Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

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/NeuralNetworkNumbers/venv/Lib/site-packages/tensorflow/_api/v2/compat/v2/nn/__init__.py

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strazhg/NeuralNetworksPython

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golubstrazh@gmail.com

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/python_fundamentals/venv/Scripts/pip-script.py

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barstow123/python_stack

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#!"C:\Users\abars\Documents\Sublime programs\School Projects\python_stack\python_fundamentals\venv\Scripts\python.exe" # EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip' __requires__ = 'pip==10.0.1' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==10.0.1', 'console_scripts', 'pip')() )

[ "39165000+barstow123@users.noreply.github.com" ]

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/pyfinnotech/tests/test_client_credential_token.py

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mahdi13/pyfinnotech

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from pyfinnotech.tests.helper import ApiClientTestCase client_invalid_mock_cards = [ '000000000000000', 'A000000000000000', ] class ClientCredentialTestCase(ApiClientTestCase): def test_fetch_client_credential(self): client_credential = self.api_client.client_credential self.assertIsNotNone(client_credential.token) self.assertIsNotNone(client_credential.refresh_token) self.assertIsNotNone(client_credential.creation_date) self.assertIsNotNone(client_credential.life_time) self.assertIsNotNone(client_credential.scopes) self.assertIsNotNone(client_credential.scopes) def test_refresh_client_credential(self): client_credential = self.api_client.client_credential client_credential.refresh(self.api_client) self.assertIsNotNone(client_credential.token) self.assertIsNotNone(client_credential.refresh_token) self.assertIsNotNone(client_credential.creation_date) self.assertIsNotNone(client_credential.life_time) self.assertIsNotNone(client_credential.scopes) self.assertIsNotNone(client_credential.scopes) def test_revoke_client_credential(self): # TODO: pass

[ "mahdi13.1373@gmail.com" ]

mahdi13.1373@gmail.com

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Ahmed-Masoud/HTTP-SERVER

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import socket # create a socket object s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # request input from user request = raw_input('Enter a file request: ') # parse input to get the port number my_list = request.split(" ") method = my_list[0] fileName = my_list[1] host = my_list[2] if len(my_list) == 4: port = int(my_list[3]) else: port = 80 # connection to hostname on the port. try: s.connect((host, port)) except: print("connection refused !!") quit() if method == "GET": s.send(request) response_code = s.recv(512) print("Status Code : "+response_code+"\n") if response_code == "404 Not Found": quit() text_file = open(fileName, "w+") while True: serverMsg = s.recv(512) if not serverMsg: break print(serverMsg) text_file.write(serverMsg) text_file.close() elif method == "POST": try: f = open(fileName, 'r') except: print("No Such File !!") quit() s.send(request) response_code = s.recv(512) print("Status Code : "+response_code+"\n") while True: data = f.readline(512) if not data: break s.send(data) f.close()

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/cases/synthetic/sieve-big-5429.py

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# A resizable list of integers class Vector(object): items: [int] = None size: int = 0 def __init__(self:"Vector"): self.items = [0] # Returns current capacity def capacity(self:"Vector") -> int: return len(self.items) # Increases capacity of vector by one element def increase_capacity(self:"Vector") -> int: self.items = self.items + [0] return self.capacity() # Appends one item to end of vector def append(self:"Vector", item: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends many items to end of vector def append_all(self:"Vector", new_items: [int]) -> object: item:int = 0 for item in new_items: self.append(item) # Removes an item from the middle of vector def remove_at(self:"Vector", idx: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Retrieves an item at a given index def get(self:"Vector", idx: int) -> int: return self.items[idx] # Retrieves the current size of the vector def length(self:"Vector") -> int: return self.size # A resizable list of integers class Vector2(object): items: [int] = None items2: [int] = None size: int = 0 size2: int = 0 def __init__(self:"Vector2"): self.items = [0] # Returns current capacity def capacity(self:"Vector2") -> int: return len(self.items) # Returns current capacity def capacity2(self:"Vector2") -> int: return len(self.items) # Increases capacity of vector by one element def increase_capacity(self:"Vector2") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity2(self:"Vector2") -> int: self.items = self.items + [0] return self.capacity() # Appends one item to end of vector def append(self:"Vector2", item: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append2(self:"Vector2", item: int, item2: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends many items to end of vector def append_all(self:"Vector2", new_items: [int]) -> object: item:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all2(self:"Vector2", new_items: [int], new_items2: [int]) -> object: item:int = 0 item2:int = 0 for item in new_items: self.append(item) # Removes an item from the middle of vector def remove_at(self:"Vector2", idx: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at2(self:"Vector2", idx: int, idx2: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Retrieves an item at a given index def get(self:"Vector2", idx: int) -> int: return self.items[idx] # Retrieves an item at a given index def get2(self:"Vector2", idx: int, idx2: int) -> int: return self.items[idx] # Retrieves the current size of the vector def length(self:"Vector2") -> int: return self.size # Retrieves the current size of the vector def length2(self:"Vector2") -> int: return self.size # A resizable list of integers class Vector3(object): items: [int] = None items2: [int] = None items3: [int] = None size: int = 0 size2: int = 0 size3: int = 0 def __init__(self:"Vector3"): self.items = [0] # Returns current capacity def capacity(self:"Vector3") -> int: return len(self.items) # Returns current capacity def capacity2(self:"Vector3") -> int: return len(self.items) # Returns current capacity def capacity3(self:"Vector3") -> int: return len(self.items) # Increases capacity of vector by one element def increase_capacity(self:"Vector3") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity2(self:"Vector3") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity3(self:"Vector3") -> int: self.items = self.items + [0] return self.capacity() # Appends one item to end of vector def append(self:"Vector3", item: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append2(self:"Vector3", item: int, item2: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append3(self:"Vector3", item: int, item2: int, item3: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends many items to end of vector def append_all(self:"Vector3", new_items: [int]) -> object: item:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all2(self:"Vector3", new_items: [int], new_items2: [int]) -> object: item:int = 0 item2:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all3(self:"Vector3", new_items: [int], new_items2: [int], new_items3: [int]) -> object: item:int = 0 item2:int = 0 item3:int = 0 for item in new_items: self.append(item) # Removes an item from the middle of vector def remove_at(self:"Vector3", idx: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at2(self:"Vector3", idx: int, idx2: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at3(self:"Vector3", idx: int, idx2: int, idx3: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Retrieves an item at a given index def get(self:"Vector3", idx: int) -> int: return self.items[idx] # Retrieves an item at a given index def get2(self:"Vector3", idx: int, idx2: int) -> int: return self.items[idx] # Retrieves an item at a given index def get3(self:"Vector3", idx: int, idx2: int, idx3: int) -> int: return self.items[idx] # Retrieves the current size of the vector def length(self:"Vector3") -> int: return self.size # Retrieves the current size of the vector def length2(self:"Vector3") -> int: return self.size # Retrieves the current size of the vector def length3(self:"Vector3") -> int: return self.size # A resizable list of integers class Vector4(object): items: [int] = None items2: [int] = None items3: [int] = None items4: [int] = None size: int = 0 size2: int = 0 size3: int = 0 size4: int = 0 def __init__(self:"Vector4"): self.items = [0] # Returns current capacity def capacity(self:"Vector4") -> int: return len(self.items) # Returns current capacity def capacity2(self:"Vector4") -> int: return len(self.items) # Returns current capacity def capacity3(self:"Vector4") -> int: return len(self.items) # Returns current capacity def capacity4(self:"Vector4") -> int: return len(self.items) # Increases capacity of vector by one element def increase_capacity(self:"Vector4") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity2(self:"Vector4") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity3(self:"Vector4") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity4(self:"Vector4") -> int: self.items = self.items + [0] return self.capacity() # Appends one item to end of vector def append(self:"Vector4", item: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append2(self:"Vector4", item: int, item2: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append3(self:"Vector4", item: int, item2: int, item3: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append4(self:"Vector4", item: int, item2: int, item3: int, item4: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends many items to end of vector def append_all(self:"Vector4", new_items: [int]) -> object: item:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all2(self:"Vector4", new_items: [int], new_items2: [int]) -> object: item:int = 0 item2:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all3(self:"Vector4", new_items: [int], new_items2: [int], new_items3: [int]) -> object: item:int = 0 item2:int = 0 item3:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all4(self:"Vector4", new_items: [int], new_items2: [int], new_items3: [int], new_items4: [int]) -> object: item:int = 0 item2:int = 0 item3:int = 0 item4:int = 0 for item in new_items: self.append(item) # Removes an item from the middle of vector def remove_at(self:"Vector4", idx: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at2(self:"Vector4", idx: int, idx2: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at3(self:"Vector4", idx: int, idx2: int, idx3: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at4(self:"Vector4", idx: int, idx2: int, idx3: int, idx4: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Retrieves an item at a given index def get(self:"Vector4", idx: int) -> int: return self.items[idx] # Retrieves an item at a given index def get2(self:"Vector4", idx: int, idx2: int) -> int: return self.items[idx] # Retrieves an item at a given index def get3(self:"Vector4", idx: int, idx2: int, idx3: int) -> int: return self.items[idx] # Retrieves an item at a given index def get4(self:"Vector4", idx: int, idx2: int, idx3: int, idx4: int) -> int: return self.items[idx] # Retrieves the current size of the vector def length(self:"Vector4") -> int: return self.size # Retrieves the current size of the vector def length2(self:"Vector4") -> int: return self.size # Retrieves the current size of the vector def length3(self:"Vector4") -> int: return self.size # Retrieves the current size of the vector def length4(self:"Vector4") -> int: return self.size # A resizable list of integers class Vector5(object): items: [int] = None items2: [int] = None items3: [int] = None items4: [int] = None items5: [int] = None size: int = 0 size2: int = 0 size3: int = 0 size4: int = 0 size5: int = 0 def __init__(self:"Vector5"): self.items = [0] # Returns current capacity def capacity(self:"Vector5") -> int: return len(self.items) # Returns current capacity def capacity2(self:"Vector5") -> int: return len(self.items) # Returns current capacity def capacity3(self:"Vector5") -> int: return len(self.items) # Returns current capacity def capacity4(self:"Vector5") -> int: return len(self.items) # Returns current capacity def capacity5(self:"Vector5") -> int: return len(self.items) # Increases capacity of vector by one element def increase_capacity(self:"Vector5") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity2(self:"Vector5") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity3(self:"Vector5") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity4(self:"Vector5") -> int: self.items = self.items + [0] return self.capacity() # Increases capacity of vector by one element def increase_capacity5(self:"Vector5") -> int: self.items = self.items + [0] return self.capacity() # Appends one item to end of vector def append(self:"Vector5", item: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append2(self:"Vector5", item: int, item2: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append3(self:"Vector5", item: int, item2: int, item3: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append4(self:"Vector5", item: int, item2: int, item3: int, item4: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends one item to end of vector def append5(self:"Vector5", item: int, item2: int, item3: int, item4: int, item5: int) -> object: if self.size == self.capacity(): self.increase_capacity() self.items[self.size] = item self.size = self.size + 1 # Appends many items to end of vector def append_all(self:"Vector5", new_items: [int]) -> object: item:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all2(self:"Vector5", new_items: [int], new_items2: [int]) -> object: item:int = 0 item2:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all3(self:"Vector5", new_items: [int], new_items2: [int], new_items3: [int]) -> object: item:int = 0 item2:int = 0 item3:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all4(self:"Vector5", new_items: [int], new_items2: [int], new_items3: [int], new_items4: [int]) -> object: item:int = 0 $TypedVar = 0 item3:int = 0 item4:int = 0 for item in new_items: self.append(item) # Appends many items to end of vector def append_all5(self:"Vector5", new_items: [int], new_items2: [int], new_items3: [int], new_items4: [int], new_items5: [int]) -> object: item:int = 0 item2:int = 0 item3:int = 0 item4:int = 0 item5:int = 0 for item in new_items: self.append(item) # Removes an item from the middle of vector def remove_at(self:"Vector5", idx: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at2(self:"Vector5", idx: int, idx2: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at3(self:"Vector5", idx: int, idx2: int, idx3: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at4(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Removes an item from the middle of vector def remove_at5(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int, idx5: int) -> object: if idx < 0: return while idx < self.size - 1: self.items[idx] = self.items[idx + 1] idx = idx + 1 self.size = self.size - 1 # Retrieves an item at a given index def get(self:"Vector5", idx: int) -> int: return self.items[idx] # Retrieves an item at a given index def get2(self:"Vector5", idx: int, idx2: int) -> int: return self.items[idx] # Retrieves an item at a given index def get3(self:"Vector5", idx: int, idx2: int, idx3: int) -> int: return self.items[idx] # Retrieves an item at a given index def get4(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int) -> int: return self.items[idx] # Retrieves an item at a given index def get5(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int, idx5: int) -> int: return self.items[idx] # Retrieves the current size of the vector def length(self:"Vector5") -> int: return self.size # Retrieves the current size of the vector def length2(self:"Vector5") -> int: return self.size # Retrieves the current size of the vector def length3(self:"Vector5") -> int: return self.size # Retrieves the current size of the vector def length4(self:"Vector5") -> int: return self.size # Retrieves the current size of the vector def length5(self:"Vector5") -> int: return self.size # A faster (but more memory-consuming) implementation of vector class DoublingVector(Vector): doubling_limit:int = 1000 # Overriding to do fewer resizes def increase_capacity(self:"DoublingVector") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # A faster (but more memory-consuming) implementation of vector class DoublingVector2(Vector): doubling_limit:int = 1000 doubling_limit2:int = 1000 # Overriding to do fewer resizes def increase_capacity(self:"DoublingVector2") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity2(self:"DoublingVector2") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # A faster (but more memory-consuming) implementation of vector class DoublingVector3(Vector): doubling_limit:int = 1000 doubling_limit2:int = 1000 doubling_limit3:int = 1000 # Overriding to do fewer resizes def increase_capacity(self:"DoublingVector3") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity2(self:"DoublingVector3") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity3(self:"DoublingVector3") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # A faster (but more memory-consuming) implementation of vector class DoublingVector4(Vector): doubling_limit:int = 1000 doubling_limit2:int = 1000 doubling_limit3:int = 1000 doubling_limit4:int = 1000 # Overriding to do fewer resizes def increase_capacity(self:"DoublingVector4") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity2(self:"DoublingVector4") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity3(self:"DoublingVector4") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity4(self:"DoublingVector4") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # A faster (but more memory-consuming) implementation of vector class DoublingVector5(Vector): doubling_limit:int = 1000 doubling_limit2:int = 1000 doubling_limit3:int = 1000 doubling_limit4:int = 1000 doubling_limit5:int = 1000 # Overriding to do fewer resizes def increase_capacity(self:"DoublingVector5") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity2(self:"DoublingVector5") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity3(self:"DoublingVector5") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity4(self:"DoublingVector5") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Overriding to do fewer resizes def increase_capacity5(self:"DoublingVector5") -> int: if (self.capacity() <= self.doubling_limit // 2): self.items = self.items + self.items else: # If doubling limit has been reached, fall back to # standard capacity increases self.items = self.items + [0] return self.capacity() # Makes a vector in the range [i, j) def vrange(i:int, j:int) -> Vector: v:Vector = None v = DoublingVector() while i < j: v.append(i) i = i + 1 return v def vrange2(i:int, j:int, i2:int, j2:int) -> Vector: v:Vector = None v2:Vector = None v = DoublingVector() while i < j: v.append(i) i = i + 1 return v def vrange3(i:int, j:int, i2:int, j2:int, i3:int, j3:int) -> Vector: v:Vector = None v2:Vector = None v3:Vector = None v = DoublingVector() while i < j: v.append(i) i = i + 1 return v def vrange4(i:int, j:int, i2:int, j2:int, i3:int, j3:int, i4:int, j4:int) -> Vector: v:Vector = None v2:Vector = None v3:Vector = None v4:Vector = None v = DoublingVector() while i < j: v.append(i) i = i + 1 return v def vrange5(i:int, j:int, i2:int, j2:int, i3:int, j3:int, i4:int, j4:int, i5:int, j5:int) -> Vector: v:Vector = None v2:Vector = None v3:Vector = None v4:Vector = None v5:Vector = None v = DoublingVector() while i < j: v.append(i) i = i + 1 return v # Sieve of Eratosthenes (not really) def sieve(v:Vector) -> object: i:int = 0 j:int = 0 k:int = 0 while i < v.length(): k = v.get(i) j = i + 1 while j < v.length(): if v.get(j) % k == 0: v.remove_at(j) else: j = j + 1 i = i + 1 def sieve2(v:Vector, v2:Vector) -> object: i:int = 0 i2:int = 0 j:int = 0 j2:int = 0 k:int = 0 k2:int = 0 while i < v.length(): k = v.get(i) j = i + 1 while j < v.length(): if v.get(j) % k == 0: v.remove_at(j) else: j = j + 1 i = i + 1 def sieve3(v:Vector, v2:Vector, v3:Vector) -> object: i:int = 0 i2:int = 0 i3:int = 0 j:int = 0 j2:int = 0 j3:int = 0 k:int = 0 k2:int = 0 k3:int = 0 while i < v.length(): k = v.get(i) j = i + 1 while j < v.length(): if v.get(j) % k == 0: v.remove_at(j) else: j = j + 1 i = i + 1 def sieve4(v:Vector, v2:Vector, v3:Vector, v4:Vector) -> object: i:int = 0 i2:int = 0 i3:int = 0 i4:int = 0 j:int = 0 j2:int = 0 j3:int = 0 j4:int = 0 k:int = 0 k2:int = 0 k3:int = 0 k4:int = 0 while i < v.length(): k = v.get(i) j = i + 1 while j < v.length(): if v.get(j) % k == 0: v.remove_at(j) else: j = j + 1 i = i + 1 def sieve5(v:Vector, v2:Vector, v3:Vector, v4:Vector, v5:Vector) -> object: i:int = 0 i2:int = 0 i3:int = 0 i4:int = 0 i5:int = 0 j:int = 0 j2:int = 0 j3:int = 0 j4:int = 0 j5:int = 0 k:int = 0 k2:int = 0 k3:int = 0 k4:int = 0 k5:int = 0 while i < v.length(): k = v.get(i) j = i + 1 while j < v.length(): if v.get(j) % k == 0: v.remove_at(j) else: j = j + 1 i = i + 1 # Input parameter n:int = 50 n2:int = 50 n3:int = 50 n4:int = 50 n5:int = 50 # Data v:Vector = None v2:Vector = None v3:Vector = None v4:Vector = None v5:Vector = None i:int = 0 i2:int = 0 i3:int = 0 i4:int = 0 i5:int = 0 # Crunch v = vrange(2, n) v2 = vrange(2, n) v3 = vrange(2, n) v4 = vrange(2, n) v5 = vrange(2, n) sieve(v) # Print while i < v.length(): print(v.get(i)) i = i + 1

[ "647530+Virtlink@users.noreply.github.com" ]

647530+Virtlink@users.noreply.github.com

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/todo/admin.py

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[]

no_license

exuberantcoyote/mgt659-todo-django

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refs/heads/master

2021-01-10T12:08:12.018637

2016-02-19T01:57:54

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2016-02-15T21:47:49

2016-02-12T22:58:52

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py

from django.contrib import admin # Register your models here. from . import models admin.site.register(models.User) admin.site.register(models.Task)

