Cleanup (#167)

* pylint stuff

* optimize imports

petab/core.py

@@ -1,21 +1,22 @@
 """PEtab core functions"""
 
-import pandas as pd
-import numpy as np
-import libsbml
-import sympy as sp
-import re
 import itertools
-import os
+import logging
 import numbers
+import os
+import re
+import warnings
 from collections import OrderedDict
-import logging
-from . import lint
-from . import sbml
-from . import parameter_mapping
 from typing import Optional, List, Union, Iterable, Set
-import warnings
 
+import libsbml
+import numpy as np
+import pandas as pd
+import sympy as sp
+
+from . import lint
+from . import parameter_mapping
+from . import sbml
 
 logger = logging.getLogger(__name__)
 
@@ -971,25 +972,25 @@ def flatten_timepoint_specific_output_overrides(
         unique_noise = df["noiseParameters"].unique()
 
         # Loop
-        for i in range(len(unique_noise)):
-            for j in range(len(unique_sc)):
-                # Find the position of all instances of unique_noise[i]
+        for i_noise, cur_noise in enumerate(unique_noise):
+            for i_sc, cur_sc in enumerate(unique_sc):
+                # Find the position of all instances of cur_noise
                 # and unique_sc[j] in their corresponding column
                 # (full-string matches are denoted by zero)
                 idxs = (
-                        df["noiseParameters"].str.find(unique_noise[i]) +
-                        df["observableParameters"].str.find(unique_sc[j])
+                        df["noiseParameters"].str.find(cur_noise) +
+                        df["observableParameters"].str.find(cur_sc)
                 )
                 tmp_ = df.loc[idxs == 0, "observableId"]
                 # Create replicate-specific observable name
-                tmp = tmp_ + "_" + str(i + j + 1)
+                tmp = tmp_ + "_" + str(i_noise + i_sc + 1)
                 # Check if replicate-specific observable name already exists
                 # in df. If true, rename replicate-specific observable
                 counter = 2
                 while (df["observableId"].str.find(
                         tmp.to_string()
                 ) == 0).any():
-                    tmp = tmp_ + counter*"_" + str(i + j + 1)
+                    tmp = tmp_ + counter*"_" + str(i_noise + i_sc + 1)
                     counter += 1
                 df.loc[idxs == 0, "observableId"] = tmp
                 # Append the result in a new df

petab/lint.py

@@ -1,15 +1,17 @@
 """Integrity checks and tests for specific features used"""
 
-from . import core
-from . import sbml
-import numpy as np
-import numbers
-import re
 import copy
 import logging
+import numbers
+import re
+from typing import Optional
+
 import libsbml
+import numpy as np
 import pandas as pd
-from typing import Optional
+
+from . import core
+from . import sbml
 
 logger = logging.getLogger(__name__)
 
@@ -178,10 +180,10 @@ def assert_parameter_id_is_string(parameter_df):
     are string and not empty.
     """
 
-    for parameterId in parameter_df:
-        if isinstance(parameterId, str):
-            if parameterId[0].isdigit():
-                raise AssertionError('parameterId ' + parameterId
+    for parameter_id in parameter_df:
+        if isinstance(parameter_id, str):
+            if parameter_id[0].isdigit():
+                raise AssertionError('parameterId ' + parameter_id
                                      + ' starts with integer')
         else:
             raise AssertionError('Empty parameterId found')
@@ -203,11 +205,11 @@ def assert_parameter_scale_is_valid(parameter_df):
     logarithm.
     """
 
-    for parameterScale in parameter_df['parameterScale']:
-        if parameterScale not in ['lin', 'log', 'log10']:
+    for parameter_scale in parameter_df['parameterScale']:
+        if parameter_scale not in ['lin', 'log', 'log10']:
             raise AssertionError(
                 'Expected "lin", "log" or "log10" but got "' +
-                parameterScale + '"')
+                parameter_scale + '"')
 
 
 def assert_parameter_bounds_are_numeric(parameter_df):
@@ -239,7 +241,7 @@ def assert_parameter_estimate_is_boolean(parameter_df):
     0 or 1.
     """
     for estimate in parameter_df['estimate']:
-        if not int(estimate) in [True, False]:
+        if int(estimate) not in [True, False]:
             raise AssertionError(
                 f"Expected 0 or 1 but got {estimate} in estimate column.")
 

