Merge pull request #98 from neurolib-dev/example/1.3-bifurcation-diagram-aln

example-1.3

Author: caglorithm

.travis.yml

@@ -36,14 +36,13 @@ jobs:
         homebrew:
           update: true
           packages:
-            - python3
+            - [email protected]
             - llvm
             - libomp
       before_install:
-        - brew link --overwrite python
         - export PATH=/usr/local/opt/llvm/bin:$PATH
         - pip3 install virtualenv
-        - virtualenv -p python3 ~/venv
+        - virtualenv -p /usr/local/opt/[email protected]/bin/python3 ~/venv
         - source ~/venv/bin/activate
     # - stage: test
     #   os: windows

codecov.yml

@@ -7,4 +7,9 @@ coverage:
     - "neurolib/utils/devutils.py"
     - "neurolib/utils/atlases.py"
   status:
+    project:
+      default:
+        enabled: yes
+        target: auto
+        threshold: 5%
     patch: off

examples/example-0.5-aln-external-stimulus.ipynb

@@ -381,7 +381,7 @@ def loadDfResults(self, filename=None, trajectoryName=None):
         :type trajectoryName: str, optional
         """
         # chose HDF file to load
-        filename = self.HDF_FILE or filename
+        filename = filename or self.HDF_FILE
         self.pypetTrajectory = pu.loadPypetTrajectory(filename, trajectoryName)
         self.nResults = len(self.pypetTrajectory.f_get_run_names())
 

examples/example-1.3-aln-bifurcation-diagram.ipynb

@@ -1,3 +1,5 @@
+import os
+
 import numpy as np
 import pandas as pd
 
@@ -11,7 +13,7 @@
 from scipy import stats
 
 from ...utils import functions as func
-
+from ...utils import paths as paths
 
 def plotExplorationResults(
     dfResults,
@@ -26,6 +28,7 @@ def plotExplorationResults(
     one_figure=False,
     contour=None,
     alpha_mask=None,
+    savename=None,
     **kwargs,
 ):
     """
@@ -136,29 +139,37 @@ def plotExplorationResults(
             contour_alpha = kwargs["contour_alpha"] if "contour_alpha" in kwargs else 1
             contour_kwargs = kwargs["contour_kwargs"] if "contour_kwargs" in kwargs else dict()
 
-            # check if this is a dataframe
-            if isinstance(contour, pd.DataFrame):
-                contourPlotDf(
-                    contour,
-                    color=contour_color,
-                    ax=ax,
-                    levels=contour_levels,
-                    alpha=contour_alpha,
-                    contour_kwargs=contour_kwargs,
-                )
-            # if it's a string, take that value as the contour plot value
-            elif isinstance(contour, str):
-                df_contour = df.pivot_table(values=contour, index=par2, columns=par1, dropna=False)
-                if nan_to_zero:
-                    df_contour = df_contour.fillna(0)
-                contourPlotDf(
-                    df_contour,
-                    color=contour_color,
-                    ax=ax,
-                    levels=contour_levels,
-                    alpha=contour_alpha,
-                    contour_kwargs=contour_kwargs,
-                )
+            def plot_contour(contour, contour_color, contour_levels, contour_alpha, contour_kwargs):
+                # check if this is a dataframe
+                if isinstance(contour, pd.DataFrame):
+                    contourPlotDf(
+                        contour,
+                        color=contour_color,
+                        ax=ax,
+                        levels=contour_levels,
+                        alpha=contour_alpha,
+                        contour_kwargs=contour_kwargs,
+                    )
+                # if it's a string, take that value as the contour plot value
+                elif isinstance(contour, str):
+                    df_contour = df.pivot_table(values=contour, index=par2, columns=par1, dropna=False)
+                    if nan_to_zero:
+                        df_contour = df_contour.fillna(0)
+                    contourPlotDf(
+                        df_contour,
+                        color=contour_color,
+                        ax=ax,
+                        levels=contour_levels,
+                        alpha=contour_alpha,
+                        contour_kwargs=contour_kwargs,
+                    )
+
+            # check if contour is alist of variables, e.g. ["max_output", "domfr"]
+            if isinstance(contour, list):
+                for ci in range(len(contour)):
+                    plot_contour(contour[ci], contour_color[ci], contour_levels[ci], contour_alpha[ci], contour_kwargs[ci])        
+            else:
+                plot_contour(contour, contour_color, contour_levels, contour_alpha, contour_kwargs)        
 