[ "trenttolley@gmail.com" ]

trenttolley@gmail.com

178985974999fd81c154f0575eef230e8d729aa0

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/Ottieni_matrici_Rettificazione.py

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[]

no_license

GiuseppeCannata/StereoVision

4e6a8129dade58cc68a1f3c1ffdddf91a47f2482

f82c4d64509f923c8e1b46be443520da9cbc0f14

refs/heads/master

2021-07-17T10:27:23.295334

2020-05-14T21:04:38

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import numpy as np import cv2 """ Modulo per ottenere le matrici di rettificazione """ # === GESTIONE ============================================================ Folder_save_calib = '/home/giuseppe/Scrivania/ResultCalib' # cartella dove sono salvati i risultati delle matrici di rettificazione Folder_to_save_rect = '/home/giuseppe/Scrivania/ResultRect' # cartella su cui salvare i risultati della rettificazione # Nome delle due immagini da rettificare nome_img_left = "/home/giuseppe/Scrivania/_ " nome_img_right = "/home/giuseppe/Scrivania/_ " # ======================================================================= def leggi_da_file_matrici(folder, nome_file): mtx = np.load(folder + nome_file) return mtx def Salva_su_file(folder, nome_file, elemento_da_salvare): np.save(folder + nome_file, elemento_da_salvare) im_left = cv2.imread(nome_img_left) im_right = cv2.imread(nome_img_right) # LETTURA MATRICI CALIBRAZIONE mtx_left = leggi_da_file_matrici(Folder_save_calib , "/Matrice_Intrinseca_Sx.npy") mtx_right = leggi_da_file_matrici(Folder_save_calib , "/Matrice_Intrinseca_Dx.npy") dist_left = leggi_da_file_matrici(Folder_save_calib , "/Matrice_Distorsione_Sx.npy") dist_right = leggi_da_file_matrici(Folder_save_calib , "/Matrice_Distorsione_Dx.npy") R = leggi_da_file_matrici(Folder_save_calib , "/R.npy") T = leggi_da_file_matrici(Folder_save_calib , "/T.npy") # GENERAZIONE MATRICI RETTIFICAZIONE R1 = np.zeros((3,3)) # R1 --> output 3x3 matrix, rectification transform (rotation matrix) for the first camera R2 = np.zeros((3,3)) # R2 --> Output 3x3 rectification transform (rotation matrix) for the second camera. # La vista di destra e di sinistra della telecamera stereo sono spostate l'una rispetto all'altra # lungo l'asse x (e possono presentare un eventuale piccolo spostamento verticale, quindi lungo l asse y). # Nelle immagini rettificate, le corrispondenti linee epipolari nelle fotocamere sinistra e destra # sono orizzontali e hanno la stessa coordinata y. Per cui punti omologhi giacciono sulla stessa retta. P1 = np.zeros((3,4))#output 3x4 matrix P2 = np.zeros((3,4)) #output 3x4 matrix Q = np.zeros((4,4)) #output 3x4 matrix cv2.stereoRectify( mtx_left, dist_left, mtx_right, dist_right, (640,480), R, T, R1, R2, P1, P2, Q, flags = cv2.CALIB_ZERO_DISPARITY) #con il flag calib zero disparity facciamo in modo che i centri delle due telecamere coincidono tra loro --> cy1 == cy2 #questo dovrebbe grarantire che oggetti molto distanti tra loro hanno diparità zero. #ricorda oggetti vicini hanno disparità alta oggetti lontani bassa # SALVATAGGIO MATRICI PER RETTIFICAZIONE Salva_su_file(Folder_to_save_rect, "/Matrice_R1.npy", R1) Salva_su_file(Folder_to_save_rect, "/Matrice_R2.npy", R2) Salva_su_file(Folder_to_save_rect, "/Matrice_P1.npy", P1) Salva_su_file(Folder_to_save_rect, "/Matrice_P2.npy", P2)

[ "thebloodofjazz29@gmail.com" ]

thebloodofjazz29@gmail.com

7ca78d0c38317f6f641d8132aba60941648200ba

de0cabc94e287cec4ff07c186cc6c708eab168e2

/flea/translate.py

14576b0fd88fb755aa69c497aa33c9551ea9cca9

[ "MIT" ]

permissive

chudym2/flea-pipeline

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2bb29d793a1c35a2f344cca70d1f3b768a5760cc

refs/heads/master

2022-03-18T13:51:30.068639

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#!/usr/bin/env python """ Translate DNA reads from a fasta file. """ import sys import click from Bio import SeqIO from Bio.Seq import Seq from Bio.Alphabet import IUPAC, Gapped from flea.util import insert_gaps def _translate(record, gapped=False): result = record[:] if gapped: translated = record.seq.ungap('-').translate() result.seq = Seq(insert_gaps(str(record.seq), str(translated), '---', '-'), alphabet=Gapped(IUPAC.IUPACProtein)) else: result.seq = record.seq.translate() return result def translate(infile, outfile, gapped=False): alphabet=IUPAC.ambiguous_dna if gapped: alphabet = Gapped(alphabet) records = SeqIO.parse(infile, "fasta", alphabet=alphabet) result = (_translate(r, gapped) for r in records) SeqIO.write(result, outfile, "fasta") @click.command() @click.option('-g', '--gapped', is_flag=True, help='allow gaps') def main(gapped): translate(sys.stdin, sys.stdout, gapped) if __name__ == "__main__": main()

[ "kemal@kemaleren.com" ]

kemal@kemaleren.com

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/Functions/Python/MIND/__init__.py

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no_license

hsokooti/RegUn

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refs/heads/master

2022-05-04T05:10:38.485534

2022-04-21T21:42:49

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py

from .pyMIND import * from .search_region import * __version__ = "0.1.0" __author__ = "hsokooti"

[ "h.sokooti_oskooyi@lumc.nl" ]

h.sokooti_oskooyi@lumc.nl

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/lecture5/dynamic_member_variable.py

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[]

no_license

wduan1025/python-intro

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8628d8fa41182a2f69c7d75c3feeffa8d0e48090

refs/heads/master

2022-04-15T12:26:31.432536

2020-03-29T02:02:33

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class Animal: pass a = Animal() b = Animal() a.name = "Charles" print(a.name) # print(b.name) Animal.name = "Charles" print(b.name) c = Animal() print(c.name)

[ "weiduan1025@gmail.com" ]

weiduan1025@gmail.com

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/RegressionVisualizer/manage.py

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no_license

scotteskridge/RegressionApp

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refs/heads/master

2021-01-19T20:48:13.495541

2017-04-25T02:39:49

2017-04-25T02:39:56

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#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "RegressionVisualizer.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise print(sys.argv) execute_from_command_line(sys.argv)

[ "scott.eskridge@gmail.com" ]

scott.eskridge@gmail.com

8f8762deb83a2f972b91a97496916672ba78463c

672b1583ba4d741a156aa907d1c70c6624d863ea

/dataclean.py

5fa9205f88d0a82fbaef3ec614b9f8971c831512

[]

no_license

IsaDash/Lake-Acidity-Modeling

1c4b2e9601650eaec4f5bb7e30a2545926836218

82768d109981a2a9fcb827f776ddc4e5c4aadd18

refs/heads/master

2023-01-31T22:14:53.330143

2020-12-14T20:31:40

320,958,451

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import csv import pandas as pd # infile = open("acid.csv", "r") # outfile = open("lake_data_transformed.csv", "w", newline="") # in_csv = csv.reader(infile) # out_csv = csv.writer(outfile) # for row in in_csv: # to_add = True # if row["SIO2_MG_L (silicon dioxicde)"] == None: # continue # else: # out_csv.writerow(row) # infile.close() # outfile.close() acid_data = pd.read_csv('acid.csv') modified_acid = acid_data.dropna() modified_acid.to_csv('lake_data_cleaned.csv', index=False)

[ "anchald2@illinois.edu" ]

anchald2@illinois.edu

59e5cf0f81cff22c4432f07f3fb01378f5ceed91

18e4fffb093c8bb1fdbf59801bfa24345ce0f7cc

/test_ping.py

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[]

no_license

Nikisn/Check_config

fccd0d33f87a004853a5128cbbfd179c610ca8f7

8a7b0dc793570f0bfb84cc0029ef616f8751bf20

refs/heads/master

2023-01-22T01:13:00.463038

2020-12-03T12:58:46

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from netmiko import ConnectHandler from textfsm import clitable import yaml with open ("hosts.yaml") as f: ping_src = yaml.load(f) ping_dst = { 'r1':'ping 1.1.1.1 -c 4', 'r2':'ping 2.2.2.2 -c 4', 'r3':'ping 3.3.3.3 -c 4', 'r4':'ping 4.4.4.4 -c 4', 'r5':'ping 5.5.5.5 -c 4', 'r6':'ping 6.6.6.6 -c 4', 'r7':'ping 7.7.7.7 -c 4' } from_ping = input('Введите ОТ какого хоста слать ICMP-пакеты (r1,r2...)\n') to_ping = input('Введите ДО какого хоста слать ICMP-пакеты (r1,r2...)\n') cli_table = clitable.CliTable('index', 'TextFSM_templates') attributes = {'Command':ping_dst[to_ping], 'Vendor': 'Cisco'} device_params = { 'device_type': 'linux', 'ip': '127.0.0.1', 'port': ping_src[from_ping], 'username': 'sysadmin', 'password': '123'} with ConnectHandler(**device_params) as ssh: ping = ssh.send_command(ping_dst[to_ping]) cli_table.ParseCmd(ping,attributes) data_rows = [list(row) for row in cli_table] if data_rows[0][0] == '4' and data_rows[0][1] == '0': print(f'PING ОТ {from_ping} ДО {to_ping} УСПЕШНО!') print('РЕЗУЛЬТАТ:\n', cli_table.FormattedTable()) else: print(f'ОТ {from_ping} ДО {to_ping} ОТСУТСТВИЕ ОТВЕТА!') print('РЕЗУЛЬТАТ:\n', cli_table.FormattedTable()) #cfg_file='r1.txt' #command = ['hostname R1','interface enp0s8','ip address 22.1.3.1/24', 'ip ospf message-digest-key 1 md5 123'] #user = 'sysadmin' #password = '123' #enable_pass = '123' #ip = '127.0.0.1' # #devices_port = {'r1':2222,'r2':2200,'r3':2201,'r4':2202,'r5':2203,'r6':2204, # 'r7':2205, # } # # #print('connection to device') #device_params = { # 'device_type': 'linux', # 'ip': ip, # 'port': 2201, # 'username': user, # 'password': password} # #"подключение и снятие команды" # #with ConnectHandler(**device_params) as ssh: # ping = ssh.send_command('ping 2.2.2.2 -c 4') # # with open ('test.txt', 'w') as dest: # for line in ping: # t = dest.write(line) # print(type(t)) # #"работа с файлом пинга" # #a = '' #with open ('test.txt', 'r') as src: # for line in src: # if line.startswith('4'): # # send, rec, loss, _ = line.split(',') # # #print(f''' # Отправлено: {send} # Доставлено: {rec} # Потери: {loss}''') # # # #""" #ssh.enable() f"{name}.txt #'session_log': "output.txt" # #"""

[ "noreply@github.com" ]

noreply@github.com

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eaeacaef534ee2bd8f33928b615134037868b660

/createDF.py

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[]

no_license

tkShir/TOPIX-Price-Signal-Analysis

42d533a0afe44872c43fa4ee02e8b1abe2628c20

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refs/heads/master

2021-08-16T23:34:15.138854

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import pandas as pd def create_df(): pd.set_option('display.max_columns', 500) index_list = ["GSPC", "DJI", "IXIC", "STOXX50E", "RUT", "N225"] fx_df = pd.read_csv("FX.csv", encoding ="shift-jis", header = 2) fx_df["Date"] = pd.to_datetime(fx_df["Date"]) #fx_df = fx_df.add_prefix("") #print(fx_df.head()) df_lst = [] for index in index_list: target = index + ".csv" target_df =pd.read_csv(str(target), header = 0) target_df["Date"] = pd.to_datetime(target_df["Date"]) target_df.columns = target_df.columns[:1].union((index + " " +target_df.columns[1:]), sort = False) df_lst.append(target_df) res_df = fx_df for i in range(len(df_lst)): res_df = pd.merge(df_lst[i], res_df, on="Date") res_df.to_csv("result.csv",index=False) def modifyDF(): res_df = pd.read_csv("result.csv", header = 0) price_increase = [[0]] prev_close = 0 cur_close = 0 for row in range(len(res_df)): if row == 0: prev_close = res_df["N225 Close"][row] else: cur_close = res_df["N225 Close"][row] if prev_close < cur_close: price_increase.append([1]) else: price_increase.append([0]) prev_close = res_df["N225 Close"][row] answer = pd.DataFrame(price_increase, columns = ["Answer"]) answer res_df = res_df.join(answer) res_df.to_csv("result.csv",index=False) create_df() modifyDF()

[ "takashirono@gmail.com" ]

takashirono@gmail.com

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/testScripts/noiseAccuracy.py

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[]

no_license

CharupriyaSharma/eBNSLNoisyOR

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import sys import os noisefile = sys.argv[2] freg = open(noisefile, "r") fmerge = open(sys.argv[1] + "_noise_compare", "w+") #get var count n= int(sys.argv[3]) realNoise = [0]*n computedNoise = [0]*n error = [0]*n for i in range(n): #get scores for ith variable item = freg.readline() items = [float(it) for it in item.split()] scorecount = 0 if len(items)>1: scorecount = int(len(items)/2) print(items) print(scorecount) if scorecount > 0 : for j in range(scorecount): print("scoring node : ") print(items[0]) realNoise[int(items[2*j+1])] = items[2*j+2] # print(str(int(items[2*j+1])) + ":" +str(items[2*j+2])) #read moise from noisy-or file for ithvariable nifile = sys.argv[1] + "_" + str(int(items[0])) + "_20_noise" print("opening " + nifile) fno = open(nifile) f2="" if os.stat(nifile).st_size > 0 : f2 = fno.readlines() for line in f2 : print ("computed " + line) items1 = [float(it) for it in line.split()] if int(items1[0]) > 0 : paracount = int(items1[0]) for k in range(paracount): print(str(int(items1[k+1])) + ":" + str(items1[paracount+1+ k])) print(int(items1[k+1])) currenterr = abs(computedNoise[int(items1[k+1])] - realNoise[int(items1[k+1])]) newerr = abs(items1[paracount+1+ k] - realNoise[int(items1[k+1])]) print(abs(newerr < currenterr)) if computedNoise[int(items1[k+1])] == 0 or abs(newerr < currenterr): computedNoise[int(items1[k+1])] = items1[paracount+1+ k] fno.close() #write merged scores to file err = 0 errctr = 0 for i in range(n): if realNoise[i] >0 : err1 = abs(realNoise[i]-computedNoise[i]) errctr+=1 err+=err1 error[i] = err1 fmerge.write(str(i) + " " + str(realNoise[i]) + " " + str(computedNoise[i])+ " " + str(error[i]) + "\n") if errctr > 0 : fmerge.write("error : " + str(err/errctr)+ "\n") freg.close() fmerge.close() er =open("errors", "a+") er.write(sys.argv[1] + " :" + str(err/errctr) + "\n") er.close()

[ "charupriyasharma@Charupriyas-MacBook-Pro.local" ]

charupriyasharma@Charupriyas-MacBook-Pro.local

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/extras/include/boost/libs/mpl/preprocessed/pp.py

