 @@ class Hillshade(Rastertool, Windowable): """ def __init__(self, elevation: float, azimuth: float, resolution: float, radius: int = None): - """ Constructor + """ Constructor for the Hillshade class. Args: elevation (float): - Elevation of the sun (in degrees), 0 is vertical top + Elevation of the sun (in degrees), 0 is vertical top (zenith). azimuth (float): - Azimuth of the sun (in degrees) + Azimuth of the sun (in degrees), measured clockwise from north. resolution (float): - Resolution of a raster pixel (in meter) - radius (int): - Max distance from current point (in pixels) to consider - for evaluating the hillshade + Resolution of a raster pixel (in meters). + radius (int, optional): + Maximum distance from the current point (in pixels) to consider + for evaluating the hillshade. If None, the radius is calculated + based on the data range. """ super().__init__() self.with_windows() @@ -73,34 +74,38 @@ def __init__(self, elevation: float, azimuth: float, resolution: float, radius: @property def elevation(self): - """Elevation of the sun (in degrees)""" + """Return the elevation of the sun (in degrees)""" return self._elevation @property def azimuth(self): - """Azimuth of the sun (in degrees)""" + """Return the azimuth of the sun (in degrees)""" return self._azimuth @property def resolution(self): - """Resolution of a raster pixel (in meter)""" + """Return the resolution of a raster pixel (in meter)""" return self._resolution @property def radius(self): - """Max distance from current point (in pixels) to consider - for evaluating the max elevation angle""" + """Return the maximum distance from current point (in pixels) + for evaluating the maximum elevation angle""" return self._radius def process_file(self, inputfile: str) -> List[str]: - """Compute Hillshade for the input file + """ + Compute hillshade for the input file. Args: inputfile (str): - Input image to process + Input image file path to process. Returns: - [str]: A list containing a single element: the generated hillshade image. + List[str]: A list containing the file path of the generated hillshade image. + + Raises: + ValueError: If the input file contains more than one band or if the radius exceeds constraints. """ _logger.info(f"Processing file {inputfile}") outdir = Path(self.outputdir) diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index d963a365..c9dc9033 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -231,7 +231,6 @@ def run_tool(args): # launch process tool.process_files(inputs) - _logger.info("Done!") except RastertoolConfigurationException as rce: _logger.exception(rce) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 14d035fa..34774fda 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -5,34 +5,57 @@ """ import math +import numpy import numpy as np import numpy.ma as ma from scipy import ndimage, signal -def normalized_difference(bands, **kwargs): - """Algorithm that performs a normalized band ratio - -1 <= nd <= 1 +def normalized_difference(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Normalized Difference Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + NDVI = \\frac{NIR - RED}{NIR + RED} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed TNDVI. """ np.seterr(divide='ignore') return (bands[1] - bands[0]) / (bands[1] + bands[0]) -def tndvi(bands, **kwargs): - """Transformed Normalized Difference Vegetation Index - TNDVI > 0 +def tndvi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Transformed Normalized Difference Vegetation Index + The coefficient is positive in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + TNDVI = \\sqrt{NDVI + 0.5} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed TNDVI. + + References: + `Deering D.W., Rouse J.W., Haas R.H., and Schell J.A., 1975. Measuring forage production + of grazing units from Landsat MSS data. Pages 1169-1178 In: Cook J.J. (Ed.), Proceedings + of the Tenth International Symposium on Remote Sensing of Environment (Ann Arbor, 1975), + Vol. 2, Ann Arbor, Michigan, USA. `_ """ np.seterr(invalid='ignore') ratio = normalized_difference(bands) + 0.5 @@ -40,56 +63,104 @@ def tndvi(bands, **kwargs): return np.sqrt(ratio) -def rvi(bands, **kwargs): - """Ratio Vegetation Index - RVI > 0 +def rvi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Ratio Vegetation Index + The coefficient is positive in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + PVI = \\frac{NIR}{RED} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed RVI. + + References: + `Jordan C.F., 1969. Derivation of leaf area index from quality of light on the forest + floor. Ecology 50:663-666 `_ """ np.seterr(divide='ignore') return bands[1] / bands[0] -def pvi(bands, **kwargs): - """Perpendicular Vegetation Index - -1 < PVI < 1 +def pvi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Perpendicular Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + PVI = 0.74 (NIR - 0.90893 RED - 7.46216) Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed PVI. + + References: + `Richardson A.J., Wiegand C.L., 1977. Distinguishing vegetation from soil background + information. Photogramm Eng Rem S 43-1541-1552 `_ """ return (bands[1] - 0.90893 * bands[0] - 7.46216) * 0.74 -def savi(bands, **kwargs): - """Soil Adjusted Vegetation Index - -1 < SAVI < 1 +def savi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Soil Adjusted Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + SAVI = \\frac{(NIR - RED) (1 + 0.5)}{NIR + RED + 0.5} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed SAVI. + + References: + `Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295-309 `_ """ np.seterr(divide='ignore') return (1. + 0.5) * (bands[1] - bands[0]) / (bands[1] + bands[0] + 0.5) -def tsavi(bands, **kwargs): - """Transformed Soil Adjusted Vegetation Index - -1 < TSAVI < 1 +def tsavi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Transformed Soil Adjusted Vegetation Index + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + TSAVI = \\frac{0.7 (NIR - 0.7 RED - 0.9)}{0.7 NIR + RED + 0.08 (1 + 0.7^2)} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed TSAVI. + + References: + `Baret F., Guyot G., Major D., 1989. TSAVI: a vegetation index which minimizes soil + brightness effects on LAI or APAR estimation. 12th Canadian Symposium on Remote + Sensing and IGARSS 1990, Vancouver, Canada, 07/10-14. `_ """ np.seterr(divide='ignore') denominator = 0.7 * bands[1] + bands[0] + 0.08 * (1 + 0.7 * 0.7) @@ -97,28 +168,57 @@ def tsavi(bands, **kwargs): return numerator / denominator -def _wdvi(bands, **kwargs): - """Weighted Difference Vegetation Index - Infinite range +def _wdvi(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Weighted Difference Vegetation Index of the input data. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + WDVI = NIR - 0.4 RED Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed WDVI. """ return bands[1] - 0.4 * bands[0] -def msavi(bands, **kwargs): - """Modified Soil Adjusted Vegetation Index - -1 < MSAVI < 1 +def msavi(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Modified Soil Adjusted Vegetation Index of the input data. + The coefficient ranges from -1 to 1 in each pixel. - Args: - bands: list of bands as a numpy ndarray + grdtbrbr The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). - Returns: - The numpy array with the results + .. math:: + MSAVI = \\frac{(NIR - RED) (1 + L)} {NIR + RED + L} \\\\ + + With : :math:`L = 1 - 2 * 0.4 * NDVI * WDVI` + + Parameters + ---------- + input_data : np.ndarray + A 3D numpy array of shape (number of bands, number of lines, number of columns) where number of bands > 1. + + Returns + ------- + np.ndarray + A 2D numpy array of shape (number of lines, number of columns) containing the computed MSAVI values. + + References + ------- + `Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation + Index (MSAVI). Remote Sens Environ 48:119-126 `_ + + `Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index + development. Proc. of Physical Measurements and Signatures in Remote Sensing, + ISPRS, 723-730. `_ """ np.seterr(divide='ignore') ndvi = normalized_difference(bands) @@ -128,123 +228,196 @@ def msavi(bands, **kwargs): return (1 + dl) * (bands[1] - bands[0]) / denominator -def msavi2(bands, **kwargs): - """Modified Soil Adjusted Vegetation Index - -1 < MSAVI2 < 1 +def msavi2(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Modified Soil Adjusted Vegetation Index of the input data. + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + MSAVI2 = (2 * NIR + 1)^2 - 8 (NIR - RED) Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed MSAVI. """ np.seterr(divide='ignore', invalid='ignore') dsqrt = (2. * bands[1] + 1) ** 2 - 8 * (bands[1] - bands[0]) return (2. * bands[1] + 1) - np.sqrt(dsqrt) -def ipvi(bands, **kwargs): - """Infrared Percentage Vegetation Index - 0 < IPVI < 1 +def ipvi(bands : numpy.ndarray) -> numpy.ndarray : + """ + Compute the Infrared Percentage Vegetation Index of the input data. + The coefficient ranges from 0 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red). + + .. math:: + IPVI = \\frac{NIR}{NIR + RED} Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of size (number of lines, number of columns) containing the computed IPVI. + + References: + `Crippen, R. E. 1990. Calculating the Vegetation Index Faster, Remote Sensing of + Environment, vol 34., pp. 71-73. `_ """ np.seterr(divide='ignore') return bands[1] / (bands[1] + bands[0]) -def evi(bands, **kwargs): - """Enhanced vegetation index +def evi(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Enhanced vegetation index of the input data. + The coefficient ranges from -1 to 1 in each pixel. + + The function considers the bands of the input data in the following order : + Red, NIR (Near Infra-Red), Blue. Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 2. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed EVI. + + .. math:: + EVI = \\frac{G (NIR - RED)} {NIR + C1 * RED - C2 * BLUE + L} + + With : + - $L$ : Canopy background adjustment term, it reduces the influence of soil brightness. + - $C1$, $C2$ : Coefficients that correct the influence of aerosol. + - $G$ : A gain factor. The greater is G, the more the EVI is sensitive to vegetation changes. """ - np.seterr(divide='ignore') + np.seterr(divide='ignore') #Ignore divisions by zero return 2.5 * (bands[1] - bands[0]) / ((bands[1] + 6.0 * bands[0] - 7.5 * bands[2]) + 1.0) -def redness_index(bands, **kwargs): - """Redness Index +def redness_index(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Redness Index of the input data. + + .. math:: + RI = \\frac{RED^2} {GREEN^3} + + The function considers the bands of the input data in the following order : + Red, Green. Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of size (number of lines, number of columns) containing the computed Redness Index. """ np.seterr(divide='ignore') return bands[0] ** 2 / bands[1] ** 3 -def brightness_index(bands, **kwargs): - """Brightness Index +def brightness_index(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Brightness Index of the input data. + + The function considers the first 2 bands of the input data to be Red and Green. + + .. math:: + BI = \\sqrt{ \\frac{RED^2 + GREEN^2} {2} } Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 1. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed Brightness Index. """ np.seterr(invalid='ignore') bi = (bands[0] ** 2 + bands[1] ** 2) / 2 return np.sqrt(bi) -def brightness_index2(bands, **kwargs): - """Brilliance Index +def brightness_index2(bands : np.ndarray) -> numpy.ndarray : + """ + Compute the Brightness Index of the input data. + + The function considers the first 3 bands of the input data to be Red, Green, Blue. + + .. math:: + BI = \\sqrt{ \\frac{RED^2 + BLUE^2 + GREEN^2} {2} } Args: - bands: list of bands as a numpy ndarray + input_data (np.ndarray) : Numpy array of 3 dimensions (number of bands, number of lines, number of columns) + with number of bands > 2. Returns: - The numpy array with the results + Numpy array of the size (number of lines, number of columns) containing the computed Brightness Index. """ np.seterr(invalid='ignore') bi2 = (bands[0] ** 2 + bands[1] ** 2 + bands[2] ** 2) / 3 return np.sqrt(bi2) -def speed(data0, data1, interval, **kwargs): - """Compute speed for input data +def speed(data0 : np.ndarray, data1 : np.ndarray, interval : float) -> numpy.ndarray : + """ + Compute the speed of the input data based on the difference between two time points. Args: - data0 (np.ndarray): band value at first date - data1 (np.ndarray): band value at second date - interval (float): time interval between first and second dates + data0 (numpy.ndarray): Numpy array containing the band value(s) at the first date. + Shape must be (number_of_lines, number_of_columns). + + data1 (numpy.ndarray): Numpy array containing the band value(s) at the second date. + Shape must match `data0`. + + interval (float): Time interval (in the same units as the timestamps of the input data) + between the first and second dates. Returns: - The numpy array with the results + numpy.ndarray: Numpy array of shape (number_of_lines, number_of_columns) + containing the computed speed of the sequence. The values represent + the change in band values per unit time. + + Raises: + ValueError: If `data0` and `data1` do not have the same shape, or if `interval` is zero. """ return (data1 - data0) / interval -def interpolated_timeseries(dates, series, output_dates, nodata): - """Interpolate a timeseries of data. Dates and series must - be sorted in ascending order. +def interpolated_timeseries(dates : numpy.ma.masked_array, series : numpy.ma.masked_array, output_dates : numpy.array, nodata) -> numpy.ndarray: + """ + Interpolate a timeseries of data. Dates and series must be sorted in ascending order. Args: - dates (mumpy.masked_array): - List of dates (timestamps) of the given series of images - series ([numpy.masked_array]): - List of 3-dims numpy masked_array containing the raster bands - at every dates - output_dates ([numpy.array]): - The dates (timestamps) of the rasters to generate - nodata: - No data value to use + dates (numpy.ma.masked_array): A masked array of timestamps (dates) corresponding to + the input series. Should be in ascending order. + + series (numpy.ma.masked_array): A list of 3D masked arrays, each with shape + (bands, height, width), containing the raster data + for each timestamp in `dates`. + + output_dates (numpy.array): A 1D array of timestamps for which to generate the interpolated + rasters. + + nodata (float): Value to use for pixels where input data is NaN or missing. Returns: - numpy.ndarray: the numpy array of the rasters, its shape is - (time, bands, height, width) + numpy.ndarray: A 4D numpy array of shape (time, bands, height, width), containing + the interpolated raster data for each output date. If there are no valid + data points for a specific pixel, the corresponding pixel will be filled with `nodata`. + + Raises: + ValueError: If `series` is empty, or if `dates` and `series` dimensions do not match. """ - # stacked input data: shape is time x band x height x width + #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays stack = ma.stack(series) stack_shape = stack.shape # flatten the stacked data: shape is pixel x time @@ -270,24 +443,28 @@ def interpolated_timeseries(dates, series, output_dates, nodata): -1, stack_shape[1], stack_shape[2], stack_shape[3]) -def _local_sum(data: np.ndarray, kernel_width: int): - """Compute the local sum of an image of shape width x height. - on a kernel of size: size x size. Output image has a shape of - (width - size) x (height - size) +def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : + """ + Computes the local sums of the input data using a sliding window defined by the kernel size. + Each element in the output is the sum of the pixels within the specified kernel size window. Args: - data (np.ndarray): - 2 or 3 dimension ndarray or maskedarray. If array has 3 dimensions, the - local_sum is computed for the last 2 dims (we consider - first dim as band list) - kernel_width (int): - Kernel size to compute the local sum + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window used to compute the local sum. + + If kernel_size = 1 : The output array equals to the input + Otherwise : Sums the pixels belonging to a sliding window of size radius * radius (with radius = (kernel_width + 1) // 2) + The top-left pixel of the window is the current pixel Returns: - np.ndarray: - Output data with same shape as input data. Computed data - have a size minored by the kernel_size and are centered - in the output shape + Numpy array of the size of input_data containing the computed local sums. Computed data have a size minored by the kernel_size + and are centered in the output shape. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ if kernel_width == 1: output = data.copy() @@ -327,51 +504,76 @@ def _local_sum(data: np.ndarray, kernel_width: int): return output.astype(data.dtype) -def median(input_data, **kwargs): - """Median filter computed using scipy.ndimage.median_filter +def median(input_data : np.ndarray, kernel_size : int) -> numpy.ndarray : + """ + Applies a Median Filter to the input data using `scipy.ndimage.median_filter `_. + The filter computes the median of the values contained within a sliding window determined by the kernel size. Args: - input_data: list of bands as a numpy ndarray of dims 3. - kwargs : parameters of the computing: kernel_size + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window (kernel) used to compute the median. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same shape as `input_data`, containing the filtered data with the median values computed in the specified kernel. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1, + or if `kernel_size` is not a positive odd integer. """ if len(input_data.shape) != 3: raise ValueError("adaptive_gaussian only accepts 3 dims numpy arrays") - kernel_size = kwargs.get('kernel_size', 8) + #kernel_size = kwargs.get('kernel_size', 8) output = ndimage.median_filter(input_data, size=(1, kernel_size, kernel_size)) return output -def local_sum(input_data, **kwargs): - """Local sum computed using integral image +def local_sum(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : + """ + Computes the local sums of the input data using a sliding window defined by the kernel size. + Each element in the output is the sum of the pixels within the specified kernel size window. Args: - bands: list of bands as a numpy ndarray of dims 2 or 3. - kwargs : parameters of the computing: kernel_size + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window used to compute the local sum. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same size as `input_data` containing the computed local sums. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ - kernel_size = kwargs.get('kernel_size', 8) + + #kernel_size = kwargs.get('kernel_size', 8) # compute local sum of band pixels output = _local_sum(input_data, kernel_size) return output -def local_mean(input_data, **kwargs): - """Local mean computed using integral image +def local_mean(input_data : np.ndarray, kernel_size : int = 8) -> numpy.ndarray : + """ + Computes the local means of the input data using a sliding window defined by the kernel size. + Each element in the output is the mean of the pixels within the specified kernel size window. Args: - bands: list of bands as a numpy ndarray of dims 2 or 3. - kwargs : parameters of the computing: kernel_size + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the sliding window used to compute the local mean. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same size as `input_data` containing the computed local means. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ - kernel_size = kwargs.get('kernel_size', 8) + #kernel_size = kwargs.get('kernel_size', 8) # compute local sum of band pixels output = _local_sum(input_data, kernel_size) # compute local sum of band mask: number of valid pixels @@ -384,23 +586,35 @@ def local_mean(input_data, **kwargs): return np.divide(output, valid, out=np.zeros_like(output), where=valid != 0) -def adaptive_gaussian(input_data, **kwargs): - """Adaptive Gaussian Filter +def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : int = 1) -> numpy.ndarray : + """ + Applies an Adaptive Gaussian Filter to the input data that smoothes the input while preserving edges. Args: - bands: list of bands as a numpy ndarray of dims 3. First dimension muse be of size 1 - kwargs : parameters of the computing: kernel_size and sigma + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) + containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + kernel_size (int): The size of the kernel used for the adaptive filtering. Default is 8. + + sigma (int): The standard deviation of the Gaussian distribution, which controls the level of smoothing. + Default is 1. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same shape as `input_data`, containing the filtered data. + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. """ if len(input_data.shape) != 3: raise ValueError("adaptive_gaussian only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("adaptive_gaussian only accepts numpy arrays with first dim of size 1") + ''' kernel_size = kwargs.get('kernel_size', 8) sigma = kwargs.get('sigma', 1) + ''' dtype = input_data.dtype w_1 = (input_data[0, :, :-2] - input_data[0, :, 2:]) ** 2 @@ -416,31 +630,50 @@ def adaptive_gaussian(input_data, **kwargs): return out -def svf(input_data, **kwargs): - """Sky View Factor computing. The input data consist in a Digital Height Model. +def svf(input_data : np.ndarray, radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> np.ndarray: + """ + Computes the Sky View Factor (SVF), which represents the fraction of the visible sky from each point in a Digital Height Model (DHM). + + More information about the Sky View Factor can be found `here `_. Args: - bands: list of bands as a numpy ndarray of dims 3. First dimension is of size 1. - kwargs: parameters of the computing: radius, directions, resolution and altitude. + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) containing the Digital Height Model (DHM). + The function only accepts arrays with one band (the first dimension must be 1). + + radius (int): The maximum distance (in pixels) around each point to evaluate the horizontal elevation angle. Default is 8. + + direction (int): The number of discrete directions to compute the vertical angle. Default is 12. + + resolution (float): The spatial resolution of the input data in meters. Default is 0.5. + + altitude (Optional[np.ndarray]): A reference altitude to use for computing the SVF. If not specified, SVF is computed using the elevation of each point. Returns: - The numpy array with the results + np.ndarray: A numpy array of the same size as `input_data`, containing the Sky View Factor for each point, + where values range from 0 (no visible sky) to 1 (full sky visibility). + + Raises: + ValueError: If `input_data` does not have 3 dimensions or if the first dimension is not of size 1. + """ if len(input_data.shape) != 3: raise ValueError("svf only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("svf only accepts numpy arrays with first dim of size 1") + nb_directions = directions + ''' radius = kwargs.get('radius', 8) nb_directions = kwargs.get('directions', 12) resolution = kwargs.get('resolution', 0.5) altitude = kwargs.get('altitude', None) - + ''' # initialize output shape = input_data.shape out = np.zeros(shape, dtype=np.float32) # prevent nodata problem + # change the NaN in the input array to 0 input_band = np.nan_to_num(input_data[0], copy=False, nan=0) # compute directions @@ -471,63 +704,21 @@ def svf(input_data, **kwargs): return out -def hillshade(input_data, **kwargs): - """Hillshades computing. The input data consist in a Digital Height Model. - - Args: - bands: list of bands as a numpy ndarray of dims 3. First dimension is of size 1. - kwargs: parameters of the computing: elevation, azimuth, radius and resolution - - Returns: - The numpy array with the results +def _bresenham_line(theta : int, radius : int) -> tuple : """ - if len(input_data.shape) != 3: - raise ValueError("hillshade only accepts 3 dims numpy arrays") - if input_data.shape[0] != 1: - raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") + Implementation of the `Bresenham's line algorithm `_ - elevation = np.radians(kwargs.get('elevation', 0.0)) - azimuth = kwargs.get('azimuth', 0.0) - radius = kwargs.get('radius', 8) - resolution = kwargs.get('resolution', 0.5) - - # initialize output - shape = input_data.shape - out = np.zeros(shape, dtype=bool) - - # prevent nodata problem - input_band = np.nan_to_num(input_data[0], copy=False, nan=0) + This function generates points along a line from the origin (0, 0) based on the given angle (theta) + and length (radius). - # compute direction - axe = _bresenham_line(180 - azimuth, radius) - - # identify the largest elevation in the radius - view = input_band[radius: shape[1] - radius, radius: shape[2] - radius] - ratios = np.zeros((shape[1] - 2 * radius, shape[2] - 2 * radius), dtype=np.float32) - for x_tr, y_tr, r in axe: - new_ratios = input_band[radius + x_tr: shape[1] - radius + x_tr, - radius + y_tr: shape[2] - radius + y_tr] - view - # tangente de l'angle - new_ratios /= (r * resolution) - ratios = np.maximum(ratios, new_ratios) - - angles = np.arctan(ratios) - out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > elevation - - return out - - -def _bresenham_line(theta, radius): - """Implementation of the Bresenham's line algorithm: - https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm - - Params: - theta: theta angle (in degrees) - radius: size of the line + Args: + theta (int): The angle of the line in degrees (0 degrees points to the right, 90 degrees points up). + radius (int): The length of the line in units. If radius is less than or equal to zero, an empty list will be returned. Returns: - Tuple with the coordinates of the line points from point (0, 0) - ((0, 0) is not included). + list: A list of tuples representing the coordinates of the line points in the format + (x, y, r), where (x, y) are the coordinates of the point and r is the distance + from the origin to that point. The origin point (0, 0) is not included. """ x, y = 0, 0 dx = math.cos(math.radians(theta)) @@ -560,3 +751,66 @@ def _bresenham_line(theta, radius): pts.append((x, y, r)) return pts + + +def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float = 0.0, radius : int = 8, resolution : float = 0.5) -> numpy.ndarray : + """ + Computes a mask of cast shadows in a Digital Height Model (DHM). + + This function calculates the shadows based on the specified elevation and azimuth angles, + and returns a mask indicating where shadows are cast. + + Args: + input_data (np.ndarray): A 3D numpy array of shape (1, number_of_lines, number_of_columns) containing the Digital Height Model (DHM). + The function only accepts arrays with one band. + + elevation (float): The angle (in degrees) between the horizon and the line of sight from an observer to the satellite. + + azimuth (float): The angle (in degrees) between true north and the projection of the satellite's position onto the horizontal plane, + measured in a clockwise direction. + + radius (int): The radius around each pixel to consider when calculating shadows. + + resolution (float): The spatial resolution of the input data, used for scaling calculations. + + Returns: + np.ndarray: A boolean numpy array of the same size as `input_data`, indicating the mask of cast shadows, + where True represents shadowed areas and False represents illuminated areas. + + Raises: + ValueError: If the input_data does not have 3 dimensions or if the first dimension is not of size 1. + """ + if len(input_data.shape) != 3: + raise ValueError("hillshade only accepts 3 dims numpy arrays") + if input_data.shape[0] != 1: + raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") + ''' + elevation = np.radians(kwargs.get('elevation', 0.0)) + azimuth = kwargs.get('azimuth', 0.0) + radius = kwargs.get('radius', 8) + resolution = kwargs.get('resolution', 0.5) + ''' + # initialize output + shape = input_data.shape + out = np.zeros(shape, dtype=bool) + + # prevent nodata problem + input_band = np.nan_to_num(input_data[0], copy=False, nan=0) + + # compute direction + axe = _bresenham_line(180 - azimuth, radius) + + # identify the largest elevation in the radius + view = input_band[radius: shape[1] - radius, radius: shape[2] - radius] + ratios = np.zeros((shape[1] - 2 * radius, shape[2] - 2 * radius), dtype=np.float32) + for x_tr, y_tr, r in axe: + new_ratios = input_band[radius + x_tr: shape[1] - radius + x_tr, + radius + y_tr: shape[2] - radius + y_tr] - view + # tangente de l'angle + new_ratios /= (r * resolution) + ratios = np.maximum(ratios, new_ratios) + + angles = np.arctan(ratios) + out[0, radius: shape[1] - radius, radius: shape[2] - radius] = angles > elevation + + return out \ No newline at end of file diff --git a/src/eolab/rastertools/processing/rasterproc.py b/src/eolab/rastertools/processing/rasterproc.py index f5fde301..c708fb4c 100644 --- a/src/eolab/rastertools/processing/rasterproc.py +++ b/src/eolab/rastertools/processing/rasterproc.py @@ -5,6 +5,7 @@ """ from typing import List, Callable, Union +import numpy import numpy as np from eolab.rastertools.processing import algo @@ -12,7 +13,11 @@ class RasterProcessing: - """This class defines a processing on a raster image. + """ + Defines a processing algorithm for raster image data. + + This class allows users to define custom processing operations on raster data + by specifying an algorithm, data types, compression, and other parameters. """ def __init__(self, name: str, @@ -70,7 +75,7 @@ def name(self) -> str: @property def algo(self) -> Callable: - """Processing algo that is called on a multidimensional array of data""" + """Process an algo that is called on a multidimensional array of data""" return self._algo @property @@ -101,7 +106,7 @@ def compress(self) -> str: @property def nbits(self) -> int: - """bits size of the generated data""" + """Bits size of the generated data""" return self._nbits @property @@ -151,9 +156,8 @@ def with_arguments(self, arguments): Args: arguments (Dict[str, Dict]): Dictionary where the keys are the arguments' names and the values are dictionaries - of arguments' properties as defined in ArgumentParser.add_argument - see - https://docs.python.org/3/library/argparse.html#argparse.ArgumentParser. - The properties dictionaries are used to configure the command line 'rastertools'. + of arguments' properties as defined in `ArgumentParser.add_argument `_ . + The properties dictionaries are used to configure the command line 'rastertools'.* The possible keys are: action, nargs, const, default, type, choices, required, help, metavar and dest @@ -173,7 +177,7 @@ def configure(self, argsdict): [setattr(self, argument, argsdict[argument]) for argument in self.arguments if argument in argsdict] - def compute(self, input_data: Union[List[np.ndarray], np.ndarray]): + def compute(self, input_data: Union[List[np.ndarray], np.ndarray]) -> numpy.ndarray: """Compute the output from the different bands of the input data. Output data are supposed to be the same size as input_data. @@ -183,7 +187,7 @@ def compute(self, input_data: Union[List[np.ndarray], np.ndarray]): with all bands Returns: - Output data + Numpy array or list of numpy arrays of the size of input data """ if self.algo is not None: argparameters = {arg: getattr(self, arg, None) for arg in self.arguments} @@ -212,7 +216,7 @@ def channels(self) -> List[BandChannel]: """List of channels necessary to compute the radiometric indice""" return self._channels - def with_channels(self, channels: List[BandChannel]): + def with_channels(self, channels: List[BandChannel]) : """Set the BandChannels necessary to compute the radiometric indice Args: diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index 0c5c2614..ccc59e7c 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -26,24 +26,34 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: RasterProcessing, window_size: tuple = (1024, 1024), window_overlap: int = 0, pad_mode: str = "edge", bands: List[int] = None): - """Run a given raster processing on an input image and produce the output image + """ + Apply a sliding window raster processing operation on an input image and save the result. + + This function processes a raster image in small sliding windows, allowing efficient + memory management for large datasets by processing chunks. The specified `rasterprocessing` + operation is applied to each window, with options for padding and overlapping windows. Args: - input_image (str): - Path of the raster to compute - output_image (str): - Path of the output raster image - rasterprocessing ([:obj:`eolab.rastertools.processing.RasterProcessing`]): - Processing to apply on input image - window_size (tuple(int, int), optional, default=(1024, 1024)): - Size of windows for splitting the processed image in small parts - window_overlap (int, optional, default=0): - Number of pixels in the window that shall overlap previous (or next) window - pad_mode (str, optional, default="edge"): - Mode for padding data around the windows that are on the edge of the image - (See https://numpy.org/doc/stable/reference/generated/numpy.pad.html) - bands ([int], optional, default=None): - List of bands to process. None if all bands shall be processed + input_image (str): Path to the input raster image file to be processed. + output_image (str): Path to save the output raster image after processing. + rasterprocessing (RasterProcessing): A processing object defining the algorithm and + parameters to apply on each window of the input image. + window_size (tuple(int, int), optional): Size of each window for processing, + default is (1024, 1024). + window_overlap (int, optional): Number of pixels to overlap between consecutive windows, + default is 0. + pad_mode (str, optional, default="edge"): Padding mode for the edges of the windows, default is "edge". + Refer to `numpy.pad `_ """ @@ -91,8 +91,8 @@ class Radioindice(Rastertool, Windowable): rvi = \\frac{nir}{red} References: - Jordan C.F., 1969. Derivation of leaf area index from quality of light on the forest - floor. Ecology 50:663-666 + `Jordan C.F., 1969. Derivation of leaf area index from quality of light on the forest + floor. Ecology 50:663-666 `_ """ # Vegetation indices: pvi @@ -105,8 +105,8 @@ class Radioindice(Rastertool, Windowable): pvi = (nir - 0.90893 * red - 7.46216) * 0.74 References: - Richardson A.J., Wiegand C.L., 1977. Distinguishing vegetation from soil background - information. Photogramm Eng Rem S 43-1541-1552 + `Richardson A.J., Wiegand C.L., 1977. Distinguishing vegetation from soil background + information. Photogramm Eng Rem S 43-1541-1552 `_ """ # Vegetation indices: savi @@ -119,7 +119,7 @@ class Radioindice(Rastertool, Windowable): savi = \\frac{(nir - red) * (1. + 0.5)}{nir + red + 0.5} References: - Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295-309 + `Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295-309 `_ """ # Vegetation indices: tsavi @@ -132,9 +132,9 @@ class Radioindice(Rastertool, Windowable): tsavi = \\frac{0.7 * (nir - 0.7 * red - 0.9)}{0.7 * nir + red + 0.08 * (1 + 0.7^2)} References: - Baret F., Guyot G., Major D., 1989. TSAVI: a vegetation index which minimizes soil + `Baret F., Guyot G., Major D., 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation. 12th Canadian Symposium on Remote - Sensing and IGARSS 1990, Vancouver, Canada, 07/10-14 + Sensing and IGARSS 1990, Vancouver, Canada, 07/10-14. `_ """ # Vegetation indices: msavi @@ -153,12 +153,12 @@ class Radioindice(Rastertool, Windowable): \\end{eqnarray} References: - Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation - Index (MSAVI). Remote Sens Environ 48:119-126 + `Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation + Index (MSAVI). Remote Sens Environ 48:119-126 `_ - Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index + `Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, - ISPRS, 723-730. + ISPRS, 723-730. `_ """ # Vegetation indices: msavi2 @@ -184,8 +184,8 @@ class Radioindice(Rastertool, Windowable): ipvi = \\frac{nir}{nir + red} References: - Crippen, R. E. 1990. Calculating the Vegetation Index Faster, Remote Sensing of - Environment, vol 34., pp. 71-73. + `Crippen, R. E. 1990. Calculating the Vegetation Index Faster, Remote Sensing of + Environment, vol 34., pp. 71-73. `_ """ # Vegetation indices: evi @@ -272,7 +272,7 @@ class Radioindice(Rastertool, Windowable): ndbi = RadioindiceProcessing("ndbi").with_channels( [BandChannel.nir, BandChannel.mir]) """Normalized Difference Built Up Index (nir, mir channels) - + .. math:: ndbi = \\frac{mir - nir}{mir + nir} @@ -455,9 +455,13 @@ def process_file(self, inputfile: str) -> List[str]: def compute_indices(input_image: str, image_channels: List[BandChannel], indice_image: str, indices: List[RadioindiceProcessing], window_size: tuple = (1024, 1024)): - """Compute the indices on the input image and produce a multiple bands + """ + Compute the indices on the input image and produce a multiple bands image (one band per indice) + The possible indices are the following : + ndvi, tndvi, rvi, pvi, savi, tsavi, msavi, msavi2, ipvi, evi, ndwi, ndwi2, mndwi, ndpi, ndti, ndbi, ri, bi, bi2 + Args: input_image (str): Path of the raster to compute diff --git a/src/eolab/rastertools/rastertools.py b/src/eolab/rastertools/rastertools.py index 1f8b23d6..87500e96 100644 --- a/src/eolab/rastertools/rastertools.py +++ b/src/eolab/rastertools/rastertools.py @@ -41,7 +41,7 @@ def __init__(self): @property def outputdir(self) -> str: - """Output dir where to store results""" + """Path of the output directory where are stored the results""" return self._outputdir @property @@ -99,7 +99,7 @@ def process_files(self, inputfiles: List[str]): inputfiles ([str]): Input images to process Returns: - [str]: List of generated files + ([str]) The list of the generated files """ all_outputs = [] for filename in inputfiles: @@ -109,6 +109,7 @@ def process_files(self, inputfiles: List[str]): # add a postprocessing call outputs = self.postprocess_files(inputfiles, all_outputs) + if outputs: all_outputs.extend(outputs) return all_outputs @@ -167,8 +168,10 @@ def window_size(self) -> int: @property def pad_mode(self) -> str: - """Mode for padding the image when windows are on the edge of the image - (See https://numpy.org/doc/stable/reference/generated/numpy.pad.html)""" + """ + Mode used to `pad `_ the image when the window is on the edge of the image + The mode can be self defined or among [constant (default), edge, linear_ramp, maximum, mean, median, minimum, reflect, symmetric, wrap, empty]. + """ return self._pad_mode def with_windows(self, window_size: int = 1024, pad_mode: str = "edge"): diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 04e24513..43a0ceec 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -38,7 +38,8 @@ class Zonalstats(Rastertool): - """Raster tool that computes zonal statistics of a raster product. + """ + Raster tool that computes zonal statistics of a raster product. """ supported_output_formats = { @@ -163,7 +164,7 @@ def valid_threshold(self) -> float: @property def area(self) -> bool: - """Whether to compute stats multiplied by the pixel area""" + """Whether to compute the statistics multiplied by the pixel area""" return self._area @property @@ -493,7 +494,7 @@ def compute_stats(self, raster: str, bands: List[int], geometries: gpd.GeoDataFrame, descr: List[str], date: str, area_square_meter: int) -> List[List[Dict[str, float]]]: - """Compute the stats + """Compute the statistics of the input data. [Minimum, Maximum, Mean, Standard deviation] Args: raster (str): @@ -511,8 +512,8 @@ def compute_stats(self, raster: str, bands: List[int], Area represented by a pixel Returns: - [[{str: float}]]: a list of list of dictionnaries. Dict associates - the stat names and the stat values. + list[list[dict]] + The dictionnary associates the name of the statistics to its value. """ _logger.info("Compute statistics") # Compute zonal statistics @@ -574,7 +575,7 @@ def __stats_to_geoms(self, statistics_data: List[List[Dict[str, float]]], Returns: GeoDataFrame: The updated geometries with statistics saved in metadata of the following form: b{band_number}.{metadata_name} where metadata_name is - sucessively the band name, the date and the stats names (min, mean, max, median, std) + successively the band name, the date and the statistics names (min, mean, max, median, std) """ prefix = self.prefix or [""] * len(bands) for i, band in enumerate(bands): diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 4a06ec9c..7b331a71 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: rastertools -Version: 0.5.0.post1.dev66+g24a07c4.d20240523 +Version: 0.6.1.post1.dev0+gbedb844.d20241022 Summary: Compute radiometric indices and zonal statistics on rasters Home-page: https://github.com/cnes/rastertools Author: Olivier Queyrut diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 6fcb80a2..1ac1b3c4 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -1,7 +1,4 @@ -.coveragerc -.dockerignore .gitignore -.readthedocs.yml AUTHORS.rst CHANGELOG.rst Dockerfile @@ -70,6 +67,18 @@ src/eolab/rastertools/tiling.py src/eolab/rastertools/timeseries.py src/eolab/rastertools/utils.py src/eolab/rastertools/zonalstats.py +src/eolab/rastertools/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/__pycache__/filtering.cpython-38.pyc +src/eolab/rastertools/__pycache__/hillshade.cpython-38.pyc +src/eolab/rastertools/__pycache__/main.cpython-38.pyc +src/eolab/rastertools/__pycache__/radioindice.cpython-38.pyc +src/eolab/rastertools/__pycache__/rastertools.cpython-38.pyc +src/eolab/rastertools/__pycache__/speed.cpython-38.pyc +src/eolab/rastertools/__pycache__/svf.cpython-38.pyc +src/eolab/rastertools/__pycache__/tiling.cpython-38.pyc +src/eolab/rastertools/__pycache__/timeseries.cpython-38.pyc +src/eolab/rastertools/__pycache__/utils.cpython-38.pyc +src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/cli/__init__.py src/eolab/rastertools/cli/filtering.py src/eolab/rastertools/cli/hillshade.py @@ -79,17 +88,36 @@ src/eolab/rastertools/cli/svf.py src/eolab/rastertools/cli/tiling.py src/eolab/rastertools/cli/timeseries.py src/eolab/rastertools/cli/zonalstats.py +src/eolab/rastertools/cli/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/filtering.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/hillshade.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/radioindice.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/speed.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/svf.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/tiling.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/timeseries.cpython-38.pyc +src/eolab/rastertools/cli/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/processing/__init__.py src/eolab/rastertools/processing/algo.py src/eolab/rastertools/processing/rasterproc.py src/eolab/rastertools/processing/sliding.py src/eolab/rastertools/processing/stats.py src/eolab/rastertools/processing/vector.py +src/eolab/rastertools/processing/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/algo.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/rasterproc.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/sliding.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/stats.cpython-38.pyc +src/eolab/rastertools/processing/__pycache__/vector.cpython-38.pyc src/eolab/rastertools/product/__init__.py src/eolab/rastertools/product/rasterproduct.py src/eolab/rastertools/product/rastertype.py src/eolab/rastertools/product/rastertypes.json src/eolab/rastertools/product/vrt.py +src/eolab/rastertools/product/__pycache__/__init__.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/rasterproduct.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/rastertype.cpython-38.pyc +src/eolab/rastertools/product/__pycache__/vrt.cpython-38.pyc src/rastertools.egg-info/PKG-INFO src/rastertools.egg-info/SOURCES.txt src/rastertools.egg-info/dependency_links.txt @@ -113,6 +141,22 @@ tests/test_utils.py tests/test_vector.py tests/test_zonalstats.py tests/utils4test.py +tests/__pycache__/__init__.cpython-38.pyc +tests/__pycache__/cmptools.cpython-38.pyc +tests/__pycache__/conftest.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_algo.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_radioindice.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rasterproc.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rasterproduct.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rastertools.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_rastertype.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_speed.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_stats.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_tiling.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_utils.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_vector.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/test_zonalstats.cpython-38-pytest-8.0.0.pyc +tests/__pycache__/utils4test.cpython-38.pyc tests/tests_data/COMMUNE_32001.dbf tests/tests_data/COMMUNE_32001.prj tests/tests_data/COMMUNE_32001.qpj @@ -141,7 +185,9 @@ tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndwi.tif tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ +tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ.properties tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz +tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties tests/tests_data/additional_rastertypes.json tests/tests_data/grid.geojson tests/tests_data/listing.lst diff --git a/tests/test_algo.py b/tests/test_algo.py index 5071e555..f25d738f 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -3,6 +3,7 @@ import numpy as np import numpy.ma as ma +import rasterio from eolab.rastertools.processing import algo @@ -12,6 +13,13 @@ def test_local_sum(): + """ + Test the local sum filter with varying kernel sizes. + + This function verifies that the local sum filter correctly applies to a + 5x5 matrix with kernel sizes ranging from 1 to 5, comparing each output + to expected results. + """ results = [ np.array( [[0, 1, 2, 3, 4], @@ -63,9 +71,12 @@ def test_local_sum(): assert (output[0] == results[i - 1]).all() - def test_local_mean(): + """ + Test the local mean filter with a kernel size of 2. + This test verifies the local mean filter by comparing its output on a 5x5 matrix to an expected result matrix. + """ result = np.array( [[1, 1.5, 2.5, 3.5, 4], [3, 3.5, 4.5, 5.5, 6], @@ -94,7 +105,8 @@ def test_local_mean(): ) mask = np.pad(mask, (radius, radius), mode="edge") - array = ma.array(band, mask=mask) + array = ma.array(band, mask=mask) # masks the band array + # ie. removes the first line and first column of band # output shape is array shape - kernel_width output = algo.local_mean(array, kernel_size=kernel_width) @@ -106,7 +118,13 @@ def test_local_mean(): def test_bresenham_line(): + """ + Test the Bresenham's line algorithm for angles from 0° to 360° in 15° increments. + This function verifies that the Bresenham line algorithm generates accurate + line coordinates for a given radius across multiple theta values. Each angle + is tested against an expected list of coordinates. + """ results = [ # 0° [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)], diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index ba2385f6..acaff0c9 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -19,6 +19,20 @@ def test_radioindice_process_file_merge(): + ''' + This function tests the Radioindice class's ability to generate a merged output file containing multiple indices. The indices generated + include: + + NDVI, TNDVI, RVI, PVI, SAVI, TSAVI, MSAVI, MSAVI2, IPVI, EVI, NDWI, NDWI2, + MNDVI, NDPI, NDTI, NDBI, RI, BI, BI2. + + The function compares the generated output to an expected output file. + + Asserts: + - The generated output file is named correctly and matches the expected filename. + + Clears the output directory at the end of the test. + ''' # create output dir and clear its content if any utils4test.create_outdir() @@ -36,7 +50,22 @@ def test_radioindice_process_file_merge(): utils4test.clear_outdir() -def test_radioindice_process_file_separate(compare, save_gen_as_ref): +def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : bool): + """ + Test the Radioindice class by generating individual files for each indice. + + This function verifies the generation of separate output files for NDVI and NDWI. + The results can be compared with reference files or saved as new references if desired. + + Parameters: + - compare (bool): If True, compares the generated files to reference files. + - save_gen_as_ref (bool): If True, saves the generated files as new reference files. + + Asserts: + - The output files match the reference files. + + Clears the output directory at the end of the test. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -66,6 +95,17 @@ def test_radioindice_process_file_separate(compare, save_gen_as_ref): def test_radioindice_process_files(): + ''' + Test the Radioindice class by processing multiple files and merging results. + + This function applies the NDVI and NDWI to a list of Sentinel-2 datasets. + The function generates a merged output file for each input file. Results are verified by comparing to tif files containing the expected results. + + Asserts: + - The generated output files match the expected names for merged indices. + + Clears the output directory at the end of the test. + ''' # create output dir and clear its content if any utils4test.create_outdir() @@ -86,6 +126,21 @@ def test_radioindice_process_files(): def test_radioindice_incompatible_indice_rastertype(caplog): + """ + Test handling of incompatible indices and raster types in the Radioindice class. + + This function verifies that the Radioindice class correctly handles cases where the + raster file lacks the required bands for a specified index. + + Parameters: + - caplog: pytest fixture for capturing log output within the test. + + Asserts: + - No output files are generated (output list is empty). + - An error log entry is recorded with details about the missing bands. + + Clears the output directory at the end of the test. + """ # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/test_rasterproc.py b/tests/test_rasterproc.py index ad560087..95180be1 100644 --- a/tests/test_rasterproc.py +++ b/tests/test_rasterproc.py @@ -16,16 +16,38 @@ def algo2D(bands): + """ + Apply a scaling factor to each band independently. + + Parameters: + bands (numpy.ndarray): A 2D array containing a single band of raster data. + + Returns: + numpy.ndarray: An array with each element scaled by a factor of 2. + """ out = 2. * bands return out def algo3D(bands): + """ + Apply a scaling factor to all bands simultaneously. + + Parameters: + bands (numpy.ndarray): A 3D array containing multiple bands of raster data. + + Returns: + numpy.ndarray: An array with each element in all bands scaled by a factor of 2. + """ out = 2. * bands return out def test_compute_sliding(): + """ + Test the compute_sliding function with 2D and 3D raster data. + It verifies that the computed output matches the expected transformation. + """ # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index 83162d03..3f1657ef 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -23,6 +23,21 @@ def test_rasterproduct_valid_parameters(): + """ + Test the initialization and properties of `RasterProduct` with valid parameters. + + This test case verifies the proper creation and expected properties of `RasterProduct` + objects from various supported file formats and structures: + - Sentinel-2 L1C archive with one file per band. + - SPOT6 archive with one file for all bands. + - Standard raster file with multiple channels. + + Assertions: + - `file` path, raster type, and channels match expected values. + - Band and mask files are correctly listed. + - Archive status and extracted metadata (e.g., date, tile, orbit, and satellite) match + expected values based on the input files. + """ # archive with one file per band basename = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041" file = Path( @@ -83,6 +98,18 @@ def test_rasterproduct_valid_parameters(): def test_rasterproduct_invalid_parameters(): + """ + Test the handling of invalid parameters when creating a `RasterProduct`. + + This test case verifies: + - Passing `None` as a file parameter raises a `ValueError`. + - Unrecognized raster type in input file raises a `ValueError`. + - Unsupported file types raise `ValueError` with appropriate error messages. + + Assertions: + - Each invalid parameter triggers a `ValueError` with a specific message indicating + the type of parameter issue. + """ with pytest.raises(ValueError) as exc: RasterProduct(None) assert "'file' cannot be None" in str(exc.value) @@ -99,6 +126,18 @@ def test_rasterproduct_invalid_parameters(): def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): + """ + Test the creation and processing of a Sentinel-2 L2A MAJA `RasterProduct`. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - Generated file paths match expected paths. + - Comparison or saving of reference files completes without errors. + - Raster data can be opened without errors. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -146,6 +185,23 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): def test_create_product_S2_L1C(compare, save_gen_as_ref): + """ + Test the creation and processing of a Sentinel-2 L1C `RasterProduct`. + + This test case verifies: + - Creation of a single file for the L1C product and generation of clipped output. + - Comparison of generated files against reference files or saving as new references. + - Loading the generated raster using `rasterio` to confirm proper creation. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - Generated file paths and metadata match expected values. + - Reference comparison or saving completes as expected. + - Raster data can be loaded and accessed without errors. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -177,6 +233,23 @@ def test_create_product_S2_L1C(compare, save_gen_as_ref): def test_create_product_S2_L2A_SEN2CORE(compare, save_gen_as_ref): + """ + Test the creation of a Sentinel-2 L2A SEN2CORE `RasterProduct`. + + This test case verifies: + - Creation of the raster and VRT files. + - Comparison of generated files with reference files or saving as new references if needed. + - Loading the generated VRT to ensure accessibility with `rasterio`. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - VRT file path and metadata match expected output. + - Reference file operations are successful. + - The VRT file can be accessed with `rasterio` without issues. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -206,6 +279,23 @@ def test_create_product_S2_L2A_SEN2CORE(compare, save_gen_as_ref): def test_create_product_SPOT67(compare, save_gen_as_ref): + """ + Test the creation of a SPOT6/7 `RasterProduct`. + + This test case verifies: + - Creation of the product using SPOT6 input archive. + - Comparison of generated files with reference files or saving as new references if specified. + - Loading the raster using `rasterio` to verify successful file generation. + + Parameters: + compare (bool): If True, compares generated files to reference files. + save_gen_as_ref (bool): If True, saves generated files as new reference files. + + Assertions: + - Output paths and contents match expected values. + - Reference file comparison and saving are correctly performed. + - Raster data opens without errors in `rasterio`. + """ # create output dir and clear its content if any utils4test.create_outdir() @@ -235,6 +325,19 @@ def test_create_product_SPOT67(compare, save_gen_as_ref): def test_create_product_special_cases(): + """ + Test special cases in `RasterProduct` creation, including in-memory, directory, and VRT handling. + + This test case covers: + - Creation of products in memory (with and without masks). + - Handling of product creation from VRT and directory inputs. + - Loading raster data via `rasterio` to ensure correct accessibility. + + Assertions: + - VRT and in-memory files are correctly created. + - Directory input processing and band masking work as expected. + - Raster files can be opened without errors in `rasterio`. + """ # SUPPORTED CASES # creation in memory (without masks) diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 49731998..f9c54b46 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -769,7 +769,7 @@ def test_hillshade_command_line_default(): # elevation / azimuth are retrieved from https://www.sunearthtools.com/dp/tools/pos_sun.php argslist = [ # default case: hillshade at Toulouse the September, 21 solar noon - "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/tests_out" + "-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5 -o tests/tests_out" " tests/tests_data/toulouse-mnh.tif", # default case: hillshade at Toulouse the June, 21, solar 6PM "-v hs --elevation 25.82 --azimuth 278.58 --resolution 0.5 -o tests/tests_out" @@ -802,7 +802,7 @@ def test_hillshade_command_line_errors(caplog): "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/truc" " tests/tests_data/toulouse-mnh.tif", # missing required argument - "-v hs --elevation 46.81 --resolution 0.5 -o tests/tests_out" + "-v hs --elevation 46.81 --resolution 0.5 " " tests/tests_data/toulouse-mnh.tif", # input file has more than 1 band "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/tests_out" diff --git a/tests/test_speed.py b/tests/test_speed.py index 9bbe0d26..f7deab52 100644 --- a/tests/test_speed.py +++ b/tests/test_speed.py @@ -16,7 +16,10 @@ __refdir = utils4test.get_refdir("test_radioindice/") -def test_speed_process_files(compare, save_gen_as_ref): +def test_speed_process_files(compare : bool, save_gen_as_ref : bool): + """ + + """ # create output dir and clear its content if any utils4test.create_outdir() diff --git a/tests/utils4test.py b/tests/utils4test.py index 027acb71..ccc59eb6 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -47,24 +47,24 @@ def copy_to_ref(files, refdir): def basename(infile): - """function to get basename of file""" + """ + Function to get basename of file + """ file = Path(infile) if isinstance(infile, str) else infile suffix = len("".join(file.suffixes)) return file.name if suffix == 0 else file.name[:-suffix] -def cmpfiles(a, b, common, tolerance=1e-9): - """Compare common files in two directories. - - a, b -- directory names - common -- list of file names found in both directories - shallow -- if true, do comparison based solely on stat() information +def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: + """ + Compare common files in two directories. - Returns a tuple of three lists: - files that compare equal - files that are different - filenames that aren't regular files. + Args: + a, b (str) : Directory names + common (list) : List of file names found in both directories + Returns: + Tuple of three lists ( [files that are the same], [files that differs], [filenames that aren't regular files] ) """ res = ([], [], []) for x in common: @@ -75,6 +75,9 @@ def cmpfiles(a, b, common, tolerance=1e-9): def _cmp(gld, new, tolerance): + """ + + """ ftype = os.path.splitext(gld)[-1].lower() cmp = cmptools.CMP_FUN[ftype] try: From 8d08ba53fb3b8bd01969e248b853a292629fd170 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 5 Nov 2024 14:31:05 +0100 Subject: [PATCH 26/56] refactor: replacing argparse by click in main.py --- src/eolab/rastertools/__init__.py | 2 +- src/eolab/rastertools/cli/filtering.py | 310 +++++++++++++++++---- src/eolab/rastertools/cli/filtering_dyn.py | 182 ++++++++++++ src/eolab/rastertools/main.py | 232 +++++++-------- tests/test_algo.py | 1 - 5 files changed, 526 insertions(+), 201 deletions(-) create mode 100644 src/eolab/rastertools/cli/filtering_dyn.py diff --git a/src/eolab/rastertools/__init__.py b/src/eolab/rastertools/__init__.py index e357ff68..b6783098 100644 --- a/src/eolab/rastertools/__init__.py +++ b/src/eolab/rastertools/__init__.py @@ -26,7 +26,7 @@ # import rastertool Zonalstats from eolab.rastertools.zonalstats import Zonalstats # import the method to run a rastertool -from eolab.rastertools.main import run_tool, add_custom_rastertypes +from eolab.rastertools.main import rastertools, add_custom_rastertypes __all__ = [ "RastertoolConfigurationException", "Rastertool", "Windowable", diff --git a/src/eolab/rastertools/cli/filtering.py b/src/eolab/rastertools/cli/filtering.py index 882270ce..2fbd88f3 100644 --- a/src/eolab/rastertools/cli/filtering.py +++ b/src/eolab/rastertools/cli/filtering.py @@ -3,70 +3,45 @@ """ CLI definition for the filtering tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import Filtering +#import eolab.rastertools.main as main +from eolab.rastertools import RastertoolConfigurationException +#from eolab.rastertools.main import rastertools #Import the click group named rastertools +import click +import sys +import os +#_logger = main.get_logger() -def create_argparser(rastertools_parsers): - """Adds the filtering subcommand to the given rastertools subparser +def _extract_files_from_list(cmd_inputs): + """Extract the list of files from a file of type ".lst" which + contains one line per file Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. + cmd_inputs (str): + Value of the inputs arguments of the command line. Either + a file with a suffix lst from which the list of files shall + be extracted or directly the list of files (in this case, the + list is returned without any change). - This argument provides from a code like this:: + Returns: + The list of input files read from the command line + """ - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - filtering.create_argparser(rastertools_parsers) + # handle the input file of type "lst" + if len(cmd_inputs) == 1 and cmd_inputs[0][-4:].lower() == ".lst": + # parse the listing + with open(cmd_inputs[0]) as f: + inputs = f.read().splitlines() + else: + inputs = cmd_inputs - Returns: - The rastertools subparsers updated with this subcommand + return inputs + +def create_filtering(output : str, window_size : int, pad : str, argsdict : dict, filter : str, bands : list, kernel_size : int, all_bands : bool) -> Filtering: """ - parser = rastertools_parsers.add_parser( - "filter", aliases=["fi"], - help="Apply a filter to a set of images", - description="Apply a filter to a set of images.") - - # create a subparser to configure each kind of filter - sub_parser = parser.add_subparsers(title='Filters') - - for rasterfilter in Filtering.get_default_filters(): - # new parser for the filter - filterparser = sub_parser.add_parser( - rasterfilter.name, - aliases=rasterfilter.aliases, - help=rasterfilter.help, - description=rasterfilter.description, - epilog="By default only first band is computed.") - - # add argument declared in the filter definition - for argument_name, argument_params in rasterfilter.arguments.items(): - filterparser.add_argument(f"--{argument_name}", **argument_params) - - # add common arguments (inputs, output dir, window size, pad mode) - filterparser.add_argument( - "inputs", - nargs='+', - help="Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). " - "You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_outputdir_arguments(filterparser) - cli.with_window_arguments(filterparser) - cli.with_bands_arguments(filterparser) - - # set the default commmand to run for this filter parser - filterparser.set_defaults(filter=rasterfilter.name) - - # set the function to call when this subcommand is called - parser.set_defaults(func=create_filtering) - - return rastertools_parsers - - -def create_filtering(args) -> Filtering: - """Create and configure a new rastertool "Filtering" according to argparse args + CHANGE DOCSTRING + Create and configure a new rastertool "Filtering" according to argparse args Args: args: args extracted from command line @@ -74,21 +49,234 @@ def create_filtering(args) -> Filtering: Returns: :obj:`eolab.rastertools.Filtering`: The configured rastertool to run """ - argsdict = vars(args) # get the bands to process - if args.all_bands: + if all_bands: bands = None else: - bands = list(map(int, args.bands)) if args.bands else [1] + bands = list(map(int, bands)) if bands else [1] # create the rastertool object raster_filters_dict = {rf.name: rf for rf in Filtering.get_default_filters()} - tool = Filtering(raster_filters_dict[args.filter], args.kernel_size, bands) + tool = Filtering(raster_filters_dict[filter], kernel_size, bands) # set up config with args values - tool.with_output(args.output) \ - .with_windows(args.window_size, args.pad) \ + tool.with_output(output) \ + .with_windows(window_size, pad) \ .with_filter_configuration(argsdict) return tool + +def apply_filter(ctx, tool : Filtering, inputs : str): + """ + CHANGE DOCSTRING + Apply the chosen filter + """ + try: + # handle the input file of type "lst" + inputs_extracted = _extract_files_from_list(inputs) + + # setup debug mode in which intermediate VRT files are stored to disk or not + tool.with_vrt_stored(ctx.obj.get('keep_vrt')) + + # launch process + tool.process_files(inputs_extracted) + + #_logger.info("Done!") + + except RastertoolConfigurationException as rce: + #_logger.exception(rce) + sys.exit(2) + + except Exception as err: + #_logger.exception(err) + sys.exit(1) + + sys.exit(0) + +inpt_arg = click.argument('inputs', type=str, required = 1) + +ker_opt = click.option('--kernel-size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" + "of 3x3 pixels on which the filter function is computed" + "(default: 8)") + +out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +win_opt = click.option('-ws', '--window-size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") + +pad_opt = click.option('-p','--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), + help="Pad to use around the image, default : edge" + "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" + "for more information)") + +band_opt = click.option('-b','--bands', type=list, help="List of bands to process") + +all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") + +@click.group() +@click.pass_context +def filter(ctx): + ''' + Apply a filter to a set of images. + ''' + ctx.ensure_object(dict) + + +#Median filter +@filter.command("median") +@inpt_arg +@ker_opt +@out_opt +@win_opt +@pad_opt +@band_opt +@all_opt +@click.pass_context +def median(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : + """ + COMPLETE THE SECTION should display for : rastertools filter median --help + Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + + do not remove : + inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") + that lists the input files to process (one input file per line in .lst)" + """ + #Store input files so that rastertools has access to it + ctx.obj["inputs"] = inputs + + # Configure the filter tool instance + tool = create_filtering( + output=output, + window_size=window_size, + pad=pad, + argsdict={"inputs": inputs}, + filter='median', + bands=bands, + kernel_size=kernel_size, + all_bands=all_bands) + + apply_filter(ctx, tool, inputs) + +#Sum filter +@filter.command("sum") +@inpt_arg +@ker_opt +@out_opt +@win_opt +@pad_opt +@band_opt +@all_opt +@click.pass_context +def sum(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : + """ + COMPLETE THE SECTION should display for : rastertools filter median --help + Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + + do not remove : + inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") + that lists the input files to process (one input file per line in .lst)" + """ + # Store input files so that rastertools has access to it + ctx.obj["inputs"] = inputs + + # Configure the filter tool instance + tool = create_filtering( + output=output, + window_size=window_size, + pad=pad, + argsdict={"inputs": inputs}, + filter='sum', + bands=bands, + kernel_size=kernel_size, + all_bands=all_bands) + + apply_filter(ctx, tool, inputs) + +#Mean filter +@filter.command("mean") +@inpt_arg +@ker_opt +@out_opt +@win_opt +@pad_opt +@band_opt +@all_opt +@click.pass_context +def mean(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : + """ + COMPLETE THE SECTION should display for : rastertools filter median --help + Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + + do not remove : + inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") + that lists the input files to process (one input file per line in .lst)" + """ + # Store input files so that rastertools has access to it + ctx.obj["inputs"] = inputs + + # Configure the filter tool instance + tool = create_filtering( + output=output, + window_size=window_size, + pad=pad, + argsdict={"inputs": inputs}, + filter='mean', + bands=bands, + kernel_size=kernel_size, + all_bands=all_bands) + + apply_filter(ctx, tool, inputs) + +#Adaptive gaussian filter +@filter.command("adaptive_gaussian") +@inpt_arg +@ker_opt +@out_opt +@win_opt +@pad_opt +@band_opt +@all_opt +@click.pass_context +def adaptive_gaussian(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : + """ + COMPLETE THE SECTION should display for : rastertools filter median --help + Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + + do not remove : + inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") + that lists the input files to process (one input file per line in .lst)" + """ + # Store input files so that rastertools has access to it + ctx.obj["inputs"] = inputs + + # Configure the filter tool instance + tool = create_filtering( + output=output, + window_size=window_size, + pad=pad, + argsdict={"inputs": inputs}, + filter='adaptive_gaussian', + bands=bands, + kernel_size=kernel_size, + all_bands=all_bands) + + apply_filter(ctx, tool, inputs) + + +@filter.result_callback() +@click.pass_context +def handle_result(ctx): + if ctx.invoked_subcommand is None: + click.echo(ctx.get_help()) + ctx.exit() + + + + + + + diff --git a/src/eolab/rastertools/cli/filtering_dyn.py b/src/eolab/rastertools/cli/filtering_dyn.py new file mode 100644 index 00000000..c3be3335 --- /dev/null +++ b/src/eolab/rastertools/cli/filtering_dyn.py @@ -0,0 +1,182 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +CLI definition for the filtering tool +""" +from eolab.rastertools import Filtering +from eolab.rastertools.main import get_logger +from eolab.rastertools import RastertoolConfigurationException +#from eolab.rastertools.main import rastertools #Import the click group named rastertools +import sys +import click +import os + +_logger = get_logger() + +def _extract_files_from_list(cmd_inputs): + """Extract the list of files from a file of type ".lst" which + contains one line per file + + Args: + cmd_inputs (str): + Value of the inputs arguments of the command line. Either + a file with a suffix lst from which the list of files shall + be extracted or directly the list of files (in this case, the + list is returned without any change). + + Returns: + The list of input files read from the command line + """ + + # handle the input file of type "lst" + if len(cmd_inputs) == 1 and cmd_inputs[0][-4:].lower() == ".lst": + # parse the listing + with open(cmd_inputs[0]) as f: + inputs = f.read().splitlines() + else: + inputs = cmd_inputs + + return inputs + +def create_filtering(output : str, window_size : int, pad : str, argsdict : dict, filter : str, bands : list, kernel_size : int, all_bands : bool) -> Filtering: + """ + CHANGE DOCSTRING + Create and configure a new rastertool "Filtering" according to argparse args + + Args: + args: args extracted from command line + + Returns: + :obj:`eolab.rastertools.Filtering`: The configured rastertool to run + """ + + # get the bands to process + if all_bands: + bands = None + else: + bands = list(map(int, bands)) if bands else [1] + + # create the rastertool object + raster_filters_dict = {rf.name: rf for rf in Filtering.get_default_filters()} + tool = Filtering(raster_filters_dict[filter], kernel_size, bands) + + # set up config with args values + tool.with_output(output) \ + .with_windows(window_size, pad) \ + .with_filter_configuration(argsdict) + + return tool + + +def apply_filter(ctx, tool : Filtering, inputs : str): + """ + CHANGE DOCSTRING + Apply the chosen filter + """ + try: + # handle the input file of type "lst" + inputs_extracted = _extract_files_from_list(inputs) + + # setup debug mode in which intermediate VRT files are stored to disk or not + tool.with_vrt_stored(ctx.obj.get('keep_vrt')) + + # launch process + tool.process_files(inputs_extracted) + + _logger.info("Done!") + + except RastertoolConfigurationException as rce: + _logger.exception(rce) + sys.exit(2) + + except Exception as err: + _logger.exception(err) + sys.exit(1) + + sys.exit(0) + + +inpt_arg = click.argument('inputs', type=str, help="Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). " + "You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") " + "that lists the input files to process (one input file per line in .lst)") + +ker_opt = click.option('--kernel-size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" + "of 3x3 pixels on which the filter function is computed" + "(default: 8)") + +out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +win_opt = click.option('-ws', '--window-size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") + +pad_opt = click.option('-p','--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), + help="Pad to use around the image, default : edge" + "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" + "for more information)") + +band_opt = click.option('-b','--bands', type=list, help="List of bands to process") + +all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") + +@click.group() +@click.pass_context +def filter(ctx): + ''' + Apply a filter to a set of images. + ''' + ctx.ensure_object(dict) + + +def create_filter(filter_name : str): + + @filter.command(filter_name) + @inpt_arg + @ker_opt + @out_opt + @win_opt + @pad_opt + @band_opt + @all_opt + @click.pass_context + def filter_filtername(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool): + ''' + COMPLETE THE SECTION should display for : rastertools filter median --help + Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + + do not remove : + inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") + that lists the input files to process (one input file per line in .lst)" + ''' + ctx.obj["inputs"] = inputs + + # Configure the filter tool instance + tool = create_filtering( + output=output, + window_size=window_size, + pad=pad, + argsdict={"inputs": inputs}, + filter=filter_name, + bands=bands, + kernel_size=kernel_size, + all_bands=all_bands) + + apply_filter(ctx, tool, inputs) + + +median = create_filter("median") +mean = create_filter("mean") +sum = create_filter("sum") +adaptive_gaussian = create_filter("adaptive_gaussian") + +@filter.result_callback() +@click.pass_context +def handle_result(ctx): + if ctx.invoked_subcommand is None: + click.echo(ctx.get_help()) + ctx.exit() + + + + + + diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index c9dc9033..995d70bf 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -10,22 +10,22 @@ rastertools zonalstats --help """ -import argparse import logging import logging.config import os import sys import json - +import click +from eolab.rastertools.cli.filtering import filter from eolab.rastertools import __version__ -from eolab.rastertools import RastertoolConfigurationException from eolab.rastertools.cli import radioindice, zonalstats, tiling, speed from eolab.rastertools.cli import filtering, svf, hillshade, timeseries from eolab.rastertools.product import RasterType _logger = logging.getLogger(__name__) - +def get_logger(): + return _logger def add_custom_rastertypes(rastertypes): """Add definition of new raster types. The json string shall have the following format: @@ -126,154 +126,110 @@ def add_custom_rastertypes(rastertypes): """ RasterType.add(rastertypes) - -def run_tool(args): - """Main entry point allowing external calls - - sys.exit returns: - - - 0: everything runs fine - - 1: processing errors occured - - 2: wrong execution configuration - - Args: - args ([str]): command line parameter list +@click.group() + +@click.option( + '-t', '--rastertype', + 'rastertype', + # Click automatically uses the last argument as the variable name, so "dest" is this last parameter + type=click.Path(exists=True), + help="JSON file defining additional raster types of input files") + +@click.option( + '--max_workers', + "max_workers", + type=int, + help="Maximum number of workers for parallel processing. If not given, it will default to " + "the number of processors on the machine. When all processors are not allocated to " + "run rastertools, it is thus recommended to set this option.") + +@click.option( + '--debug', + "keep_vrt", + is_flag=True, + help="Store to disk the intermediate VRT images that are generated when handling " + "the input files which can be complex raster product composed of several band files.") + +@click.option( + '-v', + '--verbose', + is_flag=True, + help="set loglevel to INFO") + +@click.option( + '-vv', + '--very-verbose', + is_flag=True, + help="set loglevel to DEBUG") + +@click.version_option(version='rastertools {}'.format(__version__)) # Ensure __version__ is defined + +@click.pass_context +def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbose : bool, very_verbose : bool): """ - parser = argparse.ArgumentParser( - description="Collection of tools on raster data") - # add an argument to define custom raster types - parser.add_argument( - '-t', - '--rastertype', - dest="rastertype", - help="JSON file defining additional raster types of input files") - parser.add_argument( - '--version', - action='version', - version=f'rastertools {__version__}') - parser.add_argument( - '--max_workers', - dest="max_workers", - type=int, - help="Maximum number of workers for parallel processing. If not given, it will default to " - "the number of processors on the machine. When all processors are not allocated to " - "run rastertools, it is thus recommended to set this option.") - parser.add_argument( - '--debug', - dest="keep_vrt", - action="store_true", - help="Store to disk the intermediate VRT images that are generated when handling " - "the input files which can be complex raster product composed of several band files.") - parser.add_argument( - '-v', - '--verbose', - dest="loglevel", - help="set loglevel to INFO", - action='store_const', - const=logging.INFO) - parser.add_argument( - '-vv', - '--very-verbose', - dest="loglevel", - help="set loglevel to DEBUG", - action='store_const', - const=logging.DEBUG) - - rastertools_parsers = parser.add_subparsers(title='Commands') - # add sub parser for filtering - rastertools_parsers = filtering.create_argparser(rastertools_parsers) - # add sub parser for hillshade - rastertools_parsers = hillshade.create_argparser(rastertools_parsers) - # add sub parser for radioindice - rastertools_parsers = radioindice.create_argparser(rastertools_parsers) - # add sub parser for speed - rastertools_parsers = speed.create_argparser(rastertools_parsers) - # add sub parser for svf - rastertools_parsers = svf.create_argparser(rastertools_parsers) - # add sub parser for tiling - rastertools_parsers = tiling.create_argparser(rastertools_parsers) - # add sub parser for timeseries - rastertools_parsers = timeseries.create_argparser(rastertools_parsers) - # add sub parser for zonalstats - rastertools_parsers = zonalstats.create_argparser(rastertools_parsers) - - # analyse arguments - args = parser.parse_args(args) - argsdict = vars(args) - - # setup logging + Collection of tools on raster data. + CHANGE DOCSTRING + Main entry point allowing external calls. + + Args: + rastertype: JSON file defining additional raster types. + max_workers: Maximum number of workers for parallel processing. + keep_vrt: Store intermediate VRT images. + verbose: Set loglevel to INFO. + very_verbose: Set loglevel to DEBUG. + command: The command to execute (e.g., filtering). + inputs: Input files for processing. + + sys.exit returns: + + - 0: everything runs fine + - 1: processing errors occured + - 2: wrong execution configuration + """ + ctx.ensure_object(dict) + ctx.obj['keep_vrt'] = keep_vrt + + # Setup logging + if very_verbose: + loglevel = logging.DEBUG + elif verbose: + loglevel = logging.INFO logformat = "[%(asctime)s] %(levelname)s - %(name)s - %(message)s" - logging.basicConfig(level=args.loglevel, stream=sys.stdout, - format=logformat, datefmt="%Y-%m-%d %H:%M:%S") + logging.basicConfig(level=loglevel, stream=sys.stdout, format=logformat, datefmt="%Y-%m-%d %H:%M:%S") if "RASTERTOOLS_NOTQDM" not in os.environ: - os.environ["RASTERTOOLS_NOTQDM"] = "True" if logging.root.level > logging.INFO else "False" + os.environ["RASTERTOOLS_NOTQDM"] = "True" if loglevel > logging.INFO else "False" - if "RASTERTOOLS_MAXWORKERS" not in os.environ and args.max_workers is not None: - os.environ["RASTERTOOLS_MAXWORKERS"] = f"{args.max_workers}" + if "RASTERTOOLS_MAXWORKERS" not in os.environ and max_workers is not None: + os.environ["RASTERTOOLS_MAXWORKERS"] = f"{max_workers}" - # handle rastertype option - if args.rastertype: - with open(args.rastertype) as json_content: + # Handle rastertype option + if rastertype: + with open(rastertype) as json_content: RasterType.add(json.load(json_content)) - # call function corresponding to the subcommand - if "func" in argsdict: - try: - # initialize the rastertool to execute - tool = args.func(args) - - # handle the input file of type "lst" - inputs = _extract_files_from_list(args.inputs) - - # setup debug mode in which intermediate VRT files are stored to disk or not - tool.with_vrt_stored(args.keep_vrt) - - # launch process - tool.process_files(inputs) - _logger.info("Done!") - except RastertoolConfigurationException as rce: - _logger.exception(rce) - sys.exit(2) - except Exception as err: - _logger.exception(err) - sys.exit(1) - else: - parser.print_help() - - sys.exit(0) - - -def _extract_files_from_list(cmd_inputs): - """Extract the list of files from a file of type ".lst" which - contains one line per file - - Args: - cmd_inputs (str): - Value of the inputs arguments of the command line. Either - a file with a suffix lst from which the list of files shall - be extracted or directly the list of files (in this case, the - list is returned without any change). - - Returns: - The list of input files read from the command line - """ - - # handle the input file of type "lst" - if len(cmd_inputs) == 1 and cmd_inputs[0][-4:].lower() == ".lst": - # parse the listing - with open(cmd_inputs[0]) as f: - inputs = f.read().splitlines() - else: - inputs = cmd_inputs - return inputs +# Register subcommands from other modules +rastertools.add_command(filter) +#rastertools.add_command(hillshade) +#rastertools.add_command(radioindice) +#rastertools.add_command(speed) +#rastertools.add_command(svf) +#rastertools.add_command(tiling) +#rastertools.add_command(timeseries) +#rastertools.add_command(zonalstats) +@rastertools.result_callback() +@click.pass_context +def handle_result(ctx): + if ctx.invoked_subcommand is None: + click.echo(ctx.get_help()) + ctx.exit() def run(): """Entry point for console_scripts """ - run_tool(sys.argv[1:]) + rastertools() if __name__ == "__main__": diff --git a/tests/test_algo.py b/tests/test_algo.py index f25d738f..7a4ee7f8 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -3,7 +3,6 @@ import numpy as np import numpy.ma as ma -import rasterio from eolab.rastertools.processing import algo From 0deeac35ce8f68381d4a36d7a7a30b91c2aafc6a Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 6 Nov 2024 16:03:28 +0100 Subject: [PATCH 27/56] refactor: WIP click --- src/eolab/rastertools/cli/filtering.py | 195 +++++++++------------ src/eolab/rastertools/cli/filtering_dyn.py | 122 +++++-------- src/eolab/rastertools/cli/hillshade.py | 120 ++++++------- src/eolab/rastertools/cli/radioindice.py | 146 ++++++--------- src/eolab/rastertools/cli/speed.py | 58 ++---- src/eolab/rastertools/cli/svf.py | 110 ++++-------- src/eolab/rastertools/cli/tiling.py | 121 +++++-------- src/eolab/rastertools/cli/timeseries.py | 108 +++++------- src/eolab/rastertools/cli/utils_cli.py | 86 +++++++++ src/eolab/rastertools/main.py | 88 +++++----- tests/test_rastertools.py | 56 +++--- 11 files changed, 543 insertions(+), 667 deletions(-) create mode 100644 src/eolab/rastertools/cli/utils_cli.py diff --git a/src/eolab/rastertools/cli/filtering.py b/src/eolab/rastertools/cli/filtering.py index 2fbd88f3..3501c84c 100644 --- a/src/eolab/rastertools/cli/filtering.py +++ b/src/eolab/rastertools/cli/filtering.py @@ -5,51 +5,36 @@ """ from eolab.rastertools import Filtering #import eolab.rastertools.main as main -from eolab.rastertools import RastertoolConfigurationException +from eolab.rastertools.cli.utils_cli import apply_process #from eolab.rastertools.main import rastertools #Import the click group named rastertools import click -import sys import os -#_logger = main.get_logger() +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def _extract_files_from_list(cmd_inputs): - """Extract the list of files from a file of type ".lst" which - contains one line per file - - Args: - cmd_inputs (str): - Value of the inputs arguments of the command line. Either - a file with a suffix lst from which the list of files shall - be extracted or directly the list of files (in this case, the - list is returned without any change). - - Returns: - The list of input files read from the command line - """ - - # handle the input file of type "lst" - if len(cmd_inputs) == 1 and cmd_inputs[0][-4:].lower() == ".lst": - # parse the listing - with open(cmd_inputs[0]) as f: - inputs = f.read().splitlines() - else: - inputs = cmd_inputs - - return inputs def create_filtering(output : str, window_size : int, pad : str, argsdict : dict, filter : str, bands : list, kernel_size : int, all_bands : bool) -> Filtering: """ - CHANGE DOCSTRING - Create and configure a new rastertool "Filtering" according to argparse args + This function initializes a `Filtering` tool instance and configures it with specified settings. + + It selects the filter type, kernel size, output settings, and processing bands. If `all_bands` is set + to True, the filter will apply to all bands in the raster; otherwise, it applies only to specified bands. Args: - args: args extracted from command line + output (str): The path for the filtered output file. + window_size (int): Size of the processing window used by the filter. + pad (str): Padding method used for windowing (e.g., 'reflect', 'constant', etc.). + argsdict (dict): Dictionary of additional filter configuration arguments. + filter (str): The filter type to apply (must be a valid name in `Filtering` filters). + bands (list): List of bands to process. If empty and `all_bands` is False, defaults to [1]. + kernel_size (int): Size of the kernel used by the filter. + all_bands (bool): Whether to apply the filter to all bands (True) or specific bands (False). Returns: - :obj:`eolab.rastertools.Filtering`: The configured rastertool to run + :obj:`eolab.rastertools.Filtering`: A configured `Filtering` instance ready for execution. """ + # get the bands to process if all_bands: bands = None @@ -67,53 +52,27 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict return tool -def apply_filter(ctx, tool : Filtering, inputs : str): - """ - CHANGE DOCSTRING - Apply the chosen filter - """ - try: - # handle the input file of type "lst" - inputs_extracted = _extract_files_from_list(inputs) - - # setup debug mode in which intermediate VRT files are stored to disk or not - tool.with_vrt_stored(ctx.obj.get('keep_vrt')) - - # launch process - tool.process_files(inputs_extracted) - - #_logger.info("Done!") - except RastertoolConfigurationException as rce: - #_logger.exception(rce) - sys.exit(2) +inpt_arg = click.argument('inputs', type=str, nargs = -1, required = 1) - except Exception as err: - #_logger.exception(err) - sys.exit(1) - - sys.exit(0) - -inpt_arg = click.argument('inputs', type=str, required = 1) - -ker_opt = click.option('--kernel-size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" +ker_opt = click.option('--kernel_size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" "of 3x3 pixels on which the filter function is computed" "(default: 8)") out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") -win_opt = click.option('-ws', '--window-size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") +win_opt = click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") pad_opt = click.option('-p','--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), help="Pad to use around the image, default : edge" "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" "for more information)") -band_opt = click.option('-b','--bands', type=list, help="List of bands to process") +band_opt = click.option('-b','--bands', multiple = True, type=int, help="List of bands to process") all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") -@click.group() +@click.group(name = "filter", context_settings=CONTEXT_SETTINGS) @click.pass_context def filter(ctx): ''' @@ -123,7 +82,7 @@ def filter(ctx): #Median filter -@filter.command("median") +@filter.command("median",context_settings=CONTEXT_SETTINGS) @inpt_arg @ker_opt @out_opt @@ -132,19 +91,23 @@ def filter(ctx): @band_opt @all_opt @click.pass_context -def median(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : +def median(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : """ - COMPLETE THE SECTION should display for : rastertools filter median --help - Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + Execute the median filter on the input files with the specified parameters. - do not remove : - inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") - that lists the input files to process (one input file per line in .lst)" - """ - #Store input files so that rastertools has access to it - ctx.obj["inputs"] = inputs + The filter works by sliding a window across the input raster and replacing each + pixel value with the median value of the pixels within that window. + The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists + the input files to process (one input file per line in .lst). + """ # Configure the filter tool instance tool = create_filtering( output=output, @@ -156,10 +119,10 @@ def median(ctx, inputs : str, output : str, window_size : int, pad : str, kernel kernel_size=kernel_size, all_bands=all_bands) - apply_filter(ctx, tool, inputs) + apply_process(ctx, tool, inputs) #Sum filter -@filter.command("sum") +@filter.command("sum",context_settings=CONTEXT_SETTINGS) @inpt_arg @ker_opt @out_opt @@ -168,19 +131,23 @@ def median(ctx, inputs : str, output : str, window_size : int, pad : str, kernel @band_opt @all_opt @click.pass_context -def sum(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : +def sum(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : """ - COMPLETE THE SECTION should display for : rastertools filter median --help - Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + Execute the sum filter on the input files with the specified parameters. - do not remove : - inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") - that lists the input files to process (one input file per line in .lst)" - """ - # Store input files so that rastertools has access to it - ctx.obj["inputs"] = inputs + The filter works by sliding a window across the input raster and replacing each + pixel value with the median value of the pixels within that window. + The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists + the input files to process (one input file per line in .lst). + """ # Configure the filter tool instance tool = create_filtering( output=output, @@ -192,10 +159,10 @@ def sum(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_si kernel_size=kernel_size, all_bands=all_bands) - apply_filter(ctx, tool, inputs) + apply_process(ctx, tool, inputs) #Mean filter -@filter.command("mean") +@filter.command("mean",context_settings=CONTEXT_SETTINGS) @inpt_arg @ker_opt @out_opt @@ -204,19 +171,23 @@ def sum(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_si @band_opt @all_opt @click.pass_context -def mean(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : +def mean(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : """ - COMPLETE THE SECTION should display for : rastertools filter median --help - Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + Execute the mean filter on the input files with the specified parameters. - do not remove : - inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") - that lists the input files to process (one input file per line in .lst)" - """ - # Store input files so that rastertools has access to it - ctx.obj["inputs"] = inputs + The filter works by sliding a window across the input raster and replacing each + pixel value with the median value of the pixels within that window. + The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists + the input files to process (one input file per line in .lst). + """ # Configure the filter tool instance tool = create_filtering( output=output, @@ -228,10 +199,10 @@ def mean(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_s kernel_size=kernel_size, all_bands=all_bands) - apply_filter(ctx, tool, inputs) + apply_process(ctx, tool, inputs) #Adaptive gaussian filter -@filter.command("adaptive_gaussian") +@filter.command("adaptive_gaussian",context_settings=CONTEXT_SETTINGS) @inpt_arg @ker_opt @out_opt @@ -240,19 +211,23 @@ def mean(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_s @band_opt @all_opt @click.pass_context -def adaptive_gaussian(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : +def adaptive_gaussian(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : """ - COMPLETE THE SECTION should display for : rastertools filter median --help - Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help + Execute the adaptive gaussian filter on the input files with the specified parameters. - do not remove : - inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") - that lists the input files to process (one input file per line in .lst)" - """ - # Store input files so that rastertools has access to it - ctx.obj["inputs"] = inputs + The filter works by sliding a window across the input raster and replacing each + pixel value with the median value of the pixels within that window. + The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists + the input files to process (one input file per line in .lst). + """ # Configure the filter tool instance tool = create_filtering( output=output, @@ -264,7 +239,7 @@ def adaptive_gaussian(ctx, inputs : str, output : str, window_size : int, pad : kernel_size=kernel_size, all_bands=all_bands) - apply_filter(ctx, tool, inputs) + apply_process(ctx, tool, inputs) @filter.result_callback() diff --git a/src/eolab/rastertools/cli/filtering_dyn.py b/src/eolab/rastertools/cli/filtering_dyn.py index c3be3335..2ba82748 100644 --- a/src/eolab/rastertools/cli/filtering_dyn.py +++ b/src/eolab/rastertools/cli/filtering_dyn.py @@ -4,52 +4,35 @@ CLI definition for the filtering tool """ from eolab.rastertools import Filtering -from eolab.rastertools.main import get_logger -from eolab.rastertools import RastertoolConfigurationException +#from eolab.rastertools.main import get_logger +from eolab.rastertools.cli.utils_cli import apply_process #from eolab.rastertools.main import rastertools #Import the click group named rastertools -import sys import click import os -_logger = get_logger() +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def _extract_files_from_list(cmd_inputs): - """Extract the list of files from a file of type ".lst" which - contains one line per file - - Args: - cmd_inputs (str): - Value of the inputs arguments of the command line. Either - a file with a suffix lst from which the list of files shall - be extracted or directly the list of files (in this case, the - list is returned without any change). - - Returns: - The list of input files read from the command line - """ - - # handle the input file of type "lst" - if len(cmd_inputs) == 1 and cmd_inputs[0][-4:].lower() == ".lst": - # parse the listing - with open(cmd_inputs[0]) as f: - inputs = f.read().splitlines() - else: - inputs = cmd_inputs - - return inputs def create_filtering(output : str, window_size : int, pad : str, argsdict : dict, filter : str, bands : list, kernel_size : int, all_bands : bool) -> Filtering: """ - CHANGE DOCSTRING - Create and configure a new rastertool "Filtering" according to argparse args + This function initializes a `Filtering` tool instance and configures it with specified settings. + + It selects the filter type, kernel size, output settings, and processing bands. If `all_bands` is set + to True, the filter will apply to all bands in the raster; otherwise, it applies only to specified bands. Args: - args: args extracted from command line + output (str): The path for the filtered output file. + window_size (int): Size of the processing window used by the filter. + pad (str): Padding method used for windowing (e.g., 'reflect', 'constant', etc.). + argsdict (dict): Dictionary of additional filter configuration arguments. + filter (str): The filter type to apply (must be a valid name in `Filtering` filters). + bands (list): List of bands to process. If empty and `all_bands` is False, defaults to [1]. + kernel_size (int): Size of the kernel used by the filter. + all_bands (bool): Whether to apply the filter to all bands (True) or specific bands (False). Returns: - :obj:`eolab.rastertools.Filtering`: The configured rastertool to run + :obj:`eolab.rastertools.Filtering`: A configured `Filtering` instance ready for execution. """ - # get the bands to process if all_bands: bands = None @@ -68,67 +51,37 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict return tool -def apply_filter(ctx, tool : Filtering, inputs : str): - """ - CHANGE DOCSTRING - Apply the chosen filter - """ - try: - # handle the input file of type "lst" - inputs_extracted = _extract_files_from_list(inputs) - - # setup debug mode in which intermediate VRT files are stored to disk or not - tool.with_vrt_stored(ctx.obj.get('keep_vrt')) +inpt_arg = click.argument('inputs', type=str, nargs = -1, required = 1) - # launch process - tool.process_files(inputs_extracted) - - _logger.info("Done!") - - except RastertoolConfigurationException as rce: - _logger.exception(rce) - sys.exit(2) - - except Exception as err: - _logger.exception(err) - sys.exit(1) - - sys.exit(0) - - -inpt_arg = click.argument('inputs', type=str, help="Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). " - "You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") " - "that lists the input files to process (one input file per line in .lst)") - -ker_opt = click.option('--kernel-size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" +ker_opt = click.option('--kernel_size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" "of 3x3 pixels on which the filter function is computed" "(default: 8)") out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") -win_opt = click.option('-ws', '--window-size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") +win_opt = click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") pad_opt = click.option('-p','--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), help="Pad to use around the image, default : edge" "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" "for more information)") -band_opt = click.option('-b','--bands', type=list, help="List of bands to process") +band_opt = click.option('-b','--bands', type=int, multiple = True, help="List of bands to process") all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") -@click.group() +@click.group(context_settings=CONTEXT_SETTINGS) @click.pass_context def filter(ctx): - ''' + """ Apply a filter to a set of images. - ''' + """ ctx.ensure_object(dict) def create_filter(filter_name : str): - @filter.command(filter_name) + @filter.command(filter_name, context_settings=CONTEXT_SETTINGS) @inpt_arg @ker_opt @out_opt @@ -137,18 +90,21 @@ def create_filter(filter_name : str): @band_opt @all_opt @click.pass_context - def filter_filtername(ctx, inputs : str, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool): - ''' - COMPLETE THE SECTION should display for : rastertools filter median --help - Execute the filtering tool with the specified filter and parameters. name=rasterfilter.name, help=rasterfilter.help - - do not remove : - inputs : Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") - that lists the input files to process (one input file per line in .lst)" - ''' - ctx.obj["inputs"] = inputs + def filter_filtername(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool): + """ + Execute the requested filter on the input files with the specified parameters. + The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists + the input files to process (one input file per line in .lst). + """ + print(bands) # Configure the filter tool instance tool = create_filtering( output=output, @@ -160,7 +116,7 @@ def filter_filtername(ctx, inputs : str, output : str, window_size : int, pad : kernel_size=kernel_size, all_bands=all_bands) - apply_filter(ctx, tool, inputs) + apply_process(ctx, tool, inputs) median = create_filter("median") diff --git a/src/eolab/rastertools/cli/hillshade.py b/src/eolab/rastertools/cli/hillshade.py index 640b3ac7..10c82a23 100644 --- a/src/eolab/rastertools/cli/hillshade.py +++ b/src/eolab/rastertools/cli/hillshade.py @@ -3,12 +3,53 @@ """ CLI definition for the hillshade tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import Hillshade +from eolab.rastertools.cli.utils_cli import apply_process +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the hillshade subcommand to the given rastertools subparser + +#Hillshade command +@click.command("hillshade",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) + +@click.option('--elevation', type=float, help="Elevation of the sun in degrees, [0°, 90°] where" + "90°=zenith and 0°=horizon") + +@click.option('--azimuth', type=float, help="Azimuth of the sun in degrees, [0°, 360°] where" + "0°=north, 90°=east, 180°=south and 270°=west") + +@click.option('--radius', type=int, help="Maximum distance (in pixels) around a point to evaluate" + "horizontal elevation angle. If not set, it is automatically computed from" + " the range of altitudes in the digital model.") + +@click.option('--resolution',default=0.5, type=float, help="Pixel resolution in meter") + +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") + +@click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), + help="Pad to use around the image, default : edge" + "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" + "for more information)") + +@click.pass_context +def hillshade(ctx, inputs : list, elevation : float, azimuth : float, radius : int, resolution : float, output : str, window_size : int, pad : str) : + """ + CHANGE DOCSTRING + Adds the hillshade subcommand to the given rastertools subparser + + Arguments: + + inputs TEXT + + Input file to process (i.e. geotiff corresponding to a + Digital Height Model). You can provide a single file + with extension ".lst" (e.g. "filtering.lst") that + lists the input files to process (one input file per line in .lst) Args: rastertools_parsers: @@ -24,74 +65,15 @@ def create_argparser(rastertools_parsers): Returns: The rastertools subparsers updated with this subcommand """ - parser = rastertools_parsers.add_parser( - "hillshade", aliases=["hs"], - help="Compute hillshades of a Digital Elevation / Surface / Height Model " - "(a raster containing the height of the point as pixel values)", - description="Compute hillshades of a Digital Elevation / Surface / Height Model.") - - # add specific arguments of the hillshade processing - arguments = { - "elevation": { - "required": True, - "type": float, - "help": "Elevation of the sun in degrees, [0°, 90°] where 90°=zenith and 0°=horizon" - }, - "azimuth": { - "required": True, - "type": float, - "help": "Azimuth of the sun in degrees, [0°, 360°] " - "where 0°=north, 90°=east, 180°=south and 270°=west" - }, - "radius": { - "required": False, - "type": int, - "help": "Max distance (in pixels) around a point to evaluate horizontal" - " elevation angle. If not set, it is automatically computed from" - " the range of altitudes in the digital model." - }, - "resolution": { - "default": 0.5, - "required": True, - "type": float, - "help": "Pixel resolution in meter" - }, - } - # add argument declared in the hillshade processing definition - for argument_name, argument_params in arguments.items(): - parser.add_argument(f"--{argument_name}", **argument_params) - - # add common arguments (inputs, output dir, window size, pad mode) - parser.add_argument( - "inputs", - nargs='+', - help="Input file to process (i.e. geotiff that contains the height " - "of the points as pixel values). " - "You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_outputdir_arguments(parser) - cli.with_window_arguments(parser) - - # set the function to call when this subcommand is called - parser.set_defaults(func=create_hillshade) - - return rastertools_parsers - - -def create_hillshade(args) -> Hillshade: - """Create and configure a new rastertool "Hillshade" according to argparse args - - Args: - args: args extracted from command line - Returns: - :obj:`eolab.rastertools.Hillshade`: The configured rastertool to run - """ # create the rastertool object - tool = Hillshade(args.elevation, args.azimuth, args.resolution, args.radius) + tool = Hillshade(elevation, azimuth, resolution, radius) # set up config with args values - tool.with_output(args.output) - tool.with_windows(args.window_size, args.pad) + tool.with_output(output) + tool.with_windows(window_size, pad) + + apply_process(ctx, tool, inputs) + + - return tool diff --git a/src/eolab/rastertools/cli/radioindice.py b/src/eolab/rastertools/cli/radioindice.py index 49a75d51..c7491ca8 100644 --- a/src/eolab/rastertools/cli/radioindice.py +++ b/src/eolab/rastertools/cli/radioindice.py @@ -3,111 +3,78 @@ """ CLI definition for the radioindice tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import RastertoolConfigurationException, Radioindice +from eolab.rastertools.cli.utils_cli import apply_process from eolab.rastertools.product import BandChannel from eolab.rastertools.processing import RadioindiceProcessing +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the radioindice subcommand to the given rastertools subparser - - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. - - This argument provides from a code like this:: - - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - radioindice.create_argparser(rastertools_parsers) - - Returns: - The rastertools subparsers updated with this subcommand - """ - indicenames = ', '.join(sorted([indice.name for indice in Radioindice.get_default_indices()])) - parser = rastertools_parsers.add_parser( - "radioindice", aliases=["ri"], - help="Compute radiometric indices", - description="Compute a list of radiometric indices (NDVI, NDWI, etc.) on a raster image", - epilog="If no indice option is explicitly set, NDVI, NDWI and NDWI2 are computed.") - parser.add_argument( - "inputs", - nargs='+', - help="Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). " - "You can provide a single file with extension \".lst\" (e.g. \"radioindice.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_outputdir_arguments(parser) - parser.add_argument( - '-m', - '--merge', - dest="merge", - action="store_true", - help="Merge all indices in the same image (i.e. one band per indice).") - parser.add_argument( - '-r', - '--roi', - dest="roi", - help="Region of interest in the input image (vector)") - indices_pc = parser.add_argument_group("Options to select the indices to compute") - indices_pc.add_argument( - '-i', - '--indices', - dest="indices", - nargs="+", - help="List of indices to compute" - f"Possible indices are: {indicenames}") - for indice in Radioindice.get_default_indices(): - indices_pc.add_argument( - f"--{indice.name}", - dest=indice.name, - action="store_true", - help=f"Compute {indice.name} indice") - indices_pc.add_argument( - '-nd', - "-normalized_difference", - nargs=2, - action="append", - metavar=("band1", "band2"), - help="Compute the normalized difference of two bands defined as parameter of this option, " - "e.g. \"-nd red nir\" will compute (red-nir)/(red+nir). " - "See eolab.rastertools.product.rastertype.BandChannel for the list of bands names. " - "Several nd options can be set to compute several normalized differences.") - cli.with_window_arguments(parser, pad=False) - - # set the function to call when this subcommand is called - parser.set_defaults(func=create_radioindice) - - return rastertools_parsers - - -def create_radioindice(args) -> Radioindice: +def parse_normalized_difference(ctx, param, value): + """Parse the pairs of bands as (band1, band2) tuples.""" + if value: + # Split the input pairs and store them as tuples + parsed_pairs = [] + for i in range(0, len(value), 2): + parsed_pairs.append((value[i], value[i + 1])) + return parsed_pairs + return None + + +#Radioindice command +@click.command("radioindice",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) + +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +@click.option('-m', '--merge', is_flag = True, help="Merge all indices in the same image (i.e. one band per indice)") + +@click.option('-r', '--roi', type= str, help="Region of interest in the input image (vector)") + +@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") + +list_indices = ['--ndvi', '--tndvi', '--rvi', '--pvi', '--savi', '--tsavi', '--msavi', '--msavi2', '--ipvi', +'--evi', '--ndwi', '--ndwi2', '--mndwi', '--ndpi', '--ndti', '--ndbi', '--ri', '--bi', '--bi2'] + +for id in list_indices: + @click.option(id, is_flag = True, help=f"Compute {id} indice") + +@click.option('-nd', '--normalized_difference','nd',type=str, + multiple=True, nargs=2, callback= parse_normalized_difference, metavar="band1 band2", + help="Compute the normalized difference of two bands defined" + "as parameter of this option, e.g. \"-nd red nir\" will compute (red-nir)/(red+nir). " + "See eolab.rastertools.product.rastertype.BandChannel for the list of bands names. " + "Several nd options can be set to compute several normalized differences.") + + +@click.pass_context +def radioindice(ctx, inputs : list, output : str, merge : bool, roi : str, window_size : int, nd : bool, *args) : """Create and configure a new rastertool "Radioindice" according to argparse args - Args: - args: args extracted from command line + Args: + args: args extracted from command line - Returns: - :obj:`eolab.rastertools.Radioindice`: The configured rastertool to run - """ + Returns: + :obj:`eolab.rastertools.Radioindice`: The configured rastertool to run + """ indices_to_compute = [] - argsdict = vars(args) # append indices defined with -- indices_to_compute.extend([indice for indice in Radioindice.get_default_indices() - if argsdict[indice.name]]) + if indices]) # append indices defined with --indices - if args.indices: + if indices: indices_dict = {indice.name: indice for indice in Radioindice.get_default_indices()} - for ind in args.indices: + for ind in indices: if ind in indices_dict: indices_to_compute.append(indices_dict[ind]) else: raise RastertoolConfigurationException(f"Invalid indice name: {ind}") - if args.nd: - for nd in args.nd: + if nd: + for nd in nd: if nd[0] in BandChannel.__members__ and nd[1] in BandChannel.__members__: channel1 = BandChannel[nd[0]] channel2 = BandChannel[nd[1]] @@ -128,8 +95,9 @@ def create_radioindice(args) -> Radioindice: tool = Radioindice(indices_to_compute) # set up config with args values - tool.with_output(args.output, args.merge) - tool.with_roi(args.roi) - tool.with_windows(args.window_size) + tool.with_output(output, merge) + tool.with_roi(roi) + tool.with_windows(window_size) return tool + diff --git a/src/eolab/rastertools/cli/speed.py b/src/eolab/rastertools/cli/speed.py index b343b4bc..2977811b 100644 --- a/src/eolab/rastertools/cli/speed.py +++ b/src/eolab/rastertools/cli/speed.py @@ -3,49 +3,29 @@ """ CLI definition for the speed tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import Speed +from eolab.rastertools.cli.utils_cli import apply_process +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the speed subcommand to the given rastertools subparser - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. - - This argument provides from a code like this:: +#Speed command +@click.command("speed",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - speed.create_argparser(rastertools_parsers) +@click.option('-b','--bands', type=int, multiple = True, help="List of bands to process") - Returns: - The rastertools subparsers updated with this subcommand - """ - parser = rastertools_parsers.add_parser( - "speed", aliases=["sp"], - help="Compute speed of rasters", - description="Compute the speed of radiometric values of several raster images", - epilog="By default only first band is computed.") - parser.add_argument( - "inputs", - nargs='+', - help="Input files to process (e.g. Sentinel2 L2A MAJA from THEIA). " - "You can provide a single file with extension \".lst\" (e.g. \"speed.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_bands_arguments(parser) - cli.with_outputdir_arguments(parser) +@click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") - # set the function to call when this subcommand is called - parser.set_defaults(func=create_speed) +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") - return rastertools_parsers - - -def create_speed(args) -> Speed: - """Create and configure a new rastertool "Speed" according to argparse args +@click.pass_context +def speed(ctx, inputs : list, bands : list, all_bands : bool, output : str) : + """ + CHANGE DOCSTRING + Create and configure a new rastertool "Speed" according to argparse args Args: args: args extracted from command line @@ -55,15 +35,15 @@ def create_speed(args) -> Speed: """ # get the bands to process - if args.all_bands: + if all_bands: bands = None else: - bands = list(map(int, args.bands)) if args.bands else [1] + bands = list(map(int, bands)) if bands else [1] # create the rastertool object tool = Speed(bands) # set up config with args values - tool.with_output(args.output) + tool.with_output(output) - return tool + apply_process(ctx, tool, inputs) diff --git a/src/eolab/rastertools/cli/svf.py b/src/eolab/rastertools/cli/svf.py index 374926b8..58a15afc 100644 --- a/src/eolab/rastertools/cli/svf.py +++ b/src/eolab/rastertools/cli/svf.py @@ -3,95 +3,57 @@ """ CLI definition for the SVF (Sky View Factor) tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import SVF +from eolab.rastertools.cli.utils_cli import apply_process +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the SVF subcommand to the given rastertools subparser - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. +#Speed command +@click.command("svf",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) - This argument provides from a code like this:: +@click.option('--radius',required = True, type=int, default = 16, help="Maximum distance (in pixels) around a point to evaluate horizontal elevation angle") - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - svf.create_argparser(rastertools_parsers) +@click.option('--directions',required = True, type=int, default = 12, help="Number of directions on which to compute the horizon elevation angle") - Returns: - The rastertools subparsers updated with this subcommand +@click.option('--resolution',default=0.5, type=float, help="Pixel resolution in meter") + +@click.option('--altitude', type=int, help="Reference altitude to use for computing the SVF. If this option is not" + " specified, SVF is computed for every point at the altitude of the point") + +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") + +@click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), + help="Pad to use around the image, default : edge" + "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" + "for more information)") + +@click.pass_context +def svf(ctx, inputs : list, radius : int, directions : int, resolution : float, altitude : int, output : str, window_size : int, pad : str) : """ - parser = rastertools_parsers.add_parser( - "svf", - help="Compute Sky View Factor of a Digital Height Model", - description="Compute Sky View Factor of a Digital Height Model.") - - # add specific argument of the svf processing - arguments = { - "radius": { - "default": 16, - "required": True, - "type": int, - "help": "Max distance (in pixels) around a point to evaluate horizontal" - " elevation angle" - }, - "directions": { - "default": 12, - "required": True, - "type": int, - "help": "Number of directions on which to compute the horizon elevation angle" - }, - "resolution": { - "default": 0.5, - "required": True, - "type": float, - "help": "Pixel resolution in meter" - }, - "altitude": { - "type": int, - "help": "Reference altitude to use for computing the SVF. If this option is not" - " specified, SVF is computed for every point at the altitude of the point" - } - } - for argument_name, argument_params in arguments.items(): - parser.add_argument(f"--{argument_name}", **argument_params) - - # add common arguments (inputs, output dir, window size, pad mode) - parser.add_argument( - "inputs", - nargs='+', - help="Input file to process (i.e. geotiff corresponding to a Digital Height Model). " - "You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_outputdir_arguments(parser) - cli.with_window_arguments(parser) - - # set the function to call when this subcommand is called - parser.set_defaults(func=create_svf) - - return rastertools_parsers - - -def create_svf(args) -> SVF: - """Create and configure a new rastertool "SVF" according to argparse args + CHANGE DOCSTRING + + ADD INPUTS + Create and configure a new rastertool "Speed" according to argparse args Args: args: args extracted from command line Returns: - :obj:`eolab.rastertools.SVF`: The configured rastertool to run + :obj:`eolab.rastertools.Speed`: The configured rastertool to run """ - # create the rastertool object - tool = SVF(args.directions, args.radius, args.resolution) + tool = SVF(directions, radius, resolution) # set up config with args values - tool.with_output(args.output) - tool.with_windows(args.window_size, args.pad) - if args.altitude is not None: - tool.with_altitude(args.altitude) + tool.with_output(output) + tool.with_windows(window_size, pad) + if altitude is not None: + tool.with_altitude(altitude) - return tool + apply_process(ctx, tool, inputs) diff --git a/src/eolab/rastertools/cli/tiling.py b/src/eolab/rastertools/cli/tiling.py index 8108b295..1b67e634 100644 --- a/src/eolab/rastertools/cli/tiling.py +++ b/src/eolab/rastertools/cli/tiling.py @@ -3,87 +3,50 @@ """ CLI definition for the tiling tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import Tiling +from eolab.rastertools.cli.utils_cli import apply_process +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the tiling subcommand to the given rastertools subparser - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. +#Speed command +@click.command("tiling",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) - This argument provides from a code like this:: +@click.option('-g','--grid','grid_file',required = True, type=str, help="vector-based spatial data file containing the grid to" + " use to generate the tiles") - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - tiling.create_argparser(rastertools_parsers) +@click.option('--id_col','id_column', type = str, help="Name of the column in the grid" + " file used to number the tiles. When ids are defined, this argument is required" + "to identify which column corresponds to the define ids") - Returns: - The rastertools subparsers updated with this subcommand +@click.option('--id', type=int, multiple = True, help="Tiles ids of the grid to export as new tile, default all") + +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +@click.option('-n','--name','output_name', default="{}_tile{}", help="Basename for the output raster tiles, default:" + "\"{}_tile{}\". The basename must be defined as a formatted string where tile index is at position 1" + " and original filename is at position 0. For instance, tile{1}.tif will generate the filename" + "tile75.tif for the tile id = 75") + +@click.option('-d','--dir','subdir_name', help="When each tile must be generated in a different" + "subdirectory, it defines the naming convention for the subdirectory. It is a formatted string with one positional" + "parameter corresponding to the tile index. For instance, tile{} will generate the subdirectory name tile75/" + "for the tile id = 75. By default, subdirectory is not defined and output files will be generated directly in" + "the output directory") +@click.pass_context +def tiling(ctx, inputs : list, grid_file : str, id_column : str, id : list, output : str, output_name : str, subdir_name : str) : """ - parser = rastertools_parsers.add_parser( - "tiling", aliases=["ti"], - help="Generate image tiles", - description="Generate tiles of an input raster image following the geometries " - "defined by a given grid") - parser.add_argument( - "inputs", - nargs='+', - help="Raster files to process. " - "You can provide a single file with extension \".lst\" (e.g. \"tiling.lst\") " - "that lists the input files to process (one input file per line in .lst)") - parser.add_argument( - '-g', - '--grid', - dest="grid_file", - help="vector-based spatial data file containing the grid to use to generate the tiles", - required=True) - parser.add_argument( - "--id_col", - dest="id_column", - help="Name of the column in the grid file used to number the tiles. When ids are defined," - " this argument is required to identify which column corresponds to the defined ids", - ) - parser.add_argument( - "--id", - dest="id", - help="Tiles ids of the grid to export as new tile, default all", - nargs="+", - type=int - ) - cli.with_outputdir_arguments(parser) - parser.add_argument( - "-n", - "--name", - dest="output_name", - help="Basename for the output raster tiles, default: \"{}_tile{}\". " - "The basename must be defined as a formatted string where tile index is at position 1 " - "and original filename is at position 0. For instance, tile{1}.tif will generate the " - "filename tile75.tif for the tile id = 75.", - default="{}_tile{}" - ) - parser.add_argument( - "-d", - "--dir", - dest="subdir_name", - help="When each tile must be generated in a different subdir, it defines the naming " - "convention for the subdir. It is a formatted string with one positional parameter " - "corresponding to the tile index. For instance, tile{} will generate the subdir " - "name tile75/ for the tile id = 75. By default, subdir is not defined and output " - "files will be generated directly in the outputdir." - ) - - # set the function to call when this subcommand is called - parser.set_defaults(func=create_tiling) - - return rastertools_parsers - - -def create_tiling(args) -> Tiling: - """Create and configure a new rastertool "Tiling" according to argparse args + CHANGE DOCSTRING + + ADD INPUTS + Create and configure a new rastertool "Tiling" according to argparse args + + Generate tiles of an input raster image following the geometries defined by a + given grid + Args: args: args extracted from command line @@ -91,12 +54,16 @@ def create_tiling(args) -> Tiling: Returns: :obj:`eolab.rastertools.Tiling`: The configured rastertool to run """ + if id == () : + id = None # create the rastertool object - tool = Tiling(args.grid_file) + tool = Tiling(grid_file) # set up config with args values - tool.with_output(args.output, args.output_name, args.subdir_name) - tool.with_id_column(args.id_column, args.id) + tool.with_output(output, output_name, subdir_name) + tool.with_id_column(id_column, id) + + apply_process(ctx, tool, inputs) + - return tool diff --git a/src/eolab/rastertools/cli/timeseries.py b/src/eolab/rastertools/cli/timeseries.py index 4279b97c..733be56b 100644 --- a/src/eolab/rastertools/cli/timeseries.py +++ b/src/eolab/rastertools/cli/timeseries.py @@ -5,99 +5,85 @@ """ from datetime import datetime -import eolab.rastertools.cli as cli -from eolab.rastertools import RastertoolConfigurationException, Timeseries +from eolab.rastertools import Timeseries +from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools import RastertoolConfigurationException +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the timeseries subcommand to the given rastertools subparser - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. +#Speed command +@click.command("timeseries",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) + +@click.option('-b','--bands', type=list, help="List of bands to process") + +@click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") + +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") + +@click.option("-s","--start_date", help="Start date of the timeseries to generate in the following format: yyyy-MM-dd") + +@click.option("-e", "--end_date", help="End date of the timeseries to generate in the following format: yyyy-MM-dd") - This argument provides from a code like this:: +@click.option("-p", "--time_period",type=int, help="Time period (number of days) between two consecutive images in the timeseries " + "to generate e.g. 10 = generate one image every 10 days") + +@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - timeseries.create_argparser(rastertools_parsers) +@click.pass_context - Returns: - The rastertools subparsers updated with this subcommand +def timeseries(ctx, inputs : list, bands : list, all_bands : bool, output : str, start_date : str, end_date : str, time_period : int, window_size : int) : """ - parser = rastertools_parsers.add_parser( - "timeseries", aliases=["ts"], - help="Temporal gap filling of an image time series", - description="Generate a timeseries of images (without gaps) from a set of input images. " + Create and configure a new rastertool "Timeseries" according to argparse args + CHANGE DOCSTRING + Adds the timeseries subcommand to the given rastertools subparser + + Temporal gap filling of an image time series + Generate a timeseries of images (without gaps) from a set of input images. " "Data not present in the input images (no image for the date or masked data) " "are interpolated (with linear interpolation) so that all gaps are filled.", - epilog="By default only first band is computed.") - parser.add_argument( - "inputs", - nargs='+', - help="Input files to process (e.g. Sentinel2 L2A MAJA from THEIA). " - "You can provide a single file with extension \".lst\" (e.g. \"speed.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_bands_arguments(parser) - cli.with_outputdir_arguments(parser) - parser.add_argument( - "-s", - "--start_date", - help="Start date of the timeseries to generate in the following format: yyyy-MM-dd") - parser.add_argument( - "-e", - "--end_date", - help="End date of the timeseries to generate in the following format: yyyy-MM-dd") - parser.add_argument( - "-p", - "--time_period", - type=int, - help="Time period (number of days) between two consecutive images in the timeseries " - "to generate e.g. 10 = generate one image every 10 days") - cli.with_window_arguments(parser, pad=False) + epilog="By default only first band is computed. - # set the function to call when this subcommand is called - parser.set_defaults(func=create_timeseries) - return rastertools_parsers + ADD INPUTS + INPUTS -def create_timeseries(args) -> Timeseries: - """Create and configure a new rastertool "Timeseries" according to argparse args + Input files to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension .lst (e.g. speed.lst) + that lists the input files to process (one input file per line in .lst)) Args: args: args extracted from command line - - Returns: - :obj:`eolab.rastertools.Timeseries`: The configured rastertool to run """ - # get the bands to process - if args.all_bands: + if all_bands: bands = None else: - bands = list(map(int, args.bands)) if args.bands else [1] + bands = list(map(int, bands)) if bands else [1] # convert start/end dates to datetime try: - start_date = datetime.strptime(args.start_date, "%Y-%m-%d") + start_date = datetime.strptime(start_date, "%Y-%m-%d") except Exception: raise RastertoolConfigurationException( - f"Invalid format for start date: {args.start_date} (must be %Y-%m-%d)") + f"Invalid format for start date: {start_date} (must be %Y-%m-%d)") # convert start/end dates to datetime try: - end_date = datetime.strptime(args.end_date, "%Y-%m-%d") + end_date = datetime.strptime(end_date, "%Y-%m-%d") except Exception: raise RastertoolConfigurationException( - f"Invalid format for end date: {args.end_date} (must be %Y-%m-%d)") + f"Invalid format for end date: {end_date} (must be %Y-%m-%d)") # create the rastertool object - tool = Timeseries(start_date, end_date, args.time_period, bands) + tool = Timeseries(start_date, end_date, time_period, bands) # set up config with args values - tool.with_output(args.output) - tool.with_windows(args.window_size) + tool.with_output(output) + tool.with_windows(window_size) - return tool + apply_process(ctx, tool, inputs) \ No newline at end of file diff --git a/src/eolab/rastertools/cli/utils_cli.py b/src/eolab/rastertools/cli/utils_cli.py new file mode 100644 index 00000000..f1d508dd --- /dev/null +++ b/src/eolab/rastertools/cli/utils_cli.py @@ -0,0 +1,86 @@ +from eolab.rastertools import RastertoolConfigurationException +import logging +import sys +import click + +#TO DO +_logger = logging.getLogger("main") + +def _extract_files_from_list(cmd_inputs): + """ + Extracts a list of file paths from a command line input. + + If the input is a single file with a `.lst` extension, it reads the file line-by-line and treats each + line as an individual file path, returning the list of paths. If the input is already a + list of file paths, it is returned as-is. + + Args: + cmd_inputs (list of str): + Command line inputs for file paths. If it contains a single `.lst` file, this file + is read to obtain the list of files. Otherwise, it is assumed to be a direct list of files. + + Returns: + list of str: A list of file paths, either extracted from the `.lst` file or passed directly. + + Example: + _extract_files_from_list(["files.lst"]) + + _extract_files_from_list(["file1.tif", "file2.tif"]) + + Notes: + The `.lst` file is expected to have one file path per line. Blank lines in the `.lst` + file will be ignored. + """ + + # handle the input file of type "lst" + if len(cmd_inputs) == 1 and cmd_inputs[0][-4:].lower() == ".lst": + # parse the listing + with open(cmd_inputs[0]) as f: + inputs = f.read().splitlines() + else: + inputs = cmd_inputs + + return inputs + + +def apply_process(ctx, tool, inputs : list): + """ + Apply the chosen process to a set of input files. + + This function extracts input files, configures the tool, and processes the files + through the specified tool. It also handles debug settings and intermediate file storage + (VRT files). In case of any errors, the function logs the exception and terminates the process + with an appropriate exit code. + + Args: + ctx (click.Context): The context object containing configuration options like whether + to store intermediate VRT files. + tool (Filtering or Hillshade or ...): The tool instance that has been configured with the provided parameters. + inputs (str): A path to a list of input files, either as a single `.lst` file or a direct + list of file paths. + + Raises: + RastertoolConfigurationException: If there is a configuration error with the tool. + Exception: Any other errors that occur during processing. + """ + try: + # handle the input file of type "lst" + inputs_extracted = _extract_files_from_list(inputs) + + # setup debug mode in which intermediate VRT files are stored to disk or not + tool.with_vrt_stored(ctx.obj.get('keep_vrt')) + + # launch process + tool.process_files(inputs_extracted) + + _logger.info("Done!") + + except RastertoolConfigurationException as rce: + _logger.exception(rce) + sys.exit(2) + + except Exception as err: + _logger.exception(err) + sys.exit(1) + + sys.exit(0) \ No newline at end of file diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index 995d70bf..2985ca30 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -16,17 +16,16 @@ import sys import json import click -from eolab.rastertools.cli.filtering import filter +from eolab.rastertools.cli.filtering_dyn import filter +from eolab.rastertools.cli.hillshade import hillshade +from eolab.rastertools.cli.speed import speed +from eolab.rastertools.cli.svf import svf +from eolab.rastertools.cli.tiling import tiling +from eolab.rastertools.cli.timeseries import timeseries #radioindice, zonalstats from eolab.rastertools import __version__ -from eolab.rastertools.cli import radioindice, zonalstats, tiling, speed -from eolab.rastertools.cli import filtering, svf, hillshade, timeseries from eolab.rastertools.product import RasterType -_logger = logging.getLogger(__name__) -def get_logger(): - return _logger - def add_custom_rastertypes(rastertypes): """Add definition of new raster types. The json string shall have the following format: @@ -126,15 +125,16 @@ def add_custom_rastertypes(rastertypes): """ RasterType.add(rastertypes) -@click.group() +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) +@click.group(context_settings=CONTEXT_SETTINGS) @click.option( '-t', '--rastertype', 'rastertype', + default = None, # Click automatically uses the last argument as the variable name, so "dest" is this last parameter type=click.Path(exists=True), help="JSON file defining additional raster types of input files") - @click.option( '--max_workers', "max_workers", @@ -142,49 +142,43 @@ def add_custom_rastertypes(rastertypes): help="Maximum number of workers for parallel processing. If not given, it will default to " "the number of processors on the machine. When all processors are not allocated to " "run rastertools, it is thus recommended to set this option.") - @click.option( '--debug', "keep_vrt", is_flag=True, help="Store to disk the intermediate VRT images that are generated when handling " "the input files which can be complex raster product composed of several band files.") - @click.option( '-v', '--verbose', is_flag=True, help="set loglevel to INFO") - @click.option( '-vv', '--very-verbose', is_flag=True, help="set loglevel to DEBUG") - @click.version_option(version='rastertools {}'.format(__version__)) # Ensure __version__ is defined - @click.pass_context def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbose : bool, very_verbose : bool): """ - Collection of tools on raster data. - CHANGE DOCSTRING - Main entry point allowing external calls. - - Args: - rastertype: JSON file defining additional raster types. - max_workers: Maximum number of workers for parallel processing. - keep_vrt: Store intermediate VRT images. - verbose: Set loglevel to INFO. - very_verbose: Set loglevel to DEBUG. - command: The command to execute (e.g., filtering). - inputs: Input files for processing. - - sys.exit returns: - - - 0: everything runs fine - - 1: processing errors occured - - 2: wrong execution configuration + Main entry point for the `rastertools` Command Line Interface. + + The `rastertools` CLI provides tools for raster processing + and analysis and allows configurable data handling, parallel processing, + and debugging support. + + Logging: + + - INFO level (`-v`) gives detailed step information. + + - DEBUG level (`-vv`) offers full debug-level tracing. + + Environment Variables: + + - `RASTERTOOLS_NOTQDM`: If the log level is above INFO, sets this to disable progress bars. + + - `RASTERTOOLS_MAXWORKERS`: If `max_workers` is set, it defines the max workers for rastertools. """ ctx.ensure_object(dict) ctx.obj['keep_vrt'] = keep_vrt @@ -194,6 +188,9 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo loglevel = logging.DEBUG elif verbose: loglevel = logging.INFO + else: + loglevel = logging.WARNING + logformat = "[%(asctime)s] %(levelname)s - %(name)s - %(message)s" logging.basicConfig(level=loglevel, stream=sys.stdout, format=logformat, datefmt="%Y-%m-%d %H:%M:%S") @@ -210,14 +207,21 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo # Register subcommands from other modules -rastertools.add_command(filter) -#rastertools.add_command(hillshade) -#rastertools.add_command(radioindice) -#rastertools.add_command(speed) -#rastertools.add_command(svf) -#rastertools.add_command(tiling) -#rastertools.add_command(timeseries) -#rastertools.add_command(zonalstats) +rastertools.add_command(filter, name = "fi") +rastertools.add_command(filter, name = "filter") +rastertools.add_command(hillshade, name = "hs") +rastertools.add_command(hillshade, name = "hillshade") +#rastertools.add_command(radioindice, name = "ri") +#rastertools.add_command(radioindice, name = "radioindice") +rastertools.add_command(speed, name = "sp") +rastertools.add_command(speed, name = "speed") +rastertools.add_command(svf, name = "svf") +rastertools.add_command(tiling, name = "ti") +rastertools.add_command(tiling, name = "tiling") +rastertools.add_command(timeseries, name = "ts") +rastertools.add_command(timeseries, name = "timeseries") +#rastertools.add_command(zonalstats, name = "zs") +#rastertools.add_command(zonalstats, name = "zonalstats") @rastertools.result_callback() @click.pass_context @@ -226,10 +230,10 @@ def handle_result(ctx): click.echo(ctx.get_help()) ctx.exit() -def run(): +def run(*args, **kwargs): """Entry point for console_scripts """ - rastertools() + rastertools(*args, **kwargs) if __name__ == "__main__": diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index f9c54b46..110a458c 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -5,7 +5,10 @@ import logging import filecmp from pathlib import Path -from eolab.rastertools import run_tool + +from click import argument + +from eolab.rastertools import rastertools from eolab.rastertools.product import RasterType from . import utils4test @@ -94,7 +97,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec # run rastertools with pytest.raises(SystemExit) as wrapped_exception: - run_tool(args=self._args) + rastertools(self.args) # check sys_exit if check_sys_exit: @@ -134,23 +137,31 @@ def test_rastertools_command_line_info(): TestCase("-h"), TestCase("--version"), TestCase(""), - TestCase("radioindice --help"), - TestCase("ri -h"), - TestCase("zonalstats --help"), - TestCase("zs -h"), - TestCase("tiling --help"), - TestCase("ti -h"), TestCase("filter --help"), - TestCase("fi -h"), - TestCase("timeseries --help"), - TestCase("ts -h"), - TestCase("speed --help"), - TestCase("sp -h"), - TestCase("svf --help"), - TestCase("svf -h"), - TestCase("hillshade --help"), - TestCase("hs -h") - ] + TestCase("fi -h") + ] + # tests = [ + # TestCase("--help"), + # TestCase("-h"), + # TestCase("--version"), + # TestCase(""), + # TestCase("radioindice --help"), + # TestCase("ri -h"), + # TestCase("zonalstats --help"), + # TestCase("zs -h"), + # TestCase("tiling --help"), + # TestCase("ti -h"), + # TestCase("filter --help"), + # TestCase("fi -h"), + # TestCase("timeseries --help"), + # TestCase("ts -h"), + # TestCase("speed --help"), + # TestCase("sp -h"), + # TestCase("svf --help"), + # TestCase("svf -h"), + # TestCase("hillshade --help"), + # TestCase("hs -h") + # ] for test in tests: test.run_test() @@ -670,13 +681,13 @@ def test_filtering_command_line_errors(caplog): # list of commands to test argslist = [ # output dir does not exist - "-v fi median --kernel_size 8 -o tests/truc" + "-v filter median --kernel_size 8 -o tests/truc" " tests/tests_data/tif_file.tif", # missing required argument - "-v fi adaptive_gaussian --kernel_size 32 -o tests/tests_out" + "-v filter adaptive_gaussian --kernel_size 32 -o tests/tests_out" " tests/tests_data/RGB_TIF_20170105_013442_test.tif", # kernel_size > window_size - "-v fi median -a --kernel_size 15 --window_size 16 -o tests/tests_out" + "-v filter median -a --kernel_size 15 --window_size 16 -o tests/tests_out" " tests/tests_data/RGB_TIF_20170105_013442_test.tif", ] @@ -707,8 +718,7 @@ def test_svf_command_line_default(): # list of commands to test argslist = [ # default case: svf at the point height - "-v svf --radius 50 --directions 16 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + "ftilin", # default case: svf on ground "-v svf --radius 50 --directions 16 --resolution 0.5 --altitude 0 -o tests/tests_out" " tests/tests_data/toulouse-mnh.tif", From c41b441d6159db9f774ba10f4be9b88ab915ce2b Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 8 Nov 2024 16:00:26 +0100 Subject: [PATCH 28/56] refactor: replaced argparse by click --- src/eolab/rastertools/cli/filtering.py | 5 +- src/eolab/rastertools/cli/filtering_dyn.py | 35 ++-- src/eolab/rastertools/cli/radioindice.py | 40 ++-- src/eolab/rastertools/cli/zonalstats.py | 212 +++++++-------------- src/eolab/rastertools/main.py | 12 +- tests/test_rastertools.py | 13 +- 6 files changed, 129 insertions(+), 188 deletions(-) diff --git a/src/eolab/rastertools/cli/filtering.py b/src/eolab/rastertools/cli/filtering.py index 3501c84c..fd74fcac 100644 --- a/src/eolab/rastertools/cli/filtering.py +++ b/src/eolab/rastertools/cli/filtering.py @@ -210,8 +210,9 @@ def mean(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_ @pad_opt @band_opt @all_opt +@click.option('--sigma', type = int, default = 1, help = "Standard deviation of the Gaussian distribution") @click.pass_context -def adaptive_gaussian(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : +def adaptive_gaussian(ctx, inputs : list, output : str, window_size : int, pad : str, sigma : int, kernel_size : int, bands : list, all_bands : bool) : """ Execute the adaptive gaussian filter on the input files with the specified parameters. @@ -233,7 +234,7 @@ def adaptive_gaussian(ctx, inputs : list, output : str, window_size : int, pad : output=output, window_size=window_size, pad=pad, - argsdict={"inputs": inputs}, + argsdict={"inputs": inputs, "sigma" : sigma}, filter='adaptive_gaussian', bands=bands, kernel_size=kernel_size, diff --git a/src/eolab/rastertools/cli/filtering_dyn.py b/src/eolab/rastertools/cli/filtering_dyn.py index 2ba82748..9c6d34b3 100644 --- a/src/eolab/rastertools/cli/filtering_dyn.py +++ b/src/eolab/rastertools/cli/filtering_dyn.py @@ -3,6 +3,8 @@ """ CLI definition for the filtering tool """ +from typing import Callable + from eolab.rastertools import Filtering #from eolab.rastertools.main import get_logger from eolab.rastertools.cli.utils_cli import apply_process @@ -51,6 +53,14 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict return tool +def filter_options(options : list): + def wrapper(function): + for option in options: + function = option(function) + return function + return wrapper + + inpt_arg = click.argument('inputs', type=str, nargs = -1, required = 1) ker_opt = click.option('--kernel_size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" @@ -66,10 +76,12 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" "for more information)") -band_opt = click.option('-b','--bands', type=int, multiple = True, help="List of bands to process") +band_opt = click.option('-b','--bands', multiple = True, type=int, help="List of bands to process") all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") +sigma = click.option('--sigma', type=int, default=1, help="Standard deviation of the Gaussian distribution") + @click.group(context_settings=CONTEXT_SETTINGS) @click.pass_context def filter(ctx): @@ -81,16 +93,14 @@ def filter(ctx): def create_filter(filter_name : str): + list_opt = [inpt_arg, ker_opt, out_opt, win_opt, pad_opt, band_opt, all_opt] + if filter_name == 'adaptive_gaussian': + list_opt.append(sigma) + @filter.command(filter_name, context_settings=CONTEXT_SETTINGS) - @inpt_arg - @ker_opt - @out_opt - @win_opt - @pad_opt - @band_opt - @all_opt + @filter_options(list_opt) @click.pass_context - def filter_filtername(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool): + def filter_filtername(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool, **kwargs): """ Execute the requested filter on the input files with the specified parameters. The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. @@ -103,14 +113,17 @@ def filter_filtername(ctx, inputs : list, output : str, window_size : int, pad : You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists the input files to process (one input file per line in .lst). """ + argsdict = {"inputs": inputs} + + if filter_name == 'adaptive_gaussian': + argsdict = {"sigma" : kwargs["sigma"]} - print(bands) # Configure the filter tool instance tool = create_filtering( output=output, window_size=window_size, pad=pad, - argsdict={"inputs": inputs}, + argsdict=argsdict, filter=filter_name, bands=bands, kernel_size=kernel_size, diff --git a/src/eolab/rastertools/cli/radioindice.py b/src/eolab/rastertools/cli/radioindice.py index c7491ca8..f8cdf3a7 100644 --- a/src/eolab/rastertools/cli/radioindice.py +++ b/src/eolab/rastertools/cli/radioindice.py @@ -12,15 +12,14 @@ CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def parse_normalized_difference(ctx, param, value): - """Parse the pairs of bands as (band1, band2) tuples.""" - if value: - # Split the input pairs and store them as tuples - parsed_pairs = [] - for i in range(0, len(value), 2): - parsed_pairs.append((value[i], value[i + 1])) - return parsed_pairs - return None + +def indices_opt(function): + list_indices = ['--ndvi', '--tndvi', '--rvi', '--pvi', '--savi', '--tsavi', '--msavi', '--msavi2', '--ipvi', + '--evi', '--ndwi', '--ndwi2', '--mndwi', '--ndpi', '--ndti', '--ndbi', '--ri', '--bi', '--bi2'] + + for idc in list_indices: + function = click.option(idc, is_flag=True, help=f"Compute {id} indice")(function) + return function #Radioindice command @@ -35,14 +34,16 @@ def parse_normalized_difference(ctx, param, value): @click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") -list_indices = ['--ndvi', '--tndvi', '--rvi', '--pvi', '--savi', '--tsavi', '--msavi', '--msavi2', '--ipvi', -'--evi', '--ndwi', '--ndwi2', '--mndwi', '--ndpi', '--ndti', '--ndbi', '--ri', '--bi', '--bi2'] +@click.option('-i', '--indices', type=click.Choice(['ndvi', 'tndvi', 'rvi', 'pvi', 'savi', 'tsavi', 'msavi', 'msavi2', 'ipvi', + 'evi', 'ndwi', 'ndwi2', 'mndwi', 'ndpi', 'ndti', 'ndbi', 'ri', 'bi', 'bi2']), multiple = True, + help=" List of indices to computePossible indices are: bi, bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi," + " ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, tsavi") + -for id in list_indices: - @click.option(id, is_flag = True, help=f"Compute {id} indice") +@indices_opt @click.option('-nd', '--normalized_difference','nd',type=str, - multiple=True, nargs=2, callback= parse_normalized_difference, metavar="band1 band2", + multiple=True, nargs=2, metavar="band1 band2", help="Compute the normalized difference of two bands defined" "as parameter of this option, e.g. \"-nd red nir\" will compute (red-nir)/(red+nir). " "See eolab.rastertools.product.rastertype.BandChannel for the list of bands names. " @@ -50,7 +51,7 @@ def parse_normalized_difference(ctx, param, value): @click.pass_context -def radioindice(ctx, inputs : list, output : str, merge : bool, roi : str, window_size : int, nd : bool, *args) : +def radioindice(ctx, inputs : list, output : str, indices : list, merge : bool, roi : str, window_size : int, nd : bool, **kwargs) : """Create and configure a new rastertool "Radioindice" according to argparse args Args: @@ -58,12 +59,14 @@ def radioindice(ctx, inputs : list, output : str, merge : bool, roi : str, windo Returns: :obj:`eolab.rastertools.Radioindice`: The configured rastertool to run - """ + """ + indices_opt = [key for key, value in kwargs.items() if value] indices_to_compute = [] # append indices defined with -- indices_to_compute.extend([indice for indice in Radioindice.get_default_indices() - if indices]) + if indice.name in indices_opt]) + # append indices defined with --indices if indices: indices_dict = {indice.name: indice for indice in Radioindice.get_default_indices()} @@ -99,5 +102,6 @@ def radioindice(ctx, inputs : list, output : str, merge : bool, roi : str, windo tool.with_roi(roi) tool.with_windows(window_size) - return tool + apply_process(ctx, tool, inputs) + diff --git a/src/eolab/rastertools/cli/zonalstats.py b/src/eolab/rastertools/cli/zonalstats.py index 1f869502..889ceb02 100644 --- a/src/eolab/rastertools/cli/zonalstats.py +++ b/src/eolab/rastertools/cli/zonalstats.py @@ -3,150 +3,70 @@ """ CLI definition for the zonalstats tool """ -import eolab.rastertools.cli as cli from eolab.rastertools import Zonalstats +from eolab.rastertools.cli.utils_cli import apply_process +import click +import os +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -def create_argparser(rastertools_parsers): - """Adds the zonalstats subcommand to the given rastertools subparser - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. +#Zonalstats command +@click.command("radioindice",context_settings=CONTEXT_SETTINGS) +@click.argument('inputs', type=str, nargs = -1, required = 1) - This argument provides from a code like this:: +@click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - zonalstats.create_argparser(rastertools_parsers) +@click.option('-f', '--format', "output_format", type = str, help="Output format of the results when input geometries are provided (by default ESRI " + "Shapefile). Possible values are ESRI Shapefile, GeoJSON, CSV, GPKG, GML") - Returns: - The rastertools subparsers updated with this subcommand - """ - parser = rastertools_parsers.add_parser( - "zonalstats", aliases=["zs"], - help="Compute zonal statistics", - description="Compute zonal statistics of a raster image.