         # colorbar
         if one_figure == False:
@@ -178,14 +189,21 @@ def plotExplorationResults(
         if not isinstance(i, tuple):
             i = (i,)
         if by != ["_by"]:
-            ax.set_title(" ".join([f"{bb}={bi}" for bb, bi in zip(by_label, i)]))
+            title = " ".join([f"{bb}={bi}" for bb, bi in zip(by_label, i)])
+            ax.set_title(title)
         if one_figure == False:
+            if savename:
+                save_fname = os.path.join(paths.FIGURES_DIR, f"{title}_{savename}")
+                plt.savefig(save_fname)            
             plt.show()
         else:
             axi += 1
 
     if one_figure == True:
         plt.tight_layout()
+        if savename:
+            save_fname = os.path.join(paths.FIGURES_DIR, f"{savename}")
+            plt.savefig(save_fname)
         plt.show()
 
 
@@ -200,15 +218,18 @@ def contourPlotDf(
     clabel=False,
     **contour_kwargs,
 ):
-    levels = levels or [0, 1.0001]
+    levels = levels or [0, 1]
     Xi, Yi = np.meshgrid(dataframe.columns, dataframe.index)
     ax = ax or plt
 
     if contourf:
         contours = plt.contourf(Xi, Yi, dataframe, 10, levels=levels, alpha=alpha, cmap="plasma")
 
+    # unpack, why necessary??
+    contour_kwargs = contour_kwargs["contour_kwargs"]
+    
     contours = ax.contour(
-        Xi, Yi, dataframe, colors=color, linestyles="solid", levels=levels, zorder=1, alpha=alpha, **contour_kwargs,
+        Xi, Yi, dataframe, colors=color, levels=levels, zorder=1, alpha=alpha, **contour_kwargs,
     )
 
     clabel = contour_kwargs["clabel"] if "clabel" in contour_kwargs else False

neurolib/optimize/exploration/exploration.py

@@ -18,7 +18,7 @@ def getTrajectorynamesInFile(filename):
     :rtype: list[str]
     """
     assert pathlib.Path(filename).exists(), f"{filename} does not exist!"
-    hdf = h5py.File(filename)
+    hdf = h5py.File(filename, "r")
     all_traj_names = list(hdf.keys())
     hdf.close()
     return all_traj_names

neurolib/optimize/exploration/explorationUtils.py

@@ -11,7 +11,7 @@
 
 setuptools.setup(
     name="neurolib",
-    version="0.5.6",
+    version="0.5.7",
     description="Easy whole-brain neural mass modeling",
     long_description=long_description,
     long_description_content_type="text/markdown",

neurolib/utils/pypetUtils.py

@@ -3,7 +3,7 @@
 """
 
 import unittest
-
+import numba
 import numpy as np
 import xarray as xr
 from jitcdde import jitcdde_input

setup.py

@@ -150,7 +150,7 @@ def test_init(self):
         self.assertEqual(wc.default_output, f"q_mean_{EXC}")
 
     def test_run(self):
-        wc = WilsonCowanNetwork(self.SC, self.DELAYS)
+        wc = WilsonCowanNetwork(self.SC, self.DELAYS, exc_seed=SEED, inh_seed=SEED)
         all_results = []
         for backend, noise_func in BACKENDS_TO_TEST.items():
             result = wc.run(DURATION, DT, noise_func(ZeroInput(DURATION, DT, wc.num_noise_variables)), backend=backend,)