petab/petablint.py

@@ -3,10 +3,11 @@
 """Command line tool to check for correct format"""
 
 import argparse
+import logging
 import os
-import petab
 import sys
-import logging
+
+import petab
 from colorama import (init as init_colorama, Fore)
 
 logger = logging.getLogger(__name__)

petab/sbml.py

@@ -1,11 +1,12 @@
 """Functions for direct access of SBML models"""
 
-import libsbml
-import math
 import logging
+import math
 import warnings
 from typing import Dict, Any
 
+import libsbml
+
 logger = logging.getLogger(__name__)
 
 

petab/visualize/data_overview.py

@@ -2,11 +2,12 @@
 Functions for creating an overview report of a PEtab problem
 """
 
-import petab
 import os
-import pandas as pd
 from shutil import copyfile
 
+import pandas as pd
+import petab
+
 
 def create_report(problem: petab.Problem, model_name: str) -> None:
     """Create an HTML overview data / model overview report

setup.py

@@ -24,6 +24,7 @@ def read(fname):
 }
 
 # project metadata
+# noinspection PyUnresolvedReferences
 setup(name='petab',
       version=__version__,  # noqa: F821
       description='Parameter estimation tabular data',

tests/test_parameter_mapping.py

@@ -1,7 +1,6 @@
+import numpy as np
 import pandas as pd
 import petab
-import numpy as np
-
 from petab.sbml import add_global_parameter
 
 # import fixtures
@@ -12,7 +11,8 @@
 
 class TestGetSimulationToOptimizationParameterMapping(object):
 
-    def test_no_condition_specific(self, condition_df_2_conditions,
+    @staticmethod
+    def test_no_condition_specific(condition_df_2_conditions,
                                    minimal_sbml_model):
         # Trivial case - no condition-specific parameters
 
@@ -26,7 +26,7 @@ def test_no_condition_specific(self, condition_df_2_conditions,
             'noiseParameters': ['', '']
         })
 
-        sbml_doc, sbml_model = minimal_sbml_model
+        _, sbml_model = minimal_sbml_model
         add_global_parameter(sbml_model, 'dynamicParameter1')
         add_global_parameter(sbml_model, 'dynamicParameter2')
         add_global_parameter(sbml_model, 'dynamicParameter3')
@@ -48,12 +48,13 @@ def test_no_condition_specific(self, condition_df_2_conditions,
 
         assert actual == expected
 
-    def test_all_override(self, condition_df_2_conditions,
+    @staticmethod
+    def test_all_override(condition_df_2_conditions,
                           minimal_sbml_model):
         # Condition-specific parameters overriding original parameters
         condition_df = condition_df_2_conditions
 
-        sbml_doc, sbml_model = minimal_sbml_model
+        _, sbml_model = minimal_sbml_model
         add_global_parameter(sbml_model, 'dynamicParameter1')
         add_global_parameter(sbml_model, 'dynamicParameter2')
         add_global_parameter(sbml_model, 'observableParameter1_obs1')
@@ -94,12 +95,13 @@ def test_all_override(self, condition_df_2_conditions,
 
         assert actual == expected
 
-    def test_partial_override(self, condition_df_2_conditions,
+    @staticmethod
+    def test_partial_override(condition_df_2_conditions,
                               minimal_sbml_model):
         # Condition-specific parameters, keeping original parameters
         condition_df = condition_df_2_conditions
 
-        sbml_doc, sbml_model = minimal_sbml_model
+        _, sbml_model = minimal_sbml_model
         add_global_parameter(sbml_model, 'dynamicParameter1')
         add_global_parameter(sbml_model, 'observableParameter1_obs1')
         add_global_parameter(sbml_model, 'observableParameter2_obs1')
@@ -138,7 +140,8 @@ def test_partial_override(self, condition_df_2_conditions,
 
         assert actual == expected
 
-    def test_parameterized_condition_table(self, minimal_sbml_model):
+    @staticmethod
+    def test_parameterized_condition_table(minimal_sbml_model):
         condition_df = pd.DataFrame(data={
             'conditionId': ['condition1', 'condition2', 'condition3'],
             'conditionName': ['', 'Condition 2', ''],
@@ -179,8 +182,9 @@ def test_parameterized_condition_table(self, minimal_sbml_model):
 
         assert actual == expected
 
+    @staticmethod
     def test_parameterized_condition_table_changed_scale(
-            self, minimal_sbml_model):
+            minimal_sbml_model):
         """Test overriding a dynamic parameter `overridee` with
         - a log10 parameter to be estimated (condition 1)
         - lin parameter not estimated (condition2)