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celinelee
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python_fn_calls

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should_execute(self)
datetime.now()
schedule_next_execution(self)
instant.next_datetime()
self._schedule.instants()
min(datetimes)
ActionScheduler(object)
__init__(self)
start_session(self, schedule: Schedule)
add(self, callback: Callable, *args, **kwargs)
Action(callback, *args, *kwargs)
action.set_schedule(self._schedule)
self.actions.append(action)
end_session(self)
run(self)
action.should_execute()
action()
action.schedule_next_execution()
combine_schema(borough_name)
borough_name.lower()
open('../scraped_data/borough_schema/' + borough_name + ".json", 'r', encoding='utf-8')
json.load(json_file)
range(len(data[borough_name])
open('../scraped_data/neighborhood_schema/' + borough_name + ".json", 'r+', encoding='utf-8')
json.load(zipcode_file)
print(neighborhood_data)
open('../scraped_data/neighborhood_schema/' + borough_name + ".json", 'w', encoding='utf-8')
json.dump(neighborhood_data, combined_file, sort_keys=True, indent='\t', separators=(',', ': ')
main()
os.listdir("./boroughs")
borough.split(".")
replace("-", " ")
title()
input(name + " => ")
input("Convert " + name + " data to json format? (yes/no)
print("Writing to file ...")
combine_schema(name)
print("Will not convert data json ...")
print("Skipping borough: " + name + " ... ")
main()
average_volume_difference()
NotImplementedError()
dice(y_true, y_pred, axis=(1, 2, 3, 4)
empty (e.g., all background)
tf.convert_to_tensor(y_pred)
tf.cast(y_true, y_pred.dtype)
tf.keras.backend.epsilon()
tf.reduce_sum(y_true * y_pred, axis=axis)
tf.reduce_sum(y_true, axis=axis)
tf.reduce_sum(y_pred, axis=axis)
return (2 * intersection + eps)
generalized_dice(y_true, y_pred, axis=(1, 2, 3)
tf.convert_to_tensor(y_pred)
tf.cast(y_true, y_pred.dtype)
y_true.get_shape()
y_pred.get_shape()
ValueError("y_true and y_pred must be at least rank 2.")
tf.keras.backend.epsilon()
tf.math.reciprocal(tf.square(tf.reduce_sum(y_true, axis=axis)
tf.where(tf.math.is_finite(w)
tf.reduce_sum(w * tf.reduce_sum(y_true * y_pred, axis= axis)
tf.reduce_sum(w * tf.reduce_sum(y_true + y_pred, axis= axis)
tf.where(tf.math.is_finite(gdice)
tf.zeros_like(gdice)
hamming(y_true, y_pred, axis=(1, 2, 3)
tf.convert_to_tensor(y_pred)
tf.cast(y_true, y_pred.dtype)
tf.reduce_mean(tf.not_equal(y_pred, y_true)
haussdorf()
NotADirectoryError()
jaccard(y_true, y_pred, axis=(1, 2, 3, 4)
empty (e.g., all background)
tf.convert_to_tensor(y_pred)
tf.cast(y_true, y_pred.dtype)
tf.keras.backend.epsilon()
tf.reduce_sum(y_true * y_pred, axis=axis)
tf.reduce_sum(y_true, axis=axis)
tf.reduce_sum(y_pred, axis=axis)
return (intersection + eps)
tversky(y_true, y_pred, axis=(1, 2, 3)
tf.convert_to_tensor(y_pred)
tf.cast(y_true, y_pred.dtype)
y_true.get_shape()
y_pred.get_shape()
ValueError("y_true and y_pred must be at least rank 2.")
tf.keras.backend.epsilon()
tf.reduce_sum(y_pred * y_true, axis=axis)
tf.reduce_sum(y_pred * (1 - y_true)
tf.reduce_sum((1 - y_pred)
tf.reduce_sum((num + eps)
dice_coef_multilabel(y_true, y_pred)
tf.shape(y_pred)
range(n_classes)
dice(y_true[:,:,:,:,index], y_pred[:,:,:,:,index])
PeeringWatcherTestCase(SecurityMonkeyWatcherTestCase)
freeze_time("2016-07-18 12:00:00")
test_slurp(self)
boto.connect_vpc('the_key', 'the secret')
conn.create_vpc("10.0.0.0/16")
conn.create_vpc("10.0.0.0/16")