Minor fixes in many places

development/Examples/Basics/1. Import and plot.ipynb

@@ -52,6 +52,21 @@
     "print(pontrelli_data.available_data)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7f9edb7-7488-4722-81bd-dfff2d9219b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dict = pontrelli_data.data_dict  # Get dict of paths for the data\n",
+    "\n",
+    "set_a_path = data_dict['Set_a.shp']\n",
+    "set_b_path = data_dict['Set_b.shp']\n",
+    "set_c_path = data_dict['Set_c.shp']\n",
+    "boundary_path = data_dict['Interpretation_boundary.shp']"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dd457383-8b0c-4862-93f6-5b448dcdd9e2",
@@ -69,14 +84,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_dict = pontrelli_data.data_dict  # Get dict of paths for the data\n",
-    "\n",
     "# Create the fractures and boundary objects. \n",
-    "set_a = Entities.Fractures(shp=data_dict['Set_a.shp'], set_n=1)  # to add your data put the absolute path of the shp file\n",
-    "set_b = Entities.Fractures(shp=data_dict['Set_b.shp'], set_n=2)\n",
-    "set_c = Entities.Fractures(shp=data_dict['Set_c.shp'], set_n=3)\n",
+    "set_a = Entities.Fractures(shp=set_a_path, set_n=1)  # to add your data put the absolute path of the shp file\n",
+    "set_b = Entities.Fractures(shp=set_b_path, set_n=2)\n",
+    "set_c = Entities.Fractures(shp=set_c_path, set_n=3)\n",
     "\n",
-    "boundary = Entities.Boundary(shp=data_dict['Interpretation_boundary.shp'], group_n=1)"
+    "boundary = Entities.Boundary(shp=boundary_path, group_n=1)"
    ]
   },
   {
@@ -94,7 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(set_a.entity_df)"
+    "set_a.entity_df"
    ]
   },
   {
@@ -166,7 +179,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(fracture_net.entity_df)"
+    "fracture_net.entity_df"
    ]
   },
   {

development/Examples/Basics/2. Calculate topology.ipynb

@@ -102,7 +102,7 @@
    "id": "3125d707-653c-4560-869f-1126f8026bdd",
    "metadata": {},
    "source": [
-    "### Plot the ternary diagram"
+    "### Plot the ternary diagram and return the number of nodes"
    ]
   },
   {
@@ -112,7 +112,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fracture_net.ternary_plot()"
+    "fracture_net.ternary_plot()\n",
+    "print(fracture_net.nodes.node_count)"
    ]
   },
   {