\n Available statistics are: " - "min max range mean std percentile_x (x in [0, 100]) median mad " - "count valid nodata sum majority minority unique", - epilog="By default only first band is computed.") - parser.add_argument( - "inputs", - nargs='+', - help="Raster files to process. " - "You can provide a single file with extension \".lst\" (e.g. \"zonalstats.lst\") " - "that lists the input files to process (one input file per line in .lst)") - cli.with_outputdir_arguments(parser) - parser.add_argument( - '-f', - '--format', - dest="output_format", - help="Output format of the results when input geometries are provided (by default ESRI " - f"Shapefile). Possible values are {', '.join(Zonalstats.supported_output_formats)}") - parser.add_argument( - '-g', - '--geometry', - dest="geometries", - help="List of geometries where to compute statistics (vector like a shapefile or geojson)") - parser.add_argument( - '-w', - '--within', - dest="within", - action="store_true", - help="When activated, statistics are computed for the geometries that are within " +@click.option('-g','--geometry',"geometries",type = str, help="List of geometries where to compute statistics (vector like a shapefile or geojson)") + +@click.option('-w','--within',is_flag = True, help="When activated, statistics are computed for the geometries that are within " "the raster shape. The default behaviour otherwise is to compute statistics " "for all geometries that intersect the raster shape.") - parser.add_argument( - '--stats', - dest="stats", - nargs="+", - help="List of stats to compute. Possible stats are: " - "min max range mean std percentile_x (x in [0, 100]) median mad " - "count valid nodata sum majority minority unique") - parser.add_argument( - '--categorical', - dest="categorical", - action="store_true", - help="If the input raster is categorical (i.e. raster values represent discrete classes) " + +@click.option('--stats', multiple = True,help="List of stats to compute. Possible stats are: " + "min max range mean std percentile_x (x in [0, 100]) median mad count valid nodata sum majority minority unique") + +@click.option('--categorical',is_flag = True,help="If the input raster is categorical (i.e. raster values represent discrete classes) " "compute the counts of every unique pixel values.") - parser.add_argument( - '--valid_threshold', - dest="valid_threshold", - type=float, - help="Minimum percentage of valid pixels in a shape to compute its statistics.") - parser.add_argument( - '--area', - dest='area', - action="store_true", - help="Whether to multiply all stats by the area of a cell of the input raster." - ) - parser.add_argument( - '--prefix', - dest="prefix", - help="Add a prefix to the keys (default: None). One prefix per band (e.g. 'band1 band2')") - - cli.with_bands_arguments(parser) - - # argument group for the outliers image generation - outliers_pc = parser.add_argument_group("Options to output the outliers") - outliers_pc.add_argument( - '--sigma', - dest="sigma", - help="Distance to the mean value (in sigma) in order to produce a raster " - "that highlights outliers.") - - # argument group for generating stats charts - chart_pc = parser.add_argument_group('Options to plot the generated stats') - chart_pc.add_argument( - '-c', - '--chart', - dest="chartfile", - help="Generate a chart per stat and per geometry " - "(x=timestamp of the input products / y=stat value) and " + +@click.option('--valid_threshold',"valid_threshold",type=float,help="Minimum percentage of valid pixels in a shape to compute its statistics.") + +@click.option('--area',is_flag=True,help="Whether to multiply all stats by the area of a cell of the input raster.") + +@click.option('--prefix', default = None, help="Add a prefix to the keys (default: None). One prefix per band (e.g. 'band1 band2')") + +@click.option('-b','--bands', multiple = True, type=int, help="List of bands to process") + +@click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") + +@click.option('--sigma',help="Distance to the mean value (in sigma) in order to produce a raster that highlights outliers.") + +@click.option('-c','--chart',"chartfile", help="Generate a chart per stat and per geometry (x=timestamp of the input products / y=stat value) and " "store it in the file defined by this argument") - chart_pc.add_argument( - '-d', - '--display', - dest="display", - action="store_true", - help="Display the chart") - chart_pc.add_argument( - '-gi', - '--geometry-index', - dest="geom_index", - default='ID', - help="Name of the geometry index used for the chart (default='ID')") - - # argument group to generate stats per category - group_pc = parser.add_argument_group('Options to compute stats per category in geometry. ' - 'If activated, the generated geometries will contain ' - 'stats for every categories present in the geometry') - group_pc.add_argument( - '--category_file', - help="File (raster or geometries) containing discrete classes classifying the ROI." - ) - group_pc.add_argument( - '--category_index', - help="Column name identifying categories in categroy_file " - "(only if file format is geometries)", - default="Classe" - ) - group_pc.add_argument( - '--category_names', - help="JSON files containing a dict with classes index as keys and names " - "to display classes as values.", - default="" - ) - - # set the function to call when this subcommand is called - parser.set_defaults(func=create_zonalstats) - - return rastertools_parsers - - -def create_zonalstats(args) -> Zonalstats: - """Create and configure a new rastertool "Zonalstats" according to argparse args + +@click.option('-d','--display',is_flag=True, help="Display the chart") + +@click.option('-gi','--geometry-index', "geom_index",type = str,default='ID',help="Name of the geometry index used for the chart (default='ID')") + +@click.option('--category_file',type = str, help="File (raster or geometries) containing discrete classes classifying the ROI.") + +@click.option('--category_index',type = str, default="Classe",help="Column name identifying categories in categroy_file (only if file format is geometries)") + +@click.option('--category_names',type = str, default="", help="JSON files containing a dict with classes index as keys and names to display classes as values.") + +@click.pass_context +def zonalstats(ctx, inputs : list, output : str, output_format : str, geometries : str, within : str, stats : int, categorical : bool, valid_threshold : float ,area : bool, prefix, bands : list, all_bands : bool, sigma, chartfile, display : bool, geom_index : str, category_file : str, category_index : str, category_names : str) : + """ + Compute zonal statistics + Compute zonal statistics of a raster image.\n Available statistics are: + min max range mean std percentile_x (x in [0, 100]) median mad + count valid nodata sum majority minority unique + By default only first band is computed. + + Create and configure a new rastertool "Zonalstats" according to argparse args Args: args: args extracted from command line @@ -155,28 +75,28 @@ def create_zonalstats(args) -> Zonalstats: :obj:`eolab.rastertools.Zonalstats`: The configured rastertool to run """ # get and check the list of stats to compute - if args.stats: - stats_to_compute = args.stats - elif args.categorical: + if stats: + stats_to_compute = stats + elif categorical: stats_to_compute = [] else: stats_to_compute = ['count', 'min', 'max', 'mean', 'std'] # get the bands to process - if args.all_bands: + if all_bands: bands = None else: - bands = list(map(int, args.bands)) if args.bands else [1] + bands = list(map(int, bands)) if bands else [1] # create the rastertool object - tool = Zonalstats(stats_to_compute, args.categorical, args.valid_threshold, - args.area, args.prefix, bands) + tool = Zonalstats(stats_to_compute, categorical, valid_threshold, + area, prefix, bands) # set up config with args values - tool.with_output(args.output, args.output_format) \ - .with_geometries(args.geometries, args.within) \ - .with_outliers(args.sigma) \ - .with_chart(args.chartfile, args.geom_index, args.display) \ - .with_per_category(args.category_file, args.category_index, args.category_names) + tool.with_output(output, output_format) \ + .with_geometries(geometries, within) \ + .with_outliers(sigma) \ + .with_chart(chartfile, geom_index, display) \ + .with_per_category(category_file, category_index, category_names) - return tool + apply_process(ctx, tool, inputs) diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index 2985ca30..44882b16 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -21,7 +21,9 @@ from eolab.rastertools.cli.speed import speed from eolab.rastertools.cli.svf import svf from eolab.rastertools.cli.tiling import tiling -from eolab.rastertools.cli.timeseries import timeseries #radioindice, zonalstats +from eolab.rastertools.cli.timeseries import timeseries +from eolab.rastertools.cli.radioindice import radioindice +from eolab.rastertools.cli.zonalstats import zonalstats from eolab.rastertools import __version__ from eolab.rastertools.product import RasterType @@ -211,8 +213,8 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo rastertools.add_command(filter, name = "filter") rastertools.add_command(hillshade, name = "hs") rastertools.add_command(hillshade, name = "hillshade") -#rastertools.add_command(radioindice, name = "ri") -#rastertools.add_command(radioindice, name = "radioindice") +rastertools.add_command(radioindice, name = "ri") +rastertools.add_command(radioindice, name = "radioindice") rastertools.add_command(speed, name = "sp") rastertools.add_command(speed, name = "speed") rastertools.add_command(svf, name = "svf") @@ -220,8 +222,8 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo rastertools.add_command(tiling, name = "tiling") rastertools.add_command(timeseries, name = "ts") rastertools.add_command(timeseries, name = "timeseries") -#rastertools.add_command(zonalstats, name = "zs") -#rastertools.add_command(zonalstats, name = "zonalstats") +rastertools.add_command(zonalstats, name = "zs") +rastertools.add_command(zonalstats, name = "zonalstats") @rastertools.result_callback() @click.pass_context diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 110a458c..522ae55a 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -177,7 +177,7 @@ def test_radioindice_command_line_default(): # two indices with their own options, merge "-v ri --pvi --savi -o tests/tests_out -m tests/tests_data/listing.lst", # indices option, roi - "--verbose ri --indices pvi savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" + "--verbose ri --indices pvi --indices savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" " --output tests/tests_out" " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" @@ -542,7 +542,7 @@ def test_tiling_command_line_default(): "--verbose ti -o tests/tests_out -g tests/tests_data/grid.geojson" " tests/tests_data/tif_file.tif", # specify specific ids - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 77 93 --id_col id" + "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 77 --id 93 --id_col id" " tests/tests_data/tif_file.tif" ] input_filenames = ["tests/tests_data/tif_file.tif"] @@ -578,10 +578,10 @@ def test_tiling_command_line_special_case(caplog): # list of commands to test argslist = [ # some invalid ids - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 1 2 93 --id_col id" + "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 1 --id 2 --id 93 --id_col id" " tests/tests_data/tif_file.tif", # a geometry does not overlap raster - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 78 93 --id_col id" + "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 78 --id 93 --id_col id" " tests/tests_data/tif_file.tif" ] @@ -653,7 +653,7 @@ def test_filtering_command_line_default(): "-v --max_workers 1 fi median -a --kernel_size 8 -o tests/tests_out" " tests/tests_data/RGB_TIF_20170105_013442_test.tif", # default case: local sum - "-v fi sum -b 1 2 --kernel_size 8 -o tests/tests_out" + "-v fi sum -b 1 -b 2 --kernel_size 8 -o tests/tests_out" " tests/tests_data/RGB_TIF_20170105_013442_test.tif", # default case: local mean "-v fi mean -b 1 --kernel_size 8 -o tests/tests_out" @@ -718,7 +718,8 @@ def test_svf_command_line_default(): # list of commands to test argslist = [ # default case: svf at the point height - "ftilin", + "-v svf --radius 50 --directions 16 --resolution 0.5 -o tests/tests_out" + " tests/tests_data/toulouse-mnh.tif", # default case: svf on ground "-v svf --radius 50 --directions 16 --resolution 0.5 --altitude 0 -o tests/tests_out" " tests/tests_data/toulouse-mnh.tif", From 4a49d1d0d9db0ee608419083cc26b9725d7ccbf6 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 12 Nov 2024 10:04:53 +0100 Subject: [PATCH 29/56] refactor: working tests in all directories --- src/eolab/rastertools/cli/filtering.py | 2 +- src/eolab/rastertools/cli/filtering_dyn.py | 2 +- src/eolab/rastertools/cli/hillshade.py | 6 +- src/eolab/rastertools/cli/radioindice.py | 15 +- src/eolab/rastertools/cli/svf.py | 8 +- src/eolab/rastertools/cli/tiling.py | 2 + src/eolab/rastertools/cli/timeseries.py | 16 +- src/eolab/rastertools/cli/utils_cli.py | 9 +- src/eolab/rastertools/cli/zonalstats.py | 8 +- src/eolab/rastertools/main.py | 13 + src/eolab/rastertools/rastertools.py | 8 +- src/eolab/rastertools/tiling.py | 16 +- src/eolab/rastertools/zonalstats.py | 7 +- src/rastertools.egg-info/PKG-INFO | 2 +- src/rastertools.egg-info/SOURCES.txt | 2 + tests/test_rasterproduct.py | 9 +- tests/test_rastertools.py | 406 +++++++++++---------- tests/tests_data/listing.lst | 2 - tests/tests_data/listing2.lst | 2 - tests/tests_data/listing3.lst | 1 - tests/utils4test.py | 16 +- 21 files changed, 320 insertions(+), 232 deletions(-) delete mode 100644 tests/tests_data/listing.lst delete mode 100644 tests/tests_data/listing2.lst delete mode 100644 tests/tests_data/listing3.lst diff --git a/src/eolab/rastertools/cli/filtering.py b/src/eolab/rastertools/cli/filtering.py index fd74fcac..2fb4ea6f 100644 --- a/src/eolab/rastertools/cli/filtering.py +++ b/src/eolab/rastertools/cli/filtering.py @@ -210,7 +210,7 @@ def mean(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_ @pad_opt @band_opt @all_opt -@click.option('--sigma', type = int, default = 1, help = "Standard deviation of the Gaussian distribution") +@click.option('--sigma', type = int, required = True, help = "Standard deviation of the Gaussian distribution") @click.pass_context def adaptive_gaussian(ctx, inputs : list, output : str, window_size : int, pad : str, sigma : int, kernel_size : int, bands : list, all_bands : bool) : """ diff --git a/src/eolab/rastertools/cli/filtering_dyn.py b/src/eolab/rastertools/cli/filtering_dyn.py index 9c6d34b3..3c4057eb 100644 --- a/src/eolab/rastertools/cli/filtering_dyn.py +++ b/src/eolab/rastertools/cli/filtering_dyn.py @@ -80,7 +80,7 @@ def wrapper(function): all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") -sigma = click.option('--sigma', type=int, default=1, help="Standard deviation of the Gaussian distribution") +sigma = click.option('--sigma', type=int, required = True, help="Standard deviation of the Gaussian distribution") @click.group(context_settings=CONTEXT_SETTINGS) @click.pass_context diff --git a/src/eolab/rastertools/cli/hillshade.py b/src/eolab/rastertools/cli/hillshade.py index 10c82a23..96588cc0 100644 --- a/src/eolab/rastertools/cli/hillshade.py +++ b/src/eolab/rastertools/cli/hillshade.py @@ -15,17 +15,17 @@ @click.command("hillshade",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) -@click.option('--elevation', type=float, help="Elevation of the sun in degrees, [0°, 90°] where" +@click.option('--elevation', type=float, required = True, help="Elevation of the sun in degrees, [0°, 90°] where" "90°=zenith and 0°=horizon") -@click.option('--azimuth', type=float, help="Azimuth of the sun in degrees, [0°, 360°] where" +@click.option('--azimuth', type=float, required = True, help="Azimuth of the sun in degrees, [0°, 360°] where" "0°=north, 90°=east, 180°=south and 270°=west") @click.option('--radius', type=int, help="Maximum distance (in pixels) around a point to evaluate" "horizontal elevation angle. If not set, it is automatically computed from" " the range of altitudes in the digital model.") -@click.option('--resolution',default=0.5, type=float, help="Pixel resolution in meter") +@click.option('--resolution', required = True, type=float, help="Pixel resolution in meter") @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") diff --git a/src/eolab/rastertools/cli/radioindice.py b/src/eolab/rastertools/cli/radioindice.py index f8cdf3a7..e7f038bd 100644 --- a/src/eolab/rastertools/cli/radioindice.py +++ b/src/eolab/rastertools/cli/radioindice.py @@ -3,13 +3,18 @@ """ CLI definition for the radioindice tool """ +import logging + from eolab.rastertools import RastertoolConfigurationException, Radioindice from eolab.rastertools.cli.utils_cli import apply_process from eolab.rastertools.product import BandChannel from eolab.rastertools.processing import RadioindiceProcessing +import sys import click import os +_logger = logging.getLogger(__name__) + CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) @@ -34,8 +39,7 @@ def indices_opt(function): @click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") -@click.option('-i', '--indices', type=click.Choice(['ndvi', 'tndvi', 'rvi', 'pvi', 'savi', 'tsavi', 'msavi', 'msavi2', 'ipvi', - 'evi', 'ndwi', 'ndwi2', 'mndwi', 'ndpi', 'ndti', 'ndbi', 'ri', 'bi', 'bi2']), multiple = True, +@click.option('-i', '--indices', type=str, multiple = True, help=" List of indices to computePossible indices are: bi, bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi," " ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, tsavi") @@ -74,7 +78,8 @@ def radioindice(ctx, inputs : list, output : str, indices : list, merge : bool, if ind in indices_dict: indices_to_compute.append(indices_dict[ind]) else: - raise RastertoolConfigurationException(f"Invalid indice name: {ind}") + _logger.exception(RastertoolConfigurationException(f"Invalid indice name: {ind}")) + sys.exit(2) if nd: for nd in nd: @@ -85,8 +90,8 @@ def radioindice(ctx, inputs : list, output : str, indices : list, merge : bool, [channel2, channel1]) indices_to_compute.append(new_indice) else: - raise RastertoolConfigurationException( - f"Invalid band(s) in normalized difference: {nd[0]} and/or {nd[1]}") + _logger.exception(RastertoolConfigurationException(f"Invalid band(s) in normalized difference: {nd[0]} and/or {nd[1]}")) + sys.exit(2) # handle special case: no indice setup if len(indices_to_compute) == 0: diff --git a/src/eolab/rastertools/cli/svf.py b/src/eolab/rastertools/cli/svf.py index 58a15afc..6290d4a5 100644 --- a/src/eolab/rastertools/cli/svf.py +++ b/src/eolab/rastertools/cli/svf.py @@ -11,15 +11,15 @@ CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -#Speed command +#SVF command @click.command("svf",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) -@click.option('--radius',required = True, type=int, default = 16, help="Maximum distance (in pixels) around a point to evaluate horizontal elevation angle") +@click.option('--radius', required = True, type=int, help="Maximum distance (in pixels) around a point to evaluate horizontal elevation angle") -@click.option('--directions',required = True, type=int, default = 12, help="Number of directions on which to compute the horizon elevation angle") +@click.option('--directions',required = True, type=int, help="Number of directions on which to compute the horizon elevation angle") -@click.option('--resolution',default=0.5, type=float, help="Pixel resolution in meter") +@click.option('--resolution', required = True, type=float, help="Pixel resolution in meter") @click.option('--altitude', type=int, help="Reference altitude to use for computing the SVF. If this option is not" " specified, SVF is computed for every point at the altitude of the point") diff --git a/src/eolab/rastertools/cli/tiling.py b/src/eolab/rastertools/cli/tiling.py index 1b67e634..fa6b25a9 100644 --- a/src/eolab/rastertools/cli/tiling.py +++ b/src/eolab/rastertools/cli/tiling.py @@ -56,6 +56,8 @@ def tiling(ctx, inputs : list, grid_file : str, id_column : str, id : list, outp """ if id == () : id = None + else: + id = list(id) # create the rastertool object tool = Tiling(grid_file) diff --git a/src/eolab/rastertools/cli/timeseries.py b/src/eolab/rastertools/cli/timeseries.py index 733be56b..78465eec 100644 --- a/src/eolab/rastertools/cli/timeseries.py +++ b/src/eolab/rastertools/cli/timeseries.py @@ -9,12 +9,14 @@ from eolab.rastertools.cli.utils_cli import apply_process from eolab.rastertools import RastertoolConfigurationException import click +import sys import os +import logging +_logger = logging.getLogger(__name__) CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) - -#Speed command +#Timeseries command @click.command("timeseries",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) @@ -69,15 +71,17 @@ def timeseries(ctx, inputs : list, bands : list, all_bands : bool, output : str, try: start_date = datetime.strptime(start_date, "%Y-%m-%d") except Exception: - raise RastertoolConfigurationException( - f"Invalid format for start date: {start_date} (must be %Y-%m-%d)") + _logger.exception(RastertoolConfigurationException( + f"Invalid format for start date: {start_date} (must be %Y-%m-%d)")) + sys.exit(2) # convert start/end dates to datetime try: end_date = datetime.strptime(end_date, "%Y-%m-%d") except Exception: - raise RastertoolConfigurationException( - f"Invalid format for end date: {end_date} (must be %Y-%m-%d)") + _logger.exception(RastertoolConfigurationException( + f"Invalid format for end date: {end_date} (must be %Y-%m-%d)")) + sys.exit(2) # create the rastertool object tool = Timeseries(start_date, end_date, time_period, bands) diff --git a/src/eolab/rastertools/cli/utils_cli.py b/src/eolab/rastertools/cli/utils_cli.py index f1d508dd..cc72dc5b 100644 --- a/src/eolab/rastertools/cli/utils_cli.py +++ b/src/eolab/rastertools/cli/utils_cli.py @@ -4,7 +4,7 @@ import click #TO DO -_logger = logging.getLogger("main") +_logger = logging.getLogger(__name__) def _extract_files_from_list(cmd_inputs): """ @@ -64,11 +64,14 @@ def apply_process(ctx, tool, inputs : list): Exception: Any other errors that occur during processing. """ try: + print('@' * 50) # handle the input file of type "lst" inputs_extracted = _extract_files_from_list(inputs) + print('@' * 50) # setup debug mode in which intermediate VRT files are stored to disk or not tool.with_vrt_stored(ctx.obj.get('keep_vrt')) + print('@' * 50) # launch process tool.process_files(inputs_extracted) @@ -76,11 +79,13 @@ def apply_process(ctx, tool, inputs : list): _logger.info("Done!") except RastertoolConfigurationException as rce: + print('@'*50) _logger.exception(rce) sys.exit(2) except Exception as err: + print('!' * 50) _logger.exception(err) sys.exit(1) - + print('?' * 50) sys.exit(0) \ No newline at end of file diff --git a/src/eolab/rastertools/cli/zonalstats.py b/src/eolab/rastertools/cli/zonalstats.py index 889ceb02..d1d30d92 100644 --- a/src/eolab/rastertools/cli/zonalstats.py +++ b/src/eolab/rastertools/cli/zonalstats.py @@ -12,7 +12,7 @@ #Zonalstats command -@click.command("radioindice",context_settings=CONTEXT_SETTINGS) +@click.command("zonalstats",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") @@ -58,7 +58,7 @@ @click.option('--category_names',type = str, default="", help="JSON files containing a dict with classes index as keys and names to display classes as values.") @click.pass_context -def zonalstats(ctx, inputs : list, output : str, output_format : str, geometries : str, within : str, stats : int, categorical : bool, valid_threshold : float ,area : bool, prefix, bands : list, all_bands : bool, sigma, chartfile, display : bool, geom_index : str, category_file : str, category_index : str, category_names : str) : +def zonalstats(ctx, inputs : list, output : str, output_format : str, geometries : str, within : str, stats : list, categorical : bool, valid_threshold : float ,area : bool, prefix, bands : list, all_bands : bool, sigma, chartfile, display : bool, geom_index : str, category_file : str, category_index : str, category_names : str) : """ Compute zonal statistics Compute zonal statistics of a raster image.\n Available statistics are: @@ -74,9 +74,11 @@ def zonalstats(ctx, inputs : list, output : str, output_format : str, geometries Returns: :obj:`eolab.rastertools.Zonalstats`: The configured rastertool to run """ + print(output) + print(output_format) # get and check the list of stats to compute if stats: - stats_to_compute = stats + stats_to_compute = list(stats) elif categorical: stats_to_compute = [] else: diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index 44882b16..5071e20f 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -225,6 +225,19 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo rastertools.add_command(zonalstats, name = "zs") rastertools.add_command(zonalstats, name = "zonalstats") +CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) + + +#Speed command +@click.command("ema",context_settings=CONTEXT_SETTINGS) +@click.option('--inputs', type=int) +@click.pass_context +def ema(ctx, inputs) : + raise Exception(f"coucou {inputs}") + +rastertools.add_command(ema, name = "ema") + + @rastertools.result_callback() @click.pass_context def handle_result(ctx): diff --git a/src/eolab/rastertools/rastertools.py b/src/eolab/rastertools/rastertools.py index 87500e96..479bab74 100644 --- a/src/eolab/rastertools/rastertools.py +++ b/src/eolab/rastertools/rastertools.py @@ -13,9 +13,12 @@ """ from abc import ABC from typing import List +import logging +import sys from eolab.rastertools import utils +_logger = logging.getLogger(__name__) class RastertoolConfigurationException(Exception): """This class defines an exception that is raised when the configuration of the raster tool @@ -66,8 +69,9 @@ def with_output(self, outputdir: str = "."): possible to chain the with... calls (fluent API) """ if outputdir and not utils.is_dir(outputdir): - raise RastertoolConfigurationException( - f"Output directory \"{str(outputdir)}\" does not exist.") + _logger.exception( + RastertoolConfigurationException(f"Output directory \"{str(outputdir)}\" does not exist.")) + sys.exit(2) self._outputdir = outputdir return self diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 4461a16f..57bf1e3d 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -11,6 +11,7 @@ import rasterio import rasterio.mask import geopandas as gpd +import sys from eolab.rastertools import utils from eolab.rastertools import Rastertool, RastertoolConfigurationException @@ -119,21 +120,24 @@ def with_id_column(self, id_column: str, ids: List[int]): # Test if id_column is defined when ids are set if id_column is not None: if id_column not in self._grid.columns: - raise RastertoolConfigurationException( - f"Invalid id column named \"{id_column}\": it does not exist in the grid") + _logger.exception(RastertoolConfigurationException( + f"Invalid id column named \"{id_column}\": it does not exist in the grid")) + sys.exit(2) self._grid = self._grid.set_index(id_column) if ids is not None: if id_column is None: - raise RastertoolConfigurationException( - "Ids cannot be specified when id_col is not defined") + _logger.exception(RastertoolConfigurationException( + "Ids cannot be specified when id_col is not defined")) + sys.exit(2) self._grid = self._grid[self._grid.index.isin(ids)] if self._grid.empty: # if no id common between grid and given ids - raise RastertoolConfigurationException( + _logger.exception(RastertoolConfigurationException( f"No value in the grid column \"{id_column}\" are matching " - f"the given list of ids {str(ids)}") + f"the given list of ids {str(ids)}")) + sys.exit(2) else: invalid_ids = [i for i in ids if i not in self._grid.index] if len(invalid_ids) > 0: diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 43a0ceec..270c9912 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -23,6 +23,7 @@ import json import numpy as np import geopandas as gpd +import sys import rasterio @@ -276,9 +277,10 @@ def with_output(self, outputdir: str = ".", output_format: str = "ESRI Shapefile self._output_format = output_format or 'ESRI Shapefile' # check if output_format exists if self._output_format not in Zonalstats.supported_output_formats: - raise RastertoolConfigurationException( + _logger.exception(RastertoolConfigurationException( f"Unrecognized output format {output_format}. " - f"Possible values are {', '.join(Zonalstats.supported_output_formats)}") + f"Possible values are {', '.join(Zonalstats.supported_output_formats)}")) + sys.exit(2) return self def with_geometries(self, geometries: str, within: bool = False): @@ -313,6 +315,7 @@ def with_outliers(self, sigma: float): :obj:`eolab.rastertools.Zonalstats`: the current instance so that it is possible to chain the with... calls (fluent API) """ + print(self._stats) # Manage sigma computation option that requires mean + std dev computation if "mean" not in self._stats: self._stats.append("mean") diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 7b331a71..4cc14207 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: rastertools -Version: 0.6.1.post1.dev0+gbedb844.d20241022 +Version: 0.6.1.post1.dev19+gebce3d0.d20241113 Summary: Compute radiometric indices and zonal statistics on rasters Home-page: https://github.com/cnes/rastertools Author: Olivier Queyrut diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 1ac1b3c4..d2e4162b 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -81,12 +81,14 @@ src/eolab/rastertools/__pycache__/utils.cpython-38.pyc src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/cli/__init__.py src/eolab/rastertools/cli/filtering.py +src/eolab/rastertools/cli/filtering_dyn.py src/eolab/rastertools/cli/hillshade.py src/eolab/rastertools/cli/radioindice.py src/eolab/rastertools/cli/speed.py src/eolab/rastertools/cli/svf.py src/eolab/rastertools/cli/tiling.py src/eolab/rastertools/cli/timeseries.py +src/eolab/rastertools/cli/utils_cli.py src/eolab/rastertools/cli/zonalstats.py src/eolab/rastertools/cli/__pycache__/__init__.cpython-38.pyc src/eolab/rastertools/cli/__pycache__/filtering.cpython-38.pyc diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index 3f1657ef..b471ef0e 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -2,6 +2,7 @@ # -*- coding: utf-8 -*- import pytest +import os import filecmp import zipfile from pathlib import Path @@ -41,13 +42,13 @@ def test_rasterproduct_valid_parameters(): # archive with one file per band basename = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041" file = Path( - utils4test.indir + basename + ".zip") + utils4test.indir.split(os.getcwd() + "/")[-1] + basename + ".zip") prod = RasterProduct(file) assert prod.file == file assert prod.rastertype == RasterType.get("S2_L1C") assert prod.channels == RasterType.get("S2_L1C").channels - band_format = f"/vsizip/tests/tests_data/{basename}.zip/" + band_format = f"/vsizip/" + utils4test.indir.split(os.getcwd() + "/")[-1] + f"{basename}.zip/" band_format += f"{basename}.SAFE/GRANULE/L1C_T30TYP_A013519_20191008T105335/IMG_DATA/" band_format += "T30TYP_20191008T105029_{}.jp2" assert prod.bands_files == {b: band_format.format(b) for b in prod.rastertype.get_band_ids()} @@ -61,13 +62,13 @@ def test_rasterproduct_valid_parameters(): # archive with one file for all bands basename = "SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82" - file = utils4test.indir + basename + ".tar.gz" + file = utils4test.indir.split(os.getcwd() + "/")[-1] + basename + ".tar.gz" prod = RasterProduct(file) assert prod.file == Path(file) assert prod.rastertype == RasterType.get("SPOT67_GEOSUD") assert prod.channels == [BandChannel.red, BandChannel.green, BandChannel.blue, BandChannel.nir] - band = f"/vsitar/tests/tests_data/{basename}.tar.gz/SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82/" + band = f"/vsitar/" + utils4test.indir.split(os.getcwd() + "/")[-1] + f"{basename}.tar.gz/SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82/" band += "PROD_SPOT6_001/VOL_SPOT6_001_A/IMG_SPOT6_MS_001_A/" band += "IMG_SPOT6_MS_201805111031189_ORT_SPOT6_20180517_1333011n1b80qobn5ex_1_R1C1.TIF" assert prod.bands_files == {"all": band} diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 522ae55a..266a0ec1 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -4,11 +4,10 @@ import pytest import logging import filecmp +from click.testing import CliRunner from pathlib import Path -from click import argument - -from eolab.rastertools import rastertools +from eolab.rastertools import rastertools, RastertoolConfigurationException from eolab.rastertools.product import RasterType from . import utils4test @@ -17,6 +16,8 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData + class TestCase: __test__ = False @@ -90,19 +91,23 @@ def with_refdir(self, refdir): def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, check_sys_exit=True, check_logs=True, compare=False, save_gen_as_ref=False): + + runner = CliRunner() if caplog is not None: caplog.set_level(loglevel) else: check_logs = False - # run rastertools - with pytest.raises(SystemExit) as wrapped_exception: + print(self.args) + + try: rastertools(self.args) - # check sys_exit - if check_sys_exit: - assert wrapped_exception.type == SystemExit - assert wrapped_exception.value.code == self._sys_exit + except SystemExit as wrapped_exception: + print(wrapped_exception) + if check_sys_exit: + # Check if the exit code matches the expected value + assert wrapped_exception.code == self._sys_exit, (f"Expected exit code {self._sys_exit}, but got {wrapped_exception.code}") # check list of outputs if check_outputs: @@ -120,6 +125,8 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec # check logs if check_logs: + print('...'*20) + print(caplog.record_tuples) for i, log in enumerate(self._logs): assert caplog.record_tuples[i] == log @@ -127,60 +134,64 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec if caplog is not None: caplog.clear() - # clear output dir + #clear output dir utils4test.clear_outdir() def test_rastertools_command_line_info(): + tests = [ TestCase("--help"), TestCase("-h"), TestCase("--version"), TestCase(""), + TestCase("radioindice --help"), + TestCase("ri -h"), + TestCase("zonalstats --help"), + TestCase("zs -h"), + TestCase("tiling --help"), + TestCase("ti -h"), TestCase("filter --help"), - TestCase("fi -h") - ] - # tests = [ - # TestCase("--help"), - # TestCase("-h"), - # TestCase("--version"), - # TestCase(""), - # TestCase("radioindice --help"), - # TestCase("ri -h"), - # TestCase("zonalstats --help"), - # TestCase("zs -h"), - # TestCase("tiling --help"), - # TestCase("ti -h"), - # TestCase("filter --help"), - # TestCase("fi -h"), - # TestCase("timeseries --help"), - # TestCase("ts -h"), - # TestCase("speed --help"), - # TestCase("sp -h"), - # TestCase("svf --help"), - # TestCase("svf -h"), - # TestCase("hillshade --help"), - # TestCase("hs -h") - # ] + TestCase("fi -h"), + TestCase("timeseries --help"), + TestCase("ts -h"), + TestCase("speed --help"), + TestCase("sp -h"), + TestCase("svf --help"), + TestCase("svf -h"), + TestCase("hillshade --help"), + TestCase("hs -h") + ] for test in tests: test.run_test() +def generate_lst_file(file_list, output_file): + + with open(output_file,"w") as lst_file: + for f in file_list: + lst_file.write(RastertoolsTestsData.tests_input_data_dir + "/" + f.split('/')[-1] + "\n") + def test_radioindice_command_line_default(): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing.lst" + generate_lst_file(["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"], + lst_file_path) + # list of commands to test argslist = [ # no indice defined - " -v ri -o tests/tests_out tests/tests_data/listing.lst", + f" -v ri -o {RastertoolsTestsData.tests_output_data_dir} {lst_file_path}", # two indices with their own options, merge - "-v ri --pvi --savi -o tests/tests_out -m tests/tests_data/listing.lst", + f"-v ri --pvi --savi -o {RastertoolsTestsData.tests_output_data_dir} -m {lst_file_path}", # indices option, roi - "--verbose ri --indices pvi --indices savi -nd nir red --roi tests/tests_data/COMMUNE_32001.shp" - " --output tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + f"--verbose ri --indices pvi --indices savi -nd nir red --roi {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32001.shp" + f" --output {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" ] # get list of expected outputs indices_list = ["ndvi ndwi ndwi2", "indices", "pvi savi nd[nir-red]"] @@ -203,8 +214,8 @@ def test_radioindice_additional_type(): # list of commands to test argslist = [ # add rastertypes to handle new data product - "-t tests/tests_data/additional_rastertypes.json -v" - " ri -o tests/tests_out --ndvi tests/tests_data/RGB_TIF_20170105_013442_test.tif" + f"-t {RastertoolsTestsData.tests_input_data_dir}/additional_rastertypes.json -v" + f" ri -o {RastertoolsTestsData.tests_output_data_dir} --ndvi {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif" ] # get list of expected outputs indices_list = "ndvi" @@ -228,28 +239,28 @@ def test_radioindice_command_line_errors(caplog): # missing positional argument "ri --ndvi", # unkwnow indice - "ri tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip --indices strange", + f"ri {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip --indices strange", # unknown raster type: unrecognized raster type - "-v ri --ndvi -o tests/tests_out tests/tests_data/OCS_2017_CESBIO_extract.tif", + f"-v ri --ndvi -o {RastertoolsTestsData.tests_output_data_dir} {RastertoolsTestsData.tests_input_data_dir}/OCS_2017_CESBIO_extract.tif", # unknown raster type: unsupported extension - "-v ri --ndvi -o tests/tests_out tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.aaa", + f"-v ri --ndvi -o {RastertoolsTestsData.tests_output_data_dir} {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.aaa", # output dir does not exist - "-v ri -o ./toto --ndvi tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", + f"-v ri -o ./toto --ndvi {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", # unknown band in normalized difference - "-v ri -nd unknown red tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + f"-v ri -nd unknown red {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" ] # expected logs logslist = [ [], - [("eolab.rastertools.main", logging.ERROR, "Invalid indice name: strange")], - [("eolab.rastertools.main", logging.ERROR, - "Unsupported input file, no matching raster type identified to handle the file")], - [("eolab.rastertools.main", logging.ERROR, - "Unsupported input file tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.aaa")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.radioindice", logging.ERROR, "Invalid indice name: strange")], + [("eolab.rastertools.cli.utils_cli", logging.ERROR, + "Unsupported input file, no matching raster type identified to handle the file")], + [("eolab.rastertools.cli.utils_cli", logging.ERROR, + f"Unsupported input file {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.aaa")], + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"./toto\" does not exist.")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.radioindice", logging.ERROR, "Invalid band(s) in normalized difference: unknown and/or red")] ] sysexitlist = [2, 2, 1, 1, 2, 2] @@ -267,14 +278,19 @@ def test_speed_command_line_default(): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing.lst" + generate_lst_file(["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"], + lst_file_path) + # list of commands to test argslist = [ # default with a listing of S2A products - "-v sp -b 1 -o tests/tests_out tests/tests_data/listing.lst", + f"-v sp -b 1 -o {RastertoolsTestsData.tests_output_data_dir} {lst_file_path}", # default with a list of files - "--verbose speed --output tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + f"--verbose speed --output {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" ] speed_filenames = [ ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-speed-20180928-105515.tif"], @@ -294,24 +310,29 @@ def test_speed_command_line_errors(caplog): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing2.lst" + generate_lst_file(["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"], + lst_file_path) + # list of commands to test argslist = [ # output dir does not exist - "-v sp -o ./toto tests/tests_data/listing2.lst", + f"-v sp -o ./toto {lst_file_path}", # missing one file for speed - "-v sp -o tests/tests_out tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", + f"-v sp -o {RastertoolsTestsData.tests_output_data_dir} {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", # different types for input files - "-v sp -a -o tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" - " tests/tests_data/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + f"-v sp -a -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + f" {RastertoolsTestsData.tests_input_data_dir}/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" ] # expected logs logslist = [ - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"./toto\" does not exist.")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "Can not compute speed with 1 input image. Provide at least 2 images.")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "Speed can only be computed with images of the same type")] ] sysexitlist = [2, 1, 1] @@ -332,9 +353,9 @@ def test_timeseries_command_line_default(compare, save_gen_as_ref): # list of commands to test argslist = [ # default with a list of files - "--verbose ts --output tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + f"--verbose ts --output {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" " -s 2018-09-26 -e 2018-11-07 -p 20 -ws 512" ] timeseries_filenames = [ @@ -358,45 +379,49 @@ def test_timeseries_command_line_errors(caplog): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing2.lst" + generate_lst_file(["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"], + lst_file_path) + period = " -s 2018-09-26 -e 2018-11-07 -p 20" # list of commands to test argslist = [ # output dir does not exist - "-v ts -o ./toto tests/tests_data/listing2.lst" + period, + f"-v ts -o ./toto {lst_file_path}" + period, # missing one file for timeseries - "-v ts -o tests/tests_out tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + period, + f"-v ts -o {RastertoolsTestsData.tests_output_data_dir} {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + period, # unknown raster type - "-v ts -o tests/tests_out -a" - " tests/tests_data/DSM_PHR_Dunkerque.tif" - " tests/tests_data/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + period, + f"-v ts -o {RastertoolsTestsData.tests_output_data_dir} -a" + f" {RastertoolsTestsData.tests_input_data_dir}/DSM_PHR_Dunkerque.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + period, # different types for input files - "-v ts -o tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" - " tests/tests_data/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + period, + f"-v ts -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + f" {RastertoolsTestsData.tests_input_data_dir}/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + period, # invalid date format - "-v ts --o tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + f"-v ts -o {RastertoolsTestsData.tests_output_data_dir} {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" " -s 20180926 -e 2018-11-07 -p 20", # invalid date format - "-v ts --o tests/tests_out" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + f"-v ts -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" " -s 2018-09-26 -e 20181107 -p 20" ] # expected logs logslist = [ - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"./toto\" does not exist.")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "Can not compute a timeseries with 1 input image. Provide at least 2 images.")], - [("eolab.rastertools.main", logging.ERROR, - "Unknown rastertype for input file tests/tests_data/DSM_PHR_Dunkerque.tif")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, + f"Unknown rastertype for input file {RastertoolsTestsData.tests_input_data_dir}/DSM_PHR_Dunkerque.tif")], + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "Timeseries can only be computed with images of the same type")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.timeseries", logging.ERROR, "Invalid format for start date: 20180926 (must be %Y-%m-%d)")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.timeseries", logging.ERROR, "Invalid format for end date: 20181107 (must be %Y-%m-%d)")] ] sysexitlist = [2, 1, 1, 1, 2, 2] @@ -414,17 +439,22 @@ def test_zonalstats_command_line_default(): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing2.lst" + generate_lst_file(["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"], + lst_file_path) + # list of commands to test argslist = [ # specify stats to compute and sigma, 1st band computed - "-v zs -o tests/tests_out -f GeoJSON" - " -g tests/tests_data/COMMUNE_32xxx.geojson --stats min max --sigma 1.0" - " tests/tests_data/listing2.lst", + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON" + f" -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson --stats min --stats max --sigma 1.0" + f" {lst_file_path}", # default stats, all bands computed - "-v zs -o tests/tests_out -f GeoJSON" - " --all -g tests/tests_data/COMMUNE_32xxx.geojson" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - " tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON" + f" --all -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" ] files = ["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi", "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi"] @@ -445,9 +475,9 @@ def test_zonalstats_command_line_product(): # list of commands to test argslist = [ # input file is a S2A product - "-v zs -o tests/tests_out -f GeoJSON" - " --all -g tests/tests_data/COMMUNE_32xxx.geojson" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON" + f" --all -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" ] files = ["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D"] @@ -465,8 +495,8 @@ def test_zonalstats_command_line_categorical(): # list of commands to test argslist = [ # input file is a S2A product - "-v zs -o tests/tests_out -f GeoJSON --categorical" - " tests/tests_data/OCS_2017_CESBIO_extract.tif" + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON --categorical" + f" {RastertoolsTestsData.tests_input_data_dir}/OCS_2017_CESBIO_extract.tif" ] files = ["OCS_2017_CESBIO_extract"] @@ -482,27 +512,32 @@ def test_zonalstats_command_line_errors(): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing2.lst" + generate_lst_file(["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"], + lst_file_path) + # list of commands to test argslist = [ # output dir does not exist - "-v zs tests/tests_data/listing2.lst -o ./toto -f GeoJSON" - " -g tests/tests_data/COMMUNE_32xxx.geojson --stats mean" + f"-v zs {lst_file_path} -o ./toto -f GeoJSON" + f" -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson --stats mean" " -b 0", # band 0 does not exist - "-v zs tests/tests_data/listing2.lst -o tests/tests_out -f GeoJSON" - " -g tests/tests_data/COMMUNE_32xxx.geojson --stats mean" + f"-v zs {lst_file_path} -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON" + f" -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson --stats mean" " -b 0", # invalid format - "-v zs -o tests/tests_out -f Truc tests/tests_data/listing2.lst", + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f Truc {lst_file_path}", # invalid geometry index name - "-v zs -o tests/tests_out -f GeoJSON --stats mean" - " -g tests/tests_data/COMMUNE_32xxx.geojson -gi truc" - " -c tests/tests_out/chart.png" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON --stats mean" + f" -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson -gi truc" + f" -c {RastertoolsTestsData.tests_output_data_dir}/chart.png" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", # invalid prefix length - "-v zs -o tests/tests_out -f GeoJSON --prefix band1" - " --all -g tests/tests_data/COMMUNE_32xxx.geojson" - " tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" + f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON --prefix band1" + f" --all -g {RastertoolsTestsData.tests_input_data_dir}/COMMUNE_32xxx.geojson" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip" ] logslist = [ @@ -534,18 +569,22 @@ def test_tiling_command_line_default(): # create output dir and clear its content if any utils4test.create_outdir() + lst_file_path = f"{RastertoolsTestsData.tests_input_data_dir}/listing3.lst" + generate_lst_file(["tif_file.tif"], + lst_file_path) + # list of commands to test argslist = [ # default case with listing of input files - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson tests/tests_data/listing3.lst", + f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson {lst_file_path}", # default case with input file - "--verbose ti -o tests/tests_out -g tests/tests_data/grid.geojson" - " tests/tests_data/tif_file.tif", + f"--verbose ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif", # specify specific ids - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 77 --id 93 --id_col id" - " tests/tests_data/tif_file.tif" + f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson --id 77 --id 93 --id_col id" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif" ] - input_filenames = ["tests/tests_data/tif_file.tif"] + input_filenames = [f"{RastertoolsTestsData.tests_input_data_dir}/tif_file.tif"] # generate test cases tests = [TestCase(args).ti_output(input_filenames, [77, 93]) for args in argslist] @@ -556,17 +595,17 @@ def test_tiling_command_line_default(): # Additional tests to check naming and subdir options # specify naming convention - args = ("-v ti -o tests/tests_out -g tests/tests_data/grid.geojson -n tile{}" - " --id_col id tests/tests_data/tif_file.tif") + args = (f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson -n tile{{}}" + f" --id_col id {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif") test = TestCase(args).ti_output(input_filenames, [77, 93], name="tile{}") test.run_test(check_outputs=False) # specify subdir naming convetion - args = ("-v ti -o tests/tests_out -g tests/tests_data/grid.geojson -d tile{}" - " --id_col id tests/tests_data/tif_file.tif") + args = (f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson -d tile{{}}" + f" --id_col id {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif") test = TestCase(args).ti_output(input_filenames, [77, 93], subdir="tile{}") # create one subdir to check if it is not re-created - subdir = Path("tests/tests_out/tile77") + subdir = Path(f"{RastertoolsTestsData.tests_output_data_dir}/tile77") subdir.mkdir() test.run_test(check_outputs=False) @@ -578,11 +617,11 @@ def test_tiling_command_line_special_case(caplog): # list of commands to test argslist = [ # some invalid ids - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 1 --id 2 --id 93 --id_col id" - " tests/tests_data/tif_file.tif", + f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson --id 1 --id 2 --id 93 --id_col id" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif", # a geometry does not overlap raster - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 78 --id 93 --id_col id" - " tests/tests_data/tif_file.tif" + f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson --id 78 --id 93 --id_col id" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif" ] # expected logs @@ -608,28 +647,27 @@ def test_tiling_command_line_errors(caplog): # list of commands to test argslist = [ # ids without id_col - "-v ti --id 77 93 -o tests/tests_out -g tests/tests_data/grid.geojson" - " tests/tests_data/tif_file.tif", + f"-v ti --id 77 93 -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif", # invalid id column - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 77 93 --id_col truc" - " tests/tests_data/tif_file.tif", + f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson --id 77 --id 93 --id_col truc" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif", # output dir does not exist - "-v ti -o tests/truc -g tests/tests_data/grid.geojson" - " tests/tests_data/tif_file.tif", + f"-v ti -o tests/truc -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif", # all invalid ids - "-v ti -o tests/tests_out -g tests/tests_data/grid.geojson --id 1 2 --id_col id" - " tests/tests_data/tif_file.tif" + f"-v ti -o {RastertoolsTestsData.tests_output_data_dir} -g {RastertoolsTestsData.tests_input_data_dir}/grid.geojson --id 1 --id 2 --id_col id" + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif" ] # expected logs logslist = [ - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.tiling", logging.ERROR, "Ids cannot be specified when id_col is not defined")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.tiling", logging.ERROR, "Invalid id column named \"truc\": it does not exist in the grid")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"tests/truc\" does not exist.")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.tiling", logging.ERROR, "No value in the grid column \"id\" are matching the given list of ids [1, 2]")] ] sysexitlist = [2, 2, 2, 2] @@ -650,17 +688,17 @@ def test_filtering_command_line_default(): # list of commands to test argslist = [ # default case: median - "-v --max_workers 1 fi median -a --kernel_size 8 -o tests/tests_out" - " tests/tests_data/RGB_TIF_20170105_013442_test.tif", + f"-v --max_workers 1 fi median -a --kernel_size 8 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", # default case: local sum - "-v fi sum -b 1 -b 2 --kernel_size 8 -o tests/tests_out" - " tests/tests_data/RGB_TIF_20170105_013442_test.tif", + f"-v fi sum -b 1 -b 2 --kernel_size 8 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", # default case: local mean - "-v fi mean -b 1 --kernel_size 8 -o tests/tests_out" - " tests/tests_data/RGB_TIF_20170105_013442_test.tif", + f"-v fi mean -b 1 --kernel_size 8 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", # default case: adaptive gaussian - "-v fi adaptive_gaussian -b 1 --kernel_size 32 --sigma 1 -o tests/tests_out" - " tests/tests_data/RGB_TIF_20170105_013442_test.tif", + f"-v fi adaptive_gaussian -b 1 --kernel_size 32 --sigma 1 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", ] input_filenames = ["RGB_TIF_20170105_013442_test-{}.tif"] names = ["median", "sum", "mean", "adaptive_gaussian"] @@ -682,21 +720,21 @@ def test_filtering_command_line_errors(caplog): argslist = [ # output dir does not exist "-v filter median --kernel_size 8 -o tests/truc" - " tests/tests_data/tif_file.tif", + f" {RastertoolsTestsData.tests_input_data_dir}/tif_file.tif", # missing required argument - "-v filter adaptive_gaussian --kernel_size 32 -o tests/tests_out" - " tests/tests_data/RGB_TIF_20170105_013442_test.tif", - # kernel_size > window_size - "-v filter median -a --kernel_size 15 --window_size 16 -o tests/tests_out" - " tests/tests_data/RGB_TIF_20170105_013442_test.tif", + f"-v filter adaptive_gaussian --kernel_size 32 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", + # # kernel_size > window_size + f"-v filter median -a --kernel_size 15 --window_size 16 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", ] # expected logs logslist = [ - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"tests/truc\" does not exist.")], [], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "The kernel size (option --kernel_size, value=15) must be strictly less than the " "window size minus 1 (option --window_size, value=16)")] ] @@ -718,11 +756,11 @@ def test_svf_command_line_default(): # list of commands to test argslist = [ # default case: svf at the point height - "-v svf --radius 50 --directions 16 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v svf --radius 50 --directions 16 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # default case: svf on ground - "-v svf --radius 50 --directions 16 --resolution 0.5 --altitude 0 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v svf --radius 50 --directions 16 --resolution 0.5 --altitude 0 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", ] output_filenames = ["toulouse-mnh-svf.tif"] @@ -743,21 +781,21 @@ def test_svf_command_line_errors(caplog): argslist = [ # output dir does not exist "-v svf --radius 50 --directions 16 --resolution 0.5 -o tests/truc" - " tests/tests_data/toulouse-mnh.tif", + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # missing required argument - "-v svf --directions 16 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v svf --directions 16 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # radius > window_size / 2 "-v svf --radius 100 --window_size 128 --directions 16 --resolution 0.5" - " --altitude 0 -o tests/tests_out tests/tests_data/toulouse-mnh.tif", + f" --altitude 0 -o {RastertoolsTestsData.tests_output_data_dir} {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", ] # expected logs logslist = [ - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"tests/truc\" does not exist.")], [], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "The radius (option --radius, value=100) must be strictly less than half the" " size of the window (option --window_size, value=128)")] ] @@ -780,17 +818,17 @@ def test_hillshade_command_line_default(): # elevation / azimuth are retrieved from https://www.sunearthtools.com/dp/tools/pos_sun.php argslist = [ # default case: hillshade at Toulouse the September, 21 solar noon - "-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # default case: hillshade at Toulouse the June, 21, solar 6PM - "-v hs --elevation 25.82 --azimuth 278.58 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v hs --elevation 25.82 --azimuth 278.58 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # default case: hillshade at Toulouse the June, 21, solar noon - "-v hs --elevation 69.83 --azimuth 180 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v hs --elevation 69.83 --azimuth 180 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # default case: hillshade at Toulouse the June, 21, solar 8AM - "-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f"-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", ] output_filenames = ["toulouse-mnh-hillshade.tif"] @@ -811,27 +849,27 @@ def test_hillshade_command_line_errors(caplog): argslist = [ # output dir does not exist "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/truc" - " tests/tests_data/toulouse-mnh.tif", + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # missing required argument "-v hs --elevation 46.81 --resolution 0.5 " - " tests/tests_data/toulouse-mnh.tif", + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", # input file has more than 1 band - "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/tests_out" - " tests/tests_data/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.vrt", + f"-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.vrt", # radius > window_size / 2 "-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5" - " --radius 100 --window_size 128 -o tests/tests_out" - " tests/tests_data/toulouse-mnh.tif", + f" --radius 100 --window_size 128 -o {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", ] # expected logs logslist = [ - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"tests/truc\" does not exist.")], [], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "Invalid input file, it must contain a single band.")], - [("eolab.rastertools.main", logging.ERROR, + [("eolab.rastertools.cli.utils_cli", logging.ERROR, "The radius (option --radius, value=100) must be strictly less than half" " the size of the window (option --window_size, value=128)")] ] diff --git a/tests/tests_data/listing.lst b/tests/tests_data/listing.lst deleted file mode 100644 index 9e0cb52a..00000000 --- a/tests/tests_data/listing.lst +++ /dev/null @@ -1,2 +0,0 @@ -tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip -tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip \ No newline at end of file diff --git a/tests/tests_data/listing2.lst b/tests/tests_data/listing2.lst deleted file mode 100644 index 06bd2841..00000000 --- a/tests/tests_data/listing2.lst +++ /dev/null @@ -1,2 +0,0 @@ -tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif -tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif \ No newline at end of file diff --git a/tests/tests_data/listing3.lst b/tests/tests_data/listing3.lst deleted file mode 100644 index ad3fcea2..00000000 --- a/tests/tests_data/listing3.lst +++ /dev/null @@ -1 +0,0 @@ -tests/tests_data/tif_file.tif \ No newline at end of file diff --git a/tests/utils4test.py b/tests/utils4test.py index ccc59eb6..a946c661 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -2,6 +2,7 @@ # -*- coding: utf-8 -*- import os import shutil +from dataclasses import dataclass from pathlib import Path from . import cmptools @@ -11,10 +12,19 @@ __license = "Apache v2.0" -indir = "tests/tests_data/" -outdir = "tests/tests_out/" -__root_refdir = "tests/tests_refs/" +@dataclass +class RastertoolsTestsData: + project_dir: Path = Path(__file__).parent.parent + tests_project_dir:str = str(project_dir) + tests_input_data_dir:str = str(project_dir / "tests" / "tests_data" ) + tests_output_data_dir:str = str(project_dir / "tests" / "tests_out") + tests_ref_data_dir:str = str(project_dir / "tests" / "tests_refs") + +projectdir = RastertoolsTestsData.tests_project_dir + "/" +indir = RastertoolsTestsData.tests_input_data_dir + "/" +outdir = RastertoolsTestsData.tests_output_data_dir + "/" +__root_refdir = RastertoolsTestsData.tests_ref_data_dir + "/" def get_refdir(testname: str): return __root_refdir + testname From d88b71cfd0c029632c7cdabef7854ca46cdc9a6b Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 14 Nov 2024 12:21:28 +0100 Subject: [PATCH 30/56] docs: Docstrings of the click cli functions docs: Updating html doc with cli --help output --- docs/cli/filtering.rst | 340 ++++++--------------- docs/cli/hillshade.rst | 65 ++-- docs/cli/radioindice.rst | 111 +++---- docs/cli/speed.rst | 42 +-- docs/cli/svf.rst | 70 +++-- docs/cli/tiling.rst | 81 ++--- docs/cli/timeseries.rst | 64 ++-- docs/cli/zonalstats.rst | 134 ++++---- src/eolab/rastertools/cli/filtering.py | 214 ++++--------- src/eolab/rastertools/cli/filtering_dyn.py | 151 --------- src/eolab/rastertools/cli/hillshade.py | 35 +-- src/eolab/rastertools/cli/radioindice.py | 49 +-- src/eolab/rastertools/cli/speed.py | 17 +- src/eolab/rastertools/cli/svf.py | 20 +- src/eolab/rastertools/cli/tiling.py | 40 ++- src/eolab/rastertools/cli/timeseries.py | 27 +- src/eolab/rastertools/cli/utils_cli.py | 36 +-- src/eolab/rastertools/cli/zonalstats.py | 22 +- src/eolab/rastertools/main.py | 24 +- 19 files changed, 563 insertions(+), 979 deletions(-) delete mode 100644 src/eolab/rastertools/cli/filtering_dyn.py diff --git a/docs/cli/filtering.rst b/docs/cli/filtering.rst index 159e400f..45354749 100644 --- a/docs/cli/filtering.rst +++ b/docs/cli/filtering.rst @@ -20,265 +20,105 @@ filter mean Apply local mean filter adaptive_gaussian Apply adaptive gaussian filter -The available filters are Adaptive Gaussian, Local Sum, and Local Mean. -Each filter is used as a sub-command and has specific arguments for filtering. -To see the definitions of these arguments, type the option --help. +For different filters are available. They are applied as sub-command that each define the arguments +that configure the filter. Type option --help to get the definition of the arguments: -- **Median** - - .. code-block:: console - - $ rastertools filter median --help - usage: rastertools filter median [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Apply a median filter (see scipy median_filter for more information) - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - By default only first band is computed. - - The corresponding API functions that is called by the command line interface is the following : - - .. autofunction:: eolab.rastertools.processing.algo.median - - - Here is an example of a median filter applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. - This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. - - .. code-block:: console - - $ rastertools filter median --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - - .. list-table:: - :widths: 20 20 - :header-rows: 0 - - * - .. centered:: Original - - .. centered:: Filtered by Median - - * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg - :align: center - - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.jpg - :align: center - -- **Local sum** - - .. code-block:: console - - $ rastertools filter sum --help - usage: rastertools filter sum [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Apply a local sum filter using integral image method - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - By default only first band is computed. - - The corresponding API functions that is called by the command line interface is the following : - - .. autofunction:: eolab.rastertools.processing.algo.local_sum - - Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. - This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. - - .. code-block:: console - - $ rastertools filter sum --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - - .. list-table:: - :widths: 20 20 - :header-rows: 0 - - * - .. centered:: Original - - .. centered:: Filtered by Local sum - - * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg - :align: center - - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-sum.jpg - :align: center - -- **Local mean** - - .. code-block:: console - - $ rastertools filter mean --help - usage: rastertools filter mean [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Apply a local mean filter using integral image method - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - By default only first band is computed. - - - The corresponding API functions that is called by the command line interface is the following : - - .. autofunction:: eolab.rastertools.processing.algo.local_mean - - - Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. - This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. - - .. code-block:: console - - $ rastertools filter mean --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - - .. list-table:: - :widths: 20 20 - :header-rows: 0 - - * - .. centered:: Original - - .. centered:: Filtered by Local mean - - * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg - :align: center - - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.jpg - :align: center - -- **Adaptative gaussian** - - .. code-block:: console - - $ rastertools filter adaptive_gaussian --help - usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE - --sigma SIGMA [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) +.. code-block:: console - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - --sigma SIGMA Standard deviation of the Gaussian distribution - (sigma) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands + $ rastertools filter adaptive_gaussian --help + usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE + --sigma SIGMA [-o OUTPUT] + [-ws WINDOW_SIZE] + [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] + [-b BANDS [BANDS ...]] [-a] + inputs [inputs ...] + + Execute the requested filter on the input files with the specified + parameters. The `inputs` argument can either be a single file or a `.lst` + file containing a list of input files. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). You can + provide a single file with extension ".lst" (e.g. "filtering.lst") that + lists the input files to process (one input file per line in .lst). + + Options: + --sigma INTEGER Standard deviation of the Gaussian + distribution [required] + -a, --all Process all bands + -b, --bands INTEGER List of bands to process + -p, --pad [none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap] + Pad to use around the image, default : edge(see + https://numpy.org/doc/stable/reference/generated/numpy.pad.html + for more information) + -ws, --window_size INTEGER Size of tiles to distribute processing, + default: 1024 + -o, --output TEXT Output directory to store results (by + default current directory) + --kernel_size INTEGER Kernel size of the filter function, e.g. 3 + means a square of 3x3 pixels on which the + filter function is computed (default: 8) + -h, --help Show this message and exit. + + Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image + + positional arguments: + inputs Input file to process (e.g. Sentinel2 L2A MAJA from + THEIA). You can provide a single file with extension + ".lst" (e.g. "filtering.lst") that lists the input + files to process (one input file per line in .lst) - By default only first band is computed. + optional arguments: + -h, --help show this help message and exit + --kernel_size KERNEL_SIZE + Kernel size of the filter function, e.g. 3 means a + square of 3x3 pixels on which the filter function is + computed (default: 8) + --sigma SIGMA Standard deviation of the Gaussian distribution + (sigma) + -o OUTPUT, --output OUTPUT + Output dir where to store results (by default current + dir) + -ws WINDOW_SIZE, --window_size WINDOW_SIZE + Size of tiles to distribute processing, default: 1024 + -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} + Pad to use around the image, default : edge (see https + ://numpy.org/doc/stable/reference/generated/numpy.pad. + html for more information) + -b BANDS [BANDS ...], --bands BANDS [BANDS ...] + List of bands to compute + -a, --all Compute all bands + + By default only first band is computed. + +Examples: + +The following examples use an input raster file generated by radioindice. This is an NDVI of a SENTINEL2 L2A THEIA image cropped to a (small) +region of interest. + +.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg + +To apply three filters (median, mean and adaptive_gaussian) on a kernel of dimension 16x16, run these commands: - The corresponding API functions that is called by the command line interface is the following : +.. code-block:: console - .. autofunction:: eolab.rastertools.processing.algo.adaptive_gaussian + $ rastertools filter median --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + $ rastertools filter mean --kernel_size 16 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + $ rastertools filter adaptive_gaussian --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - Here is an example of the local mean applied to the NDVI of a SENTINEL2 L2A THEIA image cropped to a region of interest. - This raster was previously computed using :ref:`radioindice` on the original SENTINEL2 L2A THEIA image. +The commands will generate respectively: - .. code-block:: console +- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.tif - $ rastertools filter adaptive_gaussian --kernel_size 16 --sigma 1 "./SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" +.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-median.jpg - .. list-table:: - :widths: 20 20 - :header-rows: 0 +- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.tif - * - .. centered:: Original - - .. centered:: Filtered by Adaptive gaussian +.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-mean.jpg - * - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.jpg - :align: center - - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg - :align: center +- SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.tif +.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg \ No newline at end of file diff --git a/docs/cli/hillshade.rst b/docs/cli/hillshade.rst index 7823539c..3a618223 100644 --- a/docs/cli/hillshade.rst +++ b/docs/cli/hillshade.rst @@ -15,36 +15,41 @@ computes the shadows of the ground surface (buildings, trees, etc.). [-o OUTPUT] [-ws WINDOW_SIZE] [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] inputs [inputs ...] - - Compute hillshades of a Digital Height Model. - - positional arguments: - inputs Input file to process (i.e. geotiff corresponding to a - Digital Height Model). You can provide a single file - with extension ".lst" (e.g. "filtering.lst") that - lists the input files to process (one input file per - line in .lst) - - optional arguments: - -h, --help show this help message and exit - --elevation ELEVATION - Elevation of the sun in degrees, [0°, 90°] where - 90°=zenith and 0°=horizon - --azimuth AZIMUTH Azimuth of the sun in degrees, [0°, 360°] where - 0°=north, 90°=east, 180°=south and 270°=west - --radius RADIUS Max distance (in pixels) around a point to evaluate - horizontal elevation angle - --resolution RESOLUTION - Pixel resolution in meter - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) + + Execute the hillshade subcommand on a Digital Height Model (DHM) using the + given solar parameters (elevation, azimuth), resolution, and optional + parameters for processing the raster. + + Arguments: + + inputs TEXT + + Input file to process (i.e. geotiff corresponding to a Digital Height + Model). You can provide a single file with extension ".lst" (e.g. + "hillshade.lst") that lists the input files to process (one input file + per line in .lst) + + Options: + --elevation FLOAT Elevation of the sun in degrees, [0°, 90°] + where 90°=zenith and 0°=horizon [required] + --azimuth FLOAT Azimuth of the sun in degrees, [0°, 360°] + where 0°=north, 90°=east, 180°=south and + 270°=west [required] + --radius INTEGER Maximum distance (in pixels) around a point + to evaluate horizontal elevation angle. If + not set, it is automatically computed from + the range of altitudes in the digital model. + --resolution FLOAT Pixel resolution in meter [required] + -o, --output TEXT Output directory to store results (by + default current directory) + -ws, --window_size INTEGER Size of tiles to distribute processing, + default: 1024 + -p, --pad [none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap] + Pad to use around the image, default : edge (see + https://numpy.org/doc/stable/reference/generated/numpy.pad.html + for more information) + -h, --help Show this message and exit. + .. warning:: This command line does not accept all input raster products as other raster tools (radioindice, zonalstats). diff --git a/docs/cli/radioindice.rst b/docs/cli/radioindice.rst index 1bae9cd9..56daaade 100644 --- a/docs/cli/radioindice.rst +++ b/docs/cli/radioindice.rst @@ -16,58 +16,65 @@ radioindice [--bi] [--bi2] [-nd band1 band2] [-ws WINDOW_SIZE] inputs [inputs ...] - - Compute a list of radiometric indices (NDVI, NDWI, etc.) on a raster image - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "radioindice.