Update black

.github/workflows/push.yml

@@ -19,4 +19,4 @@ jobs:
       - uses: actions/checkout@v2
       - uses: psf/black@stable
         with:
-          version: '19.10b0'
+          version: '22.3.0'

dump_graph_model_v4.py

@@ -53,7 +53,7 @@ def preprocess(x):
             return tf.concat([preprocess_features(x), latent_input], axis=-1)
 
     def postprocess(x):
-        x = 10 ** x - 1
+        x = 10**x - 1
         return tf.where(x < 1.0, 0.0, x)
 
     dump_graph.model_to_graph(

legacy_code/models/baseline_fc_v4_8x16.py

@@ -199,7 +199,7 @@ def gradient_penalty_on_data(self, features, real):
         #            if self.cramer:
         #                d_real = tf.norm(d_real, axis=-1)
         grads = tf.reshape(t.gradient(d_real, real), [len(real), -1])
-        return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1))
+        return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1))
 
     @tf.function
     def calculate_losses(self, feature_batch, target_batch):

legacy_code/models/baseline_v2_10x10.py

@@ -189,7 +189,7 @@ def gradient_penalty_on_data(self, features, real):
         #            if self.cramer:
         #                d_real = tf.norm(d_real, axis=-1)
         grads = tf.reshape(t.gradient(d_real, real), [len(real), -1])
-        return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1))
+        return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1))
 
     @tf.function
     def calculate_losses(self, feature_batch, target_batch):

legacy_code/models/baseline_v3_6x15.py

@@ -212,7 +212,7 @@ def gradient_penalty_on_data(self, features, real):
         #            if self.cramer:
         #                d_real = tf.norm(d_real, axis=-1)
         grads = tf.reshape(t.gradient(d_real, real), [len(real), -1])
-        return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1))
+        return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1))
 
     @tf.function
     def calculate_losses(self, feature_batch, target_batch):

legacy_code/models/baseline_v4_8x16.py

@@ -221,7 +221,7 @@ def gradient_penalty_on_data(self, features, real):
         #            if self.cramer:
         #                d_real = tf.norm(d_real, axis=-1)
         grads = tf.reshape(t.gradient(d_real, real), [len(real), -1])
-        return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1))
+        return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1))
 
     @tf.function
     def calculate_losses(self, feature_batch, target_batch):

legacy_code/test_script_data_v0.py

@@ -27,7 +27,7 @@
 
 
 def unscale(x):
-    return 10 ** x - 1
+    return 10**x - 1
 
 
 def write_hist_summary(step):

legacy_code/test_script_data_v1.py

@@ -58,7 +58,7 @@ def save_model(step):
     writer_val = tf.summary.create_file_writer(f'logs/{args.checkpoint_name}/validation')
 
     def unscale(x):
-        return 10 ** x - 1
+        return 10**x - 1
 
     def write_hist_summary(step):
         if step % args.save_every == 0:

legacy_code/test_script_data_v1_normed.py

@@ -31,7 +31,7 @@
 
 
 def unscale(x):
-    return 10 ** x - 1
+    return 10**x - 1
 
 
 def write_hist_summary(step):

legacy_code/test_script_data_v2.py

@@ -138,7 +138,7 @@ def save_model(step):
         writer_val = tf.summary.create_file_writer(f'logs/{args.checkpoint_name}/validation')
 
     def unscale(x):
-        return 10 ** x - 1
+        return 10**x - 1
 
     def get_images(return_raw_data=False, calc_chi2=False, gen_more=None, sample=(X_test, Y_test), batch_size=128):
         X, Y = sample

legacy_code/test_script_data_v3.py

@@ -134,7 +134,7 @@ def save_model(step):
         writer_val = tf.summary.create_file_writer(f'logs/{args.checkpoint_name}/validation')
 
     def unscale(x):
-        return 10 ** x - 1
+        return 10**x - 1
 
     def get_images(return_raw_data=False, calc_chi2=False, gen_more=None, sample=(X_test, Y_test), batch_size=128):
         X, Y = sample

metrics/trends.py

@@ -31,7 +31,7 @@ def stats(arr):
     ).T
 
     if do_plot:
-        mean_p_std_err = (mean_err ** 2 + std_err ** 2) ** 0.5
+        mean_p_std_err = (mean_err**2 + std_err**2) ** 0.5
         plt.fill_between(bin_centers, mean - mean_err, mean + mean_err, **kwargs)
         kwargs['alpha'] *= 0.5
         kwargs = {k: v for k, v in kwargs.items() if k != 'label'}
@@ -78,11 +78,11 @@ def make_trend_plot(feature_real, real, feature_gen, gen, name, calc_chi2=False,
 
         gen_upper = gen_mean + gen_std
         gen_lower = gen_mean - gen_std
-        gen_err2 = gen_mean_err ** 2 + gen_std_err ** 2
+        gen_err2 = gen_mean_err**2 + gen_std_err**2
 
         real_upper = real_mean + real_std
         real_lower = real_mean - real_std
-        real_err2 = real_mean_err ** 2 + real_std_err ** 2
+        real_err2 = real_mean_err**2 + real_std_err**2
 
         chi2 = ((gen_upper - real_upper) ** 2 / (gen_err2 + real_err2)).sum() + (
             (gen_lower - real_lower) ** 2 / (gen_err2 + real_err2)

models/callbacks.py

@@ -56,6 +56,6 @@ def get_scheduler(lr, lr_decay):
         return eval(lr_decay)
 
     def schedule_lr(step):
-        return lr * lr_decay ** step
+        return lr * lr_decay**step
 
     return schedule_lr

models/model_v4.py

@@ -145,7 +145,7 @@ def gradient_penalty_on_data(self, features, real):
             d_real = self.discriminator([_f(features), real])
 
         grads = tf.reshape(t.gradient(d_real, real), [len(real), -1])
-        return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1))
+        return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1))
 
     @tf.function
     def calculate_losses(self, feature_batch, target_batch):

models/nn.py

@@ -170,7 +170,7 @@ def vector_img_connect_block(vector_shape, img_shape, block, vector_bypass=False
     if concat_outputs:
         outputs = tf.keras.layers.Concatenate(axis=-1)(outputs)
 
-    args = dict(inputs=[input_vec, input_img], outputs=outputs,)
+    args = dict(inputs=[input_vec, input_img], outputs=outputs)
 
     if name:
         args['name'] = name

models/scalers.py

@@ -16,7 +16,7 @@ def scale(self, x):
         return np.log10(1 + x)
 
     def unscale(self, x):
-        return 10 ** x - 1
+        return 10**x - 1
 
 
 class Gaussian: