Spell check source code.

pelicun/base.py

@@ -455,7 +455,7 @@ def emit_warnings(self):
 
     def warn(self, msg):
         """
-        Add an emit a warning immediatelly.
+        Add an emit a warning immediately.
 
         Parameters
         ----------
@@ -634,7 +634,7 @@ def update_vals(update, primary, update_path, primary_path):
                     f'{primary_path}["{key}"] = {primary[key]}. '
                     f'Please revise {update_path}["{key}"].'
                 )
-            # With both being dictionaries, we recurse.
+            # With both being dictionaries, we use recursion.
             update_vals(
                 update[key],
                 primary[key],
@@ -1076,7 +1076,7 @@ def str2bool(v):
         value, an error is raised indicating that a boolean value was
         expected.
     """
-    # courtesy of Maxim @ stackoverflow
+    # courtesy of Maxim @ Stackoverflow
 
     if isinstance(v, bool):
         return v
@@ -1523,7 +1523,7 @@ def stringterpolation(
 ) -> Callable[[np.ndarray], np.ndarray]:
     """
     Turns a string of specially formatted arguments into a multilinear
-    interpolating funciton.
+    interpolating function.
 
     Parameters
     ----------

pelicun/db.py

@@ -2113,7 +2113,7 @@ def create_Hazus_HU_fragility_db(
 
     This method was developed to add a json file with metadata
     accompanying `damage_DB_SimCenter_Hazus_HU_bldg.csv`. That file
-    contains fragility curves fitted to Hazus Hurricane data relaetd
+    contains fragility curves fitted to Hazus Hurricane data related
     to the Hazus Hurricane Technical Manual v4.2.
 
     Parameters
@@ -2213,7 +2213,7 @@ def create_Hazus_HU_fragility_db(
     masonry_reinforcing = {
         '1': 'Has masonry reinforcing.',
         '0': 'No masonry reinforcing.',
-        'null': 'Unknown information on masonry reinfocing.',
+        'null': 'Unknown information on masonry reinforcing.',
     }
 
     roof_frame_type = {
@@ -2222,7 +2222,7 @@ def create_Hazus_HU_fragility_db(
     }
 
     wind_debris_environment = {
-        'A': 'Residentiao/commercial wind debris environment.',
+        'A': 'Residential/commercial wind debris environment.',
         'B': 'Wind debris environment varies by direction.',
         'C': 'Residential wind debris environment.',
         'D': 'No wind debris environment.',

pelicun/model/damage_model.py

@@ -238,7 +238,7 @@ def calculate(
                 .astype('int64')
             )
         else:
-            # Otherwise asume 1.00 for the number of blocks and
+            # Otherwise assume 1.00 for the number of blocks and
             # initialize `component_blocks` using the columns of `cmp_sample`.
             component_blocks = pd.DataFrame(
                 np.ones(self._asmnt.asset.cmp_sample.shape[1]),
@@ -425,7 +425,7 @@ def __init__(self, assessment: Assessment):
 
     def _load_model_parameters(self, data):
         """
-        Load model parameters from a dataframe, extending those
+        Load model parameters from a DataFrame, extending those
         already available. Parameters already defined take precedence,
         i.e. redefinitions of parameters are ignored.
 
@@ -506,14 +506,14 @@ def _get_pg_batches(
         The method takes as input the block_batch_size, which
         specifies the maximum number of blocks per batch. The method
         first checks if performance groups have been defined in the
-        cmp_marginal_params dataframe, and if so, it uses the 'Blocks'
+        cmp_marginal_params DataFrame, and if so, it uses the 'Blocks'
         column as the performance group information. If performance
         groups have not been defined in cmp_marginal_params, the
-        method uses the cmp_sample dataframe to define the performance
+        method uses the cmp_sample DataFrame to define the performance
         groups, with each performance group having a single block.
 
