Fixing lint errors

invokers/invoker_estimate_noise_cov_matrix.py

@@ -1,8 +1,6 @@
 from pathlib import Path
-from colorama import Fore
 
 from kymata.io.yaml import load_config
-from kymata.io.cli import print_with_color
 from kymata.preproc.data_cleansing import estimate_noise_cov
 
 

invokers/invoker_run_hexel_current_estimation.py

@@ -3,7 +3,7 @@
 
 from kymata.io.yaml import load_config
 from kymata.io.cli import print_with_color
-from kymata.preproc.hexel_current_estimation import create_current_estimation_prerequisites, create_forward_model_and_inverse_solution, create_hexel_morph_maps
+from kymata.preproc.hexel_current_estimation import create_forward_model_and_inverse_solution, create_hexel_morph_maps
 
 
 # noinspection DuplicatedCode

invokers/run_gridsearch.py

@@ -7,7 +7,6 @@
 from kymata.io.functions import load_function
 from kymata.preproc.source import load_emeg_pack
 from kymata.io.nkg import save_expression_set
-from kymata.io.yaml import load_config
 from kymata.plot.plot import expression_plot
 
 _default_output_dir = Path(data_root_path(), "output")
@@ -64,7 +63,7 @@ def main():
     args = parser.parse_args()
 
     # TODO: use config for ppt lists etc
-    config = load_config(str(Path(Path(__file__).parent.parent, "kymata", "config", "dataset4.yaml")))
+    #config = load_config(str(Path(Path(__file__).parent.parent, "kymata", "config", "dataset4.yaml")))
 
     participants = [
         'pilot_01',
@@ -98,8 +97,8 @@ def main():
 
     if (len(emeg_filenames) > 1) and (not args.morph) and (args.ave_mode == "ave") and (args.inverse_operator_dir is not None):
         raise ValueError(
-            f"Averaging source-space results without morphing to a common space. "
-            f"If you are averaging over multiple participants you must morph to a common space.")
+            "Averaging source-space results without morphing to a common space. " +
+            "If you are averaging over multiple participants you must morph to a common space.")
 
     # Load data
     emeg_path = Path(args.base_dir, args.emeg_dir)

kymata/datasets/sample.py

@@ -141,7 +141,7 @@ def delete_dataset(local_dataset: SampleDataset):
     # Make sure it's not silent
     print(f"Deleting dataset {local_dataset.name}")
     # Only allow deletion if the specified url is within the data dir
-    assert data_root_path() in local_dataset.path.parents, f"Cannot delete dataset outside of data root directory"
+    assert data_root_path() in local_dataset.path.parents, "Cannot delete dataset outside of data root directory"
     if not local_dataset.path.exists():
         # Nothing to delete
         print(f"{str(local_dataset.path)} doesn't exist")

kymata/gridsearch/plain.py

@@ -43,7 +43,9 @@ def do_gridsearch(
     n_samples_per_split = int(seconds_per_split * emeg_sample_rate * 2 // downsample_rate)
 
     if ave_mode == 'add':
-        n_reps = len(EMEG_paths)
+        # TODO:  hard-code hack, fix as part of https://github.com/kymata-atlas/kymata-toolbox/issues/128
+        #n_reps = len(EMEG_paths)
+        n_reps = 1
     else:
         n_reps = 1
 

kymata/io/functions.py

@@ -1,6 +1,5 @@
 from pathlib import Path
 
-from numpy import array, float16, convolve, mean
 import numpy as np
 from numpy.typing import NDArray
 from scipy.io import loadmat
@@ -26,7 +25,6 @@ def load_function(function_path_without_suffix: path_type, func_name: str, n_der
             n_chans = func.shape[0]
             new_mr_arr = np.zeros((n_chans, 400_000))
             new_ft_arr = np.zeros((n_chans, 400_000))
-            T_end = tt[0, -1]
             if func_name == 'neurogram_mr':
                 for i in range(n_chans):
                     new_mr_arr[i] = np.interp(np.linspace(0, 400, 400_000 + 1)[:-1], tt[0], func[i])

kymata/io/nkg.py

@@ -263,7 +263,7 @@ def _load_data_current(from_path_or_file: path_type | file_type) -> dict[str, An
         with TextIOWrapper(zf.open("_metadata/expression-set-type.txt"), encoding="utf-8") as f:
             return_dict[_Keys.expressionset_type] = str(f.read()).strip()
         with TextIOWrapper(zf.open("/blocks.txt"), encoding="utf-8") as f:
-            blocks = [str(l.strip()) for l in f.readlines()]
+            blocks = [str(line.strip()) for line in f.readlines()]
         with TextIOWrapper(zf.open("/latencies.txt"), encoding="utf-8") as f:
             return_dict[_Keys.latencies] = [LatencyDType(lat.strip()) for lat in f.readlines()]
         with TextIOWrapper(zf.open("/functions.txt"), encoding="utf-8") as f:

