Merge pull request #37 from MetaSys-LISBP/dev

Dev
MetaSys-LISBP · Jun 7, 2023 · 3e648b5 · 3e648b5
2 parents 2dd62d7 + 0eb2a55
commit 3e648b5
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Showing 10 changed files with 70 additions and 89 deletions.
diff --git a/data/model_5_test_data.tsv b/data/model_5_test_data.tsv
diff --git a/physiofit/base/io.py b/physiofit/base/io.py
@@ -318,6 +318,10 @@ def output_recap(self, export_path: str):
                     f"detected at indice {idx}"
                 )
         final_df = concat(self.multiple_experiments)
+        final_df = final_df.reset_index()
+        final_df[["experiments", "parameter name"]] = final_df["index"].str.split(" ", expand=True)
+        final_df.set_index(["experiments", "parameter name"], inplace=True)
+        final_df.drop("index", axis=1, inplace=True)
         final_df.to_csv(f"{str(Path(export_path))}/summary.csv")
 
 

diff --git a/physiofit/data/test-data_single_exp.tsv b/physiofit/data/test-data_single_exp.tsv
@@ -0,0 +1,17 @@
+experiments	time	X	Glc	Ace
+A	1.694722222	6.20E-02	NA	NA
+A	2.300277778	8.62E-02	NA	NA
+A	2.698333333	0.110376	NA	NA
+A	3.436111111	0.178416	NA	NA
+A	3.821111111	0.208656	NA	NA
+A	4.201111111	0.3024	NA	NA
+A	4.598055556	0.378	NA	NA
+A	4.995	0.545832	NA	NA
+A	5.375277778	0.609336	NA	NA
+A	1.35	NA	13.62768138	0.216958848
+A	1.916666667	NA	13.57921333	0.371914222
+A	2.866666667	NA	12.62004575	0.707537683
+A	4	NA	11.24998175	1.464367755
+A	4.8	NA	9.365603478	2.13574936
+A	5.183333333	NA	8.200209151	2.546579626
+A	5.583333333	NA	6.551724322	2.885181161
diff --git a/physiofit/models/model_1.py b/physiofit/models/model_1.py
@@ -23,12 +23,12 @@ def get_params(self):
 
         self.parameters_to_estimate = {
             "X_0" : self.vini,
-            "mu" : self.vini,
+            "growth_rate" : self.vini,
             "t_lag" : self.vini
         }
         self.bounds = Bounds(
             X_0=(1e-3, 10),
-            mu=(1e-3, 3),
+            growth_rate=(1e-3, 3),
             t_lag = (0, 0.5*self.time_vector.max())
         )
         for metabolite in self.metabolites:

diff --git a/physiofit/models/model_2.py b/physiofit/models/model_2.py
@@ -22,12 +22,12 @@ def get_params(self):
 
         self.parameters_to_estimate = {
             "X_0" : self.vini,
-            "mu" : self.vini,
+            "growth_rate" : self.vini,
             "t_lag" : self.vini
         }
         self.bounds = Bounds({
             "X_0": (1e-3, 10),
-            "mu": (1e-3, 3),
+            "growth_rate": (1e-3, 3),
             "t_lag": (0, 0.5 * self.time_vector.max()),
         })
         for metabolite in self.metabolites:

diff --git a/physiofit/models/model_3.py b/physiofit/models/model_3.py
@@ -22,15 +22,15 @@ def get_params(self):
 
         self.parameters_to_estimate = {
             "X_O" : self.vini,
-            "mu" : self.vini
+            "growth_rate" : self.vini
         }
         self.fixed_parameters = {"Degradation": {
             met: 0 for met in self.metabolites
             }
         }
         self.bounds = Bounds({
             "X_0": (1e-3, 10),
-            "mu": (1e-3, 3),
+            "growth_rate": (1e-3, 3),
         })
         for metabolite in self.metabolites:
             self.parameters_to_estimate.update(

diff --git a/physiofit/models/model_4.py b/physiofit/models/model_4.py
@@ -22,11 +22,11 @@ def get_params(self):
 
         self.parameters_to_estimate = {
             "X_0" : self.vini,
-            "mu" : self.vini
+            "growth_rate" : self.vini
         }
         self.bounds = Bounds({
             "X_0": (1e-3, 10),
-            "mu": (1e-3, 3)
+            "growth_rate": (1e-3, 3)
         })
         for metabolite in self.metabolites:
             self.parameters_to_estimate.update(

diff --git a/physiofit/models/model_5.py b/physiofit/models/model_5.py
@@ -65,7 +65,10 @@ def get_params(self):
                 break
 
         if len(self.parameters_to_estimate) != 7:
-            raise ValueError("This model expects 2 metabolites in the data file (1 substrate with name starting with 'S_' and 1 product with name starting with 'P_').")
+            raise ValueError(
+                "This model expects 2 metabolites in the data file (1 substrate with name starting with 'S_' and 1 "
+                "product with name starting with 'P_')."
+            )
 
 
     @staticmethod

diff --git a/physiofit/ui/gui.py b/physiofit/ui/gui.py
@@ -112,12 +112,12 @@ def _initialize_opt_menu(self):
                 f"files or json for configuration files. Detected file: "
                 f"{self.data_file.name}")
 
