WRONG BRANCH Feature: Change logging to loguru

.github/workflows/test-notebooks.yml

@@ -42,7 +42,7 @@ jobs:
           done
 
       - name: Run notebook tests
-        run: pytest --nbval-lax --current-env tests/
+        run: pytest --nbval-lax --nbval-current-env tests/
 
       - name: Upload executed notebooks on failure
         if: failure()

.gitignore

@@ -30,5 +30,8 @@ __pycache__
 *.Identifier
 !requirements.txt
 .jupyter_cache
+
+# Ignore log files
+tests/**/*.log
 tests/multiprocess_log.txt
 .vscode

pyAMARES/__init__.py

@@ -1,8 +1,7 @@
 __author__ = "Jia Xu, MR Research Facility, University of Iowa"
-__version__ = "0.3.33"
+__version__ = "0.3.34dev"
 
-# print("Current pyAMARES version is %s" % __version__)
-# print("Author: %s" % __author__)
+# print(f"Author: {__author__)}"
 
 from .fileio import *  # noqa: F403
 from .kernel import *  # noqa: F403

pyAMARES/fileio/readfidall.py

@@ -2,12 +2,9 @@
 
 import mat73
 import numpy as np
+from loguru import logger
 from scipy import io
 
-from ..libs.logger import get_logger
-
-logger = get_logger(__name__)
-
 
 def is_mat_file_v7_3(filename):
     with open(filename, "rb") as f:
@@ -132,10 +129,9 @@ def read_fidall(filename):
 
     if len(data.shape) != 1:
         logger.warning(
-            "Note pyAMARES.fitAMARES only fits 1D MRS data, however, your data shape is {data.shape}. Is it MRSI or raw MRS data that needs to be coil-combined?"
+            f"Note pyAMARES.fitAMARES only fits 1D MRS data, however, your data shape is {data.shape}. Is it MRSI or raw MRS data that needs to be coil-combined?"
         )
 
-    # print("data.shape=", data.shape)
-    logger.debug("data.shape=%s", data.shape)
+        logger.debug(f"data.shape={data.shape}")
 
     return header, data

pyAMARES/fileio/readmat.py

@@ -1,12 +1,10 @@
 import mat73
 import numpy as np
+from loguru import logger
 from scipy import io
 
-from ..libs.logger import get_logger
 from .readfidall import is_mat_file_v7_3
 
-logger = get_logger(__name__)
-
 
 def readmrs(filename):
     """
@@ -43,28 +41,23 @@ def readmrs(filename):
         - For MATLAB files, both traditional (.mat) and V7.3 (.mat) files are supported, but the variable must be named ``fid`` or ``data``.
     """
     if filename.endswith("csv"):
-        # print("Try to load 2-column CSV")
         logger.debug("Try to load 2-column CSV")
         data = np.loadtxt(filename, delimiter=",")
         data = data[:, 0] + 1j * data[:, 1]
     elif filename.endswith("txt"):
-        # print("Try to load 2-column ASCII data")
         logger.debug("Try to load 2-column ASCII data")
         data = np.loadtxt(filename, delimiter=" ")
         data = data[:, 0] + 1j * data[:, 1]
     elif filename.endswith("npy"):
-        # print("Try to load python NPY file")
         logger.debug("Try to load python NPY file")
         data = np.load(filename)
     elif filename.endswith("mat"):
         if is_mat_file_v7_3(filename):
-            # print("Try to load Matlab V7.3 mat file with the var saved as fid or data")
             logger.debug(
                 "Try to load Matlab V7.3 mat file with the var saved as fid or data"
             )
             matdic = mat73.loadmat(filename)
         else:
-            # print("Try to load Matlab mat file with the var saved as fid or data")
             logger.debug(
                 "Try to load Matlab mat file with the var saved as fid or data"
             )
@@ -87,6 +80,5 @@ def readmrs(filename):
             "Note pyAMARES.fitAMARES only fits 1D MRS data, however, your data shape is {data.shape}. Is it MRSI or raw MRS data that needs to be coil-combined?"
         )
 
-    # print("data.shape=", data.shape)
-    logger.debug("data.shape=%s", data.shape)
+    logger.debug(f"data.shape={data.shape}")
     return data

pyAMARES/fileio/readnifti.py

@@ -1,10 +1,7 @@
 import argparse
 
 import numpy as np
-
-from ..libs.logger import get_logger
-
-logger = get_logger(__name__)
+from loguru import logger
 
 
 def read_nifti(filename):

pyAMARES/kernel/PriorKnowledge.py

@@ -5,13 +5,16 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+
+# Disable automatic string dtype inference for pandas 3.0 and above
+if hasattr(pd.options, "future") and hasattr(pd.options.future, "infer_string"):
+    pd.options.future.infer_string = False
+
 from lmfit import Parameters
+from loguru import logger
 