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# Copyright Aleksey Gurtovoy 2001-2004 # # Distributed under the Boost Software License, Version 1.0. # (See accompanying file LICENSE_1_0.txt or copy at # http://www.boost.org/LICENSE_1_0.txt) # # See http://www.boost.org/libs/mpl for documentation. # $Id: pp.py,v 1.1.1.2 2015/02/27 16:50:37 mwebster Exp $ # $Date: 2015/02/27 16:50:37 $ # $Revision: 1.1.1.2 $ import fileinput import os import re import string import sys if_else = lambda a,b,c:(a and [b] or [c])[0] max_len = 79 ident = 4 def nearest_ident_pos(text): return (len(text)/ident) * ident def block_format(limits, text, first_sep=' ', sep=',', need_last_ident=1 ): if sep == ',' and string.find( text, '<' ) != -1: sep = '%s ' % sep words = string.split( string.join( string.split( text ), ' ' ) , sep ) s = ' ' * limits[0] max_len = limits[1] return '%s\n%s' \ % ( reduce( lambda t,w,max_len=max_len,s=s,sep=sep: if_else(t[1] + len(w) < max_len , ('%s%s%s'% (t[0],t[2],w), t[1]+len(w)+len(t[2]), sep) , ('%s\n%s%s%s'% (t[0],s,sep,w), len(s)+len(w)+len(sep), sep) ) , words , (s,len(s)+len(first_sep),first_sep) )[0] , if_else(need_last_ident,s,'') ) def handle_args( match ): if re.compile('^\s*(typedef|struct|static)\s+.*?$').match(match.group(0)): return match.group(0) return '%s'\ % block_format( (nearest_ident_pos(match.group(1)),max_len) , match.group(3) , match.group(2) , ',' , 0 ) def handle_inline_args(match): if len(match.group(0)) < max_len: return match.group(0) if match.group(9) == None: return '%s%s<\n%s>\n'\ % ( match.group(1) , match.group(3) , block_format( (nearest_ident_pos(match.group(1))+ident,max_len) , match.group(4) ) ) return '%s%s<\n%s>\n%s%s'\ % ( match.group(1) , match.group(3) , block_format( (nearest_ident_pos(match.group(1))+ident,max_len-len(match.group(9))) , match.group(4) ) , string.replace(match.group(1),',',' ') , match.group(9) ) def handle_simple_list(match): if match.group(1) == 'template': return match.group(0) single_arg = re.compile('^\s*(\w|\d)+\s*$').match(match.group(2)) return if_else(single_arg,'%s<%s>','%s< %s >') %\ ( match.group(1) , string.join(string.split(match.group(2)), '') ) def handle_static(match): if len(match.group(0)) < max_len: return match.group(0) (first_sep,sep) = if_else(string.find(match.group(0),'+') == -1, (' ',' '),(' ','+')) return '%s%s\n%s%s' %\ ( match.group(1) , string.join(string.split(match.group(2)), ' ') , block_format( (nearest_ident_pos(match.group(1))+ident,max_len) , match.group(4) , first_sep , sep ) , match.group(5) ) def handle_typedefs(match): if string.count(match.group(2), ';') == 1: return match.group(0) join_sep = ';\n%s' % match.group(1) return '%s%s\n' \ % ( match.group(1) , string.join(map(string.strip, string.split(match.group(2), ';')), join_sep) ) def fix_angle_brackets( match ): return ' '.join( ''.join( match.group(1).split( ' ' ) ) ) + match.group(3) class pretty: def __init__(self, name): self.output = open(name, "w") self.prev_line = '' self.re_copyright_start = re.compile( r'^// Copyright .*$' ) self.re_copyright_end = re.compile( r'^// See .* for documentation.$' ) self.reading_copyright = 0 self.copyright = None self.re_header_name_comment = re.compile( r'^\s*//\s+\$[I]d:\s+(.*?%s\.hpp)\s+[^$]+[$]$' % os.path.splitext( name )[0] ) self.header_was_written = 0 self.re_junk = re.compile(r'^\s*(#|//[^/]|////).*$') self.re_c_comment_start = re.compile(r'^\s*/\*.*') self.re_c_comment_end = re.compile(r'^.*\*/\s*$') self.inside_c_comment = 0 self.re_empty_line = re.compile(r'^\s*$') self.re_comma = re.compile(r'(\S+)\s*,\s*') self.re_assign = re.compile(r'(\S+[^<|^!|^>])\s*(=+)\s*(\S+)') self.re_marked_empty_comment = re.compile(r'^\s*//\s*$') self.re_typedef = re.compile(r'^\s+typedef\s+.*?;$') self.re_nsl = re.compile(r'^(\s+typedef\s+.*?;|\s*(private|public):\s*|\s*{\s*|\s*(\w|\d|,)+\s*)$') self.re_templ_decl = re.compile(r'^(\s*template\s*<\s*.*?|\s*(private|public):\s*)$') self.re_type_const = re.compile(r'(const)\s+((unsigned|signed)?(bool|char|short|int|long))') #self.re_templ_args = re.compile(r'^(\s*)(, | {2})((.*::.*?,?)+)\s*$') self.re_templ_args = re.compile(r'^(\s*)(, | {2})((\s*(\w+)(\s+|::)\w+\s*.*?,?)+)\s*$') self.re_inline_templ_args = re.compile( r'^(\s+(,|:\s+)?|struct\s+)(\w+)\s*<((\s*(typename\s+)?\w+\s*(=\s*.*|<(\s*\w+\s*,?)+>\s*)?,?)+)\s*>\s+((struct|class).*?)?$' ) self.re_simple_list = re.compile(r'(\w+)\s*<((\w|,| |-)+)>') self.re_static_const = re.compile(r'(\s*)((BOOST_STATIC_CONSTANT$\s*\w+,\s*|enum\s*\w*\s*{\s*)value\s*=)(.*?)([}|$];)$') self.re_typedefs = re.compile(r'(\s*)((\s*typedef\s*.*?;)+)\s*$') self.re_fix_angle_brackets = re.compile( r'(>(\s*>)+)(,|\n$)' ) self.re_closing_curly_brace = re.compile(r'^(}|struct\s+\w+);\s*$') self.re_namespace_scope_templ = re.compile(r'^template\s*<\s*$') self.re_namespace = re.compile(r'^\n?namespace\s+\w+\s*{\s*\n?$') def process(self, line): if self.reading_copyright: if not self.re_copyright_end.match( line ): self.copyright += line return self.reading_copyright = 0 if not self.header_was_written and self.re_copyright_start.match( line ): self.copyright = line self.reading_copyright = 1 return # searching for header line if not self.header_was_written: if self.re_header_name_comment.match( line ): self.header_was_written = 1 match = self.re_header_name_comment.match( line ) self.output.write( \ '\n%s\n' \ '// *Preprocessed* version of the main "%s" header\n' \ '// -- DO NOT modify by hand!\n\n' \ % ( self.copyright, match.group(1) ) ) return # skipping preprocessor directives, comments, etc. if self.re_junk.match(line): return if self.inside_c_comment or self.re_c_comment_start.match(line): self.inside_c_comment = not self.re_c_comment_end.match(line) return # restoring some empty lines if self.re_templ_decl.match(line) and self.re_typedef.match(self.prev_line) \ or not self.re_empty_line.match(line) and self.re_closing_curly_brace.match(self.prev_line) \ or not self.re_empty_line.match(self.prev_line) \ and ( self.re_namespace_scope_templ.match(line) \ or self.re_namespace.match(line) and not self.re_namespace.match(self.prev_line) \ ): line = '\n%s' % line # removing excessive empty lines if self.re_empty_line.match(line): if self.re_empty_line.match(self.prev_line) or not self.header_was_written: return # skip empty line after typedef if self.re_nsl.match(self.prev_line): return # formatting line = self.re_comma.sub( r'\1, ', line ) line = self.re_assign.sub( r'\1 \2 \3', line ) line = self.re_marked_empty_comment.sub( r'\n', line ) line = self.re_type_const.sub( r'\2 \1', line ) line = self.re_templ_args.sub( handle_args, line ) line = self.re_inline_templ_args.sub( handle_inline_args, line ) line = self.re_simple_list.sub( handle_simple_list, line) line = self.re_static_const.sub( handle_static, line ) line = self.re_typedefs.sub( handle_typedefs, line ) line = self.re_fix_angle_brackets.sub( fix_angle_brackets, line ) # write the output self.output.write(line) self.prev_line = line def main( src, dest ): p = pretty( os.path.basename( dest ) ) for line in fileinput.input( src ): p.process(line) if __name__ == '__main__': main( sys.argv[1], sys.argv[2] )

[ "muschellij2@gmail.com" ]

muschellij2@gmail.com

4be926645d799b5ba94de45a6cc2f96033dd6cea

b8b4e487a084c707bbc994214edf36c9a2710e00

/project/analyze.py

6f5679bf3bcfc6bc0a8d68f7ee936a98c1ecf767

[]

no_license

kp-john/project

de6a26abea3076cefab50a0371661c7a5db6a756

00a9fc19d09b8a1f580c6394f8a82ea4b5179506

refs/heads/master

2023-08-15T08:51:32.189677

2021-10-21T08:46:13

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import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import stockdata, corona import sys import pandas #"Malgun Gothic" 폰트 설정 sns.set(font="Malgun Gothic", rc={"axes.unicode_minus":False}, style='darkgrid') if __name__ == "__main__": app = stockdata.QApplication(sys.argv) trade = stockdata.system_trading() # # stock1(생물공학 - 알테오젠) # trade.rq_chart_data("196170", "20211016", 1) # stock1_day_data = pandas.DataFrame(trade.day_data, columns=['date','open','high','low','close','volume','trade_volume']) # stock2(해운사 - hmm) trade.rq_chart_data("011200", "20211016", 1) stock2_day_data = pandas.DataFrame(trade.day_data, columns=['date','open','high','low','close','volume','trade_volume']) # # stock3(생명과학도구및서비스 - 씨젠) # trade.rq_chart_data("096530", "20211016", 1) # stock3_day_data = pandas.DataFrame(trade.day_data, columns=['date','open','high','low','close','volume','trade_volume']) #dtype object->int형으로 변환 # stock1_day_data = stock1_day_data.apply(pd.to_numeric) # df1 = stock1_day_data.sort_values(by='date', ascending=True) stock2_day_data = stock2_day_data.apply(pd.to_numeric) df2 = stock2_day_data.sort_values(by='date', ascending=True) # stock3_day_data = stock3_day_data.apply(pd.to_numeric) # df3 = stock3_day_data.sort_values(by='date', ascending=True) #주식데이터 df1,df2,df3와 코로나데이터 corona.df 합치기 # df1_inner = pandas.merge(df1, corona.df, on='date', how='inner') df2_inner = pandas.merge(df2, corona.df, on='date', how='inner') # df3_inner = pandas.merge(df3, corona.df, on='date', how='inner') #종가, 확진자수, 일일확진자수, 1차접종완료, 접종완료 컬럼추출 # train_df1 = df1_inner[['close', '확진자수', '일일확진자수', '1차접종완료', '접종완료']] train_df2 = df2_inner[['close', '확진자수', '일일확진자수', '1차접종완료', '접종완료']] # train_df3 = df3_inner[['close', '확진자수', '일일확진자수', '1차접종완료', '접종완료']] #상관계수 # df1_corr = train_df1.corr(method='pearson') df2_corr = train_df2.corr(method='pearson') # df3_corr = train_df3.corr(method='pearson') # print(df1_corr) print(df2_corr) # print(df3_corr) #상관계수 히트맵 # plt.rcParams['figure.figsize'] = [10,10] # # sns.heatmap(train_df1.corr(), annot=True, cmap='Blues', vmin=-1, vmax=1) # plt.title('생물공학 - 알테오젠', fontsize=15) # plt.show() plt.rcParams['figure.figsize'] = [10, 10] sns.heatmap(train_df2.corr(), annot=True, cmap='Pastel1', vmin=-1, vmax=1) plt.title('해운사 - hmm', fontsize=15) plt.show() # plt.rcParams['figure.figsize'] = [10, 10] # sns.heatmap(train_df3.corr(), annot=True, cmap='Greens', vmin=-1, vmax=1) # plt.title('생명과학도구및서비스 - 씨젠', fontsize=15) # plt.show() # K-means, decision tree - 주가예측 # 뉴스 크롤링(네이버금융? 이베스트? - 일단 긍(1),부정(0)으로만 평가) # 추가 고려사항) 정해진 3종목 이외 다른 종목까지 진행?

[ "qkfkadlemf@naver.com" ]

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2020-05-09T18:29:47.818789

2014-11-07T01:48:29

2014-11-07T01:48:42

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#!/bin/sh """:" exec python $0 ${1+"$@"} """ #" ############################################################################## # # Copyright (c) 2001 Zope Corporation and Contributors. All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.0 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE # ############################################################################## """Bobo call interface This module provides tools for accessing web objects as if they were functions or objects with methods. It also provides a simple call function that allows one to simply make a single web request. Function -- Function-like objects that return both header and body data when called. Object -- Treat a URL as a web object with methods call -- Simple interface to call a remote function. The module also provides a command-line interface for calling objects. """ __version__='$Revision: 1.45 $'[11:-2] import sys, re, socket, mimetools from httplib import HTTP from os import getpid from time import time from random import random from base64 import encodestring from urllib import urlopen, quote from types import FileType, ListType, DictType, TupleType from string import translate, maketrans from urlparse import urlparse class Function: username=None password=None method=None timeout=60 def __init__(self,url, arguments=(),method=None,username=None,password=None, timeout=None, **headers): while url[-1:]=='/': url=url[:-1] self.url=url self.headers=headers if not headers.has_key('Host') and not headers.has_key('host'): headers['Host']=urlparse(url)[1] self.func_name=url[url.rfind('/')+1:] self.__dict__['__name__']=self.func_name self.func_defaults=() self.args=arguments if method is not None: self.method=method if username is not None: self.username=username if password is not None: self.password=password if timeout is not None: self.timeout=timeout mo = urlregex.match(url) if mo is not None: host,port,rurl=mo.group(1,2,3) if port: port=int(port[1:]) else: port=80 self.host=host self.port=port rurl=rurl or '/' self.rurl=rurl else: raise ValueError, url def __call__(self,*args,**kw): method=self.method if method=='PUT' and len(args)==1 and not kw: query=[args[0]] args=() else: query=[] for i in range(len(args)): try: k=self.args[i] if kw.has_key(k): raise TypeError, 'Keyword arg redefined' kw[k]=args[i] except IndexError: raise TypeError, 'Too many arguments' headers={} for k, v in self.headers.items(): headers[translate(k,dashtrans)]=v method=self.method if headers.has_key('Content-Type'): content_type=headers['Content-Type'] if content_type=='multipart/form-data': return self._mp_call(kw) else: content_type=None if not method or method=='POST': for v in kw.values(): if hasattr(v,'read'): return self._mp_call(kw) can_marshal=type2marshal.has_key for k,v in kw.items(): t=type(v) if can_marshal(t): q=type2marshal[t](k,v) else: q='%s=%s' % (k,quote(v)) query.append(q) url=self.rurl if query: query='&'.join(query) method=method or 'POST' if method == 'PUT': headers['Content-Length']=str(len(query)) if method != 'POST': url="%s?%s" % (url,query) query='' elif not content_type: headers['Content-Type']='application/x-www-form-urlencoded' headers['Content-Length']=str(len(query)) else: method=method or 'GET' if (self.username and self.password and not headers.has_key('Authorization')): headers['Authorization']=( "Basic %s" % encodestring('%s:%s' % (self.username,self.password)).replace( '\012','') ) try: h=HTTP() h.connect(self.host, self.port) h.putrequest(method, self.rurl) for hn,hv in headers.items(): h.putheader(translate(hn,dashtrans),hv) h.endheaders() if query: h.send(query) ec,em,headers=h.getreply() response =h.getfile().read() except: raise NotAvailable, RemoteException( NotAvailable,sys.exc_info()[1],self.url,query) if (ec - (ec % 100)) == 200: return (headers,response) self.handleError(query, ec, em, headers, response) def handleError(self, query, ec, em, headers, response): try: v=headers.dict['bobo-exception-value'] except: v=ec try: f=headers.dict['bobo-exception-file'] except: f='Unknown' try: l=headers.dict['bobo-exception-line'] except: l='Unknown' try: t=exceptmap[headers.dict['bobo-exception-type']] except: if ec >= 400 and ec < 500: t=NotFound elif ec == 503: t=NotAvailable else: t=ServerError raise t, RemoteException(t,v,f,l,self.url,query,ec,em,response) def _mp_call(self,kw, type2suffix={ type(1.0): ':float', type(1): ':int', type(1L): ':long', type([]): ':list', type(()): ':tuple', } ): # Call a function using the file-upload protcol # Add type markers to special values: d={} special_type=type2suffix.has_key for k,v in kw.items(): if ':' not in k: t=type(v) if special_type(t): d['%s%s' % (k,type2suffix[t])]=v else: d[k]=v else: d[k]=v rq=[('POST %s HTTP/1.0' % self.rurl),] for n,v in self.headers.items(): rq.append('%s: %s' % (n,v)) if self.username and self.password: c=encodestring('%s:%s' % (self.username,self.password)).replace('\012','') rq.append('Authorization: Basic %s' % c) rq.append(MultiPart(d).render()) rq='\r\n'.join(rq) try: sock=socket.socket(socket.AF_INET,socket.SOCK_STREAM) sock.connect((self.host,self.port)) sock.send(rq) reply=sock.makefile('rb') sock=None line=reply.readline() try: [ver, ec, em] = line.split(None, 2) except ValueError: raise 'BadReply','Bad reply from server: '+line if ver[:5] != 'HTTP/': raise 'BadReply','Bad reply from server: '+line ec=int(ec) em=em.strip() headers=mimetools.Message(reply,0) response=reply.read() finally: if 0: raise NotAvailable, ( RemoteException(NotAvailable,sys.exc_info()[1], self.url,'<MultiPart Form>')) if ec==200: return (headers,response) self.handleError('', ec, em, headers, response) class Object: """Surrogate object for an object on the web""" username=None password=None method=None timeout=None special_methods= 'GET','POST','PUT' def __init__(self, url, method=None,username=None,password=None, timeout=None, **headers): self.url=url self.headers=headers if not headers.has_key('Host') and not headers.has_key('host'): headers['Host']=urlparse(url)[1] if method is not None: self.method=method if username is not None: self.username=username if password is not None: self.password=password if timeout is not None: self.timeout=timeout def __getattr__(self, name): if name in self.special_methods: method=name url=self.url else: method=self.method url="%s/%s" % (self.url, name) f=Function(url, method=method, username=self.username, password=self.password, timeout=self.timeout) f.headers=self.headers return f def call(url,username=None, password=None, **kw): return apply(Function(url,username=username, password=password), (), kw) ############################################################################## # Implementation details below here urlregex=re.compile(r'http://([^:/]+)(:[0-9]+)?(/.+)?', re.I) dashtrans=maketrans('_','-') def marshal_float(n,f): return '%s:float=%s' % (n,f) def marshal_int(n,f): return '%s:int=%s' % (n,f) def marshal_long(n,f): value = '%s:long=%s' % (n, f) if value[-1] == 'L': value = value[:-1] return value def marshal_list(n,l,tname='list', lt=type([]), tt=type(())): r=[] for v in l: t=type(v) if t is lt or t is tt: raise TypeError, 'Invalid recursion in data to be marshaled.' r.append(marshal_whatever("%s:%s" % (n,tname) ,v)) return '&'.join(r) def marshal_tuple(n,l): return marshal_list(n,l,'tuple') type2marshal={ type(1.0): marshal_float, type(1): marshal_int, type(1L): marshal_long, type([]): marshal_list, type(()): marshal_tuple, } def marshal_whatever(k,v): try: q=type2marshal[type(v)](k,v) except KeyError: q='%s=%s' % (k,quote(str(v))) return q def querify(items): query=[] for k,v in items: query.append(marshal_whatever(k,v)) return query and '&'.join(query) or '' NotFound ='bci.NotFound' InternalError='bci.InternalError' BadRequest ='bci.BadRequest' Unauthorized ='bci.Unauthorized' ServerError ='bci.ServerError' NotAvailable ='bci.NotAvailable' exceptmap ={'AttributeError' :AttributeError, 'BadRequest' :BadRequest, 'EOFError' :EOFError, 'IOError' :IOError, 'ImportError' :ImportError, 'IndexError' :IndexError, 'InternalError' :InternalError, 'KeyError' :KeyError, 'MemoryError' :MemoryError, 'NameError' :NameError, 'NotAvailable' :NotAvailable, 'NotFound' :NotFound, 'OverflowError' :OverflowError, 'RuntimeError' :RuntimeError, 'ServerError' :ServerError, 'SyntaxError' :SyntaxError, 'SystemError' :SystemError, 'SystemExit' :SystemExit, 'TypeError' :TypeError, 'Unauthorized' :Unauthorized, 'ValueError' :ValueError, 'ZeroDivisionError':ZeroDivisionError} class RemoteException: def __init__(self,etype=None,evalue=None,efile=None,eline=None,url=None, query=None,http_code=None,http_msg=None, http_resp=None): """Contains information about an exception which occurs in a remote method call""" self.exc_type =etype self.exc_value =evalue self.exc_file =efile self.exc_line =eline self.url =url self.query =query self.http_code =http_code self.http_message=http_msg self.response =http_resp def __repr__(self): return '%s (File: %s Line: %s)\n%s %s for %s' % ( self.exc_value,self.exc_file,self.exc_line, self.http_code,self.http_message,self.url) class MultiPart: def __init__(self,*args): c=len(args) if c==1: name,val=None,args[0] elif c==2: name,val=args[0],args[1] else: raise ValueError, 'Invalid arguments' h={'Content-Type': {'_v':''}, 'Content-Transfer-Encoding': {'_v':''}, 'Content-Disposition': {'_v':''},} dt=type(val) b=t=None if dt==DictType: t=1 b=self.boundary() d=[] h['Content-Type']['_v']='multipart/form-data; boundary=%s' % b for n,v in val.items(): d.append(MultiPart(n,v)) elif (dt==ListType) or (dt==TupleType): raise ValueError, 'Sorry, nested multipart is not done yet!' elif dt==FileType or hasattr(val,'read'): if hasattr(val,'name'): fn=val.name.replace( '\\', '/') fn=fn[(fn.rfind('/')+1):] ex=(fn[(fn.rfind('.')+1):]).lower() if self._extmap.has_key(ex): ct=self._extmap[ex] else: ct=self._extmap[''] else: fn='' ct=self._extmap[None] if self._encmap.has_key(ct): ce=self._encmap[ct] else: ce='' h['Content-Disposition']['_v'] ='form-data' h['Content-Disposition']['name'] ='"%s"' % name h['Content-Disposition']['filename']='"%s"' % fn h['Content-Transfer-Encoding']['_v']=ce h['Content-Type']['_v'] =ct d=[] l=val.read(8192) while l: d.append(l) l=val.read(8192) else: h['Content-Disposition']['_v']='form-data' h['Content-Disposition']['name']='"%s"' % name d=[str(val)] self._headers =h self._data =d self._boundary=b self._top =t def boundary(self): return '%s_%s_%s' % (int(time()), getpid(), int(random()*1000000000)) def render(self): h=self._headers s=[] if self._top: for n,v in h.items(): if v['_v']: s.append('%s: %s' % (n,v['_v'])) for k in v.keys(): if k != '_v': s.append('; %s=%s' % (k, v[k])) s.append('\r\n') p=[] t=[] b=self._boundary for d in self._data: p.append(d.render()) t.append('--%s\n' % b) t.append(('\n--%s\n' % b).join(p)) t.append('\n--%s--\n' % b) t=''.join(t) s.append('Content-Length: %s\r\n\r\n' % len(t)) s.append(t) return ''.join(s) else: for n,v in h.items(): if v['_v']: s.append('%s: %s' % (n,v['_v'])) for k in v.keys(): if k != '_v': s.append('; %s=%s' % (k, v[k])) s.append('\r\n') s.append('\r\n') if self._boundary: p=[] b=self._boundary for d in self._data: p.append(d.render()) s.append('--%s\n' % b) s.append(('\n--%s\n' % b).join(p)) s.append('\n--%s--\n' % b) return ''.join(s) else: return ''.join(s+self._data) _extmap={'': 'text/plain', 'rdb': 'text/plain', 'html': 'text/html', 'dtml': 'text/html', 'htm': 'text/html', 'dtm': 'text/html', 'gif': 'image/gif', 'jpg': 'image/jpeg', 'exe': 'application/octet-stream', None : 'application/octet-stream', } _encmap={'image/gif': 'binary', 'image/jpg': 'binary', 'application/octet-stream': 'binary', } def ErrorTypes(code): if code >= 400 and code < 500: return NotFound if code >= 500 and code < 600: return ServerError return 'HTTP_Error_%s' % code usage=""" Usage: %s [-u username:password] url [name=value ...] where url is the web resource to call. The -u option may be used to provide a user name and password. Optional arguments may be provides as name=value pairs. In a name value pair, if a name ends in ":file", then the value is treated as a file name and the file is send using the file-upload protocol. If the file name is "-", then data are taken from standard input. The body of the response is written to standard output. The headers of the response are written to standard error. """ % sys.argv[0] def main(): import getopt user=None try: optlist, args = getopt.getopt(sys.argv[1:],'u:') url=args[0] u =filter(lambda o: o[0]=='-u', optlist) if u: [user, pw] = u[0][1].split(':') kw={} for arg in args[1:]: [name,v]=arg.split('=') if name[-5:]==':file': name=name[:-5] if v=='-': v=sys.stdin else: v=open(v, 'rb') kw[name]=v except: print usage sys.exit(1) # The "main" program for this module f=Function(url) if user: f.username, f.password = user, pw headers, body = apply(f,(),kw) sys.stderr.write(''.join(map(lambda h: "%s: %s\n" % h, headers.items())) +"\n\n") print body if __name__ == "__main__": main()