development/Examples/Basics/3. Fitting a distribution.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d3122f6d-7e05-46c2-bf97-c4a523477ae9",
    "metadata": {},
    "outputs": [],
@@ -41,24 +41,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "9c33a71a-33ff-4ed6-8ff2-e937d29f6bb1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "Calculating intersections: 1944/1945\n",
-      "\n",
-      "\n",
-      "Analyzing nodes:20982/20983\r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pontrelli_data = data.Pontrelli()\n",
     "data_dict = pontrelli_data.data_dict  # Get dict of paths for the data\n",
@@ -94,52 +80,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "b4829d42-ada1-47da-85c9-213cc98ec076",
    "metadata": {},
    "outputs": [],
    "source": [
     "fitter = Statistics.NetworkFitter(fracture_net)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bcad090e-855a-4429-b5f9-4a86432bbc83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(fitter.net_data.data)\n",
-    "print(fitter.net_data.censoring_percentage)  # get the % of censored values\n",
-    "print(fitter.net_data.mean) # get the sample mean (i.e. ignoring censoring) \n",
-    "print(fitter.net_data.std) # get the sample std\n",
-    "print(fitter.net_data.mode)  # get the sample mode\n",
-    "# ... See docs for the full list of sample statitistics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0f2f594-2423-4a5d-bad6-47e9ba78736f",
-   "metadata": {},
-   "source": [
-    "We can also estimate the empirical cumulative or survival curve using Kaplan Meier and compare it to a ecdf calculated on a \"ignoring censoring\" dataset (i.e. all the lentghs without censoring flag)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "38632b10-61ba-45e9-a0b7-d4d9377103f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "KM = fitter.net_data.ecdf\n",
-    "lengths = fitter.network_data.lengths  # get only the length data (without knowing which one are censored or not)\n",
-    "ignore_ecdf = ss.ecdf(lengths).cdf\n",
-    "plt.plot(lengths, KM, '-k')\n",
-    "plt.plot(lengths, ignore_ecdf.probabilities, '--k')\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "9764c325-a97f-47cd-ab44-a890fce3ca94",
@@ -154,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "fc7b36c7-0251-4d3a-8447-747ec6051020",
    "metadata": {},
    "outputs": [],
@@ -182,7 +130,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Plotters.matplot_stats_uniform(fitter)"
+    "Plotters.matplot_stats_uniform(fitter,sort_by='Akaike') # Plot all the models\n",
+    "\n",
+    "# Plot specific model\n",
+    "Plotters.matplot_stats_uniform(fitter,position=3,sort_by='Akaike',show_plot=True)\n",
+    "\n",
+    "# Plot specific models together\n",
+    "Plotters.matplot_stats_uniform(fitter,position=1,sort_by='Akaike',show_plot=False)\n",
+    "Plotters.matplot_stats_uniform(fitter,position=2,sort_by='Akaike',show_plot=False)\n",
+    "Plotters.matplot_stats_uniform(fitter,position=3,sort_by='Akaike',show_plot=True)"
    ]
   },
   {
@@ -225,22 +181,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "2bd844d0-ea57-444c-9c41-ec712d9b1bb0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Plotters.matplot_stats_summary(fitter, best=True,sort_by='Mean_rank')\n",
-    "Plotters.matplot_stats_summary(fitter, best=False,sort_by='Mean_rank') # plot them all"
+    "Plotters.matplot_stats_summary(fitter, position=1, sort_by='Mean_rank') # Plot specific model\n",
+    "Plotters.matplot_stats_summary(fitter, position=False, sort_by='Mean_rank') # plot them all"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53ca949e-81ee-4dbf-8463-03d04af906e1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

fracability/AbstractClasses.py

@@ -287,9 +287,11 @@ def save_shp(self, path: str):
         if not self.entity_df.empty:
             cwd = os.getcwd()
             output_path = os.path.join(cwd, path, 'output', 'shp')
-
+            style_out = os.path.join(output_path, 'qgis_style')
             if not os.path.isdir(output_path):
                 os.makedirs(output_path)
+            if not os.path.isdir(style_out):
+                os.makedirs(style_out)
 
             if self.name == 'Fractures':
                 set_n = list(set(self.entity_df['f_set']))
@@ -311,7 +313,7 @@ def save_shp(self, path: str):
             qgis_style_paths = QgisStyle().available_paths
 
             for qgis_path in qgis_style_paths:
-                shutil.copy(qgis_path, output_path)
+                shutil.copy(qgis_path, style_out)
 
         else:
             print('Cannot save an empty entity')

fracability/Entities.py

@@ -91,15 +91,12 @@ def network_object(self) -> Graph:
         return network_obj
 
     @property
-    def node_count(self) -> tuple[float, float, float, float, float]:
+    def ternary_node_count(self) -> tuple[float, float, float, float, float]:
         """
         Calculate the node proportions and precise connectivity value following Manzocchi 2002
-
-        :return: A tuple of values PI, PY, PX, PU, precise_n
+        :return: A tuple of values PI, PY, PX, PU, CI
         """
-        nodes = self.vtk_object['n_type']
-        unique, count = np.unique(nodes, return_counts=True)
-        count_dict = dict(zip(unique, count))
+        count_dict = self.node_count
 
         if 1 in count_dict.keys():
             I_nodes = count_dict[1]
@@ -153,6 +150,18 @@ def node_count(self) -> tuple[float, float, float, float, float]:
         return PI, PY, PX, PU, precise_n
 
