Ruff check fixes

examples/example_gee_ci/src/main.py

@@ -62,10 +62,10 @@ def mann_kendall_stat(imageCollection):
     NumberOfItems = TimeSeriesList.length().getInfo()
     ConcordantArray = []
     DiscordantArray = []
-    for k in range(0, NumberOfItems - 2):
-        CurrentImage = ee.Image(TimeSeriesList.get(k))
-        for l in range(k + 1, NumberOfItems - 1):
-            nextImage = ee.Image(TimeSeriesList.get(l))
+    for j in range(0, NumberOfItems - 2):
+        CurrentImage = ee.Image(TimeSeriesList.get(j))
+        for k in range(j + 1, NumberOfItems - 1):
+            nextImage = ee.Image(TimeSeriesList.get(k))
             Concordant = CurrentImage.lt(nextImage)
             ConcordantArray.append(Concordant)
             Discordant = CurrentImage.gt(nextImage)

examples/example_tensorflow/src/main.py

@@ -1,12 +1,9 @@
 """Custom Script"""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.examples.tutorials.mnist import input_data
+from __future__ import absolute_import, division, print_function
 
 import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
 
 
 def model(x):
@@ -18,7 +15,7 @@ def model(x):
 
 def run(params, logger):
     # Import data
-    mnist = input_data.read_data_sets('/tmp/tensorflow/mnist/input_data', one_hot=True)
+    mnist = input_data.read_data_sets("/tmp/tensorflow/mnist/input_data", one_hot=True)
     learning_rate = 0.5
 
     # Create the model
@@ -27,8 +24,9 @@ def run(params, logger):
     y_ = tf.placeholder(tf.float32, [None, 10])
 
     cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
-    loss = tf.reduce_mean(cross_entropy)
-    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
+    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(
+        cross_entropy
+    )
 
     sess = tf.InteractiveSession()
     tf.global_variables_initializer().run()
@@ -41,6 +39,5 @@ def run(params, logger):
     # Test trained model
     correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-    print(sess.run(accuracy, feed_dict={x: mnist.test.images,
-                                      y_: mnist.test.labels}))
+    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
     return "Done"

tecli/logs.py

@@ -1,17 +1,12 @@
 """Logs command"""
 
-from __future__ import absolute_import
-
-from __future__ import division
-
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import json
 import logging
 import os
 import time
-from datetime import datetime
-from datetime import timedelta
+from datetime import datetime, timedelta
 
 import dateutil.parser
 import pytz
@@ -66,7 +61,7 @@ def show_logs(script, since):
     printed = False
     for log in script["logs"]:
         last_date = dateutil.parser.parse(log["register_date"]).replace(tzinfo=pytz.utc)
-        if log["text"] != None and (last_date > (now - since)):
+        if log["text"] is not None and (last_date > (now - since)):
             print(log["register_date"] + ": " + log["text"])
             printed = True
 
@@ -77,12 +72,12 @@ def show_logs(script, since):
 
     if script["status"] != "FAIL" and script["status"] != "SUCCESS":
         next = True
-        while next == True:
+        while next:
             next, logs = get_logs(script, last_date)
             if logs:
                 for log in logs:
                     last_date = dateutil.parser.parse(log["register_date"])
-                    if log["text"] != None:
+                    if log["text"] is not None:
                         print(log["register_date"] + ": " + log["text"])
 
             time.sleep(2)