Joss review edits

examples/example_3_model_properties.ipynb

@@ -51,7 +51,7 @@
     "\n",
     "path = os.getcwd()\n",
     "\n",
-    "%matplotlib widget\n",
+    "%matplotlib inline\n",
     "%load_ext autoreload\n",
     "%autoreload 2"
    ]

examples/example_4_porespy_analysis.ipynb

@@ -16,6 +16,15 @@
     "Maruti K. Mudunuru"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install tqdm # For modules that use tdqm loading bars in notebook environments"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -37,7 +46,7 @@
     "\n",
     "path = os.getcwd()\n",
     "\n",
-    "%matplotlib widget\n",
+    "%matplotlib inline\n",
     "%load_ext autoreload\n",
     "%autoreload 2"
    ]

examples/example_5_vtk_exporting.ipynb

@@ -16,6 +16,15 @@
     "Maruti K. Mudunuru"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install tqdm # For modules that use tdqm loading bars in notebook environments"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -40,9 +49,10 @@
     "\n",
     "path = os.getcwd()\n",
     "\n",
-    "%matplotlib widget\n",
+    "%matplotlib inline\n",
     "%load_ext autoreload\n",
-    "%autoreload 2"
+    "%autoreload 2\n",
+    "os.makedirs('example_outputs', exist_ok=True) # Ensures output folder exists"
    ]
   },
   {

requirements.txt

@@ -5,12 +5,11 @@ numpy
 opencv-python
 openpnm
 porespy
-pypardiso
 scikit-image
 scipy
 yapf
 svglib
-reportlab
+reportlab==3.6.13
 cairosvg
 gstools
 jaxlib

src/flow/plotting_results.py

@@ -79,63 +79,46 @@ def plot_2d_permeability_seg(X, Y, perm_field, results_dir):
     plt.savefig(results_dir + filename, dpi=dpi)
 
 
-def plot_boundary_conditions(x_b1, y_b1, bc_1, x_b2, y_b2, bc_2, x_b3, y_b3,
-                             bc_3, x_b4, y_b4, bc_4, x_c, y_c, results_dir):
+def plot_boundary_conditions(x_b1, y_b1, bc_1, x_b2, y_b2, bc_2,
+                              x_b3, y_b3, bc_3, x_b4, y_b4, bc_4,
+                              x_c, y_c, results_dir):
     plt.figure(figsize=(width, height), dpi=dpi)
-    s = 10
-    plt.scatter(x_b1,
-                y_b1,
-                c=bc_1,
-                marker='x',
-                vmin=0,
-                vmax=2,
-                label='P[0,y] = 0',
-                cmap=cm.jet,
-                s=s)
-    plt.scatter(x_b2,
-                y_b2,
-                c=bc_2,
-                marker='^',
-                vmin=0,
-                vmax=2,
-                label='P[1,y] = y',
-                cmap=cm.jet,
-                s=s)
-    plt.scatter(x_b3,
-                y_b3,
-                c=bc_3,
-                marker='*',
-                vmin=0,
-                vmax=2,
-                label='$\\partial P/\\partial y[x,0] = 0$',
-                cmap=cm.jet,
-                s=s)
-    plt.scatter(x_b4,
-                y_b4,
-                c=bc_4,
-                marker='o',
-                vmin=0,
-                vmax=2,
-                label='$\\partial P/\\partial y[x,1] = 0$',
-                cmap=cm.jet,
-                s=s)
-    s = 5
-    plt.scatter(x_c,
-                y_c,
-                c='k',
-                marker='.',
-                alpha=0.5,
-                label='Collocation points',
-                s=s)
+
+    # Compute vmin and vmax across all boundary condition values
+    all_bc_values = np.concatenate([bc_1, bc_2, bc_3, bc_4])
+    vmin = np.min(all_bc_values)
+    vmax = np.max(all_bc_values)
+
+    s = 3
+
+    # Plot the first boundary condition and link it to the colorbar
+    sc = plt.scatter(x_b1, y_b1, c=bc_1, marker='x', vmin=vmin, vmax=vmax,
+                     label='P[0,y]', cmap=cm.jet, s=s)
+
+    # Remaining boundary conditions
+    plt.scatter(x_b2, y_b2, c=bc_2, marker='^', vmin=vmin, vmax=vmax,
+                label='P[1,y]', cmap=cm.jet, s=s)
+    plt.scatter(x_b3, y_b3, c=bc_3, marker='*', vmin=vmin, vmax=vmax,
+                label='$\\partial P/\\partial y[x,0]$', cmap=cm.jet, s=s)
+    plt.scatter(x_b4, y_b4, c=bc_4, marker='o', vmin=vmin, vmax=vmax,
+                label='$\\partial P/\\partial y[x,1]$', cmap=cm.jet, s=s)
+
+    # Collocation points
+    plt.scatter(x_c, y_c, c='k', marker='.', alpha=0.5,
+                label='Collocation points', s=1)
+
     # Labels and colorbar
     plt.xlabel('$X$')
     plt.ylabel('$Y$')
-    cbar = plt.colorbar(mpl.cm.ScalarMappable(norm=mpl.colors.Normalize(
-        vmin=0, vmax=2),
-                                              cmap=cm.jet),
-                        aspect=30)
+    cbar = plt.colorbar(sc, aspect=30)
     cbar.set_label('Pressure (kPa)')
+
+    #plt.legend(fontsize=6) 
+    # plt.legend(fontsize=6, loc='best', frameon=True,
+    #        title='Legend', title_fontsize=6)
+    plt.legend(fontsize=6, loc='upper left', bbox_to_anchor=(0.50, 0.95))
     plt.tight_layout()
+
     figure_name = 'bcs_collocs'
     plt.savefig(results_dir + figure_name + figure_format,
                 dpi=dpi,
@@ -144,53 +127,6 @@ def plot_boundary_conditions(x_b1, y_b1, bc_1, x_b2, y_b2, bc_2, x_b3, y_b3,
     plt.show()
 