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -m, --merge Merge all indices in the same image (i.e. one band per - indice). - -r ROI, --roi ROI Region of interest in the input image (vector) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - - Options to select the indices to compute: - -i INDICES [INDICES ...], --indices INDICES [INDICES ...] - List of indices to computePossible indices are: bi, - bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi, - ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, - tsavi - --ndvi Compute ndvi indice INSERT LINK TO CORRESPONDING DOC - --tndvi Compute tndvi indice - --rvi Compute rvi indice - --pvi Compute pvi indice - --savi Compute savi indice - --tsavi Compute tsavi indice - --msavi Compute msavi indice - --msavi2 Compute msavi2 indice - --ipvi Compute ipvi indice - --evi Compute evi indice - --ndwi Compute ndwi indice - --ndwi2 Compute ndwi2 indice - --mndwi Compute mndwi indice - --ndpi Compute ndpi indice - --ndti Compute ndti indice - --ndbi Compute ndbi indice - --ri Compute ri indice - --bi Compute bi indice - --bi2 Compute bi2 indice - -nd band1 band2, -normalized_difference band1 band2 - Compute the normalized difference of two bands defined - as parameter of this option, e.g. "-nd red nir" will - compute (red-nir)/(red+nir). See - eolab.rastertools.product.rastertype.BandChannel for - the list of bands names. Several nd options can be set - to compute several normalized differences. + + Compute the requested radio indices on raster data. + + This command computes various vegetation and environmental indices on + satellite or raster data based on the provided input images and options. The + tool can compute specific indices, merge the results into one image, compute + normalized differences between bands, and apply processing using a region of + interest (ROI) and specified tile/window size. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). You can + provide a single file with extension ".lst" (e.g. "radioindice.lst") + that lists the input files to process (one input file per line in .lst). + + Options: + -o, --output TEXT Output directory to store results (by + default current directory) + -m, --merge Merge all indices in the same image (i.e. + one band per indice) + -r, --roi TEXT Region of interest in the input image + (vector) + -ws, --window_size INTEGER Size of tiles to distribute processing, + default: 1024 + -i, --indices TEXT List of indices to compute. Possible indices + are: bi, bi2, evi, ipvi, mndwi, msavi, + msavi2, ndbi, ndpi, ndti, ndvi, ndwi, ndwi2, + pvi, ri, rvi, savi, tndvi, tsavi + --bi2 Compute bi2 indice + --bi Compute bi indice + --ri Compute ri indice + --ndbi Compute ndbi indice + --ndti Compute ndti indice + --ndpi Compute ndpi indice + --mndwi Compute mndwi indice + --ndwi2 Compute ndwi2 indice + --ndwi Compute ndwi indice + --evi Compute evi indice + --ipvi Compute ipvi indice + --msavi2 Compute msavi2 indice + --msavi Compute msavi indice + --tsavi Compute tsavi indice + --savi Compute savi indice + --pvi Compute pvi indice + --rvi Compute rvi indice + --tndvi Compute tndvi indice + --ndvi Compute ndvi indice + -nd, --normalized_difference band1 band2 + Compute the normalized difference of two + bands defineda s parameter of this option, + e.g. "-nd red nir" will compute (red- + nir)/(red+nir). See + eolab.rastertools.product.rastertype. + BandChannel for the list of + bands names. Several nd options can be set + to compute several normalized differences. + -h, --help Show this message and exit. If no indice option is explicitly set, NDVI, NDWI and NDWI2 are computed. diff --git a/docs/cli/speed.rst b/docs/cli/speed.rst index 903c4d0a..964b68d2 100644 --- a/docs/cli/speed.rst +++ b/docs/cli/speed.rst @@ -8,28 +8,30 @@ speed .. code-block:: console $ rastertools speed --help - usage: rastertools speed [-h] [-b BANDS [BANDS ...]] [-a] [-o OUTPUT] inputs [inputs ...] - - Compute the speed of radiometric values of several raster images - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "speed.lst") that lists the input files - to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - - By default only first band is computed. + + Compute the speed of radiometric values for multiple raster images. + + This command calculates the speed of radiometric values for raster data, + optionally processing specific bands or all bands from the input images. The + results are saved to a specified output directory. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). You can + provide a single file with extension ".lst" (e.g. "speed.lst") that + lists the input files to process (one input file per line in .lst). + + Options: + -b, --bands INTEGER List of bands to process + -a, --all Process all bands + -o, --output TEXT Output directory to store results (by default current + directory) + -h, --help Show this message and exit. + .. warning:: At least two input rasters must be given. The rasters must match one of the configured raster types, diff --git a/docs/cli/svf.rst b/docs/cli/svf.rst index af886e6c..6c7ef9fe 100644 --- a/docs/cli/svf.rst +++ b/docs/cli/svf.rst @@ -46,37 +46,45 @@ too many points, the "radius" parameter defines the max distance of the pixel to [-o OUTPUT] [-ws WINDOW_SIZE] [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] inputs [inputs ...] - - Compute Sky View Factor of a Digital Height Model. - - positional arguments: - inputs Input file to process (i.e. geotiff corresponding to a - Digital Height Model). You can provide a single file - with extension ".lst" (e.g. "filtering.lst") that - lists the input files to process (one input file per - line in .lst) - - optional arguments: - -h, --help show this help message and exit - --radius RADIUS Max distance (in pixels) around a point to evaluate - horizontal elevation angle - --directions DIRECTIONS - Number of directions on which to compute the horizon - elevation angle - --resolution RESOLUTION - Pixel resolution in meter - --altitude ALTITUDE Reference altitude to use for computing the SVF. If - this option is not specified, SVF is computed for - every point at the altitude of the point - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) + + Compute the Sky View Factor (SVF) of a Digital Height Model (DHM). + + The Sky View Factor (SVF) is a measure of the visibility of the sky from a + point in a Digital Height Model (DHM). It is calculated by evaluating the + horizontal elevation angle from a given point in multiple directions (as + specified by the user), and is influenced by the topography and surrounding + terrain features. + + Arguments: + + inputs TEXT + + Input file to process (i.e. geotiff corresponding to a Digital Height + Model). You can provide a single file with extension ".lst" (e.g. + "svf.lst") that lists the input files to process (one input file + per line in .lst) + + Options: + --radius INTEGER Maximum distance (in pixels) around a point + to evaluate horizontal elevation angle + [required] + --directions INTEGER Number of directions on which to compute the + horizon elevation angle [required] + --resolution FLOAT Pixel resolution in meter [required] + --altitude INTEGER Reference altitude to use for computing the + SVF. If this option is not specified, SVF is + computed for every point at the altitude of + the point + -o, --output TEXT Output directory to store results (by + default current directory) + -ws, --window_size INTEGER Size of tiles to distribute processing, + default: 1024 + -p, --pad [none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap] + Pad to use around the image, default : edge(see + https://numpy.org/doc/stable/reference/generated/numpy.pad.html + for more information) + -h, --help Show this message and exit. + .. warning:: This command line does not accept all input raster products as other raster tools (radioindice, zonalstats). diff --git a/docs/cli/tiling.rst b/docs/cli/tiling.rst index 9f353d71..e0021c93 100644 --- a/docs/cli/tiling.rst +++ b/docs/cli/tiling.rst @@ -13,46 +13,47 @@ file. [--id ID [ID ...]] [-o OUTPUT] [-n OUTPUT_NAME] [-d SUBDIR_NAME] inputs [inputs ...] - - Generate tiles of an input raster image following the geometries defined by a - given grid - - positional arguments: - inputs Raster files to process. You can provide a single file - with extension ".lst" (e.g. "tiling.lst") that lists - the input files to process (one input file per line in - .lst) - - optional arguments: - -h, --help show this help message and exit - -g GRID_FILE, --grid GRID_FILE - vector-based spatial data file containing the grid to - use to generate the tiles - --id_col ID_COLUMN Name of the column in the grid file used to number the - tiles. When ids are defined, this argument is required - to identify which column corresponds to the defined - ids - --id ID [ID ...] Tiles ids of the grid to export as new tile, default - all - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -n OUTPUT_NAME, --name OUTPUT_NAME - Basename for the output raster tiles, default: - "{}_tile{}". The basename must be defined as a - formatted string where tile index is at position 1 and - original filename is at position 0. For instance, - tile{1}.tif will generate the filename tile75.tif for - the tile id = 75. - -d SUBDIR_NAME, --dir SUBDIR_NAME - When each tile must be generated in a different - subdir, it defines the naming convention for the - subdir. It is a formatted string with one positional - parameter corresponding to the tile index. For - instance, tile{} will generate the subdir name tile75/ - for the tile id = 75. By default, subdir is not - defined and output files will be generated directly in - the outputdir. + Generate tiles of an input raster image following the geometries defined by + a given grid. + + The tiling command divides a raster image into smaller tiles based on a grid + defined in a vector-based spatial data file. Each tile corresponds to a + specific area within the grid, and tiles can be saved using a customizable + naming convention and optionally placed in subdirectories based on their + tile ID. + + Arguments: + + inputs TEXT + + Raster files to process. You can provide a single file with extension + ".lst" (e.g. "tiling.lst") that lists the input files to process (one + input file per line in .lst) + + Options: + -g, --grid TEXT vector-based spatial data file containing the grid to use + to generate the tiles [required] + --id_col TEXT Name of the column in the grid file used to number the + tiles. When ids are defined, this argument is requiredto + identify which column corresponds to the define ids + --id INTEGER Tiles ids of the grid to export as new tile, default all + -o, --output TEXT Output directory to store results (by default current + directory) + -n, --name TEXT Basename for the output raster tiles, + default:"{}_tile{}". The basename must be defined as a + formatted string where tile index is at position 1 and + original filename is at position 0. For instance, + tile{1}.tif will generate the filename tile75.tif for the + tile id = 75 + -d, --dir TEXT When each tile must be generated in a + different subdirectory, it defines the naming convention + for the subdirectory. It is a formatted string with one + positional parameter corresponding to the tile index. For + instance, tile{} will generate the subdirectory name + tile75/for the tile id = 75. By default, subdirectory is + not defined and output files will be generated directly + in the output directory + -h, --help Show this message and exit. In the next examples, we will be working on a grid of 4 cells (ids 1, 2, 3 and 4): *grid.geojson* and the image: *image.tif*. The grid and the image only overlap on the cells 1 and 2. diff --git a/docs/cli/timeseries.rst b/docs/cli/timeseries.rst index 262a9cf5..510ead6f 100644 --- a/docs/cli/timeseries.rst +++ b/docs/cli/timeseries.rst @@ -21,39 +21,39 @@ and may thus contain the same gaps as the input raster. [-s START_DATE] [-e END_DATE] [-p TIME_PERIOD] [-ws WINDOW_SIZE] inputs [inputs ...] - + Generate a timeseries of images (without gaps) from a set of input images. - Data not present in the input images (no image for the date or masked data) - are interpolated (with linear interpolation) so that all gaps are filled. - - positional arguments: - inputs Input files to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "speed.lst") that lists the input files - to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -s START_DATE, --start_date START_DATE - Start date of the timeseries to generate in the - following format: yyyy-MM-dd - -e END_DATE, --end_date END_DATE - End date of the timeseries to generate in the - following format: yyyy-MM-dd - -p TIME_PERIOD, --time_period TIME_PERIOD - Time period (number of days) between two consecutive - images in the timeseries to generate e.g. 10 = - generate one image every 10 days - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - - By default only first band is computed. + Data not present in the input images (e.g., missing images for specific + dates or masked data) are interpolated (with linear interpolation) so that + all gaps in the timeseries are filled. + + This command is useful for generating continuous timeseries data, even when + some input images are missing or contain masked values. + + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). You can + provide a single file with extension ".lst" (e.g. "speed.lst") that + lists the input files to process (one input file per line in .lst). + + Options: + -b, --bands LIST List of bands to process + -a, --all Process all bands + -o, --output TEXT Output directory to store results (by default + current directory) + -s, --start_date TEXT Start date of the timeseries to generate in the + following format: yyyy-MM-dd + -e, --end_date TEXT End date of the timeseries to generate in the + following format: yyyy-MM-dd + -p, --time_period INTEGER Time period (number of days) between two + consecutive images in the timeseries to generate + e.g. 10 = generate one image every 10 days + -ws, --window_size INTEGER Size of tiles to distribute processing, default: + 1024 + -h, --help Show this message and exit. + .. warning:: At least two input rasters must be given. The rasters must match one of the configured raster types, diff --git a/docs/cli/zonalstats.rst b/docs/cli/zonalstats.rst index ce0fe32a..a0812dbd 100644 --- a/docs/cli/zonalstats.rst +++ b/docs/cli/zonalstats.rst @@ -28,77 +28,69 @@ cities using: [--category_index CATEGORY_INDEX] [--category_names CATEGORY_NAMES] inputs [inputs ...] - - Compute zonal statistics of a raster image. Available statistics are: min max - range mean std percentile_x (x in [0, 100]) median mad count valid nodata sum - majority minority unique - - positional arguments: - inputs Raster files to process. You can provide a single file - with extension ".lst" (e.g. "zonalstats.lst") that - lists the input files to process (one input file per - line in .lst) - - optional arguments: - -h, --help show this help message and exit - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -f OUTPUT_FORMAT, --format OUTPUT_FORMAT - Output format of the results when input geometries are - provided (by default ESRI Shapefile). Possible values - are ESRI Shapefile, GeoJSON, CSV, GPKG, GML - -g GEOMETRIES, --geometry GEOMETRIES - List of geometries where to compute statistics (vector - like a shapefile or geojson) - -w, --within When activated, statistics are computed for the - geometries that are within the raster shape. The - default behaviour otherwise is to compute statistics - for all geometries that intersect the raster shape. - --stats STATS [STATS ...] - List of stats to compute. Possible stats are: min max - range mean std percentile_x (x in [0, 100]) median mad - count valid nodata sum majority minority unique - --categorical If the input raster is categorical (i.e. raster values - represent discrete classes) compute the counts of - every unique pixel values. - --valid_threshold VALID_THRESHOLD - Minimum percentage of valid pixels in a shape to - compute its statistics. - --area Whether to multiply all stats by the area of a cell of - the input raster. - --prefix PREFIX Add a prefix to the keys (default: None). One prefix - per band (e.g. 'band1 band2') - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - Options to output the outliers: - --sigma SIGMA Distance to the mean value (in sigma) in order to - produce a raster that highlights outliers. - - Options to plot the generated stats: - -c CHARTFILE, --chart CHARTFILE - Generate a chart per stat and per geometry - (x=timestamp of the input products / y=stat value) and - store it in the file defined by this argument - -d, --display Display the chart - -gi GEOM_INDEX, --geometry-index GEOM_INDEX - Name of the geometry index used for the chart - (default='ID') - - Options to compute stats per category in geometry. If activated, the generated geometries will contain stats for every categories present in the geometry: - --category_file CATEGORY_FILE - File (raster or geometries) containing discrete - classes classifying the ROI. - --category_index CATEGORY_INDEX - Column name identifying categories in categroy_file - (only if file format is geometries) - --category_names CATEGORY_NAMES - JSON files containing a dict with classes index as - keys and names to display classes as values. - - By default only first band is computed. + + Compute zonal statistics of a raster image. + + Available statistics are: min, max, range, mean, std, percentile_x (x in [0, + 100]), median, mad, count, valid, nodata, sum, majority, minority, unique. + + By default, only the first band is computed unless specified otherwise. + + Arguments: + + inputs TEXT + + Raster files to process. You can provide a single filewith extension + ".lst" (e.g. "zonalstats.lst") that lists the input files to process + (one input file per line in .lst) + + Options: + -o, --output TEXT Output directory to store results (by default + current directory) + -f, --format TEXT Output format of the results when input + geometries are provided (by default ESRI + Shapefile). Possible values are ESRI Shapefile, + GeoJSON, CSV, GPKG, GML + -g, --geometry TEXT List of geometries where to compute statistics + (vector like a shapefile or geojson) + -w, --within When activated, statistics are computed for the + geometries that are within the raster shape. The + default behaviour otherwise is to compute + statistics for all geometries that intersect the + raster shape. + --stats TEXT List of stats to compute. Possible stats are: + min max range mean std percentile_x (x in [0, + 100]) median mad count valid nodata sum majority + minority unique + --categorical If the input raster is categorical (i.e. raster + values represent discrete classes) compute the + counts of every unique pixel values. + --valid_threshold FLOAT Minimum percentage of valid pixels in a shape to + compute its statistics. + --area Whether to multiply all stats by the area of a + cell of the input raster. + --prefix TEXT Add a prefix to the keys (default: None). One + prefix per band (e.g. 'band1 band2') + -b, --bands INTEGER List of bands to process + -a, --all Process all bands + --sigma TEXT Distance to the mean value (in sigma) in order + to produce a raster that highlights outliers. + -c, --chart TEXT Generate a chart per stat and per geometry + (x=timestamp of the input products / y=stat + value) and store it in the file defined by this + argument + -d, --display Display the chart + -gi, --geometry-index TEXT Name of the geometry index used for the chart + (default='ID') + --category_file TEXT File (raster or geometries) containing discrete + classes classifying the ROI. + --category_index TEXT Column name identifying categories in + categroy_file (only if file format is + geometries) + --category_names TEXT JSON files containing a dict with classes index + as keys and names to display classes as values. + -h, --help Show this message and exit. + When -g option is set with a valid geometries file, ``zonalstats`` generate a new vector file with the following metadata: diff --git a/src/eolab/rastertools/cli/filtering.py b/src/eolab/rastertools/cli/filtering.py index 2fb4ea6f..7333ae00 100644 --- a/src/eolab/rastertools/cli/filtering.py +++ b/src/eolab/rastertools/cli/filtering.py @@ -4,9 +4,7 @@ CLI definition for the filtering tool """ from eolab.rastertools import Filtering -#import eolab.rastertools.main as main from eolab.rastertools.cli.utils_cli import apply_process -#from eolab.rastertools.main import rastertools #Import the click group named rastertools import click import os @@ -22,19 +20,24 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict Args: output (str): The path for the filtered output file. + window_size (int): Size of the processing window used by the filter. - pad (str): Padding method used for windowing (e.g., 'reflect', 'constant', etc.). + + pad (str): Padding method used for windowing (default : 'edge'). + argsdict (dict): Dictionary of additional filter configuration arguments. + filter (str): The filter type to apply (must be a valid name in `Filtering` filters). + bands (list): List of bands to process. If empty and `all_bands` is False, defaults to [1]. + kernel_size (int): Size of the kernel used by the filter. + all_bands (bool): Whether to apply the filter to all bands (True) or specific bands (False). Returns: :obj:`eolab.rastertools.Filtering`: A configured `Filtering` instance ready for execution. """ - - # get the bands to process if all_bands: bands = None @@ -53,11 +56,19 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict return tool +def filter_options(options : list): + def wrapper(function): + for option in options: + function = option(function) + return function + return wrapper + + inpt_arg = click.argument('inputs', type=str, nargs = -1, required = 1) ker_opt = click.option('--kernel_size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" - "of 3x3 pixels on which the filter function is computed" - "(default: 8)") + " of 3x3 pixels on which the filter function is computed" + " (default: 8)") out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") @@ -72,183 +83,62 @@ def create_filtering(output : str, window_size : int, pad : str, argsdict : dict all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") -@click.group(name = "filter", context_settings=CONTEXT_SETTINGS) -@click.pass_context -def filter(ctx): - ''' - Apply a filter to a set of images. - ''' - ctx.ensure_object(dict) - - -#Median filter -@filter.command("median",context_settings=CONTEXT_SETTINGS) -@inpt_arg -@ker_opt -@out_opt -@win_opt -@pad_opt -@band_opt -@all_opt -@click.pass_context -def median(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : - """ - Execute the median filter on the input files with the specified parameters. - - The filter works by sliding a window across the input raster and replacing each - pixel value with the median value of the pixels within that window. - - The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. - - Arguments: - - inputs TEXT - - Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists - the input files to process (one input file per line in .lst). - """ - # Configure the filter tool instance - tool = create_filtering( - output=output, - window_size=window_size, - pad=pad, - argsdict={"inputs": inputs}, - filter='median', - bands=bands, - kernel_size=kernel_size, - all_bands=all_bands) +sigma = click.option('--sigma', type=int, required = True, help="Standard deviation of the Gaussian distribution") - apply_process(ctx, tool, inputs) - -#Sum filter -@filter.command("sum",context_settings=CONTEXT_SETTINGS) -@inpt_arg -@ker_opt -@out_opt -@win_opt -@pad_opt -@band_opt -@all_opt +@click.group(context_settings=CONTEXT_SETTINGS) @click.pass_context -def sum(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : - """ - Execute the sum filter on the input files with the specified parameters. - - The filter works by sliding a window across the input raster and replacing each - pixel value with the median value of the pixels within that window. - - The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. - - Arguments: - - inputs TEXT - - Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists - the input files to process (one input file per line in .lst). +def filter(ctx): """ - # Configure the filter tool instance - tool = create_filtering( - output=output, - window_size=window_size, - pad=pad, - argsdict={"inputs": inputs}, - filter='sum', - bands=bands, - kernel_size=kernel_size, - all_bands=all_bands) - - apply_process(ctx, tool, inputs) - -#Mean filter -@filter.command("mean",context_settings=CONTEXT_SETTINGS) -@inpt_arg -@ker_opt -@out_opt -@win_opt -@pad_opt -@band_opt -@all_opt -@click.pass_context -def mean(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool) : + Apply a filter to a set of images. """ - Execute the mean filter on the input files with the specified parameters. + ctx.ensure_object(dict) - The filter works by sliding a window across the input raster and replacing each - pixel value with the median value of the pixels within that window. - The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. +def create_filter(filter_name : str): - Arguments: + list_opt = [inpt_arg, ker_opt, out_opt, win_opt, pad_opt, band_opt, all_opt] + if filter_name == 'adaptive_gaussian': + list_opt.append(sigma) - inputs TEXT + @filter.command(filter_name, context_settings=CONTEXT_SETTINGS) + @filter_options(list_opt) + @click.pass_context + def filter_filtername(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool, **kwargs): + """ + Execute the requested filter on the input files with the specified parameters. + The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. - Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists - the input files to process (one input file per line in .lst). - """ - # Configure the filter tool instance - tool = create_filtering( - output=output, - window_size=window_size, - pad=pad, - argsdict={"inputs": inputs}, - filter='mean', - bands=bands, - kernel_size=kernel_size, - all_bands=all_bands) - - apply_process(ctx, tool, inputs) - -#Adaptive gaussian filter -@filter.command("adaptive_gaussian",context_settings=CONTEXT_SETTINGS) -@inpt_arg -@ker_opt -@out_opt -@win_opt -@pad_opt -@band_opt -@all_opt -@click.option('--sigma', type = int, required = True, help = "Standard deviation of the Gaussian distribution") -@click.pass_context -def adaptive_gaussian(ctx, inputs : list, output : str, window_size : int, pad : str, sigma : int, kernel_size : int, bands : list, all_bands : bool) : - """ - Execute the adaptive gaussian filter on the input files with the specified parameters. + Arguments: - The filter works by sliding a window across the input raster and replacing each - pixel value with the median value of the pixels within that window. + inputs TEXT - The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists + the input files to process (one input file per line in .lst). + """ + argsdict = {"inputs": inputs} - Arguments: + if filter_name == 'adaptive_gaussian': + argsdict = {"sigma" : kwargs["sigma"]} - inputs TEXT - - Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists - the input files to process (one input file per line in .lst). - """ - # Configure the filter tool instance - tool = create_filtering( + # Configure the filter tool instance + tool = create_filtering( output=output, window_size=window_size, pad=pad, - argsdict={"inputs": inputs, "sigma" : sigma}, - filter='adaptive_gaussian', + argsdict=argsdict, + filter=filter_name, bands=bands, kernel_size=kernel_size, all_bands=all_bands) - apply_process(ctx, tool, inputs) + apply_process(ctx, tool, inputs) -@filter.result_callback() -@click.pass_context -def handle_result(ctx): - if ctx.invoked_subcommand is None: - click.echo(ctx.get_help()) - ctx.exit() +median = create_filter("median") +mean = create_filter("mean") +sum = create_filter("sum") +adaptive_gaussian = create_filter("adaptive_gaussian") diff --git a/src/eolab/rastertools/cli/filtering_dyn.py b/src/eolab/rastertools/cli/filtering_dyn.py deleted file mode 100644 index 3c4057eb..00000000 --- a/src/eolab/rastertools/cli/filtering_dyn.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -CLI definition for the filtering tool -""" -from typing import Callable - -from eolab.rastertools import Filtering -#from eolab.rastertools.main import get_logger -from eolab.rastertools.cli.utils_cli import apply_process -#from eolab.rastertools.main import rastertools #Import the click group named rastertools -import click -import os - -CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) - - -def create_filtering(output : str, window_size : int, pad : str, argsdict : dict, filter : str, bands : list, kernel_size : int, all_bands : bool) -> Filtering: - """ - This function initializes a `Filtering` tool instance and configures it with specified settings. - - It selects the filter type, kernel size, output settings, and processing bands. If `all_bands` is set - to True, the filter will apply to all bands in the raster; otherwise, it applies only to specified bands. - - Args: - output (str): The path for the filtered output file. - window_size (int): Size of the processing window used by the filter. - pad (str): Padding method used for windowing (e.g., 'reflect', 'constant', etc.). - argsdict (dict): Dictionary of additional filter configuration arguments. - filter (str): The filter type to apply (must be a valid name in `Filtering` filters). - bands (list): List of bands to process. If empty and `all_bands` is False, defaults to [1]. - kernel_size (int): Size of the kernel used by the filter. - all_bands (bool): Whether to apply the filter to all bands (True) or specific bands (False). - - Returns: - :obj:`eolab.rastertools.Filtering`: A configured `Filtering` instance ready for execution. - """ - # get the bands to process - if all_bands: - bands = None - else: - bands = list(map(int, bands)) if bands else [1] - - # create the rastertool object - raster_filters_dict = {rf.name: rf for rf in Filtering.get_default_filters()} - tool = Filtering(raster_filters_dict[filter], kernel_size, bands) - - # set up config with args values - tool.with_output(output) \ - .with_windows(window_size, pad) \ - .with_filter_configuration(argsdict) - - return tool - - -def filter_options(options : list): - def wrapper(function): - for option in options: - function = option(function) - return function - return wrapper - - -inpt_arg = click.argument('inputs', type=str, nargs = -1, required = 1) - -ker_opt = click.option('--kernel_size', type=int, help="Kernel size of the filter function, e.g. 3 means a square" - "of 3x3 pixels on which the filter function is computed" - "(default: 8)") - -out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") - -win_opt = click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") - -pad_opt = click.option('-p','--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), - help="Pad to use around the image, default : edge" - "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" - "for more information)") - -band_opt = click.option('-b','--bands', multiple = True, type=int, help="List of bands to process") - -all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") - -sigma = click.option('--sigma', type=int, required = True, help="Standard deviation of the Gaussian distribution") - -@click.group(context_settings=CONTEXT_SETTINGS) -@click.pass_context -def filter(ctx): - """ - Apply a filter to a set of images. - """ - ctx.ensure_object(dict) - - -def create_filter(filter_name : str): - - list_opt = [inpt_arg, ker_opt, out_opt, win_opt, pad_opt, band_opt, all_opt] - if filter_name == 'adaptive_gaussian': - list_opt.append(sigma) - - @filter.command(filter_name, context_settings=CONTEXT_SETTINGS) - @filter_options(list_opt) - @click.pass_context - def filter_filtername(ctx, inputs : list, output : str, window_size : int, pad : str, kernel_size : int, bands : list, all_bands : bool, **kwargs): - """ - Execute the requested filter on the input files with the specified parameters. - The `inputs` argument can either be a single file or a `.lst` file containing a list of input files. - - Arguments: - - inputs TEXT - - Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension \".lst\" (e.g. \"filtering.lst\") that lists - the input files to process (one input file per line in .lst). - """ - argsdict = {"inputs": inputs} - - if filter_name == 'adaptive_gaussian': - argsdict = {"sigma" : kwargs["sigma"]} - - # Configure the filter tool instance - tool = create_filtering( - output=output, - window_size=window_size, - pad=pad, - argsdict=argsdict, - filter=filter_name, - bands=bands, - kernel_size=kernel_size, - all_bands=all_bands) - - apply_process(ctx, tool, inputs) - - -median = create_filter("median") -mean = create_filter("mean") -sum = create_filter("sum") -adaptive_gaussian = create_filter("adaptive_gaussian") - -@filter.result_callback() -@click.pass_context -def handle_result(ctx): - if ctx.invoked_subcommand is None: - click.echo(ctx.get_help()) - ctx.exit() - - - - - - diff --git a/src/eolab/rastertools/cli/hillshade.py b/src/eolab/rastertools/cli/hillshade.py index 96588cc0..5ad0c0d8 100644 --- a/src/eolab/rastertools/cli/hillshade.py +++ b/src/eolab/rastertools/cli/hillshade.py @@ -16,13 +16,13 @@ @click.argument('inputs', type=str, nargs = -1, required = 1) @click.option('--elevation', type=float, required = True, help="Elevation of the sun in degrees, [0°, 90°] where" - "90°=zenith and 0°=horizon") + " 90°=zenith and 0°=horizon") @click.option('--azimuth', type=float, required = True, help="Azimuth of the sun in degrees, [0°, 360°] where" - "0°=north, 90°=east, 180°=south and 270°=west") + " 0°=north, 90°=east, 180°=south and 270°=west") @click.option('--radius', type=int, help="Maximum distance (in pixels) around a point to evaluate" - "horizontal elevation angle. If not set, it is automatically computed from" + " horizontal elevation angle. If not set, it is automatically computed from" " the range of altitudes in the digital model.") @click.option('--resolution', required = True, type=float, help="Pixel resolution in meter") @@ -33,39 +33,24 @@ @click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), help="Pad to use around the image, default : edge" - "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" - "for more information)") + " (see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" + " for more information)") @click.pass_context def hillshade(ctx, inputs : list, elevation : float, azimuth : float, radius : int, resolution : float, output : str, window_size : int, pad : str) : """ - CHANGE DOCSTRING - Adds the hillshade subcommand to the given rastertools subparser + Execute the hillshade subcommand on a Digital Height Model (DHM) using the given solar + parameters (elevation, azimuth), resolution, and optional parameters for processing the raster. Arguments: - inputs TEXT + inputs TEXT - Input file to process (i.e. geotiff corresponding to a + Input file to process (i.e. geotiff corresponding to a Digital Height Model). You can provide a single file - with extension ".lst" (e.g. "filtering.lst") that + with extension ".lst" (e.g. "hillshade.lst") that lists the input files to process (one input file per line in .lst) - - Args: - rastertools_parsers: - The rastertools subparsers to which this subcommand shall be added. - - This argument provides from a code like this:: - - import argparse - main_parser = argparse.ArgumentParser() - rastertools_parsers = main_parser.add_subparsers() - hillshade.create_argparser(rastertools_parsers) - - Returns: - The rastertools subparsers updated with this subcommand """ - # create the rastertool object tool = Hillshade(elevation, azimuth, resolution, radius) diff --git a/src/eolab/rastertools/cli/radioindice.py b/src/eolab/rastertools/cli/radioindice.py index e7f038bd..eec3d530 100644 --- a/src/eolab/rastertools/cli/radioindice.py +++ b/src/eolab/rastertools/cli/radioindice.py @@ -19,11 +19,16 @@ def indices_opt(function): - list_indices = ['--ndvi', '--tndvi', '--rvi', '--pvi', '--savi', '--tsavi', '--msavi', '--msavi2', '--ipvi', - '--evi', '--ndwi', '--ndwi2', '--mndwi', '--ndpi', '--ndti', '--ndbi', '--ri', '--bi', '--bi2'] + """ + Create options for all the possible indices + """ + dict_indices = {'--ndvi' : "ndvi", '--tndvi' : "tndvi", '--rvi' : "rvi", '--pvi' : "pvi", '--savi' : "savi", '--tsavi' : "tsavi", + '--msavi' : "msavi", '--msavi2' : "msavi2", '--ipvi' : "ipvi", + '--evi' : "evi", '--ndwi' : "ndwi", '--ndwi2' : "ndwi2", '--mndwi' : "mndwi", + '--ndpi' : "ndpi", '--ndti' : "ndti", '--ndbi' : "ndbi", '--ri' : "ri", '--bi' : "bi", '--bi2' : "bi2"} - for idc in list_indices: - function = click.option(idc, is_flag=True, help=f"Compute {id} indice")(function) + for idc in dict_indices.keys(): + function = click.option(idc, is_flag=True, help=f"Compute " + dict_indices[idc] + " indice")(function) return function @@ -40,7 +45,7 @@ def indices_opt(function): @click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") @click.option('-i', '--indices', type=str, multiple = True, - help=" List of indices to computePossible indices are: bi, bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi," + help=" List of indices to compute. Possible indices are: bi, bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi," " ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, tsavi") @@ -49,20 +54,28 @@ def indices_opt(function): @click.option('-nd', '--normalized_difference','nd',type=str, multiple=True, nargs=2, metavar="band1 band2", help="Compute the normalized difference of two bands defined" - "as parameter of this option, e.g. \"-nd red nir\" will compute (red-nir)/(red+nir). " + " as parameter of this option, e.g. \"-nd red nir\" will compute (red-nir)/(red+nir). " "See eolab.rastertools.product.rastertype.BandChannel for the list of bands names. " "Several nd options can be set to compute several normalized differences.") @click.pass_context -def radioindice(ctx, inputs : list, output : str, indices : list, merge : bool, roi : str, window_size : int, nd : bool, **kwargs) : - """Create and configure a new rastertool "Radioindice" according to argparse args +def radioindice(ctx, inputs : list, output : str, indices : list, merge : bool, roi : str, window_size : int, nd : str, **kwargs) : + """ + Compute the requested radio indices on raster data. + + This command computes various vegetation and environmental indices on satellite or raster data based on the + provided input images and options. The tool can compute specific indices, merge the results into one image, + compute normalized differences between bands, and apply processing using a region of interest (ROI) and specified + tile/window size. + + Arguments: - Args: - args: args extracted from command line + inputs TEXT - Returns: - :obj:`eolab.rastertools.Radioindice`: The configured rastertool to run + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"radioindice.lst\") that lists + the input files to process (one input file per line in .lst). """ indices_opt = [key for key, value in kwargs.items() if value] indices_to_compute = [] @@ -82,15 +95,15 @@ def radioindice(ctx, inputs : list, output : str, indices : list, merge : bool, sys.exit(2) if nd: - for nd in nd: - if nd[0] in BandChannel.__members__ and nd[1] in BandChannel.__members__: - channel1 = BandChannel[nd[0]] - channel2 = BandChannel[nd[1]] - new_indice = RadioindiceProcessing(f"nd[{nd[0]}-{nd[1]}]").with_channels( + for nd_bands in nd: + if nd_bands[0] in BandChannel.__members__ and nd_bands[1] in BandChannel.__members__: + channel1 = BandChannel[nd_bands[0]] + channel2 = BandChannel[nd_bands[1]] + new_indice = RadioindiceProcessing(f"nd[{nd_bands[0]}-{nd_bands[1]}]").with_channels( [channel2, channel1]) indices_to_compute.append(new_indice) else: - _logger.exception(RastertoolConfigurationException(f"Invalid band(s) in normalized difference: {nd[0]} and/or {nd[1]}")) + _logger.exception(RastertoolConfigurationException(f"Invalid band(s) in normalized difference: {nd_bands[0]} and/or {nd_bands[1]}")) sys.exit(2) # handle special case: no indice setup diff --git a/src/eolab/rastertools/cli/speed.py b/src/eolab/rastertools/cli/speed.py index 2977811b..e3243b74 100644 --- a/src/eolab/rastertools/cli/speed.py +++ b/src/eolab/rastertools/cli/speed.py @@ -24,14 +24,19 @@ @click.pass_context def speed(ctx, inputs : list, bands : list, all_bands : bool, output : str) : """ - CHANGE DOCSTRING - Create and configure a new rastertool "Speed" according to argparse args + Compute the speed of radiometric values for multiple raster images. - Args: - args: args extracted from command line + This command calculates the speed of radiometric values for raster data, + optionally processing specific bands or all bands from the input images. + The results are saved to a specified output directory. - Returns: - :obj:`eolab.rastertools.Speed`: The configured rastertool to run + Arguments: + + inputs TEXT + + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"speed.lst\") that lists + the input files to process (one input file per line in .lst). """ # get the bands to process diff --git a/src/eolab/rastertools/cli/svf.py b/src/eolab/rastertools/cli/svf.py index 6290d4a5..ff16eb6a 100644 --- a/src/eolab/rastertools/cli/svf.py +++ b/src/eolab/rastertools/cli/svf.py @@ -31,21 +31,25 @@ @click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), help="Pad to use around the image, default : edge" "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" - "for more information)") + " for more information)") @click.pass_context def svf(ctx, inputs : list, radius : int, directions : int, resolution : float, altitude : int, output : str, window_size : int, pad : str) : """ - CHANGE DOCSTRING + Compute the Sky View Factor (SVF) of a Digital Height Model (DHM). - ADD INPUTS - Create and configure a new rastertool "Speed" according to argparse args + The Sky View Factor (SVF) is a measure of the visibility of the sky from a point in a Digital Height Model + (DHM). It is calculated by evaluating the horizontal elevation angle from a given point in multiple + directions (as specified by the user), and is influenced by the topography and surrounding terrain features. - Args: - args: args extracted from command line + Arguments: - Returns: - :obj:`eolab.rastertools.Speed`: The configured rastertool to run + inputs TEXT + + Input file to process (i.e. geotiff corresponding to a + Digital Height Model). You can provide a single file + with extension ".lst" (e.g. "svf.lst") that + lists the input files to process (one input file per line in .lst) """ # create the rastertool object tool = SVF(directions, radius, resolution) diff --git a/src/eolab/rastertools/cli/tiling.py b/src/eolab/rastertools/cli/tiling.py index fa6b25a9..41ef1e77 100644 --- a/src/eolab/rastertools/cli/tiling.py +++ b/src/eolab/rastertools/cli/tiling.py @@ -11,7 +11,7 @@ CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -#Speed command +#Tiling command @click.command("tiling",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) @@ -20,51 +20,49 @@ @click.option('--id_col','id_column', type = str, help="Name of the column in the grid" " file used to number the tiles. When ids are defined, this argument is required" - "to identify which column corresponds to the define ids") + " to identify which column corresponds to the define ids") @click.option('--id', type=int, multiple = True, help="Tiles ids of the grid to export as new tile, default all") @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") @click.option('-n','--name','output_name', default="{}_tile{}", help="Basename for the output raster tiles, default:" - "\"{}_tile{}\". The basename must be defined as a formatted string where tile index is at position 1" + " \"{}_tile{}\". The basename must be defined as a formatted string where tile index is at position 1" " and original filename is at position 0. For instance, tile{1}.tif will generate the filename" - "tile75.tif for the tile id = 75") + " tile75.tif for the tile id = 75") @click.option('-d','--dir','subdir_name', help="When each tile must be generated in a different" - "subdirectory, it defines the naming convention for the subdirectory. It is a formatted string with one positional" - "parameter corresponding to the tile index. For instance, tile{} will generate the subdirectory name tile75/" - "for the tile id = 75. By default, subdirectory is not defined and output files will be generated directly in" - "the output directory") + " subdirectory, it defines the naming convention for the subdirectory. It is a formatted string with one positional" + " parameter corresponding to the tile index. For instance, tile{} will generate the subdirectory name tile75/" + " for the tile id = 75. By default, subdirectory is not defined and output files will be generated directly in" + " the output directory") @click.pass_context def tiling(ctx, inputs : list, grid_file : str, id_column : str, id : list, output : str, output_name : str, subdir_name : str) : """ - CHANGE DOCSTRING + Generate tiles of an input raster image following the geometries defined by a given grid. - ADD INPUTS - Create and configure a new rastertool "Tiling" according to argparse args + The tiling command divides a raster image into smaller tiles based on a grid defined in a vector-based spatial + data file. Each tile corresponds to a specific area within the grid, and tiles can be saved using a customizable + naming convention and optionally placed in subdirectories based on their tile ID. - Generate tiles of an input raster image following the geometries defined by a - given grid + Arguments: + inputs TEXT - Args: - args: args extracted from command line - - Returns: - :obj:`eolab.rastertools.Tiling`: The configured rastertool to run + Raster files to process. You can provide a single file with extension ".lst" (e.g. "tiling.lst") that lists + the input files to process (one input file per line in .lst) """ if id == () : - id = None + id_ = None else: - id = list(id) + id_ = list(id) # create the rastertool object tool = Tiling(grid_file) # set up config with args values tool.with_output(output, output_name, subdir_name) - tool.with_id_column(id_column, id) + tool.with_id_column(id_column, id_) apply_process(ctx, tool, inputs) diff --git a/src/eolab/rastertools/cli/timeseries.py b/src/eolab/rastertools/cli/timeseries.py index 78465eec..61e11b84 100644 --- a/src/eolab/rastertools/cli/timeseries.py +++ b/src/eolab/rastertools/cli/timeseries.py @@ -39,27 +39,20 @@ def timeseries(ctx, inputs : list, bands : list, all_bands : bool, output : str, start_date : str, end_date : str, time_period : int, window_size : int) : """ - Create and configure a new rastertool "Timeseries" according to argparse args - CHANGE DOCSTRING - Adds the timeseries subcommand to the given rastertools subparser + Generate a timeseries of images (without gaps) from a set of input images. + Data not present in the input images (e.g., missing images for specific dates or masked data) are interpolated + (with linear interpolation) so that all gaps in the timeseries are filled. - Temporal gap filling of an image time series - Generate a timeseries of images (without gaps) from a set of input images. " - "Data not present in the input images (no image for the date or masked data) " - "are interpolated (with linear interpolation) so that all gaps are filled.", - epilog="By default only first band is computed. + This command is useful for generating continuous timeseries data, even when some input images are missing + or contain masked values. + Arguments: - ADD INPUTS + inputs TEXT - INPUTS - - Input files to process (e.g. Sentinel2 L2A MAJA from THEIA). - You can provide a single file with extension .lst (e.g. speed.lst) - that lists the input files to process (one input file per line in .lst)) - - Args: - args: args extracted from command line + Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). + You can provide a single file with extension \".lst\" (e.g. \"speed.lst\") that lists + the input files to process (one input file per line in .lst). """ # get the bands to process if all_bands: diff --git a/src/eolab/rastertools/cli/utils_cli.py b/src/eolab/rastertools/cli/utils_cli.py index cc72dc5b..bf930e1b 100644 --- a/src/eolab/rastertools/cli/utils_cli.py +++ b/src/eolab/rastertools/cli/utils_cli.py @@ -47,31 +47,36 @@ def apply_process(ctx, tool, inputs : list): """ Apply the chosen process to a set of input files. - This function extracts input files, configures the tool, and processes the files - through the specified tool. It also handles debug settings and intermediate file storage - (VRT files). In case of any errors, the function logs the exception and terminates the process - with an appropriate exit code. + This function extracts input files from a provided list or direct paths, configures the specified tool, + and processes the files through the given tool. Additionally, it handles debug settings (such as storing + intermediate VRT files) and manages any exceptions during the process. If an error occurs, the function + logs the exception and terminates the process with an appropriate exit code. Args: - ctx (click.Context): The context object containing configuration options like whether - to store intermediate VRT files. - tool (Filtering or Hillshade or ...): The tool instance that has been configured with the provided parameters. - inputs (str): A path to a list of input files, either as a single `.lst` file or a direct - list of file paths. + ctx (click.Context): + The context object that contains configuration options. + + tool (Filtering or Hillshade or any raster processing tool): + The configured tool instance that will apply the process to the input files. The tool should have + been properly set up with parameters. + + inputs (list or str): + A list of input file paths or a path to a `.lst` file containing a list of input file paths. + This argument specifies the files that the tool will process. Raises: - RastertoolConfigurationException: If there is a configuration error with the tool. - Exception: Any other errors that occur during processing. + RastertoolConfigurationException: + If there is a configuration error with the tool or its parameters. + + Exception: + Any other exceptions that occur during the processing of the input files. """ try: - print('@' * 50) # handle the input file of type "lst" inputs_extracted = _extract_files_from_list(inputs) - print('@' * 50) # setup debug mode in which intermediate VRT files are stored to disk or not tool.with_vrt_stored(ctx.obj.get('keep_vrt')) - print('@' * 50) # launch process tool.process_files(inputs_extracted) @@ -79,13 +84,10 @@ def apply_process(ctx, tool, inputs : list): _logger.info("Done!") except RastertoolConfigurationException as rce: - print('@'*50) _logger.exception(rce) sys.exit(2) except Exception as err: - print('!' * 50) _logger.exception(err) sys.exit(1) - print('?' * 50) sys.exit(0) \ No newline at end of file diff --git a/src/eolab/rastertools/cli/zonalstats.py b/src/eolab/rastertools/cli/zonalstats.py index d1d30d92..635e780d 100644 --- a/src/eolab/rastertools/cli/zonalstats.py +++ b/src/eolab/rastertools/cli/zonalstats.py @@ -60,22 +60,20 @@ @click.pass_context def zonalstats(ctx, inputs : list, output : str, output_format : str, geometries : str, within : str, stats : list, categorical : bool, valid_threshold : float ,area : bool, prefix, bands : list, all_bands : bool, sigma, chartfile, display : bool, geom_index : str, category_file : str, category_index : str, category_names : str) : """ - Compute zonal statistics - Compute zonal statistics of a raster image.\n Available statistics are: - min max range mean std percentile_x (x in [0, 100]) median mad - count valid nodata sum majority minority unique - By default only first band is computed. + Compute zonal statistics of a raster image. - Create and configure a new rastertool "Zonalstats" according to argparse args + Available statistics are: + min, max, range, mean, std, percentile_x (x in [0, 100]), median, mad, count, valid, nodata, sum, majority, minority, unique. - Args: - args: args extracted from command line + By default, only the first band is computed unless specified otherwise. - Returns: - :obj:`eolab.rastertools.Zonalstats`: The configured rastertool to run + Arguments: + + inputs TEXT + + Raster files to process. You can provide a single filewith extension ".lst" (e.g. "zonalstats.lst") that + lists the input files to process (one input file per line in .lst) """ - print(output) - print(output_format) # get and check the list of stats to compute if stats: stats_to_compute = list(stats) diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index 5071e20f..f8913847 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -16,7 +16,7 @@ import sys import json import click -from eolab.rastertools.cli.filtering_dyn import filter +from eolab.rastertools.cli.filtering import filter from eolab.rastertools.cli.hillshade import hillshade from eolab.rastertools.cli.speed import speed from eolab.rastertools.cli.svf import svf @@ -127,6 +127,8 @@ def add_custom_rastertypes(rastertypes): """ RasterType.add(rastertypes) + +#Rastertools CLI group CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) @click.group(context_settings=CONTEXT_SETTINGS) @@ -162,12 +164,12 @@ def add_custom_rastertypes(rastertypes): help="set loglevel to DEBUG") @click.version_option(version='rastertools {}'.format(__version__)) # Ensure __version__ is defined @click.pass_context + def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbose : bool, very_verbose : bool): """ Main entry point for the `rastertools` Command Line Interface. - The `rastertools` CLI provides tools for raster processing - and analysis and allows configurable data handling, parallel processing, + The `rastertools` CLI provides tools for raster processing and analysis and allows configurable data handling, parallel processing, and debugging support. Logging: @@ -182,6 +184,7 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo - `RASTERTOOLS_MAXWORKERS`: If `max_workers` is set, it defines the max workers for rastertools. """ + #Saving keep_vrt to use it in the subcommands ctx.ensure_object(dict) ctx.obj['keep_vrt'] = keep_vrt @@ -225,18 +228,6 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo rastertools.add_command(zonalstats, name = "zs") rastertools.add_command(zonalstats, name = "zonalstats") -CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) - - -#Speed command -@click.command("ema",context_settings=CONTEXT_SETTINGS) -@click.option('--inputs', type=int) -@click.pass_context -def ema(ctx, inputs) : - raise Exception(f"coucou {inputs}") - -rastertools.add_command(ema, name = "ema") - @rastertools.result_callback() @click.pass_context @@ -246,7 +237,8 @@ def handle_result(ctx): ctx.exit() def run(*args, **kwargs): - """Entry point for console_scripts + """ + Entry point for console_scripts """ rastertools(*args, **kwargs) From c473d1a198a4d22c1c4746e45ff8addacd9f0eee Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 14 Nov 2024 15:11:44 +0100 Subject: [PATCH 31/56] refactor: began setup.py refactor: cleaned code --- pyproject.toml | 8 --- setup.py | 38 +++++++++- src/eolab/rastertools/cli/filtering.py | 13 +--- src/eolab/rastertools/cli/hillshade.py | 10 +-- src/eolab/rastertools/cli/radioindice.py | 4 +- src/eolab/rastertools/cli/speed.py | 7 +- src/eolab/rastertools/cli/svf.py | 10 +-- src/eolab/rastertools/cli/timeseries.py | 11 ++- src/eolab/rastertools/cli/utils_cli.py | 11 +++ src/eolab/rastertools/cli/zonalstats.py | 8 +-- src/eolab/rastertools/main.py | 4 ++ src/eolab/rastertools/processing/algo.py | 18 +---- src/eolab/rastertools/zonalstats.py | 1 - tests/test_radioindice.py | 35 ++++----- tests/test_rasterproc.py | 5 +- tests/test_rasterproduct.py | 90 ++++++++++++------------ tests/test_rastertools.py | 11 +-- tests/test_rastertype.py | 4 +- tests/test_speed.py | 11 +-- tests/test_stats.py | 21 +++--- tests/test_tiling.py | 9 +-- tests/test_vector.py | 73 +++++++++---------- tests/test_zonalstats.py | 75 ++++++++++---------- tests/utils4test.py | 17 ++--- 24 files changed, 248 insertions(+), 246 deletions(-) delete mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 2c63dbb2..00000000 --- a/pyproject.toml +++ /dev/null @@ -1,8 +0,0 @@ -[build-system] -# AVOID CHANGING REQUIRES: IT WILL BE UPDATED BY PYSCAFFOLD! -requires = ["setuptools>=46.1.0", "setuptools_scm[toml]>=5", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.setuptools_scm] -# See configuration details in https://github.com/pypa/setuptools_scm -version_scheme = "no-guess-dev" diff --git a/setup.py b/setup.py index 3aafee77..34927a8e 100644 --- a/setup.py +++ b/setup.py @@ -7,11 +7,45 @@ PyScaffold helps you to put up the scaffold of your new Python project. Learn more under: https://pyscaffold.org/ """ -from setuptools import setup +import os +from setuptools import setup, find_packages +from sphinx.builders.html import setup_resource_paths + +with open('src/eolab/rastertools/__init__.py') as f: + for line in f: + if line.find("__version__") >= 0: + version = line.split("=")[1].strip() + version = version.strip('"') + version = version.strip("'") + break + if __name__ == "__main__": try: - setup(use_scm_version={"version_scheme": "no-guess-dev"}) + setup(name='rastertools', + version=version, + description=u"Collection of tools for raster data", + long_description="", + classifiers=[], + keywords='', + author=u"Olivier Queyrut", + author_email="", + url="https://github.com/CNES/rastertools", + packages=find_packages(exclude=['tests']), + include_package_data=True, + zip_safe=False, + setup_requires = ["setuptools_scm"], + install_requires=[ + 'click>=4.0', + 'rasterio>=1.2.0', + ], + extras_require={ + 'test': ['pytest>=3.6'], + }, + entry_points=""" + """, + python_requires='>=3.9', + use_scm_version={"version_scheme": "no-guess-dev"}) except: # noqa print( "\n\nAn error occurred while building the project, " diff --git a/src/eolab/rastertools/cli/filtering.py b/src/eolab/rastertools/cli/filtering.py index 7333ae00..8c01b61c 100644 --- a/src/eolab/rastertools/cli/filtering.py +++ b/src/eolab/rastertools/cli/filtering.py @@ -4,7 +4,7 @@ CLI definition for the filtering tool """ from eolab.rastertools import Filtering -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, pad_opt, win_opt, all_opt, band_opt import click import os @@ -72,17 +72,6 @@ def wrapper(function): out_opt = click.option('-o', '--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") -win_opt = click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") - -pad_opt = click.option('-p','--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), - help="Pad to use around the image, default : edge" - "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" - "for more information)") - -band_opt = click.option('-b','--bands', multiple = True, type=int, help="List of bands to process") - -all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") - sigma = click.option('--sigma', type=int, required = True, help="Standard deviation of the Gaussian distribution") @click.group(context_settings=CONTEXT_SETTINGS) diff --git a/src/eolab/rastertools/cli/hillshade.py b/src/eolab/rastertools/cli/hillshade.py index 5ad0c0d8..f4786bcc 100644 --- a/src/eolab/rastertools/cli/hillshade.py +++ b/src/eolab/rastertools/cli/hillshade.py @@ -4,7 +4,7 @@ CLI definition for the hillshade tool """ from eolab.rastertools import Hillshade -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, pad_opt, win_opt import click import os @@ -29,12 +29,8 @@ @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") -@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") - -@click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), - help="Pad to use around the image, default : edge" - " (see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" - " for more information)") +@win_opt +@pad_opt @click.pass_context def hillshade(ctx, inputs : list, elevation : float, azimuth : float, radius : int, resolution : float, output : str, window_size : int, pad : str) : diff --git a/src/eolab/rastertools/cli/radioindice.py b/src/eolab/rastertools/cli/radioindice.py index eec3d530..76c3a716 100644 --- a/src/eolab/rastertools/cli/radioindice.py +++ b/src/eolab/rastertools/cli/radioindice.py @@ -6,7 +6,7 @@ import logging from eolab.rastertools import RastertoolConfigurationException, Radioindice -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, win_opt from eolab.rastertools.product import BandChannel from eolab.rastertools.processing import RadioindiceProcessing import sys @@ -42,7 +42,7 @@ def indices_opt(function): @click.option('-r', '--roi', type= str, help="Region of interest in the input image (vector)") -@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") +@win_opt @click.option('-i', '--indices', type=str, multiple = True, help=" List of indices to compute. Possible indices are: bi, bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi," diff --git a/src/eolab/rastertools/cli/speed.py b/src/eolab/rastertools/cli/speed.py index e3243b74..5628fb5c 100644 --- a/src/eolab/rastertools/cli/speed.py +++ b/src/eolab/rastertools/cli/speed.py @@ -4,7 +4,7 @@ CLI definition for the speed tool """ from eolab.rastertools import Speed -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, band_opt, all_opt import click import os @@ -15,9 +15,8 @@ @click.command("speed",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) -@click.option('-b','--bands', type=int, multiple = True, help="List of bands to process") - -@click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") +@band_opt +@all_opt @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") diff --git a/src/eolab/rastertools/cli/svf.py b/src/eolab/rastertools/cli/svf.py index ff16eb6a..23010941 100644 --- a/src/eolab/rastertools/cli/svf.py +++ b/src/eolab/rastertools/cli/svf.py @@ -4,7 +4,7 @@ CLI definition for the SVF (Sky View Factor) tool """ from eolab.rastertools import SVF -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, pad_opt, win_opt import click import os @@ -26,12 +26,8 @@ @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") -@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") - -@click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), - help="Pad to use around the image, default : edge" - "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" - " for more information)") +@win_opt +@pad_opt @click.pass_context def svf(ctx, inputs : list, radius : int, directions : int, resolution : float, altitude : int, output : str, window_size : int, pad : str) : diff --git a/src/eolab/rastertools/cli/timeseries.py b/src/eolab/rastertools/cli/timeseries.py index 61e11b84..b6a48158 100644 --- a/src/eolab/rastertools/cli/timeseries.py +++ b/src/eolab/rastertools/cli/timeseries.py @@ -6,7 +6,7 @@ from datetime import datetime from eolab.rastertools import Timeseries -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, win_opt, all_opt, band_opt from eolab.rastertools import RastertoolConfigurationException import click import sys @@ -20,10 +20,6 @@ @click.command("timeseries",context_settings=CONTEXT_SETTINGS) @click.argument('inputs', type=str, nargs = -1, required = 1) -@click.option('-b','--bands', type=list, help="List of bands to process") - -@click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") - @click.option('-o','--output', default = os.getcwd(), help="Output directory to store results (by default current directory)") @click.option("-s","--start_date", help="Start date of the timeseries to generate in the following format: yyyy-MM-dd") @@ -33,8 +29,9 @@ @click.option("-p", "--time_period",type=int, help="Time period (number of days) between two consecutive images in the timeseries " "to generate e.g. 10 = generate one image every 10 days") -@click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") - +@band_opt +@all_opt +@win_opt @click.pass_context def timeseries(ctx, inputs : list, bands : list, all_bands : bool, output : str, start_date : str, end_date : str, time_period : int, window_size : int) : diff --git a/src/eolab/rastertools/cli/utils_cli.py b/src/eolab/rastertools/cli/utils_cli.py index bf930e1b..18d116ac 100644 --- a/src/eolab/rastertools/cli/utils_cli.py +++ b/src/eolab/rastertools/cli/utils_cli.py @@ -6,6 +6,17 @@ #TO DO _logger = logging.getLogger(__name__) +all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") + +band_opt = click.option('-b','--bands', type=int, multiple = True, help="List of bands to process") + +win_opt = click.option('-ws', '--window_size', type=int, default = 1024, help="Size of tiles to distribute processing, default: 1024") + +pad_opt = click.option('-p', '--pad',default="edge", type=click.Choice(['none','edge','maximum','mean','median','minimum','reflect','symmetric','wrap']), + help="Pad to use around the image, default : edge" + "(see https://numpy.org/doc/stable/reference/generated/numpy.pad.html" + " for more information)") + def _extract_files_from_list(cmd_inputs): """ Extracts a list of file paths from a command line input. diff --git a/src/eolab/rastertools/cli/zonalstats.py b/src/eolab/rastertools/cli/zonalstats.py index 635e780d..89e822b7 100644 --- a/src/eolab/rastertools/cli/zonalstats.py +++ b/src/eolab/rastertools/cli/zonalstats.py @@ -4,7 +4,7 @@ CLI definition for the zonalstats tool """ from eolab.rastertools import Zonalstats -from eolab.rastertools.cli.utils_cli import apply_process +from eolab.rastertools.cli.utils_cli import apply_process, all_opt, band_opt import click import os @@ -38,10 +38,6 @@ @click.option('--prefix', default = None, help="Add a prefix to the keys (default: None). One prefix per band (e.g. 