     @property
+    def node_count(self) -> dict:
+        """
+        Calculate the node count dictionary
+
+        :return: A dictionary of nodes and their count
+        """
+
+        nodes = self.vtk_object['n_type']
+        unique, count = np.unique(nodes, return_counts=True)
+        count_dict = dict(zip(unique, count))
+        return count_dict
+    @property
     def n_censored(self) -> int:
         """ Return the number of censored nodes"""
 

fracability/Plotters.py

@@ -815,7 +815,7 @@ def matplot_stats_summary(fitter: NetworkFitter,
                           function_list: list = ['pdf', 'cdf', 'sf'],
                           table: bool = True,
                           show_plot: bool = True,
-                          best: bool = True,
+                          position: int = 1,
                           sort_by: str = 'Akaike'):
     """
     Summarize PDF, CDF and SF functions and display summary table all
@@ -832,15 +832,15 @@ def matplot_stats_summary(fitter: NetworkFitter,
 
     show_plot: Bool. If true show the plot. By default, is True
     
-    best: Bool. If true show only the best fit sorted by the sort_by field. If false show the plots for each fit.  By default is True.
+    position: int. Show the summary plot for the model at the indicated position (1 indexed) sorted by the sort_by field. If False show the plots for each fit.  By default is 1.
 
     sort_by: str. If best is True, show the best fit using the indicated column name. By default is Akaike
 
     """
     sns.set_theme()
 
-    if best:
-        names = [fitter.get_fitted_distribution_names(sort_by)[0]]
+    if position:
+        names = [fitter.get_fitted_distribution_names(sort_by)[position-1]]
     else:
         names = fitter.get_fitted_distribution_names(sort_by)
 
@@ -875,35 +875,51 @@ def matplot_stats_summary(fitter: NetworkFitter,
             plt.show()
 
 
-def matplot_stats_uniform(network_fit: NetworkFitter):
+def matplot_stats_uniform(fitter: NetworkFitter,
+                          show_plot: bool = True,
+                          position: int = 0,
+                          sort_by: str = 'Akaike'):
     """
     Confront the fitted data with the standard uniform 0,1. Following Kim 2019
 
-    :param network_fit:
-    :return:
+    Parameters
+    -------
+    fitter: Input NetworkFitter object
+
+    show_plot: Bool. If true show the plot. By default, is True
+
+    position: int. Show the summary plot for the model at the indicated position (1 indexed) sorted by the sort_by field. If False show the plots for each fit.  By default is False.
+
+    sort_by: str. If best is True, show the best fit using the indicated column name. By default is Akaike
+
     """
     #sns.set_theme()
 
-    fitted_list = network_fit.get_fitted_distribution_names()
+    if position:
+        names = [fitter.get_fitted_distribution_names(sort_by)[position-1]]
+    else:
+        names = fitter.get_fitted_distribution_names(sort_by)
 
     fig = plt.figure(num=f'Comparison plot', figsize=(13, 7))
 
     uniform_list = np.linspace(0, 1, num=10)
     uniform_cdf = uniform.cdf(uniform_list)
 
-    for fit_name in fitted_list:
-        fitter = network_fit.get_fitted_distribution(fit_name)
-        Z = fitter.cdf()
-        delta = fitter.fit_data.delta
+    for name in names:
+        network_distribution = fitter.get_fitted_distribution(name)
+        Z = network_distribution.cdf()
+        delta = network_distribution.fit_data.delta
         G_n = KM(Z, Z, delta)
-        plt.plot(Z, G_n, label=f'G_n {fit_name}') # plot the Kaplan-Meier curves with steps
+        plt.plot(Z, G_n, label=f'G_n {name}') # plot the Kaplan-Meier curves with steps
 
     setFigLinesBW(fig)
-    plt.plot(uniform_list, uniform_cdf, 'r', label='U(0, 1)')
-    plt.title('Distance to Uniform comparison')
-    plt.grid(True)
-    plt.legend()
-    plt.show()
+
+    if show_plot:
+        plt.plot(uniform_list, uniform_cdf, 'r', label='U(0, 1)')
+        plt.title('Distance to Uniform comparison')
+        plt.grid(False)
+        plt.legend()
+        plt.show()
 
 
 
@@ -934,10 +950,11 @@ def matplot_ternary(entity,
     elif entity.name == 'Nodes':
         nodes = entity
 
-    PI, PY, PX = nodes.node_count[: 3]
+    PI, PY, PX, _, CI = nodes.ternary_node_count
     points = [(PX, PI, PY)]
 
     tax.scatter(points, marker='o', color='red', label='Classification')
+    tax.annotate(f'{np.round(CI,2)}', position=[PX, PI, PY])
 
     for n in range(8):
         n += 1

fracability/__init__.py

@@ -1,4 +1,4 @@
 from os.path import dirname, join as joinpath
 
-__version__ = "1.3.1"
+__version__ = "1.3.2"
 __author__ = 'Gabriele Benedetti'