Paper hydroroot

README.md

@@ -76,7 +76,7 @@ mamba env create -f ./conda/environment.yml
 
 #### Usage
 
-See notebooks in example directory, they are listed [here](doc/example/notebook_list.ipynb) and can be played following [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openalea/hydroroot/HEAD?urlpath=%2Fdoc%2Ftree%2Fexample%2Fnotebook_list.ipynb)
+See notebooks in example directory, they are listed [here](doc/example/index.ipynb) and can be played following [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openalea/hydroroot/HEAD?urlpath=%2Fdoc%2Ftree%2Fexample%2Fnotebook_list.ipynb)
 
 
 ## Documentation

doc/example/example_cut_and_flow_pure_water.py

@@ -52,7 +52,7 @@
 import matplotlib.pyplot as plt
 
 from openalea.hydroroot.init_parameter import Parameters
-from openalea.hydroroot.solver_wrapper import pure_hydraulic_model
+from openalea.hydroroot.cut_and_flow import pure_hydraulic_model
 
 start_time = time.time()
 
@@ -95,22 +95,21 @@
 fig, axs = plt.subplots(2,2)
 axs[0,0].axis('off')
 ### Display the plot J vs Lcut
-dresults.plot.scatter('max_length', 'Jexp (uL/s)', c = 'black', ax = axs[0,1], label = 'Jexp(P) cnf')
-dresults.plot.line('max_length', 'Jv (uL/s)', c = 'purple', ax = axs[0,1], label = 'Jv(P)')
+axs[0,1].scatter(dresults['max_length'], dresults['Jexp (uL/s)'], c = 'black', label = 'Jexp(P) cnf')
+axs[0,1].plot(dresults['max_length'], dresults['Jv (uL/s)'], c = 'purple', label = 'Jv(P)')
 axs[0,1].set_xlabel('max length (m)')
 axs[0,1].set_ylabel('Jv (uL/s)')
 
 ### Plot K vs x and comparing radial k between 1st guess and optim value
-dresults.plot.scatter('x', 'K 1st', c = 'black', ax = axs[1,0], label = 'K1st')
-dresults.plot.line('x', 'K optimized', c = 'purple', ax = axs[1,0], label = 'K adjusted')
+axs[1,0].scatter(dresults['x'], dresults['K 1st'], c = 'black', label = 'K1st')
+axs[1,0].plot(dresults['x'], dresults['K optimized'], c = 'purple', label = 'K adjusted')
 axs[1,0].set_xlabel('dist. to tip (m)')
 axs[1,0].set_ylabel('K (10-9 m4/(s.Mpa))')
 
 d = pd.DataFrame({'radial':['k', 'k adjusted'], 'val':[parameter.hydro['k0'], dresults['k (10-9 m/s/MPa)'][0]]})
-d.plot.bar(x='radial', y='val', rot=0, ax = axs[1,1])
+axs[1,1].bar(x=d['radial'], height=d['val'])
 axs[1,1].set_ylabel('k (10-9 m/(s.MPa))')
 axs[1,1].xaxis.label.set_visible(False)
-axs[1,1].get_legend().remove()
 
 fig.patch.set_facecolor('lightgrey')
 fig.tight_layout()

doc/example/example_cut_and_flow_water_solute.py

@@ -63,7 +63,7 @@
 import matplotlib.pyplot as plt
 
 from openalea.hydroroot.init_parameter import Parameters
-from openalea.hydroroot.solver_wrapper import water_solute_model
+from openalea.hydroroot.cut_and_flow import water_solute_model
 
 results = {}
 g_cut = {}
@@ -134,8 +134,8 @@
 if 'Jexp(P)' in list(dresults.columns):
     d = dresults[['dp', 'Jexp(P)', 'Jv(P)']].dropna()
     d.sort_values(['dp'], inplace=True)
-    d.plot.scatter('dp', 'Jexp(P)', ax = axs[0, 0], label = 'Jexp(P)')
-    d.plot.line('dp', 'Jv(P)', ax = axs[0, 0], label = 'Jv(P)')
+    axs[0,0].scatter(d['dp'], d['Jexp(P)'], label = 'Jexp(P)')
+    axs[0,0].plot(d['dp'], d['Jv(P)'], label = 'Jv(P)')
     j = np.array(d.loc[:, ['Jv(P)', 'Jexp(P)']])
     axs[0, 0].set_ylim(j.min(),j.max())
     axs[0,0].set_xlabel('P (Mpa)')
@@ -144,28 +144,27 @@
 #Jv CnF data and fit
 if 'Jexp cnf (uL/s)' in list(dresults.columns):
     d = dresults[['max_length', 'Jexp cnf (uL/s)', 'Jv cnf (uL/s)']].dropna()
-    d.plot.scatter('max_length', 'Jexp cnf (uL/s)', ax = axs[0, 1], label = 'Jexp(P) cnf')
-    d.plot.line('max_length', 'Jv cnf (uL/s)', ax = axs[0, 1], label = 'Jv(P)')
+    axs[0,1].scatter(d['max_length'], d['Jexp cnf (uL/s)'], label = 'Jexp(P) cnf')
+    axs[0,1].plot(d['max_length'], d['Jv cnf (uL/s)'], label = 'Jv(P)')
     j = np.array(d.loc[:, ['Jv cnf (uL/s)', 'Jexp cnf (uL/s)']])
     axs[0, 1].set_ylim(j.min(),j.max())
     axs[0,1].set_xlabel('max length (m)')
     axs[0,1].set_ylabel('Jv (uL/s)')
 
 #K 1st guess and optim
 d = dresults[['x pr', 'K1st pr', 'K pr']].dropna()
-d.plot.scatter('x pr', 'K1st pr', ax = axs[1, 0], label = 'K1st')
-d.plot.line('x pr', 'K pr', ax = axs[1, 0], label = 'K adjusted')
+axs[1, 0].scatter(d['x pr'], d['K1st pr'], label = 'K1st')
+axs[1, 0].plot(d['x pr'], d['K pr'], label = 'K adjusted')
 axs[1, 0].set_ylim(min(d['K1st pr'].min(), d['K pr'].min()),
                    max(d['K1st pr'].max(), d['K pr'].max()))
 axs[1,0].set_xlabel('dist. to tip (m)')
 axs[1,0].set_ylabel('K (10-9 m4/(s.Mpa)')
 
 #radial k 1st guess and optim
 d = pd.DataFrame({'lab':['k', 'k adjusted'], 'val':[parameter.hydro['k0'], dresults['kpr'][0]]})
-d.plot.bar(x='lab', y='val', rot=0, ax = axs[1, 1])
+axs[1,1].bar(x=d['lab'], height=d['val'])
 axs[1,1].set_ylabel('k (10-9 m/(s.MPa)')
 axs[1,1].xaxis.label.set_visible(False)
-axs[1,1].get_legend().remove()
 
 fig.patch.set_facecolor('lightgrey')
 fig.tight_layout()