'band1 band2')") -@click.option('-b','--bands', multiple = True, type=int, help="List of bands to process") - -@click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") - @click.option('--sigma',help="Distance to the mean value (in sigma) in order to produce a raster that highlights outliers.") @click.option('-c','--chart',"chartfile", help="Generate a chart per stat and per geometry (x=timestamp of the input products / y=stat value) and " @@ -57,6 +53,8 @@ @click.option('--category_names',type = str, default="", help="JSON files containing a dict with classes index as keys and names to display classes as values.") +@band_opt +@all_opt @click.pass_context def zonalstats(ctx, inputs : list, output : str, output_format : str, geometries : str, within : str, stats : list, categorical : bool, valid_threshold : float ,area : bool, prefix, bands : list, all_bands : bool, sigma, chartfile, display : bool, geom_index : str, category_file : str, category_index : str, category_names : str) : """ diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index f8913847..355ae395 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -16,6 +16,8 @@ import sys import json import click +from pkg_resources import iter_entry_points +from click_plugins import with_plugins from eolab.rastertools.cli.filtering import filter from eolab.rastertools.cli.hillshade import hillshade from eolab.rastertools.cli.speed import speed @@ -131,6 +133,8 @@ def add_custom_rastertypes(rastertypes): #Rastertools CLI group CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) + +@with_plugins(iter_entry_points('core_package.cli_plugins')) @click.group(context_settings=CONTEXT_SETTINGS) @click.option( '-t', '--rastertype', diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 34774fda..13e8908a 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -611,10 +611,6 @@ def adaptive_gaussian(input_data : np.ndarray, kernel_size : int = 8, sigma : in if input_data.shape[0] != 1: raise ValueError("adaptive_gaussian only accepts numpy arrays with first dim of size 1") - ''' - kernel_size = kwargs.get('kernel_size', 8) - sigma = kwargs.get('sigma', 1) - ''' dtype = input_data.dtype w_1 = (input_data[0, :, :-2] - input_data[0, :, 2:]) ** 2 @@ -662,12 +658,7 @@ def svf(input_data : np.ndarray, radius : int = 8, directions : int = 12, resolu raise ValueError("svf only accepts numpy arrays with first dim of size 1") nb_directions = directions - ''' - radius = kwargs.get('radius', 8) - nb_directions = kwargs.get('directions', 12) - resolution = kwargs.get('resolution', 0.5) - altitude = kwargs.get('altitude', None) - ''' + # initialize output shape = input_data.shape out = np.zeros(shape, dtype=np.float32) @@ -784,12 +775,7 @@ def hillshade(input_data : np.ndarray, elevation : float = 0.0, azimuth : float raise ValueError("hillshade only accepts 3 dims numpy arrays") if input_data.shape[0] != 1: raise ValueError("hillshade only accepts numpy arrays with first dim of size 1") - ''' - elevation = np.radians(kwargs.get('elevation', 0.0)) - azimuth = kwargs.get('azimuth', 0.0) - radius = kwargs.get('radius', 8) - resolution = kwargs.get('resolution', 0.5) - ''' + # initialize output shape = input_data.shape out = np.zeros(shape, dtype=bool) diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 270c9912..bb366213 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -315,7 +315,6 @@ def with_outliers(self, sigma: float): :obj:`eolab.rastertools.Zonalstats`: the current instance so that it is possible to chain the with... calls (fluent API) """ - print(self._stats) # Manage sigma computation option that requires mean + std dev computation if "mean" not in self._stats: self._stats.append("mean") diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index acaff0c9..a0306b29 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -14,6 +14,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_radioindice/") @@ -36,16 +37,16 @@ def test_radioindice_process_file_merge(): # create output dir and clear its content if any utils4test.create_outdir() - inputfile = utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" indices = [indice for indice in Radioindice.get_default_indices()] tool = Radioindice(indices) - tool.with_output(utils4test.outdir, merge=True) - tool.with_roi(utils4test.indir + "COMMUNE_32001.shp") + tool.with_output(RastertoolsTestsData.tests_output_data_dir , merge=True) + tool.with_roi(RastertoolsTestsData.tests_input_data_dir + "/COMMUNE_32001.shp") outputs = tool.process_file(inputfile) assert outputs == [ - utils4test.outdir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-indices.tif"] + RastertoolsTestsData.tests_output_data_dir + "/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-indices.tif"] utils4test.clear_outdir() @@ -73,17 +74,17 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo indices = [Radioindice.ndvi, Radioindice.ndwi] tool = Radioindice(indices) - tool.with_output(utils4test.outdir, merge=False) + tool.with_output(RastertoolsTestsData.tests_output_data_dir , merge=False) tool.with_vrt_stored(False) - outputs = tool.process_file(utils4test.indir + inputfile + ".zip") + outputs = tool.process_file(RastertoolsTestsData.tests_input_data_dir + "/" + inputfile + ".zip") # check outputs - assert outputs == [utils4test.outdir + inputfile + "-ndvi.tif", - utils4test.outdir + inputfile + "-ndwi.tif"] + assert outputs == [RastertoolsTestsData.tests_output_data_dir + "/" + inputfile + "-ndvi.tif", + RastertoolsTestsData.tests_output_data_dir + "/" + inputfile + "-ndwi.tif"] gen_files = [inputfile + "-ndvi.tif", inputfile + "-ndwi.tif"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir , __refdir, gen_files) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -109,18 +110,18 @@ def test_radioindice_process_files(): # create output dir and clear its content if any utils4test.create_outdir() - inputfiles = [utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", - utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"] + inputfiles = [RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D.zip", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"] indices = [Radioindice.ndvi, Radioindice.ndwi] tool = Radioindice(indices) - tool.with_output(utils4test.outdir, merge=True) - tool.with_roi(utils4test.indir + "COMMUNE_32001.shp") + tool.with_output(RastertoolsTestsData.tests_output_data_dir, merge=True) + tool.with_roi(RastertoolsTestsData.tests_input_data_dir + "/COMMUNE_32001.shp") outputs = tool.process_files(inputfiles) assert outputs == [ - utils4test.outdir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-indices.tif", - utils4test.outdir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-indices.tif"] + RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-indices.tif", + RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-indices.tif"] utils4test.clear_outdir() @@ -145,11 +146,11 @@ def test_radioindice_incompatible_indice_rastertype(caplog): utils4test.create_outdir() file = "SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz" - inputfile = utils4test.indir + file + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + file indice = RadioindiceProcessing("my_indice").with_channels([BandChannel.mir, BandChannel.swir]) tool = Radioindice([indice]) - tool.with_output(utils4test.outdir) + tool.with_output(RastertoolsTestsData.tests_output_data_dir) caplog.set_level(logging.ERROR) outputs = tool.process_file(inputfile) diff --git a/tests/test_rasterproc.py b/tests/test_rasterproc.py index 95180be1..aa68cc63 100644 --- a/tests/test_rasterproc.py +++ b/tests/test_rasterproc.py @@ -11,6 +11,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_rasterproc/") @@ -51,8 +52,8 @@ def test_compute_sliding(): # create output dir and clear its content if any utils4test.create_outdir() - input_image = utils4test.indir + "RGB_TIF_20170105_013442_test.tif" - output_image = utils4test.outdir + "RGB_TIF_20170105_013442_test-out.tif" + input_image = RastertoolsTestsData.tests_input_data_dir + "/" + "RGB_TIF_20170105_013442_test.tif" + output_image = RastertoolsTestsData.tests_output_data_dir + "/" + "RGB_TIF_20170105_013442_test-out.tif" # Test 2D proc2D = RasterProcessing("Processing per band", algo=algo2D, per_band_algo=True) diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index b471ef0e..1135e2d1 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -19,6 +19,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_rasterproduct/") @@ -41,14 +42,15 @@ def test_rasterproduct_valid_parameters(): """ # archive with one file per band basename = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041" + origin_path = RastertoolsTestsData.tests_input_data_dir + "/".split(os.getcwd() + "/")[-1] file = Path( - utils4test.indir.split(os.getcwd() + "/")[-1] + basename + ".zip") + origin_path + basename + ".zip") prod = RasterProduct(file) assert prod.file == file assert prod.rastertype == RasterType.get("S2_L1C") assert prod.channels == RasterType.get("S2_L1C").channels - band_format = f"/vsizip/" + utils4test.indir.split(os.getcwd() + "/")[-1] + f"{basename}.zip/" + band_format = f"/vsizip/" + origin_path + f"{basename}.zip/" band_format += f"{basename}.SAFE/GRANULE/L1C_T30TYP_A013519_20191008T105335/IMG_DATA/" band_format += "T30TYP_20191008T105029_{}.jp2" assert prod.bands_files == {b: band_format.format(b) for b in prod.rastertype.get_band_ids()} @@ -62,13 +64,13 @@ def test_rasterproduct_valid_parameters(): # archive with one file for all bands basename = "SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82" - file = utils4test.indir.split(os.getcwd() + "/")[-1] + basename + ".tar.gz" + file = origin_path + basename + ".tar.gz" prod = RasterProduct(file) assert prod.file == Path(file) assert prod.rastertype == RasterType.get("SPOT67_GEOSUD") assert prod.channels == [BandChannel.red, BandChannel.green, BandChannel.blue, BandChannel.nir] - band = f"/vsitar/" + utils4test.indir.split(os.getcwd() + "/")[-1] + f"{basename}.tar.gz/SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82/" + band = f"/vsitar/" + origin_path + f"{basename}.tar.gz/SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82/" band += "PROD_SPOT6_001/VOL_SPOT6_001_A/IMG_SPOT6_MS_001_A/" band += "IMG_SPOT6_MS_201805111031189_ORT_SPOT6_20180517_1333011n1b80qobn5ex_1_R1C1.TIF" assert prod.bands_files == {"all": band} @@ -82,7 +84,7 @@ def test_rasterproduct_valid_parameters(): # regular raster file basename = "RGB_TIF_20170105_013442_test.tif" - file = utils4test.indir + basename + file = RastertoolsTestsData.tests_input_data_dir + "/" + basename prod = RasterProduct(file) assert prod.file == Path(file) @@ -115,12 +117,12 @@ def test_rasterproduct_invalid_parameters(): RasterProduct(None) assert "'file' cannot be None" in str(exc.value) - file = utils4test.indir + "InvalidName.zip" + file = RastertoolsTestsData.tests_input_data_dir + "/" + "InvalidName.zip" with pytest.raises(ValueError) as exc: RasterProduct(file) assert f"Unrecognized raster type for input file {file}" in str(exc.value) - file = utils4test.indir + "grid.geojson" + file = RastertoolsTestsData.tests_input_data_dir + "/" + "grid.geojson" with pytest.raises(ValueError) as exc: RasterProduct(file) assert f"Unsupported input file {file}" in str(exc.value) @@ -143,31 +145,31 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): utils4test.create_outdir() # unzip SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip - file = utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + file = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" with zipfile.ZipFile(file) as myzip: - myzip.extractall(utils4test.outdir) + myzip.extractall(RastertoolsTestsData.tests_output_data_dir + "/") # creation of S2 L2A MAJA products - files = [Path(utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"), - Path(utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar"), - Path(utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ"), - Path(utils4test.outdir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9")] + files = [Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"), + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar"), + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ"), + Path(RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9")] for file in files: - with RasterProduct(file, vrt_outputdir=Path(utils4test.outdir)) as prod: - raster = prod.get_raster(roi=Path(utils4test.indir + "COMMUNE_32001.shp"), + with RasterProduct(file, vrt_outputdir=Path(RastertoolsTestsData.tests_output_data_dir + "/")) as prod: + raster = prod.get_raster(roi=Path(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32001.shp"), masks="all") assert Path(raster).exists() - assert raster == utils4test.outdir + utils4test.basename(file) + "-mask.vrt" + assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(file) + "-mask.vrt" ref = [utils4test.basename(file) + ".vrt", utils4test.basename(file) + "-clipped.vrt", utils4test.basename(file) + "-mask.vrt"] if compare: - print(f"compare {utils4test.outdir} ,{__refdir}, {ref}") - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, ref) + print(f"compare {RastertoolsTestsData.tests_output_data_dir} ,{__refdir}, {ref}") + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, ref) assert len(match) == len(ref) assert len(mismatch) == 0 assert len(err) == 0 @@ -182,7 +184,7 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): utils4test.clear_outdir(subdirs=False) # delete the dir resulting from unzip - utils4test.delete_dir(utils4test.outdir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") + utils4test.delete_dir(RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") def test_create_product_S2_L1C(compare, save_gen_as_ref): @@ -207,18 +209,18 @@ def test_create_product_S2_L1C(compare, save_gen_as_ref): utils4test.create_outdir() # creation of S2 L1C product - infile = utils4test.indir + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" + infile = RastertoolsTestsData.tests_input_data_dir + "/" + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" - with RasterProduct(infile, vrt_outputdir=utils4test.outdir) as prod: - raster = prod.get_raster(roi=utils4test.indir + "/COMMUNE_32001.shp", + with RasterProduct(infile, vrt_outputdir=RastertoolsTestsData.tests_output_data_dir + "/") as prod: + raster = prod.get_raster(roi=RastertoolsTestsData.tests_input_data_dir + "/" + "/COMMUNE_32001.shp", masks="all") assert Path(raster).exists() - assert raster == utils4test.outdir + utils4test.basename(infile) + "-clipped.vrt" + assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(infile) + "-clipped.vrt" gen_files = [utils4test.basename(infile) + ".vrt", utils4test.basename(infile) + "-clipped.vrt"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -255,16 +257,16 @@ def test_create_product_S2_L2A_SEN2CORE(compare, save_gen_as_ref): utils4test.create_outdir() # creation of S2 L2A SEN2CORE product - infile = utils4test.indir + "S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" - with RasterProduct(infile, vrt_outputdir=utils4test.outdir) as prod: + infile = RastertoolsTestsData.tests_input_data_dir + "/" + "S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + with RasterProduct(infile, vrt_outputdir=RastertoolsTestsData.tests_output_data_dir + "/") as prod: raster = prod.get_raster() assert Path(raster).exists() - assert raster == utils4test.outdir + utils4test.basename(infile) + ".vrt" + assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(infile) + ".vrt" gen_files = [utils4test.basename(infile) + ".vrt"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -302,15 +304,15 @@ def test_create_product_SPOT67(compare, save_gen_as_ref): # creation of SPOT67 product infile = "SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz" - with RasterProduct(utils4test.indir + infile, vrt_outputdir=utils4test.outdir) as prod: + with RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + infile, vrt_outputdir=RastertoolsTestsData.tests_output_data_dir + "/") as prod: raster = prod.get_raster() assert Path(raster).exists() - assert raster == utils4test.outdir + utils4test.basename(infile) + ".vrt" + assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(infile) + ".vrt" gen_files = [utils4test.basename(infile) + ".vrt"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -343,7 +345,7 @@ def test_create_product_special_cases(): # creation in memory (without masks) file = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" - with RasterProduct(utils4test.indir + file) as prod: + with RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + file) as prod: assert prod.get_raster(masks=None).endswith(utils4test.basename(file) + ".vrt") # check if product can be opened by rasterio dataset = prod.open() @@ -351,8 +353,8 @@ def test_create_product_special_cases(): # creation in memory (with masks) file = "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar" - with RasterProduct(utils4test.indir + file) as prod: - raster = prod.get_raster(roi=Path(utils4test.indir + "COMMUNE_32001.shp"), + with RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + file) as prod: + raster = prod.get_raster(roi=Path(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32001.shp"), bands=prod.rastertype.get_band_ids(), masks=prod.rastertype.get_mask_ids()) @@ -367,9 +369,9 @@ def test_create_product_special_cases(): # creation from a vrt file = "S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.vrt" - with RasterProduct(utils4test.indir + file) as prod: + with RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + file) as prod: raster = prod.get_raster() - assert raster == utils4test.indir + utils4test.basename(file) + ".vrt" + assert raster == RastertoolsTestsData.tests_input_data_dir + "/" + utils4test.basename(file) + ".vrt" assert prod.rastertype == RasterType.get("S2_L2A_SEN2CORE") # check if product can be opened by rasterio dataset = rasterio.open(raster) @@ -377,14 +379,14 @@ def test_create_product_special_cases(): # creation from a directory # unzip S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip - file = utils4test.indir + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" + file = RastertoolsTestsData.tests_input_data_dir + "/" + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" with zipfile.ZipFile(file) as myzip: - myzip.extractall(utils4test.outdir) + myzip.extractall(RastertoolsTestsData.tests_output_data_dir + "/") dirname = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.SAFE" - with RasterProduct(file, vrt_outputdir=Path(utils4test.outdir)) as prod: + with RasterProduct(file, vrt_outputdir=Path(RastertoolsTestsData.tests_output_data_dir + "/")) as prod: raster = prod.get_raster() - assert raster == utils4test.outdir + utils4test.basename(file) + ".vrt" + assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(file) + ".vrt" # check if product can be opened by rasterio dataset = rasterio.open(raster) dataset.close() @@ -395,7 +397,7 @@ def test_create_product_special_cases(): # file = "SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz" # channels = [BandChannel.red, BandChannel.green, BandChannel.blue, BandChannel.swir] # with pytest.raises(ValueError) as exc: - # prod = RasterProduct(utils4test.indir + file, + # prod = RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + file, # RasterType.get("SPOT67_GEOSUD"), # channels) # msg = f"RasterType does not contain all the channels in {channels}" @@ -403,7 +405,7 @@ def test_create_product_special_cases(): # creation of a product with bands that do not exist in the product (but that # do exist in rastertype) - file = utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + file = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" with zipfile.ZipFile(file) as myzip: names = myzip.namelist() selection = [] @@ -413,9 +415,9 @@ def test_create_product_special_cases(): n.endswith("FRE_B4.tif") or \ n.endswith("CLM_R1.tif"): selection.append(n) - myzip.extractall(utils4test.outdir, selection) + myzip.extractall(RastertoolsTestsData.tests_output_data_dir + "/", selection) - file = Path(utils4test.outdir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") + file = Path(RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") with pytest.raises(ValueError) as exc: prod = RasterProduct(file) diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 266a0ec1..5a984484 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -7,7 +7,7 @@ from click.testing import CliRunner from pathlib import Path -from eolab.rastertools import rastertools, RastertoolConfigurationException +from eolab.rastertools import rastertools from eolab.rastertools.product import RasterType from . import utils4test @@ -98,24 +98,21 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec else: check_logs = False - print(self.args) - try: rastertools(self.args) except SystemExit as wrapped_exception: - print(wrapped_exception) if check_sys_exit: # Check if the exit code matches the expected value assert wrapped_exception.code == self._sys_exit, (f"Expected exit code {self._sys_exit}, but got {wrapped_exception.code}") # check list of outputs if check_outputs: - outdir = Path(utils4test.outdir) + outdir = Path(RastertoolsTestsData.tests_output_data_dir + "/") assert sorted([x.name for x in outdir.iterdir()]) == sorted(self._outputs) if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, self._refdir, self._outputs) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", self._refdir, self._outputs) assert len(match) == 3 assert len(mismatch) == 0 assert len(err) == 0 @@ -125,8 +122,6 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec # check logs if check_logs: - print('...'*20) - print(caplog.record_tuples) for i, log in enumerate(self._logs): assert caplog.record_tuples[i] == log diff --git a/tests/test_rastertype.py b/tests/test_rastertype.py index b0611741..9981136c 100644 --- a/tests/test_rastertype.py +++ b/tests/test_rastertype.py @@ -13,6 +13,8 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData + def test_rastertype_valid_parameters(): # S2 L1C @@ -352,7 +354,7 @@ def test_rastertype_SPOT67(): def test_rastertype_additional(): - file = utils4test.indir + "additional_rastertypes.json" + file = RastertoolsTestsData.tests_input_data_dir + "/" + "additional_rastertypes.json" with open(file) as json_content: RasterType.add(json.load(json_content)) diff --git a/tests/test_speed.py b/tests/test_speed.py index f7deab52..18295637 100644 --- a/tests/test_speed.py +++ b/tests/test_speed.py @@ -12,6 +12,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_radioindice/") @@ -28,19 +29,19 @@ def test_speed_process_files(compare : bool, save_gen_as_ref : bool): date1 = RasterType.get("S2_L2A_MAJA").get_date(name1) - files = [utils4test.indir + name1 + "-ndvi.tif", - utils4test.indir + name2 + "-ndvi.tif"] + files = [RastertoolsTestsData.tests_input_data_dir + "/" + name1 + "-ndvi.tif", + RastertoolsTestsData.tests_input_data_dir + "/" + name2 + "-ndvi.tif"] tool = Speed() - tool.with_output(utils4test.outdir) + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/") outputs = tool.process_files(files) exp_outs = [name2 + "-ndvi-speed-" + date1.strftime('%Y%m%d-%H%M%S') + ".tif"] - assert outputs == [utils4test.outdir + exp_out for exp_out in exp_outs] + assert outputs == [RastertoolsTestsData.tests_output_data_dir + "/" + exp_out for exp_out in exp_outs] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, exp_outs) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, exp_outs) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_stats.py b/tests/test_stats.py index 692a1b3b..cef6a987 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -8,6 +8,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_stats/") @@ -16,8 +17,8 @@ def test_compute_zonal_default_stats(): - raster = utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - geojson = utils4test.indir + "COMMUNE_32xxx.geojson" + raster = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + geojson = RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson" stats_to_compute = DEFAULT_STATS categorical = False bands = [1] @@ -63,8 +64,8 @@ def test_compute_zonal_default_stats(): def test_compute_zonal_extra_stats(): - raster = utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - geojson = utils4test.indir + "COMMUNE_32xxx.geojson" + raster = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + geojson = RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson" stats_to_compute = EXTRA_STATS + ["valid"] categorical = False bands = [1] @@ -167,8 +168,8 @@ def test_compute_zonal_extra_stats(): def test_compute_zonal_categorical(): - raster = utils4test.indir + "OCS_2017_CESBIO_extract.tif" - geojson = utils4test.indir + "COMMUNE_59xxx.geojson" + raster = RastertoolsTestsData.tests_input_data_dir + "/" + "OCS_2017_CESBIO_extract.tif" + geojson = RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_59xxx.geojson" stats_to_compute = ['count'] categorical = True bands = [1] @@ -205,10 +206,10 @@ def test_compute_zonal_categorical(): def test_compute_zonal_stats_per_category(): - raster = utils4test.indir + "DSM_PHR_Dunkerque.tif" - geojson = utils4test.indir + "COMMUNE_59xxx.geojson" - catgeojson = utils4test.indir + "OSO_2017_classification_dep59.shp" - catlabels = utils4test.indir + "OSO_nomenclature_2017.json" + raster = RastertoolsTestsData.tests_input_data_dir + "/" + "DSM_PHR_Dunkerque.tif" + geojson = RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_59xxx.geojson" + catgeojson = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_2017_classification_dep59.shp" + catlabels = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017.json" stats_to_compute = DEFAULT_STATS bands = [1] geometries = vector.reproject(vector.filter(geojson, raster), raster) diff --git a/tests/test_tiling.py b/tests/test_tiling.py index 54180919..92197c4c 100644 --- a/tests/test_tiling.py +++ b/tests/test_tiling.py @@ -10,6 +10,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_tiling/") @@ -21,14 +22,14 @@ def test_tiling_process_file(compare, save_gen_as_ref): inputfile = "tif_file" geometryfile = "grid.geojson" - tool = Tiling(utils4test.indir + geometryfile) - tool.with_output(utils4test.outdir) + tool = Tiling(RastertoolsTestsData.tests_input_data_dir + "/" + geometryfile) + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/") tool.with_id_column("id", [77, 93]) - tool.process_file(utils4test.indir + inputfile + ".tif") + tool.process_file(RastertoolsTestsData.tests_input_data_dir + "/" + inputfile + ".tif") gen_files = [inputfile + "_tile77.tif", inputfile + "_tile93.tif"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_vector.py b/tests/test_vector.py index a8661393..8d9e7c25 100644 --- a/tests/test_vector.py +++ b/tests/test_vector.py @@ -13,6 +13,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_vector/") @@ -22,41 +23,41 @@ def test_reproject_filter(compare, save_gen_as_ref): utils4test.create_outdir() reproj_geoms = vector.reproject( - utils4test.indir + "COMMUNE_32xxx.geojson", - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") filtered_geoms = vector.filter( reproj_geoms, - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") assert len(filtered_geoms) == 19 filtered_geoms = vector.filter( reproj_geoms, - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", within=True) assert len(filtered_geoms) == 2 geoms = vector.reproject( - vector.filter(utils4test.indir + "COMMUNE_32xxx.geojson", - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + vector.filter(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") assert len(geoms) == 19 geoms = vector.reproject( - vector.filter(utils4test.indir + "COMMUNE_32xxx.geojson", - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + vector.filter(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", within=True), - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") assert len(geoms) == 2 geoms = vector.reproject( - vector.filter(Path(utils4test.indir + "COMMUNE_32xxx.geojson"), - Path(utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif")), - Path(utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), - output=Path(utils4test.outdir + "reproject_filter.geojson")) + vector.filter(Path(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson"), + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif")), + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), + output=Path(RastertoolsTestsData.tests_output_data_dir + "/" + "reproject_filter.geojson")) assert len(geoms) == 19 gen_files = ["reproject_filter.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -64,16 +65,16 @@ def test_reproject_filter(compare, save_gen_as_ref): # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(gen_files, __refdir) - geometries = gpd.read_file(utils4test.indir + "COMMUNE_32xxx.geojson") + geometries = gpd.read_file(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson") geoms = vector.reproject( vector.filter(geometries, - Path(utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif")), - Path(utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), - output=Path(utils4test.outdir + "reproject_filter.geojson")) + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif")), + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), + output=Path(RastertoolsTestsData.tests_output_data_dir + "/" + "reproject_filter.geojson")) assert len(geoms) == 19 gen_files = ["reproject_filter.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -89,18 +90,18 @@ def test_reproject_dissolve(compare, save_gen_as_ref): utils4test.create_outdir() geoms = vector.reproject( - vector.dissolve(utils4test.indir + "COMMUNE_32xxx.geojson"), - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + vector.dissolve(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson"), + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") assert len(geoms) == 1 geoms = vector.reproject( - vector.dissolve(Path(utils4test.indir + "COMMUNE_32xxx.geojson")), - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", - output=Path(utils4test.outdir + "reproject_dissolve.geojson")) + vector.dissolve(Path(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson")), + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + output=Path(RastertoolsTestsData.tests_output_data_dir + "/" + "reproject_dissolve.geojson")) assert len(geoms) == 1 gen_files = ["reproject_dissolve.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -115,18 +116,18 @@ def test_clip(compare, save_gen_as_ref): # create output dir and clear its content if any utils4test.create_outdir() - geoms = vector.clip(utils4test.indir + "COMMUNE_32xxx.geojson", - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + geoms = vector.clip(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") assert len(geoms) == 19 geoms = vector.clip( - Path(utils4test.indir + "COMMUNE_32xxx.geojson"), - Path(utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), - output=Path(utils4test.outdir + "clip.geojson")) + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson"), + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), + output=Path(RastertoolsTestsData.tests_output_data_dir + "/" + "clip.geojson")) assert len(geoms) == 19 gen_files = ["clip.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -142,16 +143,16 @@ def test_get_raster_shape(compare, save_gen_as_ref): utils4test.create_outdir() geoms = vector.get_raster_shape( - utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif") assert len(geoms) == 1 geoms = vector.get_raster_shape( - Path(utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), - Path(utils4test.outdir + "raster_outline.geojson")) + Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif"), + Path(RastertoolsTestsData.tests_output_data_dir + "/" + "raster_outline.geojson")) assert len(geoms) == 1 gen_files = ["raster_outline.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 906b5bac..11c7cb12 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -13,6 +13,7 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" +from .utils4test import RastertoolsTestsData __refdir = utils4test.get_refdir("test_zonalstats/") @@ -22,20 +23,20 @@ def test_zonalstats_global(compare, save_gen_as_ref): utils4test.create_outdir() # cas 1 - inputfile = utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" outformat = "ESRI Shapefile" statistics = "min max mean std count range sum".split() categorical = False tool = Zonalstats(statistics, categorical) - tool.with_output(utils4test.outdir, output_format=outformat) + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/", output_format=outformat) tool.with_outliers(1.0) tool.process_file(inputfile) gen_files = ["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-stats.shp", "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-stats-outliers.tif"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -44,18 +45,18 @@ def test_zonalstats_global(compare, save_gen_as_ref): utils4test.copy_to_ref(gen_files, __refdir) # cas 2 - inputfile = utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" outformat = "GeoJSON" statistics = "median majority minority unique percentile_10 percentile_90".split() categorical = False tool = Zonalstats(statistics, categorical) - tool.with_output(utils4test.outdir, output_format=outformat) + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/", output_format=outformat) tool.process_file(inputfile) gen_files = ["SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-stats.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -64,13 +65,13 @@ def test_zonalstats_global(compare, save_gen_as_ref): utils4test.copy_to_ref(gen_files, __refdir) # cas 3 : unrecognized rastertype that disables charting capability, no output file for stats - inputfile = utils4test.indir + "toulouse-mnh.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "toulouse-mnh.tif" statistics = "mean std".split() categorical = False tool = Zonalstats(statistics, categorical) tool.with_output(None) - tool.with_chart(utils4test.outdir + "chart.png") + tool.with_chart(RastertoolsTestsData.tests_output_data_dir + "/" + "chart.png") tool.process_file(inputfile) assert len(tool.generated_stats) == 1 assert len(tool.generated_stats_per_date) == 0 @@ -83,21 +84,21 @@ def test_zonalstats_zonal(compare, save_gen_as_ref): utils4test.create_outdir() # cas 1 - inputfile = utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" outformat = "ESRI Shapefile" statistics = "min max mean std count range sum".split() categorical = False tool = Zonalstats(statistics, categorical) - tool.with_output(utils4test.outdir, output_format=outformat) - tool.with_geometries(geometries=utils4test.indir + "COMMUNE_32xxx.geojson") + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/", output_format=outformat) + tool.with_geometries(geometries=RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson") tool.with_outliers(sigma=1.0) tool.process_file(inputfile) gen_files = ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-stats.shp", "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-stats-outliers.tif"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -106,19 +107,19 @@ def test_zonalstats_zonal(compare, save_gen_as_ref): utils4test.copy_to_ref(gen_files, __refdir) # cas 2 - inputfile = utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" outformat = "GeoJSON" statistics = "median majority minority unique percentile_10 percentile_90".split() categorical = False tool = Zonalstats(statistics, categorical) - tool.with_output(utils4test.outdir, output_format=outformat) - tool.with_geometries(geometries=utils4test.indir + "COMMUNE_32xxx.geojson") + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/", output_format=outformat) + tool.with_geometries(geometries=RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson") tool.process_file(inputfile) gen_files = ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-stats.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -133,21 +134,21 @@ def test_zonalstats_process_files(compare, save_gen_as_ref): # create output dir and clear its content if any utils4test.create_outdir() - inputfiles = [utils4test.indir + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", - utils4test.indir + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"] + inputfiles = [RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"] outformat = "GeoJSON" statistics = "min max mean std count range sum".split() tool = Zonalstats(statistics, prefix="indice") tool.with_output(None, output_format=outformat) - tool.with_geometries(geometries=utils4test.indir + "COMMUNE_32xxx.geojson") - tool.with_chart(chart_file=utils4test.outdir + "chart.png") + tool.with_geometries(geometries=RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson") + tool.with_chart(chart_file=RastertoolsTestsData.tests_output_data_dir + "/" + "chart.png") tool.process_files(inputfiles) gen_files = ["chart.png"] - assert Path(utils4test.outdir + "chart.png").exists() + assert Path(RastertoolsTestsData.tests_output_data_dir + "/" + "chart.png").exists() if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -163,23 +164,23 @@ def test_zonalstats_category(compare, save_gen_as_ref): utils4test.create_outdir() # cas 1 - classif shapefile sur une ROI composée de plusieurs géométries - inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "DSM_PHR_Dunkerque.tif" outformat = "GeoJSON" statistics = "min max mean std count range sum".split() # Category inputs - categoryfile = utils4test.indir + "OSO_2017_classification_dep59.shp" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + categoryfile = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_2017_classification_dep59.shp" + categorydic = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017.json" tool = Zonalstats(statistics, area=True) - tool.with_output(utils4test.outdir, output_format=outformat) - tool.with_geometries(geometries=utils4test.indir + "COMMUNE_59xxx.geojson") + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/", output_format=outformat) + tool.with_geometries(geometries=RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_59xxx.geojson") tool.with_per_category(category_file=categoryfile, category_index="Classe", category_labels_json=categorydic) tool.process_file(inputfile) gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -188,13 +189,13 @@ def test_zonalstats_category(compare, save_gen_as_ref): utils4test.copy_to_ref(gen_files, __refdir) # cas 2 - classif raster sur l'emprise globale du DSM - inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" + inputfile = RastertoolsTestsData.tests_input_data_dir + "/" + "DSM_PHR_Dunkerque.tif" outformat = "GeoJSON" - categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + categoryfile = RastertoolsTestsData.tests_input_data_dir + "/" + "OCS_2017_CESBIO_extract.tif" + categorydic = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017.json" tool = Zonalstats(statistics, area=True) - tool.with_output(utils4test.outdir, output_format=outformat) + tool.with_output(RastertoolsTestsData.tests_output_data_dir + "/", output_format=outformat) tool.with_per_category(category_file=categoryfile, category_index="Classe", category_labels_json=categorydic) tool.process_file(inputfile) @@ -202,7 +203,7 @@ def test_zonalstats_category(compare, save_gen_as_ref): # gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] # do not compare, order of features can change in output # if compare: - # match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + # match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) # assert len(match) == 1 # assert len(mismatch) == 0 # assert len(err) == 0 @@ -238,8 +239,8 @@ def test_zonalstats_category_errors(): # cas 1 - Invalid category file statistics = "min max mean std count range sum".split() # Category inputs - categoryfile = utils4test.indir + "unknown.tif" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + categoryfile = RastertoolsTestsData.tests_input_data_dir + "/" + "unknown.tif" + categorydic = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017.json" tool = Zonalstats(statistics) with pytest.raises(RastertoolConfigurationException) as err: @@ -248,8 +249,8 @@ def test_zonalstats_category_errors(): assert f"File {categoryfile} cannot be read: check format and existence" in str(err.value) # cas 2 - invalid category dictionary - categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" - categorydic = utils4test.indir + "OSO_nomenclature_2017_wrong.json" + categoryfile = RastertoolsTestsData.tests_input_data_dir + "/" + "OCS_2017_CESBIO_extract.tif" + categorydic = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017_wrong.json" tool = Zonalstats(statistics) with pytest.raises(RastertoolConfigurationException) as err: diff --git a/tests/utils4test.py b/tests/utils4test.py index a946c661..d9ec5f4e 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -21,19 +21,14 @@ class RastertoolsTestsData: tests_output_data_dir:str = str(project_dir / "tests" / "tests_out") tests_ref_data_dir:str = str(project_dir / "tests" / "tests_refs") -projectdir = RastertoolsTestsData.tests_project_dir + "/" -indir = RastertoolsTestsData.tests_input_data_dir + "/" -outdir = RastertoolsTestsData.tests_output_data_dir + "/" -__root_refdir = RastertoolsTestsData.tests_ref_data_dir + "/" - def get_refdir(testname: str): - return __root_refdir + testname + return RastertoolsTestsData.tests_ref_data_dir + '/' + testname def clear_outdir(subdirs=True): """function to clear content of a dir""" - for file in os.listdir(outdir): - file_path = os.path.join(outdir, file) + for file in os.listdir(RastertoolsTestsData.tests_output_data_dir + '/'): + file_path = os.path.join(RastertoolsTestsData.tests_output_data_dir + '/', file) if os.path.isfile(file_path): os.unlink(file_path) elif subdirs: @@ -42,8 +37,8 @@ def clear_outdir(subdirs=True): def create_outdir(): - if not os.path.isdir(outdir): - os.makedirs(outdir) + if not os.path.isdir(RastertoolsTestsData.tests_output_data_dir + '/'): + os.makedirs(RastertoolsTestsData.tests_output_data_dir + '/') clear_outdir() @@ -53,7 +48,7 @@ def delete_dir(dir): def copy_to_ref(files, refdir): for f in files: - os.replace(outdir + f, refdir + f) + os.replace(RastertoolsTestsData.tests_output_data_dir + '/' + f, refdir + f) def basename(infile): From ae532aa2f475d58b96ee58afe32e3170182ad6b4 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 15 Nov 2024 13:53:44 +0100 Subject: [PATCH 32/56] install: update setup.py --- environment.yml | 2 +- setup.py | 53 +++-- src/eolab/rastertools/main.py | 14 +- src/eolab/rastertools/processing/stats.py | 192 ++++++++++++--- src/eolab/rastertools/processing/vector.py | 103 +++++--- .../rastertools/product/rasterproduct.py | 127 +++++++++- src/eolab/rastertools/radioindice.py | 221 ++++++++++++++---- src/rastertools.egg-info/PKG-INFO | 26 ++- src/rastertools.egg-info/SOURCES.txt | 4 - src/rastertools.egg-info/entry_points.txt | 4 +- src/rastertools.egg-info/requires.txt | 21 +- tests/test_radioindice.py | 9 +- tests/test_rastertools.py | 6 +- tests/test_stats.py | 1 + 14 files changed, 624 insertions(+), 159 deletions(-) diff --git a/environment.yml b/environment.yml index 414e3d24..96531f3e 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: rastertools +name: temp_test channels: - conda-forge diff --git a/setup.py b/setup.py index 34927a8e..6c91ecfb 100644 --- a/setup.py +++ b/setup.py @@ -1,29 +1,10 @@ # -*- coding: utf-8 -*- -""" - Setup file for rastertools. - Use setup.cfg to configure your project. - - This file was generated with PyScaffold 4.0.2. - PyScaffold helps you to put up the scaffold of your new Python project. - Learn more under: https://pyscaffold.org/ -""" -import os from setuptools import setup, find_packages -from sphinx.builders.html import setup_resource_paths - -with open('src/eolab/rastertools/__init__.py') as f: - for line in f: - if line.find("__version__") >= 0: - version = line.split("=")[1].strip() - version = version.strip('"') - version = version.strip("'") - break - if __name__ == "__main__": try: setup(name='rastertools', - version=version, + version="0.1.0", description=u"Collection of tools for raster data", long_description="", classifiers=[], @@ -36,15 +17,33 @@ zip_safe=False, setup_requires = ["setuptools_scm"], install_requires=[ - 'click>=4.0', - 'rasterio>=1.2.0', + 'click', + 'rasterio==1.3.0', + 'pytest>=3.6', + 'pytest-cov', + 'geopandas==0.13', + 'python-dateutil==2.9.0', + 'kiwisolver==1.4.5', + 'fonttools==4.53.1', + 'matplotlib==3.7.3', + 'packaging==24.1', + 'Shapely==1.8.5.post1', + 'tomli==2.0.2', + 'Rtree==1.3.0', + 'Pillow==9.2.0', + 'pip==24.2', + 'pyproj==3.4.0', + 'matplotlib', + 'scipy==1.8', + 'pyscaffold', + 'gdal==3.5.0', + 'tqdm==4.66' ], - extras_require={ - 'test': ['pytest>=3.6'], - }, entry_points=""" - """, - python_requires='>=3.9', + [rasterio.rio_plugins] + rastertools=eolab.rastertools.main:rastertools + """, + python_requires='==3.8.13', use_scm_version={"version_scheme": "no-guess-dev"}) except: # noqa print( diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index 355ae395..4e00af5d 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -134,7 +134,7 @@ def add_custom_rastertypes(rastertypes): CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) -@with_plugins(iter_entry_points('core_package.cli_plugins')) +# @with_plugins(iter_entry_points('rasterio.plugins')) @click.group(context_settings=CONTEXT_SETTINGS) @click.option( '-t', '--rastertype', @@ -233,12 +233,12 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo rastertools.add_command(zonalstats, name = "zonalstats") -@rastertools.result_callback() -@click.pass_context -def handle_result(ctx): - if ctx.invoked_subcommand is None: - click.echo(ctx.get_help()) - ctx.exit() +# @rastertools.result_callback() +# @click.pass_context +# def handle_result(ctx): +# if ctx.invoked_subcommand is None: +# click.echo(ctx.get_help()) +# ctx.exit() def run(*args, **kwargs): """ diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index b4ae720b..a4284ab8 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -26,25 +26,49 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, bands: List[int] = [1], stats: List[str] = ["min", "max", "mean", "std"], categorical: bool = False) -> List[List[Dict[str, float]]]: - """Compute the statistics of an input image for each feature in the shapefile + """ + Compute zonal statistics for an input raster image over specified geometries. + + This function calculates statistical summaries (e.g., min, max, mean, standard deviation) + for each feature in the provided geometries (GeoDataFrame) using the specified raster image. + If the raster is categorical, the function can compute counts of unique values. Args: geoms (GeoDataFrame): - Geometries where to compute stats + A GeoDataFrame containing geometries (e.g., polygons) for which statistics will be computed. image (str): - Filename of the input image to process - bands ([int], optional, default=[1]): - List of bands to process in the input image - stats ([str], optional, default=["min", "max","mean", "std"]): - List of stats to computed - categorical (bool, optional, default=False): - Whether to treat the input raster as categorical + Path to the raster image file to process. + bands (List[int], optional): + A list of raster band indices to process. Defaults to [1] (the first band). + stats (List[str], optional): + A list of statistical metrics to compute. Possible values include: + - "min": Minimum value within the geometry. + - "max": Maximum value within the geometry. + - "mean": Mean (average) value within the geometry. + - "std": Standard deviation within the geometry. + - Other metrics may be supported based on the implementation of `_compute_stats`. + Defaults to ["min", "max", "mean", "std"]. + categorical (bool, optional): + If True, treats the raster as categorical, computing counts of unique values within each geometry. + Defaults to False. Returns: - statistics: a list of list of dictionnaries. First list on ROI, second on bands. - Dict associates the stat names and the stat values. + List[List[Dict[str, float]]]: + A nested list where: + - The outer list corresponds to each geometry in the GeoDataFrame. + - The inner list corresponds to each band processed. + - Each dictionary contains the computed statistics, with stat names as keys and their values as values. + + Example: + ``` + import geopandas as gpd + from rastertools import compute_zonal_stats + + geoms = gpd.read_file("polygons.shp") + stats = compute_zonal_stats(geoms, "input_image.tif", bands=[1, 2], stats=["mean", "std"]) + print(stats) + ``` """ - statistics = [] nb_geoms = len(geoms) with rasterio.open(image) as src: geom_gen = (geoms.iloc[i].geometry for i in range(nb_geoms)) @@ -69,29 +93,77 @@ def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, categories: gpd.GeoDataFrame = None, category_index: str = 'Classe', category_labels: Dict[str, str] = None): - """Compute the statistics of an input image for each feature in the shapefile + """ + Compute zonal statistics for an input raster image, categorized by specified subregions. + + This function calculates statistical metrics for a raster image over a set of geometries + (e.g., polygons) provided in `geoms`. If a set of categories (subregions within each geometry) + is provided, statistics are computed separately for each category within each geometry. Args: geoms (GeoDataFrame): - Geometries where to compute stats + A GeoDataFrame containing the input geometries (e.g., polygons) to compute + statistics over. image (str): - Filename of the input image to process - bands ([int], optional, default=[1]): - List of bands to process in the input image - stats ([str], optional, default=["min", "max", "mean", "std"]): - List of stats to computed - categories (GeoDataFrame, optional, default=None): - The geometries defining the categories. - category_index (str, optional, default='Classe'): - Name of the column in category file (when it is a vector) that - contains the category index - category_labels (Dict[str, str], optional, default=None): - Dict that associates the category values and category names + The file path to the input raster image. + bands (List[int], optional): + A list of raster band indices to process. Defaults to [1] (the first band). + stats (List[str], optional): + A list of statistical metrics to compute. Supported values include: + - "min": Minimum value within the geometry. + - "max": Maximum value within the geometry. + - "mean": Mean value within the geometry. + - "std": Standard deviation within the geometry. + Defaults to ["min", "max", "mean", "std"]. + categories (GeoDataFrame, optional): + A GeoDataFrame containing category geometries that define subregions of the + input geometries. Defaults to None. + category_index (str, optional): + The column in the `categories` GeoDataFrame that identifies category labels + for each geometry. Defaults to 'Classe'. + category_labels (Dict[str, str], optional): + A dictionary mapping category values (from `category_index`) to human-readable + labels. If provided, these labels replace category values in the output. + Defaults to None. Returns: - statistics ([[Dict[str, float]]]): a list of list of dictionnaries. - First list on ROI, second on bands. Dict associates the stat names and the stat values. - + List[List[Dict[str, float]]]: + A nested list of dictionaries containing the computed statistics: + - Outer list corresponds to each input geometry in `geoms`. + - Inner list corresponds to each raster band being processed. + - Each dictionary maps statistic names (e.g., "mean", "max") to their respective values. + + Raises: + IOError: + If any input geometry is not of type Polygon or MultiPolygon. + + Notes: + - For each geometry in `geoms`, the function subdivides it into subregions based + on the geometries in `categories` (if provided). Statistics are then computed + for each subregion separately. + - The function assumes that the raster image is georeferenced and aligned with + the coordinate system of the input geometries. + + Example: + ``` + import geopandas as gpd + from rastertools import compute_zonal_stats_per_category + + geoms = gpd.read_file("regions.shp") + categories = gpd.read_file("landcover.shp") + image_path = "satellite_image.tif" + + stats = compute_zonal_stats_per_category( + geoms=geoms, + image=image_path, + categories=categories, + category_index="Land_Type", + category_labels={1: "Forest", 2: "Urban", 3: "Water"} + ) + + for geometry_stats in stats: + print(geometry_stats) + ``` """ def _get_list_of_polygons(geom): """Get the list of polygons from the geometry""" @@ -214,19 +286,63 @@ def extract_zonal_outliers(geoms: gpd.GeoDataFrame, image: str, outliers_image: def plot_stats(chartfile: str, stats_per_date: Dict[datetime.datetime, gpd.GeoDataFrame], stats: List[str] = ["min", "max", "mean", "std"], index_name: str = 'ID', display: bool = False): - """Plot the statistics. + """ + Plot temporal statistics for geometries across multiple dates. + + This function visualizes the evolution of specified statistics (e.g., "min", "mean") + over time for different zones defined in the input GeoDataFrames. The output is + saved as a chart file, and optionally displayed. Args: chartfile (str): - Name of the chartfile to generate + Path to the file where the generated chart will be saved. stats_per_date (Dict[datetime.datetime, gpd.GeoDataFrame]): - A dict that associates a date and the statistics for this date - stats ([str], optional, default=["min", "max", "mean", "std"]): - List of stats to plot - index_name (str, optional, default='ID'): - List of bands to process in the input image - display (bool, optional, default=False): - Whether to display the generated plot + A dictionary mapping each date to a GeoDataFrame containing the statistics + for that date. Each GeoDataFrame should include the specified `index_name` + column and relevant statistics columns. + stats (List[str], optional): + A list of statistics to plot (e.g., "min", "max", "mean", "std"). Defaults + to ["min", "max", "mean", "std"]. + index_name (str, optional): + Name of the column in the GeoDataFrames that uniquely identifies the zones + (e.g., region IDs). Defaults to 'ID'. + display (bool, optional): + If `True`, the generated plot is displayed after saving. Defaults to `False`. + + Raises: + ValueError: + If the specified `index_name` is not present in the combined GeoDataFrame. + + Notes: + - The `stats_per_date` dictionary must be ordered or sortable by date to ensure + proper time-series plotting. + - Each GeoDataFrame in `stats_per_date` should have columns named in the format + `.` (e.g., "temperature.mean"). + + Example: + ``` + import geopandas as gpd + import datetime + from plot_tools import plot_stats + + # Example input + stats_per_date = { + datetime.datetime(2023, 1, 1): gpd.GeoDataFrame({...}), + datetime.datetime(2023, 2, 1): gpd.GeoDataFrame({...}), + } + + plot_stats( + chartfile="output_chart.png", + stats_per_date=stats_per_date, + stats=["mean", "std"], + index_name="RegionID", + display=True + ) + ``` + + Output: + - Saves a time-series plot of the specified statistics as `chartfile`. + - Optionally displays the plot if `display=True`. """ # convert dates to datenumber format diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 22b0b1de..fa524a97 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -157,17 +157,34 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str geoms_crs = _get_geoms_crs(geometries) file = raster.as_posix() if isinstance(raster, Path) else raster - with rasterio.open(file) as dataset: - if(geoms_crs != dataset.crs): - reprojected_geoms = geometries.to_crs(dataset.crs) - else: - reprojected_geoms = geometries + print(file) + + + # with rasterio.open(file) as dataset: + # print(type(dataset.crs)) + # print(geoms_crs) + # if (geoms_crs != dataset.crs): + # reprojected_geoms = geometries.to_crs(dataset.crs) + # else: + # reprojected_geoms = geometries + # print(reprojected_geoms) + # if output: + # outfile = output.as_posix() if isinstance(output, Path) else output + # reprojected_geoms.to_file(outfile, driver=driver) + + dataset = gdal.Open(file) + print(dataset.GetProjection()) + if(geoms_crs != dataset.GetProjection()): + reprojected_geoms = geometries.to_crs(dataset.GetProjection()) + else: + reprojected_geoms = geometries + print(reprojected_geoms) - if output: - outfile = output.as_posix() if isinstance(output, Path) else output - reprojected_geoms.to_file(outfile, driver=driver) + if output: + outfile = output.as_posix() if isinstance(output, Path) else output + reprojected_geoms.to_file(outfile, driver=driver) - return reprojected_geoms + return reprojected_geoms def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], @@ -189,6 +206,7 @@ def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], geometries = _get_geoms(geoms) geometries['COMMON'] = 0 + print(geometries) union = geometries.dissolve(by='COMMON', as_index=False) union = union.drop(columns='COMMON') @@ -196,6 +214,7 @@ def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], outfile = output.as_posix() if isinstance(output, Path) else output union.to_file(outfile, driver=driver) + print(union) return union @@ -308,29 +327,59 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], """ pinput = input_image.as_posix() if isinstance(input_image, Path) else input_image + print(pinput) poutput = output_image.as_posix() if isinstance(output_image, Path) else output_image geometries = reproject(dissolve(roi), pinput) geom_bounds = geometries.total_bounds - with rasterio.open(pinput) as raster: - rst_bounds = raster.bounds - bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), - math.floor(max(rst_bounds[1], geom_bounds[1])), - math.ceil(min(rst_bounds[2], geom_bounds[2])), - math.ceil(min(rst_bounds[3], geom_bounds[3]))) - geotransform = raster.get_transform() - width = np.abs(geotransform[1]) - height = np.abs(geotransform[5]) - - ds = gdal.Warp(destNameOrDestDS=poutput, - srcDSOrSrcDSTab=pinput, - outputBounds=bounds, targetAlignedPixels=True, - cutlineDSName=roi, - cropToCutline=False, - xRes=width, yRes=height, - format="VRT") - del ds + # with rasterio.open(pinput) as raster: + # rst_bounds = raster.bounds + # + # print(rst_bounds) + # bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), + # math.floor(max(rst_bounds[1], geom_bounds[1])), + # math.ceil(min(rst_bounds[2], geom_bounds[2])), + # math.ceil(min(rst_bounds[3], geom_bounds[3]))) + # geotransform = raster.get_transform() + # + # print(geotransform) + # width = np.abs(geotransform[1]) + # height = np.abs(geotransform[5]) + # + # ds = gdal.Warp(destNameOrDestDS=poutput, + # srcDSOrSrcDSTab=pinput, + # outputBounds=bounds, targetAlignedPixels=True, + # cutlineDSName=roi, + # cropToCutline=False, + # xRes=width, yRes=height, + # format="VRT") + # del ds + + raster = gdal.Open(pinput) + geotransform = list(raster.GetGeoTransform()) + bottom = geotransform[3] + raster.RasterYSize * geotransform[5] + right = geotransform[0] + raster.RasterXSize * geotransform[1] + rst_bounds = [geotransform[0],bottom,right,geotransform[3]] + print(rst_bounds) + + bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), + math.floor(max(rst_bounds[1], geom_bounds[1])), + math.ceil(min(rst_bounds[2], geom_bounds[2])), + math.ceil(min(rst_bounds[3], geom_bounds[3]))) + width = np.abs(geotransform[1]) + height = np.abs(geotransform[5]) + + ds = gdal.Warp(destNameOrDestDS=poutput, + srcDSOrSrcDSTab=pinput, + outputBounds=bounds, targetAlignedPixels=True, + cutlineDSName=roi, + cropToCutline=False, + xRes=width, yRes=height, + format="VRT") + del ds + + def vectorize(category_raster: Union[Path, str], raster: Union[Path, str], diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 573fe00b..018d0119 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -12,7 +12,11 @@ import zipfile import tempfile from uuid import uuid4 +import xml.etree.ElementTree as ET +from numpy import dtype +from rasterio.io import MemoryFile +from rasterio.vrt import WarpedVRT from osgeo import gdal import rasterio @@ -111,11 +115,29 @@ def __exit__(self, *args): """Exit method for with statement, it cleans the in memory vrt products""" self.free_in_memory_vrts() + def create_in_memory_vrt(self, vrt_content): + """ + Create an in-memory VRT using Rasterio's MemoryFile. + + Args: + vrt_content (str): The XML content of the VRT file. + """ + with MemoryFile() as memfile: + # Write the VRT content into the memory file + memfile.write(vrt_content.encode('utf-8')) + dataset = memfile.open() # Open the VRT as a dataset + self._in_memory_vrts.append(memfile) + def free_in_memory_vrts(self): - """Free in memory vrts""" + """ + Free in-memory VRTs by closing all MemoryFile objects. + """ for vrt in self._in_memory_vrts: gdal.Unlink(vrt.as_posix()) self._in_memory_vrts = [] + # for vrt in self._in_memory_vrts: + # vrt.close() # Closes and cleans up the memory file + # self._in_memory_vrts = [] @property def file(self) -> Path: @@ -379,6 +401,8 @@ def __create_vrt(self, """ # convert parameters defined as str to Path outdir = utils.to_path(self._vrt_outputdir, "/vsimem/") + print(bands_files) + print(outdir) basename = utils.get_basename(self.file) _logger.debug("Creating a VRT that handles the bands of the archive product") @@ -403,6 +427,7 @@ def __create_vrt(self, # Create a VRT image with GDAL rasterfile = outdir.joinpath(f"{uuid}{basename}.vrt") + # rasterfile = rasterfile.as_posix() ds = gdal.BuildVRT(rasterfile.as_posix(), bands, VRTNodata=' '.join(nodatavals), @@ -413,6 +438,79 @@ def __create_vrt(self, # free resource from GDAL del ds + # with rasterio.open(list(bands_files.values())[0]) as src: + # width = src.width + # height = src.height + # + # # with rasterio.open(rasterfile, 'w', driver="GTiff", width=width, height=height, count=1) as dataset: + # # dataset.write(bands[0]) + # + # with rasterio.open(rasterfile, 'w', driver="GTiff", width=width, height=height) as src: + # with WarpedVRT(src) as vrt: + # print("VRT created with dimensions:", vrt.width, vrt.height) + + + # free resource from GDAL + # - - - - - - + # # convert parameters defined as str to Path + # outdir = utils.to_path(self._vrt_outputdir, "/vsimem/") + # print(outdir) + # basename = utils.get_basename(self.file) + # rasterfile = outdir.joinpath(f"{uuid}{basename}.vrt") + # + # _logger.debug("Creating a VRT that handles the bands of the archive product") + # + # # Initialize VRT XML structure + # vrt_root = ET.Element("VRTDataset") + # + # # Open the first band to get metadata + # with rasterio.open(list(bands_files.values())[0]) as src: + # vrt_root.set("rasterXSize", str(src.width)) + # vrt_root.set("rasterYSize", str(src.height)) + # vrt_root.set("subClass", "VRTDataset") + # crs_element = ET.SubElement(vrt_root, "SRS") + # crs_element.text = src.crs.to_wkt() + # + # # Add bands + # for i, (band_id, band_path) in enumerate(bands_files.items(), start=1): + # with rasterio.open(band_path) as src_band: + # band_element = ET.SubElement(vrt_root, "VRTRasterBand", attrib={ + # "dataType": src_band.dtypes[0].upper(), + # "band": str(i) + # }) + # source_element = ET.SubElement(band_element, "SimpleSource") + # ET.SubElement(source_element, "SourceFilename", attrib={"relativeToVRT": "1"}).text = str(band_path) + # ET.SubElement(source_element, "SourceBand").text = "1" + # ET.SubElement(source_element, "SourceProperties", attrib={ + # "RasterXSize": str(src_band.width), + # "RasterYSize": str(src_band.height), + # "DataType": src_band.dtypes[0].upper(), + # "BlockXSize": str(src_band.block_shapes[0][0]), + # "BlockYSize": str(src_band.block_shapes[0][1]) + # }) + # ET.SubElement(source_element, "SrcRect", attrib={ + # "xOff": "0", "yOff": "0", + # "xSize": str(src_band.width), "ySize": str(src_band.height) + # }) + # ET.SubElement(source_element, "DstRect", attrib={ + # "xOff": "0", "yOff": "0", + # "xSize": str(src_band.width), "ySize": str(src_band.height) + # }) + # + # # Add masks + # for i, (mask_id, mask_path) in enumerate(masks_files.items(), start=len(bands_files) + 1): + # mask_element = ET.SubElement(vrt_root, "VRTRasterBand", attrib={ + # "dataType": "Byte", + # "band": str(i) + # }) + # source_element = ET.SubElement(mask_element, "SimpleSource") + # ET.SubElement(source_element, "SourceFilename", attrib={"relativeToVRT": "1"}).text = str(mask_path) + # ET.SubElement(source_element, "SourceBand").text = "1" + # + # # Write the VRT to file + # tree = ET.ElementTree(vrt_root) + # with open(rasterfile, "wb") as f: + # tree.write(f, encoding="utf-8", xml_declaration=True) return rasterfile def __wrap(self, input_vrt: Path, roi: Path, uuid: str = "") -> Path: @@ -489,6 +587,33 @@ def __apply_masks(self, input_vrt: Path, nb_bands: int, nb_masks: int, uuid: str return masked_image + # # convert parameters defined as str to Path + # outdir = utils.to_path(self._vrt_outputdir, "/vsimem/") + # basename = utils.get_basename(self.file) + # + # # create a tempdir for generated temporary files + # tempdir = Path(tempfile.gettempdir()) + # temp_image = tempdir.joinpath(f"{uuid}{basename}-temp.vrt") + # with rasterio.open(input_vrt) as src: + # # Create a new in-memory VRT for the bands + # with WarpedVRT(src, crs=src.crs, transform=src.transform, width=src.width, height=src.height, + # count=nb_bands) as vrt: + # # Here you would apply any mask band logic. For simplicity, we assume a method that adds the masks. + # # For each mask, you could apply it using rasterio to create new bands with the mask applied. + # # Add the masks to the VRT using the provided add_masks_to_vrt function + # _logger.debug("Adding mask bands to VRT") + # masks_index = list(range(nb_bands + 1, nb_bands + nb_masks + 1)) + # vrt_new_content = add_masks_to_vrt(temp_image, input_vrt, masks_index, + # self.rastertype.maskfunc) + # + # # Now save this VRT to a file + # masked_image = outdir.joinpath(f"{uuid}{basename}-mask.vrt") + # with open(masked_image, 'wb') as out_vrt: + # out_vrt.write(vrt_new_content) + # + # _logger.debug(f"Generated masked VRT saved to {masked_image}") + # return masked_image + def _extract_bands(inputfile: Path, bands_pattern: str, diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 87dd168f..88c68aac 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -10,9 +10,13 @@ from pathlib import Path from typing import List import threading +import geopandas as gpd +import numpy as np import rasterio import numpy.ma as ma +from osgeo import gdal +from rasterio import CRS from tqdm import tqdm from eolab.rastertools import utils @@ -427,7 +431,11 @@ def process_file(self, inputfile: str) -> List[str]: indices.append(indice) # get the raster + print(self.roi) + # self.roi = "tests/tests_data/COMMUNE_32001.shp" + # print(self.roi) raster = product.get_raster(roi=self.roi) + print(type(raster)) # STEP 2: Compute the indices outputs = [] @@ -451,6 +459,51 @@ def process_file(self, inputfile: str) -> List[str]: # return the list of generated files return outputs +def get_raster_profile(raster): + # Open the dataset + + # Get the raster driver + driver = raster.GetDriver().ShortName + + # Get raster dimensions + width = raster.RasterXSize + height = raster.RasterYSize + count = raster.RasterCount + + # Get geotransform and projection + geotransform = raster.GetGeoTransform() + crs = raster.GetProjection() + + # Get data type and block size from the first band + band = raster.GetRasterBand(1) + dtype = gdal.GetDataTypeName(band.DataType) + dtype_rasterio = rasterio.dtypes.get_minimum_dtype(band.DataType) + nodata = band.GetNoDataValue() + + # Get block size and tiled status + blockxsize, blockysize = band.GetBlockSize() + tiled = raster.GetMetadata('IMAGE_STRUCTURE').get('TILED', 'NO') == 'YES' + + # Convert geotransform to Rasterio-compatible affine transform + transform = rasterio.Affine.from_gdal(*geotransform) + + # Build a profile dictionary similar to rasterio + profile = { + "driver": driver, # e.g., "GTiff" + "width": width, + "height": height, + "count": count, + "crs": crs, + "transform": transform, # Affine geotransform + "dtype": dtype_rasterio, + "nodata": nodata, # Nodata value + "blockxsize": blockxsize, # Block width + "blockysize": blockysize, # Block height + "tiled": tiled # Whether the raster is tiled + } + + return profile + def compute_indices(input_image: str, image_channels: List[BandChannel], indice_image: str, indices: List[RadioindiceProcessing], @@ -474,56 +527,142 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], window_size (tuple(int, int), optional, default=(1024, 1024)): Size of windows for splitting the processed image in small parts """ - with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with rasterio.open(input_image) as src: - profile = src.profile - - # set block size to the configured window_size of first indice - blockxsize, blockysize = window_size - if src.width < blockxsize: - blockxsize = utils.highest_power_of_2(src.width) - if src.height < blockysize: - blockysize = utils.highest_power_of_2(src.height) - - # dtype of output data - dtype = indices[0].dtype or rasterio.float32 - - # setup profile for output image - profile.update(driver='GTiff', + print('...'*50) + print(input_image) + # with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + # with rasterio.open(input_image) as src: + # profile = src.profile + # + # print(profile) + # + # # set block size to the configured window_size of first indice + # blockxsize, blockysize = window_size + # print(src.width) + # print(blockxsize) + # if src.width < blockxsize: + # blockxsize = utils.highest_power_of_2(src.width) + # if src.height < blockysize: + # blockysize = utils.highest_power_of_2(src.height) + # + # # dtype of output data + # dtype = indices[0].dtype or rasterio.float32 + # + # # setup profile for output image + # profile.update(driver='GTiff', + # blockxsize=blockysize, blockysize=blockxsize, tiled=True, + # dtype=dtype, nodata=indices[0].nodata, + # count=len(indices)) + # print(type(profile)) + # with rasterio.open(indice_image, "w", **profile) as dst: + # # Materialize a list of destination block windows + # windows = [window for ij, window in dst.block_windows()] + # + # # disable status of tqdm progress bar + # disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + # + # # compute every indices + # for i, indice in enumerate(indices, 1): + # # Get the bands necessary to compute the indice + # bands = [image_channels.index(channel) + 1 for channel in indice.channels] + # + # read_lock = threading.Lock() + # write_lock = threading.Lock() + # + # def process(window): + # """Read input raster, compute indice and write output raster""" + # with read_lock: + # src_array = src.read(bands, window=window, masked=True) + # src_array[src_array == src.nodata] = ma.masked + # src_array = src_array.astype(dtype) + # + # # The computation can be performed concurrently + # result = indice.algo(src_array).astype(dtype).filled(indice.nodata) + # + # with write_lock: + # dst.write_band(i, result, window=window) + # + # # compute using concurrent.futures.ThreadPoolExecutor and tqdm + # for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): + # process(window) + # + # dst.set_band_description(i, indice.name) + # with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + # with rasterio.open(input_image) as src: + # profile = src.profile + + src = gdal.Open(input_image) + profile = get_raster_profile(src) ############################# + print(profile) + geotransform = list(src.GetGeoTransform()) + width = src.RasterXSize + height = src.RasterYSize + + # set block size to the configured window_size of first indice + blockxsize, blockysize = window_size + print(width) + print(blockxsize) + + if width < blockxsize: + blockxsize = utils.highest_power_of_2(width) + if height < blockysize: + blockysize = utils.highest_power_of_2(height) + + # dtype of output data + dtype = indices[0].dtype or rasterio.float32 + + # setup profile for output image + profile.update(driver='GTiff', blockxsize=blockysize, blockysize=blockxsize, tiled=True, dtype=dtype, nodata=indices[0].nodata, count=len(indices)) - with rasterio.open(indice_image, "w", **profile) as dst: - # Materialize a list of destination block windows - windows = [window for ij, window in dst.block_windows()] + print(profile) + print(type(profile)) + nodata = src.GetRasterBand(1).GetNoDataValue() + + with rasterio.open(indice_image, "w", **profile) as dst: + # Materialize a list of destination block windows + windows = [window for ij, window in dst.block_windows()] + print(windows) + + # disable status of tqdm progress bar + disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + + # compute every indices + for i, indice in enumerate(indices, 1): + # Get the bands necessary to compute the indice + bands = [image_channels.index(channel) + 1 for channel in indice.channels] + + read_lock = threading.Lock() + write_lock = threading.Lock() - # disable status of tqdm progress bar - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + def process(window): + """Read input raster, compute indice and write output raster""" + with read_lock: + x_offset = int(window.col_off) + y_offset = int(window.row_off) + x_size = int(window.width) + y_size = int(window.height) - # compute every indices - for i, indice in enumerate(indices, 1): - # Get the bands necessary to compute the indice - bands = [image_channels.index(channel) + 1 for channel in indice.channels] + src_array = np.array([ + src.GetRasterBand(band).ReadAsArray(x_offset, y_offset, x_size, y_size) + for band in bands], dtype=dtype) - read_lock = threading.Lock() - write_lock = threading.Lock() + # Mask nodata values + src_array = np.ma.masked_equal(src_array, nodata) - def process(window): - """Read input raster, compute indice and write output raster""" - with read_lock: - src_array = src.read(bands, window=window, masked=True) - src_array[src_array == src.nodata] = ma.masked - src_array = src_array.astype(dtype) + # src_array = src.read(bands, window=window, masked=True) + # src_array[src_array == src.nodata] = ma.masked + # src_array = src_array.astype(dtype) - # The computation can be performed concurrently - result = indice.algo(src_array).astype(dtype).filled(indice.nodata) + # The computation can be performed concurrently + result = indice.algo(src_array).astype(dtype).filled(indice.nodata) - with write_lock: - dst.write_band(i, result, window=window) + with write_lock: + dst.write_band(i, result, window=window) - # compute using concurrent.futures.ThreadPoolExecutor and tqdm - for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): - process(window) + # compute using concurrent.futures.ThreadPoolExecutor and tqdm + for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): + process(window) - dst.set_band_description(i, indice.name) + dst.set_band_description(i, indice.name) diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 4cc14207..7be60b8c 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -1,8 +1,8 @@ Metadata-Version: 2.1 Name: rastertools -Version: 0.6.1.post1.dev19+gebce3d0.d20241113 -Summary: Compute radiometric indices and zonal statistics on rasters -Home-page: https://github.com/cnes/rastertools +Version: 0.1.0 +Summary: Collection of tools for raster data +Home-page: https://github.com/CNES/rastertools Author: Olivier Queyrut Author-email: olivier.queyrut@cnes.fr License: apache v2 @@ -12,10 +12,28 @@ Project-URL: Issues, https://github.com/cnes/rastertools/issues Platform: any Classifier: Development Status :: 5 - Production/Stable Classifier: Programming Language :: Python +Requires-Python: ==3.8.13 Description-Content-Type: text/x-rst; charset=UTF-8 License-File: LICENSE.txt License-File: AUTHORS.rst -Requires-Dist: importlib-metadata; python_version < "3.8" +Requires-Dist: click +Requires-Dist: rasterio==1.3.0 +Requires-Dist: pytest>=3.6 +Requires-Dist: pytest-cov +Requires-Dist: geopandas==0.13 +Requires-Dist: python-dateutil==2.9.0 +Requires-Dist: kiwisolver==1.4.5 +Requires-Dist: fonttools==4.53.1 +Requires-Dist: matplotlib==3.7.3 +Requires-Dist: packaging==24.1 +Requires-Dist: Shapely==1.8.5.post1 +Requires-Dist: tomli==2.0.2 +Requires-Dist: Pillow==9.2.0 +Requires-Dist: pip==24.2 +Requires-Dist: pyproj==3.4.0 +Requires-Dist: matplotlib +Requires-Dist: scipy==1.8 +Requires-Dist: tqdm==4.66 Provides-Extra: testing Requires-Dist: setuptools; extra == "testing" Requires-Dist: pytest; extra == "testing" diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index d2e4162b..5d7f921e 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -81,7 +81,6 @@ src/eolab/rastertools/__pycache__/utils.cpython-38.pyc src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc src/eolab/rastertools/cli/__init__.py src/eolab/rastertools/cli/filtering.py -src/eolab/rastertools/cli/filtering_dyn.py src/eolab/rastertools/cli/hillshade.py src/eolab/rastertools/cli/radioindice.py src/eolab/rastertools/cli/speed.py @@ -192,9 +191,6 @@ tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GE tests/tests_data/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz.properties tests/tests_data/additional_rastertypes.json tests/tests_data/grid.geojson -tests/tests_data/listing.lst -tests/tests_data/listing2.lst -tests/tests_data/listing3.lst tests/tests_data/tif_file.tif tests/tests_data/toulouse-mnh.tif tests/tests_refs/test_radioindice/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-speed-20180928-105515.tif diff --git a/src/rastertools.egg-info/entry_points.txt b/src/rastertools.egg-info/entry_points.txt index 4366a5f5..7844cc62 100644 --- a/src/rastertools.egg-info/entry_points.txt +++ b/src/rastertools.egg-info/entry_points.txt @@ -1,2 +1,2 @@ -[console_scripts] -rastertools = eolab.rastertools.main:run +[rasterio.plugins] +rastertools = src.eolab.rastertools.main:rastertools diff --git a/src/rastertools.egg-info/requires.txt b/src/rastertools.egg-info/requires.txt index a5ca8f7c..b92b22ec 100644 --- a/src/rastertools.egg-info/requires.txt +++ b/src/rastertools.egg-info/requires.txt @@ -1,6 +1,21 @@ - -[:python_version < "3.8"] -importlib-metadata +click +rasterio==1.3.0 +pytest>=3.6 +pytest-cov +geopandas==0.13 +python-dateutil==2.9.0 +kiwisolver==1.4.5 +fonttools==4.53.1 +matplotlib==3.7.3 +packaging==24.1 +Shapely==1.8.5.post1 +tomli==2.0.2 +Pillow==9.2.0 +pip==24.2 +pyproj==3.4.0 +matplotlib +scipy==1.8 +tqdm==4.66 [testing] setuptools diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index a0306b29..a121497d 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -1,5 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import os import pytest import filecmp @@ -37,12 +38,18 @@ def test_radioindice_process_file_merge(): # create output dir and clear its content if any utils4test.create_outdir() + origin_path = RastertoolsTestsData.tests_input_data_dir + "/".split(os.getcwd() + "/")[-1] + inputfile = RastertoolsTestsData.tests_input_data_dir + "/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + print(inputfile) + inputfile = origin_path+ "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + print(inputfile) indices = [indice for indice in Radioindice.get_default_indices()] tool = Radioindice(indices) + shapefile = RastertoolsTestsData.tests_input_data_dir + "/COMMUNE_32001.shp" tool.with_output(RastertoolsTestsData.tests_output_data_dir , merge=True) - tool.with_roi(RastertoolsTestsData.tests_input_data_dir + "/COMMUNE_32001.shp") + tool.with_roi(shapefile) outputs = tool.process_file(inputfile) assert outputs == [ diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 5a984484..57f7493a 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -283,9 +283,9 @@ def test_speed_command_line_default(): # default with a listing of S2A products f"-v sp -b 1 -o {RastertoolsTestsData.tests_output_data_dir} {lst_file_path}", # default with a list of files - f"--verbose speed --output {RastertoolsTestsData.tests_output_data_dir}" - f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + # f"--verbose speed --output {RastertoolsTestsData.tests_output_data_dir}" + # f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + # f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" ] speed_filenames = [ ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-speed-20180928-105515.tif"], diff --git a/tests/test_stats.py b/tests/test_stats.py index cef6a987..d4b00e5e 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -212,6 +212,7 @@ def test_compute_zonal_stats_per_category(): catlabels = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017.json" stats_to_compute = DEFAULT_STATS bands = [1] + print(vector.clip(catgeojson, raster)) geometries = vector.reproject(vector.filter(geojson, raster), raster) categories = vector.reproject(vector.clip(catgeojson, raster), raster) From a0b8766bbca663f6f9d618ec9dbcf424ef8fe9f0 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Fri, 29 Nov 2024 14:38:07 +0100 Subject: [PATCH 33/56] feat: add vsimem_to_rasterio function --- src/eolab/rastertools/processing/stats.py | 27 +++--- src/eolab/rastertools/processing/vector.py | 31 ++++--- .../rastertools/product/rasterproduct.py | 8 +- src/eolab/rastertools/utils.py | 91 +++++++++++++++++++ src/eolab/rastertools/zonalstats.py | 22 +++-- tests/test_rasterproduct.py | 12 +-- 6 files changed, 140 insertions(+), 51 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index a4284ab8..ae782719 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -18,7 +18,7 @@ from rasterio import features from tqdm import tqdm -from eolab.rastertools.utils import get_metadata_name +from eolab.rastertools.utils import get_metadata_name, vsimem_to_rasterio from eolab.rastertools.processing.vector import rasterize, filter_dissolve @@ -70,20 +70,21 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, ``` """ nb_geoms = len(geoms) - with rasterio.open(image) as src: - geom_gen = (geoms.iloc[i].geometry for i in range(nb_geoms)) - geom_windows = ((geom, features.geometry_window(src, [geom])) for geom in geom_gen) - - statistics = [] - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - for geom, window in tqdm(geom_windows, total=nb_geoms, disable=disable, desc="zonalstats"): - data = src.read(bands, window=window) - transform = src.window_transform(window) - s = _compute_stats((data, transform, [geom], window), - src.nodata, stats, categorical) - statistics.append(s) + src = vsimem_to_rasterio(image) + geom_gen = (geoms.iloc[i].geometry for i in range(nb_geoms)) + geom_windows = ((geom, features.geometry_window(src, [geom])) for geom in geom_gen) + statistics = [] + disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + for geom, window in tqdm(geom_windows, total=nb_geoms, disable=disable, desc="zonalstats"): + data = src.read(bands, window=window) + transform = src.window_transform(window) + + s = _compute_stats((data, transform, [geom], window), + src.nodata, stats, categorical) + statistics.append(s) + src.close() return statistics diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index fa524a97..9787fa7c 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -66,25 +66,26 @@ def filter(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], geoms_crs = _get_geoms_crs(geometries) file = raster.as_posix() if isinstance(raster, Path) else raster - with rasterio.open(file) as dataset: - l, b, r, t = dataset.bounds - px, py = ([l, l, r, r], [b, t, t, b]) - if(geoms_crs != dataset.crs): - px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) + dataset = utils.vsimem_to_rasterio(file) + l, b, r, t = dataset.bounds + px, py = ([l, l, r, r], [b, t, t, b]) - polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) - if within: - # convert geometries into GeoPandasBaseExtended to use the new cix property - filtered_geoms = geometries[geometries.within(polygon)] - else: - filtered_geoms = geometries[geometries.intersects(polygon)] + if(geoms_crs != dataset.crs): + px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) - if output: - outfile = output.as_posix() if isinstance(output, Path) else output - filtered_geoms.to_file(outfile, driver=driver) + polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) + if within: + # convert geometries into GeoPandasBaseExtended to use the new cix property + filtered_geoms = geometries[geometries.within(polygon)] + else: + filtered_geoms = geometries[geometries.intersects(polygon)] - return filtered_geoms + if output: + outfile = output.as_posix() if isinstance(output, Path) else output + filtered_geoms.to_file(outfile, driver=driver) + dataset.close() + return filtered_geoms def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 018d0119..294dda80 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -12,11 +12,8 @@ import zipfile import tempfile from uuid import uuid4 -import xml.etree.ElementTree as ET -from numpy import dtype from rasterio.io import MemoryFile -from rasterio.vrt import WarpedVRT from osgeo import gdal import rasterio @@ -24,6 +21,7 @@ from eolab.rastertools.product import RasterType from eolab.rastertools.product.vrt import add_masks_to_vrt, set_band_descriptions from eolab.rastertools.processing.vector import crop +from eolab.rastertools.utils import vsimem_to_rasterio __author__ = "Olivier Queyrut" __copyright__ = "Copyright 2019, CNES" @@ -229,8 +227,7 @@ def open(self, bands: Union[str, List[str]] = "all", masks: Union[str, List[str]] = "all", roi: Union[Path, str] = None): - """Proxy method to rasterio.open(rasterproduct.get_raster(...))""" - return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) + return vsimem_to_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi)) def get_raster(self, bands: Union[str, List[str]] = "all", @@ -304,7 +301,6 @@ def get_raster(self, set_band_descriptions(masked_image, band_descriptions) rasterfile = masked_image - return rasterfile.as_posix() def __get_bands(self, bands: Union[str, List[str]] = "all"): diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index 7dd9b145..417e2c78 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -9,8 +9,99 @@ - ... """ import math +import tempfile from pathlib import Path +import rasterio +from osgeo import gdal + + +def vsimem_to_rasterio(vsimem_file:str, nodata=None) -> rasterio.io.DatasetReader: + """ + Converts a VSIMEM (in-memory) raster dataset to a Rasterio dataset with optional nodata masking. + + This function opens a raster dataset stored in VSIMEM (virtual file system in memory) + using GDAL, extracts its metadata and data, handles optional nodata values and masks, + and saves it to a temporary GeoTIFF file. It then reopens the file with Rasterio and + returns a Rasterio dataset reader. + + Parameters + ---------- + vsimem_file : str + The path to the VSIMEM file to be converted. This file should be an in-memory GDAL dataset. + + nodata : float, optional + A user-defined nodata value to override the nodata value in the GDAL dataset. + If not provided, the nodata value from the GDAL dataset is used (if available). + + Returns + ------- + rasterio.io.DatasetReader + A Rasterio dataset reader object corresponding to the temporary GeoTIFF created from the VSIMEM file. + + Notes + ----- + - The function assumes the dataset is in a format that is compatible with both GDAL and Rasterio. + - The created temporary file is not deleted automatically. It can be removed manually after use. + - The function reads all raster bands from the dataset, applies the optional nodata masking, + and writes the data to a new GeoTIFF file. + - If a nodata value is provided, the function will apply the mask based on that value to each band. + If no nodata value is set, no mask is applied. + """ + gdal_ds = gdal.Open(vsimem_file) + cols = gdal_ds.RasterXSize + rows = gdal_ds.RasterYSize + bands = gdal_ds.RasterCount + geo_transform = gdal_ds.GetGeoTransform() + projection = gdal_ds.GetProjection() + + gdal_dtype_to_numpy = { + gdal.GDT_Byte: "uint8", + gdal.GDT_UInt16: "uint16", + gdal.GDT_Int16: "int16", + gdal.GDT_UInt32: "uint32", + gdal.GDT_Int32: "int32", + gdal.GDT_Float32: "float32", + gdal.GDT_Float64: "float64", + } + dtype = gdal_dtype_to_numpy[gdal_ds.GetRasterBand(1).DataType] + + data = [gdal_ds.GetRasterBand(i + 1).ReadAsArray() for i in range(bands)] + + masks = [] + for i in range(bands): + band = gdal_ds.GetRasterBand(i + 1) + band_nodata = band.GetNoDataValue() + # Prioriser la valeur nodata de l'utilisateur + nodata_value = nodata if nodata is not None else band_nodata + if nodata_value is not None: + masks.append(data[i] == nodata_value) + else: + masks.append(None) + + with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmpfile: + temp_filename = tmpfile.name + + profile = { + "driver": "GTiff", + "height": rows, + "width": cols, + "count": bands, + "dtype": dtype, + "crs": projection, + "transform": rasterio.transform.Affine.from_gdal(*geo_transform), + "nodata": nodata_value, + } + + with rasterio.open(temp_filename, "w", **profile) as dst: + # Écrire les données + for i, band_data in enumerate(data, start=1): + dst.write(band_data, i) + # Si un masque est défini, l'écrire + if masks[i - 1] is not None: + dst.write_mask((masks[i - 1]).astype("uint8") * 255) + + return rasterio.open(temp_filename) def to_tuple(val): """Convert val as a tuple of two val""" diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index bb366213..29d7bd60 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -17,7 +17,6 @@ """ from typing import List, Dict import datetime -import logging import logging.config from pathlib import Path import json @@ -33,6 +32,7 @@ from eolab.rastertools.processing import extract_zonal_outliers, plot_stats from eolab.rastertools.processing import vector from eolab.rastertools.product import RasterProduct +from eolab.rastertools.utils import vsimem_to_rasterio _logger = logging.getLogger(__name__) @@ -408,15 +408,17 @@ def process_file(self, inputfile: str) -> List[str]: # open raster to get metadata raster = product.get_raster() - with rasterio.open(raster) as rst: - bound = int(rst.count) - indexes = rst.indexes - descr = rst.descriptions - - geotransform = rst.get_transform() - width = np.abs(geotransform[1]) - height = np.abs(geotransform[5]) - area_square_meter = width * height + + rst = vsimem_to_rasterio(raster) + bound = int(rst.count) + indexes = rst.indexes + descr = rst.descriptions + + geotransform = rst.get_transform() + width = np.abs(geotransform[1]) + height = np.abs(geotransform[5]) + area_square_meter = width * height + rst.close() date_str = product.get_date_string('%Y%m%d-%H%M%S') diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index 1135e2d1..7d470aab 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -1,9 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- - import pytest import os -import filecmp import zipfile from pathlib import Path from datetime import datetime @@ -12,7 +10,7 @@ from eolab.rastertools.product import RasterType, BandChannel from eolab.rastertools.product import RasterProduct - +from eolab.rastertools.utils import vsimem_to_rasterio from . import utils4test __author__ = "Olivier Queyrut" @@ -362,10 +360,10 @@ def test_create_product_special_cases(): # check if product can be opened by rasterio with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with rasterio.open(raster) as dataset: - data = dataset.read([1], masked=True) - # pixel corresponding to a value > 0 for a band mask => masked value - assert data.mask[0][350][250] + dataset = vsimem_to_rasterio(raster, nodata=-10000) + data = dataset.read([1], masked=True) + # pixel corresponding to a value > 0 for a band mask => masked value + assert data.mask[0][350][250] # creation from a vrt file = "S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.vrt" From 26af4c683cd1a6b74f7577aa88c1af5cff0ac799 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 2 Dec 2024 10:54:09 +0100 Subject: [PATCH 34/56] refactor: cleaning --- setup.py | 4 +- src/eolab/rastertools/filtering.py | 1 - src/eolab/rastertools/hillshade.py | 1 - src/eolab/rastertools/main.py | 9 - src/eolab/rastertools/processing/sliding.py | 83 ++++---- src/eolab/rastertools/processing/vector.py | 66 ++---- .../rastertools/product/rasterproduct.py | 106 ---------- src/eolab/rastertools/radioindice.py | 197 +++++------------- src/eolab/rastertools/speed.py | 1 - src/eolab/rastertools/svf.py | 1 - src/eolab/rastertools/timeseries.py | 1 - src/eolab/rastertools/utils.py | 2 +- src/rastertools.egg-info/PKG-INFO | 4 + src/rastertools.egg-info/SOURCES.txt | 1 - src/rastertools.egg-info/entry_points.txt | 4 +- src/rastertools.egg-info/requires.txt | 4 + tests/test_radioindice.py | 9 +- tests/test_rasterproduct.py | 4 +- tests/test_rastertools.py | 18 +- tests/test_rastertype.py | 1 - tests/test_speed.py | 2 - tests/test_stats.py | 2 - tests/test_tiling.py | 1 - tests/test_vector.py | 1 - tests/test_zonalstats.py | 1 - tests/utils4test.py | 1 + 26 files changed, 137 insertions(+), 388 deletions(-) diff --git a/setup.py b/setup.py index 6c91ecfb..0c119b19 100644 --- a/setup.py +++ b/setup.py @@ -30,10 +30,12 @@ 'Shapely==1.8.5.post1', 'tomli==2.0.2', 'Rtree==1.3.0', + 'fiona==1.8.21', 'Pillow==9.2.0', + 'sphinx_rtd_theme==3.0.1', 'pip==24.2', 'pyproj==3.4.0', - 'matplotlib', + 'sphinx==7.1.2', 'scipy==1.8', 'pyscaffold', 'gdal==3.5.0', diff --git a/src/eolab/rastertools/filtering.py b/src/eolab/rastertools/filtering.py index 3f503b1c..64fcea2b 100644 --- a/src/eolab/rastertools/filtering.py +++ b/src/eolab/rastertools/filtering.py @@ -4,7 +4,6 @@ This module defines a rastertool named Filtering that can apply different kind of filters on raster images. """ -import logging import logging.config from typing import List, Dict from pathlib import Path diff --git a/src/eolab/rastertools/hillshade.py b/src/eolab/rastertools/hillshade.py index 01646361..7fbbeb99 100644 --- a/src/eolab/rastertools/hillshade.py +++ b/src/eolab/rastertools/hillshade.py @@ -4,7 +4,6 @@ This module defines a rastertool named Hillshade which computes the hillshade of a Digital Height Model corresponding to a given solar position (elevation and azimuth). """ -import logging import logging.config from typing import List from pathlib import Path diff --git a/src/eolab/rastertools/main.py b/src/eolab/rastertools/main.py index 4e00af5d..88bbf0f3 100644 --- a/src/eolab/rastertools/main.py +++ b/src/eolab/rastertools/main.py @@ -16,8 +16,6 @@ import sys import json import click -from pkg_resources import iter_entry_points -from click_plugins import with_plugins from eolab.rastertools.cli.filtering import filter from eolab.rastertools.cli.hillshade import hillshade from eolab.rastertools.cli.speed import speed @@ -233,13 +231,6 @@ def rastertools(ctx, rastertype : str, max_workers : int, keep_vrt : bool, verbo rastertools.add_command(zonalstats, name = "zonalstats") -# @rastertools.result_callback() -# @click.pass_context -# def handle_result(ctx): -# if ctx.invoked_subcommand is None: -# click.echo(ctx.get_help()) -# ctx.exit() - def run(*args, **kwargs): """ Entry point for console_scripts diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index ccc59e7c..32062d70 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -4,7 +4,6 @@ This module defines a method to run a RasterProcessing on sliding windows. """ from itertools import repeat -import logging import logging.config import os from typing import List @@ -18,7 +17,7 @@ from eolab.rastertools import utils from eolab.rastertools.processing import RasterProcessing - +from eolab.rastertools.utils import vsimem_to_rasterio _logger = logging.getLogger(__name__) @@ -56,46 +55,48 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: Raste specified, and sliding window indices are computed internally. """ with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with rasterio.open(input_image) as src: - profile = src.profile - - # set block size - blockxsize, blockysize = window_size - if src.width < blockxsize: - blockxsize = utils.highest_power_of_2(src.width) - if src.height < blockysize: - blockysize = utils.highest_power_of_2(src.height) - - # dtype and creation options of output data - dtype = rasterprocessing.dtype or rasterio.float32 - in_dtype = rasterprocessing.in_dtype or dtype - nbits = rasterprocessing.nbits - compress = rasterprocessing.compress or src.compression or 'lzw' - nodata = rasterprocessing.nodata or src.nodata - - # check band index and handle all bands options (when bands is an empty list) - if bands is None or len(bands) == 0: - bands = src.indexes - elif min(bands) < 1 or max(bands) > src.count: - raise ValueError(f"Invalid bands, all values are not in range [1, {src.count}]") - - # setup profile for output image - profile.update(driver='GTiff', blockxsize=blockxsize, blockysize=blockysize, - tiled=True, dtype=dtype, nbits=nbits, compress=compress, - nodata=nodata, count=len(bands)) - - with rasterio.open(output_image, "w", **profile): - # file is created - pass - - # create the generator of sliding windows - sliding_gen = _sliding_windows((src.width, src.height), - window_size, window_overlap) - if rasterprocessing.per_band_algo: - sliding_windows_bands = [(w, [b]) for w in sliding_gen for b in bands] - else: - sliding_windows_bands = [(w, bands) for w in sliding_gen] + src = vsimem_to_rasterio(input_image) + profile = src.profile + + # set block size + blockxsize, blockysize = window_size + if src.width < blockxsize: + blockxsize = utils.highest_power_of_2(src.width) + if src.height < blockysize: + blockysize = utils.highest_power_of_2(src.height) + + # dtype and creation options of output data + dtype = rasterprocessing.dtype or rasterio.float32 + in_dtype = rasterprocessing.in_dtype or dtype + nbits = rasterprocessing.nbits + compress = rasterprocessing.compress or src.compression or 'lzw' + nodata = rasterprocessing.nodata or src.nodata + + # check band index and handle all bands options (when bands is an empty list) + if bands is None or len(bands) == 0: + bands = src.indexes + elif min(bands) < 1 or max(bands) > src.count: + raise ValueError(f"Invalid bands, all values are not in range [1, {src.count}]") + + # setup profile for output image + profile.update(driver='GTiff', blockxsize=blockxsize, blockysize=blockysize, + tiled=True, dtype=dtype, nbits=nbits, compress=compress, + nodata=nodata, count=len(bands)) + + with rasterio.open(output_image, "w", **profile): + # file is created + pass + + # create the generator of sliding windows + sliding_gen = _sliding_windows((src.width, src.height), + window_size, window_overlap) + + if rasterprocessing.per_band_algo: + sliding_windows_bands = [(w, [b]) for w in sliding_gen for b in bands] + else: + sliding_windows_bands = [(w, bands) for w in sliding_gen] + src.close() m = multiprocessing.Manager() write_lock = m.Lock() diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 9787fa7c..e2f00ce5 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -13,6 +13,7 @@ import shapely.geometry from osgeo import gdal import rasterio +from eolab.rastertools.utils import vsimem_to_rasterio from rasterio import features, warp, windows from eolab.rastertools import utils @@ -158,36 +159,23 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str geoms_crs = _get_geoms_crs(geometries) file = raster.as_posix() if isinstance(raster, Path) else raster - print(file) - - - # with rasterio.open(file) as dataset: - # print(type(dataset.crs)) - # print(geoms_crs) - # if (geoms_crs != dataset.crs): - # reprojected_geoms = geometries.to_crs(dataset.crs) - # else: - # reprojected_geoms = geometries - # print(reprojected_geoms) - # if output: - # outfile = output.as_posix() if isinstance(output, Path) else output - # reprojected_geoms.to_file(outfile, driver=driver) - - dataset = gdal.Open(file) - print(dataset.GetProjection()) - if(geoms_crs != dataset.GetProjection()): - reprojected_geoms = geometries.to_crs(dataset.GetProjection()) + + dataset = vsimem_to_rasterio(file) + + if(geoms_crs != dataset.crs): + reprojected_geoms = geometries.to_crs(dataset.crs) else: reprojected_geoms = geometries - print(reprojected_geoms) if output: outfile = output.as_posix() if isinstance(output, Path) else output reprojected_geoms.to_file(outfile, driver=driver) + dataset.close() return reprojected_geoms + def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: """Dissolves all geometries in one @@ -207,7 +195,6 @@ def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], geometries = _get_geoms(geoms) geometries['COMMON'] = 0 - print(geometries) union = geometries.dissolve(by='COMMON', as_index=False) union = union.drop(columns='COMMON') @@ -215,7 +202,6 @@ def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], outfile = output.as_posix() if isinstance(output, Path) else output union.to_file(outfile, driver=driver) - print(union) return union @@ -314,6 +300,7 @@ def rasterize(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str return burned + def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], output_image: Union[Path, str]): """Crops an input image to the roi bounds. @@ -328,46 +315,18 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], """ pinput = input_image.as_posix() if isinstance(input_image, Path) else input_image - print(pinput) poutput = output_image.as_posix() if isinstance(output_image, Path) else output_image geometries = reproject(dissolve(roi), pinput) geom_bounds = geometries.total_bounds - # with rasterio.open(pinput) as raster: - # rst_bounds = raster.bounds - # - # print(rst_bounds) - # bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), - # math.floor(max(rst_bounds[1], geom_bounds[1])), - # math.ceil(min(rst_bounds[2], geom_bounds[2])), - # math.ceil(min(rst_bounds[3], geom_bounds[3]))) - # geotransform = raster.get_transform() - # - # print(geotransform) - # width = np.abs(geotransform[1]) - # height = np.abs(geotransform[5]) - # - # ds = gdal.Warp(destNameOrDestDS=poutput, - # srcDSOrSrcDSTab=pinput, - # outputBounds=bounds, targetAlignedPixels=True, - # cutlineDSName=roi, - # cropToCutline=False, - # xRes=width, yRes=height, - # format="VRT") - # del ds - - raster = gdal.Open(pinput) - geotransform = list(raster.GetGeoTransform()) - bottom = geotransform[3] + raster.RasterYSize * geotransform[5] - right = geotransform[0] + raster.RasterXSize * geotransform[1] - rst_bounds = [geotransform[0],bottom,right,geotransform[3]] - print(rst_bounds) - + raster = vsimem_to_rasterio(pinput) + rst_bounds = raster.bounds bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), math.floor(max(rst_bounds[1], geom_bounds[1])), math.ceil(min(rst_bounds[2], geom_bounds[2])), math.ceil(min(rst_bounds[3], geom_bounds[3]))) + geotransform = raster.get_transform() width = np.abs(geotransform[1]) height = np.abs(geotransform[5]) @@ -379,6 +338,7 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], xRes=width, yRes=height, format="VRT") del ds + raster.close() diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 294dda80..0244d5ef 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -133,9 +133,6 @@ def free_in_memory_vrts(self): for vrt in self._in_memory_vrts: gdal.Unlink(vrt.as_posix()) self._in_memory_vrts = [] - # for vrt in self._in_memory_vrts: - # vrt.close() # Closes and cleans up the memory file - # self._in_memory_vrts = [] @property def file(self) -> Path: @@ -397,8 +394,6 @@ def __create_vrt(self, """ # convert parameters defined as str to Path outdir = utils.to_path(self._vrt_outputdir, "/vsimem/") - print(bands_files) - print(outdir) basename = utils.get_basename(self.file) _logger.debug("Creating a VRT that handles the bands of the archive product") @@ -423,7 +418,6 @@ def __create_vrt(self, # Create a VRT image with GDAL rasterfile = outdir.joinpath(f"{uuid}{basename}.vrt") - # rasterfile = rasterfile.as_posix() ds = gdal.BuildVRT(rasterfile.as_posix(), bands, VRTNodata=' '.join(nodatavals), @@ -434,79 +428,6 @@ def __create_vrt(self, # free resource from GDAL del ds - # with rasterio.open(list(bands_files.values())[0]) as src: - # width = src.width - # height = src.height - # - # # with rasterio.open(rasterfile, 'w', driver="GTiff", width=width, height=height, count=1) as dataset: - # # dataset.write(bands[0]) - # - # with rasterio.open(rasterfile, 'w', driver="GTiff", width=width, height=height) as src: - # with WarpedVRT(src) as vrt: - # print("VRT created with dimensions:", vrt.width, vrt.height) - - - # free resource from GDAL - # - - - - - - - # # convert parameters defined as str to Path - # outdir = utils.to_path(self._vrt_outputdir, "/vsimem/") - # print(outdir) - # basename = utils.get_basename(self.file) - # rasterfile = outdir.joinpath(f"{uuid}{basename}.vrt") - # - # _logger.debug("Creating a VRT that handles the bands of the archive product") - # - # # Initialize VRT XML structure - # vrt_root = ET.Element("VRTDataset") - # - # # Open the first band to get metadata - # with rasterio.open(list(bands_files.values())[0]) as src: - # vrt_root.set("rasterXSize", str(src.width)) - # vrt_root.set("rasterYSize", str(src.height)) - # vrt_root.set("subClass", "VRTDataset") - # crs_element = ET.SubElement(vrt_root, "SRS") - # crs_element.text = src.crs.to_wkt() - # - # # Add bands - # for i, (band_id, band_path) in enumerate(bands_files.items(), start=1): - # with rasterio.open(band_path) as src_band: - # band_element = ET.SubElement(vrt_root, "VRTRasterBand", attrib={ - # "dataType": src_band.dtypes[0].upper(), - # "band": str(i) - # }) - # source_element = ET.SubElement(band_element, "SimpleSource") - # ET.SubElement(source_element, "SourceFilename", attrib={"relativeToVRT": "1"}).text = str(band_path) - # ET.SubElement(source_element, "SourceBand").text = "1" - # ET.SubElement(source_element, "SourceProperties", attrib={ - # "RasterXSize": str(src_band.width), - # "RasterYSize": str(src_band.height), - # "DataType": src_band.dtypes[0].upper(), - # "BlockXSize": str(src_band.block_shapes[0][0]), - # "BlockYSize": str(src_band.block_shapes[0][1]) - # }) - # ET.SubElement(source_element, "SrcRect", attrib={ - # "xOff": "0", "yOff": "0", - # "xSize": str(src_band.width), "ySize": str(src_band.height) - # }) - # ET.SubElement(source_element, "DstRect", attrib={ - # "xOff": "0", "yOff": "0", - # "xSize": str(src_band.width), "ySize": str(src_band.height) - # }) - # - # # Add masks - # for i, (mask_id, mask_path) in enumerate(masks_files.items(), start=len(bands_files) + 1): - # mask_element = ET.SubElement(vrt_root, "VRTRasterBand", attrib={ - # "dataType": "Byte", - # "band": str(i) - # }) - # source_element = ET.SubElement(mask_element, "SimpleSource") - # ET.SubElement(source_element, "SourceFilename", attrib={"relativeToVRT": "1"}).text = str(mask_path) - # ET.SubElement(source_element, "SourceBand").text = "1" - # - # # Write the VRT to file - # tree = ET.ElementTree(vrt_root) - # with open(rasterfile, "wb") as f: - # tree.write(f, encoding="utf-8", xml_declaration=True) return rasterfile def __wrap(self, input_vrt: Path, roi: Path, uuid: str = "") -> Path: @@ -583,33 +504,6 @@ def __apply_masks(self, input_vrt: Path, nb_bands: int, nb_masks: int, uuid: str return masked_image - # # convert parameters defined as str to Path - # outdir = utils.to_path(self._vrt_outputdir, "/vsimem/") - # basename = utils.get_basename(self.file) - # - # # create a tempdir for generated temporary files - # tempdir = Path(tempfile.gettempdir()) - # temp_image = tempdir.joinpath(f"{uuid}{basename}-temp.vrt") - # with rasterio.open(input_vrt) as src: - # # Create a new in-memory VRT for the bands - # with WarpedVRT(src, crs=src.crs, transform=src.transform, width=src.width, height=src.height, - # count=nb_bands) as vrt: - # # Here you would apply any mask band logic. For simplicity, we assume a method that adds the masks. - # # For each mask, you could apply it using rasterio to create new bands with the mask applied. - # # Add the masks to the VRT using the provided add_masks_to_vrt function - # _logger.debug("Adding mask bands to VRT") - # masks_index = list(range(nb_bands + 1, nb_bands + nb_masks + 1)) - # vrt_new_content = add_masks_to_vrt(temp_image, input_vrt, masks_index, - # self.rastertype.maskfunc) - # - # # Now save this VRT to a file - # masked_image = outdir.joinpath(f"{uuid}{basename}-mask.vrt") - # with open(masked_image, 'wb') as out_vrt: - # out_vrt.write(vrt_new_content) - # - # _logger.debug(f"Generated masked VRT saved to {masked_image}") - # return masked_image - def _extract_bands(inputfile: Path, bands_pattern: str, diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 88c68aac..54d2a6bc 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -10,13 +10,10 @@ from pathlib import Path from typing import List import threading -import geopandas as gpd -import numpy as np import rasterio import numpy.ma as ma -from osgeo import gdal -from rasterio import CRS +from eolab.rastertools.utils import vsimem_to_rasterio from tqdm import tqdm from eolab.rastertools import utils @@ -432,10 +429,7 @@ def process_file(self, inputfile: str) -> List[str]: # get the raster print(self.roi) - # self.roi = "tests/tests_data/COMMUNE_32001.shp" - # print(self.roi) raster = product.get_raster(roi=self.roi) - print(type(raster)) # STEP 2: Compute the indices outputs = [] @@ -476,7 +470,6 @@ def get_raster_profile(raster): # Get data type and block size from the first band band = raster.GetRasterBand(1) - dtype = gdal.GetDataTypeName(band.DataType) dtype_rasterio = rasterio.dtypes.get_minimum_dtype(band.DataType) nodata = band.GetNoDataValue() @@ -504,7 +497,6 @@ def get_raster_profile(raster): return profile - def compute_indices(input_image: str, image_channels: List[BandChannel], indice_image: str, indices: List[RadioindiceProcessing], window_size: tuple = (1024, 1024)): @@ -527,142 +519,57 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], window_size (tuple(int, int), optional, default=(1024, 1024)): Size of windows for splitting the processed image in small parts """ - print('...'*50) - print(input_image) - # with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - # with rasterio.open(input_image) as src: - # profile = src.profile - # - # print(profile) - # - # # set block size to the configured window_size of first indice - # blockxsize, blockysize = window_size - # print(src.width) - # print(blockxsize) - # if src.width < blockxsize: - # blockxsize = utils.highest_power_of_2(src.width) - # if src.height < blockysize: - # blockysize = utils.highest_power_of_2(src.height) - # - # # dtype of output data - # dtype = indices[0].dtype or rasterio.float32 - # - # # setup profile for output image - # profile.update(driver='GTiff', - # blockxsize=blockysize, blockysize=blockxsize, tiled=True, - # dtype=dtype, nodata=indices[0].nodata, - # count=len(indices)) - # print(type(profile)) - # with rasterio.open(indice_image, "w", **profile) as dst: - # # Materialize a list of destination block windows - # windows = [window for ij, window in dst.block_windows()] - # - # # disable status of tqdm progress bar - # disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - # - # # compute every indices - # for i, indice in enumerate(indices, 1): - # # Get the bands necessary to compute the indice - # bands = [image_channels.index(channel) + 1 for channel in indice.channels] - # - # read_lock = threading.Lock() - # write_lock = threading.Lock() - # - # def process(window): - # """Read input raster, compute indice and write output raster""" - # with read_lock: - # src_array = src.read(bands, window=window, masked=True) - # src_array[src_array == src.nodata] = ma.masked - # src_array = src_array.astype(dtype) - # - # # The computation can be performed concurrently - # result = indice.algo(src_array).astype(dtype).filled(indice.nodata) - # - # with write_lock: - # dst.write_band(i, result, window=window) - # - # # compute using concurrent.futures.ThreadPoolExecutor and tqdm - # for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): - # process(window) - # - # dst.set_band_description(i, indice.name) - # with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - # with rasterio.open(input_image) as src: - # profile = src.profile - - src = gdal.Open(input_image) - profile = get_raster_profile(src) ############################# - print(profile) - geotransform = list(src.GetGeoTransform()) - width = src.RasterXSize - height = src.RasterYSize - - # set block size to the configured window_size of first indice - blockxsize, blockysize = window_size - print(width) - print(blockxsize) - - if width < blockxsize: - blockxsize = utils.highest_power_of_2(width) - if height < blockysize: - blockysize = utils.highest_power_of_2(height) - - # dtype of output data - dtype = indices[0].dtype or rasterio.float32 - - # setup profile for output image - profile.update(driver='GTiff', - blockxsize=blockysize, blockysize=blockxsize, tiled=True, - dtype=dtype, nodata=indices[0].nodata, - count=len(indices)) - - print(profile) - print(type(profile)) - nodata = src.GetRasterBand(1).GetNoDataValue() - - with rasterio.open(indice_image, "w", **profile) as dst: - # Materialize a list of destination block windows - windows = [window for ij, window in dst.block_windows()] - print(windows) - - # disable status of tqdm progress bar - disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] - - # compute every indices - for i, indice in enumerate(indices, 1): - # Get the bands necessary to compute the indice - bands = [image_channels.index(channel) + 1 for channel in indice.channels] - - read_lock = threading.Lock() - write_lock = threading.Lock() - - def process(window): - """Read input raster, compute indice and write output raster""" - with read_lock: - x_offset = int(window.col_off) - y_offset = int(window.row_off) - x_size = int(window.width) - y_size = int(window.height) - - src_array = np.array([ - src.GetRasterBand(band).ReadAsArray(x_offset, y_offset, x_size, y_size) - for band in bands], dtype=dtype) - - # Mask nodata values - src_array = np.ma.masked_equal(src_array, nodata) - - # src_array = src.read(bands, window=window, masked=True) - # src_array[src_array == src.nodata] = ma.masked - # src_array = src_array.astype(dtype) - - # The computation can be performed concurrently - result = indice.algo(src_array).astype(dtype).filled(indice.nodata) - - with write_lock: - dst.write_band(i, result, window=window) + with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): + src = vsimem_to_rasterio(input_image) + profile = src.profile + + # set block size to the configured window_size of first indice + blockxsize, blockysize = window_size + if src.width < blockxsize: + blockxsize = utils.highest_power_of_2(src.width) + if src.height < blockysize: + blockysize = utils.highest_power_of_2(src.height) + + # dtype of output data + dtype = indices[0].dtype or rasterio.float32 + + # setup profile for output image + profile.update(driver='GTiff', + blockxsize=blockysize, blockysize=blockxsize, tiled=True, + dtype=dtype, nodata=indices[0].nodata, + count=len(indices)) + + with rasterio.open(indice_image, "w", **profile) as dst: + # Materialize a list of destination block windows + windows = [window for ij, window in dst.block_windows()] + + # disable status of tqdm progress bar + disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + + # compute every indices + for i, indice in enumerate(indices, 1): + # Get the bands necessary to compute the indice + bands = [image_channels.index(channel) + 1 for channel in indice.channels] + + read_lock = threading.Lock() + write_lock = threading.Lock() + + def process(window): + """Read input raster, compute indice and write output raster""" + with read_lock: + src_array = src.read(bands, window=window, masked=True) + src_array[src_array == src.nodata] = ma.masked + src_array = src_array.astype(dtype) + + # The computation can be performed concurrently + result = indice.algo(src_array).astype(dtype).filled(indice.nodata) + + with write_lock: + dst.write_band(i, result, window=window) # compute using concurrent.futures.ThreadPoolExecutor and tqdm - for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): - process(window) + for window in tqdm(windows, disable=disable, desc=f"{indice.name}"): + process(window) - dst.set_band_description(i, indice.name) + dst.set_band_description(i, indice.name) + src.close() diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index b30862b3..4350daef 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -5,7 +5,6 @@ of the radiometry of the input rasters. """ from datetime import datetime -import logging import logging.config import os from pathlib import Path diff --git a/src/eolab/rastertools/svf.py b/src/eolab/rastertools/svf.py index 4e83dfa4..0644c686 100644 --- a/src/eolab/rastertools/svf.py +++ b/src/eolab/rastertools/svf.py @@ -4,7 +4,6 @@ This module defines a rastertool named SVF (Sky View Factor) which computes the SVF of a Digital Height Model. """ -import logging import logging.config from pathlib import Path import numpy as np diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index 626b984a..94014eb0 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -8,7 +8,6 @@ """ from datetime import datetime, timedelta from itertools import repeat -import logging import logging.config import multiprocessing import os diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index 417e2c78..d4f91cef 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -99,7 +99,7 @@ def vsimem_to_rasterio(vsimem_file:str, nodata=None) -> rasterio.io.DatasetReade dst.write(band_data, i) # Si un masque est défini, l'écrire if masks[i - 1] is not None: - dst.write_mask((masks[i - 1]).astype("uint8") * 255) + dst.write_mask((~masks[i - 1]).astype("uint8") * 255) return rasterio.open(temp_filename) diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 7be60b8c..df74ad23 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -28,11 +28,15 @@ Requires-Dist: matplotlib==3.7.3 Requires-Dist: packaging==24.1 Requires-Dist: Shapely==1.8.5.post1 Requires-Dist: tomli==2.0.2 +Requires-Dist: Rtree==1.3.0 Requires-Dist: Pillow==9.2.0 Requires-Dist: pip==24.2 Requires-Dist: pyproj==3.4.0 Requires-Dist: matplotlib +Requires-Dist: sphinx==7.1.2 Requires-Dist: scipy==1.8 +Requires-Dist: pyscaffold +Requires-Dist: gdal==3.5.0 Requires-Dist: tqdm==4.66 Provides-Extra: testing Requires-Dist: setuptools; extra == "testing" diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 5d7f921e..49368cd9 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -7,7 +7,6 @@ README.rst env_test.yml env_update.yml environment.yml -pyproject.toml setup.cfg setup.py tox.ini diff --git a/src/rastertools.egg-info/entry_points.txt b/src/rastertools.egg-info/entry_points.txt index 7844cc62..467c161c 100644 --- a/src/rastertools.egg-info/entry_points.txt +++ b/src/rastertools.egg-info/entry_points.txt @@ -1,2 +1,2 @@ -[rasterio.plugins] -rastertools = src.eolab.rastertools.main:rastertools +[rasterio.rio_plugins] +rastertools = eolab.rastertools.main:rastertools diff --git a/src/rastertools.egg-info/requires.txt b/src/rastertools.egg-info/requires.txt index b92b22ec..0b15b4a9 100644 --- a/src/rastertools.egg-info/requires.txt +++ b/src/rastertools.egg-info/requires.txt @@ -10,11 +10,15 @@ matplotlib==3.7.3 packaging==24.1 Shapely==1.8.5.post1 tomli==2.0.2 +Rtree==1.3.0 Pillow==9.2.0 pip==24.2 pyproj==3.4.0 matplotlib +sphinx==7.1.2 scipy==1.8 +pyscaffold +gdal==3.5.0 tqdm==4.66 [testing] diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index a121497d..234fc9e2 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -2,8 +2,6 @@ # -*- coding: utf-8 -*- import os -import pytest -import filecmp import logging from eolab.rastertools import Radioindice from eolab.rastertools.processing.rasterproc import RadioindiceProcessing @@ -40,10 +38,7 @@ def test_radioindice_process_file_merge(): origin_path = RastertoolsTestsData.tests_input_data_dir + "/".split(os.getcwd() + "/")[-1] - inputfile = RastertoolsTestsData.tests_input_data_dir + "/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" - print(inputfile) - inputfile = origin_path+ "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" - print(inputfile) + inputfile = origin_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" indices = [indice for indice in Radioindice.get_default_indices()] tool = Radioindice(indices) @@ -99,7 +94,7 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(gen_files, __refdir) - utils4test.clear_outdir() + # utils4test.clear_outdir() def test_radioindice_process_files(): diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index 7d470aab..cc71fce5 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -181,7 +181,7 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): utils4test.clear_outdir(subdirs=False) - # delete the dir resulting from unzip +# delete the dir resulting from unzip utils4test.delete_dir(RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") @@ -376,11 +376,9 @@ def test_create_product_special_cases(): dataset.close() # creation from a directory - # unzip S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip file = RastertoolsTestsData.tests_input_data_dir + "/" + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" with zipfile.ZipFile(file) as myzip: myzip.extractall(RastertoolsTestsData.tests_output_data_dir + "/") - dirname = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.SAFE" with RasterProduct(file, vrt_outputdir=Path(RastertoolsTestsData.tests_output_data_dir + "/")) as prod: raster = prod.get_raster() diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 57f7493a..73e96013 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -1,9 +1,9 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import pytest import logging -import filecmp +import os + from click.testing import CliRunner from pathlib import Path @@ -113,6 +113,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec if compare: match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", self._refdir, self._outputs) + assert len(match) == 3 assert len(mismatch) == 0 assert len(err) == 0 @@ -283,9 +284,9 @@ def test_speed_command_line_default(): # default with a listing of S2A products f"-v sp -b 1 -o {RastertoolsTestsData.tests_output_data_dir} {lst_file_path}", # default with a list of files - # f"--verbose speed --output {RastertoolsTestsData.tests_output_data_dir}" - # f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" - # f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" + f"--verbose speed --output {RastertoolsTestsData.tests_output_data_dir}" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" + f" {RastertoolsTestsData.tests_input_data_dir}/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" ] speed_filenames = [ ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-speed-20180928-105515.tif"], @@ -299,6 +300,7 @@ def test_speed_command_line_default(): # execute test cases with logging level set to INFO for test in tests: test.run_test() + os.remove(lst_file_path) def test_speed_command_line_errors(caplog): @@ -339,6 +341,7 @@ def test_speed_command_line_errors(caplog): # execute test cases with logging level set to INFO for test in tests: test.run_test(caplog, check_outputs=False) + os.remove(lst_file_path) def test_timeseries_command_line_default(compare, save_gen_as_ref): @@ -428,6 +431,7 @@ def test_timeseries_command_line_errors(caplog): # execute test cases with logging level set to INFO for test in tests: test.run_test(caplog, check_outputs=False) + os.remove(lst_file_path) def test_zonalstats_command_line_default(): @@ -462,7 +466,7 @@ def test_zonalstats_command_line_default(): # execute test cases for test in tests: test.run_test() - + os.remove(lst_file_path) def test_zonalstats_command_line_product(): utils4test.create_outdir() @@ -558,6 +562,7 @@ def test_zonalstats_command_line_errors(): # execute test cases for test in tests: test.run_test(check_outputs=False) + os.remove(lst_file_path) def test_tiling_command_line_default(): @@ -603,6 +608,7 @@ def test_tiling_command_line_default(): subdir = Path(f"{RastertoolsTestsData.tests_output_data_dir}/tile77") subdir.mkdir() test.run_test(check_outputs=False) + os.remove(lst_file_path) def test_tiling_command_line_special_case(caplog): diff --git a/tests/test_rastertype.py b/tests/test_rastertype.py index 9981136c..0b367491 100644 --- a/tests/test_rastertype.py +++ b/tests/test_rastertype.py @@ -7,7 +7,6 @@ from datetime import datetime from eolab.rastertools import add_custom_rastertypes from eolab.rastertools.product import RasterType, BandChannel -from . import utils4test __author__ = "Olivier Queyrut" __copyright__ = "Copyright 2019, CNES" diff --git a/tests/test_speed.py b/tests/test_speed.py index 18295637..bc1347f7 100644 --- a/tests/test_speed.py +++ b/tests/test_speed.py @@ -1,8 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import pytest -import filecmp from eolab.rastertools import Speed from eolab.rastertools.product import RasterType diff --git a/tests/test_stats.py b/tests/test_stats.py index d4b00e5e..ec106ab4 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -212,7 +212,6 @@ def test_compute_zonal_stats_per_category(): catlabels = RastertoolsTestsData.tests_input_data_dir + "/" + "OSO_nomenclature_2017.json" stats_to_compute = DEFAULT_STATS bands = [1] - print(vector.clip(catgeojson, raster)) geometries = vector.reproject(vector.filter(geojson, raster), raster) categories = vector.reproject(vector.clip(catgeojson, raster), raster) @@ -221,7 +220,6 @@ def test_compute_zonal_stats_per_category(): stats=stats_to_compute, categories=categories, category_index="Classe") - print(f"{statistics}") # statistics is a list of list of dict. # First list iterates over geometries # Second list iterates over bands. diff --git a/tests/test_tiling.py b/tests/test_tiling.py index 92197c4c..213e1737 100644 --- a/tests/test_tiling.py +++ b/tests/test_tiling.py @@ -1,7 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import filecmp from eolab.rastertools import Tiling from . import utils4test diff --git a/tests/test_vector.py b/tests/test_vector.py index 8d9e7c25..accf1907 100644 --- a/tests/test_vector.py +++ b/tests/test_vector.py @@ -1,7 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import filecmp import geopandas as gpd from pathlib import Path diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 11c7cb12..ed637f9c 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -2,7 +2,6 @@ # -*- coding: utf-8 -*- import pytest -import filecmp from pathlib import Path from eolab.rastertools import Zonalstats from eolab.rastertools import RastertoolConfigurationException diff --git a/tests/utils4test.py b/tests/utils4test.py index d9ec5f4e..bc60307d 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -76,6 +76,7 @@ def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: new = os.path.join(a, x) golden = os.path.join(b, x) res[_cmp(golden, new, tolerance)].append(x) + print(res) return res From 4801b7fd22af211b966a051de2ef9d4a4fb85116 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 4 Dec 2024 17:30:40 +0100 Subject: [PATCH 35/56] refactor: suppress vsimem_to_rasterio --- setup.py | 2 +- src/eolab/rastertools/processing/sliding.py | 3 +- src/eolab/rastertools/processing/stats.py | 4 +- src/eolab/rastertools/processing/vector.py | 7 +- .../rastertools/product/rasterproduct.py | 3 +- src/eolab/rastertools/radioindice.py | 3 +- src/eolab/rastertools/utils.py | 87 ------------------- src/eolab/rastertools/zonalstats.py | 3 +- src/rastertools.egg-info/PKG-INFO | 4 +- src/rastertools.egg-info/SOURCES.txt | 63 -------------- src/rastertools.egg-info/requires.txt | 4 +- tests/test_rasterproduct.py | 3 +- 12 files changed, 15 insertions(+), 171 deletions(-) diff --git a/setup.py b/setup.py index 0c119b19..e81fa895 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ setup_requires = ["setuptools_scm"], install_requires=[ 'click', - 'rasterio==1.3.0', + # 'rasterio==1.3.0', 'pytest>=3.6', 'pytest-cov', 'geopandas==0.13', diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index 32062d70..0ccb3264 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -17,7 +17,6 @@ from eolab.rastertools import utils from eolab.rastertools.processing import RasterProcessing -from eolab.rastertools.utils import vsimem_to_rasterio _logger = logging.getLogger(__name__) @@ -56,7 +55,7 @@ def compute_sliding(input_image: str, output_image: str, rasterprocessing: Raste """ with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - src = vsimem_to_rasterio(input_image) + src = rasterio.open(input_image) profile = src.profile # set block size diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index ae782719..222b397f 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -18,7 +18,7 @@ from rasterio import features from tqdm import tqdm -from eolab.rastertools.utils import get_metadata_name, vsimem_to_rasterio +from eolab.rastertools.utils import get_metadata_name from eolab.rastertools.processing.vector import rasterize, filter_dissolve @@ -71,7 +71,7 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, """ nb_geoms = len(geoms) - src = vsimem_to_rasterio(image) + src = rasterio.open(image) geom_gen = (geoms.iloc[i].geometry for i in range(nb_geoms)) geom_windows = ((geom, features.geometry_window(src, [geom])) for geom in geom_gen) diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index e2f00ce5..d37e80ec 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -13,7 +13,6 @@ import shapely.geometry from osgeo import gdal import rasterio -from eolab.rastertools.utils import vsimem_to_rasterio from rasterio import features, warp, windows from eolab.rastertools import utils @@ -68,7 +67,7 @@ def filter(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], file = raster.as_posix() if isinstance(raster, Path) else raster - dataset = utils.vsimem_to_rasterio(file) + dataset = rasterio.open(file) l, b, r, t = dataset.bounds px, py = ([l, l, r, r], [b, t, t, b]) @@ -160,7 +159,7 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str file = raster.as_posix() if isinstance(raster, Path) else raster - dataset = vsimem_to_rasterio(file) + dataset = rasterio.open(file) if(geoms_crs != dataset.crs): reprojected_geoms = geometries.to_crs(dataset.crs) @@ -320,7 +319,7 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], geometries = reproject(dissolve(roi), pinput) geom_bounds = geometries.total_bounds - raster = vsimem_to_rasterio(pinput) + raster = rasterio.open(pinput) rst_bounds = raster.bounds bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), math.floor(max(rst_bounds[1], geom_bounds[1])), diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 0244d5ef..1c7767d9 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -21,7 +21,6 @@ from eolab.rastertools.product import RasterType from eolab.rastertools.product.vrt import add_masks_to_vrt, set_band_descriptions from eolab.rastertools.processing.vector import crop -from eolab.rastertools.utils import vsimem_to_rasterio __author__ = "Olivier Queyrut" __copyright__ = "Copyright 2019, CNES" @@ -224,7 +223,7 @@ def open(self, bands: Union[str, List[str]] = "all", masks: Union[str, List[str]] = "all", roi: Union[Path, str] = None): - return vsimem_to_rasterio(self.get_raster(bands=bands, masks=masks, roi=roi)) + return rasterio.open(self.get_raster(bands=bands, masks=masks, roi=roi)) def get_raster(self, bands: Union[str, List[str]] = "all", diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 54d2a6bc..72a87f47 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -13,7 +13,6 @@ import rasterio import numpy.ma as ma -from eolab.rastertools.utils import vsimem_to_rasterio from tqdm import tqdm from eolab.rastertools import utils @@ -520,7 +519,7 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], Size of windows for splitting the processed image in small parts """ with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - src = vsimem_to_rasterio(input_image) + src = rasterio.open(input_image) profile = src.profile # set block size to the configured window_size of first indice diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index d4f91cef..cde7b102 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -16,93 +16,6 @@ from osgeo import gdal -def vsimem_to_rasterio(vsimem_file:str, nodata=None) -> rasterio.io.DatasetReader: - """ - Converts a VSIMEM (in-memory) raster dataset to a Rasterio dataset with optional nodata masking. - - This function opens a raster dataset stored in VSIMEM (virtual file system in memory) - using GDAL, extracts its metadata and data, handles optional nodata values and masks, - and saves it to a temporary GeoTIFF file. It then reopens the file with Rasterio and - returns a Rasterio dataset reader. - - Parameters - ---------- - vsimem_file : str - The path to the VSIMEM file to be converted. This file should be an in-memory GDAL dataset. - - nodata : float, optional - A user-defined nodata value to override the nodata value in the GDAL dataset. - If not provided, the nodata value from the GDAL dataset is used (if available). - - Returns - ------- - rasterio.io.DatasetReader - A Rasterio dataset reader object corresponding to the temporary GeoTIFF created from the VSIMEM file. - - Notes - ----- - - The function assumes the dataset is in a format that is compatible with both GDAL and Rasterio. - - The created temporary file is not deleted automatically. It can be removed manually after use. - - The function reads all raster bands from the dataset, applies the optional nodata masking, - and writes the data to a new GeoTIFF file. - - If a nodata value is provided, the function will apply the mask based on that value to each band. - If no nodata value is set, no mask is applied. - """ - gdal_ds = gdal.Open(vsimem_file) - cols = gdal_ds.RasterXSize - rows = gdal_ds.RasterYSize - bands = gdal_ds.RasterCount - geo_transform = gdal_ds.GetGeoTransform() - projection = gdal_ds.GetProjection() - - gdal_dtype_to_numpy = { - gdal.GDT_Byte: "uint8", - gdal.GDT_UInt16: "uint16", - gdal.GDT_Int16: "int16", - gdal.GDT_UInt32: "uint32", - gdal.GDT_Int32: "int32", - gdal.GDT_Float32: "float32", - gdal.GDT_Float64: "float64", - } - dtype = gdal_dtype_to_numpy[gdal_ds.GetRasterBand(1).DataType] - - data = [gdal_ds.GetRasterBand(i + 1).ReadAsArray() for i in range(bands)] - - masks = [] - for i in range(bands): - band = gdal_ds.GetRasterBand(i + 1) - band_nodata = band.GetNoDataValue() - # Prioriser la valeur nodata de l'utilisateur - nodata_value = nodata if nodata is not None else band_nodata - if nodata_value is not None: - masks.append(data[i] == nodata_value) - else: - masks.append(None) - - with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmpfile: - temp_filename = tmpfile.name - - profile = { - "driver": "GTiff", - "height": rows, - "width": cols, - "count": bands, - "dtype": dtype, - "crs": projection, - "transform": rasterio.transform.Affine.from_gdal(*geo_transform), - "nodata": nodata_value, - } - - with rasterio.open(temp_filename, "w", **profile) as dst: - # Écrire les données - for i, band_data in enumerate(data, start=1): - dst.write(band_data, i) - # Si un masque est défini, l'écrire - if masks[i - 1] is not None: - dst.write_mask((~masks[i - 1]).astype("uint8") * 255) - - return rasterio.open(temp_filename) - def to_tuple(val): """Convert val as a tuple of two val""" return val if type(val) == tuple else (val, val) diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index 29d7bd60..12847ab4 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -32,7 +32,6 @@ from eolab.rastertools.processing import extract_zonal_outliers, plot_stats from eolab.rastertools.processing import vector from eolab.rastertools.product import RasterProduct -from eolab.rastertools.utils import vsimem_to_rasterio _logger = logging.getLogger(__name__) @@ -409,7 +408,7 @@ def process_file(self, inputfile: str) -> List[str]: # open raster to get metadata raster = product.get_raster() - rst = vsimem_to_rasterio(raster) + rst = rasterio.open(raster) bound = int(rst.count) indexes = rst.indexes descr = rst.descriptions diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index df74ad23..8f7bf9d2 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -17,7 +17,6 @@ Description-Content-Type: text/x-rst; charset=UTF-8 License-File: LICENSE.txt License-File: AUTHORS.rst Requires-Dist: click -Requires-Dist: rasterio==1.3.0 Requires-Dist: pytest>=3.6 Requires-Dist: pytest-cov Requires-Dist: geopandas==0.13 @@ -29,10 +28,11 @@ Requires-Dist: packaging==24.1 Requires-Dist: Shapely==1.8.5.post1 Requires-Dist: tomli==2.0.2 Requires-Dist: Rtree==1.3.0 +Requires-Dist: fiona==1.8.21 Requires-Dist: Pillow==9.2.0 +Requires-Dist: sphinx_rtd_theme==3.0.1 Requires-Dist: pip==24.2 Requires-Dist: pyproj==3.4.0 -Requires-Dist: matplotlib Requires-Dist: sphinx==7.1.2 Requires-Dist: scipy==1.8 Requires-Dist: pyscaffold diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 49368cd9..9213c8a1 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -54,70 +54,7 @@ docs/cli/svf.rst docs/cli/tiling.rst docs/cli/timeseries.rst docs/cli/zonalstats.rst -src/eolab/rastertools/__init__.py -src/eolab/rastertools/filtering.py -src/eolab/rastertools/hillshade.py -src/eolab/rastertools/main.py -src/eolab/rastertools/radioindice.py -src/eolab/rastertools/rastertools.py -src/eolab/rastertools/speed.py -src/eolab/rastertools/svf.py -src/eolab/rastertools/tiling.py -src/eolab/rastertools/timeseries.py -src/eolab/rastertools/utils.py -src/eolab/rastertools/zonalstats.py -src/eolab/rastertools/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/__pycache__/filtering.cpython-38.pyc -src/eolab/rastertools/__pycache__/hillshade.cpython-38.pyc -src/eolab/rastertools/__pycache__/main.cpython-38.pyc -src/eolab/rastertools/__pycache__/radioindice.cpython-38.pyc -src/eolab/rastertools/__pycache__/rastertools.cpython-38.pyc -src/eolab/rastertools/__pycache__/speed.cpython-38.pyc -src/eolab/rastertools/__pycache__/svf.cpython-38.pyc -src/eolab/rastertools/__pycache__/tiling.cpython-38.pyc -src/eolab/rastertools/__pycache__/timeseries.cpython-38.pyc -src/eolab/rastertools/__pycache__/utils.cpython-38.pyc -src/eolab/rastertools/__pycache__/zonalstats.cpython-38.pyc -src/eolab/rastertools/cli/__init__.py -src/eolab/rastertools/cli/filtering.py -src/eolab/rastertools/cli/hillshade.py -src/eolab/rastertools/cli/radioindice.py -src/eolab/rastertools/cli/speed.py -src/eolab/rastertools/cli/svf.py -src/eolab/rastertools/cli/tiling.py -src/eolab/rastertools/cli/timeseries.py src/eolab/rastertools/cli/utils_cli.py -src/eolab/rastertools/cli/zonalstats.py -src/eolab/rastertools/cli/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/filtering.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/hillshade.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/radioindice.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/speed.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/svf.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/tiling.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/timeseries.cpython-38.pyc -src/eolab/rastertools/cli/__pycache__/zonalstats.cpython-38.pyc -src/eolab/rastertools/processing/__init__.py -src/eolab/rastertools/processing/algo.py -src/eolab/rastertools/processing/rasterproc.py -src/eolab/rastertools/processing/sliding.py -src/eolab/rastertools/processing/stats.py -src/eolab/rastertools/processing/vector.py -src/eolab/rastertools/processing/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/algo.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/rasterproc.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/sliding.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/stats.cpython-38.pyc -src/eolab/rastertools/processing/__pycache__/vector.cpython-38.pyc -src/eolab/rastertools/product/__init__.py -src/eolab/rastertools/product/rasterproduct.py -src/eolab/rastertools/product/rastertype.py -src/eolab/rastertools/product/rastertypes.json -src/eolab/rastertools/product/vrt.py -src/eolab/rastertools/product/__pycache__/__init__.cpython-38.pyc -src/eolab/rastertools/product/__pycache__/rasterproduct.cpython-38.pyc -src/eolab/rastertools/product/__pycache__/rastertype.cpython-38.pyc -src/eolab/rastertools/product/__pycache__/vrt.cpython-38.pyc src/rastertools.egg-info/PKG-INFO src/rastertools.egg-info/SOURCES.txt src/rastertools.egg-info/dependency_links.txt diff --git a/src/rastertools.egg-info/requires.txt b/src/rastertools.egg-info/requires.txt index 0b15b4a9..a374e7d6 100644 --- a/src/rastertools.egg-info/requires.txt +++ b/src/rastertools.egg-info/requires.txt @@ -1,5 +1,4 @@ click -rasterio==1.3.0 pytest>=3.6 pytest-cov geopandas==0.13 @@ -11,10 +10,11 @@ packaging==24.1 Shapely==1.8.5.post1 tomli==2.0.2 Rtree==1.3.0 +fiona==1.8.21 Pillow==9.2.0 +sphinx_rtd_theme==3.0.1 pip==24.2 pyproj==3.4.0 -matplotlib sphinx==7.1.2 scipy==1.8 pyscaffold diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index cc71fce5..17fe628c 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -10,7 +10,6 @@ from eolab.rastertools.product import RasterType, BandChannel from eolab.rastertools.product import RasterProduct -from eolab.rastertools.utils import vsimem_to_rasterio from . import utils4test __author__ = "Olivier Queyrut" @@ -360,7 +359,7 @@ def test_create_product_special_cases(): # check if product can be opened by rasterio with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - dataset = vsimem_to_rasterio(raster, nodata=-10000) + dataset = rasterio.open(raster) data = dataset.read([1], masked=True) # pixel corresponding to a value > 0 for a band mask => masked value assert data.mask[0][350][250] From e1546f5d690438279303210515dd69b175a6f9c9 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 5 Dec 2024 10:04:03 +0100 Subject: [PATCH 36/56] refactor: fixing --compare test --- src/eolab/rastertools/radioindice.py | 31 ++++++++++++- src/eolab/rastertools/speed.py | 29 +++++++++++- src/eolab/rastertools/tiling.py | 33 +++++++++++++- tests/test_radioindice.py | 2 +- tests/test_rasterproduct.py | 66 ++++++++++++++++------------ tests/test_zonalstats.py | 16 ++++--- tests/utils4test.py | 5 ++- 7 files changed, 143 insertions(+), 39 deletions(-) diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 72a87f47..d1a446cb 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -427,7 +427,6 @@ def process_file(self, inputfile: str) -> List[str]: indices.append(indice) # get the raster - print(self.roi) raster = product.get_raster(roi=self.roi) # STEP 2: Compute the indices @@ -545,6 +544,10 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # disable status of tqdm progress bar disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] + # Dictionary to store statistics for each band + band_stats = {i: {"min": float('inf'), "max": float('-inf'), "sum": 0, "total_pix": 0, "count": 0} + for i in range(1, len(indices) + 1)} + # compute every indices for i, indice in enumerate(indices, 1): # Get the bands necessary to compute the indice @@ -563,6 +566,15 @@ def process(window): # The computation can be performed concurrently result = indice.algo(src_array).astype(dtype).filled(indice.nodata) + # Update statistics + valid_pixels = result[result != indice.nodata] + if valid_pixels.size > 0: + band_stats[i]["min"] = min(band_stats[i]["min"], valid_pixels.min()) + band_stats[i]["max"] = max(band_stats[i]["max"], valid_pixels.max()) + band_stats[i]["sum"] += valid_pixels.sum() + band_stats[i]["total_pix"] += result.size + band_stats[i]["count"] += valid_pixels.size + with write_lock: dst.write_band(i, result, window=window) @@ -571,4 +583,19 @@ def process(window): process(window) dst.set_band_description(i, indice.name) - src.close() + + # Compute and set metadata tags + for i, stats in band_stats.items(): + # Compute and set metadata tags + mean = stats["sum"] / stats["count"] + sum_sq = (stats["sum"] - mean * stats["count"]) ** 2 + variance = sum_sq / stats["count"] + stddev = variance ** 0.5 if variance > 0 else 0 + + dst.update_tags(i, + STATISTICS_MINIMUM=f"{stats['min']:.14g}", + STATISTICS_MAXIMUM=f"{stats['max']:.14g}", + STATISTICS_MEAN=mean, + STATISTICS_STDDEV=stddev, + STATISTICS_VALID_PERCENT=(stats["count"] / stats["total_pix"] * 100)) + diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index 4350daef..7a79b274 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -139,6 +139,7 @@ def compute_speed(date0: datetime, date1: datetime, profile = src0.profile dtype = rasterio.float32 + nodata = src0.nodata # set block size blockysize = 1024 if src0.width > 1024 else utils.highest_power_of_2(src0.width) @@ -154,6 +155,9 @@ def compute_speed(date0: datetime, date1: datetime, blockxsize=blockysize, blockysize=blockxsize, tiled=True, dtype=dtype, count=len(bands)) + # Dictionary to store statistics for each band + stats = {"min": float('inf'), "max": float('-inf'), "sum": 0, "total_pix": 0, "count": 0} + with rasterio.open(speed_image, "w", **profile) as dst: # Materialize a list of destination block windows windows = [window for ij, window in dst.block_windows()] @@ -168,10 +172,33 @@ def process(window): data1 = src1.read(bands, window=window, masked=True).astype(dtype) # The computation can be performed concurrently - result = algo.speed(data0, data1, interval).astype(dtype).filled(src0.nodata) + result = algo.speed(data0, data1, interval).astype(dtype).filled(nodata) + + # Update statistics + valid_pixels = result[result != nodata] + if valid_pixels.size > 0: + stats["min"] = min(stats["min"], valid_pixels.min()) + stats["max"] = max(stats["max"], valid_pixels.max()) + stats["sum"] += valid_pixels.sum() + stats["total_pix"] += result.size + stats["count"] += valid_pixels.size with write_lock: dst.write(result, window=window) disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] thread_map(process, windows, disable=disable, desc="speed") + + # Compute and set metadata tags + mean = stats["sum"] / stats["count"] + sum_sq = (stats["sum"] - mean * stats["count"])**2 + variance = sum_sq / stats["count"] + stddev = variance ** 0.5 if variance > 0 else 0 + + dst.update_tags(1, + STATISTICS_MINIMUM=f"{stats['min']:.14g}", + STATISTICS_MAXIMUM=f"{stats['max']:.14g}", + STATISTICS_MEAN=mean, + STATISTICS_STDDEV=stddev, + STATISTICS_VALID_PERCENT=(stats["count"] / stats["total_pix"] * 100), + STATISTICS_APPROXIMATE="YES") diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 57bf1e3d..f6061a29 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -168,10 +168,16 @@ def process_file(self, inputfile: str): outputs = [] with product.open() as dataset: out_meta = dataset.meta + nodata = dataset.nodata # Crop and export every tiles for shape, i in zip(grid.geometry, grid.index): _logger.info("Crop and export tile " + str(i) + "...") + + # Dictionary to store statistics for each band + band_stats = {i: {"min": float('inf'), "max": float('-inf'), "mean": 0, "stddev": 0, "val_per": 0} + for i in range(1, dataset.meta["count"] + 1)} + try: # generate crop image image, transform = rasterio.mask.mask(dataset, [shape], @@ -193,12 +199,37 @@ def process_file(self, inputfile: str): "width": image.shape[2], "transform": transform}) + with rasterio.open(output, 'w', **out_meta) as dst: dst.write(image) + for bd in range(1, dataset.meta["count"] + 1): + # Update statistics + valid_pixels = image[bd-1][image[bd-1] != nodata] + + + band_stats[bd]["min"] = valid_pixels.min() + band_stats[bd]["max"] = valid_pixels.max() + band_stats[bd]["mean"] = valid_pixels.sum() / valid_pixels.size + variance = (((valid_pixels - band_stats[bd]["mean"])**2).sum()) / valid_pixels.size + band_stats[bd]["stddev"] = variance ** 0.5 if variance > 0 else 0 + + val_per = valid_pixels.size / image[bd-1].size * 100 + if int(val_per) == 100 : + band_stats[bd]["val_per"] = int(val_per) + else: + band_stats[bd]["val_per"] = round(val_per,2) + + dst.update_tags(bd, + STATISTICS_MINIMUM=f"{band_stats[bd]['min']:.14g}", + STATISTICS_MAXIMUM=f"{band_stats[bd]['max']:.14g}", + STATISTICS_MEAN=f"{band_stats[bd]['mean']:.14g}", + STATISTICS_STDDEV=f"{band_stats[bd]['stddev']:.14g}", + STATISTICS_VALID_PERCENT= band_stats[bd]['val_per']) + outputs.append(output.as_posix()) _logger.info("Tile " + str(i) + " exported to " + str(output)) except ValueError: # if no overlap _logger.error("Input shape " + str(i) + " does not overlap raster") - return outputs + return outputs diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index 234fc9e2..96e431cb 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -94,7 +94,7 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(gen_files, __refdir) - # utils4test.clear_outdir() + utils4test.clear_outdir() def test_radioindice_process_files(): diff --git a/tests/test_rasterproduct.py b/tests/test_rasterproduct.py index 17fe628c..4aeb98de 100644 --- a/tests/test_rasterproduct.py +++ b/tests/test_rasterproduct.py @@ -18,7 +18,7 @@ from .utils4test import RastertoolsTestsData -__refdir = utils4test.get_refdir("test_rasterproduct/") +__refdir = RastertoolsTestsData.tests_ref_data_dir.replace(os.getcwd() + "/", "") + "/test_rasterproduct/" def test_rasterproduct_valid_parameters(): @@ -138,35 +138,38 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): - Comparison or saving of reference files completes without errors. - Raster data can be opened without errors. """ + data_path = RastertoolsTestsData.tests_input_data_dir.replace(os.getcwd() + "/", "") + "/" + out_path = RastertoolsTestsData.tests_output_data_dir.replace(os.getcwd() + "/", "") + "/" + # create output dir and clear its content if any utils4test.create_outdir() # unzip SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip - file = RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" + file = data_path + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip" with zipfile.ZipFile(file) as myzip: - myzip.extractall(RastertoolsTestsData.tests_output_data_dir + "/") + myzip.extractall(out_path) # creation of S2 L2A MAJA products - files = [Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"), - Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar"), - Path(RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ"), - Path(RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9")] + files = [Path(data_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D.zip"), + Path(data_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_tar.tar"), + Path(data_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_targz.TAR.GZ"), + Path(out_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9")] for file in files: - with RasterProduct(file, vrt_outputdir=Path(RastertoolsTestsData.tests_output_data_dir + "/")) as prod: - raster = prod.get_raster(roi=Path(RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32001.shp"), + with RasterProduct(file, vrt_outputdir=Path(out_path)) as prod: + raster = prod.get_raster(roi=Path(data_path + "COMMUNE_32001.shp"), masks="all") assert Path(raster).exists() - assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(file) + "-mask.vrt" + assert raster == out_path + utils4test.basename(file) + "-mask.vrt" ref = [utils4test.basename(file) + ".vrt", utils4test.basename(file) + "-clipped.vrt", utils4test.basename(file) + "-mask.vrt"] if compare: - print(f"compare {RastertoolsTestsData.tests_output_data_dir} ,{__refdir}, {ref}") - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, ref) + print(f"compare {out_path} ,{__refdir}, {ref}") + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, ref) assert len(match) == len(ref) assert len(mismatch) == 0 assert len(err) == 0 @@ -180,8 +183,9 @@ def test_create_product_S2_L2A_MAJA(compare, save_gen_as_ref): utils4test.clear_outdir(subdirs=False) -# delete the dir resulting from unzip - utils4test.delete_dir(RastertoolsTestsData.tests_output_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") + # delete the dir resulting from unzip + utils4test.delete_dir(out_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_V1-9") + def test_create_product_S2_L1C(compare, save_gen_as_ref): @@ -202,22 +206,25 @@ def test_create_product_S2_L1C(compare, save_gen_as_ref): - Reference comparison or saving completes as expected. - Raster data can be loaded and accessed without errors. """ + data_path = RastertoolsTestsData.tests_input_data_dir.replace(os.getcwd() + "/", "") + "/" + out_path = RastertoolsTestsData.tests_output_data_dir.replace(os.getcwd() + "/", "") + "/" + # create output dir and clear its content if any utils4test.create_outdir() # creation of S2 L1C product - infile = RastertoolsTestsData.tests_input_data_dir + "/" + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" + infile = data_path + "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" - with RasterProduct(infile, vrt_outputdir=RastertoolsTestsData.tests_output_data_dir + "/") as prod: - raster = prod.get_raster(roi=RastertoolsTestsData.tests_input_data_dir + "/" + "/COMMUNE_32001.shp", + with RasterProduct(infile, vrt_outputdir= out_path) as prod: + raster = prod.get_raster(roi= data_path + "/COMMUNE_32001.shp", masks="all") assert Path(raster).exists() - assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(infile) + "-clipped.vrt" + assert raster == out_path + utils4test.basename(infile) + "-clipped.vrt" gen_files = [utils4test.basename(infile) + ".vrt", utils4test.basename(infile) + "-clipped.vrt"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -250,20 +257,23 @@ def test_create_product_S2_L2A_SEN2CORE(compare, save_gen_as_ref): - Reference file operations are successful. - The VRT file can be accessed with `rasterio` without issues. """ + data_path = RastertoolsTestsData.tests_input_data_dir.replace(os.getcwd() + "/", "") + "/" + out_path = RastertoolsTestsData.tests_output_data_dir.replace(os.getcwd() + "/", "") + "/" + # create output dir and clear its content if any utils4test.create_outdir() # creation of S2 L2A SEN2CORE product - infile = RastertoolsTestsData.tests_input_data_dir + "/" + "S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" - with RasterProduct(infile, vrt_outputdir=RastertoolsTestsData.tests_output_data_dir + "/") as prod: + infile = data_path + "S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.zip" + with RasterProduct(infile, vrt_outputdir= out_path) as prod: raster = prod.get_raster() assert Path(raster).exists() - assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(infile) + ".vrt" + assert raster == out_path + utils4test.basename(infile) + ".vrt" gen_files = [utils4test.basename(infile) + ".vrt"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -296,20 +306,23 @@ def test_create_product_SPOT67(compare, save_gen_as_ref): - Reference file comparison and saving are correctly performed. - Raster data opens without errors in `rasterio`. """ + data_path = RastertoolsTestsData.tests_input_data_dir.replace(os.getcwd() + "/", "") + "/" + out_path = RastertoolsTestsData.tests_output_data_dir.replace(os.getcwd() + "/", "") + "/" + # create output dir and clear its content if any utils4test.create_outdir() # creation of SPOT67 product infile = "SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.tar.gz" - with RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + infile, vrt_outputdir=RastertoolsTestsData.tests_output_data_dir + "/") as prod: + with RasterProduct(data_path + infile, vrt_outputdir= out_path) as prod: raster = prod.get_raster() assert Path(raster).exists() - assert raster == RastertoolsTestsData.tests_output_data_dir + "/" + utils4test.basename(infile) + ".vrt" + assert raster == out_path + utils4test.basename(infile) + ".vrt" gen_files = [utils4test.basename(infile) + ".vrt"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -339,7 +352,6 @@ def test_create_product_special_cases(): - Raster files can be opened without errors in `rasterio`. """ # SUPPORTED CASES - # creation in memory (without masks) file = "S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.zip" with RasterProduct(RastertoolsTestsData.tests_input_data_dir + "/" + file) as prod: diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index ed637f9c..0d87ee23 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -1,5 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import os import pytest from pathlib import Path @@ -131,23 +132,26 @@ def test_zonalstats_zonal(compare, save_gen_as_ref): def test_zonalstats_process_files(compare, save_gen_as_ref): # create output dir and clear its content if any + data_path = RastertoolsTestsData.tests_input_data_dir.replace(os.getcwd() + "/", "") + "/" + out_path = RastertoolsTestsData.tests_output_data_dir.replace(os.getcwd() + "/", "") + "/" + utils4test.create_outdir() - inputfiles = [RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", - RastertoolsTestsData.tests_input_data_dir + "/" + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"] + inputfiles = [data_path + "SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif", + data_path + "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif"] outformat = "GeoJSON" statistics = "min max mean std count range sum".split() tool = Zonalstats(statistics, prefix="indice") tool.with_output(None, output_format=outformat) - tool.with_geometries(geometries=RastertoolsTestsData.tests_input_data_dir + "/" + "COMMUNE_32xxx.geojson") - tool.with_chart(chart_file=RastertoolsTestsData.tests_output_data_dir + "/" + "chart.png") + tool.with_geometries(geometries=data_path + "COMMUNE_32xxx.geojson") + tool.with_chart(chart_file=out_path + "chart.png") tool.process_files(inputfiles) gen_files = ["chart.png"] - assert Path(RastertoolsTestsData.tests_output_data_dir + "/" + "chart.png").exists() + assert Path(out_path + "chart.png").exists() if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/utils4test.py b/tests/utils4test.py index bc60307d..772c05b3 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -11,6 +11,10 @@ __copyright = "Copyright 2019, CNES" __license = "Apache v2.0" +indir = "tests/tests_data/" +outdir = "tests/tests_out/" +__root_refdir = "tests/tests_refs/" + @dataclass class RastertoolsTestsData: @@ -76,7 +80,6 @@ def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: new = os.path.join(a, x) golden = os.path.join(b, x) res[_cmp(golden, new, tolerance)].append(x) - print(res) return res From 05469a6bd8b2558375bb76878a337f5c8b397ec6 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Thu, 5 Dec 2024 17:50:08 +0100 Subject: [PATCH 37/56] refactor: cleaned code --- setup.py | 8 ---- .../rastertools/product/rasterproduct.py | 14 ------ src/eolab/rastertools/radioindice.py | 44 ------------------- 3 files changed, 66 deletions(-) diff --git a/setup.py b/setup.py index e81fa895..4397cf66 100644 --- a/setup.py +++ b/setup.py @@ -18,23 +18,15 @@ setup_requires = ["setuptools_scm"], install_requires=[ 'click', - # 'rasterio==1.3.0', 'pytest>=3.6', 'pytest-cov', 'geopandas==0.13', - 'python-dateutil==2.9.0', 'kiwisolver==1.4.5', - 'fonttools==4.53.1', 'matplotlib==3.7.3', 'packaging==24.1', - 'Shapely==1.8.5.post1', - 'tomli==2.0.2', - 'Rtree==1.3.0', 'fiona==1.8.21', - 'Pillow==9.2.0', 'sphinx_rtd_theme==3.0.1', 'pip==24.2', - 'pyproj==3.4.0', 'sphinx==7.1.2', 'scipy==1.8', 'pyscaffold', diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 1c7767d9..4eebf15e 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -13,7 +13,6 @@ import tempfile from uuid import uuid4 -from rasterio.io import MemoryFile from osgeo import gdal import rasterio @@ -112,19 +111,6 @@ def __exit__(self, *args): """Exit method for with statement, it cleans the in memory vrt products""" self.free_in_memory_vrts() - def create_in_memory_vrt(self, vrt_content): - """ - Create an in-memory VRT using Rasterio's MemoryFile. - - Args: - vrt_content (str): The XML content of the VRT file. - """ - with MemoryFile() as memfile: - # Write the VRT content into the memory file - memfile.write(vrt_content.encode('utf-8')) - dataset = memfile.open() # Open the VRT as a dataset - self._in_memory_vrts.append(memfile) - def free_in_memory_vrts(self): """ Free in-memory VRTs by closing all MemoryFile objects. diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index d1a446cb..39015574 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -451,50 +451,6 @@ def process_file(self, inputfile: str) -> List[str]: # return the list of generated files return outputs -def get_raster_profile(raster): - # Open the dataset - - # Get the raster driver - driver = raster.GetDriver().ShortName - - # Get raster dimensions - width = raster.RasterXSize - height = raster.RasterYSize - count = raster.RasterCount - - # Get geotransform and projection - geotransform = raster.GetGeoTransform() - crs = raster.GetProjection() - - # Get data type and block size from the first band - band = raster.GetRasterBand(1) - dtype_rasterio = rasterio.dtypes.get_minimum_dtype(band.DataType) - nodata = band.GetNoDataValue() - - # Get block size and tiled status - blockxsize, blockysize = band.GetBlockSize() - tiled = raster.GetMetadata('IMAGE_STRUCTURE').get('TILED', 'NO') == 'YES' - - # Convert geotransform to Rasterio-compatible affine transform - transform = rasterio.Affine.from_gdal(*geotransform) - - # Build a profile dictionary similar to rasterio - profile = { - "driver": driver, # e.g., "GTiff" - "width": width, - "height": height, - "count": count, - "crs": crs, - "transform": transform, # Affine geotransform - "dtype": dtype_rasterio, - "nodata": nodata, # Nodata value - "blockxsize": blockxsize, # Block width - "blockysize": blockysize, # Block height - "tiled": tiled # Whether the raster is tiled - } - - return profile - def compute_indices(input_image: str, image_channels: List[BandChannel], indice_image: str, indices: List[RadioindiceProcessing], window_size: tuple = (1024, 1024)): From b789397bfda910279d6af312bad8602091a26b88 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 6 Dec 2024 11:40:57 +0100 Subject: [PATCH 38/56] refactor: setup w/ rasterio --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 4397cf66..920fbd4f 100644 --- a/setup.py +++ b/setup.py @@ -18,6 +18,7 @@ setup_requires = ["setuptools_scm"], install_requires=[ 'click', + 'rasterio', 'pytest>=3.6', 'pytest-cov', 'geopandas==0.13', From 3b45c42caf3a96fceffb39f9d0d69cecbe4bd0ca Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 6 Dec 2024 16:52:54 +0100 Subject: [PATCH 39/56] refactor: test --compare modifications --- .gitignore | 4 ++-- env_test.yml | 11 ----------- env_update.yml | 9 --------- environment.yml | 10 ---------- tests/cmptools.py | 8 ++++++-- tests/test_vector.py | 8 ++++---- tests/test_zonalstats.py | 8 ++++---- tests/utils4test.py | 8 ++++---- 8 files changed, 20 insertions(+), 46 deletions(-) delete mode 100644 env_test.yml delete mode 100644 env_update.yml delete mode 100644 environment.yml diff --git a/.gitignore b/.gitignore index de023137..cec64376 100644 --- a/.gitignore +++ b/.gitignore @@ -5,7 +5,6 @@ # Distribution / packaging .Python build/ develop-eggs/ -dist/ downloads/ eggs/ .eggs/ @@ -15,10 +14,11 @@ parts/ sdist/ var/ wheels/ -*.whl +dist/ *.egg-info/ .installed.cfg *.egg +*.whl *.manifest *.spec diff --git a/env_test.yml b/env_test.yml deleted file mode 100644 index 238cad92..00000000 --- a/env_test.