Fix flake8 style warnings for all files except torch_runner.py

Author: felker

plasma/conf_parser.py

@@ -96,10 +96,10 @@ def parameters(input_file):
             sig.jet, params['paths']['shot_list_dir'],
             ['ILW_unint_late.txt', 'ILW_clear_late.txt'],
             'Late jet iter like wall data')
-        jet_iterlike_wall_full = ShotListFiles(
-            sig.jet, params['paths']['shot_list_dir'],
-            ['ILW_unint_full.txt', 'ILW_clear_full.txt'],
-            'Full jet iter like wall data')
+        # jet_iterlike_wall_full = ShotListFiles(
+        #     sig.jet, params['paths']['shot_list_dir'],
+        #     ['ILW_unint_full.txt', 'ILW_clear_full.txt'],
+        #     'Full jet iter like wall data')
 
         jenkins_jet_carbon_wall = ShotListFiles(
             sig.jet, params['paths']['shot_list_dir'],
@@ -169,12 +169,12 @@ def parameters(input_file):
             params['paths']['shot_files'] = [jet_carbon_wall]
             params['paths']['shot_files_test'] = [jet_iterlike_wall]
             params['paths']['use_signals_dict'] = {
-                'etemp_profile': etemp_profile}
+                'etemp_profile': sig.etemp_profile}
         elif params['paths']['data'] == 'jet_data_dens_profile':
             params['paths']['shot_files'] = [jet_carbon_wall]
             params['paths']['shot_files_test'] = [jet_iterlike_wall]
             params['paths']['use_signals_dict'] = {
-                'edens_profile': edens_profile}
+                'edens_profile': sig.edens_profile}
         elif params['paths']['data'] == 'jet_carbon_data':
             params['paths']['shot_files'] = [jet_carbon_wall]
             params['paths']['shot_files_test'] = []
@@ -302,13 +302,13 @@ def parameters(input_file):
             params['paths']['shot_files'] = [d3d_full]
             params['paths']['shot_files_test'] = []
             params['paths']['use_signals_dict'] = {
-                'etemp_profile': etemp_profile}  # fully_defined_signals_0D
+                'etemp_profile': sig.etemp_profile}  # fully_defined_signals_0D
         elif params['paths']['data'] == 'd3d_data_dens_profile':
             # jet data but with fully defined signals
             params['paths']['shot_files'] = [d3d_full]
             params['paths']['shot_files_test'] = []
             params['paths']['use_signals_dict'] = {
-                'edens_profile': edens_profile}  # fully_defined_signals_0D
+                'edens_profile': sig.edens_profile}  # fully_defined_signals_0D
 
         # cross-machine
         elif params['paths']['data'] == 'jet_to_d3d_data':

plasma/models/builder.py

@@ -82,6 +82,7 @@ def build_model(self, predict, custom_batch_size=None):
         conf = self.conf
         model_conf = conf['model']
         rnn_size = model_conf['rnn_size']
+        use_bidirectional = model_conf['use_bidirectional']
         rnn_type = model_conf['rnn_type']
         regularization = model_conf['regularization']
         dense_regularization = model_conf['dense_regularization']
@@ -353,7 +354,8 @@ def get_all_saved_files(self):
         self.ensure_save_directory()
         unique_id = self.get_unique_id()
         path = self.conf['paths']['model_save_path']
-        filenames = [name for name in os.listdir(path) if os.path.isfile(os.path.join(path, name))]
+        filenames = [name for name in os.listdir(path)
+                     if os.path.isfile(os.path.join(path, name))]
         epochs = []
         for file in filenames:
             curr_id, epoch = self.extract_id_and_epoch_from_filename(file)

plasma/models/mpi_runner.py

@@ -786,7 +786,7 @@ def mpi_make_predictions_and_evaluate_multiple_times(conf, shot_list, loader,
 
 
 def mpi_train(conf, shot_list_train, shot_list_validate, loader,
-              callbacks_list=None shot_list_test=None):
+              callbacks_list=None, shot_list_test=None):
     loader.set_inference_mode(False)
     conf['num_workers'] = comm.Get_size()
 