-from ..libs.logger import get_logger
 from .fid import fft_params
 
-logger = get_logger(__name__)
-
 
 def safe_convert_to_numeric(x):
     try:
@@ -181,6 +184,9 @@ def unitconverter(df_ini, MHz=120.0):
         pandas.DataFrame: A DataFrame with converted unit values in specified rows.
     """
     df = deepcopy(df_ini)
+    df = df.apply(
+        pd.to_numeric, errors="raise", downcast="float"
+    )  # By this point the values should only be numeric
     if "chemicalshift" in df.index:
         df.loc["chemicalshift", df.notna().loc["chemicalshift"]] *= MHz
 
@@ -189,7 +195,7 @@ def unitconverter(df_ini, MHz=120.0):
 
     if "phase" in df.index:
         df.loc["phase", df.notna().loc["phase"]] = np.deg2rad(
-            df.loc["phase"][df.notna().loc["phase"]].astype(float)
+            df.loc["phase"][df.notna().loc["phase"]]
         )
 
     return df
@@ -262,30 +268,27 @@ def assert_peak_format(input_str):
     if re.search(r"\.\d+$", input_str):
         logger.error(msg)
         raise ValueError(
-            "The peak name %s cannot end with a floating-point number!" % input_str
+            f"The peak name {input_str} cannot end with a floating-point number!"
         )
     if re.search(r"\d+[\D]", input_str) or re.search(r"^\d+", input_str):
         logger.error(msg)
         raise ValueError(
-            "The peak name %s cannot contain numbers at the beginning or in the middle!"
-            % input_str
+            f"The peak name {input_str} cannot contain numbers at the beginning or in the middle!"
         )
 
 
 def find_header_row(filename, comment_char="#"):
     """Determine the index of the first non-commented line."""
     with open(filename, "r") as file:
-        logger.info("Checking comment lines in the prior knowledge file")
+        logger.debug("Checking comment lines in the prior knowledge file")
         for i, line in enumerate(file):
             if "#" in line:
-                logger.info("Comment: in line %d: %s", i, line)
+                logger.debug(f"Comment: in line {i}: {line}")
     with open(filename, "r") as file:
         for i, line in enumerate(file):
             processedline = line.replace('"', "").replace("'", "").strip()
             if not processedline.startswith(comment_char):
                 return i
-            # else:
-            #     print("Comment:", processedline)
     return None  # Return None if all lines are comments or file is empty
 
 
@@ -326,41 +329,36 @@ def generateparameter(
     else:
         raise NotImplementedError("file format must be Excel (xlsx) or CSV!")
 
-    # Compatible with pandas both older and newer than 2.1.0
-    pk = (
-        pk.map(safe_convert_to_numeric)
-        if hasattr(pk, "map")
-        else pk.applymap(safe_convert_to_numeric)
-    )  # To be compatible with CSV
+    def backward_compatible_map(df, func):
+        # Check if the newer 'map' method exists (pandas >= 2.1.0)
+        if hasattr(df, "map"):
+            return df.map(func)
+        # Fallback to the older 'applymap' (pandas < 2.1.0)
+        elif hasattr(df, "applymap"):
+            return df.applymap(func)  # type: ignore
+        else:
+            raise AttributeError(
+                "Pandas DataFrame has neither 'map' nor 'applymap' method."
+            )
 
+    pk = backward_compatible_map(pk, safe_convert_to_numeric)
     peaklist = pk.columns.to_list()  # generate a peak list directly from the
     [assert_peak_format(x) for x in peaklist]
     dfini = extractini(pk, MHz=MHz)  # Parse initial values
     dfini2 = unitconverter(
         dfini, MHz=MHz
     )  # Convert ppm to Hz, convert FWHM to dk, convert degree to radians.
-    # print(f"{dfini2=}")
     df_lb, df_ub = parse_bounds(pk)  # Parse bounds
     df_expr = extract_expr(pk, MHz=MHz)  # Parse expression
     df_lb2 = unitconverter(df_lb, MHz=MHz)
     df_ub2 = unitconverter(df_ub, MHz=MHz)
     # Make sure the bounds are numeric
-    # Compatible with pandas both older and newer than 2.1.0
-    df_lb2 = (
-        df_lb2.map(safe_convert_to_numeric)
-        if hasattr(df_lb2, "map")
-        else df_lb2.applymap(safe_convert_to_numeric)
-    )
-    df_ub2 = (
-        df_ub2.map(safe_convert_to_numeric)
-        if hasattr(df_ub2, "map")
-        else df_ub2.applymap(safe_convert_to_numeric)
-    )
+    df_lb2 = backward_compatible_map(df_lb2, safe_convert_to_numeric)
+    df_ub2 = backward_compatible_map(df_ub2, safe_convert_to_numeric)
     if g_global is False:
         logger.debug(
-            "Parameter g will be fit with the initial value set in the file %s" % fname
+            f"Parameter g will be fit with the initial value set in the file {fname}"
         )
-        # print(f"Parameter g will be fit with the initial value set in the file {fname}")
     allpara = Parameters()
     for peak in dfini2.columns:
         for i, para in enumerate(paramter_prefix):
@@ -492,27 +490,21 @@ def initialize_FID(
     deadtime = float(deadtime)
     dwelltime = 1.0 / sw
     if truncate_initial_points > 0:
-        logger.info(
-            "Truncating %i points from the beginning of the FID signal"
-            % truncate_initial_points
+        logger.debug(
+            f"Truncating {truncate_initial_points} points from the beginning of the FID signal"
         )
         deadtime_old = deadtime * 1.0
         deadtime = deadtime + truncate_initial_points * dwelltime
         fid = fid[truncate_initial_points:]
-        logger.info(
-            "The deadtime is changing from %f seconds to %f seconds"
-            % (deadtime_old, deadtime)
+        logger.debug(
+            f"The deadtime is changing from {deadtime} seconds to {deadtime_old} seconds"
         )
     fidpt = len(fid)
     # TD = fidpt * 2
     # at = TD / (2 * sw)
 