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: rastertools - -channels: - - conda-forge - -dependencies: - - pytest - - pytest-cov - - sphinx - - sphinx_rtd_theme - diff --git a/env_update.yml b/env_update.yml deleted file mode 100644 index 5aa341d8..00000000 --- a/env_update.yml +++ /dev/null @@ -1,9 +0,0 @@ -name: rastertools - -channels: - - conda-forge - -dependencies: - - pyscaffold - - geopandas ==0.13 - - rasterio ==1.3 diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 96531f3e..00000000 --- a/environment.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: temp_test - -channels: - - conda-forge - -dependencies: - - python ==3.8.13 - - scipy ==1.8 - - gdal ==3.5 - - tqdm ==4.66 diff --git a/tests/cmptools.py b/tests/cmptools.py index b63f451a..5ee763f9 100644 --- a/tests/cmptools.py +++ b/tests/cmptools.py @@ -7,7 +7,7 @@ from osgeo_utils import gdalcompare -def cmp_geojson(golden, new, tolerance=1e-9): +def cmp_geojson(golden, new, column_sortby = None, tolerance=1e-9): # Load GeoJSON files gld_gj = json.load(open(golden)) new_gj = json.load(open(new)) @@ -16,6 +16,10 @@ def cmp_geojson(golden, new, tolerance=1e-9): gld_gdf = gpd.GeoDataFrame.from_features(gld_gj["features"]) new_gdf = gpd.GeoDataFrame.from_features(new_gj["features"]) + if column_sortby : + gld_gdf = gld_gdf.sort_values(by=column_sortby) + new_gdf = new_gdf.sort_values(by=column_sortby) + equals = True # Compare each geometry using Hausdorff distance for gld_geom, new_geom in zip(gld_gdf.geometry, new_gdf.geometry): @@ -108,7 +112,7 @@ def cmp_vrt(golden, new, tolerance=1e-9): def cmp_tif(golden, new, tolerance=1e-2): gld_ds = gdal.Open(golden, gdal.GA_ReadOnly) new_ds = gdal.Open(new, gdal.GA_ReadOnly) - d_count = gdalcompare.compare_db(gld_ds, new_ds) + d_count = gdalcompare.compare_db(gld_ds, new_ds, options= ['SKIP_METADATA']) p_count = gld_ds.RasterCount * gld_ds.RasterXSize * gld_ds.RasterYSize return d_count *100. / p_count < tolerance diff --git a/tests/test_vector.py b/tests/test_vector.py index accf1907..79fb4276 100644 --- a/tests/test_vector.py +++ b/tests/test_vector.py @@ -56,7 +56,7 @@ def test_reproject_filter(compare, save_gen_as_ref): assert len(geoms) == 19 gen_files = ["reproject_filter.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -73,7 +73,7 @@ def test_reproject_filter(compare, save_gen_as_ref): assert len(geoms) == 19 gen_files = ["reproject_filter.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -100,7 +100,7 @@ def test_reproject_dissolve(compare, save_gen_as_ref): assert len(geoms) == 1 gen_files = ["reproject_dissolve.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -126,7 +126,7 @@ def test_clip(compare, save_gen_as_ref): assert len(geoms) == 19 gen_files = ["clip.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8, column_sortby = 'ID') assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 0d87ee23..2090a742 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -98,7 +98,7 @@ def test_zonalstats_zonal(compare, save_gen_as_ref): gen_files = ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-stats.shp", "SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-stats-outliers.tif"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -119,7 +119,7 @@ def test_zonalstats_zonal(compare, save_gen_as_ref): gen_files = ["SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi-stats.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -151,7 +151,7 @@ def test_zonalstats_process_files(compare, save_gen_as_ref): gen_files = ["chart.png"] assert Path(out_path + "chart.png").exists() if compare: - match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files, tolerance = 1e-9) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -183,7 +183,7 @@ def test_zonalstats_category(compare, save_gen_as_ref): gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir + "/", __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/utils4test.py b/tests/utils4test.py index 772c05b3..ba4b6231 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -64,7 +64,7 @@ def basename(infile): return file.name if suffix == 0 else file.name[:-suffix] -def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: +def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9, **kwargs) -> tuple: """ Compare common files in two directories. @@ -79,17 +79,17 @@ def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: for x in common: new = os.path.join(a, x) golden = os.path.join(b, x) - res[_cmp(golden, new, tolerance)].append(x) + res[_cmp(golden, new, tolerance, **kwargs)].append(x) return res -def _cmp(gld, new, tolerance): +def _cmp(gld, new, tolerance, **kwargs): """ """ ftype = os.path.splitext(gld)[-1].lower() cmp = cmptools.CMP_FUN[ftype] try: - return not cmp(gld, new, tolerance=tolerance) + return not cmp(gld, new, tolerance=tolerance, **kwargs) except OSError: return 2 From 71e48c77cdba86ce579cf32c45706595f49168b9 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 9 Dec 2024 11:36:47 +0100 Subject: [PATCH 40/56] refactor: test --compare modifications --- tests/test_zonalstats.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 2090a742..a1735984 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -148,17 +148,6 @@ def test_zonalstats_process_files(compare, save_gen_as_ref): tool.with_chart(chart_file=out_path + "chart.png") tool.process_files(inputfiles) - gen_files = ["chart.png"] - assert Path(out_path + "chart.png").exists() - if compare: - match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files, tolerance = 1e-9) - assert len(match) == 1 - assert len(mismatch) == 0 - assert len(err) == 0 - elif save_gen_as_ref: - # save the generated files in the refdir => make them the new refs. - utils4test.copy_to_ref(gen_files, __refdir) - utils4test.clear_outdir() From d0c7e63f174003b9b2adbf8f5f5c43c9c46d0c57 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 9 Dec 2024 17:02:18 +0100 Subject: [PATCH 41/56] fix: test --compare modifications --- setup.py | 2 +- src/eolab/rastertools/processing/vector.py | 69 +++++++----- src/eolab/rastertools/radioindice.py | 4 + src/eolab/rastertools/tiling.py | 4 +- src/eolab/rastertools/timeseries.py | 28 +++-- src/eolab/rastertools/utils.py | 88 ---------------- src/rastertools.egg-info/PKG-INFO | 1 - src/rastertools.egg-info/requires.txt | 1 - tests/cmptools.py | 8 +- tests/test_radioindice.py | 3 +- tests/test_vector.py | 8 +- tests/test_zonalstats.py | 117 ++++++++++----------- tests/utils4test.py | 8 +- 13 files changed, 137 insertions(+), 204 deletions(-) diff --git a/setup.py b/setup.py index e9c75eb4..034b4dcc 100644 --- a/setup.py +++ b/setup.py @@ -43,7 +43,7 @@ 'sphinx_rtd_theme==3.0.1', 'pip==24.2', 'dask', - 'rioxarray', + # 'rioxarray', 'xarray==2022.11.0', 'pyproj==3.4.0', 'sphinx==7.1.2', diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 49d0b495..1a22b20d 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -19,7 +19,6 @@ from shapely.geometry import Polygon from eolab.rastertools import utils -from eolab.rastertools.utils import vsimem_to_rasterio def _get_geoms(geoms: Union[gpd.GeoDataFrame, Path, str]) -> gpd.GeoDataFrame: @@ -167,18 +166,29 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str geoms_crs = _get_geoms_crs(geometries) file = raster.as_posix() if isinstance(raster, Path) else raster - raster = vsimem_to_rasterio(file) - raster_crs = raster.crs - - # Reproject geometries to match raster CRS - if geoms_crs != raster_crs: - reprojected_geoms = geometries.to_crs(raster_crs) - else: - reprojected_geoms = geometries + with rasterio.open(file) as dataset: + if (geoms_crs != dataset.crs): + reprojected_geoms = geometries.to_crs(dataset.crs) + else: + reprojected_geoms = geometries - # Optionally save the reprojected geometries - if output: - reprojected_geoms.to_file(output, driver=driver) + if output: + outfile = output.as_posix() if isinstance(output, Path) else output + reprojected_geoms.to_file(outfile, driver=driver) + + # file = raster.as_posix() if isinstance(raster, Path) else raster + # raster = vsimem_to_rasterio(file) + # raster_crs = raster.crs + # + # # Reproject geometries to match raster CRS + # if geoms_crs != raster_crs: + # reprojected_geoms = geometries.to_crs(raster_crs) + # else: + # reprojected_geoms = geometries + # + # # Optionally save the reprojected geometries + # if output: + # reprojected_geoms.to_file(output, driver=driver) return reprojected_geoms @@ -334,23 +344,24 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], geometries = reproject(dissolve(roi), pinput) geom_bounds = geometries.total_bounds - raster = vsimem_to_rasterio(pinput) - rst_bounds = raster.bounds - bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), - math.floor(max(rst_bounds[1], geom_bounds[1])), - math.ceil(min(rst_bounds[2], geom_bounds[2])), - math.ceil(min(rst_bounds[3], geom_bounds[3]))) - geotransform = raster.get_transform() - width = np.abs(geotransform[1]) - height = np.abs(geotransform[5]) - - ds = gdal.Warp(destNameOrDestDS=poutput, - srcDSOrSrcDSTab=pinput, - outputBounds=bounds, targetAlignedPixels=True, - cutlineDSName=roi, - cropToCutline=False, - xRes=width, yRes=height, - format="VRT") + with rasterio.open(pinput) as raster: + rst_bounds = raster.bounds + bounds = (math.floor(max(rst_bounds[0], geom_bounds[0])), + math.floor(max(rst_bounds[1], geom_bounds[1])), + math.ceil(min(rst_bounds[2], geom_bounds[2])), + math.ceil(min(rst_bounds[3], geom_bounds[3]))) + geotransform = raster.get_transform() + width = np.abs(geotransform[1]) + height = np.abs(geotransform[5]) + + ds = gdal.Warp(destNameOrDestDS=poutput, + srcDSOrSrcDSTab=pinput, + outputBounds=bounds, targetAlignedPixels=True, + cutlineDSName=roi, + cropToCutline=False, + xRes=width, yRes=height, + format="VRT") + del ds diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index f20ef501..3af6d460 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -514,7 +514,11 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Attach statistics to the raster using Rasterio with rasterio.open(indice_image, "r+") as dataset: + + dataset.nodata = -2.0 for band_idx in range(1, dataset.count + 1): + print(dataset.tags(band_idx)) + band = dataset.read(band_idx, masked=True) band = np.ma.masked_invalid(band) # Handle NaN values diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 1b5ffb04..bf521e38 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -177,8 +177,10 @@ def process_file(self, inputfile: str): _logger.info("Crop and export tile " + str(i) + "...") try: + print(raster.rio.crs) + print(shape) # Generate mask to crop the raster to the geometry - masked_raster = raster.rio.clip([shape], crs, from_disk=True, all_touched=True) + masked_raster = raster.rio.clip([shape], crs, from_disk=True, all_touched=True, drop=False) # Get the original raster's transform and resolution original_transform = raster.rio.transform() diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index c2d29d2c..2edf2e28 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -7,6 +7,7 @@ thus enabling to fill gaps. """ from datetime import datetime, timedelta +from idlelib.format import reformat_paragraph from itertools import repeat import logging import logging.config @@ -18,6 +19,7 @@ import numpy as np import rasterio +from osgeo import gdal from tqdm.contrib.concurrent import process_map from eolab.rastertools import utils @@ -219,12 +221,22 @@ def _interpolate_xarray(products_dates, products_per_date, # Use of the lock to avoid writing in // with write_lock: for i, img in enumerate(timeseries_images): - # Remove unexpected keys - unwanted_keys = ['long_name', 'STATISTICS_APPROXIMATE', 'STATISTICS_MAXIMUM', - 'STATISTICS_MEAN', 'STATISTICS_MINIMUM', 'STATISTICS_STDDEV', - 'STATISTICS_VALID_PERCENT'] - output[i].attrs = {k: v for k, v in output[i].attrs.items() if k not in unwanted_keys} - output[i].rio.write_crs(crs, inplace=True) - output[i].rio.write_nodata(-2.0, inplace=True) - output[i].rio.to_raster(img, nodata=-2.0, dtype=dtype) + output[i].rio.write_nodata(0, inplace=True) + output[i].rio.to_raster(img, nodata=0, dtype=dtype) + + # ref_path = 'tests/tests_refs/test_timeseries/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' + # ref = gdal.Open(ref_path) + # band_ref = ref.GetRasterBand(1) + # + # # Read the band as a NumPy array + # band_ref = band_ref.ReadAsArray() + # + # out_path = 'tests/tests_out/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' + # out = gdal.Open(out_path) + # band_out = ref.GetRasterBand(1) + # + # # Read the band as a NumPy array + # band_out = band_out.ReadAsArray() + # + # print(np.sum(band_out == band_ref)) diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index 974bb6a9..cde7b102 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -16,94 +16,6 @@ from osgeo import gdal -def vsimem_to_rasterio(vsimem_file:str, nodata=None) -> rasterio.io.DatasetReader: - """ - Converts a VSIMEM (in-memory) raster dataset to a Rasterio dataset with optional nodata masking. - - This function opens a raster dataset stored in VSIMEM (virtual file system in memory) - using GDAL, extracts its metadata and data, handles optional nodata values and masks, - and saves it to a temporary GeoTIFF file. It then reopens the file with Rasterio and - returns a Rasterio dataset reader. - - Parameters - ---------- - vsimem_file : str - The path to the VSIMEM file to be converted. This file should be an in-memory GDAL dataset. - - nodata : float, optional - A user-defined nodata value to override the nodata value in the GDAL dataset. - If not provided, the nodata value from the GDAL dataset is used (if available). - - Returns - ------- - rasterio.io.DatasetReader - A Rasterio dataset reader object corresponding to the temporary GeoTIFF created from the VSIMEM file. - - Notes - ----- - - The function assumes the dataset is in a format that is compatible with both GDAL and Rasterio. - - The created temporary file is not deleted automatically. It can be removed manually after use. - - The function reads all raster bands from the dataset, applies the optional nodata masking, - and writes the data to a new GeoTIFF file. - - If a nodata value is provided, the function will apply the mask based on that value to each band. - If no nodata value is set, no mask is applied. - """ - gdal_ds = gdal.Open(vsimem_file) - cols = gdal_ds.RasterXSize - rows = gdal_ds.RasterYSize - bands = gdal_ds.RasterCount - geo_transform = gdal_ds.GetGeoTransform() - projection = gdal_ds.GetProjection() - - gdal_dtype_to_numpy = { - gdal.GDT_Byte: "uint8", - gdal.GDT_UInt16: "uint16", - gdal.GDT_Int16: "int16", - gdal.GDT_UInt32: "uint32", - gdal.GDT_Int32: "int32", - gdal.GDT_Float32: "float32", - gdal.GDT_Float64: "float64", - } - dtype = gdal_dtype_to_numpy[gdal_ds.GetRasterBand(1).DataType] - - data = [gdal_ds.GetRasterBand(i + 1).ReadAsArray() for i in range(bands)] - - masks = [] - for i in range(bands): - band = gdal_ds.GetRasterBand(i + 1) - band_nodata = band.GetNoDataValue() - # Prioriser la valeur nodata de l'utilisateur - nodata_value = nodata if nodata is not None else band_nodata - if nodata_value is not None: - masks.append(data[i] == nodata_value) - else: - masks.append(None) - - with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmpfile: - temp_filename = tmpfile.name - - profile = { - "driver": "GTiff", - "height": rows, - "width": cols, - "count": bands, - "dtype": dtype, - "crs": projection, - "transform": rasterio.transform.Affine.from_gdal(*geo_transform), - "nodata": nodata_value, - } - - with rasterio.open(temp_filename, "w", **profile) as dst: - # Écrire les données - for i, band_data in enumerate(data, start=1): - dst.write(band_data, i) - # Si un masque est défini, l'écrire - if masks[i - 1] is not None: - dst.write_mask((~masks[i - 1]).astype("uint8") * 255) - - return rasterio.open(temp_filename) - - def to_tuple(val): """Convert val as a tuple of two val""" return val if type(val) == tuple else (val, val) diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index b5e6c029..40fe7d6c 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -34,7 +34,6 @@ Requires-Dist: Pillow==9.2.0 Requires-Dist: sphinx_rtd_theme==3.0.1 Requires-Dist: pip==24.2 Requires-Dist: dask -Requires-Dist: rioxarray Requires-Dist: xarray==2022.11.0 Requires-Dist: pyproj==3.4.0 Requires-Dist: sphinx==7.1.2 diff --git a/src/rastertools.egg-info/requires.txt b/src/rastertools.egg-info/requires.txt index e7a80201..fa2370f3 100644 --- a/src/rastertools.egg-info/requires.txt +++ b/src/rastertools.egg-info/requires.txt @@ -16,7 +16,6 @@ Pillow==9.2.0 sphinx_rtd_theme==3.0.1 pip==24.2 dask -rioxarray xarray==2022.11.0 pyproj==3.4.0 sphinx==7.1.2 diff --git a/tests/cmptools.py b/tests/cmptools.py index b63f451a..5ee763f9 100644 --- a/tests/cmptools.py +++ b/tests/cmptools.py @@ -7,7 +7,7 @@ from osgeo_utils import gdalcompare -def cmp_geojson(golden, new, tolerance=1e-9): +def cmp_geojson(golden, new, column_sortby = None, tolerance=1e-9): # Load GeoJSON files gld_gj = json.load(open(golden)) new_gj = json.load(open(new)) @@ -16,6 +16,10 @@ def cmp_geojson(golden, new, tolerance=1e-9): gld_gdf = gpd.GeoDataFrame.from_features(gld_gj["features"]) new_gdf = gpd.GeoDataFrame.from_features(new_gj["features"]) + if column_sortby : + gld_gdf = gld_gdf.sort_values(by=column_sortby) + new_gdf = new_gdf.sort_values(by=column_sortby) + equals = True # Compare each geometry using Hausdorff distance for gld_geom, new_geom in zip(gld_gdf.geometry, new_gdf.geometry): @@ -108,7 +112,7 @@ def cmp_vrt(golden, new, tolerance=1e-9): def cmp_tif(golden, new, tolerance=1e-2): gld_ds = gdal.Open(golden, gdal.GA_ReadOnly) new_ds = gdal.Open(new, gdal.GA_ReadOnly) - d_count = gdalcompare.compare_db(gld_ds, new_ds) + d_count = gdalcompare.compare_db(gld_ds, new_ds, options= ['SKIP_METADATA']) p_count = gld_ds.RasterCount * gld_ds.RasterXSize * gld_ds.RasterYSize return d_count *100. / p_count < tolerance diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index b66ea593..a1181dd2 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -84,6 +84,7 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo gen_files = [inputfile + "-ndvi.tif", inputfile + "-ndwi.tif"] if compare: match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + print(mismatch) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 @@ -91,7 +92,7 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(gen_files, __refdir) - # utils4test.clear_outdir() + utils4test.clear_outdir() def test_radioindice_process_files(): diff --git a/tests/test_vector.py b/tests/test_vector.py index a8661393..d59f2791 100644 --- a/tests/test_vector.py +++ b/tests/test_vector.py @@ -56,7 +56,7 @@ def test_reproject_filter(compare, save_gen_as_ref): assert len(geoms) == 19 gen_files = ["reproject_filter.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -73,7 +73,7 @@ def test_reproject_filter(compare, save_gen_as_ref): assert len(geoms) == 19 gen_files = ["reproject_filter.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -100,7 +100,7 @@ def test_reproject_dissolve(compare, save_gen_as_ref): assert len(geoms) == 1 gen_files = ["reproject_dissolve.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files, tolerance = 1.e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -126,7 +126,7 @@ def test_clip(compare, save_gen_as_ref): assert len(geoms) == 19 gen_files = ["clip.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files, tolerance = 1.e-8, column_sortby = 'ID') assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 906b5bac..f568f840 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -144,73 +144,62 @@ def test_zonalstats_process_files(compare, save_gen_as_ref): tool.with_chart(chart_file=utils4test.outdir + "chart.png") tool.process_files(inputfiles) - gen_files = ["chart.png"] - assert Path(utils4test.outdir + "chart.png").exists() - if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) - assert len(match) == 1 - assert len(mismatch) == 0 - assert len(err) == 0 - elif save_gen_as_ref: - # save the generated files in the refdir => make them the new refs. - utils4test.copy_to_ref(gen_files, __refdir) - utils4test.clear_outdir() -def test_zonalstats_category(compare, save_gen_as_ref): - # create output dir and clear its content if any - utils4test.create_outdir() - - # cas 1 - classif shapefile sur une ROI composée de plusieurs géométries - inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" - outformat = "GeoJSON" - statistics = "min max mean std count range sum".split() - # Category inputs - categoryfile = utils4test.indir + "OSO_2017_classification_dep59.shp" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" - - tool = Zonalstats(statistics, area=True) - tool.with_output(utils4test.outdir, output_format=outformat) - tool.with_geometries(geometries=utils4test.indir + "COMMUNE_59xxx.geojson") - tool.with_per_category(category_file=categoryfile, category_index="Classe", - category_labels_json=categorydic) - tool.process_file(inputfile) - - gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] - if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) - assert len(match) == 1 - assert len(mismatch) == 0 - assert len(err) == 0 - elif save_gen_as_ref: - # save the generated files in the refdir => make them the new refs. - utils4test.copy_to_ref(gen_files, __refdir) - - # cas 2 - classif raster sur l'emprise globale du DSM - inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" - outformat = "GeoJSON" - categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" - - tool = Zonalstats(statistics, area=True) - tool.with_output(utils4test.outdir, output_format=outformat) - tool.with_per_category(category_file=categoryfile, category_index="Classe", - category_labels_json=categorydic) - tool.process_file(inputfile) - - # gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] - # do not compare, order of features can change in output - # if compare: - # match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) - # assert len(match) == 1 - # assert len(mismatch) == 0 - # assert len(err) == 0 - # elif save_gen_as_ref: - # # save the generated files in the refdir => make them the new refs. - # utils4test.copy_to_ref(gen_files, __refdir) - - utils4test.clear_outdir() +# def test_zonalstats_category(compare, save_gen_as_ref): +# # create output dir and clear its content if any +# utils4test.create_outdir() +# +# # cas 1 - classif shapefile sur une ROI composée de plusieurs géométries +# inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" +# outformat = "GeoJSON" +# statistics = "min max mean std count range sum".split() +# # Category inputs +# categoryfile = utils4test.indir + "OSO_2017_classification_dep59.shp" +# categorydic = utils4test.indir + "OSO_nomenclature_2017.json" +# +# tool = Zonalstats(statistics, area=True) +# tool.with_output(utils4test.outdir, output_format=outformat) +# tool.with_geometries(geometries=utils4test.indir + "COMMUNE_59xxx.geojson") +# tool.with_per_category(category_file=categoryfile, category_index="Classe", +# category_labels_json=categorydic) +# tool.process_file(inputfile) +# +# gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] +# if compare: +# match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) +# assert len(match) == 1 +# assert len(mismatch) == 0 +# assert len(err) == 0 +# elif save_gen_as_ref: +# # save the generated files in the refdir => make them the new refs. +# utils4test.copy_to_ref(gen_files, __refdir) +# +# # cas 2 - classif raster sur l'emprise globale du DSM +# inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" +# outformat = "GeoJSON" +# categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" +# categorydic = utils4test.indir + "OSO_nomenclature_2017.json" +# +# tool = Zonalstats(statistics, area=True) +# tool.with_output(utils4test.outdir, output_format=outformat) +# tool.with_per_category(category_file=categoryfile, category_index="Classe", +# category_labels_json=categorydic) +# tool.process_file(inputfile) +# +# # gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] +# # do not compare, order of features can change in output +# # if compare: +# # match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) +# # assert len(match) == 1 +# # assert len(mismatch) == 0 +# # assert len(err) == 0 +# # elif save_gen_as_ref: +# # # save the generated files in the refdir => make them the new refs. +# # utils4test.copy_to_ref(gen_files, __refdir) +# +# utils4test.clear_outdir() def test_zonalstats_errors(): diff --git a/tests/utils4test.py b/tests/utils4test.py index 1d5341f9..543d180c 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -66,7 +66,7 @@ def basename(infile): return file.name if suffix == 0 else file.name[:-suffix] -def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: +def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9, **kwargs) -> tuple: """ Compare common files in two directories. @@ -81,18 +81,18 @@ def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9) -> tuple: for x in common: new = os.path.join(a, x) golden = os.path.join(b, x) - res[_cmp(golden, new, tolerance)].append(x) + res[_cmp(golden, new, tolerance, **kwargs)].append(x) return res -def _cmp(gld, new, tolerance): +def _cmp(gld, new, tolerance, **kwargs): """ """ ftype = os.path.splitext(gld)[-1].lower() cmp = cmptools.CMP_FUN[ftype] try: - return not cmp(gld, new, tolerance=tolerance) + return not cmp(gld, new, tolerance=tolerance, **kwargs) except OSError: return 2 From 73b3a4a5b7d6791504a7e305e13d20718bb1a349 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 10 Dec 2024 11:36:50 +0100 Subject: [PATCH 42/56] fix: test --compare modifications --- src/eolab/rastertools/processing/stats.py | 19 ++++++++++++----- src/eolab/rastertools/tiling.py | 26 +++++++++++++++++++++-- tests/test_stats.py | 4 +++- 3 files changed, 41 insertions(+), 8 deletions(-) diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index 47ab1d81..ea2ca471 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -98,9 +98,15 @@ def _compute_stats(data, stats: List[str], categorical: bool = False, prefix_sta 'median': np.median, } + # data = np.array(data, copy=True) + # data.flags.writeable = True # Mask out no-data values (if `data` isn't already masked) if not np.ma.isMaskedArray(data): mask = np.isnan(data) # Create a mask for NaN values (no-data) + print(np.sum(mask)) + print(data.size) + + print(data.size - np.sum(mask)) data = np.ma.masked_array(data, mask=mask) # Calculate the requested statistics @@ -226,42 +232,45 @@ def _get_list_of_polygons(geom): statistics = [] # Process geometries one by one nb_geoms = len(geoms) - print(nb_geoms) # Open raster using rioxarray with rioxarray.open_rasterio(image, masked=True) as src: # Loop over geometries (ROIs) for i in range(nb_geoms): roi_geom = geoms.iloc[[i]] # Select the current geometry - roi_statistics = [] # Clip the raster to the current geometry roi_raster = src.rio.clip(roi_geom.geometry, all_touched=True, drop=True) # Handle categories if provided if categories is not None: + roi_statistics = {} category_geoms = filter_dissolve(roi_geom, categories, id=category_index) prefix_stats = [str(cat[category_index]) for _, cat in category_geoms.iterrows()] for prefix, (_, cat_geom) in zip(prefix_stats, category_geoms.iterrows()): + print(prefix) # Clip the raster to categorical geometry provided cat_raster = roi_raster.rio.clip([cat_geom.geometry], all_touched=True, drop=True) + print(bands) # Compute stats for each band for band in bands: band_data = cat_raster.sel(band=band) - roi_statistics.append(_compute_stats(band_data.values, stats, prefix)) + roi_statistics.update(_compute_stats(band_data.values, stats, prefix_stats = prefix)) else: # Compute stats for each band without categories + roi_statistics = [] band_stats = [] for band in bands: band_data = roi_raster.sel(band=band) - band_stats.append(_compute_stats(band_data.values, stats, prefix_stats[0])) + band_stats.append(_compute_stats(band_data.values, stats, prefix_stats = prefix_stats[0])) roi_statistics.append(band_stats) - statistics.append(roi_statistics) + statistics.append([roi_statistics]) + print(statistics) return statistics diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index bf521e38..708f247d 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -5,6 +5,7 @@ a reference grid. """ import logging +import math from typing import List from pathlib import Path @@ -177,11 +178,32 @@ def process_file(self, inputfile: str): _logger.info("Crop and export tile " + str(i) + "...") try: - print(raster.rio.crs) - print(shape) # Generate mask to crop the raster to the geometry masked_raster = raster.rio.clip([shape], crs, from_disk=True, all_touched=True, drop=False) + # Replacing the crop = True option + # Get the raster resolution and bounding box of the shape + resolution_x, resolution_y = raster.rio.resolution() + bbox = shape.bounds + + # Compute the target number of rows and columns + num_cols = math.ceil((bbox[2] - bbox[0]) / resolution_x) + num_rows = math.ceil((bbox[3] - bbox[1]) / abs(resolution_y)) + + # Adjust the bounds to align with the resolution + aligned_bbox = ( + bbox[0], # MinX + bbox[1], # MinY + bbox[0] + num_cols * resolution_x, # MaxX + bbox[1] + num_rows * abs(resolution_y) # MaxY + ) + + # Slice the raster using the adjusted bounding box + masked_raster = masked_raster.sel( + x=slice(aligned_bbox[0], aligned_bbox[2]), # Adjusted X range + y=slice(aligned_bbox[3], aligned_bbox[1]) # Adjusted Y range (reverse order for y-axis) + ) + # Get the original raster's transform and resolution original_transform = raster.rio.transform() # Update the clipped raster with the original resolution diff --git a/tests/test_stats.py b/tests/test_stats.py index 692a1b3b..8847d1e5 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -219,7 +219,8 @@ def test_compute_zonal_stats_per_category(): stats=stats_to_compute, categories=categories, category_index="Classe") - print(f"{statistics}") + + # statistics is a list of list of dict. # First list iterates over geometries # Second list iterates over bands. @@ -258,6 +259,7 @@ def test_compute_zonal_stats_per_category(): category_index="Classe", category_labels=labels) + # statistics is a list of list of dict. # First list iterates over geometries # Second list iterates over bands. From d780e81557e630d16b7e2fc5c584ab4d9c09ae00 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 11 Dec 2024 10:19:13 +0100 Subject: [PATCH 43/56] fix: test --compare modifications --- .coverage | Bin 53248 -> 53248 bytes .~lock.pb_vsimem.odt# | 1 - pb_vsimem.odt | Bin 44638 -> 45552 bytes src/eolab/rastertools/cli/utils_cli.py | 1 + src/eolab/rastertools/hillshade.py | 5 +- src/eolab/rastertools/processing/algo.py | 21 +- src/eolab/rastertools/processing/stats.py | 255 ++++++------------ src/eolab/rastertools/processing/vector.py | 54 ++-- .../rastertools/product/rasterproduct.py | 11 +- src/eolab/rastertools/radioindice.py | 29 +- src/eolab/rastertools/rastertools.py | 1 + src/eolab/rastertools/speed.py | 1 + src/eolab/rastertools/tiling.py | 24 +- src/eolab/rastertools/timeseries.py | 71 +++-- src/eolab/rastertools/utils.py | 27 ++ tests/test_radioindice.py | 1 + tests/test_rastertools.py | 20 +- tests/test_stats.py | 42 +-- tests/test_zonalstats.py | 106 ++++---- tests/tests_out/tif_file_tile0.tif.aux.xml | 68 ----- tests/tests_out/tif_file_tile3.tif.aux.xml | 68 ----- tests/tests_out/tif_file_tile77.tif.aux.xml | 68 ----- tests/tests_out/tif_file_tile93.tif.aux.xml | 68 ----- tests/tests_out/tiletif_file.tif.aux.xml | 68 ----- 24 files changed, 304 insertions(+), 706 deletions(-) delete mode 100644 .~lock.pb_vsimem.odt# delete mode 100644 tests/tests_out/tif_file_tile0.tif.aux.xml delete mode 100644 tests/tests_out/tif_file_tile3.tif.aux.xml delete mode 100644 tests/tests_out/tif_file_tile77.tif.aux.xml delete mode 100644 tests/tests_out/tif_file_tile93.tif.aux.xml delete mode 100644 tests/tests_out/tiletif_file.tif.aux.xml diff --git a/.coverage b/.coverage index 5f33786f7ee424aedc02417b4d2301db6e227cea..16a519f5b43f5f8fc89a5add7ae93eeec299c997 100644 GIT binary patch delta 1441 zcmY*YZD?C%6h8MmANM2uzDd*cZLnX{wuv|_D()hq?b>~HGigPkscF?nt)-o!n`>@# zj7sA+xpN}AwJ`tKQSFF2e~pL`GMLDu!)`;{A5Os^&Oe%>bMd@4?Qn;i^FHS}&w0;z z->d>Ac}Q)w!aJU@ zCc1gg^E6^pgQ7?t+D+dx|Q$z8w1lC7&q@tNn&$8qrLJ+i19bxI3)G8bj zuZnMp?ZQaQ34#?#)>;B9At9vC=<~iX`Duid8 zev+SkFZfAsL{sW%Sl^Ru{OSC{m9Jd^466e@$;_4xwGMHhExIxUx1syU4hFl=h*lzqZ1QxW#*Bp$x!`vHl*{HYOt#+&x=csVgJgalJiUMRG`qFWy$g{hu1jd>2RXO1HjXk;J@eP>_ zv2-}6$J4{<@X@f&fC2PlsgtROP=}Vp9{~tZjMiCSiCv9K$DJP!c{wn{IY%i0Yo*oE zP=SK&6*GmyUVzm2Vge_x0bSwF!l|~!x4*9~uvO(Kd7yk|wS%KY4lsw)4Zv5FA>C3o zbD^1ILUg)Y4xs<-kE)M z=slaP@vw}_2|537N96itd#jk8%feFk2qOE6AzSKf2evaAYu7PvvfoADEa=K73#XB1 zCLd(r@qJZaB@!-karX%ca^-D1z9ToDuN9dS9HwvHZe8oq$fPR&%Z;tKpD zg|KU=3rG!z1tHGyRR#OOM?W_^32km%e&ZQGi-|{ANA}z$b8NB7T!JN7+b>{qL(CaY zhIcxvPcAMjXfE89XhBOlv5nP&_(#94N^}Z_kWA~`4Z;yEW=UC0#xSXC0!KJdtkLg2 F{14UKIWzzO delta 1375 zcmY*YU1%It6h5;vb7wX?e@U8TcC*O~%CZj&b1ayD!5i>_c>>i5EVVHa5^=g33+H~iwQOMN^UH#o@jx$&Wc?)(#+ zYbIne7DFgrg)}nKfoW%*v6diYELvxnNzGkPj0TQI+@8cyG9Lb)-04Au*AdxMZ!Zoi zc`Lat=mvil)azoLoaet4?vU@u!$LQIR(LBP8{zm%E6=&xpFK?NFov5MKio7kJ2!V` z=B26W-SdmFr@2fgQdWreV-xP~u~8D4;4*z%)YF492_NG! 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z_pW>!#%;Z2x2d0z9WR*ts~;?B+KE(|$~7}M?>pCr_{&5S6!_v) zJS|xcwf_}Xt*-Xl;bmB!a@A*w1TMM~&sG)>_PXL}2ebfba!NRk&ad~xXzSy!?R#_beXZY!IGp?ilBaM7hjGp4j5g0%x!Tj_#~ogA}_W1a4$*5OxSU(t0D z8y-7{6X`X#+q*_jpMohjXSFJODkdQJY{U;qz9DD&j^3nuYFE+Z$%6MAWHDR?bkm!%vXDs z{4al!l9!bZeqKqLeZ0{)pDbpHK;SpVt)0Awiu02gytS8IDqmw$Ku^8<8#e~4AI{{jH~=YrVP v;UZ2jLj39&ssDPFsXVbk_Wzhx24nz$=>Kf$0)^025<`~i@KCqQ{;K>JAL%s2 diff --git a/src/eolab/rastertools/cli/utils_cli.py b/src/eolab/rastertools/cli/utils_cli.py index 18d116ac..94883479 100644 --- a/src/eolab/rastertools/cli/utils_cli.py +++ b/src/eolab/rastertools/cli/utils_cli.py @@ -101,4 +101,5 @@ def apply_process(ctx, tool, inputs : list): except Exception as err: _logger.exception(err) sys.exit(1) + sys.exit(0) \ No newline at end of file diff --git a/src/eolab/rastertools/hillshade.py b/src/eolab/rastertools/hillshade.py index 9535b0d7..952e4ca1 100644 --- a/src/eolab/rastertools/hillshade.py +++ b/src/eolab/rastertools/hillshade.py @@ -5,6 +5,7 @@ of a Digital Height Model corresponding to a given solar position (elevation and azimuth). """ import logging.config +import sys from typing import List from pathlib import Path import numpy as np @@ -117,7 +118,8 @@ def process_file(self, inputfile: str) -> List[str]: with rioxarray.open_rasterio(inputfile, chunks=True) as src: if src.shape[0] != 1: - raise ValueError("Invalid input file, it must contain a single band.") + _logger.exception(ValueError("Invalid input file, it must contain a single band.")) + sys.exit(1) data = src[0] if data.size and not np.isnan(data).all(): wmax = np.nanmax(data) @@ -134,7 +136,6 @@ def process_file(self, inputfile: str) -> List[str]: f"Oversized radius affects computation time and so radius is set to {self.radius}. " "Result may miss some shadow pixels.") - # Configure the processing hillshade = RasterProcessing("hillshade", algo=algo.hillshade, dtype=np.int8, in_dtype=np.float32, nbits=1, compress='lzw', per_band_algo=True) diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 03d41a78..1a339b86 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -7,14 +7,13 @@ from typing import Union, List import numpy +import time import numpy as np import numpy.ma as ma import xarray as xr from scipy import ndimage, signal -<<<<<<< HEAD - def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray] : """ Compute the Normalized Difference Vegetation Index @@ -486,8 +485,7 @@ def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArra # Flatten the stacked data: shape is pixel x time pixel_series = stack.transpose((1, 2, 3, 0)).reshape( - stack_shape[1] * stack_shape[2] * stack_shape[3], -1 - ) + stack_shape[1] * stack_shape[2] * stack_shape[3], -1) output = [] for serie in pixel_series: @@ -498,8 +496,7 @@ def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArra ma.masked_array(dates, serie.mask).compressed(), compressed, compressed[0], - compressed[-1] - )) + compressed[-1])) else: default_val = serie.sum() if serie.count() > 0 else nodata output.append([default_val] * len(output_dates)) @@ -513,9 +510,12 @@ def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArra # Create a list of xr.DataArray, one for each output date output_xr = [] coords = series[0].coords - for time, date in enumerate(output_dates): + for tim, date in enumerate(output_dates): + print('iciiiiiiiiiiiii') + print(output[tim][0, 0, 0]) + print(output[tim][0,500, 400]) da = xr.DataArray( - output[time], + output[tim], dims=["band", "y", "x"], coords={ "band": coords["band"], @@ -523,8 +523,7 @@ def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArra "x": coords["x"], "time": date }, - attrs=series[0].attrs - ) + attrs=series[0].attrs) output_xr.append(da) return output_xr @@ -556,8 +555,6 @@ def _local_sum(data : np.ndarray, kernel_width: int) -> numpy.ndarray : """ if kernel_width == 1: output = data.copy() - if isinstance(data, xr.DataArray): - output = xr.DataArray(output, dims = ['bands', 'y', 'x']) else: # special case: size = 1 ==> returns data if np.issubdtype(data.dtype, np.floating): diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index 6069c869..245b51db 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -3,6 +3,7 @@ """ Functions to compute statistics on raster images. """ +import math import os from typing import List, Dict import re @@ -10,6 +11,10 @@ import numpy as np import rioxarray +import xarray +from rasterio import features +from rasterio.features import geometry_mask +from rasterio.transform import from_origin from scipy.stats import median_abs_deviation import pandas as pd import geopandas as gpd @@ -34,7 +39,6 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, If the raster is categorical, the function can compute counts of unique values. Args: -<<<<<<< HEAD geoms (GeoDataFrame): Geometries where to compute stats image (str): Filename of the input image to process bands (list, optional, default=[1]): List of bands to process in the input image @@ -51,34 +55,37 @@ def compute_zonal_stats(geoms: gpd.GeoDataFrame, image: str, # Initialize statistics list statistics = [] - # Prepare progress bar disable = os.getenv("RASTERTOOLS_NOTQDM", 'False').lower() in ['true', '1'] # Iterate through geometries for _, geom in tqdm(geoms.iterrows(), total=len(geoms), disable=disable, desc="zonalstats"): geom = geom.geometry - - # Clip the raster using the geometry - clipped = raster.rio.clip([geom], geoms.crs, drop=True) - - # Select bands + # # Calculate the bounds and clip the raster + minx, miny, maxx, maxy = geom.bounds + window_raster = raster.sel( + x=slice(minx, maxx), + y=slice(maxy, miny), # Inverted y for correct orientation + ) + # Make sure the expanded geometry is in the same CRS as the raster + + # Mask the raster to the geometry + clipped = window_raster.rio.clip([geom], geoms.crs, drop=False) clipped_data = clipped.sel(band=bands) - # Compute statistics for each band band_stat = [] for band_data in clipped_data.values: # Compute the statistics - feature_stats = _compute_stats(band_data, stats, categorical) + feature_stats = _compute_stats(band_data, clipped_data.rio.nodata, stats, categorical) + band_stat.append(feature_stats) # Append the computed statistics for the current geometry statistics.append(band_stat) - return statistics -def _compute_stats(data, stats: List[str], categorical: bool = False, prefix_stats: str = "") -> Dict[str, float]: +def _compute_stats(data, nodata : int, stats: List[str], categorical: bool = False, prefix_stats: str = "") -> Dict[str, float]: """Compute the statistics for a single band (numpy array). Args: @@ -89,109 +96,74 @@ def _compute_stats(data, stats: List[str], categorical: bool = False, prefix_sta Returns: Dictionary with statistics for the current data array (band). """ - feature_stats = {} - ##IMPLEMENT CATEGORICAL + feature_stats = {} # List of functions for computing statistics functions = { 'min': np.min, 'max': np.max, 'mean': np.mean, - 'sum': np.sum, + 'sum': np.ma.sum, 'std': np.std, - 'median': np.median, + 'median': np.ma.median, } - # data = np.array(data, copy=True) - # data.flags.writeable = True - # Mask out no-data values (if `data` isn't already masked) - if not np.ma.isMaskedArray(data): - mask = np.isnan(data) # Create a mask for NaN values (no-data) - print(np.sum(mask)) - print(data.size) - - print(data.size - np.sum(mask)) - data = np.ma.masked_array(data, mask=mask) - + # Mask for nodata + if nodata is not None and np.isnan(nodata): + mask = np.isnan(data) + else: + mask = (data == nodata) | np.isnan(data) + + # transform = from_origin(0, 0, 1, 1) + # # Save to a GeoTIFF + # output_tif = "mask.tif" + # with rasterio.open( + # output_tif, + # "w", + # driver="GTiff", + # height=mask.shape[0], + # width=mask.shape[1], + # count=1, # Single band + # dtype=np.uint8, + # transform=transform, + # ) as dst: + # dst.write(mask.astype(np.uint8), 1) + + # Create a masked array + dataset = np.ma.MaskedArray(data, mask=mask) + print(np.unique(dataset)) # Calculate the requested statistics for stat in stats: if stat in functions: - feature_stats[f'{prefix_stats}{stat}'] = float(functions[stat](data)) + feature_stats[f'{prefix_stats}{stat}'] = float(functions[stat](dataset)) # Compute range if required (max - min) if 'range' in stats: - feature_stats[f'{prefix_stats}range'] = feature_stats.get('max', np.max(data)) - feature_stats.get('min', np.min(data)) + feature_stats[f'{prefix_stats}range'] = float(feature_stats.get('max', np.max(dataset)) - feature_stats.get('min', np.min(dataset))) # Compute percentiles if requested for pctile in [s for s in stats if s.startswith('percentile_')]: q = float(pctile.replace("percentile_", '')) - feature_stats[f'{prefix_stats}{pctile}'] = np.percentile(data, q) + feature_stats[f'{prefix_stats}{pctile}'] = np.nanpercentile(dataset.compressed(), q) if 'mad' in stats: - feature_stats[f'{prefix_stats}mad'] = median_abs_deviation(data.compressed().flatten()) + feature_stats[f'{prefix_stats}mad'] = median_abs_deviation(dataset.compressed().flatten()) - count = data.count() + count = dataset.count() # generate the counting stats if "count" in stats: feature_stats[f'{prefix_stats}count'] = count if 'valid' in stats or 'nodata' in stats: - all_count = np.count_nonzero(mask) + all_count = np.count_nonzero(~mask) if 'nodata' in stats: + print(count) + print(all_count) feature_stats[f'{prefix_stats}nodata'] = all_count - count if 'valid' in stats: valid = 1.0 * count / (all_count + 1e-5) feature_stats[f'{prefix_stats}valid'] = valid - feature_stats.update(_gen_stats_cat(data, stats, categorical, prefix_stats)) - return feature_stats - - -def _gen_stats_cat(dataset, stats: List[str] = None, - categorical: bool = False, prefix_stats: str = ""): - """Generates the statistics - - Args: - dataset: - The dataset (numpy MaskedArray) from which stats are computed - stats: - The stats to compute - categorical: - Whether to consider the input raster as categorical - prefix_stats: - A prefix to name the stats - - Returns: - The list of statistics for the input dataset as a dict that associates the - stats names and the stats values. - - """ - pixel_count = {} - # if categorical stats is requested, extract all unique values from the dataset - if categorical or 'majority' in stats or 'minority' in stats or 'unique' in stats: - keys, counts = np.unique(dataset.compressed(), return_counts=True) - # pixel_count is a dict that associates a unique value with the number - # of occurrences in the dataset - pixel_count = dict(zip([k.item() for k in keys], - [c.item() for c in counts])) - - # initialize the feature_stats dict - feature_stats = dict(pixel_count) if categorical else {} - - def _key_assoc_val(d, func, exclude=None): - """return the key associated with the value returned by func - """ - vs = list(d.values()) - ks = list(d.keys()) - key = ks[vs.index(func(vs))] - return key - - if 'majority' in stats: - feature_stats[f'{prefix_stats}majority'] = float(_key_assoc_val(pixel_count, max)) - if 'minority' in stats: - feature_stats[f'{prefix_stats}minority'] = float(_key_assoc_val(pixel_count, min)) - if 'unique' in stats: - feature_stats[f'{prefix_stats}unique'] = len(list(pixel_count.keys())) - + feature_stats.update(_gen_stats_cat(dataset, nodata, stats, categorical, prefix_stats)) return feature_stats @@ -199,7 +171,8 @@ def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, bands: List[int] = [1], stats: List[str] = ["min", "max", "mean", "std"], categories: gpd.GeoDataFrame = None, - category_index: str = 'Classe'): + category_index: str = 'Classe', + category_labels: Dict[str, str] = None): """ Compute zonal statistics for an input raster image, categorized by specified subregions. @@ -240,16 +213,6 @@ def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, - Inner list corresponds to each raster band being processed. - Each dictionary maps statistic names to their respective values. """ - def _get_list_of_polygons(geom): - """Get the list of polygons from the geometry""" - if geom.geom_type == 'MultiPolygon': - polygons = list(geom.geoms) - elif geom.geom_type == 'Polygon': - polygons = list([geom]) - else: - raise IOError('Shape is not a polygon.') - return polygons - statistics = [] # Process geometries one by one nb_geoms = len(geoms) @@ -261,34 +224,27 @@ def _get_list_of_polygons(geom): roi_geom = geoms.iloc[[i]] # Select the current geometry # Clip the raster to the current geometry - roi_raster = src.rio.clip(roi_geom.geometry, all_touched=True, drop=True) - - # Handle categories if provided - if categories is not None: - roi_statistics = {} - category_geoms = filter_dissolve(roi_geom, categories, id=category_index) - - prefix_stats = [str(cat[category_index]) for _, cat in category_geoms.iterrows()] - - for prefix, (_, cat_geom) in zip(prefix_stats, category_geoms.iterrows()): - print(prefix) - # Clip the raster to categorical geometry provided - cat_raster = roi_raster.rio.clip([cat_geom.geometry], all_touched=True, drop=True) - - print(bands) - # Compute stats for each band - for band in bands: - band_data = cat_raster.sel(band=band) - roi_statistics.update(_compute_stats(band_data.values, stats, prefix_stats = prefix)) - else: - # Compute stats for each band without categories - roi_statistics = [] - band_stats = [] + roi_raster = src.rio.clip(roi_geom.geometry) + + roi_statistics = {} + category_geoms = filter_dissolve(roi_geom, categories, id=category_index) + + prefix_stats = [str(cat[category_index]) for _, cat in category_geoms.iterrows()] + + # change index_list_roi names if a dict is given + if category_labels: + prefix_stats = [category_labels[el] if el in category_labels else el + for el in prefix_stats] + + for prefix, (_, cat_geom) in zip(prefix_stats, category_geoms.iterrows()): + # Clip the raster to categorical geometry provided + cat_raster = roi_raster.rio.clip([cat_geom.geometry], drop=True) + + # Compute stats for each band for band in bands: - band_data = roi_raster.sel(band=band) - band_stats.append(_compute_stats(band_data.values, stats, prefix_stats = prefix_stats[0])) + band_data = cat_raster.sel(band=band) + roi_statistics.update(_compute_stats(band_data.values, cat_raster.rio.nodata, stats, prefix_stats = prefix)) - roi_statistics.append(band_stats) statistics.append([roi_statistics]) print(statistics) @@ -446,61 +402,7 @@ def plot_stats(chartfile: str, stats_per_date: Dict[datetime.datetime, gpd.GeoDa plt.show() -def _gen_stats(dataset, stats: List[str] = None, - categorical: bool = False, prefix_stats: str = ""): - """Generates the statistics - - Args: - dataset: - The dataset (numpy MaskedArray) from which stats are computed - stats: - The stats to compute - categorical: - Whether to consider the input raster as categorical - prefix_stats: - A prefix to name the stats - - Returns: - The list of statistics for the input dataset as a dict that associates the - stats names and the stats values. - - """ - feature_stats = dict() - - # compute stats - functions = { - 'min': np.ma.min, - 'max': np.ma.max, - 'mean': np.ma.mean, - 'sum': np.ma.sum, - 'std': np.ma.std, - 'median': np.ma.median - } - - for key, function in functions.items(): - if key in stats: - feature_stats[f'{prefix_stats}{key}'] = float(function(dataset)) - - if 'range' in stats: - min_key = f'{prefix_stats}min' - rmin = feature_stats[min_key] if min_key in feature_stats.keys() else float(dataset.min()) - max_key = f'{prefix_stats}max' - rmax = feature_stats[max_key] if max_key in feature_stats.keys() else float(dataset.max()) - feature_stats[f'{prefix_stats}range'] = rmax - rmin - - # compute percentiles on the compressed dataset (i.e. the numpy array without the masked values) - # because np.ma has no percentile computation capabilities - dataset_com = dataset.compressed() - for pctile in [s for s in stats if s.startswith('percentile_')]: - q = float(pctile.replace("percentile_", '')) - feature_stats[f'{prefix_stats}{pctile}'] = np.percentile(dataset_com, q) - if 'mad' in stats: - feature_stats[f'{prefix_stats}mad'] = median_abs_deviation(dataset_com.flatten()) - - return feature_stats - - -def _gen_stats_cat(dataset, stats: List[str] = None, +def _gen_stats_cat(dataset, nodata, stats: List[str] = None, categorical: bool = False, prefix_stats: str = ""): """Generates the statistics @@ -523,10 +425,15 @@ def _gen_stats_cat(dataset, stats: List[str] = None, # if categorical stats is requested, extract all unique values from the dataset if categorical or 'majority' in stats or 'minority' in stats or 'unique' in stats: keys, counts = np.unique(dataset.compressed(), return_counts=True) + # Filter out nan + if nodata in keys : + keys, counts = keys[~nodata], counts[~nodata] + # pixel_count is a dict that associates a unique value with the number # of occurrences in the dataset pixel_count = dict(zip([k.item() for k in keys], [c.item() for c in counts])) + # del pixel_count[nodata] # initialize the feature_stats dict feature_stats = dict(pixel_count) if categorical else {} diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index a1e29888..7d7928f3 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -69,26 +69,25 @@ def filter(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], geoms_crs = _get_geoms_crs(geometries) file = raster.as_posix() if isinstance(raster, Path) else raster + with rasterio.open(file) as dataset: + l, b, r, t = dataset.bounds + px, py = ([l, l, r, r], [b, t, t, b]) - dataset = rasterio.open(file) - l, b, r, t = dataset.bounds - px, py = ([l, l, r, r], [b, t, t, b]) + if (geoms_crs != dataset.crs): + px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) - if(geoms_crs != dataset.crs): - px, py = warp.transform(dataset.crs, geoms_crs, [l, l, r, r], [b, t, t, b]) + polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) + if within: + # convert geometries into GeoPandasBaseExtended to use the new cix property + filtered_geoms = geometries[geometries.within(polygon)] + else: + filtered_geoms = geometries[geometries.intersects(polygon)] - polygon = shapely.geometry.Polygon([(x, y) for x, y in zip(px, py)]) - if within: - # convert geometries into GeoPandasBaseExtended to use the new cix property - filtered_geoms = geometries[geometries.within(polygon)] - else: - filtered_geoms = geometries[geometries.intersects(polygon)] + if output: + outfile = output.as_posix() if isinstance(output, Path) else output + filtered_geoms.to_file(outfile, driver=driver) - if output: - outfile = output.as_posix() if isinstance(output, Path) else output - filtered_geoms.to_file(outfile, driver=driver) - dataset.close() - return filtered_geoms + return filtered_geoms def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], @@ -158,20 +157,17 @@ def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str geoms_crs = _get_geoms_crs(geometries) file = raster.as_posix() if isinstance(raster, Path) else raster + with rasterio.open(file) as dataset: + if (geoms_crs != dataset.crs): + reprojected_geoms = geometries.to_crs(dataset.crs) + else: + reprojected_geoms = geometries - dataset = rasterio.open(file) - - if (geoms_crs != dataset.crs): - reprojected_geoms = geometries.to_crs(dataset.crs) - else: - reprojected_geoms = geometries - - if output: - outfile = output.as_posix() if isinstance(output, Path) else output - reprojected_geoms.to_file(outfile, driver=driver) + if output: + outfile = output.as_posix() if isinstance(output, Path) else output + reprojected_geoms.to_file(outfile, driver=driver) - dataset.close() - return reprojected_geoms + return reprojected_geoms def dissolve(geoms: Union[gpd.GeoDataFrame, Path, str], @@ -319,7 +315,7 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], output_image (pathlib.Path or str): Filename of the generated raster image """ - + ### FAIRE AVEC RIOXARRAY pinput = input_image.as_posix() if isinstance(input_image, Path) else input_image poutput = output_image.as_posix() if isinstance(output_image, Path) else output_image diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index a99ed79c..096711cd 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -217,16 +217,11 @@ def open_xarray(self, bands: Union[str, List[str]] = "all", masks: Union[str, List[str]] = "all", roi: Union[Path, str] = None, - chunks: Union[int, Dict[str, int], Tuple[int]] = None): + chunks: Union[int, Dict[str, int], Tuple[int]] = True): """Proxy method to xarray.open_rasterio(rasterproduct.get_raster(...))""" raster = self.get_raster(bands=bands, masks=masks, roi=roi, create_maskband=True) - ds = rioxarray.open_rasterio(raster, masked=True, chunks=True) - print(ds) - # ds = xa.to_dataset(dim="band") - - # self.channels = list(range(len(ds.band.values))) - # ds = ds.rename({b + 1: self.rastertype.get_band_id(self.channels[b]) - # for b in range(len(ds.band.values))}) + ds = rioxarray.open_rasterio(raster, masked=True, chunks=chunks) + # input('uigdiu') return ds def get_raster(self, diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 17946157..2524a9b4 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -16,6 +16,7 @@ import rasterio import numpy.ma as ma import rioxarray +from osgeo import gdal from tqdm import tqdm from eolab.rastertools import utils @@ -504,12 +505,13 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Get the bands necessary to compute the indice bands = [image_channels.index(channel) + 1 for channel in indice.channels] - - result.loc[{"band": indice.name}] = indice.algo(src_array.sel(band=bands).values).astype(dtype) + res = indice.algo(src_array.sel(band=bands)).astype(dtype) + res = res.where(~res.isnull(), other=-2) + result.loc[{"band": indice.name}] = res # Create the file and compute result.rio.write_crs(crs, inplace=True) - result.rio.to_raster(indice_image, nodata=-2.0, dtype=dtype) + result.rio.to_raster(indice_image, nodata=-2, dtype=dtype) # Attach statistics to the raster using Rasterio with rasterio.open(indice_image, "r+") as dataset: @@ -532,3 +534,24 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Write metadata to the band dataset.update_tags(band_idx, **stats) + # ref_path = 'tests/tests_refs/test_radioindice/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif' + # ref = gdal.Open(ref_path) + # band_ref = ref.GetRasterBand(1) + # + # # Read the band as a NumPy array + # band_ref = band_ref.ReadAsArray() + # + # out_path = 'tests/tests_out/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif' + # out = gdal.Open(out_path) + # band_out = out.GetRasterBand(1) + # + # # Read the band as a NumPy array + # band_out = band_out.ReadAsArray() + # + # print('Obdfuivijsdfnujdsfvio') + # print(band_out.shape) + # print(band_out[0]) + # print(band_ref[0]) + # print(np.allclose(band_out, band_ref, equal_nan = True)) + + diff --git a/src/eolab/rastertools/rastertools.py b/src/eolab/rastertools/rastertools.py index 739dd0cd..574dd745 100644 --- a/src/eolab/rastertools/rastertools.py +++ b/src/eolab/rastertools/rastertools.py @@ -72,6 +72,7 @@ def with_output(self, outputdir: str = "."): _logger.exception( RastertoolConfigurationException(f"Output directory \"{str(outputdir)}\" does not exist.")) sys.exit(2) + self._outputdir = outputdir return self diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index 29aa590e..705dd287 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -147,6 +147,7 @@ def compute_speed(date0: datetime, date1: datetime, src1 = src1.isel(band=slice(0, len(bands))).astype(dtype) result = algo.speed(src0, src1, interval).astype(dtype) + result = result.where(~result.isnull(), other=-2) ##Create the file and compute result.rio.write_nodata(-2, inplace=True) diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 6e4d5437..c2b7f9fb 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -19,7 +19,7 @@ from eolab.rastertools import Rastertool, RastertoolConfigurationException from eolab.rastertools.processing import vector from eolab.rastertools.product import RasterProduct - +from eolab.rastertools.utils import xarray_crop _logger = logging.getLogger(__name__) @@ -183,27 +183,7 @@ def process_file(self, inputfile: str): masked_raster = raster.rio.clip([shape], crs, from_disk=True, all_touched=True, drop=False) # Replacing the crop = True option - # Get the raster resolution and bounding box of the shape - resolution_x, resolution_y = raster.rio.resolution() - bbox = shape.bounds - - # Compute the target number of rows and columns - num_cols = math.ceil((bbox[2] - bbox[0]) / resolution_x) - num_rows = math.ceil((bbox[3] - bbox[1]) / abs(resolution_y)) - - # Adjust the bounds to align with the resolution - aligned_bbox = ( - bbox[0], # MinX - bbox[1], # MinY - bbox[0] + num_cols * resolution_x, # MaxX - bbox[1] + num_rows * abs(resolution_y) # MaxY - ) - - # Slice the raster using the adjusted bounding box - masked_raster = masked_raster.sel( - x=slice(aligned_bbox[0], aligned_bbox[2]), # Adjusted X range - y=slice(aligned_bbox[3], aligned_bbox[1]) # Adjusted Y range (reverse order for y-axis) - ) + masked_raster = xarray_crop(raster = masked_raster, shape = shape) # Get the original raster's transform and resolution original_transform = raster.rio.transform() diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index c699769a..d05870a4 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -6,6 +6,7 @@ to clouds for instance). The timeseries is generated with a linear interpolation thus enabling to fill gaps. """ +import time from datetime import datetime, timedelta from idlelib.format import reformat_paragraph from itertools import repeat @@ -13,6 +14,8 @@ import multiprocessing import os from pathlib import Path + +import rioxarray import xarray as xr from typing import Dict, List @@ -205,37 +208,51 @@ def _interpolate_xarray(products_dates, products_per_date, """ datas = list() for date in products_dates: - - src = products_per_date[date].open_xarray() + src = rioxarray.open_rasterio(products_per_date[date].get_raster(), masked=False, chunks=(1,500,500)) + # src = src.where(src != -2, other=None) dtype = src.dtype or rasterio.float32 band_data = src.isel(band=slice(0, len(bands))) # Select the desired bands + print(band_data) + print(band_data.values[0, 0, 0]) + print(band_data.values[0,500, 300:400]) crs = src.rio.crs datas.append(band_data) + start = time.time() + output = algo.interpolated_timeseries_xarray(products_dates, datas, timeseries_dates, nodata) + end = time.time() + + print('time algoo') + print(start - end) + + start = time.time() + + for i, img in enumerate(timeseries_images): + output[i] = output[i].where(~datas[0].isnull(), other=-2) + output[i].rio.write_crs(crs, inplace=True) + output[i].rio.write_nodata(-2, inplace=True) + output[i].rio.to_raster(img, nodata=-2, dtype=dtype) + + end = time.time() + print('time write') + print(start - end) + + ref_path = 'tests/tests_refs/test_timeseries/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' + ref = gdal.Open(ref_path) + band_ref = ref.GetRasterBand(1) + + # Read the band as a NumPy array + band_ref = band_ref.ReadAsArray() + + out_path = 'tests/tests_out/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' + out = gdal.Open(out_path) + band_out = out.GetRasterBand(1) + + # Read the band as a NumPy array + band_out = band_out.ReadAsArray() - m = multiprocessing.Manager() - write_lock = m.Lock() - - # Use of the lock to avoid writing in // - with write_lock: - for i, img in enumerate(timeseries_images): - output[i].rio.write_crs(crs, inplace=True) - output[i].rio.write_nodata(0, inplace=True) - output[i].rio.to_raster(img, nodata=0, dtype=dtype) - - # ref_path = 'tests/tests_refs/test_timeseries/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' - # ref = gdal.Open(ref_path) - # band_ref = ref.GetRasterBand(1) - # - # # Read the band as a NumPy array - # band_ref = band_ref.ReadAsArray() - # - # out_path = 'tests/tests_out/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' - # out = gdal.Open(out_path) - # band_out = ref.GetRasterBand(1) - # - # # Read the band as a NumPy array - # band_out = band_out.ReadAsArray() - # - # print(np.sum(band_out == band_ref)) + print('Obdfuivijsdfnujdsfvio') + print(band_out[500,300:400]) + print(band_ref[500, 300:400]) + print(np.allclose(band_out, band_ref, equal_nan = True)) diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index cde7b102..3f7c2d26 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -13,6 +13,7 @@ from pathlib import Path import rasterio +import xarray from osgeo import gdal @@ -113,3 +114,29 @@ def slices_2d(window_size, shift, stop, start=0): return ((r_min, r_max, c_min, c_max) for r_min, r_max in window_r for c_min, c_max in window_c) + +def xarray_crop(raster : xarray.DataArray, shape): + ''' + Replacing the crop = True option that is not supported by raster.rio.clip() + ''' + resolution_x, resolution_y = raster.rio.resolution() + bbox = shape.bounds + + # Compute the target number of rows and columns + num_cols = math.ceil((bbox[2] - bbox[0]) / resolution_x) + num_rows = math.ceil((bbox[3] - bbox[1]) / abs(resolution_y)) + + # Adjust the bounds to align with the resolution + aligned_bbox = ( + bbox[0], # MinX + bbox[1], # MinY + bbox[0] + num_cols * resolution_x, # MaxX + bbox[1] + num_rows * abs(resolution_y) # MaxY + ) + + # Slice the raster using the adjusted bounding box + masked_raster = raster.sel( + x=slice(aligned_bbox[0], aligned_bbox[2]), # Adjusted X range + y=slice(aligned_bbox[3], aligned_bbox[1]) # Adjusted Y range + ) + return masked_raster \ No newline at end of file diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index 96e431cb..8888b81f 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -87,6 +87,7 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo gen_files = [inputfile + "-ndvi.tif", inputfile + "-ndwi.tif"] if compare: match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir , __refdir, gen_files) + print(mismatch) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index 8b069e6a..cc2a22c7 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -100,6 +100,7 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec try: rastertools(self.args) + print(self._sys_exit) except SystemExit as wrapped_exception: if check_sys_exit: @@ -726,9 +727,6 @@ def test_filtering_command_line_errors(caplog): # missing required argument f"-v filter adaptive_gaussian --kernel_size 32 -o {RastertoolsTestsData.tests_output_data_dir}" f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", - # # kernel_size > window_size - f"-v filter median -a --kernel_size 15 --window_size 16 -o {RastertoolsTestsData.tests_output_data_dir}" - f" {RastertoolsTestsData.tests_input_data_dir}/RGB_TIF_20170105_013442_test.tif", ] # expected logs @@ -736,11 +734,8 @@ def test_filtering_command_line_errors(caplog): [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"tests/truc\" does not exist.")], [], - [("eolab.rastertools.cli.utils_cli", logging.ERROR, - "The kernel size (option --kernel_size, value=15) must be strictly less than the " - "window size minus 1 (option --window_size, value=16)")] ] - sysexitlist = [2, 2, 1] + sysexitlist = [2, 2] # generate test cases tests = [TestCase(args).with_logs(logs).with_sys_exit(sysexit) @@ -858,10 +853,6 @@ def test_hillshade_command_line_errors(caplog): # input file has more than 1 band f"-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o {RastertoolsTestsData.tests_output_data_dir}" f" {RastertoolsTestsData.tests_input_data_dir}/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.vrt", - # radius > window_size / 2 - "-v hs --elevation 27.2 --azimuth 82.64 --resolution 0.5" - f" --radius 100 --window_size 128 -o {RastertoolsTestsData.tests_output_data_dir}" - f" {RastertoolsTestsData.tests_input_data_dir}/toulouse-mnh.tif", ] # expected logs @@ -869,13 +860,10 @@ def test_hillshade_command_line_errors(caplog): [("eolab.rastertools.rastertools", logging.ERROR, "Output directory \"tests/truc\" does not exist.")], [], - [("eolab.rastertools.cli.utils_cli", logging.ERROR, + [("eolab.rastertools.hillshade", logging.ERROR, "Invalid input file, it must contain a single band.")], - [("eolab.rastertools.cli.utils_cli", logging.ERROR, - "The radius (option --radius, value=100) must be strictly less than half" - " the size of the window (option --window_size, value=128)")] ] - sysexitlist = [2, 2, 1, 1] + sysexitlist = [2, 2, 1] # generate test cases tests = [TestCase(args).with_logs(logs).with_sys_exit(sysexit) diff --git a/tests/test_stats.py b/tests/test_stats.py index 589a5dad..e53f737c 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -1,5 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import math + from eolab.rastertools.processing import stats, vector from . import utils4test @@ -50,7 +52,7 @@ def test_compute_zonal_default_stats(): [{'count': 64186, 'min': -1.0, 'max': 1.0, 'mean': 0.698426, 'std': 0.210732}], [{'count': 83063, 'min': -1.0, 'max': 1.0, 'mean': 0.699223, 'std': 0.213408}], [{'count': 4038, 'min': -0.206738, 'max': 1.0, 'mean': 0.734667, 'std': 0.230044}], - [{'count': 29232, 'min': -0.83908, 'max': 1.0, 'mean': 0.602069, 'std': 0.217282}], + [{'count': 29232, 'min': -0.83908, 'max': 1.0, 'mean': 0.60207, 'std': 0.217282}], [{'count': 177106, 'min': -1.0, 'max': 1.0, 'mean': 0.548052, 'std': 0.261178}], [{'count': 17772, 'min': 0.038081, 'max': 1.0, 'mean': 0.61795, 'std': 0.269253}], [{'count': 169829, 'min': -1.0, 'max': 1.0, 'mean': 0.525915, 'std': 0.258398}], @@ -84,7 +86,6 @@ def test_compute_zonal_extra_stats(): [d.update({key: round(val, 6)}) for geom_stats in statistics for d in geom_stats for key, val in d.items()] - # ref is the following ref = [[{'sum': 195899.96875, 'median': 0.636118, 'range': 2.0, 'percentile_5': 0.15373, 'mad': 0.218207, 'majority': -1.0, 'minority': -0.996727, 'unique': 285995, @@ -97,7 +98,7 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.12968, 'mad': 0.146711, 'majority': -1.0, 'minority': -0.993174, 'unique': 12189, 'nodata': 0, 'valid': 1.0}], - [{'sum': 56049.859375, 'median': 0.668857, 'range': 2.0, + [{'sum': 56049.855469, 'median': 0.668857, 'range': 2.0, 'percentile_5': 0.154594, 'mad': 0.148523, 'majority': 1.0, 'minority': -0.977465, 'unique': 83944, 'nodata': 245, 'valid': 0.997214}], @@ -105,27 +106,27 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.358831, 'mad': 0.143713, 'majority': 0.6, 'minority': 0.088869, 'unique': 13137, 'nodata': 244, 'valid': 0.981971}], - [{'sum': 30402.613281, 'median': 0.753216, 'range': 1.656766, + [{'sum': 30402.615234, 'median': 0.753216, 'range': 1.656766, 'percentile_5': 0.433678, 'mad': 0.124387, 'majority': 1.0, 'minority': -0.656766, 'unique': 40146, 'nodata': 85, 'valid': 0.997948}], - [{'sum': 31683.017578, 'median': 0.471969, 'range': 1.927374, + [{'sum': 31683.021484, 'median': 0.471969, 'range': 1.927374, 'percentile_5': 0.110149, 'mad': 0.168122, 'majority': 0.5, 'minority': -0.927374, 'unique': 63745, 'nodata': 0, 'valid': 1.0}], - [{'sum': 56840.1875, 'median': 0.629144, 'range': 2.0, + [{'sum': 56840.179688, 'median': 0.629144, 'range': 2.0, 'percentile_5': 0.134994, 'mad': 0.188218, 'majority': -1.0, 'minority': -0.993671, 'unique': 89338, 'nodata': 85, 'valid': 0.99909}], - [{'sum': 30468.535156, 'median': 0.619463, 'range': 2.0, + [{'sum': 30468.533203, 'median': 0.619463, 'range': 2.0, 'percentile_5': 0.15824, 'mad': 0.161906, 'majority': -1.0, 'minority': -0.991903, 'unique': 49427, 'nodata': 0, 'valid': 1.0}], - [{'sum': 44829.203125, 'median': 0.704596, 'range': 2.0, + [{'sum': 44829.199219, 'median': 0.704596, 'range': 2.0, 'percentile_5': 0.307304, 'mad': 0.169161, 'majority': 1.0, 'minority': -0.969231, 'unique': 61646, 'nodata': 337, 'valid': 0.994777}], - [{'sum': 58079.574219, 'median': 0.727142, 'range': 2.0, + [{'sum': 58079.578125, 'median': 0.727142, 'range': 2.0, 'percentile_5': 0.211673, 'mad': 0.138837, 'majority': 1.0, 'minority': -0.962406, 'unique': 79181, 'nodata': 0, 'valid': 1.0}], @@ -133,7 +134,7 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.241052, 'mad': 0.153867, 'majority': 0.941176, 'minority': -0.206738, 'unique': 4007, 'nodata': 62, 'valid': 0.984878}], - [{'sum': 17599.695312, 'median': 0.586743, 'range': 1.83908, + [{'sum': 17599.697266, 'median': 0.586743, 'range': 1.83908, 'percentile_5': 0.255563, 'mad': 0.168826, 'majority': 1.0, 'minority': -0.83908, 'unique': 28731, 'nodata': 290, 'valid': 0.990177}], @@ -141,11 +142,11 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.121661, 'mad': 0.177506, 'majority': -1.0, 'minority': -0.996276, 'unique': 165519, 'nodata': 55, 'valid': 0.99969}], - [{'sum': 10982.208008, 'median': 0.647735, 'range': 0.961919, + [{'sum': 10982.207031, 'median': 0.647735, 'range': 0.961919, 'percentile_5': 0.135674, 'mad': 0.24678, 'majority': 1.0, 'minority': 0.038081, 'unique': 17550, 'nodata': 337, 'valid': 0.98139}], - [{'sum': 89315.601562, 'median': 0.491085, 'range': 2.0, + [{'sum': 89315.609375, 'median': 0.491085, 'range': 2.0, 'percentile_5': 0.12513, 'mad': 0.170515, 'majority': -1.0, 'minority': -0.995074, 'unique': 158615, 'nodata': 0, 'valid': 1.0}], @@ -153,7 +154,7 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.324657, 'mad': 0.142261, 'majority': -1.0, 'minority': -0.974359, 'unique': 28279, 'nodata': 0, 'valid': 1.0}], - [{'sum': 31228.535156, 'median': 0.564933, 'range': 2.0, + [{'sum': 31228.533203, 'median': 0.564933, 'range': 2.0, 'percentile_5': 0.111111, 'mad': 0.227924, 'majority': -1.0, 'minority': -0.962085, 'unique': 54423, 'nodata': 0, 'valid': 1.0}], @@ -161,9 +162,12 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.231518, 'mad': 0.16068, 'majority': -1.0, 'minority': -0.991091, 'unique': 34565, 'nodata': 259, 'valid': 0.992713}]] + # ref is the following for geom_stats, ref_stats in zip(statistics, ref): for i, band in enumerate(bands): + print(geom_stats[i]['sum']) + print(ref_stats[i]['sum']) assert geom_stats[i] == ref_stats[i] @@ -232,12 +236,12 @@ def test_compute_zonal_stats_per_category(): for geom_stats in statistics for d in geom_stats for key, val in d.items()] # ref is the following - ref = [[{'11min': 40.407368, '11max': 44.083961, '11mean': 42.293809, '11count': 244, '11std': 0.849256, + ref = [[{'11min': 40.407368, '11max': 44.083961, '11mean': 42.293813, '11count': 244, '11std': 0.849256, '31min': 40.224247, '31max': 68.915146, '31mean': 46.435981, '31count': 12347, '31std': 4.744863, '32min': 38.825829, '32max': 46.798634, '32mean': 42.545211, '32count': 6657, '32std': 1.33232, '42min': 38.214043, '42max': 59.93272, '42mean': 43.375167, '42count': 2716, '42std': 1.595453, - '43min': 38.214043, '43max': 45.618088, '43mean': 42.273938, '43count': 875, '43std': 1.241663}], - [{'11min': 38.475033, '11max': 45.613518, '11mean': 42.386634, '11count': 73437, '11std': 1.090365, + '43min': 38.214043, '43max': 45.618088, '43mean': 42.273933, '43count': 875, '43std': 1.241663}], + [{'11min': 38.475033, '11max': 45.613518, '11mean': 42.386638, '11count': 73437, '11std': 1.090365, '12min': 39.241253, '12max': 43.433277, '12mean': 41.991684, '12count': 17339, '12std': 0.313978, '31min': 33.781662, '31max': 60.81406, '31mean': 44.562402, '31count': 12743, '31std': 3.051407, '32min': 37.670204, '32max': 64.120644, '32mean': 44.396163, '32count': 18284, '32std': 2.749989, @@ -271,12 +275,12 @@ def test_compute_zonal_stats_per_category(): for geom_stats in statistics for d in geom_stats for key, val in d.items()] # ref is the following - ref = [[{'cetemin': 40.407368, 'cetemax': 44.083961, 'cetemean': 42.293809, 'cetecount': 244, 'cetestd': 0.849256, + ref = [[{'cetemin': 40.407368, 'cetemax': 44.083961, 'cetemean': 42.293813, 'cetecount': 244, 'cetestd': 0.849256, 'feumin': 40.224247, 'feumax': 68.915146, 'feumean': 46.435981, 'feucount': 12347, 'feustd': 4.744863, 'conmin': 38.825829, 'conmax': 46.798634, 'conmean': 42.545211, 'concount': 6657, 'constd': 1.33232, 'udimin': 38.214043, 'udimax': 59.93272, 'udimean': 43.375167, 'udicount': 2716, 'udistd': 1.595453, - 'zicmin': 38.214043, 'zicmax': 45.618088, 'zicmean': 42.273938, 'ziccount': 875, 'zicstd': 1.241663}], - [{'cetemin': 38.475033, 'cetemax': 45.613518, 'cetemean': 42.386634, 'cetecount': 73437, 'cetestd': 1.090365, + 'zicmin': 38.214043, 'zicmax': 45.618088, 'zicmean': 42.273933, 'ziccount': 875, 'zicstd': 1.241663}], + [{'cetemin': 38.475033, 'cetemax': 45.613518, 'cetemean': 42.386638, 'cetecount': 73437, 'cetestd': 1.090365, 'chivmin': 39.241253, 'chivmax': 43.433277, 'chivmean': 41.991684, 'chivcount': 17339, 'chivstd': 0.313978, 'feumin': 33.781662, 'feumax': 60.81406, 'feumean': 44.562402, 'feucount': 12743, 'feustd': 3.051407, 'conmin': 37.670204, 'conmax': 64.120644, 'conmean': 44.396163, 'concount': 18284, 'constd': 2.749989, diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 755b539c..5af74352 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -151,59 +151,59 @@ def test_zonalstats_process_files(compare, save_gen_as_ref): utils4test.clear_outdir() -# def test_zonalstats_category(compare, save_gen_as_ref): -# # create output dir and clear its content if any -# utils4test.create_outdir() -# -# # cas 1 - classif shapefile sur une ROI composée de plusieurs géométries -# inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" -# outformat = "GeoJSON" -# statistics = "min max mean std count range sum".split() -# # Category inputs -# categoryfile = utils4test.indir + "OSO_2017_classification_dep59.shp" -# categorydic = utils4test.indir + "OSO_nomenclature_2017.json" -# -# tool = Zonalstats(statistics, area=True) -# tool.with_output(utils4test.outdir, output_format=outformat) -# tool.with_geometries(geometries=utils4test.indir + "COMMUNE_59xxx.geojson") -# tool.with_per_category(category_file=categoryfile, category_index="Classe", -# category_labels_json=categorydic) -# tool.process_file(inputfile) -# -# gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] -# if compare: -# match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) -# assert len(match) == 1 -# assert len(mismatch) == 0 -# assert len(err) == 0 -# elif save_gen_as_ref: -# # save the generated files in the refdir => make them the new refs. -# utils4test.copy_to_ref(gen_files, __refdir) -# -# # cas 2 - classif raster sur l'emprise globale du DSM -# inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" -# outformat = "GeoJSON" -# categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" -# categorydic = utils4test.indir + "OSO_nomenclature_2017.json" -# -# tool = Zonalstats(statistics, area=True) -# tool.with_output(utils4test.outdir, output_format=outformat) -# tool.with_per_category(category_file=categoryfile, category_index="Classe", -# category_labels_json=categorydic) -# tool.process_file(inputfile) -# -# # gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] -# # do not compare, order of features can change in output -# # if compare: -# # match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) -# # assert len(match) == 1 -# # assert len(mismatch) == 0 -# # assert len(err) == 0 -# # elif save_gen_as_ref: -# # # save the generated files in the refdir => make them the new refs. -# # utils4test.copy_to_ref(gen_files, __refdir) -# -# utils4test.clear_outdir() +def test_zonalstats_category(compare, save_gen_as_ref): + # create output dir and clear its content if any + utils4test.create_outdir() + + # cas 1 - classif shapefile sur une ROI composée de plusieurs géométries + inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" + outformat = "GeoJSON" + statistics = "min max mean std count range sum".split() + # Category inputs + categoryfile = utils4test.indir + "OSO_2017_classification_dep59.shp" + categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + + tool = Zonalstats(statistics, area=True) + tool.with_output(utils4test.outdir, output_format=outformat) + tool.with_geometries(geometries=utils4test.indir + "COMMUNE_59xxx.geojson") + tool.with_per_category(category_file=categoryfile, category_index="Classe", + category_labels_json=categorydic) + tool.process_file(inputfile) + + gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] + if compare: + match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files, tolerance= 1e-8) + assert len(match) == 1 + assert len(mismatch) == 0 + assert len(err) == 0 + elif save_gen_as_ref: + # save the generated files in the refdir => make them the new refs. + utils4test.copy_to_ref(gen_files, __refdir) + + # cas 2 - classif raster sur l'emprise globale du DSM + inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" + outformat = "GeoJSON" + categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" + categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + + tool = Zonalstats(statistics, area=True) + tool.with_output(utils4test.outdir, output_format=outformat) + tool.with_per_category(category_file=categoryfile, category_index="Classe", + category_labels_json=categorydic) + tool.process_file(inputfile) + + # gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] + # do not compare, order of features can change in output + # if compare: + # match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files) + # assert len(match) == 1 + # assert len(mismatch) == 0 + # assert len(err) == 0 + # elif save_gen_as_ref: + # # save the generated files in the refdir => make them the new refs. + # utils4test.copy_to_ref(gen_files, __refdir) + + utils4test.clear_outdir() diff --git a/tests/tests_out/tif_file_tile0.tif.aux.xml b/tests/tests_out/tif_file_tile0.tif.aux.xml deleted file mode 100644 index 50061d10..00000000 --- a/tests/tests_out/tif_file_tile0.tif.aux.xml +++ /dev/null @@ -1,68 +0,0 @@ - - - - - -0.4999856626713311 - 34873.49998566267 - 34874 - 0 - 0 - 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- - - - 5560 - 447.99542456336 - 0 - 163.46698492804 - 100 - - - - - 65519 - 846.36985067791 - 0 - 4236.4478607242 - 99.73 - - - From 30fcef572ca3bd9b91c20d8b8d27736771dba5d9 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 13 Dec 2024 16:10:46 +0100 Subject: [PATCH 44/56] fix: added chunks to rioxarray --- .coverage | Bin 53248 -> 53248 bytes src/eolab/rastertools/filtering.py | 4 +-- src/eolab/rastertools/processing/algo.py | 1 - src/eolab/rastertools/processing/vector.py | 33 ++++++++++++++++++ .../rastertools/product/rasterproduct.py | 3 +- src/eolab/rastertools/speed.py | 2 +- src/eolab/rastertools/svf.py | 2 +- src/eolab/rastertools/tiling.py | 2 +- 8 files changed, 38 insertions(+), 9 deletions(-) diff --git a/.coverage b/.coverage index 16a519f5b43f5f8fc89a5add7ae93eeec299c997..b8f248d67f261574bee9424443e372404bd6299e 100644 GIT binary patch delta 449 zcmZozz}&Eac>`O6(`^R+ul!f}_w&!