Fix Flake8 Errors

clarite/internal/utilities.py

@@ -49,7 +49,7 @@ def _validate_skip_only(
         only = [only]
 
     if skip is not None and only is not None:
-        raise ValueError(f"It isn't possible to specify 'skip' and 'only' at the same time.")
+        raise ValueError("It isn't possible to specify 'skip' and 'only' at the same time.")
     elif skip is not None and only is None:
         invalid_cols = set(skip) - set(list(data))
         if len(invalid_cols) > 0:
@@ -178,7 +178,7 @@ def validate_ewas_params(covariates, data, phenotype, survey_design_spec):
 
     # Make sure the index of each dataset is not a multiindex and give it a consistent name
     if isinstance(data.index, pd.MultiIndex):
-        raise ValueError(f"Data must not have a multiindex")
+        raise ValueError("Data must not have a multiindex")
     data.index.name = "ID"
 
     # Collects lists of regression variables
@@ -188,7 +188,7 @@ def validate_ewas_params(covariates, data, phenotype, survey_design_spec):
     rv_cont = [v for v, t in types.iteritems() if t == 'continuous' and v not in covariates and v != phenotype]
     # Ensure there are variables which can be regressed
     if len(rv_bin + rv_cat + rv_cont) == 0:
-        raise ValueError(f"No variables are available to run regression on")
+        raise ValueError("No variables are available to run regression on")
     else:
         click.echo(
             f"Running {len(rv_bin):,} binary, {len(rv_cat):,} categorical, and {len(rv_cont):,} continuous variables")
@@ -214,7 +214,7 @@ def validate_ewas_params(covariates, data, phenotype, survey_design_spec):
     elif pheno_kind == 'categorical':
         raise NotImplementedError("Categorical Phenotypes are not yet supported.")
     elif pheno_kind == 'continuous':
-        click.echo(f"Running EWAS on a Continuous Outcome (family = Gaussian)")
+        click.echo("Running EWAS on a Continuous Outcome (family = Gaussian)")
     elif pheno_kind == 'binary':
         # Use the order according to the categorical
         counts = data[phenotype].value_counts().to_dict()
@@ -226,7 +226,7 @@ def validate_ewas_params(covariates, data, phenotype, survey_design_spec):
                                f"\t{counts[categories[1]]:,} occurrences of '{categories[1]}' coded as 1",
                                fg='green'))
     else:
-        raise ValueError(f"The phenotype's type could not be determined.  Please report this error.")
+        raise ValueError("The phenotype's type could not be determined.  Please report this error.")
 
     # Log how many NA outcomes
     na_outcome_count = data[phenotype].isna().sum()

clarite/modules/modify.py

@@ -526,7 +526,7 @@ def recode_values(data, replacement_dict,
     if cells_with_changes > 0:
         click.echo(f"Replaced {cells_with_changes:,} values from {len(data):,} observations in {cols_with_changes:,} variables")
     else:
-        click.echo(f"No occurences of replaceable values were found, so nothing was replaced.")
+        click.echo("No occurences of replaceable values were found, so nothing was replaced.")
 
     # Return
     return result
@@ -897,7 +897,7 @@ def drop_extra_categories(data: pd.DataFrame,
         message = f"\t{str(var)} had categories with no occurrences: {', '.join([str(c) for c in cats])}"
         click.echo(message)
     elif len(removed_cats) > 1:
-        message = f"\tMultiple categorical variables had categories with no occurrences:"
+        message = "\tMultiple categorical variables had categories with no occurrences:"
         for var, cats in removed_cats.items():
             message += f"\n\t{str(var)}: {', '.join([str(c) for c in cats])}"
         click.echo(message)

clarite/modules/plot.py

@@ -755,12 +755,12 @@ def top_results(
     # Ensure corrected pvalues are present
     if pvalue_name == 'pvalue_fdr' or pvalue_name == 'pvalue_bonferroni':
         if pvalue_name not in list(ewas_result):
-            raise ValueError(f"Missing corrected pvalues in ewas result.  Run clarite.analyze.add_corrected_pvalues")
+            raise ValueError("Missing corrected pvalues in ewas result.  Run clarite.analyze.add_corrected_pvalues")
     elif pvalue_name == 'pvalue':
         pass
     else:
-        raise ValueError(f"Incorrect value specified for 'pvalue_name': must be one of 'pvalue', 'pvalue_fdr',"
-                         f" or 'pvalue_bonferroni'.")
+        raise ValueError("Incorrect value specified for 'pvalue_name': must be one of 'pvalue', 'pvalue_fdr',"
+                         " or 'pvalue_bonferroni'.")
 
     # Sort and filter data
     df = ewas_result.sort_values(pvalue_name, ascending=True).head(num_rows).reset_index()

clarite/modules/survey/survey_design.py

@@ -136,8 +136,8 @@ def __init__(self,
 
         # Load single_cluster
         if single_cluster not in {'fail', 'adjust', 'average', 'certainty'}:
-            raise ValueError(f"if provided, 'single_cluster' must be one of "
-                             f"'fail', 'adjust', 'average', or 'certainty'.")
+            raise ValueError("if provided, 'single_cluster' must be one of "
+                             "'fail', 'adjust', 'average', or 'certainty'.")
         else:
             self.single_cluster = single_cluster
 
@@ -148,27 +148,27 @@ def __str__(self):
         if self.has_strata:
             result += f"\tStrata: {len(self.strata.unique())} unique values of {self.strata_name}\n"
         else:
-            result += f"\tStrata: None\n"
+            result += "\tStrata: None\n"
         # Clusters
         if self.has_cluster:
             result += f"\tCluster: {len(self.cluster.unique())} unique values of {self.cluster_name}\n"
             # Nest
             result += f"\t\tClusters nested in Strata: {self.nest}\n"
         else:
-            result += f"\tCluster: None\n"
+            result += "\tCluster: None\n"
         # FPC
         if self.has_fpc:
             result += f"\tFPC: {self.fpc_name}\n"
         else:
-            result += f"\tFPC: None\n"
+            result += "\tFPC: None\n"
         # Weights
         if self.single_weight:
             result += f"\tWeight: {self.weight_name}\n"
         elif self.multi_weight:
             result += f"\tMultiple Weights: {len(set(self.weight_names.values())):,} " \
                       f"unique weights associated with {len(set(self.weight_names.keys())):,} variables\n"
         else:
-            result += f"\tWeights: None\n"
+            result += "\tWeights: None\n"
         # single cluster
         result += f"\tSingle Cluster ('Lonely PSU') Option: {self.single_cluster}"
 

clarite/tests/conftest.py

@@ -1,6 +1,7 @@
 import pytest
 from statsmodels import datasets
 
+
 # Datasets for testing
 @pytest.fixture
 def plantTraits():