[ "martin@os2world.com" ]

martin@os2world.com

af65bdd5edd78745f8c63e9d570bc61d19f4926b

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/advent_of_code/2021/day15/main.py

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antoinemadec/test

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refs/heads/master

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#!/usr/bin/env python3 class Pos: pos_dict = {} def __init__(self, pos, weight): self.pos = pos self.weight = weight self.lowest_risk = None self.pos_dict[pos] = self def get_ripple_positions(size, i, reverse=False): positions = [] for x in range(size): if x > i: positions.append((i, x)) for x in range(size): if x > i: positions.append((x, i)) positions.reverse() positions.append((i, i)) if reverse: positions.reverse() return positions def compute_lowest_risks(d, size): for i in range(size): for pos in get_ripple_positions(size, (size-i)-1): point = d[pos] risks = [] # FIXME: missing up case when on left, and left case when on top for i, j in [(0, 1), (1, 0)]: x, y = pos[0]+i, pos[1]+j if (x, y) not in d: continue risks.append(d[(x, y)].lowest_risk) if len(risks) == 0: point.lowest_risk = point.weight else: point.lowest_risk = min(risks) + point.weight def build_meta_grid(lines, meta_grid_size, size): for meta_i in range(meta_grid_size): for meta_pos in get_ripple_positions(meta_grid_size, meta_i, reverse=True): for i, row in enumerate(lines): for j, weight in enumerate(row): x = i + meta_pos[0]*size y = j + meta_pos[1]*size weight += meta_pos[0] + meta_pos[1] if weight > 9: weight -= 9 Pos((x, y), weight) def part12(lines, p2=False): size = len(lines) meta_grid_size = (1, 5)[p2] build_meta_grid(lines, meta_grid_size, size) compute_lowest_risks(Pos.pos_dict, size * meta_grid_size) return Pos.pos_dict[(0, 0)].lowest_risk - Pos.pos_dict[(0, 0)].weight with open('input.txt', 'r') as f: lines = [[int(c) for c in l.strip()] for l in f.readlines()] print(part12(lines)) print(part12(lines, True))

[ "ajamadec@gmail.com" ]

ajamadec@gmail.com

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/worm_plates/collect/refine_coords.py

c86eb3c3b422cbf802411536855a272433f692d0

[]

no_license

ver228/worm-eggs

fd4afa13cba12f6553c0e8225fb591d9ea3806f1

0b2db08d9d81c3b31d9ebcd593059db02b3ee2fe

refs/heads/master

2022-04-01T06:29:56.358944

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Apr 1 16:22:37 2019 @author: avelinojaver """ from pathlib import Path import pandas as pd import tables import tqdm import cv2 import numpy as np from skimage.feature import peak_local_max from scipy.spatial.distance import cdist from scipy.optimize import linear_sum_assignment #%% def correct_coords(img_, coords_, min_distance = 1, max_dist = 5): #%% peaks = peak_local_max(img_, min_distance = min_distance) peaks = peaks[:, ::-1] #remove `peaks` that is not close by to any `coord` by at most `max_dist` D = cdist(coords_, peaks) #peaks with an intensity smaller than the coords intensities will be spurious peaks_ints = img_[peaks[:, 1], peaks[:, 0]] cc = coords_.astype(np.int) coords_int = img_[cc[:, 1], cc[:, 0]] good = (D <= max_dist).any(axis=0) good &= peaks_ints >= coords_int.min() D = D[:, good] valid_peaks = peaks[good] #find the closest peaks closest_indexes = np.argmin(D, axis=1) #we will consider as an easy assigment if the closest peak is assigned to only one coord u_indexes = np.unique(closest_indexes) counts = np.bincount(closest_indexes)[u_indexes] easy_assigments = u_indexes[counts == 1] valid_pairs = [(ii, x) for ii, x in enumerate(closest_indexes) if x in easy_assigments] if len(valid_pairs) > 0: easy_rows, easy_cols = map(np.array, zip(*valid_pairs)) easy_cost = D[easy_rows, easy_cols] good = easy_cost<max_dist easy_rows = easy_rows[good] easy_cols = easy_cols[good] assert (D[easy_rows, easy_cols] <= max_dist).all() #now hard assigments are if a peak is assigned to more than one peak ambigous_rows = np.ones(D.shape[0], np.bool) ambigous_rows[easy_rows] = False ambigous_rows, = np.where(ambigous_rows) ambigous_cols = np.ones(D.shape[1], np.bool) ambigous_cols[easy_cols] = False ambigous_cols, = np.where(ambigous_cols) else: ambigous_rows = np.arange(D.shape[0]) ambigous_cols = np.arange(D.shape[1]) easy_rows = np.array([], dtype=np.int) easy_cols = np.array([], dtype=np.int) D_r = D[ambigous_rows][:, ambigous_cols] good = (D_r <= max_dist).any(axis=0) D_r = D_r[:, good] ambigous_cols = ambigous_cols[good] #for this one we use the hungarian algorithm for the assigment. This assigment is to slow over the whole matrix ri, ci = linear_sum_assignment(D_r) hard_rows, hard_cols = ambigous_rows[ri], ambigous_cols[ci] assert (D_r[ri, ci] == D[hard_rows, hard_cols]).all() hard_cost = D[hard_rows, hard_cols] good = hard_cost<max_dist hard_rows = hard_rows[good] hard_cols = hard_cols[good] #let's combine both and assign the corresponding peak rows = np.concatenate((easy_rows, hard_rows)) cols = np.concatenate((easy_cols, hard_cols)) new_coords = coords_.copy() new_coords[rows] = valid_peaks[cols] #coords that do not satisfy the close peak condition will not be changed return new_coords #%% if __name__ == '__main__': _debug = False min_distance = 2 max_dist = 5 r = max_dist*2+1 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(r, r)) src_root_dir = Path.home() / 'workspace/localization/data/worm_eggs_adam/' dst_root_dir = Path.home() / 'workspace/localization/data/worm_eggs_adam_refined/' src_files = [x for x in src_root_dir.rglob('*.hdf5') if not x.name.startswith('.')] for src_file in tqdm.tqdm(src_files): with pd.HDFStore(src_file, 'r') as fid: df = fid['/coords'] img = fid.get_node('/img')[:] #%% #create a mask using the known coordinates valid_mask = np.zeros_like(img) cols = df['cx'].astype(np.int) rows = df['cy'].astype(np.int) valid_mask[rows, cols] = 1 valid_mask = cv2.dilate(valid_mask, kernel) > 0 #then I will use the inverted maxima to to create local maxima corresponding to the refined eggs peaks img_peaks = ~img img_peaks -= img_peaks[valid_mask].min() img_peaks[~valid_mask] = 0 #img_peaks = cv2.blur(img_peaks, (1,1)) #%% #finaly use the correct coords function to assing each labelled coords to a local maxima cc = df[['cx','cy']].values new_coords = correct_coords(img_peaks, cc, min_distance, max_dist) coords = pd.DataFrame({'type_id':1, 'cx':new_coords[:,0], 'cy':new_coords[:,1]}) coords = coords.to_records(index=False) dst_file = str(src_file).replace(str(src_root_dir), str(dst_root_dir)) dst_file = Path(dst_file) dst_file.parent.mkdir(exist_ok=True, parents=True) with tables.File(str(dst_file), 'w') as fid: fid.create_carray('/', 'img', obj = img) fid.create_table('/', 'coords', obj = coords) #%% if _debug: #%% import matplotlib.pylab as plt fig, axs = plt.subplots(1, 2, sharex=True, sharey=True) axs[0].imshow(img, cmap = 'gray') axs[1].imshow(img_peaks, cmap = 'gray') for ax in axs: ax.plot(df['cx'], df['cy'], '.r') ax.plot(coords['cx'], coords['cy'], '.g') plt.show() #%% break

[ "ver228@gmail.com" ]

ver228@gmail.com

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/pysnmp-with-texts/ALCATEL-IND1-E-SERVICE-MIB.py