 
-def save_legend_as_image(results_dir):
-    plt.figure(figsize=(width, height), dpi=dpi)
-    fig, ax = plt.subplots()
-    handles = [
-        mlines.Line2D([], [],
-                      color='red',
-                      marker='x',
-                      linestyle='None',
-                      markersize=10,
-                      label='P[0,y] = 0'),
-        mlines.Line2D([], [],
-                      color='blue',
-                      marker='^',
-                      linestyle='None',
-                      markersize=10,
-                      label='P[1,y] = y'),
-        mlines.Line2D([], [],
-                      color='green',
-                      marker='*',
-                      linestyle='None',
-                      markersize=10,
-                      label='$\\partial P/\\partial y[x,0] = 0$'),
-        mlines.Line2D([], [],
-                      color='purple',
-                      marker='o',
-                      linestyle='None',
-                      markersize=10,
-                      label='$\\partial P/\\partial y[x,1] = 0$'),
-        mlines.Line2D([], [],
-                      color='black',
-                      marker='.',
-                      linestyle='None',
-                      markersize=10,
-                      label='Collocation points')
-    ]
-    legend = ax.legend(handles=handles,
-                       loc='upper right',
-                       fontsize=fontsize,
-                       fancybox=True)
-    plt.tight_layout()
-    ax.axis('off')
-    fig.canvas.draw()
-    bbox = legend.get_window_extent().transformed(
-        fig.dpi_scale_trans.inverted())
-    filename = 'bc_colloc_legends.png'
-    fig.savefig(results_dir + filename, dpi=dpi, bbox_inches=bbox)
-    plt.close()
 
 
 #%% plot for pinn loss
@@ -232,6 +168,7 @@ def plot_2d_pressure_distribution(X, Y, pressure, cbar_lebel, title, fig_name,
     plt.ylabel('$Y$')
     filename = fig_name + '.png'
     plt.savefig(results_dir + filename, dpi=dpi)
+    plt.show()
 
 
 def plot_pressure_along_x(X, Y, pressure, title, fig_name, results_dir):
@@ -298,35 +235,6 @@ def plot_2d_pressure_distribution_masked(X, Y, pressure, perm_field,
     plt.savefig(results_dir + filename, dpi=dpi)
     # Set masked values to zero
     pressure_with_zeros = masked_pressure.filled(0)
-
+    plt.show()
     return pressure_with_zeros
 
