Cancer_modules_depression_epilepsy convert excel files to csv files

src/tlo/methods/bladder_cancer.py

@@ -23,6 +23,7 @@
 from tlo.methods.hsi_event import HSI_Event
 from tlo.methods.hsi_generic_first_appts import GenericFirstAppointmentsMixin
 from tlo.methods.symptommanager import Symptom
+from tlo.util import read_csv_files
 
 if TYPE_CHECKING:
     from tlo.methods.hsi_generic_first_appts import HSIEventScheduler
@@ -208,8 +209,8 @@ def read_parameters(self, data_folder):
 
         # Update parameters from the resourcefile
         self.load_parameters_from_dataframe(
-            pd.read_excel(Path(self.resourcefilepath) / "ResourceFile_Bladder_Cancer.xlsx",
-                          sheet_name="parameter_values")
+            read_csv_files(Path(self.resourcefilepath) / "ResourceFile_Bladder_Cancer",
+                           files="parameter_values")
         )
 
         # Register Symptom that this module will use
@@ -268,7 +269,7 @@ def initialise_population(self, population):
         if bc_status_any_stage.sum():
             sum_probs = sum(p['init_prop_bladder_cancer_stage'])
             if sum_probs > 0:
-                prob_by_stage_of_cancer_if_cancer = [i/sum_probs for i in p['init_prop_bladder_cancer_stage']]
+                prob_by_stage_of_cancer_if_cancer = [i / sum_probs for i in p['init_prop_bladder_cancer_stage']]
                 assert (sum(prob_by_stage_of_cancer_if_cancer) - 1.0) < 1e-10
                 df.loc[bc_status_any_stage, "bc_status"] = self.rng.choice(
                     [val for val in df.bc_status.cat.categories if val != 'none'],
@@ -366,7 +367,7 @@ def initialise_population(self, population):
             df.is_alive &
             (df.bc_status == 'metastatic') &
             ~pd.isnull(df.bc_date_diagnosis)
-        ]
+            ]
 
         select_for_care = self.rng.random_sample(size=len(in_metastatic_diagnosed)) < p['init_prob_palliative_care']
         select_for_care = in_metastatic_diagnosed[select_for_care]
@@ -432,7 +433,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_t2p_bladder_cancer_undergone_curative_treatment']),
             Predictor('bc_status').when('tis_t1', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         lm['metastatic'] = LinearModel(
@@ -441,7 +442,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_metastatic_undergone_curative_treatment']),
             Predictor('bc_status').when('t2p', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         # Check that the dict labels are correct as these are used to set the value of bc_status
@@ -495,7 +496,7 @@ def initialise_simulation(self, sim):
                 sensitivity=self.parameters['sensitivity_of_cystoscopy_for_bladder_cancer_pelvic_pain'],
                 target_categories=["tis_t1", "t2p", "metastatic"]
             )
-         )
+        )
 
         # ----- DISABILITY-WEIGHT -----
         if "HealthBurden" in self.sim.modules:
@@ -584,13 +585,13 @@ def report_daly_values(self):
         disability_series_for_alive_persons.loc[
             (df.bc_status == "metastatic") &
             (pd.isnull(df.bc_date_palliative_care))
-        ] = self.daly_wts['metastatic']
+            ] = self.daly_wts['metastatic']
 
         # Assign daly_wt to those in metastatic cancer, who have had palliative care
         disability_series_for_alive_persons.loc[
             (df.bc_status == "metastatic") &
             (~pd.isnull(df.bc_date_palliative_care))
-        ] = self.daly_wts['metastatic_palliative_care']
+            ] = self.daly_wts['metastatic_palliative_care']
 
         return disability_series_for_alive_persons
 
@@ -702,6 +703,7 @@ class HSI_BladderCancer_Investigation_Following_Blood_Urine(HSI_Event, Individua
     treatment or palliative care.
     It is for people with the symptom blood_urine.
     """
+
     def __init__(self, module, person_id):
         super().__init__(module, person_id=person_id)
 