     ppm = np.linspace(-sw / 2, sw / 2, fidpt) / np.abs(MHz)
     Hz = np.linspace(-sw / 2, sw / 2, fidpt)
-    # print(f"{sw=}")
-    # print(f"{np.max(ppm)=} {np.min(ppm)=}")
-    # print(f"{np.max(Hz)=} {np.min(Hz)=}")
-    # print(f"{-sw/2=}")
 
     opts = argparse.Namespace()
     opts.deadtime = deadtime
@@ -523,7 +515,7 @@ def initialize_FID(
         # This must be done before the shifting FID for carrier.
         fid = np.conj(fid)
     if carrier != 0:
-        logger.info("Shift FID so that center frequency is at %s ppm!" % carrier)
+        logger.debug(f"Shift FID so that center frequency is at {carrier} ppm!")
         fid = fid * np.exp(1j * 2 * np.pi * carrier * MHz * opts.timeaxis)
         # ppm = ppm + carrier
         # Hz = Hz + carrier / np.abs(MHz)
@@ -584,7 +576,7 @@ def initialize_FID(
             timeaxis=opts.timeaxis, params=opts.initialParams, fid=True
         )
         if ppm_offset != 0:
-            logger.info("Shifting the ppm by ppm_offset=%2.2f ppm" % ppm_offset)
+            logger.debug(f"Shifting the ppm by ppm_offset={ppm_offset:.2f} ppm")
             for p in opts.initialParams:
                 if p.startswith("freq"):
                     hz_offset = opts.ppm_offset * opts.MHz
@@ -597,25 +589,17 @@ def initialize_FID(
                     ):  # Check if there's an upper bound set
                         opts.initialParams[p].max += hz_offset
                     logger.debug(
-                        "before opts.initialParams[%s].value=%s"
-                        % (p, opts.initialParams[p].value)
+                        f"before opts.initialParams[{p}].value={opts.initialParams[p].value}"
                     )
                     logger.debug(
-                        "new value should be opts.initialParams[%s].value + opts.ppm_offset * opts.MHz=%s"
-                        % (p, opts.initialParams[p].value + opts.ppm_offset * opts.MHz)
+                        f"new value should be opts.initialParams[{p}].value + opts.ppm_offset * opts.MHz="
+                        f"{opts.initialParams[p].value + opts.ppm_offset * opts.MHz}"
                     )
-
-                    # print(f"before {opts.initialParams[p].value=}")
-                    # print(
-                    #     f"new value should be {opts.initialParams[p].value + opts.ppm_offset * opts.MHz=}"
-                    # )
                     opts.initialParams[p].value = (
                         opts.initialParams[p].value + hz_offset
                     )
-                    # print(f"after {opts.initialParams[p].value=}")
                     logger.debug(
-                        "after opts.initialParams[%s].value=%s"
-                        % (p, opts.initialParams[p].value)
+                        f"after opts.initialParams[{p}].value={opts.initialParams[p].value}"
                     )
 
         opts.allpara = opts.initialParams  # obsolete API, will be removed
@@ -635,12 +619,12 @@ def initialize_FID(
         plt.xlabel("ppm")
         plt.show()
         if priorknowledgefile is not None:
-            logger.info("Printing the Prior Knowledge File %s" % priorknowledgefile)
+            logger.debug(f"Printing the Prior Knowledge File {priorknowledgefile}")
             try:
                 from IPython.display import display
 
                 display(opts.PK_table)  # display table
             except ImportError:
-                logger.info(opts.PK_table)
+                logger.debug(opts.PK_table)
 
     return opts