@@ -949,7 +949,7 @@ def mpi_train(conf, shot_list_train, shot_list_validate, loader,
                         round(e)), epoch_logs)
 
         print_unique("end epoch {} 0".format(e))
-        stop_training = comm.bcast(stop_training,root=0)
+        stop_training = comm.bcast(stop_training, root=0)
         print_unique("end epoch {} 1".format(e))
         if stop_training:
             print("Stopping training due to early stopping")

plasma/models/shallow_runner.py

@@ -21,7 +21,6 @@
 import time
 import numpy as np
 from copy import deepcopy
-from keras.utils.generic_utils import Progbar
 
 # import matplotlib.pyplot as plt
 import matplotlib

plasma/preprocessor/normalize.py

@@ -153,12 +153,13 @@ def cut_end_of_shot(self, shot):
         # only cut shots during training
         if not self.inference_mode and cut_shot_ends:
             T_min_warn = self.conf['data']['T_min_warn']
-            if shot.ttd.shape[0] - T_min_warn <= max(self.conf['model']['length'],0):
-                print("not cutting shot since length of shot after cutting by T_min_warn would be shorter than RNN length")
+            if shot.ttd.shape[0] - T_min_warn <= max(
+                    self.conf['model']['length'], 0):
+                print("not cutting shot; length of shot after cutting by ",
+                      "T_min_warn would be shorter than RNN length")
                 return
             for key in shot.signals_dict:
-                shot.signals_dict[key] = shot.signals_dict[key][:-T_min_warn,
-                                                                :]
+                shot.signals_dict[key] = shot.signals_dict[key][:-T_min_warn,:]  # noqa
             shot.ttd = shot.ttd[:-T_min_warn]
 
     # def apply_mask(self,shot):

plasma/utils/downloading.py

@@ -33,9 +33,11 @@
 
 
 def general_object_hash(o):
-    """Makes a hash from a dictionary, list, tuple or set to any level, that contains
-       only other hashable types (including any lists, tuples, sets, and
-       dictionaries). Relies on dill for serialization"""
+    """
+    Makes a hash from a dictionary, list, tuple or set to any level, that
+    contains only other hashable types (including any lists, tuples, sets, and
+    dictionaries). Relies on dill for serialization
+"""
 
     if isinstance(o, (set, tuple, list)):
         return tuple([general_object_hash(e) for e in o])
@@ -51,9 +53,12 @@ def general_object_hash(o):
 
 
 def myhash(x):
-    return int(hashlib.md5((dill.dumps(x).decode('unicode_escape')).encode('utf-8')).hexdigest(),16)
+    return int(hashlib.md5((dill.dumps(x).decode('unicode_escape')).encode(
+        'utf-8')).hexdigest(), 16)
     # return int(hashlib.md5((dill.dumps(x))).hexdigest(),16)
-    # return int(hashlib.md5((dill.dumps(x))))#.decode('unicode_escape')).encode('utf-8')).hexdigest(),16)
+    # return
+    # int(hashlib.md5((dill.dumps(x))))#.decode('unicode_escape')).encode(
+    # 'utf-8')).hexdigest(),16)
 
 
 def get_missing_value_array():

plasma/utils/performance.py

@@ -14,16 +14,8 @@
 
 
 class PerformanceAnalyzer():
-    def __init__(
-            self,
-            results_dir=None,
-            shots_dir=None,
-            i=0,
-            T_min_warn=None,
-            T_max_warn=None,
-            verbose=False,
-            pred_ttd=False,
-            conf=None):
+    def __init__(self, results_dir=None, shots_dir=None, i=0, T_min_warn=None,
+                 T_max_warn=None, verbose=False, pred_ttd=False, conf=None):
         self.T_min_warn = T_min_warn
         self.T_max_warn = T_max_warn
         dt = conf['data']['dt']
@@ -35,7 +27,9 @@ def __init__(
         if T_max_warn is None:
             self.T_max_warn = T_max_warn_def
         if self.T_max_warn < self.T_min_warn:
-            print("T max warn is too small: need to increase artificially.") #computation of statistics is only correct if T_max_warn is larger than T_min_warn
+            # computation of statistics is only correct if T_max_warn is larger
+            # than T_min_warn
+            print("T max warn is too small: need to increase artificially.")
             self.T_max_warn = self.T_min_warn + 1
         self.verbose = verbose
         self.results_dir = results_dir