@@ -852,6 +854,7 @@ class HSI_BladderCancer_StartTreatment(HSI_Event, IndividualScopeEventMixin):
     diagnosis of bladder Cancer using cystoscopy. It initiates the treatment of bladder Cancer.
     It is only for persons with a cancer that is not in metastatic and who have been diagnosed.
     """
+
     def __init__(self, module, person_id):
         super().__init__(module, person_id=person_id)
 
@@ -1024,6 +1027,7 @@ def apply(self, person_id, squeeze_factor):
 
 class BladderCancerLoggingEvent(RegularEvent, PopulationScopeEventMixin):
     """The only logging event for this module"""
+
     def __init__(self, module):
         """schedule logging to repeat every 1 month
         """

src/tlo/methods/breast_cancer.py

@@ -22,6 +22,7 @@
 from tlo.methods.hsi_event import HSI_Event
 from tlo.methods.hsi_generic_first_appts import GenericFirstAppointmentsMixin
 from tlo.methods.symptommanager import Symptom
+from tlo.util import read_csv_files
 
 if TYPE_CHECKING:
     from tlo.methods.hsi_generic_first_appts import HSIEventScheduler
@@ -197,8 +198,8 @@ def read_parameters(self, data_folder):
 
         # Update parameters from the resourcefile
         self.load_parameters_from_dataframe(
-            pd.read_excel(Path(self.resourcefilepath) / "ResourceFile_Breast_Cancer.xlsx",
-                          sheet_name="parameter_values")
+            read_csv_files(Path(self.resourcefilepath) / "ResourceFile_Breast_Cancer",
+                           files="parameter_values")
         )
 
         # Register Symptom that this module will use
@@ -244,7 +245,7 @@ def initialise_population(self, population):
         if brc_status_any_stage.sum():
             sum_probs = sum(p['init_prop_breast_cancer_stage'])
             if sum_probs > 0:
-                prob_by_stage_of_cancer_if_cancer = [i/sum_probs for i in p['init_prop_breast_cancer_stage']]
+                prob_by_stage_of_cancer_if_cancer = [i / sum_probs for i in p['init_prop_breast_cancer_stage']]
                 assert (sum(prob_by_stage_of_cancer_if_cancer) - 1.0) < 1e-10
                 df.loc[brc_status_any_stage, "brc_status"] = self.rng.choice(
                     [val for val in df.brc_status.cat.categories if val != 'none'],
@@ -550,10 +551,10 @@ def report_daly_values(self):
         disability_series_for_alive_persons.loc[
             (
                 ~pd.isnull(df.brc_date_treatment) & (
-                    (df.brc_status == "stage1") |
-                    (df.brc_status == "stage2") |
-                    (df.brc_status == "stage3")
-                ) & (df.brc_status == df.brc_stage_at_which_treatment_given)
+                (df.brc_status == "stage1") |
+                (df.brc_status == "stage2") |
+                (df.brc_status == "stage3")
+            ) & (df.brc_status == df.brc_stage_at_which_treatment_given)
             )
         ] = self.daly_wts['stage_1_3_treated']
 
@@ -656,6 +657,8 @@ def apply(self, population):
             df.loc[selected_to_die, 'brc_date_death'] = self.sim.date
 
     # ---------------------------------------------------------------------------------------------------------
+
+
 #   HEALTH SYSTEM INTERACTION EVENTS
 # ---------------------------------------------------------------------------------------------------------
 
@@ -742,6 +745,7 @@ def apply(self, person_id, squeeze_factor):
                         tclose=None
                     )
 