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# # PySNMP MIB module ALCATEL-IND1-E-SERVICE-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ALCATEL-IND1-E-SERVICE-MIB # Produced by pysmi-0.3.4 at Wed May 1 11:17:28 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # softentIND1eService, = mibBuilder.importSymbols("ALCATEL-IND1-BASE", "softentIND1eService") ObjectIdentifier, Integer, OctetString = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueSizeConstraint, SingleValueConstraint, ConstraintsUnion, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "SingleValueConstraint", "ConstraintsUnion", "ValueRangeConstraint", "ConstraintsIntersection") InterfaceIndex, = mibBuilder.importSymbols("IF-MIB", "InterfaceIndex") SnmpAdminString, = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString") NotificationGroup, ModuleCompliance, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance", "ObjectGroup") Gauge32, Counter32, iso, NotificationType, ModuleIdentity, ObjectIdentity, IpAddress, MibScalar, MibTable, MibTableRow, MibTableColumn, MibIdentifier, Counter64, Unsigned32, Integer32, TimeTicks, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "Gauge32", "Counter32", "iso", "NotificationType", "ModuleIdentity", "ObjectIdentity", "IpAddress", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "MibIdentifier", "Counter64", "Unsigned32", "Integer32", "TimeTicks", "Bits") RowStatus, DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "RowStatus", "DisplayString", "TextualConvention") alcatelIND1EServiceMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1)) if mibBuilder.loadTexts: alcatelIND1EServiceMIB.setLastUpdated('200705230000Z') if mibBuilder.loadTexts: alcatelIND1EServiceMIB.setOrganization('Alcatel-Lucent') if mibBuilder.loadTexts: alcatelIND1EServiceMIB.setContactInfo('Please consult with Customer Service to ensure the most appropriate version of this document is used with the products in question: Alcatel-Lucent, Enterprise Solutions Division (Formerly Alcatel Internetworking, Incorporated) 26801 West Agoura Road Agoura Hills, CA 91301-5122 United States Of America Telephone: North America +1 800 995 2696 Latin America +1 877 919 9526 Europe +31 23 556 0100 Asia +65 394 7933 All Other +1 818 878 4507 Electronic Mail: support@ind.alcatel.com World Wide Web: http://alcatel-lucent.com/wps/portal/enterprise File Transfer Protocol: ftp://ftp.ind.alcatel.com/pub/products/mibs') if mibBuilder.loadTexts: alcatelIND1EServiceMIB.setDescription('The parameters for configuration of the E-Service feature. The right to make changes in specification and other information contained in this document without prior notice is reserved. No liability shall be assumed for any incidental, indirect, special, or consequential damages whatsoever arising from or related to this document or the information contained herein. Vendors, end-users, and other interested parties are granted non-exclusive license to use this specification in connection with management of the products for which it is intended to be used. Copyright (C) 1995-2006 Alcatel-Lucent ALL RIGHTS RESERVED WORLDWIDE') class AlaEServiceUNIProfileProtocolTreatment(TextualConvention, Integer32): description = 'The behavior of the bridge in regards to the given protocols packets received on the UNI. Tunnel (1) enables the packets to be tunneled across the provider network. Discard (2) causes the packets to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol.' status = 'current' subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3)) namedValues = NamedValues(("tunnel", 1), ("drop", 2), ("peer", 3)) alcatelIND1eServiceMIBObjects = ObjectIdentity((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1)) if mibBuilder.loadTexts: alcatelIND1eServiceMIBObjects.setStatus('current') if mibBuilder.loadTexts: alcatelIND1eServiceMIBObjects.setDescription('Branch For E-Service Managed Objects.') alcatelIND1EServiceMIBConformance = ObjectIdentity((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2)) if mibBuilder.loadTexts: alcatelIND1EServiceMIBConformance.setStatus('current') if mibBuilder.loadTexts: alcatelIND1EServiceMIBConformance.setDescription('Branch For E-Service Conformance Information.') alcatelIND1EServiceMIBGroups = ObjectIdentity((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1)) if mibBuilder.loadTexts: alcatelIND1EServiceMIBGroups.setStatus('current') if mibBuilder.loadTexts: alcatelIND1EServiceMIBGroups.setDescription('Branch For E-Service Units Of Conformance.') alcatelIND1EServiceMIBCompliances = ObjectIdentity((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 2)) if mibBuilder.loadTexts: alcatelIND1EServiceMIBCompliances.setStatus('current') if mibBuilder.loadTexts: alcatelIND1EServiceMIBCompliances.setDescription('Branch For E-Service Compliance Statements.') alaEService = MibIdentifier((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1)) alaEServiceInfo = MibIdentifier((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 1)) alaEServiceMode = MibScalar((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("legacyMode", 1), ("eServiceMode", 2))).clone('legacyMode')).setMaxAccess("readwrite") if mibBuilder.loadTexts: alaEServiceMode.setStatus('current') if mibBuilder.loadTexts: alaEServiceMode.setDescription('The current mode configured for Vlan Stacking and Layer 2 tunnel configuration. legacyMode (1) indicates that the commands from AlcatelIND1VLANStacking.mib are to be used. eServiceMode (2) indicates the commands from this MIB are to be used.') alaEServiceSapProfileTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2), ) if mibBuilder.loadTexts: alaEServiceSapProfileTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileTable.setDescription('A table that contains service profiles containing performance and control attributes. An entry in this table is created when a new service profile is defined.') alaEServiceSapProfileEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1), ).setIndexNames((1, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileID")) if mibBuilder.loadTexts: alaEServiceSapProfileEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileEntry.setDescription('A E-Service Service Profile entry.') alaEServiceSapProfileID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 1), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 31))) if mibBuilder.loadTexts: alaEServiceSapProfileID.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileID.setDescription('A label given to uniquely identify this profile. Must be at least one character long.') alaEServiceSapProfileCVLANTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("stackSVLAN", 1), ("translate", 2), ("changeCVLAN", 3))).clone('stackSVLAN')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileCVLANTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileCVLANTreatment.setDescription('The type of VLAN stacking operation to be performed on a customer frame entering this service. Stack Svlan (1) indicates that the SVLAN is to be pre-pended on the frame before any existing 802.1Q tag. Translate (2) means to replace the existing 802.1Q tag with the SVLAN. Change CVLAN (3) indicates that the customer tag is to remain on the frame but its value is to be changed to the supplied value.') alaEServiceSapProfileReplacementCVLAN = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 4094))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileReplacementCVLAN.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileReplacementCVLAN.setDescription('The CVLAN ID to use when using the Change CVLAN treatment mode.') alaEServiceSapProfilePriorityMapMode = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2, 3))).clone(namedValues=NamedValues(("notAssigned", 0), ("mapInnerPtoOuterP", 1), ("mapInnerDscpToOuterP", 2), ("fixedP", 3))).clone('fixedP')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfilePriorityMapMode.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfilePriorityMapMode.setDescription('This object describes the source of the value for the priority field of the SVLAN 802.1Q tag when pre-pended to the customer data frame.NotAssigned(0), MapInnerPtoOuterP (1) uses the priority field of the incoming frame when tagged to fill in the priority field of the SVLAN tag. mapInnerDscpToOuterP (2) uses the frames priority bits in its IP DSCP field to fill in the priority field of the SVLAN tag. FixedP (3) uses the supplied FixedPriorityValue to fill in the SVLAN tag priority bits.') alaEServiceSapProfileFixedPriority = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 7))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileFixedPriority.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileFixedPriority.setDescription('This object describes the value of the priority field of the 802.1Q SVLAN tag pre-pended to customer data frames when the fixed priority mapping mode is selected.') alaEServiceSapProfileIngressBW = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 6), Integer32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileIngressBW.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileIngressBW.setDescription('This object describes this limit of ingress bandwidth for the traffic to which this profile is applied. If 0, no bandwidth limit is applied. This number represents traffic in units of 1,000,000 bits per second. Note that all CVLAN that belong to this SAP will share this aggregated limit.') alaEServiceSapProfileBandwidthShare = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("notApplicable", 0), ("shared", 1), ("notShared", 2))).clone('shared')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileBandwidthShare.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileBandwidthShare.setDescription('This object describes the use of the bandwidth limit in how it is applied across multiple ports of the SAP. If set to notApplicable(0), the SAP is not used. If set to Shared (1), all the ports that are part of the SAP will use aggregated bandwidth, sharing some part of the bandwidth limit. If set to notShared (2), each port will use its own bandwidth meter for this SAP. This value is not used if ingressBandwidth is 0.') alaEServiceSapProfileRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 8), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileRowStatus.setDescription('The status of this table entry.') alaEServiceSapProfileEgressBW = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 2, 1, 9), Integer32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfileEgressBW.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileEgressBW.setDescription('This object describes this limit of egress bandwidth for each UNI of the SAP to which this profile is applied. If 0, no bandwidth limit is applied. This number represents traffic in units of Megabits per second. Note that all CVLAN that belong to this SAP will share this aggregated limit.') alaEServiceUNIProfileTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3), ) if mibBuilder.loadTexts: alaEServiceUNIProfileTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileTable.setDescription('A table that contains service profiles containing performance and control attributes. An entry in this table is created when a new service profile is defined.') alaEServiceUNIProfileEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1), ).setIndexNames((1, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileID")) if mibBuilder.loadTexts: alaEServiceUNIProfileEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileEntry.setDescription('A E-Service Service Profile entry.') alaEServiceUNIProfileID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 1), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 31))) if mibBuilder.loadTexts: alaEServiceUNIProfileID.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileID.setDescription('A label given to uniquely identify this profile. Must be at least one character long.') alaEServiceUNIProfileStpBpduTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 2), AlaEServiceUNIProfileProtocolTreatment().clone('tunnel')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfileStpBpduTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileStpBpduTreatment.setDescription('This object describes the behavior of the bridge in regards to the spanning tree protocol BPDU received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currnetly Peer is not supported for Spanning Tree') alaEServiceUNIProfile8021xTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 3), AlaEServiceUNIProfileProtocolTreatment().clone('drop')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfile8021xTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfile8021xTreatment.setDescription('This object describes the behavior of the bridge in regards to the IEEE 802.1x PDU frames received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currnetly only drop is supported') alaEServiceUNIProfile8021ABTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 4), AlaEServiceUNIProfileProtocolTreatment().clone('drop')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfile8021ABTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfile8021ABTreatment.setDescription('This object describes the behavior of the bridge in regards to the IEEE 802.1AB PDU frames received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currently only drop is supported') alaEServiceUNIProfile8023adTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 5), AlaEServiceUNIProfileProtocolTreatment().clone('peer')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfile8023adTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfile8023adTreatment.setDescription('This object describes the behavior of the bridge in regards to the IEEE 802.1ad PDU frames received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currently only peer is supported') alaEServiceUNIProfileGvrpTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 6), AlaEServiceUNIProfileProtocolTreatment().clone('tunnel')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfileGvrpTreatment.setStatus('deprecated') if mibBuilder.loadTexts: alaEServiceUNIProfileGvrpTreatment.setDescription('This object describes the behavior of the bridge in regards to the GVRP PDU frames received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currently peer is not supported for GVRP') alaEServiceUNIProfileAmapTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 7), AlaEServiceUNIProfileProtocolTreatment().clone('drop')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfileAmapTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileAmapTreatment.setDescription('This object describes the behavior of the bridge in regards to the Alcatel propietary AMAP PDU frames received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currently drop is only supported') alaEServiceUNIProfileMvrpTreatment = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 8), AlaEServiceUNIProfileProtocolTreatment().clone('tunnel')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfileMvrpTreatment.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileMvrpTreatment.setDescription('This object describes the behavior of the bridge in regards to the MVRP PDU frames received on the UNI. Tunnel (1) enables the PDU to be tunneled across the provider network. Discard (2) causes the PDU of the protocol to be discarded and not enter the provider network. Peer (3) means that on this port the bridge is to participate in the protocol. Currently peer is not supported for MVRP') alaEServiceUNIProfileRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 3, 1, 9), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceUNIProfileRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileRowStatus.setDescription('The status of this table entry.') alaEServiceTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 4), ) if mibBuilder.loadTexts: alaEServiceTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceTable.setDescription('A table that contains the services and their assigned SVLAN for the E-Service feature.') alaEServiceEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 4, 1), ).setIndexNames((1, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceID")) if mibBuilder.loadTexts: alaEServiceEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceEntry.setDescription('The svlan/ipmvlan-port association.') alaEServiceID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 4, 1, 1), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 32))) if mibBuilder.loadTexts: alaEServiceID.setStatus('current') if mibBuilder.loadTexts: alaEServiceID.setDescription('A label given to uniquely identify this Service. Must be at least one character long.') alaEServiceSVLAN = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 4, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 4094))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSVLAN.setStatus('current') if mibBuilder.loadTexts: alaEServiceSVLAN.setDescription('The SVLAN number of the SVLAN chosen to the be transport for this service.') alaEServiceVlanType = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 4, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("unknown", 0), ("svlan", 1), ("ipmvlan", 2)))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceVlanType.setStatus('current') if mibBuilder.loadTexts: alaEServiceVlanType.setDescription('The type of the vlan this service is going to attach to. When creating the service, the type should match the vlanId specified in the request.') alaEServiceRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 4, 1, 4), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceRowStatus.setDescription('The status of this table entry. The supported value for set are createAndGo (4) and destroy(6), to add or remove a service. When creating or deleting the service, the user needs to provide both the svlan and the vlantype objects.') alaEServiceSapTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 5), ) if mibBuilder.loadTexts: alaEServiceSapTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapTable.setDescription("A table that contains the Service Access Points (Sap) listed by ID. This table is used to create, delete, and modify the SAP's profile") alaEServiceSapEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 5, 1), ).setIndexNames((0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapID")) if mibBuilder.loadTexts: alaEServiceSapEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapEntry.setDescription('The list of SAP.') alaEServiceSapID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 5, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 2147483647))) if mibBuilder.loadTexts: alaEServiceSapID.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapID.setDescription('A Number given to uniquely identify the SAP.') alaEServiceSapServiceID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 5, 1, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 32))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapServiceID.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapServiceID.setDescription('A label given to uniquely identify the Service this SAP is for. Must be at least one character long.') alaEServiceSapProfile = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 5, 1, 3), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 32))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapProfile.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfile.setDescription('The string identifying the SAP Profile this sap is to use. If specified, must match an existing SAP Profile.') alaEServiceSapRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 5, 1, 4), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapRowStatus.setDescription('The status of this table entry. The supported value for set are createAndGo (4) and destroy(6), to add or remove a sap. When creating the sap, the user needs to provide the service name in the same set request.') alaEServiceSapCvlanTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 6), ) if mibBuilder.loadTexts: alaEServiceSapCvlanTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanTable.setDescription('A table that contains the Service Access Points (Sap) where the CVLANs are bound to their service.') alaEServiceSapCvlanEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 6, 1), ).setIndexNames((0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapCvlanSapID"), (0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapCvlanCvlan")) if mibBuilder.loadTexts: alaEServiceSapCvlanEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanEntry.setDescription('The CVLAN to Sap binding.') alaEServiceSapCvlanSapID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 6, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 2147483647))) if mibBuilder.loadTexts: alaEServiceSapCvlanSapID.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanSapID.setDescription('A Number given to uniquely identify this SAP.') alaEServiceSapCvlanCvlan = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 6, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 4094))) if mibBuilder.loadTexts: alaEServiceSapCvlanCvlan.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanCvlan.setDescription('This object is the CVLAN ID that this binding is targeted at. The CVLAN ID may be 0, which indicates an all or untagged only mapping type.') alaEServiceSapCvlanMapType = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 6, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("single", 1), ("all", 2), ("untaggedOnly", 3))).clone('single')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapCvlanMapType.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanMapType.setDescription('This object is the mapping type that defines what CVLANs are mapped into this service. Multiple mappings can be defined for CVLAN to service, however only one all (2) or untaggedOnly (3) mapping entry can be created per UNI. A mapping type of Single (1) denotes a specific CVLAN value to bind to the service. A mapping type of All (2) denotes that all customer frames that do not map to any other SAP, will be mapped into this service. A mapping type of Untagged (3) denotes that only the untagged frames will be mapped into this service.') alaEServiceSapCvlanRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 6, 1, 4), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapCvlanRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanRowStatus.setDescription('The status of this table entry. The supported value for set are createAndGo (4) and destroy(6), to add or remove a SAP.') alaEServicePortTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7), ) if mibBuilder.loadTexts: alaEServicePortTable.setStatus('current') if mibBuilder.loadTexts: alaEServicePortTable.setDescription('A table that contains the ports used by the EService feature. Both UNI and NNI are listed here.') alaEServicePortEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1), ).setIndexNames((0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortID")) if mibBuilder.loadTexts: alaEServicePortEntry.setStatus('current') if mibBuilder.loadTexts: alaEServicePortEntry.setDescription('The list of ports being used by EService.') alaEServicePortID = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 1), InterfaceIndex()) if mibBuilder.loadTexts: alaEServicePortID.setStatus('current') if mibBuilder.loadTexts: alaEServicePortID.setDescription('The IfIndex of this UNI or NNI Port.') alaEServicePortType = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 3))).clone(namedValues=NamedValues(("uni", 1), ("nni", 3))).clone('uni')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortType.setStatus('current') if mibBuilder.loadTexts: alaEServicePortType.setDescription('The type of port for Vlan Stacking operation. uni (1) represents a customer facing port on which traffic may enter the E-Service. nni (2) respresents a provider network port over which the E-Service may be connected.') alaEServicePortVendorTpid = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 3), Integer32().clone(33024)).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortVendorTpid.setStatus('current') if mibBuilder.loadTexts: alaEServicePortVendorTpid.setDescription('he TPID for this port if type is NNI. It is used for the incoming data traffic parsing and it is substituted to the 802.1Q standard Tpid for the outgoing data traffic. This is used for compatibility with other vendor equipment. The default value is the standard value 0x8100.') alaEServicePortLegacyStpBpdu = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("notApplicable", 0), ("enable", 1), ("disable", 2))).clone('disable')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortLegacyStpBpdu.setStatus('current') if mibBuilder.loadTexts: alaEServicePortLegacyStpBpdu.setDescription('The legacy STP BPDU treatment for this port if NNI. It defines the type of processing applied to STP legacy BPDUs on network ports. Legacy BPDU refer to conventional/customer BPDUs with MAC address 01:80:c2:00:00:00 and its processing on network ports can be enabled/disabled by this object.By default the value is disabled i.e provider MAC BPDU with MAC address 01:80:c2:00:00:08 would be processed at network ports.') alaEServicePortLegacyGvrpPdu = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("notApplicable", 0), ("enable", 1), ("disable", 2))).clone('disable')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortLegacyGvrpPdu.setStatus('deprecated') if mibBuilder.loadTexts: alaEServicePortLegacyGvrpPdu.setDescription('The legacy GVRP PDU treatment for this port if NNI. It defines the type of processing applied to GVRP PDUs on network ports. ') alaEServicePortUniProfile = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 6), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 31))).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortUniProfile.setStatus('current') if mibBuilder.loadTexts: alaEServicePortUniProfile.setDescription('The label of an existing UNI profile that which contains various properties to be applied to this port if UNI.') alaEServicePortTransBridging = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enable", 1), ("disable", 2))).clone('disable')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortTransBridging.setStatus('current') if mibBuilder.loadTexts: alaEServicePortTransBridging.setDescription('The Transparent Bridging status for the nni Port.') alaEServicePortLegacyMvrpPdu = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("notApplicable", 0), ("enable", 1), ("disable", 2))).clone('disable')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortLegacyMvrpPdu.setStatus('current') if mibBuilder.loadTexts: alaEServicePortLegacyMvrpPdu.setDescription('The legacy MVRP PDU treatment for this port if NNI. It defines the type of processing applied to MVRP PDUs on network ports. ') alaEServicePortRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 7, 1, 9), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServicePortRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServicePortRowStatus.setDescription('The status of this table entry. The supported value for set are createAndGo (4) and destroy(6), to add or remove a binding') alaEServiceSapUniTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 8), ) if mibBuilder.loadTexts: alaEServiceSapUniTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapUniTable.setDescription('A table that contains the UNI that are bound to each SAP for classifying traffic into each EService. Not that writing to this table may create a new UNI.') alaEServiceSapUniEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 8, 1), ).setIndexNames((0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapUniSap"), (0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapUniUni")) if mibBuilder.loadTexts: alaEServiceSapUniEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapUniEntry.setDescription('The list of SAP-UNI bindings being used by EService.') alaEServiceSapUniSap = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 8, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 2147483647))) if mibBuilder.loadTexts: alaEServiceSapUniSap.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapUniSap.setDescription('The SAP ID that is configured onto this port.') alaEServiceSapUniUni = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 8, 1, 2), InterfaceIndex()) if mibBuilder.loadTexts: alaEServiceSapUniUni.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapUniUni.setDescription('The IfIndex of this UNI Port.') alaEServiceSapUniRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 8, 1, 3), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceSapUniRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapUniRowStatus.setDescription('The status of this table entry. The supported value for set are createAndGo (4) and destroy(6), to add or remove a binding') alaEServiceNniSvlanTable = MibTable((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 9), ) if mibBuilder.loadTexts: alaEServiceNniSvlanTable.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanTable.setDescription('A table that contains the SVLANs bound to each NNI for use by the EService feature.') alaEServiceNniSvlanEntry = MibTableRow((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 9, 1), ).setIndexNames((0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceNniSvlanNni"), (0, "ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceNniSvlanSvlan")) if mibBuilder.loadTexts: alaEServiceNniSvlanEntry.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanEntry.setDescription('The list of NNI-SVLAN bindings being used by EService.') alaEServiceNniSvlanNni = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 9, 1, 1), InterfaceIndex()) if mibBuilder.loadTexts: alaEServiceNniSvlanNni.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanNni.setDescription('The IfIndex of this NNI Port.') alaEServiceNniSvlanSvlan = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 9, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(2, 4094))) if mibBuilder.loadTexts: alaEServiceNniSvlanSvlan.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanSvlan.setDescription('The SVLAN bound to this port. SVLAN cannot be 1.') alaEServiceNniSvlanRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 9, 1, 3), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceNniSvlanRowStatus.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanRowStatus.setDescription('The status of this table entry. The supported value for set are createAndGo (4) and destroy(6), to add or remove a binding') alaEServiceNniSvlanVpaType = MibTableColumn((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 1, 1, 9, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("stp", 1), ("erp", 2))).clone('stp')).setMaxAccess("readcreate") if mibBuilder.loadTexts: alaEServiceNniSvlanVpaType.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanVpaType.setDescription('The object is used to specify whether the VPA state is to be controlled by an ERP or a STP. By default VPA state is controlled by STP.') alcatelIND1EServiceMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 2, 1)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapUniGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapCvlanGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceNniSvlanGroup"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceInfoGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alcatelIND1EServiceMIBCompliance = alcatelIND1EServiceMIBCompliance.setStatus('current') if mibBuilder.loadTexts: alcatelIND1EServiceMIBCompliance.setDescription('Compliance statement for E-Service.') alaEServiceSapProfileGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 1)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileCVLANTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileReplacementCVLAN"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfilePriorityMapMode"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileFixedPriority"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileIngressBW"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileBandwidthShare"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileRowStatus"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfileEgressBW")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceSapProfileGroup = alaEServiceSapProfileGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapProfileGroup.setDescription('Collection of objects for management of E-Service Sap Profiles.') alaEServiceUNIProfileGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 2)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileStpBpduTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfile8021xTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfile8021ABTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfile8023adTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileGvrpTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileAmapTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileMvrpTreatment"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceUNIProfileRowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceUNIProfileGroup = alaEServiceUNIProfileGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceUNIProfileGroup.setDescription('Collection of objects for management of EService UNI Profiles.') alaEServiceGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 3)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSVLAN"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceVlanType"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceRowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceGroup = alaEServiceGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceGroup.setDescription('Collection of objects for management of E-Services.') alaEServiceSapGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 4)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapServiceID"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapProfile"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapRowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceSapGroup = alaEServiceSapGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapGroup.setDescription('Collection of objects for management of E-Service SAPs.') alaEServiceSapCvlanGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 5)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapCvlanMapType"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapCvlanRowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceSapCvlanGroup = alaEServiceSapCvlanGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapCvlanGroup.setDescription('Collection of objects for management of E-Service SAP CVLAN bindings.') alaEServicePortGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 6)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortType"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortVendorTpid"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortLegacyStpBpdu"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortLegacyGvrpPdu"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortUniProfile"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortTransBridging"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortLegacyMvrpPdu"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServicePortRowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServicePortGroup = alaEServicePortGroup.setStatus('current') if mibBuilder.loadTexts: alaEServicePortGroup.setDescription('Collection of objects for management of E-Service Ports.') alaEServiceSapUniGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 7)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceSapUniRowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceSapUniGroup = alaEServiceSapUniGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceSapUniGroup.setDescription('Collection of objects for management of E-Service SAP to UNI Binding.') alaEServiceNniSvlanGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 8)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceNniSvlanRowStatus"), ("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceNniSvlanVpaType")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceNniSvlanGroup = alaEServiceNniSvlanGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceNniSvlanGroup.setDescription('Collection of objects for management of E-Service SVLAN to NNI Binding.') alaEServiceInfoGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 6486, 801, 1, 2, 1, 50, 1, 2, 1, 9)).setObjects(("ALCATEL-IND1-E-SERVICE-MIB", "alaEServiceMode")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): alaEServiceInfoGroup = alaEServiceInfoGroup.setStatus('current') if mibBuilder.loadTexts: alaEServiceInfoGroup.setDescription('Collection of objects for management of E-Service Info Binding.') mibBuilder.exportSymbols("ALCATEL-IND1-E-SERVICE-MIB", alaEServiceUNIProfile8021xTreatment=alaEServiceUNIProfile8021xTreatment, alaEServiceNniSvlanEntry=alaEServiceNniSvlanEntry, alaEServiceUNIProfileGroup=alaEServiceUNIProfileGroup, alaEServicePortTable=alaEServicePortTable, alaEServiceUNIProfileAmapTreatment=alaEServiceUNIProfileAmapTreatment, alaEServiceTable=alaEServiceTable, alcatelIND1EServiceMIB=alcatelIND1EServiceMIB, alaEServiceSapCvlanCvlan=alaEServiceSapCvlanCvlan, alcatelIND1EServiceMIBCompliance=alcatelIND1EServiceMIBCompliance, alaEServiceUNIProfileTable=alaEServiceUNIProfileTable, alaEServicePortType=alaEServicePortType, alaEServiceUNIProfileMvrpTreatment=alaEServiceUNIProfileMvrpTreatment, alaEServiceNniSvlanVpaType=alaEServiceNniSvlanVpaType, alaEServiceInfo=alaEServiceInfo, alaEServicePortUniProfile=alaEServicePortUniProfile, alaEServiceSapProfileReplacementCVLAN=alaEServiceSapProfileReplacementCVLAN, alaEServiceSapProfileTable=alaEServiceSapProfileTable, alaEServiceSapProfileID=alaEServiceSapProfileID, alaEServiceSapEntry=alaEServiceSapEntry, alaEServiceUNIProfileStpBpduTreatment=alaEServiceUNIProfileStpBpduTreatment, alaEServicePortTransBridging=alaEServicePortTransBridging, alaEServicePortEntry=alaEServicePortEntry, alaEServiceSapUniSap=alaEServiceSapUniSap, alaEServiceSapProfileGroup=alaEServiceSapProfileGroup, alaEServiceSapProfileCVLANTreatment=alaEServiceSapProfileCVLANTreatment, alaEServiceSapProfileRowStatus=alaEServiceSapProfileRowStatus, alaEServiceRowStatus=alaEServiceRowStatus, alaEServiceSapProfileEgressBW=alaEServiceSapProfileEgressBW, alaEServicePortLegacyStpBpdu=alaEServicePortLegacyStpBpdu, alaEServiceSapRowStatus=alaEServiceSapRowStatus, alaEServiceUNIProfile8021ABTreatment=alaEServiceUNIProfile8021ABTreatment, alaEServiceSapCvlanGroup=alaEServiceSapCvlanGroup, alaEServiceSapUniEntry=alaEServiceSapUniEntry, alaEServicePortRowStatus=alaEServicePortRowStatus, alaEServiceNniSvlanRowStatus=alaEServiceNniSvlanRowStatus, alaEServiceSapTable=alaEServiceSapTable, alaEServiceNniSvlanSvlan=alaEServiceNniSvlanSvlan, alcatelIND1EServiceMIBGroups=alcatelIND1EServiceMIBGroups, alcatelIND1EServiceMIBConformance=alcatelIND1EServiceMIBConformance, alaEServiceUNIProfileID=alaEServiceUNIProfileID, alaEServiceSapProfilePriorityMapMode=alaEServiceSapProfilePriorityMapMode, alaEServiceSapServiceID=alaEServiceSapServiceID, alaEServiceID=alaEServiceID, 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# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: 2017 Scott Shawcroft, written for Adafruit Industries # # SPDX-License-Identifier: MIT import os import sys sys.path.insert(0, os.path.abspath("..")) # -- General configuration ------------------------------------------------ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.todo", ] # TODO: Please Read! # Uncomment the below if you use native CircuitPython modules such as # digitalio, micropython and busio. List the modules you use. Without it, the # autodoc module docs will fail to generate with a warning. autodoc_mock_imports = ["displayio", "vectorio"] intersphinx_mapping = { "python": ("https://docs.python.org/3.4", None), "CircuitPython": ("https://circuitpython.readthedocs.io/en/latest/", None), } # Show the docstring from both the class and its __init__() method. autoclass_content = "both" # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = " CircuitPython Candlesticks Library" copyright = "2021 Jose David" author = "Jose David" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = "1.0" # The full version, including alpha/beta/rc tags. release = "1.0" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = [ "_build", "Thumbs.db", ".DS_Store", ".env", "CODE_OF_CONDUCT.md", ] # The reST default role (used for this markup: `text`) to use for all # documents. # default_role = "any" # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # If this is True, todo emits a warning for each TODO entries. The default is False. todo_emit_warnings = True napoleon_numpy_docstring = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # on_rtd = os.environ.get("READTHEDOCS", None) == "True" if not on_rtd: # only import and set the theme if we're building docs locally try: import sphinx_rtd_theme html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path(), "."] except: html_theme = "default" html_theme_path = ["."] else: html_theme_path = ["."] # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = "_static/favicon.ico" # Output file base name for HTML help builder. htmlhelp_basename = "CircuitPython_CandlesticksLibrarydoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # 'preamble': '', # Latex figure (float) alignment # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( master_doc, "CircuitPython_CandlesticksLibrary.tex", "CircuitPython Candlesticks Library Documentation", author, "manual", ), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ( master_doc, "CircuitPython_CandlesticksLibrary", "CircuitPython Candlesticks Library Documentation", [author], 1, ), ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "CircuitPython_CandlesticksLibrary", "CircuitPython Candlesticks Library Documentation", author, "CircuitPython_CandlesticksLibrary", "One line description of project.", "Miscellaneous", ), ]