kymata/io/nkg_compatibility.py

@@ -37,7 +37,7 @@ def _load_data_0_3(from_path_or_file: path_type | file_type) -> dict[str, Any]:
         with TextIOWrapper(zf.open("_metadata/expression-set-type.txt"), encoding="utf-8") as f:
             return_dict["expressionset-type"] = str(f.read()).strip()
         with TextIOWrapper(zf.open("/layers.txt"), encoding="utf-8") as f:
-            layers = [str(l.strip()) for l in f.readlines()]
+            layers = [str(line.strip()) for line in f.readlines()]
         with TextIOWrapper(zf.open("/channels.txt"), encoding="utf-8") as f:
             return_dict["channels"] = [c.strip() for c in f.readlines()]
         with TextIOWrapper(zf.open("/latencies.txt"), encoding="utf-8") as f:
@@ -76,7 +76,7 @@ def _load_data_0_2(from_path_or_file: path_type | file_type) -> dict[str, Any]:
         with TextIOWrapper(zf.open("_metadata/expression-set-type.txt"), encoding="utf-8") as f:
             return_dict["expressionset-type"] = str(f.read()).strip()
         with TextIOWrapper(zf.open("/layers.txt"), encoding="utf-8") as f:
-            layers = [str(l.strip()) for l in f.readlines()]
+            layers = [str(line.strip()) for line in f.readlines()]
         with TextIOWrapper(zf.open("/channels.txt"), encoding="utf-8") as f:
             return_dict["channels"] = [c.strip() for c in f.readlines()]
         with TextIOWrapper(zf.open("/latencies.txt"), encoding="utf-8") as f:

kymata/ippm/__init__.py

@@ -0,0 +1 @@
+

kymata/ippm/data_tools.py

@@ -10,7 +10,6 @@
 import pandas as pd
 from sklearn.preprocessing import normalize
 from sklearn.metrics.pairwise import euclidean_distances
-from copy import deepcopy
 import requests
 import seaborn as sns
 from matplotlib.lines import Line2D
@@ -102,7 +101,7 @@ def build_hexel_dict_from_expression_set(expression_set: HexelExpressionSet) ->
             func = row[DIM_FUNCTION]
             latency = row[DIM_LATENCY] * 1000  # convert to ms
             pval = row["value"]
-            if not func in hexels:
+            if func not in hexels:
                 hexels[func] = IPPMHexel(func)
             hexels[func].add_pairing(hemi, (latency, pval))
     return hexels
@@ -127,7 +126,7 @@ def build_hexel_dict_from_api_response(dict_: Dict) -> Dict[str, IPPMHexel]:
         for (_, latency, pval, func) in dict_[hemi]:
             # we have id, latency (ms), pvalue (log_10), function name.
             # discard id as it conveys no useful information
-            if not func in hexels:
+            if func not in hexels:
                 # first time seeing function, so create key and hexel object.
                 hexels[func] = IPPMHexel(func)
 
@@ -209,7 +208,7 @@ def stem_plot(
     right_hem_expression_plot.invert_yaxis()
     left_hem_expression_plot.text(-180, y_limit * 10000000, 'left hemisphere', style='italic', verticalalignment='center')
     right_hem_expression_plot.text(-180, y_limit * 10000000, 'right hemisphere', style='italic', verticalalignment='center')
-    y_axis_label = f'p-value (with α at 5-sigma, Bonferroni corrected)'
+    y_axis_label = 'p-value (with α at 5-sigma, Bonferroni corrected)'
     left_hem_expression_plot.text(-275, 1, y_axis_label, verticalalignment='center',rotation='vertical')
     right_hem_expression_plot.text(0, 1, '   onset of environment   ', color='white', fontsize='x-small', bbox={'facecolor': 'grey', 'edgecolor': 'none'}, verticalalignment='center', horizontalalignment='center', rotation='vertical')
     left_hem_expression_plot.legend(handles=custom_handles, labels=custom_labels, fontsize='x-small', bbox_to_anchor=(1.2, 1))
@@ -265,7 +264,7 @@ def function_recall(hexels: Dict[str, IPPMHexel], funcs: List[str], ippm_dict: D
     for func in funcs:
         pairings = hexels[func].right_best_pairings if hemi == 'rightHemisphere' else hexels[func].left_best_pairings
         for latency, spike in pairings:
-            if spike <= alpha:
+            if spike <= bonferroni_corrected_alpha:
                 funcs_present_in_data += 1
 
                 # Step 2: Found a function, look in ippm_dict.keys() for the function.
@@ -291,7 +290,7 @@ def convert_to_power10(hexels: Dict[str, IPPMHexel]) -> Dict[str, IPPMHexel]:
 def remove_excess_funcs(to_retain: List[str], hexels: Dict[str, IPPMHexel]) -> Dict[str, IPPMHexel]:
     funcs = list(hexels.keys())
     for func in funcs:
-        if not func in to_retain:
+        if func not in to_retain:
             # delete
             hexels.pop(func)
     return hexels