         The method then checks if the performance groups are available
-        in the damage parameters dataframe, and removes any
+        in the damage parameters DataFrame, and removes any
         performance groups that are not found in the damage
         parameters. The method then groups the performance groups
         based on the locations and directions of the components, and
@@ -522,14 +522,14 @@ def _get_pg_batches(
         batches of size specified by block_batch_size and assigns a
         batch number to each group. Finally, the method groups the
         performance groups by batch number, component, location, and
-        direction, and returns a dataframe that shows the number of
+        direction, and returns a DataFrame that shows the number of
         blocks for each batch.
 
         Parameters
         ----------
 
         component_blocks: pd.DataFrame
-            Dataframe containing a singe column, `Blocks`, which lists
+            DataFrame containing a singe column, `Blocks`, which lists
             the number of blocks for each (`cmp`-`loc`-`dir`-`uid`).
         block_batch_size: int
             Maximum number of components in each batch.
@@ -543,7 +543,7 @@ def _get_pg_batches(
             A DataFrame indexed by batch number, component identifier,
             location, direction, and unique ID, with a column
             indicating the number of blocks assigned to each
-            batch. This dataframe facilitates the management and
+            batch. This DataFrame facilitates the management and
             execution of damage assessment tasks by grouping
             components into manageable batches based on the specified
             block batch size.
@@ -641,7 +641,7 @@ def probabilities(self):
         Returns
         -------
         pd.DataFrame
-            Dataframe with the probability of each damage state for
+            DataFrame with the probability of each damage state for
             each component block.
 
         """
@@ -926,7 +926,7 @@ def _evaluate_damage_state(
         if self._asmnt.log.verbose:
             self.log.msg('Evaluating damage states...', prepend_timestamp=True)
 
-        # Create an empty dataframe with columns and index taken from
+        # Create an empty DataFrame with columns and index taken from
         # the input capacity sample
         dmg_eval = pd.DataFrame(
             columns=capacity_sample.columns, index=capacity_sample.index
@@ -946,25 +946,25 @@ def _evaluate_damage_state(
                 [dmg_eval.loc[:1, PG_i] for PG_i in PG_list], axis=1, keys=PG_list
             ).columns
             PG_cols.names = ['cmp', 'loc', 'dir', 'uid', 'block', 'ls']
-            # Create a dataframe with demand values repeated for the
+            # Create a DataFrame with demand values repeated for the
             # number of PGs and assign the columns as PG_cols
             demand_df.append(
                 pd.concat(
                     [pd.Series(demand_vals)] * len(PG_cols), axis=1, keys=PG_cols
                 )
             )
 
-        # Concatenate all demand dataframes into a single dataframe
+        # Concatenate all demand DataFrames into a single DataFrame
         demand_df = pd.concat(demand_df, axis=1)
-        # Sort the columns of the demand dataframe
+        # Sort the columns of the demand DataFrame
         demand_df.sort_index(axis=1, inplace=True)
 
         # Evaluate the damage exceedance by subtracting demand from
         # capacity and checking if the result is less than zero
         dmg_eval = (capacity_sample - demand_df) < 0
 
         # Remove any columns with NaN values from the damage
-        # exceedance dataframe
+        # exceedance DataFrame
         dmg_eval.dropna(axis=1, inplace=True)
 
         # initialize the DataFrames that store the damage states and
@@ -1018,7 +1018,7 @@ def _create_dmg_RVs(self, PGB, scaling_specification=None):
         The method initializes two random variable registries,
         capacity_RV_reg and lsds_RV_reg, and loops through each
         performance group in the input performance group batch (PGB)
-        dataframe. For each performance group, it retrieves the
+        DataFrame. For each performance group, it retrieves the
         component sample and blocks and checks if the limit state is
         defined for the component. If the limit state is defined, the
         method gets the list of limit states and the parameters for
@@ -1178,7 +1178,7 @@ def map_ds(values, offset=int(ds_id + 1)):
                 css = 'capacity adjustment specification'
                 if not isinstance(value, str):
                     raise ValueError(
-                        f'Invalud entry in {css}: {value}. It has to be a string. '
+                        f'Invalid entry in {css}: {value}. It has to be a string. '
                         f'See docstring of DamageModel._create_dmg_RVs.'
                     )
                 capacity_adjustment_operation = value[0]
@@ -1504,7 +1504,7 @@ def _perform_dmg_task(self, task):
             match_locations = False
 