-
-def plot_gradient_field(X, Y, grad_head_x, grad_head_y, fig_title, scale,
-                        results_dir):
-    plt.figure(figsize=(width, height), dpi=dpi)
-
-    # Calculate the magnitude of the gradient vectors
-    magnitude = np.sqrt(grad_head_x**2 + grad_head_y**2)
-
-    # Determine vmin and vmax based on the actual values of grad_head_x and grad_head_y
-    vmin = min(np.min(grad_head_x), np.min(grad_head_y))
-    vmax = max(np.max(grad_head_x), np.max(grad_head_y))
-
-    # Plot gradient vectors with color range based on actual vmin and vmax
-    quiver = plt.quiver(X,
-                        Y,
-                        grad_head_x,
-                        grad_head_y,
-                        magnitude,
-                        scale=scale,
-                        cmap='jet')
-    quiver.set_clim(vmin, vmax)  # Set the color range without normalization
-
-    plt.colorbar(quiver, label='Gradient magnitude')
-    plt.title(f'{fig_title}')
-    plt.xlabel('X (m)')
-    plt.ylabel('Y (m)')
-    plt.xlim([X.min(), X.max()])
-    plt.ylim([Y.min(), Y.max()])
-    plt.show()

src/pore2chip/export.py

@@ -21,8 +21,8 @@ def network2svg(
     throat_random_debug=False,
     no_throats=False,
     middle_pores=None,
-    disconnected=None
-):  # Boolean to draw perp. lines for throat placement (debugging)
+    disconnected=None,
+    throat_vector_thres=None): # Boolean to draw perp. lines for throat placement (debugging) 
     r"""
     Create an SVG file from an OpenPNM network. 
     The network2svg function converts an OpenPNM network into an SVG image. 
@@ -210,6 +210,18 @@ def network2svg(
             pore1_coords = [pore1_x, (-pore1_y) + d2]
             pore2_coords = [pore2_x, (-pore2_y) + d2]
 
+            # If small throats are vectors
+            if throat_vector_thres is not None:
+                if generated_network['throat.diameter'][throat_index] < throat_vector_thres:
+                    design.append(
+                        dr.Line(pore1_coords[0],
+                                pore1_coords[1],
+                                pore2_coords[0],
+                                pore2_coords[1],
+                                stroke='red',
+                                stroke_width=1))
+                    continue
+
             # Distance between the two pores
             distance = math.dist(pore1_coords, pore2_coords)
 

src/pore2chip/filter_im.py

@@ -25,6 +25,7 @@ def filter_single(image,
         thresh (int): Threshold value. If not given, then Otsu's threshold is used 
         gauss (int): Radius of Gaussian Blur. Default is 5 
         invert (Boolean): Inverts pixel values of image 
+        returnthres (Boolean): Returns the threshold value calculated using Otsu 
 
     Returns:
         2D numpy array: Filtered image

tests/test_export.py

@@ -12,7 +12,7 @@
 
 # Set up the module path (I am in tests folder)
 # Modify these lines if the pore2chip package is not in the PYTHONPATH
-mod_path = Path("__file__").resolve().parents[2]
+mod_path = Path(os.getcwd()).resolve().parent
 sys.path.append(os.path.abspath(mod_path))
 
 # Importing specific functions for exporting network data to SVG and DXF formats

tests/test_filter_im.py

@@ -71,8 +71,6 @@ def main():
     ax[2].imshow(filtered2)
     ax[3].imshow(filtered3)
     plt.show()
-    plt.waitforbuttonpress()  # Wait for a user interaction before closing
-    plt.close()
 
     ### Advanced Tests ###
 
@@ -93,8 +91,6 @@ def main():
     ax[2].imshow(filtered_stack[1])
     ax[3].imshow(filtered_stack[2])
     plt.show()
-    plt.waitforbuttonpress()  # Wait for user interaction before closing
-    plt.close()
 
 
 if __name__ == "__main__":