+
 #   todo: we would like to note that the symptom has been investigated in a diagnostic test and the diagnosis was
 #   todo: was missed, so the same test will not likely be repeated, at least not in the short term, so we even
 #   todo: though the symptom remains we don't want to keep repeating the HSI which triggers the diagnostic test
@@ -776,8 +780,8 @@ def apply(self, person_id, squeeze_factor):
 
             hs.schedule_hsi_event(
                 hsi_event=HSI_BreastCancer_PalliativeCare(
-                     module=self.module,
-                     person_id=person_id,
+                    module=self.module,
+                    person_id=person_id,
                 ),
                 topen=self.sim.date,
                 tclose=None,
@@ -819,7 +823,7 @@ def apply(self, person_id, squeeze_factor):
                 topen=self.sim.date + DateOffset(months=12),
                 tclose=None,
                 priority=0
-        )
+            )
 
 
 class HSI_BreastCancer_PostTreatmentCheck(HSI_Event, IndividualScopeEventMixin):
@@ -1007,7 +1011,7 @@ def apply(self, population):
             'n_newly_diagnosed_stage3': n_newly_diagnosed_stage3,
             'n_newly_diagnosed_stage4': n_newly_diagnosed_stage4,
             'n_diagnosed_age_15_29': n_diagnosed_age_15_29,
-            'n_diagnosed_age_30_49':  n_diagnosed_age_30_49,
+            'n_diagnosed_age_30_49': n_diagnosed_age_30_49,
             'n_diagnosed_age_50p': n_diagnosed_age_50p,
             'n_diagnosed': n_diagnosed
         })

src/tlo/methods/oesophagealcancer.py

@@ -24,6 +24,7 @@
 from tlo.methods.hsi_event import HSI_Event
 from tlo.methods.hsi_generic_first_appts import GenericFirstAppointmentsMixin
 from tlo.methods.symptommanager import Symptom
+from tlo.util import read_csv_files
 
 if TYPE_CHECKING:
     from tlo.methods.hsi_generic_first_appts import HSIEventScheduler
@@ -217,8 +218,8 @@ def read_parameters(self, data_folder):
         """Setup parameters used by the module, register it with healthsystem and register symptoms"""
         # Update parameters from the resourcefile
         self.load_parameters_from_dataframe(
-            pd.read_excel(Path(self.resourcefilepath) / "ResourceFile_Oesophageal_Cancer.xlsx",
-                          sheet_name="parameter_values")
+            read_csv_files(Path(self.resourcefilepath) / "ResourceFile_Oesophageal_Cancer",
+                           files="parameter_values")
         )
 
         # Register Symptom that this module will use
@@ -395,7 +396,7 @@ def initialise_simulation(self, sim):
             Predictor('li_tob').when(True, p['rr_low_grade_dysplasia_none_tobacco']),
             Predictor('li_ex_alc').when(True, p['rr_low_grade_dysplasia_none_ex_alc']),
             Predictor('oc_status').when('none', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         lm['high_grade_dysplasia'] = LinearModel(
@@ -404,7 +405,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_high_grade_dysp_undergone_curative_treatment']),
             Predictor('oc_status').when('low_grade_dysplasia', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         lm['stage1'] = LinearModel(
@@ -413,7 +414,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_stage1_undergone_curative_treatment']),
             Predictor('oc_status').when('high_grade_dysplasia', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         lm['stage2'] = LinearModel(
@@ -422,7 +423,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_stage2_undergone_curative_treatment']),
             Predictor('oc_status').when('stage1', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         lm['stage3'] = LinearModel(
@@ -431,7 +432,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_stage3_undergone_curative_treatment']),
             Predictor('oc_status').when('stage2', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         lm['stage4'] = LinearModel(
@@ -440,7 +441,7 @@ def initialise_simulation(self, sim):
             Predictor('had_treatment_during_this_stage',
                       external=True).when(True, p['rr_stage4_undergone_curative_treatment']),
             Predictor('oc_status').when('stage3', 1.0)
-                                  .otherwise(0.0)
+            .otherwise(0.0)
         )
 
         # Check that the dict labels are correct as these are used to set the value of oc_status
@@ -560,7 +561,7 @@ def report_daly_values(self):
                 (df.oc_status == "stage2") |
                 (df.oc_status == "stage3")
             ) & (df.oc_status == df.oc_stage_at_which_treatment_applied)
-            )
+             )
         ] = self.daly_wts['stage_1_3_treated']
 
         # Assign daly_wt to those in stage4 cancer (who have not had palliative care)