         # check if the source component exists in the damage state
-        # dataframe
+        # DataFrame
         if source_cmp not in self.ds_sample.columns.get_level_values('cmp'):
             self.log.warn(
                 f"Source component `{source_cmp}` in the prescribed "
@@ -1639,7 +1639,7 @@ def _perform_dmg_event_loc(self, source_cmp, ds_source, target_cmp, ds_target):
 
     def _complete_ds_cols(self, dmg_sample):
         """
-        Completes the damage sample dataframe with all possible damage
+        Completes the damage sample DataFrame with all possible damage
         states for each component.
 
         Parameters
@@ -1682,7 +1682,7 @@ def _complete_ds_cols(self, dmg_sample):
         # get the number of possible limit states
         ls_list = [col for col in DP.columns.unique(level=0) if 'LS' in col]
 
-        # initialize the result dataframe
+        # initialize the result DataFrame
         res = pd.DataFrame()
 
         # TODO: For the code below, store the number of damage states
@@ -1720,7 +1720,7 @@ def _complete_ds_cols(self, dmg_sample):
                 ],
             ]
 
-            # Create a dataframe where they are repeated ds_count times in the
+            # Create a DataFrame where they are repeated ds_count times in the
             # columns. The keys put the DS id in the first level of the
             # multiindexed column
             cmp_headers = pd.concat(
@@ -1730,10 +1730,10 @@ def _complete_ds_cols(self, dmg_sample):
             )
             cmp_headers.columns.names = ['ds', *cmp_headers.columns.names[1::]]
 
-            # add these new columns to the result dataframe
+            # add these new columns to the result DataFrame
             res = pd.concat([res, cmp_headers], axis=1)
 
-        # Fill the result dataframe with zeros and reorder its columns to have
+        # Fill the result DataFrame with zeros and reorder its columns to have
         # the damage states at the lowest like - matching the dmg_sample input
         res = pd.DataFrame(
             0.0,

pelicun/model/demand_model.py

@@ -404,7 +404,7 @@ def calibrate_model(self, config):
         Parameters
         ----------
         config: dict
-            A dictionary, typically read from a json file, that specifies the
+            A dictionary, typically read from a JSON file, that specifies the
             distribution family, truncation and censoring limits, and other
             settings for the calibration.
 
@@ -921,8 +921,8 @@ def turn_to_tuples(demand_cloning):
 
         demand_cloning = turn_to_tuples(demand_cloning)
 
-        # The demand cloning confuguration should not include
-        # columns that are not present in the orignal sample.
+        # The demand cloning configuration should not include
+        # columns that are not present in the original sample.
         warn_columns = []
         for column in demand_cloning:
             if column not in self.sample.columns:

pelicun/model/loss_model.py

@@ -416,7 +416,7 @@ def apply_consequence_scaling(self, scaling_conditions, scaling_factor):
         Applies a scaling factor to selected columns of the loss
         samples.
 
-        The scaling conditiones are passed as a dictionary mapping
+        The scaling conditions are passed as a dictionary mapping
         level names with their required value for the condition to be
         met. It has to contain `dv` as one of its keys, defining the
         decision variable where the factors should be applied. Other
@@ -464,7 +464,7 @@ def apply_consequence_scaling(self, scaling_conditions, scaling_factor):
             if model.sample is None:
                 continue
 
-            # ensure the levels exist (but don't check if speicfied
+            # ensure the levels exist (but don't check if specified
             # values exist yet)
             for name in scaling_conditions:
                 if name not in model.sample.columns.names:
@@ -673,7 +673,7 @@ def _loss_map(self):
             The loss map.
 