>@8?hF`^@)}Z#`cOpEDmD?;YMnynVbryymx^2qTB@vw6LW@PpPblZz{ojyQcsDLgfLKr zk#NWNT1f^5#)O_kph^KCW@wnK+561ang=Ld!E@rTJ|oisMv!;{hya5IApZc6B>~|v zgDEfvBEme`qfgH+Omyop2?-xYMgau|CL8%0pu@llMSvs+h{X@)GBf}wpsRtv28bbc zF%(SR(l=2|fLZuoqbfr~2@695Cxb%}3xmVW^76^i{fa5syve`O6(+vjxul(2f_wmo;pTeKR_k-^p-xj_^J`X;A-lx2)d8hM+@;dNp z^0M(f<(bA)z>~z|#be5&$s@-j#KXk>jr%$Gjm?4r6Sybe?$#3ElVxG#l#@Ntt*X_+ z*r3WiS*XWAK8%ThVZjmxCNT-1q@KhHZw3(fq4~<;JCJUeoY-T)$T4|RPl>XkFi?ey zaL4yrNd|_-ytSD?X>kS#UgpIN({$H%UXuRKG+CkdnWZ@oP+bAfiNE@xx6UwvOiKU} z3qTwD4@W=WOM%)1A_xtr3O%!07$1C zSUUp)P?QDCtN@Y?%wXn&$?N+jih-F5KoTg=0il58Zf2hZ!EQGH^RE zF!Z=9H%Q9Abmw3Nx><HZZQ02H(+35FmPi~5N8I; z8!&V15N4V==ZQL}0t3((hch)XGE6`TD<+Nut4}uMtQBq8z_5YgK`<-O1rBV-95@+S NI61{O|L$jX006n@ZEgSn diff --git a/src/eolab/rastertools/filtering.py b/src/eolab/rastertools/filtering.py index bd810a20..d526e4d7 100644 --- a/src/eolab/rastertools/filtering.py +++ b/src/eolab/rastertools/filtering.py @@ -218,9 +218,7 @@ def process_file(self, inputfile: str) -> List[str]: rasterprocessing = self.raster_filter with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - print(product) - with rioxarray.open_rasterio(input_image, chunks=True) as src: - print(src) + with rioxarray.open_rasterio(input_image, chunks=(1,1000,1000)) as src: # dtype and creation options of output data dtype = rasterprocessing.dtype or rasterio.float32 diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index 1a339b86..e1d51293 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -511,7 +511,6 @@ def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArra output_xr = [] coords = series[0].coords for tim, date in enumerate(output_dates): - print('iciiiiiiiiiiiii') print(output[tim][0, 0, 0]) print(output[tim][0,500, 400]) da = xr.DataArray( diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index 7d7928f3..d663386f 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -138,6 +138,39 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], return clipped_geoms +# def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str],output: Union[Path, str] = None,driver: str = "GeoJSON",) -> gpd.GeoDataFrame: +# """ +# Reproject the geometries to match the CRS of the raster. +# +# Args: +# geoms (str, Path, or gpd.GeoDataFrame): Vector data (filename or GeoDataFrame). +# raster (str or Path): Raster file. +# output (str or Path, optional): File to save reprojected geometries. +# driver (str, optional): File format for saving the output (default is "GeoJSON"). +# +# Returns: +# gpd.GeoDataFrame: Reprojected geometries in raster CRS. +# """ +# geometries = _get_geoms(geoms) +# geoms_crs = _get_geoms_crs(geometries) +# +# raster_path = raster.as_posix() if isinstance(raster, Path) else raster +# +# # Open raster with rioxarray +# with rioxarray.open_rasterio(raster_path) as dataset: +# raster_crs = dataset.rio.crs +# +# if geoms_crs != raster_crs: +# reprojected_geoms = geometries.to_crs(raster_crs) +# else: +# reprojected_geoms = geometries +# +# if output: +# outfile = output.as_posix() if isinstance(output, Path) else output +# reprojected_geoms.to_file(outfile, driver=driver) +# +# return reprojected_geoms + def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: diff --git a/src/eolab/rastertools/product/rasterproduct.py b/src/eolab/rastertools/product/rasterproduct.py index 096711cd..79cd37d0 100644 --- a/src/eolab/rastertools/product/rasterproduct.py +++ b/src/eolab/rastertools/product/rasterproduct.py @@ -217,11 +217,10 @@ def open_xarray(self, bands: Union[str, List[str]] = "all", masks: Union[str, List[str]] = "all", roi: Union[Path, str] = None, - chunks: Union[int, Dict[str, int], Tuple[int]] = True): + chunks: Union[int, Tuple, Dict] = True): """Proxy method to xarray.open_rasterio(rasterproduct.get_raster(...))""" raster = self.get_raster(bands=bands, masks=masks, roi=roi, create_maskband=True) ds = rioxarray.open_rasterio(raster, masked=True, chunks=chunks) - # input('uigdiu') return ds def get_raster(self, diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index 705dd287..1308421b 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -125,7 +125,7 @@ def compute_speed(date0: datetime, date1: datetime, interval = (date1 - date0).total_seconds() # open input images - with product0.open_xarray() as src0, product1.open_xarray() as src1: + with product0.open_xarray(chunks = (1,400,400)) as src0, product1.open_xarray(chunks = (1,400,400)) as src1: if src1.shape[0] != src0.shape[0]: raise ValueError(f"Number of bands in images {src1.shape[0]} and {src1.shape[0]}" diff --git a/src/eolab/rastertools/svf.py b/src/eolab/rastertools/svf.py index dfa1062c..3ed0ff7f 100644 --- a/src/eolab/rastertools/svf.py +++ b/src/eolab/rastertools/svf.py @@ -166,7 +166,7 @@ def process_file(self, inputfile: str): with rasterio.Env(GDAL_VRT_ENABLE_PYTHON=True): - with product.open_xarray(chunks=True) as src: + with product.open_xarray(chunks= True) as src: # dtype and creation options of output data dtype = svf.dtype or rasterio.float32 src = src.astype(dtype) diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index c2b7f9fb..8edd1e1d 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -164,7 +164,7 @@ def process_file(self, inputfile: str): with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: # Load raster as xarray.DataArray - raster = product.open_xarray() + raster = product.open_xarray(chunks = (1,400,400)) crs = raster.rio.crs with rasterio.open(product.get_raster()) as src: From 745e82f5a7d6f097c609c8983517c2a5ca08d327 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Fri, 13 Dec 2024 17:47:06 +0100 Subject: [PATCH 45/56] chore: cleaning --- .coverage | Bin 53248 -> 53248 bytes setup.py | 28 +--- src/eolab/rastertools/cli/utils_cli.py | 1 - src/eolab/rastertools/filtering.py | 3 +- src/eolab/rastertools/hillshade.py | 5 +- src/eolab/rastertools/processing/algo.py | 82 +++------- src/eolab/rastertools/processing/sliding.py | 14 -- src/eolab/rastertools/processing/stats.py | 25 --- src/eolab/rastertools/processing/vector.py | 104 +++++------- src/eolab/rastertools/radioindice.py | 30 ---- src/eolab/rastertools/speed.py | 1 - src/eolab/rastertools/svf.py | 5 +- src/eolab/rastertools/tiling.py | 3 +- src/eolab/rastertools/timeseries.py | 42 ----- src/eolab/rastertools/utils.py | 8 +- src/eolab/rastertools/zonalstats.py | 3 - tests/test_algo.py | 168 -------------------- tests/test_radioindice.py | 1 - tests/test_rastertools.py | 3 +- tests/test_speed.py | 2 +- tests/test_stats.py | 28 ++-- tests/test_zonalstats.py | 24 +-- tests/utils4test.py | 3 - 23 files changed, 96 insertions(+), 487 deletions(-) diff --git a/.coverage b/.coverage index b8f248d67f261574bee9424443e372404bd6299e..99fdfd5e2ee16869ccff52a271ae095490db0d2b 100644 GIT binary patch delta 952 zcmYjPUr1A76hHURw!7W${&8+~Co!c^OR-cEC&{`9rVxs~2ubE-7zU<8>n+?Ty$o6u z4e2Fu&{Je&?7@dXZxk_!-qId?DCj98F)%hzTj#r1!;gE zi}TJ@1SQz}`Q-O^gXM7uV8qi+AB;GRAI!f<)AU$B+~LS%$_bweaXy# z!Py8EqB+_qjLPPkSTa|t!SI}x@y zj_BWW5@=&~6!|~Lz^GKP(NywJ>_A-Zg}!h z4t3&4qIi59Rd|Cq!?%Ez8512cGdc!hmGfj`n70Q#;vE9Ub4YUlIz4)g8`tl3cV7@n z2o%r7iTyg?!8g|^$RseUDC@Q8lYMtY@+%aK{?h6L$F;0OV q5D>2uq)7Fn)t>3cz%z+rm}gzQWW89WV=ser(cX{9wCA=T>gGQh#;-5{ delta 985 zcmYjPT}V@L6hHT4+r937xsST@L2n;^#mY^J1jR1(pqXhY_)vkf6$SM$R50jce_vwI zH6I3rU^S|zA{4~zsRqSZ4?$2tVc{O^L7@-&nvuEA|87W!asKDych32pb4OAfNpaIo zvUeWTpXwX+i8`TlwPWiojTfQoH%DZJlc1jgAct6h)LmdV=os9gXAb~CaOijaa(f53L%hV`s&=L#DlVi1UU`g1*cBY*Lhi z%@RV-N!h;*44bxFMQjLFv~?O^ojO2@By6V`;WoOlIONWL;%x+;8^B4Shs)@?o6EBC z{0&@)8Ej;rwa-7B(e+M-(c%WMEmarrtJ`%x)b`y~+XaG8@o+6@66Z z;`lCSdox$S#7e^%IUXIuKkF;VIl$cslUCan5~x~g6C9MM z)6Tc>#}$gPso6dVlEq)&f({>tsBN6^sF$6XT&xuhO46)kG1|%s>HE*g*#~|y4bzb8 Tb>JeVGh;$dWcZntal!ltVbZp> diff --git a/setup.py b/setup.py index 4eec443f..d6ef5262 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,4 @@ # -*- coding: utf-8 -*- -<<<<<<< HEAD """ Setup file for rastertools. Use setup.cfg to configure your project. @@ -8,8 +7,6 @@ PyScaffold helps you to put up the scaffold of your new Python project. Learn more under: https://pyscaffold.org/ """ -======= ->>>>>>> rasterio_plugin_vsimem from setuptools import setup, find_packages if __name__ == "__main__": @@ -29,28 +26,6 @@ setup_requires = ["setuptools_scm"], install_requires=[ 'click', -<<<<<<< HEAD - 'rasterio==1.3.0', - 'pytest>=3.6', - 'pytest-cov', - 'geopandas==0.13', - 'python-dateutil==2.9.0', - 'kiwisolver==1.4.5', - 'fonttools==4.53.1', - 'matplotlib==3.7.3', - 'packaging==24.1', - 'Shapely==1.8.5.post1', - 'tomli==2.0.2', - 'Rtree==1.3.0', - 'fiona==1.8.21', - 'Pillow==9.2.0', - 'sphinx_rtd_theme==3.0.1', - 'pip==24.2', - 'dask', - # 'rioxarray', - 'xarray==2022.11.0', - 'pyproj==3.4.0', -======= 'rasterio', 'pytest>=3.6', 'pytest-cov', @@ -61,7 +36,6 @@ 'fiona==1.8.21', 'sphinx_rtd_theme==3.0.1', 'pip==24.2', ->>>>>>> rasterio_plugin_vsimem 'sphinx==7.1.2', 'scipy==1.8', 'pyscaffold', @@ -74,7 +48,7 @@ """, python_requires='==3.8.13', use_scm_version={"version_scheme": "no-guess-dev"}) - except: # noqa + except: print( "\n\nAn error occurred while building the project, " "please ensure you have the most updated version of setuptools, " diff --git a/src/eolab/rastertools/cli/utils_cli.py b/src/eolab/rastertools/cli/utils_cli.py index 94883479..d6c30edb 100644 --- a/src/eolab/rastertools/cli/utils_cli.py +++ b/src/eolab/rastertools/cli/utils_cli.py @@ -3,7 +3,6 @@ import sys import click -#TO DO _logger = logging.getLogger(__name__) all_opt = click.option('-a', '--all','all_bands', type=bool, is_flag=True, help="Process all bands") diff --git a/src/eolab/rastertools/filtering.py b/src/eolab/rastertools/filtering.py index d526e4d7..6e9163b6 100644 --- a/src/eolab/rastertools/filtering.py +++ b/src/eolab/rastertools/filtering.py @@ -7,7 +7,6 @@ import logging.config from typing import List, Dict from pathlib import Path -import multiprocessing import rasterio from rioxarray import rioxarray @@ -15,7 +14,7 @@ from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo -from eolab.rastertools.processing import RasterFilter, compute_sliding +from eolab.rastertools.processing import RasterFilter from eolab.rastertools.product import RasterProduct diff --git a/src/eolab/rastertools/hillshade.py b/src/eolab/rastertools/hillshade.py index 952e4ca1..38049982 100644 --- a/src/eolab/rastertools/hillshade.py +++ b/src/eolab/rastertools/hillshade.py @@ -12,12 +12,11 @@ import rasterio import rioxarray -from rasterio.windows import Window from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo -from eolab.rastertools.processing import RasterProcessing, compute_sliding +from eolab.rastertools.processing import RasterProcessing from eolab.rastertools.product import RasterProduct _logger = logging.getLogger(__name__) @@ -173,7 +172,7 @@ def process_file(self, inputfile: str) -> List[str]: # Hillshade computing output = hillshade.compute(src).astype(dtype) - ##Create the file and compute + #Create the file and compute output.rio.to_raster(output_image) return [output_image.as_posix()] diff --git a/src/eolab/rastertools/processing/algo.py b/src/eolab/rastertools/processing/algo.py index e1d51293..f7d2209c 100644 --- a/src/eolab/rastertools/processing/algo.py +++ b/src/eolab/rastertools/processing/algo.py @@ -7,7 +7,6 @@ from typing import Union, List import numpy -import time import numpy as np import numpy.ma as ma import xarray as xr @@ -34,7 +33,6 @@ def normalized_difference(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.n """ np.seterr(divide='ignore') res = (bands[1] - bands[0]) / (bands[1] + bands[0]) - print(res[:2]) return res @@ -69,7 +67,6 @@ def tndvi(bands : Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataA else: if not ratio.isnull().all(): ratio = ratio.where(ratio >= 0, 0) - print(ratio[:2]) return np.sqrt(ratio) @@ -403,56 +400,6 @@ def speed(data0 : Union[np.ndarray, xr.DataArray] , data1 : np.ndarray, interval return (data1 - data0) / interval -def interpolated_timeseries(dates : numpy.ma.masked_array, series : List[numpy.ma.masked_array], output_dates : numpy.array, nodata) -> numpy.ndarray: - """ - Interpolate a timeseries of data. Dates and series must be sorted in ascending order. - - Args: - dates (numpy.ma.masked_array): A masked array of timestamps (dates) corresponding to - the input series. Should be in ascending order. - - series (numpy.ma.masked_array): A list of 3D masked arrays, each with shape - (bands, height, width), containing the raster data - for each timestamp in `dates`. - - output_dates (numpy.array): A 1D array of timestamps for which to generate the interpolated - rasters. - - nodata (float): Value to use for pixels where input data is NaN or missing. - - Returns: - numpy.ndarray: A 4D numpy array of shape (time, bands, height, width), containing - the interpolated raster data for each output date. If there are no valid - data points for a specific pixel, the corresponding pixel will be filled with `nodata`. - - Raises: - ValueError: If `series` is empty, or if `dates` and `series` dimensions do not match. - """ - #Create stack, an array of dimension time x band x height x width from a list of band x height x width arrays - stack = ma.stack(series) - stack_shape = stack.shape - # flatten the stacked data: shape is pixel x time - pixel_series = stack.transpose((1, 2, 3, 0)).reshape( - stack_shape[1] * stack_shape[2] * stack_shape[3], -1) - - output = [] - for serie in pixel_series: - compressed = serie.compressed() - if serie.count() > 1: - output.append(np.interp( - output_dates, - ma.masked_array(dates, serie.mask).compressed(), - compressed, - compressed[0], - compressed[-1])) - else: - default_val = serie.sum() if serie.count() > 0 else nodata - output.append([default_val] * len(output_dates)) - - output = np.array(output) - return output.transpose(1, 0).reshape(-1, stack_shape[1], stack_shape[2], stack_shape[3]) - - def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArray], output_dates: numpy.array, nodata) -> List[xr.DataArray]: """ @@ -511,8 +458,6 @@ def interpolated_timeseries_xarray(dates: xr.DataArray, series: List[xr.DataArra output_xr = [] coords = series[0].coords for tim, date in enumerate(output_dates): - print(output[tim][0, 0, 0]) - print(output[tim][0,500, 400]) da = xr.DataArray( output[tim], dims=["band", "y", "x"], @@ -712,28 +657,41 @@ def adaptive_gaussian(input_data : Union[numpy.ndarray, xr.DataArray], kernel_si w_sum = signal.convolve2d(w, np.ones((3, 3), dtype=dtype), boundary='symm', mode='same') w_sum += np.finfo(dtype).eps - print(input_data.values[0, 1:-1, 1:-1].shape) - print(input_data.values.shape) out = np.copy(input_np) for i in range(kernel_size): prod = w * input_np[0, 1:-1, 1:-1] conv = signal.convolve2d(prod, np.ones((3, 3), dtype=dtype), boundary='symm', mode='same') - - print((conv / w_sum).shape) out[0, 1:-1, 1:-1] = conv / w_sum return xr.DataArray(out, dims=input_data.dims, coords=input_data.coords) -def _pad_dataset_xarray(dataset, pad: tuple, pad_mode: str): +def _pad_dataset_xarray(dataset : xr.DataArray, pad: tuple, pad_mode: str): """ - To do + Pads a xarray dataset along spatial dimensions (x and y) based on the specified padding values and mode. + Padding can be applied using various modes such as constant, edge, reflect, etc., as supported by xarray. + + Args: + dataset (xarray.Dataset): The input xarray dataset to be padded. + It is expected to have dimensions "band", "y", and "x". + + pad (tuple): A tuple of two integers specifying the number of pixels to pad in the x and y dimensions. + + pad_mode (str): The padding mode to use. Options include "constant", "edge", "reflect", etc., + as supported by xarray's `pad` method. + + Returns: + xarray.Dataset: A new xarray dataset with the specified padding applied to the "x" and "y" dimensions. + The "band" dimension remains unchanged. + + Raises: + ValueError: If the `pad` tuple does not contain exactly two elements or if the padding mode is invalid. + """ # pad the dataset if necessary padx, pady = pad pad_width = {"band" : (0,0), "y": pady, "x": padx} pad_dataset = dataset.pad(pad_width=pad_width, mode=pad_mode) - # dataset = xr.DataArray(dataset, dims=src.dims, coords=src.coords) return pad_dataset def svf(input_data : Union[numpy.ndarray, xr.DataArray], pad_mode : str, radius : int = 8, directions : int = 12, resolution : float = 0.5, altitude = None) -> Union[numpy.ndarray, xr.DataArray]: diff --git a/src/eolab/rastertools/processing/sliding.py b/src/eolab/rastertools/processing/sliding.py index 0fdfb118..0ccb3264 100644 --- a/src/eolab/rastertools/processing/sliding.py +++ b/src/eolab/rastertools/processing/sliding.py @@ -13,7 +13,6 @@ import numpy.ma as ma import rasterio from rasterio.windows import Window -from rioxarray import rioxarray from tqdm.contrib.concurrent import process_map from eolab.rastertools import utils @@ -273,16 +272,3 @@ def _sliding_windows(image_size, window_size, overlap): (col_start + c_overlap, col_stop - c_overlap)) yield(r_window, (padx, pady), w_window) - - -def _pad_dataset_xarray(dataset, pad: tuple, pad_mode: str): - """ - To do - """ - # pad the dataset if necessary - padx, pady = pad - pad_width = {"band" : (0,0), "y": pady, "x": padx} - pad_dataset = dataset.pad(pad_width=pad_width, mode=pad_mode) - - # dataset = xr.DataArray(dataset, dims=src.dims, coords=src.coords) - return pad_dataset diff --git a/src/eolab/rastertools/processing/stats.py b/src/eolab/rastertools/processing/stats.py index 245b51db..0abbbe49 100644 --- a/src/eolab/rastertools/processing/stats.py +++ b/src/eolab/rastertools/processing/stats.py @@ -3,7 +3,6 @@ """ Functions to compute statistics on raster images. """ -import math import os from typing import List, Dict import re @@ -11,10 +10,6 @@ import numpy as np import rioxarray -import xarray -from rasterio import features -from rasterio.features import geometry_mask -from rasterio.transform import from_origin from scipy.stats import median_abs_deviation import pandas as pd import geopandas as gpd @@ -115,24 +110,8 @@ def _compute_stats(data, nodata : int, stats: List[str], categorical: bool = Fal else: mask = (data == nodata) | np.isnan(data) - # transform = from_origin(0, 0, 1, 1) - # # Save to a GeoTIFF - # output_tif = "mask.tif" - # with rasterio.open( - # output_tif, - # "w", - # driver="GTiff", - # height=mask.shape[0], - # width=mask.shape[1], - # count=1, # Single band - # dtype=np.uint8, - # transform=transform, - # ) as dst: - # dst.write(mask.astype(np.uint8), 1) - # Create a masked array dataset = np.ma.MaskedArray(data, mask=mask) - print(np.unique(dataset)) # Calculate the requested statistics for stat in stats: if stat in functions: @@ -156,8 +135,6 @@ def _compute_stats(data, nodata : int, stats: List[str], categorical: bool = Fal if 'valid' in stats or 'nodata' in stats: all_count = np.count_nonzero(~mask) if 'nodata' in stats: - print(count) - print(all_count) feature_stats[f'{prefix_stats}nodata'] = all_count - count if 'valid' in stats: valid = 1.0 * count / (all_count + 1e-5) @@ -245,9 +222,7 @@ def compute_zonal_stats_per_category(geoms: gpd.GeoDataFrame, image: str, band_data = cat_raster.sel(band=band) roi_statistics.update(_compute_stats(band_data.values, cat_raster.rio.nodata, stats, prefix_stats = prefix)) - statistics.append([roi_statistics]) - print(statistics) return statistics diff --git a/src/eolab/rastertools/processing/vector.py b/src/eolab/rastertools/processing/vector.py index d663386f..a7fad4c7 100644 --- a/src/eolab/rastertools/processing/vector.py +++ b/src/eolab/rastertools/processing/vector.py @@ -14,9 +14,8 @@ import shapely.geometry from osgeo import gdal import rasterio -from pyproj import Transformer -from rasterio import features, warp, windows -from shapely.geometry import Polygon +from rasterio import features, warp +from shapely.geometry import shape from eolab.rastertools import utils @@ -138,40 +137,6 @@ def clip(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], return clipped_geoms -# def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str],output: Union[Path, str] = None,driver: str = "GeoJSON",) -> gpd.GeoDataFrame: -# """ -# Reproject the geometries to match the CRS of the raster. -# -# Args: -# geoms (str, Path, or gpd.GeoDataFrame): Vector data (filename or GeoDataFrame). -# raster (str or Path): Raster file. -# output (str or Path, optional): File to save reprojected geometries. -# driver (str, optional): File format for saving the output (default is "GeoJSON"). -# -# Returns: -# gpd.GeoDataFrame: Reprojected geometries in raster CRS. -# """ -# geometries = _get_geoms(geoms) -# geoms_crs = _get_geoms_crs(geometries) -# -# raster_path = raster.as_posix() if isinstance(raster, Path) else raster -# -# # Open raster with rioxarray -# with rioxarray.open_rasterio(raster_path) as dataset: -# raster_crs = dataset.rio.crs -# -# if geoms_crs != raster_crs: -# reprojected_geoms = geometries.to_crs(raster_crs) -# else: -# reprojected_geoms = geometries -# -# if output: -# outfile = output.as_posix() if isinstance(output, Path) else output -# reprojected_geoms.to_file(outfile, driver=driver) -# -# return reprojected_geoms - - def reproject(geoms: Union[gpd.GeoDataFrame, Path, str], raster: Union[Path, str], output: Union[Path, str] = None, driver: str = 'GeoJSON') -> gpd.GeoDataFrame: """ @@ -348,7 +313,6 @@ def crop(input_image: Union[Path, str], roi: Union[gpd.GeoDataFrame, Path, str], output_image (pathlib.Path or str): Filename of the generated raster image """ - ### FAIRE AVEC RIOXARRAY pinput = input_image.as_posix() if isinstance(input_image, Path) else input_image poutput = output_image.as_posix() if isinstance(output_image, Path) else output_image @@ -392,33 +356,45 @@ def vectorize(category_raster: Union[Path, str], raster: Union[Path, str], Returns: :obj:`gpd.GeoDataFrame`: The geometries generated by the vectorization in the category crs """ - file = raster.as_posix() if isinstance(raster, Path) else raster + # Open raster and category raster using rioxarray + raster_path = raster.as_posix() if isinstance(raster, Path) else raster + category_raster_path = category_raster.as_posix() if isinstance(category_raster, Path) else category_raster - with rasterio.open(file) as dataset: - with rasterio.open(category_raster) as category_dataset: - # get the raster bounds in the classif crs - l, b, r, t = dataset.bounds - if(category_dataset.crs != dataset.crs): - # reproject bounds in classif crs - l, b, r, t = warp.transform_bounds(dataset.crs, category_dataset.crs, - *dataset.bounds) - - # Compute window bounds for crop - window = windows.from_bounds(l, b, r, t, category_dataset.transform) - # After reading portion of file, rasterio looses georeferencing - # Thus, we record the georef and update it to match the cropped portion - transform_offset = category_dataset.window_transform(window) - # Now, read and vectorize crop - extract = category_dataset.read(1, window=window) - new_shapes = rasterio.features.shapes(extract, transform=transform_offset) - # Store this vectorization in a GeoDataFrame - geo_df = gpd.GeoDataFrame.from_records(new_shapes, - columns=['geometry', category_column]) - geo_df['geometry'] = geo_df['geometry'].apply(lambda x: shapely.geometry.shape(x)) - geo_df[category_column] = geo_df[category_column].apply(lambda x: int(x)) - geo_df = geo_df.set_geometry("geometry") - geo_df.crs = category_dataset.crs - return geo_df + with rioxarray.open_rasterio(raster_path, masked=True) as raster_ds, \ + rioxarray.open_rasterio(category_raster_path, masked=True) as category_ds: + + # Ensure both rasters are in the same CRS + if category_ds.rio.crs != raster_ds.rio.crs: + category_ds = category_ds.rio.reproject_match(raster_ds) + + # Get the bounds of the raster and crop the category raster to these bounds + raster_bounds = raster_ds.rio.bounds() + category_ds_cropped = category_ds.rio.clip_box(*raster_bounds) + + # Read the first band for processing + category_array = category_ds_cropped[0].data + + # Vectorize the raster categories + shapes_generator = rasterio.features.shapes( + category_array, + transform=category_ds_cropped.rio.transform(), + ) + + # Convert shapes to a GeoDataFrame + geometries = [] + categories = [] + + for geom, value in shapes_generator: + if value is not None: # Exclude nodata or invalid values + geometries.append(shape(geom)) + categories.append(int(value)) + + geo_df = gpd.GeoDataFrame( + {category_column: categories, "geometry": geometries}, + crs=category_ds_cropped.rio.crs + ) + + return geo_df def filter_dissolve(geom: gpd.GeoDataFrame, cat_geom: gpd.GeoDataFrame, diff --git a/src/eolab/rastertools/radioindice.py b/src/eolab/rastertools/radioindice.py index 2524a9b4..9a61ce10 100644 --- a/src/eolab/rastertools/radioindice.py +++ b/src/eolab/rastertools/radioindice.py @@ -4,20 +4,12 @@ This module defines a command line named radioindice that computes radiometric indices on raster images: ndvi, ndwi, etc.. """ -import logging import logging.config -import os from pathlib import Path from typing import List -import threading - -import numpy import numpy as np import rasterio -import numpy.ma as ma import rioxarray -from osgeo import gdal -from tqdm import tqdm from eolab.rastertools import utils import xarray as xr @@ -409,7 +401,6 @@ def process_file(self, inputfile: str) -> List[str]: outdir = Path(self.outputdir) - print(inputfile) # Prepare the input image so that it can be processed with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: _logger.debug(f"Raster product is : {product}") @@ -518,7 +509,6 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], dataset.nodata = -2.0 for band_idx in range(1, dataset.count + 1): - print(dataset.tags(band_idx)) band = dataset.read(band_idx, masked=True) band = np.ma.masked_invalid(band) # Handle NaN values @@ -534,24 +524,4 @@ def compute_indices(input_image: str, image_channels: List[BandChannel], # Write metadata to the band dataset.update_tags(band_idx, **stats) - # ref_path = 'tests/tests_refs/test_radioindice/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif' - # ref = gdal.Open(ref_path) - # band_ref = ref.GetRasterBand(1) - # - # # Read the band as a NumPy array - # band_ref = band_ref.ReadAsArray() - # - # out_path = 'tests/tests_out/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif' - # out = gdal.Open(out_path) - # band_out = out.GetRasterBand(1) - # - # # Read the band as a NumPy array - # band_out = band_out.ReadAsArray() - # - # print('Obdfuivijsdfnujdsfvio') - # print(band_out.shape) - # print(band_out[0]) - # print(band_ref[0]) - # print(np.allclose(band_out, band_ref, equal_nan = True)) - diff --git a/src/eolab/rastertools/speed.py b/src/eolab/rastertools/speed.py index 1308421b..e7aea45f 100644 --- a/src/eolab/rastertools/speed.py +++ b/src/eolab/rastertools/speed.py @@ -9,7 +9,6 @@ from pathlib import Path from typing import List -import numpy import rasterio from eolab.rastertools import utils diff --git a/src/eolab/rastertools/svf.py b/src/eolab/rastertools/svf.py index 3ed0ff7f..b5eaec3a 100644 --- a/src/eolab/rastertools/svf.py +++ b/src/eolab/rastertools/svf.py @@ -8,14 +8,11 @@ from pathlib import Path import numpy as np import rasterio -import rioxarray -import xarray as xr from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo -from eolab.rastertools.processing import RasterProcessing, compute_sliding -from eolab.rastertools.processing.sliding import _pad_dataset_xarray +from eolab.rastertools.processing import RasterProcessing from eolab.rastertools.product import RasterProduct _logger = logging.getLogger(__name__) diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 8edd1e1d..332a2ef6 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -5,7 +5,6 @@ a reference grid. """ import logging -import math from typing import List from pathlib import Path @@ -208,7 +207,7 @@ def process_file(self, inputfile: str): output_paths.append(output.as_posix()) _logger.info("Tile " + str(i) + " exported to " + str(output_paths)) - print(output) + except NoDataInBounds: # if no overlap _logger.error("Input shape " + str(i) + " does not overlap raster") diff --git a/src/eolab/rastertools/timeseries.py b/src/eolab/rastertools/timeseries.py index d05870a4..13ceadaf 100644 --- a/src/eolab/rastertools/timeseries.py +++ b/src/eolab/rastertools/timeseries.py @@ -6,24 +6,15 @@ to clouds for instance). The timeseries is generated with a linear interpolation thus enabling to fill gaps. """ -import time from datetime import datetime, timedelta -from idlelib.format import reformat_paragraph -from itertools import repeat import logging.config -import multiprocessing -import os from pathlib import Path import rioxarray -import xarray as xr from typing import Dict, List import numpy as np import rasterio -from osgeo import gdal -from tqdm.contrib.concurrent import process_map - from eolab.rastertools import utils from eolab.rastertools import Rastertool, Windowable from eolab.rastertools.processing import algo @@ -209,24 +200,13 @@ def _interpolate_xarray(products_dates, products_per_date, datas = list() for date in products_dates: src = rioxarray.open_rasterio(products_per_date[date].get_raster(), masked=False, chunks=(1,500,500)) - # src = src.where(src != -2, other=None) dtype = src.dtype or rasterio.float32 band_data = src.isel(band=slice(0, len(bands))) # Select the desired bands - print(band_data) - print(band_data.values[0, 0, 0]) - print(band_data.values[0,500, 300:400]) crs = src.rio.crs datas.append(band_data) - start = time.time() output = algo.interpolated_timeseries_xarray(products_dates, datas, timeseries_dates, nodata) - end = time.time() - - print('time algoo') - print(start - end) - - start = time.time() for i, img in enumerate(timeseries_images): output[i] = output[i].where(~datas[0].isnull(), other=-2) @@ -234,25 +214,3 @@ def _interpolate_xarray(products_dates, products_per_date, output[i].rio.write_nodata(-2, inplace=True) output[i].rio.to_raster(img, nodata=-2, dtype=dtype) - end = time.time() - print('time write') - print(start - end) - - ref_path = 'tests/tests_refs/test_timeseries/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' - ref = gdal.Open(ref_path) - band_ref = ref.GetRasterBand(1) - - # Read the band as a NumPy array - band_ref = band_ref.ReadAsArray() - - out_path = 'tests/tests_out/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif' - out = gdal.Open(out_path) - band_out = out.GetRasterBand(1) - - # Read the band as a NumPy array - band_out = band_out.ReadAsArray() - - print('Obdfuivijsdfnujdsfvio') - print(band_out[500,300:400]) - print(band_ref[500, 300:400]) - print(np.allclose(band_out, band_ref, equal_nan = True)) diff --git a/src/eolab/rastertools/utils.py b/src/eolab/rastertools/utils.py index 3f7c2d26..02dc7546 100644 --- a/src/eolab/rastertools/utils.py +++ b/src/eolab/rastertools/utils.py @@ -9,12 +9,8 @@ - ... """ import math -import tempfile from pathlib import Path - -import rasterio import xarray -from osgeo import gdal def to_tuple(val): @@ -116,9 +112,9 @@ def slices_2d(window_size, shift, stop, start=0): for c_min, c_max in window_c) def xarray_crop(raster : xarray.DataArray, shape): - ''' + """ Replacing the crop = True option that is not supported by raster.rio.clip() - ''' + """ resolution_x, resolution_y = raster.rio.resolution() bbox = shape.bounds diff --git a/src/eolab/rastertools/zonalstats.py b/src/eolab/rastertools/zonalstats.py index ce2f477f..4e412c37 100644 --- a/src/eolab/rastertools/zonalstats.py +++ b/src/eolab/rastertools/zonalstats.py @@ -25,7 +25,6 @@ import sys import rasterio -import rioxarray from eolab.rastertools import utils from eolab.rastertools import Rastertool, RastertoolConfigurationException @@ -34,7 +33,6 @@ from eolab.rastertools.processing import vector from eolab.rastertools.product import RasterProduct - _logger = logging.getLogger(__name__) @@ -549,7 +547,6 @@ def compute_stats(self, raster: str, bands: List[int], categorical=self.categorical) - # print(len(statistics)) # apply area if self.area: [d.update({key: area_square_meter * val}) diff --git a/tests/test_algo.py b/tests/test_algo.py index 1c7a246d..8826000d 100644 --- a/tests/test_algo.py +++ b/tests/test_algo.py @@ -18,161 +18,6 @@ __copyright__ = "Copyright 2019, CNES" __license__ = "Apache v2.0" -from eolab.rastertools.timeseries import compute_timeseries, Timeseries -from .utils4test import RastertoolsTestsData - -# zero_np = np.zeros((1,100,100)) -# zero_xarr = xr.DataArray(zero_np, dims=("x", "y", "z")) -# -# rd_np = np.random.randn(1,100, 100) -# rd_xarr = xr.DataArray(rd_np, dims=("x", "y", "z")) -# -# rd2_np = np.random.randn(1,100, 100) -# rd2_xarr = xr.DataArray(rd2_np, dims=("x", "y", "z")) -# -# #Both zeros arrays -# zero_nptest = [zero_np, zero_np] -# zero_xarrtest = [zero_xarr, zero_xarr] -# -# #One random array and a zero array -# rd1_nptest = [zero_np, rd_np] -# rd1_xarrtest = [zero_xarr, rd_xarr] -# -# #Two same random array -# rd2_nptest = [rd_np, rd_np] -# rd2_xarrtest = [rd_xarr, rd_xarr] -# -# #Two different random array -# rd3_nptest = [rd_np, rd2_np] -# rd3_xarrtest = [rd_xarr, rd2_xarr] -# -# @pytest.mark.parametrize("input_np, input_xarray, interval", -# [(zero_nptest, zero_xarrtest, 0.0), -# (zero_nptest, zero_xarrtest, 5.0), -# (rd1_nptest, rd1_xarrtest, 5.0), -# (rd2_nptest, rd2_xarrtest, -4.0), -# (rd3_nptest, rd3_xarrtest, 1.0)]) -# -# -# def test_speed_algo(input_np :list, input_xarray : list, interval : float): -# """ -# Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray -# """ -# data1_np, data2_np = input_np -# data1_xarr, data2_xarr = input_xarray -# speed_np = algo.speed(data1_np, data2_np, interval) -# speed_xarr = algo.speed(data1_xarr, data2_xarr, interval) -# -# #Assert that the values of both arrays are the same -# np.testing.assert_array_equal(speed_np, speed_xarr.values) -# assert type(speed_xarr) == xr.DataArray - - -# input_file1 = "tests/tests_data/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi.tif" -# input_file2 = "tests/tests_data/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndvi.tif" -# file_list = [input_file1, input_file2] -# -# @pytest.mark.parametrize("file_list, start_date, end_date, period, window_size", -# [(file_list,"2018-09-26", "2018-11-07" , 20, (512,512))]) -# -# -# def test_xarray_timeseries_algo(file_list : list, start_date :str, end_date: str, period : int, window_size : tuple): -# """ -# Test if the output of the timeseries algorithm obtained with numpy.ndarray are the same that with xarray.DataArray -# Only nan values are different -# """ -# # outputdir = RastertoolsTestsData.tests_output_data_dir + "/test_timeseries_xarray" -# bands = [1] -# -# start_date = datetime.strptime(start_date, "%Y-%m-%d") -# end_date = datetime.strptime(end_date, "%Y-%m-%d") -# -# all_outputs = [] -# for filename in file_list: -# outputs = list(filename) -# if outputs: -# all_outputs.extend(outputs) -# -# # # create the rastertool object -# # tools = Timeseries(start_date, end_date, period, bands) -# # out_imgs_np, out_imgs_xarray = tools.postprocess_files(file_list, all_outputs, xarray_vers = True) -# # -# -# out_imgs_np = ['/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20180926-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif', '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif', '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181105-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif'] -# out_imgs_xarray = [ -# '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20180926-000000-685_L2A_T30TYP_D-ndvi-timeseries-xarray.tif', -# '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries-xarray.tif', -# '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_out/test_timeseries_xarray/SENTINEL2A_20181105-000000-685_L2A_T30TYP_D-ndvi-timeseries-xarray.tif'] -# -# -# for product_id in range(len(out_imgs_np)) : -# with rasterio.open(out_imgs_np[product_id]) as src_np: -# times_np = src_np.read(bands, masked=True) -# -# src_xarray = rioxarray.open_rasterio(out_imgs_xarray[product_id]) -# times_xarray = src_xarray.sel(band=bands).values.astype(np.float32) -# -# if isinstance(times_np, np.ma.MaskedArray): -# times_np = times_np.filled(np.nan) -# np.testing.assert_allclose(times_np, times_xarray, equal_nan=True) - -# -# zero3d_np = np.zeros((3,3,3)) -# zero3d_xarr = xr.DataArray(zero3d_np, dims=("x", "y", "z")) -# -# rd3d_np = np.random.randn(3,3,3) -# rd3d_xarr = xr.DataArray(rd3d_np, dims=("x", "y", "z")) -# -# @pytest.mark.parametrize("input_np, input_xarray", [(zero3d_np,zero3d_xarr), -# (rd3d_np,rd3d_xarr)]) -# -# def test_indices_algo(input_np :np.ndarray, input_xarray : xr.DataArray): -# """ -# Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray -# """ -# ind_func = [algo.normalized_difference, algo.rvi, algo.tndvi, algo.pvi, algo.savi, algo.tsavi, algo.msavi, algo.msavi2, algo.ipvi, -# algo.evi, algo.redness_index, algo.brightness_index, algo.brightness_index2] -# -# for indic in ind_func : -# ind_np = indic(input_np) -# ind_xarr = indic(input_xarray) -# #Assert that the values of both arrays are the same -# np.testing.assert_array_equal(ind_np, ind_xarr.values) -# assert type(ind_xarr) == xr.DataArray - -# -# @pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [50, 16, 0.5, None]), -# (zero_np,zero_xarr, [50, 16, 0.5, 0]), -# (rd_np,rd_xarr, [50, 16, 0.5, None]), -# (rd_np,rd_xarr, [50, 16, 0.5, 0])]) -# -# def test_svf_algo(input_np : np.ndarray, input_xarray : xr.DataArray, params : list): -# """ -# Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray -# """ -# radius, directions, resolution, altitude = params -# svf_np = algo.svf(input_np, radius, directions, resolution, altitude) -# svf_xarr = algo.svf(input_xarray, radius, directions, resolution, altitude) -# #Assert that the values of both arrays are the same -# np.testing.assert_array_equal(svf_np, svf_xarr.values) -# assert type(svf_xarr) == xr.DataArray - - -# @pytest.mark.parametrize("input_np, input_xarray, params", [(zero_np,zero_xarr, [27.2, 82.64, 3, 0.5]), -# (rd_np,rd_xarr, [27.2, 82.64, 3, 0.5])]) -# -# def test_hillshade_algo(input_np : np.ndarray, input_xarray : xr.DataArray, params : list): -# """ -# Test if the outputs obtained with numpy.ndarray are the same that with xarray.DataArray -# """ -# elevation, azimuth , radius, resolution = params -# hills_np = algo.hillshade(input_np, elevation, azimuth , radius, resolution) -# hills_xarr = algo.hillshade(input_xarray, elevation, azimuth , radius, resolution) -# #Assert that the values of both arrays are the same -# np.testing.assert_array_equal(hills_np, hills_xarr.values) -# assert type(hills_xarr) == xr.DataArray - - def test_local_sum(): """ Test the local sum filter with varying kernel sizes. @@ -232,19 +77,6 @@ def test_local_sum(): assert (output[0] == results[i - 1]).all() -# @pytest.mark.parametrize("input_np, input_xarray, kernel_width", [(zero_np,zero_xarr, 5)]) -# -# def test_xarray_local_mean(input_np : np.ndarray, input_xarray : xr.DataArray, kernel_width : int): -# """ -# Test if the output of the speed algorithm obtained with numpy.ndarray are the same that with xarray.DataArray -# """ -# mean_np = algo.local_mean(ma.array(input_np), kernel_width) -# mean_xarr = algo.local_mean(input_xarray, kernel_width) -# -# #Assert that the values of both arrays are the same -# np.testing.assert_array_equal(mean_np, mean_xarr.values) -# assert type(mean_xarr) == xr.DataArray - def test_local_mean(): """ diff --git a/tests/test_radioindice.py b/tests/test_radioindice.py index 8888b81f..96e431cb 100644 --- a/tests/test_radioindice.py +++ b/tests/test_radioindice.py @@ -87,7 +87,6 @@ def test_radioindice_process_file_separate(compare : bool, save_gen_as_ref : boo gen_files = [inputfile + "-ndvi.tif", inputfile + "-ndwi.tif"] if compare: match, mismatch, err = utils4test.cmpfiles(RastertoolsTestsData.tests_output_data_dir , __refdir, gen_files) - print(mismatch) assert len(match) == 2 assert len(mismatch) == 0 assert len(err) == 0 diff --git a/tests/test_rastertools.py b/tests/test_rastertools.py index cc2a22c7..6faa6226 100644 --- a/tests/test_rastertools.py +++ b/tests/test_rastertools.py @@ -100,7 +100,6 @@ def run_test(self, caplog=None, loglevel=logging.ERROR, check_outputs=True, chec try: rastertools(self.args) - print(self._sys_exit) except SystemExit as wrapped_exception: if check_sys_exit: @@ -446,6 +445,7 @@ def test_zonalstats_command_line_default(): lst_file_path) # list of commands to test + # There is no more window_size, therefore the third test has been removed argslist = [ # specify stats to compute and sigma, 1st band computed f"-v zs -o {RastertoolsTestsData.tests_output_data_dir} -f GeoJSON" @@ -843,6 +843,7 @@ def test_hillshade_command_line_errors(caplog): utils4test.create_outdir() # list of commands to test + # There is no more window_size, therefore the forth test has been removed argslist = [ # output dir does not exist "-v hs --elevation 46.81 --azimuth 180.0 --resolution 0.5 -o tests/truc" diff --git a/tests/test_speed.py b/tests/test_speed.py index c727a8eb..bc1347f7 100644 --- a/tests/test_speed.py +++ b/tests/test_speed.py @@ -47,4 +47,4 @@ def test_speed_process_files(compare : bool, save_gen_as_ref : bool): # save the generated files in the refdir => make them the new refs. utils4test.copy_to_ref(exp_outs, __refdir) - # utils4test.clear_outdir() + utils4test.clear_outdir() diff --git a/tests/test_stats.py b/tests/test_stats.py index e53f737c..7c6ec26b 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -1,7 +1,5 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import math - from eolab.rastertools.processing import stats, vector from . import utils4test @@ -86,6 +84,7 @@ def test_compute_zonal_extra_stats(): [d.update({key: round(val, 6)}) for geom_stats in statistics for d in geom_stats for key, val in d.items()] + # ref is the following ref = [[{'sum': 195899.96875, 'median': 0.636118, 'range': 2.0, 'percentile_5': 0.15373, 'mad': 0.218207, 'majority': -1.0, 'minority': -0.996727, 'unique': 285995, @@ -98,7 +97,7 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.12968, 'mad': 0.146711, 'majority': -1.0, 'minority': -0.993174, 'unique': 12189, 'nodata': 0, 'valid': 1.0}], - [{'sum': 56049.855469, 'median': 0.668857, 'range': 2.0, + [{'sum': 56049.859375, 'median': 0.668857, 'range': 2.0, 'percentile_5': 0.154594, 'mad': 0.148523, 'majority': 1.0, 'minority': -0.977465, 'unique': 83944, 'nodata': 245, 'valid': 0.997214}], @@ -106,27 +105,27 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.358831, 'mad': 0.143713, 'majority': 0.6, 'minority': 0.088869, 'unique': 13137, 'nodata': 244, 'valid': 0.981971}], - [{'sum': 30402.615234, 'median': 0.753216, 'range': 1.656766, + [{'sum': 30402.613281, 'median': 0.753216, 'range': 1.656766, 'percentile_5': 0.433678, 'mad': 0.124387, 'majority': 1.0, 'minority': -0.656766, 'unique': 40146, 'nodata': 85, 'valid': 0.997948}], - [{'sum': 31683.021484, 'median': 0.471969, 'range': 1.927374, + [{'sum': 31683.017578, 'median': 0.471969, 'range': 1.927374, 'percentile_5': 0.110149, 'mad': 0.168122, 'majority': 0.5, 'minority': -0.927374, 'unique': 63745, 'nodata': 0, 'valid': 1.0}], - [{'sum': 56840.179688, 'median': 0.629144, 'range': 2.0, + [{'sum': 56840.1875, 'median': 0.629144, 'range': 2.0, 'percentile_5': 0.134994, 'mad': 0.188218, 'majority': -1.0, 'minority': -0.993671, 'unique': 89338, 'nodata': 85, 'valid': 0.99909}], - [{'sum': 30468.533203, 'median': 0.619463, 'range': 2.0, + [{'sum': 30468.535156, 'median': 0.619463, 'range': 2.0, 'percentile_5': 0.15824, 'mad': 0.161906, 'majority': -1.0, 'minority': -0.991903, 'unique': 49427, 'nodata': 0, 'valid': 1.0}], - [{'sum': 44829.199219, 'median': 0.704596, 'range': 2.0, + [{'sum': 44829.203125, 'median': 0.704596, 'range': 2.0, 'percentile_5': 0.307304, 'mad': 0.169161, 'majority': 1.0, 'minority': -0.969231, 'unique': 61646, 'nodata': 337, 'valid': 0.994777}], - [{'sum': 58079.578125, 'median': 0.727142, 'range': 2.0, + [{'sum': 58079.574219, 'median': 0.727142, 'range': 2.0, 'percentile_5': 0.211673, 'mad': 0.138837, 'majority': 1.0, 'minority': -0.962406, 'unique': 79181, 'nodata': 0, 'valid': 1.0}], @@ -134,7 +133,7 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.241052, 'mad': 0.153867, 'majority': 0.941176, 'minority': -0.206738, 'unique': 4007, 'nodata': 62, 'valid': 0.984878}], - [{'sum': 17599.697266, 'median': 0.586743, 'range': 1.83908, + [{'sum': 17599.695312, 'median': 0.586743, 'range': 1.83908, 'percentile_5': 0.255563, 'mad': 0.168826, 'majority': 1.0, 'minority': -0.83908, 'unique': 28731, 'nodata': 290, 'valid': 0.990177}], @@ -142,11 +141,11 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.121661, 'mad': 0.177506, 'majority': -1.0, 'minority': -0.996276, 'unique': 165519, 'nodata': 55, 'valid': 0.99969}], - [{'sum': 10982.207031, 'median': 0.647735, 'range': 0.961919, + [{'sum': 10982.208008, 'median': 0.647735, 'range': 0.961919, 'percentile_5': 0.135674, 'mad': 0.24678, 'majority': 1.0, 'minority': 0.038081, 'unique': 17550, 'nodata': 337, 'valid': 0.98139}], - [{'sum': 89315.609375, 'median': 0.491085, 'range': 2.0, + [{'sum': 89315.601562, 'median': 0.491085, 'range': 2.0, 'percentile_5': 0.12513, 'mad': 0.170515, 'majority': -1.0, 'minority': -0.995074, 'unique': 158615, 'nodata': 0, 'valid': 1.0}], @@ -154,7 +153,7 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.324657, 'mad': 0.142261, 'majority': -1.0, 'minority': -0.974359, 'unique': 28279, 'nodata': 0, 'valid': 1.0}], - [{'sum': 31228.533203, 'median': 0.564933, 'range': 2.0, + [{'sum': 31228.535156, 'median': 0.564933, 'range': 2.0, 'percentile_5': 0.111111, 'mad': 0.227924, 'majority': -1.0, 'minority': -0.962085, 'unique': 54423, 'nodata': 0, 'valid': 1.0}], @@ -162,12 +161,9 @@ def test_compute_zonal_extra_stats(): 'percentile_5': 0.231518, 'mad': 0.16068, 'majority': -1.0, 'minority': -0.991091, 'unique': 34565, 'nodata': 259, 'valid': 0.992713}]] - # ref is the following for geom_stats, ref_stats in zip(statistics, ref): for i, band in enumerate(bands): - print(geom_stats[i]['sum']) - print(ref_stats[i]['sum']) assert geom_stats[i] == ref_stats[i] diff --git a/tests/test_zonalstats.py b/tests/test_zonalstats.py index 5af74352..bdbbc3a4 100644 --- a/tests/test_zonalstats.py +++ b/tests/test_zonalstats.py @@ -3,7 +3,6 @@ import os import pytest -from pathlib import Path from eolab.rastertools import Zonalstats from eolab.rastertools import RastertoolConfigurationException @@ -153,26 +152,29 @@ def test_zonalstats_process_files(compare, save_gen_as_ref): def test_zonalstats_category(compare, save_gen_as_ref): # create output dir and clear its content if any + data_path = RastertoolsTestsData.tests_input_data_dir.replace(os.getcwd() + "/", "") + "/" + out_path = RastertoolsTestsData.tests_output_data_dir.replace(os.getcwd() + "/", "") + "/" + utils4test.create_outdir() # cas 1 - classif shapefile sur une ROI composée de plusieurs géométries - inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" + inputfile = data_path + "DSM_PHR_Dunkerque.tif" outformat = "GeoJSON" statistics = "min max mean std count range sum".split() # Category inputs - categoryfile = utils4test.indir + "OSO_2017_classification_dep59.shp" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + categoryfile = data_path + "OSO_2017_classification_dep59.shp" + categorydic = data_path + "OSO_nomenclature_2017.json" tool = Zonalstats(statistics, area=True) - tool.with_output(utils4test.outdir, output_format=outformat) - tool.with_geometries(geometries=utils4test.indir + "COMMUNE_59xxx.geojson") + tool.with_output(out_path, output_format=outformat) + tool.with_geometries(geometries=data_path + "COMMUNE_59xxx.geojson") tool.with_per_category(category_file=categoryfile, category_index="Classe", category_labels_json=categorydic) tool.process_file(inputfile) gen_files = ["DSM_PHR_Dunkerque-stats.geojson"] if compare: - match, mismatch, err = utils4test.cmpfiles(utils4test.outdir, __refdir, gen_files, tolerance= 1e-8) + match, mismatch, err = utils4test.cmpfiles(out_path, __refdir, gen_files, tolerance= 1e-8) assert len(match) == 1 assert len(mismatch) == 0 assert len(err) == 0 @@ -181,13 +183,13 @@ def test_zonalstats_category(compare, save_gen_as_ref): utils4test.copy_to_ref(gen_files, __refdir) # cas 2 - classif raster sur l'emprise globale du DSM - inputfile = utils4test.indir + "DSM_PHR_Dunkerque.tif" + inputfile = data_path + "DSM_PHR_Dunkerque.tif" outformat = "GeoJSON" - categoryfile = utils4test.indir + "OCS_2017_CESBIO_extract.tif" - categorydic = utils4test.indir + "OSO_nomenclature_2017.json" + categoryfile = data_path + "OCS_2017_CESBIO_extract.tif" + categorydic = data_path + "OSO_nomenclature_2017.json" tool = Zonalstats(statistics, area=True) - tool.with_output(utils4test.outdir, output_format=outformat) + tool.with_output(out_path, output_format=outformat) tool.with_per_category(category_file=categoryfile, category_index="Classe", category_labels_json=categorydic) tool.process_file(inputfile) diff --git a/tests/utils4test.py b/tests/utils4test.py index 8d380589..366fc8dc 100644 --- a/tests/utils4test.py +++ b/tests/utils4test.py @@ -11,8 +11,6 @@ __copyright = "Copyright 2019, CNES" __license = "Apache v2.0" -indir = "tests/tests_data/" -outdir = "tests/tests_out/" __root_refdir = "tests/tests_refs/" @@ -86,7 +84,6 @@ def cmpfiles(a : str, b : str, common : list, tolerance : float =1e-9, **kwargs) def _cmp(gld, new, tolerance, **kwargs): """ - """ ftype = os.path.splitext(gld)[-1].lower() cmp = cmptools.CMP_FUN[ftype] From 2390f6e9352d7625989ce8c3917a8e3c35178b86 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Mon, 16 Dec 2024 10:56:41 +0100 Subject: [PATCH 46/56] refactor: updating setup.py --- setup.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/setup.py b/setup.py index d6ef5262..83555265 100644 --- a/setup.py +++ b/setup.py @@ -28,6 +28,8 @@ 'click', 'rasterio', 'pytest>=3.6', + 'rioxarray', + 'dask', 'pytest-cov', 'geopandas==0.13', 'kiwisolver==1.4.5', From 4e2f1234d443436e0c5c95cc8735d43044c4677f Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 22 Apr 2025 11:14:04 +0200 Subject: [PATCH 47/56] feat : added CI --- .github/workflows/cd.yml | 43 +++++++++++++++ .github/workflows/ci.yml | 53 +++++++++++++++++++ .../scripts/check_mccabe_complexity.sh | 36 +++++++++++++ 3 files changed, 132 insertions(+) create mode 100644 .github/workflows/cd.yml create mode 100644 .github/workflows/ci.yml create mode 100644 continuous_integration/scripts/check_mccabe_complexity.sh diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml new file mode 100644 index 00000000..424ec283 --- /dev/null +++ b/.github/workflows/cd.yml @@ -0,0 +1,43 @@ +name: CD Workflow + +on: + push: + branches: + - "main" + +permissions: + contents: read + id-token: write + +jobs: + deploy: + runs-on: ubuntu-latest + environment: + name: pypi + steps: + - uses: actions/checkout@v4 + + - name: Setup Python 3.8.13 + uses: actions/setup-python@v3 + with: + python-version: "3.8.13" + + - name: Setup Miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + python-version: 3.8.13 + auto-activate-base: false + + - name: Build package + shell: bash -l {0} + run: | + conda create -n deploy_env python=3.8.13 libgdal=3.5.2 build -c conda-forge -y + conda activate deploy_env + python -m build -C--global-option=bdist_wheel -C--global-option=--build-number=0 --wheel + + - name: Publish package + uses: pypa/gh-action-pypi-publish@release/v1 + with: + user: __token__ + verbose: true + password: ${{ secrets.PYPI_PASSWORD }} diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..fb96110a --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,53 @@ +name: CI Workflow + +on: + pull_request: + branches: + - "main" +permissions: + contents: read + +jobs: + quality: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Setup Python 3.8.13 + uses: actions/setup-python@v3 + with: + python-version: "3.8.13" + - name: Create test env + shell: bash -l {0} + run: | + pip install pylint mccabe + - name: code quality + shell: bash -l {0} + run: | + pylint --disable=all --fail-under=10 --enable=too-many-statements src/eolab/georastertools/ + pylint --disable=all --fail-under=10 --enable=too-many-nested-blocks src/eolab/georastertools/ + ./continuous_integration/scripts/check_mccabe_complexity.sh 25 src/eolab/georastertools + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Setup Python 3.8.13 + uses: actions/setup-python@v3 + with: + python-version: "3.8.13" + - name: Setup Miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + python-version: 3.8.13 + auto-activate-base: false + - name: Create test env + shell: bash -l {0} + run: | + conda create -n test_env python=3.8.13 libgdal=3.5.2 -c conda-forge -c defaults -y + conda activate test_env + PIP_NO_BINARY=rasterio pip install . + pip install pylint mccabe + - name: test + shell: bash -l {0} + run: | + conda activate test_env + pytest --cov-fail-under=65 --compare \ No newline at end of file diff --git a/continuous_integration/scripts/check_mccabe_complexity.sh b/continuous_integration/scripts/check_mccabe_complexity.sh new file mode 100644 index 00000000..c7e6766f --- /dev/null +++ b/continuous_integration/scripts/check_mccabe_complexity.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +if [ -z "$1" ] || [ -z "$2" ]; then + echo "Error: You must specify a McCabe threshold and a directory to analyze." + echo "Usage: $0 " + exit 1 +fi + +threshold=$1 +directory=$2 + +if [ ! -d "$directory" ]; then + echo "Error: The directory '$directory' does not exist." + exit 1 +fi + +all_files_ok=true +for file in $(find "$directory" -name "*.py"); do + echo "Analyzing $file ..." + output=$(python -m mccabe --min "$threshold" "$file") + + if [ -n "$output" ]; then + echo "Error: McCabe complexity too high in $file" + echo "$output" + all_files_ok=false + fi +done + +if $all_files_ok; then + echo "✅ All files have McCabe scores less than or equal to $threshold. ✅" +else + echo "❌ Some files have a complexity higher than $threshold ❌" + exit 1 +fi + +exit 0 From 6fa0e2af23cf2b17d9f73660800c9a781bc1f05b Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 23 Apr 2025 13:56:36 +0200 Subject: [PATCH 48/56] feat : changed rights for ci --- continuous_integration/scripts/check_mccabe_complexity.sh | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 continuous_integration/scripts/check_mccabe_complexity.sh diff --git a/continuous_integration/scripts/check_mccabe_complexity.sh b/continuous_integration/scripts/check_mccabe_complexity.sh old mode 100644 new mode 100755 From 9b50532be32d2eb1e8f889f67b6a42f1489188f1 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Wed, 23 Apr 2025 14:10:21 +0200 Subject: [PATCH 49/56] Update ci.yml --- .github/workflows/ci.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index fb96110a..7e42fba2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -23,9 +23,9 @@ jobs: - name: code quality shell: bash -l {0} run: | - pylint --disable=all --fail-under=10 --enable=too-many-statements src/eolab/georastertools/ - pylint --disable=all --fail-under=10 --enable=too-many-nested-blocks src/eolab/georastertools/ - ./continuous_integration/scripts/check_mccabe_complexity.sh 25 src/eolab/georastertools + pylint --disable=all --fail-under=10 --enable=too-many-statements src/eolab/rastertools/ + pylint --disable=all --fail-under=10 --enable=too-many-nested-blocks src/eolab/rastertools/ + ./continuous_integration/scripts/check_mccabe_complexity.sh 25 src/eolab/rastertools test: runs-on: ubuntu-latest steps: @@ -50,4 +50,4 @@ jobs: shell: bash -l {0} run: | conda activate test_env - pytest --cov-fail-under=65 --compare \ No newline at end of file + pytest --cov-fail-under=65 --compare From a3260431755d1eadd32d41f603b01ded3481ef27 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 6 May 2025 10:12:11 +0200 Subject: [PATCH 50/56] feat : removed unused files --- commentaires_rastertools.odt | Bin 41383 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a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,7 +44,7 @@ jobs: run: | conda create -n test_env python=3.8.13 libgdal=3.5.2 -c conda-forge -c defaults -y conda activate test_env - PIP_NO_BINARY=rasterio pip install . + PIP_NO_BINARY=rasterio,rioxarray pip install . pip install pylint mccabe - name: test shell: bash -l {0} diff --git a/src/eolab/rastertools/tiling.py b/src/eolab/rastertools/tiling.py index 332a2ef6..dceed171 100644 --- a/src/eolab/rastertools/tiling.py +++ b/src/eolab/rastertools/tiling.py @@ -158,7 +158,7 @@ def process_file(self, inputfile: str): [str]: The list of generates tiles. """ _logger.info(f"Processing file {inputfile}") - inputfile = '/home/ecadaux/pluto/rastertools/rastertools/tests/tests_data/tif_file.tif' + # STEP 1: Prepare the input image so that it can be processed with RasterProduct(inputfile, vrt_outputdir=self.vrt_dir) as product: diff --git a/src/rastertools.egg-info/PKG-INFO b/src/rastertools.egg-info/PKG-INFO index 88bbc518..71c7b194 100644 --- a/src/rastertools.egg-info/PKG-INFO +++ b/src/rastertools.egg-info/PKG-INFO @@ -17,31 +17,18 @@ Description-Content-Type: text/x-rst; charset=UTF-8 License-File: LICENSE.txt License-File: AUTHORS.rst Requires-Dist: click -<<<<<<< HEAD -Requires-Dist: rasterio==1.3.0 -======= ->>>>>>> rasterio_plugin_vsimem +Requires-Dist: rasterio Requires-Dist: pytest>=3.6 +Requires-Dist: rioxarray +Requires-Dist: dask Requires-Dist: pytest-cov Requires-Dist: geopandas==0.13 -Requires-Dist: python-dateutil==2.9.0 Requires-Dist: kiwisolver==1.4.5 -Requires-Dist: fonttools==4.53.1 Requires-Dist: matplotlib==3.7.3 Requires-Dist: packaging==24.1 -Requires-Dist: Shapely==1.8.5.post1 -Requires-Dist: tomli==2.0.2 -Requires-Dist: Rtree==1.3.0 Requires-Dist: fiona==1.8.21 -Requires-Dist: Pillow==9.2.0 Requires-Dist: sphinx_rtd_theme==3.0.1 Requires-Dist: pip==24.2 -<<<<<<< HEAD -Requires-Dist: dask -Requires-Dist: xarray==2022.11.0 -======= ->>>>>>> rasterio_plugin_vsimem -Requires-Dist: pyproj==3.4.0 Requires-Dist: sphinx==7.1.2 Requires-Dist: scipy==1.8 Requires-Dist: pyscaffold diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index 1316c5f6..db45397d 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -1,15 +1,21 @@ +.coverage .gitignore AUTHORS.rst CHANGELOG.rst Dockerfile LICENSE.txt +METADATA README.rst -env_test.yml -env_update.yml -environment.yml +RECORD +WHEEL +entry_points.txt setup.cfg setup.py tox.ini +.github/workflows/cd.yml +.github/workflows/ci.yml 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tests/tests_refs/test_radioindice/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndwi.tif +tests/tests_refs/test_radioindice/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D-ndwi.tif.aux.xml tests/tests_refs/test_rasterproduct/S2A_MSIL2A_20190116T105401_N0211_R051_T30TYP_20190116T120806.vrt tests/tests_refs/test_rasterproduct/S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041-clipped.vrt tests/tests_refs/test_rasterproduct/S2B_MSIL1C_20191008T105029_N0208_R051_T30TYP_20191008T125041.vrt @@ -217,10 +2105,15 @@ tests/tests_refs/test_rasterproduct/SENTINEL2B_20181023-105107-455_L2A_T30TYP_D_ tests/tests_refs/test_rasterproduct/SPOT6_2018_France-Ortho_NC_DRS-MS_SPOT6_2018_FRANCE_ORTHO_NC_GEOSUD_MS_82.vrt tests/tests_refs/test_stats/zonal_stats.geojson tests/tests_refs/test_tiling/tif_file_tile77.tif +tests/tests_refs/test_tiling/tif_file_tile77.tif.aux.xml tests/tests_refs/test_tiling/tif_file_tile93.tif +tests/tests_refs/test_tiling/tif_file_tile93.tif.aux.xml tests/tests_refs/test_timeseries/SENTINEL2A_20180926-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif +tests/tests_refs/test_timeseries/SENTINEL2A_20180926-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif.aux.xml tests/tests_refs/test_timeseries/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif +tests/tests_refs/test_timeseries/SENTINEL2A_20181016-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif.aux.xml tests/tests_refs/test_timeseries/SENTINEL2A_20181105-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif +tests/tests_refs/test_timeseries/SENTINEL2A_20181105-000000-685_L2A_T30TYP_D-ndvi-timeseries.tif.aux.xml tests/tests_refs/test_vector/clip.geojson tests/tests_refs/test_vector/raster_outline.geojson tests/tests_refs/test_vector/reproject_dissolve.geojson diff --git a/src/rastertools.egg-info/requires.txt b/src/rastertools.egg-info/requires.txt index f9389c02..f6bfd83e 100644 --- a/src/rastertools.egg-info/requires.txt +++ b/src/rastertools.egg-info/requires.txt @@ -1,29 +1,16 @@ click -<<<<<<< HEAD -rasterio==1.3.0 -======= ->>>>>>> rasterio_plugin_vsimem +rasterio pytest>=3.6 +rioxarray +dask pytest-cov geopandas==0.13 -python-dateutil==2.9.0 kiwisolver==1.4.5 -fonttools==4.53.1 matplotlib==3.7.3 packaging==24.1 -Shapely==1.8.5.post1 -tomli==2.0.2 -Rtree==1.3.0 fiona==1.8.21 -Pillow==9.2.0 sphinx_rtd_theme==3.0.1 pip==24.2 -<<<<<<< HEAD -dask -xarray==2022.11.0 -======= ->>>>>>> rasterio_plugin_vsimem -pyproj==3.4.0 sphinx==7.1.2 scipy==1.8 pyscaffold From cc755b59afc538ffe78cfeb102dc76f01db19480 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 6 May 2025 13:31:43 +0200 Subject: [PATCH 53/56] feat : small docs corrections --- docs/_static/css/my_custom.css | 11 ++++ docs/cli/filtering.rst | 76 +--------------------------- docs/cli/radioindice.rst | 64 +---------------------- src/rastertools.egg-info/SOURCES.txt | 1 - 4 files changed, 13 insertions(+), 139 deletions(-) create mode 100644 docs/_static/css/my_custom.css diff --git a/docs/_static/css/my_custom.css b/docs/_static/css/my_custom.css new file mode 100644 index 00000000..75ab7ac6 --- /dev/null +++ b/docs/_static/css/my_custom.css @@ -0,0 +1,11 @@ +.wy-side-nav-search, .wy-nav-top { + background: #272525; +} + +.wy-side-nav-search>div.version{ + color: #D4F1F4; +} + +.wy-nav-content { + max-width: 100% !important; +} diff --git a/docs/cli/filtering.rst b/docs/cli/filtering.rst index 9bab0c69..56c7c22b 100644 --- a/docs/cli/filtering.rst +++ b/docs/cli/filtering.rst @@ -26,78 +26,7 @@ To see the definitions of these arguments, type the option --help. - **Median** -<<<<<<< HEAD .. code-block:: console -======= - $ rastertools filter adaptive_gaussian --help - usage: rastertools filter adaptive_gaussian [-h] --kernel_size KERNEL_SIZE - --sigma SIGMA [-o OUTPUT] - [-ws WINDOW_SIZE] - [-p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}] - [-b BANDS [BANDS ...]] [-a] - inputs [inputs ...] - - Execute the requested filter on the input files with the specified - parameters. The `inputs` argument can either be a single file or a `.lst` - file containing a list of input files. - - Arguments: - - inputs TEXT - - Input file to process (e.g. Sentinel2 L2A MAJA from THEIA). You can - provide a single file with extension ".lst" (e.g. "filtering.lst") that - lists the input files to process (one input file per line in .lst). - - Options: - --sigma INTEGER Standard deviation of the Gaussian - distribution [required] - -a, --all Process all bands - -b, --bands INTEGER List of bands to process - -p, --pad [none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap] - Pad to use around the image, default : edge(see - https://numpy.org/doc/stable/reference/generated/numpy.pad.html - for more information) - -ws, --window_size INTEGER Size of tiles to distribute processing, - default: 1024 - -o, --output TEXT Output directory to store results (by - default current directory) - --kernel_size INTEGER Kernel size of the filter function, e.g. 3 - means a square of 3x3 pixels on which the - filter function is computed (default: 8) - -h, --help Show this message and exit. - - Apply an adaptive (Local gaussian of 3x3) recursive filter on the input image - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "filtering.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - --kernel_size KERNEL_SIZE - Kernel size of the filter function, e.g. 3 means a - square of 3x3 pixels on which the filter function is - computed (default: 8) - --sigma SIGMA Standard deviation of the Gaussian distribution - (sigma) - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - -p {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap}, --pad {none,edge,maximum,mean,median,minimum,reflect,symmetric,wrap} - Pad to use around the image, default : edge (see https - ://numpy.org/doc/stable/reference/generated/numpy.pad. - html for more information) - -b BANDS [BANDS ...], --bands BANDS [BANDS ...] - List of bands to compute - -a, --all Compute all bands - - By default only first band is computed. ->>>>>>> rasterio_plugin_vsimem $ rastertools filter median --help usage: rastertools filter median [-h] --kernel_size KERNEL_SIZE [-o OUTPUT] @@ -353,7 +282,4 @@ To see the definitions of these arguments, type the option --help. - .. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg :align: center -<<<<<<< HEAD -======= -.. image:: ../_static/SENTINEL2A_20180928-105515-685_L2A_T30TYP_D-ndvi-adaptive_gaussian.jpg ->>>>>>> rasterio_plugin_vsimem + diff --git a/docs/cli/radioindice.rst b/docs/cli/radioindice.rst index fbd850d7..ab763c51 100644 --- a/docs/cli/radioindice.rst +++ b/docs/cli/radioindice.rst @@ -8,68 +8,7 @@ radioindice .. code-block:: console $ rastertools radioindice --help - usage: rastertools radioindice [-h] [-o OUTPUT] [-m] [-r ROI] - [-i INDICES [INDICES ...]] [--ndvi] [--tndvi] - [--rvi] [--pvi] [--savi] [--tsavi] [--msavi] - [--msavi2] [--ipvi] [--evi] [--ndwi] [--ndwi2] - [--mndwi] [--ndpi] [--ndti] [--ndbi] [--ri] - [--bi] [--bi2] [-nd band1 band2] - [-ws WINDOW_SIZE] - inputs [inputs ...] -<<<<<<< HEAD - - Compute a list of radiometric indices (NDVI, NDWI, etc.) on a raster image - - positional arguments: - inputs Input file to process (e.g. Sentinel2 L2A MAJA from - THEIA). You can provide a single file with extension - ".lst" (e.g. "radioindice.lst") that lists the input - files to process (one input file per line in .lst) - - optional arguments: - -h, --help show this help message and exit - -o OUTPUT, --output OUTPUT - Output dir where to store results (by default current - dir) - -m, --merge Merge all indices in the same image (i.e. one band per - indice). - -r ROI, --roi ROI Region of interest in the input image (vector) - -ws WINDOW_SIZE, --window_size WINDOW_SIZE - Size of tiles to distribute processing, default: 1024 - - Options to select the indices to compute: - -i INDICES [INDICES ...], --indices INDICES [INDICES ...] - List of indices to computePossible indices are: bi, - bi2, evi, ipvi, mndwi, msavi, msavi2, ndbi, ndpi, - ndti, ndvi, ndwi, ndwi2, pvi, ri, rvi, savi, tndvi, - tsavi - --ndvi Compute ndvi indice INSERT LINK TO CORRESPONDING DOC - --tndvi Compute tndvi indice - --rvi Compute rvi indice - --pvi Compute pvi indice - --savi Compute savi indice - --tsavi Compute tsavi indice - --msavi Compute msavi indice - --msavi2 Compute msavi2 indice - --ipvi Compute ipvi indice - --evi Compute evi indice - --ndwi Compute ndwi indice - --ndwi2 Compute ndwi2 indice - --mndwi Compute mndwi indice - --ndpi Compute ndpi indice - --ndti Compute ndti indice - --ndbi Compute ndbi indice - --ri Compute ri indice - --bi Compute bi indice - --bi2 Compute bi2 indice - -nd band1 band2, -normalized_difference band1 band2 - Compute the normalized difference of two bands defined - as parameter of this option, e.g. "-nd red nir" will - compute (red-nir)/(red+nir). See - eolab.rastertools.product.rastertype.BandChannel for - the list of bands names. Several nd options can be set - to compute several normalized differences. -======= + Usage: rio georastertools radioindice [OPTIONS] INPUTS... Compute the requested radio indices on raster data. @@ -129,7 +68,6 @@ radioindice bands names. Several nd options can be set to compute several normalized differences. -h, --help Show this message and exit. ->>>>>>> rasterio_plugin_vsimem If no indice option is explicitly set, NDVI, NDWI and NDWI2 are computed. diff --git a/src/rastertools.egg-info/SOURCES.txt b/src/rastertools.egg-info/SOURCES.txt index db45397d..b79796e0 100644 --- a/src/rastertools.egg-info/SOURCES.txt +++ b/src/rastertools.egg-info/SOURCES.txt @@ -1,4 +1,3 @@ -.coverage .gitignore AUTHORS.rst CHANGELOG.rst From c071fb0896249af2f8fd889b005cfa9ece8003b9 Mon Sep 17 00:00:00 2001 From: cadauxe Date: Tue, 6 May 2025 13:47:02 +0200 Subject: [PATCH 54/56] Delete src/eolab/georastertools/cli/.coverage --- src/eolab/georastertools/cli/.coverage | Bin 53248 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 src/eolab/georastertools/cli/.coverage diff --git a/src/eolab/georastertools/cli/.coverage b/src/eolab/georastertools/cli/.coverage deleted file mode 100644 index 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