-        if "experiments" in self.io.data.columns:
-            to_sort = ["experiments", "time"]
-        else:
-            to_sort = "time"
+        if "experiments" not in self.io.data.columns:
+            raise ValueError(
+                "'experiments' column missing from dataset"
+            )
         self.io.data = self.io.data.sort_values(
-            to_sort, ignore_index=True
+            ["experiments", "time"], ignore_index=True
         )
 
         try:
@@ -150,93 +150,60 @@ def _initialize_opt_menu(self):
                 iterations = self.iterations
             )
 
-            if "experiments" in self.io.data.columns:
-                full_dataframe = self.io.data.copy()
-                results_path = copy(self.io.res_path)
-                experiments = list(self.io.data["experiments"].unique())
-                self.io.multiple_experiments = []
-                for experiment in experiments:
-                    with st.spinner(f"Running optimization for {experiment}"):
-                        final_table_dict = {}
-                        self.model.data = full_dataframe[
-                            full_dataframe["experiments"] == experiment
-                        ].drop("experiments", axis=1).copy()
-
-                        self.io.res_path = results_path / experiment
-                        if not self.io.res_path.is_dir():
-                            self.io.res_path.mkdir()
-                        # Initialize the fitter object
-                        self.io.names = self.io.data.columns[1:].to_list()
-                        kwargs = self._build_fitter_kwargs()
-                        fitter = self.io.initialize_fitter(
-                            self.model.data,
-                            model=kwargs["model"],
-                            mc=kwargs["mc"],
-                            iterations=kwargs["iterations"],
-                            sd=kwargs["sd"],
-                            debug_mode=kwargs["debug_mode"]
-                        )
-                        # Do the work
-                        fitter.optimize()
-                        if self.mc:
-                            fitter.monte_carlo_analysis()
-                        fitter.khi2_test()
-                        df = pd.DataFrame.from_dict(
-                            fitter.parameter_stats,
-                            orient="columns"
-                        )
-                        df.index = [
-                            f"{experiment} {param}" for param in fitter.model.parameters_to_estimate.keys()
-                        ]
-                        st.write(df)
-                        self.io.multiple_experiments.append(df)
-
-                        # Export results
-                        self.io.output_report(fitter, self.io.res_path)
-                        self.io.plot_data(fitter)
-                        self.io.output_plots(fitter, self.io.res_path)
-                        with st.expander(f"{experiment} plots"):
-                            for fig in self.io.figures:
-                                st.pyplot(fig[1])
-                        self.io.output_pdf(fitter, self.io.res_path)
-                        # Reset figures to free memory
-                        self.io.figures = []
-                        self.config_parser.export_config(self.io.res_path)
-                self.io.data = full_dataframe
-                self.io.res_path = results_path
-                self.io.output_recap(results_path)
-            else:
-                with st.spinner("Running flux calculation..."):
+            full_dataframe = self.io.data.copy()
+            results_path = copy(self.io.res_path)
+            experiments = list(self.io.data["experiments"].unique())
+            self.io.multiple_experiments = []
+            for experiment in experiments:
+                with st.spinner(f"Running optimization for {experiment}"):
+                    # final_table_dict = {}
+                    self.model.data = full_dataframe[
+                        full_dataframe["experiments"] == experiment
+                    ].drop("experiments", axis=1).copy()
+
+                    self.io.res_path = results_path / experiment
+                    if not self.io.res_path.is_dir():
+                        self.io.res_path.mkdir()
                     # Initialize the fitter object
                     self.io.names = self.io.data.columns[1:].to_list()
                     kwargs = self._build_fitter_kwargs()
                     fitter = self.io.initialize_fitter(
-                        self.io.data,
+                        self.model.data,
                         model=kwargs["model"],
                         mc=kwargs["mc"],
                         iterations=kwargs["iterations"],
                         sd=kwargs["sd"],
                         debug_mode=kwargs["debug_mode"]
                     )
-                    # Do the work and export results
+                    # Do the work
                     fitter.optimize()
                     if self.mc:
                         fitter.monte_carlo_analysis()
                     fitter.khi2_test()
+                    df = pd.DataFrame.from_dict(
+                        fitter.parameter_stats,
+                        orient="columns"
+                    )
+                    df.index = [
+                        f"{experiment} {param}" for param in fitter.model.parameters_to_estimate.keys()
+                    ]
+                    st.write(df)
+                    self.io.multiple_experiments.append(df)
+
+                    # Export results
                     self.io.output_report(fitter, self.io.res_path)
                     self.io.plot_data(fitter)
                     self.io.output_plots(fitter, self.io.res_path)
-                    with st.expander("Plots"):
+                    with st.expander(f"{experiment} plots"):
                         for fig in self.io.figures:
                             st.pyplot(fig[1])
                     self.io.output_pdf(fitter, self.io.res_path)
                     # Reset figures to free memory
                     self.io.figures = []
                     self.config_parser.export_config(self.io.res_path)
-                st.success(
-                    f"Run is finished. Check {self.io.res_path} for "
-                    f"the results."
-                )
+            self.io.data = full_dataframe
+            self.io.res_path = results_path
+            self.io.output_recap(results_path)
 
     def silent_sim(self):
 

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "physiofit"
-version = "3.0.6"
+version = "3.1.0"
 description = "Calculate extracellular fluxes from metabolite concentrations and biomass data"
 authors = ["llegregam <[email protected]>"]
 license = "GNU General Public License (GPL)"