         """
-        # Retrieve the dataframe from one of the included loss models.
+        # Retrieve the DataFrame from one of the included loss models.
         # We use a single loss map for all.
         return self.ds_model._loss_map
 
@@ -688,7 +688,7 @@ def _loss_map(self, loss_map):
             The loss map.
 
         """
-        # Add the dataframe to the included loss models.
+        # Add the DataFrame to the included loss models.
         # We use a single loss map for all.
         for model in self._loss_models:
             model._loss_map = loss_map
@@ -781,7 +781,7 @@ def __init__(self, assessment: Assessment):
 
     def _load_model_parameters(self, data):
         """
-        Load model parameters from a dataframe, extending those
+        Load model parameters from a DataFrame, extending those
         already available. Parameters already defined take precedence,
         i.e. redefinitions of parameters are ignored.
 
@@ -1481,7 +1481,7 @@ def _calculate(
         loss_map = self._loss_map['Repair'].to_dict()
         sample_size = len(demand_sample)
 
-        # TODO: this can be taken out and simly passed as blocks in
+        # TODO: this can be taken out and simply passed as blocks in
         # the arguments, and cast to a dict in here. Index can be
         # obtained from there.
         index = [

pelicun/resources/auto/Hazus_Earthquake_IM.py

@@ -214,7 +214,7 @@ def convertTunnelToHAZUSclass(AIM):
     elif ("Cut" in AIM["ConstructType"]) or ("Cover" in AIM["ConstructType"]):
         return "HTU2"
     else:
-        # Select HTU2 for unclassfied tunnels because it is more conservative.
+        # Select HTU2 for unclassified tunnels because it is more conservative.
         return "HTU2"
 
 

pelicun/tests/model/test_damage_model.py

@@ -134,17 +134,17 @@ def test_load_model_parameters(self, damage_model):
         assert damage_model.ds_model.damage_params.empty
 
     def test_calculate(self):
-        # User-facing methods are coupled with other assessment objets
+        # User-facing methods are coupled with other assessment objects
         # and are tested in the verification examples.
         pass
 
     def test_save_sample(self):
-        # User-facing methods are coupled with other assessment objets
+        # User-facing methods are coupled with other assessment objects
         # and are tested in the verification examples.
         pass
 
     def test_load_sample(self):
-        # User-facing methods are coupled with other assessment objets
+        # User-facing methods are coupled with other assessment objects
         # and are tested in the verification examples.
         pass
 

pelicun/tests/model/test_loss_model.py

@@ -802,7 +802,7 @@ def test__calc_median_consequence(self, assessment_instance):
                 performance_group, loss_map, required_edps, demand_dict, cmp_sample
             )
         assert (
-            'Loss function intepolation for consequence '
+            'Loss function interpolation for consequence '
             '`cmp.A-dv.A` has failed. Ensure a sufficient '
             'interpolation domain  for the X values '
             '(those after the `|` symbol)  and verify '

pelicun/tests/model/test_model.py

@@ -39,7 +39,7 @@
 # John Vouvakis Manousakis
 
 """
-This file defines a clas used by the model unit tests.
+This file defines a class used by the model unit tests.
 """
 
 from __future__ import annotations

pelicun/tests/test_assessment.py

@@ -96,7 +96,7 @@ def test_assessment_get_default_metadata():
 
     for data_source in data_sources:
         # here we just test that we can load the data file, without
-        # checking the contens.
+        # checking the contents.
         asmt.get_default_data(data_source)
         asmt.get_default_metadata(data_source)
 

pelicun/tests/test_base.py

@@ -358,7 +358,7 @@ def test_convert_dtypes():
         check_column_type=False,
     )
 
-    # Empty dataframe
+    # Empty DataFrame
 
     df_input = pd.DataFrame({})
     df_expected = pd.DataFrame({})
@@ -626,7 +626,7 @@ def test_describe():
     )
 
     # case 1:
-    # passing a dataframe
+    # passing a DataFrame
 
     df = pd.DataFrame(
         ((1.00, 2.00, 3.00), (4.00, 5.00, 6.00)), columns=['A', 'B', 'C']