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import cv2 img = "ibm.png" img1 = cv2.imread(img) cv2.imwrite("ibm2.jpg",img1)

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# Generated by Django 2.2.8 on 2020-01-19 14:57 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('products', '0027_auto_20191217_0738'), ] operations = [ migrations.AlterField( model_name='product', name='price', field=models.PositiveIntegerField(default=0, help_text='This price must be valued in euro cents. For example: 500 for 5.00€, 120 for 1.20€ etc.', verbose_name='price'), ), ]

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""" Django settings for wordcount project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'yhl)dpm7y0+ow17i#8o()@-r&)9v^7_)o&djz-v1b$@i(++a9#' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'wordcount.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'wordcount.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/'

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import sys sys.path.append('../../../projects') import cutouts import hhana from astropy.table import Table galex = Table.read('data/gemini-ft-sample.csv') targets = [139, 118, 230, 27] for ID in targets: prefix = 'figures/hugs-'+str(ID) ra, dec = galex[galex['hugs-id']==ID]['MatchRA', 'MatchDEC'][0] cutouts.galex.getGalexCutout( ra, dec, size=35, name=prefix, label_survey=False)

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import raytracer import objects3d import time import pygame import math3d caseNum = 2 # Pygame setup if caseNum == 1: win_width = 700; win_height = 150; elif caseNum == 2: win_width = 800; win_height = 600; else: win_width = 300; win_height = 200; pygame.display.init() screen = pygame.display.set_mode((win_width, win_height)) clock = pygame.time.Clock() done = False # Raytracer setup if caseNum == 1: cameraPos = math3d.VectorN(0, 0, -20) cameraUp = math3d.VectorN(0, 1, 0) cameraCoi = math3d.VectorN(0, 0, 0) cameraNear = 3.2 cameraFov = 45.0 elif caseNum == 2: cameraPos = math3d.VectorN(5, 7, -20) cameraUp = math3d.VectorN(1, 10, 0).normalized() cameraCoi = math3d.VectorN(2, 5, 3) cameraNear = 1.5 cameraFov = 60.0 elif caseNum == 3: cameraPos = math3d.VectorN(-5, 7, -30) cameraUp = math3d.VectorN(0, 1, 0) cameraCoi = math3d.VectorN(2, 5, 3) cameraNear = 1.5 cameraFov = 60.0 camera = objects3d.Camera(cameraPos, cameraCoi, cameraUp, screen, cameraFov, cameraNear, True) sphere1 = objects3d.Sphere(math3d.VectorN(2,5,3), 7.0, math3d.VectorN(1,0,0)) plane1 = objects3d.Plane(math3d.VectorN(0,1,0), 5.0, math3d.VectorN(0,1,0)) plane2 = objects3d.Plane(math3d.VectorN(0.1,1,0), 4.0, math3d.VectorN(0,0,1)) box1 = objects3d.AABB(math3d.VectorN(2, 9, -6), math3d.VectorN(8, 15, 0), math3d.VectorN(1,1,0)) #mesh1 = objects3d.Polymesh("sword.obj", math3d.VectorN(-10,8,3), 1.0, math3d.VectorN(1.0,0.3,0.8)) rt = raytracer.Raytracer(camera) rt.addObject(sphere1) rt.addObject(plane1) rt.addObject(plane2) rt.addObject(box1) #rt.addObject(mesh1) totalTime = 0.0 currentLine = 0 print("\n+==============================================+") print("| PHASE II tests |") print("+==============================================+") if caseNum == 1: testPts = [(0, 0), (win_width - 1, win_height - 1), (win_width // 2, win_height // 2), (113, 23), (623,83)] else: testPts = [(0, 0), (win_width - 1, win_height - 1), (win_width // 2, win_height // 2), (113, 542), (723,11)] for pygamePos in testPts: camera.getViewplanePosition(pygamePos[0], pygamePos[1], True) # Game Loop while not done: # Update if currentLine < win_height: rt.renderOneLine(currentLine) currentLine += 1 dt = clock.tick() totalTime += dt # Input event = pygame.event.poll() if event.type == pygame.QUIT or (event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE): done = True # Draw (nothing to do!) pygame.display.flip() # Pygame shutdown pygame.display.quit()

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__author__ = 'Dominic Miglar <dominic.miglar@bitmovin.net>' import unittest from bitcodin import create_output from bitcodin import delete_output from bitcodin import get_output from bitcodin import S3Output from bitcodin.test.settings import aws_config from bitcodin.test.bitcodin_test_case import BitcodinTestCase class GetS3OutputTestCase(BitcodinTestCase): def setUp(self): super(GetS3OutputTestCase, self).setUp() self.s3_configuration = { 'name': 'Python API Test Output', 'host': aws_config.get('host', None), 'access_key': aws_config.get('access_key', None), 'secret_key': aws_config.get('secret_key', None), 'bucket': aws_config.get('bucket', None), 'prefix': aws_config.get('prefix', None), 'region': aws_config.get('region', None), 'make_public': False } output = S3Output( access_key=self.s3_configuration.get('access_key'), secret_key=self.s3_configuration.get('secret_key'), name=self.s3_configuration.get('name'), host=self.s3_configuration.get('host'), bucket=self.s3_configuration.get('bucket'), prefix=self.s3_configuration.get('prefix'), region=self.s3_configuration.get('region'), make_public=self.s3_configuration.get('make_public') ) self.output = create_output(output) def runTest(self): output = get_output(self.output.output_id) self.assertEquals(self.output.name, output.name) self.assertEquals(self.output.bucket, output.bucket) self.assertEquals(self.output.prefix, output.prefix) self.assertEquals(self.output.region, output.region) self.assertEquals(self.output.make_public, output.make_public) def tearDown(self): delete_output(self.output.output_id) super(GetS3OutputTestCase, self).tearDown() if __name__ == '__main__': unittest.main()

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import json import os import numpy as np from PIL import Image from tqdm import tqdm from glob import glob from .dataset import Dataset from .video import Video def ca(): path='/home/louis/Documents/V4RFlight112' name_list=os.listdir(path+'/data_seq') name_list.sort() a=len(name_list) b=[] for i in range(a): b.append(name_list[i]) c=[] for jj in range(a): imgs=path+'/data_seq/'+str(name_list[jj]) txt=path+'/anno/'+str(name_list[jj])+'.txt' bbox=[] f = open(txt) # 返回一个文件对象 file= f.readlines() li=os.listdir(imgs) li.sort() for ii in range(len(file)): try: li[ii]=name_list[jj]+'/'+li[ii] except: a=1 line = file[ii].strip('\n').split(' ') if len(line)!=4: line = file[ii].strip('\n').split(',') if len(line)!=4: line = file[ii].strip('\n').split('\t') try: line[0]=int(line[0]) except: line[0]=float(line[0]) try: line[1]=int(line[1]) except: line[1]=float(line[1]) try: line[2]=int(line[2]) except: line[2]=float(line[2]) try: line[3]=int(line[3]) except: line[3]=float(line[3]) bbox.append(line) if len(bbox)!=len(li): print (jj) f.close() c.append({'attr':[],'gt_rect':bbox,'img_names':li,'init_rect':bbox[0],'video_dir':name_list[jj]}) d=dict(zip(b,c)) return d class UAVVideo(Video): """ Args: name: video name root: dataset root video_dir: video directory init_rect: init rectangle img_names: image names gt_rect: groundtruth rectangle attr: attribute of video """ def __init__(self, name, root, video_dir, init_rect, img_names, gt_rect, attr, load_img=False): super(UAVVideo, self).__init__(name, root, video_dir, init_rect, img_names, gt_rect, attr, load_img) class V4RDataset(Dataset): """ Args: name: dataset name, should be 'UAV123', 'UAV20L' dataset_root: dataset root load_img: wether to load all imgs """ def __init__(self, name, dataset_root, load_img=False): super(V4RDataset, self).__init__(name, dataset_root) meta_data = ca() # load videos pbar = tqdm(meta_data.keys(), desc='loading '+name, ncols=100) self.videos = {} for video in pbar: pbar.set_postfix_str(video) self.videos[video] = UAVVideo(video, dataset_root, meta_data[video]['video_dir'], meta_data[video]['init_rect'], meta_data[video]['img_names'], meta_data[video]['gt_rect'], meta_data[video]['attr']) # set attr attr = [] for x in self.videos.values(): attr += x.attr attr = set(attr) self.attr = {} self.attr['ALL'] = list(self.videos.keys()) for x in attr: self.attr[x] = [] for k, v in self.videos.items(): for attr_ in v.attr: self.attr[attr_].append(k)

[ "17721296636@163.com" ]

17721296636@163.com

2620861cf54ff2d1ae44aed67d936dd807aed01f

f6e561772157156db860284aac46c2a596686a4d

/PyQt4 IDE/microhope_lib/__init__.py

e75a29d31a77730446531eebe0e3b4e0ba82b5c1

[]

no_license

JosekuttyMJ/microHOPE

5cb1c9fda4ff353caa34c915e7ac1390fe52aa1d

fce58402bf5fbf4fc6ed980bb7721823a431174f

refs/heads/master

2020-12-03T09:33:38.599012

2015-01-28T06:56:11

null

0

null

UTF-8

Python

false

27

py

__all__ = ["mh_latest_ui"]

[ "arunjayan32@gmail.com" ]

arunjayan32@gmail.com

2f944d70bb962407accb344b9ede5c40b2f7a3a7

cd7166c7b6332b4c331f9bb10986d7af298a605a

/Config.py

5c50cb5ed342bf9368be64c712354e695b96d045

[]

no_license

tuanquanghpvn/socket4py

a795ff3bbb64e41f72e42a619d70165fc77084fe

53e21524cfd97010ccbd0971b1b969467d09e193

refs/heads/master

2020-05-18T00:49:59.976982

2015-03-11T06:23:05

null

0

null

UTF-8

Python

false

95

py

__author__ = 'JsAndPy' class Config: # Init Socket HOST = 'localhost' PORT = 2222

[ "tuanquanghpvn@gmail.com" ]

tuanquanghpvn@gmail.com

0bdb9d4f4e95b0c5eefe477b1bd396427419e433

28686b6c3670539ae00f80ced4533a41dc59d165

/efs-server/dropbox/dropbox.py

acd0644a74f13bd76da9970855e862babf72d842

[]

no_license

hicksmatt/EFS

61eb326246ff114227d42a2ccf5606e844bc7e3c

c101f1bdb07091b2ae82f22dea0f9d0182be2919

refs/heads/master

2021-01-10T10:02:31.266934

2015-12-04T17:51:55

47,127,610

0

null

UTF-8

Python

false

15,338

py

__all__ = [ 'Dropbox', ] # TODO(kelkabany): We need to auto populate this as done in the v1 SDK. __version__ = '3.42' import contextlib import json import logging import os import random import six import time import requests #from . import babel_serializers import babel_serializers from .base import DropboxBase from .exceptions import ( ApiError, AuthError, BadInputError, HttpError, InternalServerError, RateLimitError, ) from .session import pinned_session class RouteResult(object): """The successful result of a call to a route.""" def __init__(self, obj_result, http_resp=None): """ :param str obj_result: The result of a route not including the binary payload portion, if one exists. Must be serialized JSON. :param requests.models.Response http_resp: A raw HTTP response. It will be used to stream the binary-body payload of the response. """ assert isinstance(obj_result, six.string_types), \ 'obj_result: expected string, got %r' % type(obj_result) if http_resp is not None: assert isinstance(http_resp, requests.models.Response), \ 'http_resp: expected requests.models.Response, got %r' % \ type(http_resp) self.obj_result = obj_result self.http_resp = http_resp class RouteErrorResult(object): """The error result of a call to a route.""" def __init__(self, obj_result): """ :param str obj_result: The result of a route not including the binary payload portion, if one exists. """ self.obj_result = obj_result class Dropbox(DropboxBase): """ Use this to make requests to the Dropbox API. """ API_VERSION = '2' DEFAULT_DOMAIN = '.dropboxapi.com' # Host for RPC-style routes. HOST_API = 'api' # Host for upload and download-style routes. HOST_CONTENT = 'content' # Host for longpoll routes. HOST_NOTIFY = 'notify' # Download style means that the route argument goes in a Dropbox-API-Arg # header, and the result comes back in a Dropbox-API-Result header. The # HTTP response body contains a binary payload. ROUTE_STYLE_DOWNLOAD = 'download' # Upload style means that the route argument goes in a Dropbox-API-Arg # header. The HTTP request body contains a binary payload. The result # comes back in a Dropbox-API-Result header. ROUTE_STYLE_UPLOAD = 'upload' # RPC style means that the argument and result of a route are contained in # the HTTP body. ROUTE_STYLE_RPC = 'rpc' def __init__(self, oauth2_access_token, max_connections=8, max_retries_on_error=4, user_agent=None, proxies=None): """ :param str oauth2_access_token: OAuth2 access token for making client requests. :param int max_connections: Maximum connection pool size. :param int max_retries_on_error: On 5xx errors, the number of times to retry. :param str user_agent: The user agent to use when making requests. This helps us identify requests coming from your application. We recommend you use the format "AppName/Version". If set, we append "/OfficialDropboxPythonV2SDK/__version__" to the user_agent, :param dict proxies: See the `requests module <http://docs.python-requests.org/en/latest/user/advanced/#proxies>`_ for more details. """ assert len(oauth2_access_token) > 0, \ 'OAuth2 access token cannot be empty.' self._oauth2_access_token = oauth2_access_token # We only need as many pool_connections as we have unique hostnames. self._session = pinned_session(pool_maxsize=max_connections) if proxies: self._session.proxies = proxies self._max_retries_on_error = max_retries_on_error base_user_agent = 'OfficialDropboxPythonV2SDK/' + __version__ if user_agent: self._user_agent = '{}/{}'.format(user_agent, base_user_agent) else: self._user_agent = base_user_agent self._logger = logging.getLogger('dropbox') self._domain = os.environ.get('DROPBOX_DOMAIN', Dropbox.DEFAULT_DOMAIN) self._api_hostname = os.environ.get( 'DROPBOX_API_HOST', 'api' + self._domain) self._api_content_hostname = os.environ.get( 'DROPBOX_API_CONTENT_HOST', 'content' + self._domain) self._api_notify_hostname = os.environ.get( 'DROPBOX_API_NOTIFY_HOST', 'notify' + self._domain) self._host_map = {self.HOST_API: self._api_hostname, self.HOST_CONTENT: self._api_content_hostname, self.HOST_NOTIFY: self._api_notify_hostname} def request(self, host, route_name, route_style, arg_data_type, result_data_type, error_data_type, request_arg, request_binary): """ Makes a request to the Dropbox API and in the process validates that the route argument and result are the expected data types. The request_arg is converted to JSON based on the arg_data_type. Likewise, the response is deserialized from JSON and converted to an object based on the {result,error}_data_type. :param host: The Dropbox API host to connect to. :param route_name: The name of the route to invoke. :param route_style: The style of the route. :type arg_data_type: :class:`.datatypes.babel_validators.DataType` :type result_data_type: :class:`.datatypes.babel_validators.DataType` :type error_data_type: :class:`.datatypes.babel_validators.DataType` :param request_arg: Argument for the route that conforms to the validator specified by arg_data_type. :param request_binary: String or file pointer representing the binary payload. Use None if there is no binary payload. :return: The route's result. """ serialized_arg = babel_serializers.json_encode(arg_data_type, request_arg) res = self.request_json_string_with_retry(host, route_name, route_style, serialized_arg, request_binary) decoded_obj_result = json.loads(res.obj_result) if isinstance(res, RouteResult): returned_data_type = result_data_type obj = decoded_obj_result elif isinstance(res, RouteErrorResult): returned_data_type = error_data_type obj = decoded_obj_result['error'] user_message = decoded_obj_result.get('user_message') user_message_text = user_message and user_message.get('text') user_message_locale = user_message and user_message.get('locale') else: raise AssertionError('Expected RouteResult or RouteErrorResult, ' 'but res is %s' % type(res)) deserialized_result = babel_serializers.json_compat_obj_decode( returned_data_type, obj, strict=False) if isinstance(res, RouteErrorResult): raise ApiError(deserialized_result, user_message_text, user_message_locale) elif route_style == self.ROUTE_STYLE_DOWNLOAD: return (deserialized_result, res.http_resp) else: return deserialized_result def request_json_object(self, host, route_name, route_style, request_arg, request_binary): """ Makes a request to the Dropbox API, taking a JSON-serializable Python object as an argument, and returning one as a response. :param host: The Dropbox API host to connect to. :param route_name: The name of the route to invoke. :param route_style: The style of the route. :param str request_arg: A JSON-serializable Python object representing the argument for the route. :param request_binary: String or file pointer representing the binary payload. Use None if there is no binary payload. :return: The route's result as a JSON-serializable Python object. """ serialized_arg = json.dumps(request_arg) res = self.request_json_string_with_retry(host, route_name, route_style, serialized_arg, request_binary) # This can throw a ValueError if the result is not deserializable, # but that would be completely unexpected. deserialized_result = json.loads(res.obj_result) if isinstance(res, RouteResult) and res.http_resp is not None: return (deserialized_result, res.http_resp) else: return deserialized_result def request_json_string_with_retry(self, host, route_name, route_style, request_json_arg, request_binary): """ See :meth:`request_json_object` for description of parameters. :param request_json_arg: A string representing the serialized JSON argument to the route. """ attempt = 0 while True: self._logger.info('Request to %s', route_name) try: return self.request_json_string(host, route_name, route_style, request_json_arg, request_binary) except (InternalServerError, RateLimitError) as e: if isinstance(e, InternalServerError): # Do not count a rate limiting error as an attempt attempt += 1 if attempt <= self._max_retries_on_error: # Use exponential backoff backoff = 2**attempt * random.random() self._logger.info('HttpError status_code=%s: ' 'Retrying in %.1f seconds', e.status_code, backoff) time.sleep(backoff) else: raise def request_json_string(self, host, func_name, route_style, request_json_arg, request_binary): """ See :meth:`request_json_string_with_retry` for description of parameters. """ if host not in self._host_map: raise ValueError('Unknown value for host: %r' % host) # Fully qualified hostname fq_hostname = self._host_map[host] url = self._get_route_url(fq_hostname, func_name) headers = {'User-Agent': self._user_agent} if host != self.HOST_NOTIFY: headers['Authorization'] = 'Bearer %s' % self._oauth2_access_token # The contents of the body of the HTTP request body = None # Whether the response should be streamed incrementally, or buffered # entirely. If stream is True, the caller is responsible for closing # the HTTP response. stream = False if route_style == self.ROUTE_STYLE_RPC: headers['Content-Type'] = 'application/json' body = request_json_arg elif route_style == self.ROUTE_STYLE_DOWNLOAD: headers['Dropbox-API-Arg'] = request_json_arg stream = True elif route_style == self.ROUTE_STYLE_UPLOAD: headers['Content-Type'] = 'application/octet-stream' headers['Dropbox-API-Arg'] = request_json_arg body = request_binary else: raise ValueError('Unknown operation style: %r' % route_style) r = self._session.post(url, headers=headers, data=body, stream=stream, verify=True, ) if r.status_code >= 500: raise InternalServerError(r.status_code, r.text) elif r.status_code == 400: raise BadInputError(r.text) elif r.status_code == 401: assert r.headers.get('content-type') == 'application/json', ( 'Expected content-type to be application/json, got %r' % r.headers.get('content-type')) raise AuthError(r.json()) elif r.status_code == 429: # TODO(kelkabany): Use backoff if provided in response. raise RateLimitError() elif 200 <= r.status_code <= 299: if route_style == self.ROUTE_STYLE_DOWNLOAD: raw_resp = r.headers['dropbox-api-result'] else: assert r.headers.get('content-type') == 'application/json', ( 'Expected content-type to be application/json, got %r' % r.headers.get('content-type')) raw_resp = r.content.decode('utf-8') if route_style == self.ROUTE_STYLE_DOWNLOAD: return RouteResult(raw_resp, r) else: return RouteResult(raw_resp) elif r.status_code in (403, 404, 409): raw_resp = r.content.decode('utf-8') return RouteErrorResult(raw_resp) else: raise HttpError(r.status_code, r.text) def _get_route_url(self, hostname, route_name): """Returns the URL of the route. :param str hostname: Hostname to make the request to. :param str route_name: Name of the route. :rtype: str """ return 'https://{hostname}/{version}/{route_name}'.format( hostname=hostname, version=Dropbox.API_VERSION, route_name=route_name, ) def _save_body_to_file(self, download_path, http_resp, chunksize=2**16): """ Saves the body of an HTTP response to a file. :param str download_path: Local path to save data to. :param http_resp: The HTTP response whose body will be saved. :type http_resp: :class:`requests.models.Response` :rtype: None """ with open(download_path, 'wb') as f: with contextlib.closing(http_resp): for c in http_resp.iter_content(chunksize): f.write(c)

