update analysis function

Author: samirdarouich

examples/AnalysisThermoML.ipynb

@@ -522,14 +522,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Extract density data over the composition of choline chloride for different temperatures\n",
-    "density_data = datareport.analysis_property_over_composition( component = \"choline chloride\",\n",
-    "                                                              prop_name = \"Mass density\"\n",
-    "                                                            )"
+    "# Extract density data of the whole system\n",
+    "density_data = datareport.analysis_property( prop_name = \"Mass density\",\n",
+    "                                             prop_identifier = \"\"\n",
+    "                                            )"
    ]
   },
   {
@@ -541,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -558,8 +558,8 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
-    "for _,df in density_data.groupby(\"composition\"):\n",
-    "    ax.errorbar( df[\"Temperature, K\"], df[\"mean\"], df[\"95_confidence\"]/2, label=r'$x_\\mathrm{Ch^+,Cl^-} =$' + f'{df[\"composition\"].iloc[0]}' )\n",
+    "for _,df in density_data.groupby(\"Mole fraction of choline chloride\"):\n",
+    "    ax.errorbar( df[\"Temperature, K\"], df[\"mean\"], df[\"95_confidence\"]/2, label=r'$x_\\mathrm{Ch^+,Cl^-} =$' + f'{df[\"Mole fraction of choline chloride\"].iloc[0]}' )\n",
     "ax.legend()\n",
     "ax.set_xlabel(\"Temperature / K\")\n",
     "ax.set_ylabel(\"Density / kg m$^{-3}$\")\n",

examples/AnalysisThermoML2.ipynb

@@ -119,23 +119,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "exists\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Extract molality data of carbon dioxide over the composition of choline chloride\n",
-    "molality_data = datareport.analysis_property_over_composition( component = \"choline chloride\",\n",
-    "                                                               prop_name = \"Molality\",\n",
-    "                                                               prop_identifier = \"carbon dioxide\"\n",
-    "                                                            )"
+    "# Extract molality data of carbon dioxide\n",
+    "molality_data = datareport.analysis_property( prop_name = \"Molality\",\n",
+    "                                              prop_identifier = \"carbon dioxide\"\n",
+    "                                            )"
    ]
   },
   {
@@ -147,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -164,10 +155,10 @@
    "source": [
     "fig, ax = plt.subplots( figsize = (12,8) )\n",
     "\n",
-    "for _,df in molality_data.groupby(\"composition\"):\n",
+    "for _,df in molality_data.groupby(\"Solvent: Mole fraction of choline chloride\"):\n",
     "\n",
     "    for _, dff in df.groupby(\"Temperature, K\"):\n",
-    "        ax.errorbar( dff[\"Pressure, kPa\"]*1000, dff[\"mean\"],dff[\"95_confidence\"]/2, label=r'$x_\\mathrm{Ch^+,Cl^-} =$' + f'{dff[\"composition\"].iloc[0]}; T = {dff['Temperature, K'].iloc[0]}' )\n",
+    "        ax.errorbar( dff[\"Pressure, kPa\"]*1000, dff[\"mean\"],dff[\"95_confidence\"]/2, label=r'$x_\\mathrm{Ch^+,Cl^-} =$' + f'{dff[\"Solvent: Mole fraction of choline chloride\"].iloc[0]}; T = {dff['Temperature, K'].iloc[0]}' )\n",
     "\n",
     "ax.legend()\n",
     "ax.set_xlabel(\"Pressure / Pa\")\n",

pyThermoML/core/datareport.py

@@ -480,25 +480,21 @@ def get_all_data(self, verbose: bool = True):
 
         return final
 
-    def analysis_property_over_composition(
+    def analysis_property(
         self,
-        component: str | int,
         prop_name: str,
         prop_identifier: str = "",
     ):
         """
-        Analyzes a specific property over the composition of a component in a DataReport.
+        Analyzes a specific property in a DataReport.
 
         Parameters:
-            component (str|int): The (common) name of the component, or the reg num identifier of it.
             prop_name (str): The name of the property to analyze. This can also be a substring of the property.
             prop_identifier (RegNum, optional): Component identifier for the property. Defaults to "".
 
         Returns:
             pd.DataFrame: A DataFrame containing the analyzed property values over the composition of the component.
         """
-        # Get reg_num identifier for component
-        identifier = self.get_reg_num(component)
 
         # Get reg_num for property identifier
         property_identifier = self.get_reg_num(prop_identifier)
@@ -518,17 +514,23 @@ def analysis_property_over_composition(
             # Check if property exists in pure of mixture data
             if pomd.property_exists(prop_name, property_identifier):
 
-                # Get composition
-                composition = pomd.get_composition(identifier, id_dict)
 
                 # Get properties
                 prop_df = pomd.get_property()
+
+                # Get variables
                 variables = pomd.get_variables()
 
                 for variable in variables:
                     prop_df[variable["type"]] = variable["values"]
-
-                prop_df["composition"] = [composition] * prop_df.shape[0]
+                
+                # Get constraints
+                constraints = pomd.get_constraints()
+
+                for const in constraints:
+                    txt = f'{const["type"]} of'
+                    txt += f' {id_dict[const["component_identifier"].n_org_num][0] if const["component_identifier"].n_org_num else "system"}'
+                    prop_df[txt] = [const["value"]] * prop_df.shape[0]
 
                 final.append(prop_df)