[ "mhicks34@gatech.edu" ]

mhicks34@gatech.edu

f58c19c5218fc279438b07e3ca1976d176013a3a

2868a3f3bca36328b4fcff5cce92f8adeb25b033

/+100ns/Co_optimized/step1_dc/set.py

25b40663a2257d720ef9bd0d368b0791db804c94

[]

no_license

linfranksong/TM-enzyme_input

1c2a5e12e69c48febd5b5900aa00fe2339d42298

6e46a5b2c451efb93761707b77917a98ca0bfedc

refs/heads/master

2022-03-19T19:49:09.373397

2019-12-04T00:11:59

205,220,795

0

1

null

UTF-8

Python

false

2,131

py

import os dir = os.path.dirname(os.path.realpath(__file__)) + '/' #for a in [150,200,250,300,350,400,450,500,550,600]: for a in [150]: #for a in [200,250,300,350,400,450,500,550,600]: os.system("rm -r %s_dc_repe"%(a)) os.system("cp -r temp/ %s_dc_repe"%(a)) adir=dir+ "%s_dc_repe/"%(a) os.chdir(adir) os.system("sed -i 's/MMM/%s/g' */*pbs"%(a)) array= [0,0.00922, 0.04794, 0.11505, 0.20634, 0.31608, 0.43738, 0.56262, 0.68392, 0.79366, 0.88495, 0.95206, 0.99078,1.0] for n in range(1,len(array)-1): i=array[n] os.system("rm -r %s"%(i)) os.system("cp -r files %s"%(i)) wdir=adir+"%s/"%(i) os.chdir(wdir) os.system("mv eq.in %s_eq.in"%(i)) os.system("mv us.in %s_us.in"%(i)) os.system("sed -i 's/XXX/%s/g' %s_eq.in"%(i,i)) os.system("sed -i 's/XXX/%s/g' %s_us.in"%(i,i)) os.system("sed -i 's/MMM/%s/g' dis.RST"%(a)) os.system("mv eq.pbs %s_eq.pbs"%(i)) os.system("mv us.pbs %s_us.pbs"%(i)) os.system("sed -i 's/XXX/%s/g' *.pbs"%(i)) os.system("sed -i 's/NNN/%s/g' *.pbs"%(array[n+1])) os.system("sed -i 's/PPP/%s/g' *.pbs"%(array[n-1])) os.chdir(adir) sdir=adir+"0/" os.chdir(sdir) i=0 os.system("cp /mnt/gs18/scratch/users/songlin3/run/glx-0904/+100ns/Co_optimized/step0_fep/%s_fep/1.0/%s_1.0_eq_center.rst ."%(a,a)) os.system("mv eq.in %s_eq.in"%(i)) os.system("mv us.in %s_us.in"%(i)) os.system("sed -i 's/XXX/%s/g' %s_eq.in"%(i,i)) os.system("sed -i 's/XXX/%s/g' %s_us.in"%(i,i)) os.system("mv eq.pbs %s_eq.pbs"%(i)) os.system("mv us.pbs %s_us.pbs"%(i)) os.system("sed -i 's/XXX/%s/g' *.pbs"%(i)) os.system("sed -i 's/MMM/%s/g' dis.RST"%(a)) os.system("sbatch 0_eq.pbs") sdir=adir+"1.0/" os.chdir(sdir) i=1.0 os.system("mv eq.in %s_eq.in"%(i)) os.system("mv us.in %s_us.in"%(i)) os.system("sed -i 's/XXX/%s/g' %s_eq.in"%(i,i)) os.system("sed -i 's/XXX/%s/g' %s_us.in"%(i,i)) os.system("mv eq.pbs %s_eq.pbs"%(i)) os.system("mv us.pbs %s_us.pbs"%(i)) os.system("sed -i 's/XXX/%s/g' *.pbs"%(i)) os.system("sed -i 's/MMM/%s/g' dis.RST"%(a)) os.system("sed -i 's/MMM/%s/g' center.in"%(a)) os.chdir(dir)

[ "songlin3@msu.edu" ]

songlin3@msu.edu

30591b5a81ad213a23a3274b5b6e3b178d939eb7

578ff132c94d9f423951f16168e18de5e845d50e

/worktestauthproblem/models/permissions.py

ea142b192197c9c989466617beb8440690bd2c84

[]

no_license

shsaraswat06/application-security-toptracer

fe7b27778d7f8a1ed3bef4ef851b9774dea9040c

a1452d9d1db7c05e9d2feb766bcc2b46a00b2bf9

refs/heads/master

2023-06-10T07:07:25.324402

2021-06-30T10:19:42

381,659,557

0

null

UTF-8

Python

false

819

py

from .orm import Orm class Permissions: def __init__(self, service_name): self.service_name = service_name self.resource = 'permissions' self.orm = Orm(resource_name=self.resource) self.permissions = self.orm.get() def get_permissions(self): permissions = next( ( permission['permissions'] for permission in self.permissions if permission["service"] == self.service_name ), None, ) return permissions def add_permission(self, service_name, applications): permissions = [{app: {'can-access-data': True}} for app in applications] permissions_data = {'service': service_name, 'permissions': permissions} self.orm.create(data=permissions_data)

[ "shubham.saraswat@oneflow.com" ]

shubham.saraswat@oneflow.com

12b7d32876e7618f91c79671a447d6a02a6f5d9a

4be92d2ea9ede0084f31cda1f51acdadf1392043

/dags/src/authorization.py

0672971540249f899e58b98d999d9e7947038a58

[]

no_license

rohitamale18/airflow_demo

3c5a55d4d40197090e42f2081577f746abb1dc96

6c6eca90c148179f577483258a4efa9f85e8af67

refs/heads/master

2022-12-07T12:02:27.390489

2020-07-28T19:08:07

283,300,111

0

null

UTF-8

Python

false

2,038

py

import os from src.logger import get_logger import boto3 from airflow.models import Variable logger = get_logger(__name__) class Authorization(object): def __init__(self) -> None: self.value = None def set_key(self, key): self.value = Variable.get(key) def get_key(self): return self.value @staticmethod def validate_env_existence(key) -> bool: if not Variable.get(key): logger.info("Environment variable, " + key + " not present in system") return False else: return True class AWSClient(Authorization): def __init__(self) -> None: # super().__init__() self.access_key_value = None self.secret_access_key_value = None self.client = None self.aws_session = boto3.Session self.access_key_name = "AWS_ACCESS_KEY_ID" self.secret_access_key_name = "AWS_SECRET_ACCESS_KEY" self.get_aws_client() def set_aws_keys(self) -> None: if self.validate_env_existence(self.access_key_name): self.set_key(self.access_key_name) self.access_key_value = self.get_key() if self.validate_env_existence(self.secret_access_key_name): self.set_key(self.secret_access_key_name) self.secret_access_key_value = self.get_key() def set_aws_session(self) -> None: try: self.aws_session = boto3.Session(aws_access_key_id=self.access_key_value, aws_secret_access_key=self.secret_access_key_value, region_name="us-east-1") except Exception as e: logger.error("Failed to set AWS Session. Error: " + str(e)) def get_aws_client(self): self.set_aws_keys() self.set_aws_session() try: self.client = self.aws_session.client('firehose') except Exception as e: logger.info("Exception occurred while initiating AWS Connection: " + str(e))

[ "rohit.amale@woodmac.com" ]

rohit.amale@woodmac.com

fb3af5de3c27d3150ece8709175402b61b6aeb73

08dcd0424d8ea0ae357d75b69a25c4b90653dec7

/DRP/migrations/0025_auto_20160524_1613.py

17dcd2cca4980f60fc0f8d0c5fc94471283384ed

[]

no_license

zhaojhao/DRP

fb2a834ce8297855bd8e74e41140940db5501b68

eae2009eadf87ffd2378233f3e153d385f4654d2

refs/heads/master

2021-01-18T10:33:26.395991

2016-06-20T17:31:50

null

0

null

UTF-8

Python

false

1,496

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import django.core.validators class Migration(migrations.Migration): dependencies = [ ('DRP', '0024_auto_20160512_1658'), ] operations = [ migrations.AlterField( model_name='performedreaction', name='convertedLegacyRef', field=models.CharField(blank=True, max_length=40, null=True, validators=[django.core.validators.RegexValidator( b'^[a-z0-9._]*[a-z][a-z0-9._]*$', b'Please include only values which are limited to alphanumeric characters, underscores, periods, and must include at least one alphabetic character.')]), ), migrations.AlterField( model_name='performedreaction', name='performedDateTime', field=models.DateTimeField(default=None, help_text=b'Timezone assumed EST, Date in format YYYY-MM-DD', null=True, verbose_name=b'Date Reaction Performed', blank=True), ), migrations.AlterField( model_name='performedreaction', name='reference', field=models.CharField(max_length=40, validators=[django.core.validators.RegexValidator( b'^[a-z0-9\\._]*[a-z][a-z0-9\\._]*$', b'Please include only values which are limited to alphanumeric characters, underscores, periods, and must include at least one alphabetic character.')]), ), ]

[ "padler1@haverford.edu" ]

padler1@haverford.edu

eb04f67d1f28d2896c4f7cf56d21b890101b979e

dea576f4d05f91eba81c31a0a98f2d2bad93a7a5

/pandasDataFrame.py

2e5678ab5cf10173f09fcbf0b0effc83e5943765

[]

no_license

bastianposey/PANDAS

f4d896500dcdb3cfb319fae1c1ffc7387c91a74a

26a59bcda0c9ed91f492f7a06ba279191cfc3af7

refs/heads/master

2023-09-05T01:44:44.324899

2021-10-13T22:18:13

null

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py

import pandas as pd grades_dict = { 'Wally': [87,96,70], 'Eva':[100,87,90], 'Sam':[94,77,90], 'Katie':[100,81,82], 'Bob':[83,65,85]} grades = pd.DataFrame(grades_dict) grades.index = ['Test1', 'Test2', 'Test3'] #print(grades) #print(grades['Eva']) #print(grades.Sam) # using the loc and iloc methods #print(grades.loc['Test2']) #print(grades.iloc[1]) # For consecutive rows #print(grades.loc['Test1':'Test3']) #print(grades.iloc[:3]) #for non-consecutive rows #print(grades.loc[['Test1','Test3']]) #print(grades.iloc[[0,2]]) #View only Eva's and Katie's grades for Test1 and Test2 #print(grades.Eva.loc[['Test1','Test2']]) #print(grades.Katie.loc[['Test1','Test2']]) #print("") #View only Sam's THRU Bob's grades for Test1 and Test3 print(grades.Sam.loc["Test1":"Test3"]) #print(grades.Bob.loc["Test1":"Test3"]) print(grades.loc[['Test1', 'Test3'], "Sam":"Bob"]) grades_A = grades[grades >= 90] print(grades_A) # create a dataframe for everyone wiht a B grade grades_B = [(grades>=80) & (grades < 90)] print(grades_B) # creates a dataframe for everyone with a A or B grade grades_A_or_B = [(grades>=90) | (grades>-80)] print(grades_A_or_B) pd.set_option('precision',2) #print(grades.T.describe()) print(grades.sort_index(ascending=False)) print(grades.sort_index(axis=1, ascending=False)) print(grades.sort_values(by='Test1', axis=1,ascending=False)) print(grades.T.sort_values(by='Test1', ascending=False)) print(grades.loc['Test1'].sort_values(ascending=False))

[ "bastian_posey2@baylor.edu" ]

bastian_posey2@baylor.edu

c0c758ec3f45045fd732d1505955fd973d3253de

54f352a242a8ad6ff5516703e91da61e08d9a9e6

/Source Codes/AtCoder/abc036/D/4119191.py

5214b136ffa3fbda10cfeb4ddda4f643d5080a9d

[]

no_license

Kawser-nerd/CLCDSA

5cbd8a4c3f65173e4e8e0d7ed845574c4770c3eb

aee32551795763b54acb26856ab239370cac4e75

refs/heads/master

2022-02-09T11:08:56.588303

2022-01-26T18:53:40

211,783,197

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import sys stdin = sys.stdin sys.setrecursionlimit(10**5) def li(): return map(int, stdin.readline().split()) def li_(): return map(lambda x: int(x)-1, stdin.readline().split()) def lf(): return map(float, stdin.readline().split()) def ls(): return stdin.readline().split() def ns(): return stdin.readline().rstrip() def lc(): return list(ns()) def ni(): return int(stdin.readline()) def nf(): return float(stdin.readline()) def dfs(graph:list, par:int, cur:int, mod:int): children = [] for child in graph[cur]: if child == par: continue children.append(child) if len(children) == 0: return 2, 1 else: topall = 1 topwht = 1 for child in children: topallchild, topwhtchild = dfs(graph, cur, child, mod) topwht *= topallchild topwht %= mod topall *= topwhtchild topall %= mod return (topall+topwht)%mod, topwht n = ni() graph = [[] for _ in range(n)] MOD = 10**9+7 for _ in range(n-1): a,b = li_() graph[a].append(b) graph[b].append(a) ans, _ = dfs(graph, 0, 0, MOD) print(ans)

[ "kwnafi@yahoo.com" ]

kwnafi@yahoo.com

6a31dadc459e9cff086a628b3036733ed01e6692

d4f28073663e228e8bd119a70d17a8a21fc849c9

/algorithms/libHIN/dataStructures.py

4fda0163757175cd407c888d0454542bb85622da

[]

no_license

wsgan001/embedding_graph

fe81fa6cd81265a1b371d5de0dc4889bf7572763

93b49015dd2610e4348b2f7e3dc90405bd803c36

refs/heads/master

2021-08-27T18:54:47.803771

2017-11-23T09:36:05

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py

## core data structures import networkx as nx import numpy as np import scipy.sparse as sp from .decomposition import get_calculation_method class Class: def __init__(self, lab_id, name, members): self.name = name self.id = lab_id self.index = -1 self.members = members # ids of members calculated using hierarchy of labels self.member_indices = [] self.train_indices = [] # indices of training instances with this label self.validate_indices = [] # indices of validate instances with this label self.test_indices = [] # indices of test instances with this label self.train_members = set() # ids of train members (intersection of basic_members and the train set) self.test_members = set() # ids of test members (intersection of basic_members and the test set) self.validate_members = set() # ids of validate members (intersection of basic_members and the validate set) self.not_test_members = set() def __repr__(self): return self.name def __str__(self): return self.name class HeterogeneousInformationNetwork: def __init__(self, network, label_delimiter, weight_tag=False, target_tag=True): self.label_list = [] # list of labels. self.labels_by_id = {} # IDs of each label self.graph = network self.target_tag = target_tag self.node_list = [] # List of all nodes in decomposition self.node_indices = {} # Indices of all nodes in decomposition self.basic_type = None # Basic node type (for decomposition) self.label_array = None self.label_matrix = None self.train_indices = [] self.validate_indices = [] self.test_indices = [] self.train_ids = set() self.validate_ids = set() self.test_ids = set() self.weighted = weight_tag ## include info on weighted edges self.decomposed = {} # Dictionary of all performed decompositions (self.decomposed['PAP'] is one) self.pairs = {} self.midpoints = {} self.validate_pprs = {} self.test_pprs = {} self.train_pprs = {} self.validate_distances = {} self.test_distances = {} self.midpoint_files = {} self.feature_vectors = {} if network != None: self.process_network(label_delimiter) def add_label(self, node, label_id, label_name=None): if label_name is None: label_name = str(label_id) if label_id in self.labels_by_id: if self.labels_by_id[label_id] not in self.graph.node[node]['labels']: self.graph.node[node]['labels'].append(self.labels_by_id[label_id]) self.labels_by_id[label_id].members.append(node) else: new_class = Class(label_id, label_name, [node]) self.label_list.append(new_class) self.labels_by_id[label_id] = new_class new_class.index = len(self.label_list) - 1 self.graph.node[node]['labels'].append(new_class) def process_network(self, label_delimiter): if self.target_tag: basic_types = set([self.graph.node[x]['type'] for x in self.graph.node if 'labels' in self.graph.node[x]]) if len(basic_types) != 1: ## tukej naredi, da enostavno sejvne grafek, to je uporabno za embedding raise Exception('Unclear target type!') self.basic_type = basic_types.pop() self.node_list = [x for x in self.graph.node if self.graph.node[x]['type'] == self.basic_type] try: self.node_list.sort(key=lambda x: float(x)) except ValueError: self.node_list.sort() self.node_indices = dict([(item, index) for index, item in enumerate(self.node_list)]) for node_id in self.node_list: if len(self.graph.node[node_id]['labels']) > 0: labels = self.graph.node[node_id]['labels'].split(label_delimiter) self.graph.node[node_id]['labels'] = [] for label in labels: self.add_label(node_id, label, label_name=label) for lab in self.label_list: if lab is not None: temp_list = [mem for mem in lab.members if self.graph.node[mem]['type'] == self.basic_type] lab.basic_members = set(temp_list) self.label_array = - np.ones((max([len(self.graph.node[node]['labels']) for node in self.node_list]), len(self.node_list))) for node in self.node_list: tmp = self.graph.node[node]['labels'] self.label_array[:len(tmp), self.node_indices[node]] = [label.index for label in tmp] self.create_label_matrix() def create_label_matrix(self, weights=None): self.label_matrix = np.zeros((len(self.node_list), len(self.label_list))) for i, label in enumerate(self.label_list): member_indices = [self.node_indices[x] for x in label.members] if weights == 'balanced': self.label_matrix[member_indices, i] = 1.0 / max(len(label.train_indices), 1) else: self.label_matrix[member_indices, i] = 1 def calculate_schema(self): schema = nx.MultiDiGraph() for node_start in self.graph.node: for node_end in self.graph[node_start]: for key in self.graph[node_start][node_end]: start_type = self.graph.node[node_start]['type'] end_type = self.graph.node[node_end]['type'] edge_type = self.graph[node_start][node_end][key]['type'] has_type = False if schema.has_edge(start_type, end_type): for key in schema[start_type][end_type]: if schema[start_type][end_type][key]['type'] == edge_type: has_type = True break # if schema[start_type][end_type]['type'] != edge_type: # raise Exception('Multiple edge types between equal node types are not supported!') if not has_type: schema.add_edge(start_type, end_type, type=edge_type) return schema def calculate_decomposition_candidates(self, max_decomposition_length=10): schema = self.calculate_schema() under_construction = [{'node_list': [self.basic_type], 'edge_list': []}] candidate_lists = [] for i in range(max_decomposition_length - 1): next_gens = [] for list_so_far in under_construction: if list_so_far['node_list'][-1] != self.basic_type or len(list_so_far['node_list']) == 1: current = list_so_far['node_list'][-1] for neighbor in schema[current]: if neighbor == self.basic_type: append_to = candidate_lists else: append_to = next_gens for key in schema[current][neighbor]: append_to.append({ 'node_list': list_so_far['node_list'] + [neighbor], 'edge_list': list_so_far['edge_list'] + [schema[current][neighbor][key]['type']] }) under_construction = next_gens return candidate_lists def split_to_indices(self, train_indices=(), validate_indices=(), test_indices=()): self.train_indices = train_indices self.validate_indices = validate_indices self.test_indices = test_indices self.train_ids = set([self.node_list[i] for i in self.train_indices]) self.validate_ids = set([self.node_list[i] for i in self.validate_indices]) self.test_ids = set([self.node_list[i] for i in self.test_indices]) # calculate test representatives: for train_index in self.train_indices: train_node = self.node_list[train_index] for label in self.graph.node[train_node]['labels']: label.train_indices.append(train_index) label.train_members.add(self.node_list[train_index]) label.not_test_members.add(self.node_list[train_index]) for validate_index in self.validate_indices: validate_node = self.node_list[validate_index] for label in self.graph.node[validate_node]['labels']: label.validate_indices.append(validate_index) label.validate_members.add(self.node_list[validate_index]) label.not_test_members.add(self.node_list[validate_index]) for test_index in self.test_indices: test_node = self.node_list[test_index] for label in self.graph.node[test_node]['labels']: label.test_indices.append(test_index) label.test_members.add(self.node_list[test_index]) for label in self.label_list: label.not_test_members_num = len(label.not_test_members) def split_to_parts(self,lst,n): return [lst[i::n] for i in range(n)] def decompose_from_iterator(self, name, weighing, summing ,generator=None, degrees=None, parallel=True,pool=None): classes = [lab for lab in self.label_list if lab and len(lab.not_test_members) > 0] universal_set = list(set(self.train_ids).union(self.validate_ids)) universal_inv = {} for i, item in enumerate(universal_set): universal_inv[item] = i universal_set = set(universal_set) label_matrix = np.zeros((len(universal_set), len(classes))) for i, label in enumerate(classes): label_matrix[[universal_inv[item] for item in label.not_test_members], i] = 1 nn = len(self.node_list) matrix = sp.csr_matrix((nn, nn)) n = len(universal_set) importance_calculator = get_calculation_method(weighing) if generator is None: raise Exception('No midpoint generator!') avgdegree = None if weighing != 'okapi': degrees = None avgdegree = None if degrees is not None: avgdegree = sum(degrees.values()) * 1.0 / len(degrees) i=0 tmp_container = [] bsize = 5 if parallel: ## parallel for edge type while True: tmp_container = list(next(generator) for _ in range(bsize)) if len(tmp_container) == 0: break pinput = [] for j in tmp_container: pinput.append((classes,universal_set,j,n)) results = pool.starmap(importance_calculator,pinput) ## construct main matrix for item, importances in zip(tmp_container, results): importance = np.sum(importances, axis=0) i1 = [self.node_indices[x] for x in item] i2 = [[x] for x in i1] to_add = sp.csr_matrix((nn, nn)) if len(i1) > 1000: ## split to prevent memory leaks when doing hadamand products parts_first = self.split_to_parts(i1,4) parts_second = self.split_to_parts(i2,4) for x in range(len(parts_first)): to_add[parts_first[x], parts_second[x]] = importance else: to_add[i2, i1] = importance to_add = to_add.tocsr() matrix += to_add else: ## non-parallel for item in generator: ## to za vsak class poracun importance importances = importance_calculator(classes, universal_set, item, n, degrees=degrees, avgdegree=avgdegree) importance = np.sum(importances, axis=0) i1 = [self.node_indices[x] for x in item] i2 = [[x] for x in i1] to_add = sp.csr_matrix((nn, nn)) to_add[i2, i1] = importance to_add = to_add.tocsr() # this prevents memory leaks matrix += to_add ## hadamand product self.decomposed[name] = matrix def midpoint_generator(self, node_sequence, edge_sequence): if len(node_sequence) % 2 == 0: raise Exception('In a split of length %i, a midpoint is not well defined!' % len(node_sequence)) middle_type = node_sequence[int(len(node_sequence) / 2)] # forward_sequence = %TODO: INVERSE SEQUENCES!!!!!!!!! for node in self.graph: if self.graph.node[node]['type'] == middle_type: points = [node] i = int(len(node_sequence)/2 + 1) while i < len(node_sequence): current_type = node_sequence[i] new_points = [] for point in points: new_points += [x for x in self.graph[point] if self.graph.node[x]['type'] == current_type] points = new_points i += 1 if len(points) > 1: yield points

[ "skrljblaz@gmail.com" ]

skrljblaz@gmail.com

b444b19a3bf1da0d3d18e4fd044a8a7ef7641a4a

3e607e43f264a4020c010422f0b2f9919c298033

/start.py

1fdf1a1cb2c657528449253971922a2165d8f301

[]

no_license

MagnusBordewich/Pibot

a74529371d93950bdecc9e73aa4254038a443a74

484dc97d5b8cf7f770cd6dabd1896cf29ba1564f

refs/heads/master

2021-01-10T16:17:21.072658

2016-03-01T12:48:34

52,875,832

1

2

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UTF-8

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py

import numpy as np import copy import cv import cv2 import time import os import StringIO from BrickPi import * #import BrickPi.py file to use BrickPi operations import threading os.system('sudo modprobe bcm2835-v4l2') side = 0 w=80 h=60 turning_rate = 60 running = True time_limit=60 startTime=time.time() BrickPiSetup() # setup the serial port for communication BrickPi.MotorEnable[PORT_A] = 1 #Enable the Motor A BrickPi.MotorEnable[PORT_D] = 1 #Enable the Motor D #This thread is used for keeping the motors running class myThread (threading.Thread): def __init__(self, threadID, name, counter): threading.Thread.__init__(self) self.threadID = threadID self.name = name self.counter = counter def run(self): while running and (time.time()<startTime+time_limit): BrickPiUpdateValues() # Ask BrickPi to update values for senso # time.sleep(.1) # sleep for 200 ms thread1 = myThread(1, "Thread-1", 1) #Setup and start the thread thread1.setDaemon(True) thread1.start() # This sets up the video capture cap = cv2.VideoCapture(0) cap.set(3,w) cap.set(4,h) time.sleep(2) #cap.set(15,-80.0) seeking=True seekTime=time.time() # Main loop while True and (time.time()<startTime+time_limit): try: found = False ret, image = cap.read() image = cv2.flip(image,-1) #image2 = copy.deepcopy(image) #image2 = cv2.cvtColor(image2,cv2.COLOR_RGB2BGR) binary = cv2.GaussianBlur(image,(5,5),0) binary = cv2.cvtColor(binary,cv2.COLOR_BGR2HSV) lower_pink = np.array([164,50,50]) upper_pink = np.array([176,255,255]) kernel = np.ones((5,5),np.uint8) mask = cv2.inRange(binary,lower_pink,upper_pink) mask = cv2.erode(mask,kernel,iterations=1) mask = cv2.dilate(mask,kernel,iterations=1) contours, hierarchy = cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) blob_x = w/2 area = 0 if len(contours)>0: largest = 0 area = cv2.contourArea(contours[0]) if len(contours)>1: for i in range(1,len(contours)): temp_area = cv2.contourArea(contours[i]) if temp_area>area: area=temp_area largest = i if area > 100: found=True coords = cv2.moments(contours[largest]) blob_x = int(coords['m10']/coords['m00']) #blob_y = int(coords['m01']/coords['m00']) #diam = int(np.sqrt(area)/4) #cv2.circle(image,(blob_x,blob_y),diam,(0,255,0),1) #cv2.line(image,(blob_x-2*diam,blob_y),(blob_x+2*diam,blob_y),(0,255,0),1) #cv2.line(image,(blob_x,blob_y-2*diam),(blob_x,blob_y+2*diam),(0,255,0),1) #cv2.drawContours(image,contours,largest,(255,0,0),3) if not found: if seeking == False: L_motor_speed=0 R_motor_speed=0 else: if time.time()<seekTime+10: if side == -1: L_motor_speed=-70 R_motor_speed=70 else: L_motor_speed=70 R_motor_speed=-70 else: seeking=False else: seeking=True seekTime=time.time() direction = blob_x -w/2 if direction <0: side = -1 else: side = 1 L_motor_speed=220*side*direction/w R_motor_speed=-220*side*direction/w BrickPi.MotorSpeed[PORT_A] = L_motor_speed BrickPi.MotorSpeed[PORT_D] = R_motor_speed found = False except KeyboardInterrupt: break key_pressed = cv2.waitKey(33) if key_pressed ==27: break cap.release()

[ "m.j.r.bordewich@durham.ac.uk" ]

m.j.r.bordewich@durham.ac.uk

bd7b287b4684f03a23b44c0db107bd72b40c3163

747ec225125c19acb0838ce4270c990deb0e1a15

/src/utils/writecsv.py

2496cb08a7e972a273c4c1fe994eb39de2bd6e58

[]

no_license

juanjo3ns/gridworldRL

c2d01d9b3add464e22ea6a7687774a9967ca009d

e119d97c9b9a0d401f16be45b90e1d1fe7dfe52d

refs/heads/master

2022-07-10T04:16:53.167556

2019-08-10T19:11:06

188,305,100

0

null

2022-06-21T22:03:04

2019-05-23T20:54:09

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import csv import os class CSV: def __init__(self,type, version , file): self.path = '/data/src/csvdata/' if not os.path.exists(os.path.join(self.path,type)): os.mkdir(os.path.join(self.path,type)) if not os.path.exists(os.path.join(self.path,type,version)): os.mkdir(os.path.join(self.path,type,version)) self.file = file self.csvfile = open(os.path.join(self.path, type, version, self.file + '.csv'), 'w') self.csvwriter = csv.writer(self.csvfile, delimiter=',') def write(self, row): self.csvwriter.writerow(row) def close(self): self.csvfile.close()

[ "juanjo.3ns@gmail.com" ]

juanjo.3ns@gmail.com

55e82a61b41e201ad163f255e9179fc6a9b7ce58

a4c3eb6ce6e1a04d2d4909a808e61fe22d6c7817

/Supplier/migrations/0033_auto_20200826_1232.py

38233fdf5ea709c812d7344e611f1c9b6598257e

[]

no_license

AIMsnet/Backend

2522d192ef1e1a95e49a7d4c0b3a83c77660dcf1

42a32850a88eebd038df3fee47f94309c76e005a

refs/heads/master

2023-02-22T13:51:54.531918

2021-01-28T09:08:44

267,277,083

0

null

UTF-8

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py

# Generated by Django 3.0.6 on 2020-08-26 07:02 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('Supplier', '0032_auto_20200825_1354'), ] operations = [ migrations.AddField( model_name='product', name='description_eight', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_five', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_four', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_nine', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_one', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_seven', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_six', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_ten', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_three', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='description_two', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_eight', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_five', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_four', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_nine', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_one', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_seven', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_six', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_ten', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_three', field=models.CharField(blank=True, default='', max_length=255, null=True), ), migrations.AddField( model_name='product', name='specification_two', field=models.CharField(blank=True, default='', max_length=255, null=True), ), ]

[ "hasnainshaikh31@gmail.com" ]

hasnainshaikh31@gmail.com

60597c73a482cbddf64360493f4c0f493476a87e

8b0b2711dd95943a3623e6cdd63a4bc4aa906cb0

/app/config.py

141f98655d89fa601206eb482c4cf3564399780f

[]

no_license

raycursive/overwatch_stats

18a71934cbf1bb219e807978aafe5fb98ee9fafe

99f95f84548846336c3d0449a41a68dc34e98102

refs/heads/master

2020-06-22T14:36:12.302372

2019-07-19T08:26:00

197,730,610

0

null

UTF-8

Python

false

49

py

MONGO_URL = 'mongodb://127.0.0.1:27017/ow_stats'

[ "m@raycursive.com" ]

m@raycursive.com

fadd8b2d4391ccc33a5c230fc0d56aaf7713c9fb

e247fb17dcdce92f3cb439880b0c7df7a0b0f869

/rooms/migrations/0019_auto_20210731_1450.py

9600f52897a28df09f19198d0465011ecb6a8755

[]

no_license

hongsoojong/airbnb-clone-2021

6807bc3cec5ff70039c71ac44c9c28b13841c894

17a46b31d808f6e531c5c4b2f13aa7204cb51293

refs/heads/master

2023-07-15T12:36:00.429074

2021-08-24T07:42:00

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null

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Python

false

665

py

# Generated by Django 2.2.5 on 2021-07-31 05:50 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('rooms', '0018_auto_20210731_1441'), ] operations = [ migrations.AlterField( model_name='photo', name='file', field=models.ImageField(upload_to='room_photos'), ), migrations.AlterField( model_name='room', name='room_type', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='rooms', to='rooms.RoomType'), ), ]

[ "84722193+hongsoojong@users.noreply.github.com" ]

84722193+hongsoojong@users.noreply.github.com

98dedda4af53cd513aba1f2a0550e46f1b00586d

0afc304e4b808e729d62cdd9e7d9643a411d1677

/config/urls.py

6bc9ecf2264a784c50b80eec74046851ef8e28c5

[]

no_license

lisa4930007/One

f9a85bd4e1b7c931f65f3bd64deaf4e79435465f

a225096fc7e2a3004a0f26049276613f03df7802

refs/heads/master

2023-05-14T04:48:47.321243

2021-04-22T13:36:17

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null

2021-06-05T02:29:32

2021-04-14T05:06:36

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Python

false

980

py

"""config URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('accounts/', include('django.contrib.auth.urls')), path('accounts/', include('accounts.urls')), path('social-auth/', include('social_django.urls', namespace='social')), path('', include('pages.urls')), ]

[ "lisalai@gapp.nthu.edu.tw" ]

lisalai@gapp.nthu.edu.tw

2cc01a5bcad3b82724cee5c8768f5a5ce0700964

29929e5236a44fb315792f0aa21f7d81f8ce3b3e

/autos/migrations/0001_initial.py

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[]

no_license

hell0ut/DjangoTesting

fa175ec48b3e420f7f5324d9211830ff0c695d80

a00e8bbc947cd7522144945c3f8b2f1d72fca37f

refs/heads/master

2023-08-16T07:00:19.149461

2021-09-25T23:02:50

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1,302

py

# Generated by Django 3.1 on 2020-08-08 14:21 import django.core.validators from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Make', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(help_text='Enter a make (e.g. Dodge)', max_length=200, validators=[django.core.validators.MinLengthValidator(2, 'Make must be greater than 1 character')])), ], ), migrations.CreateModel( name='Auto', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('nickname', models.CharField(max_length=200, validators=[django.core.validators.MinLengthValidator(2, 'Nickname must be greater than 1 character')])), ('mileage', models.PositiveIntegerField()), ('comments', models.CharField(max_length=300)), ('make', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='autos.make')), ], ), ]

[ "hell0ut@users.noreply.github.com" ]

hell0ut@users.noreply.github.com

c45fd4f5a8e15cd035acf90dac7bafd543d04c90

4c88b359375197bbfd96028a08ded14a48bb60ad

/Conditionals.py

9f45513fcde2632e826850e618c85cfd85c9fc04

[]

no_license

Joexder/Python-Curse

6a8247f98760ba331a9e25973c73d97b90348b17

ac3fb169da78da48599024755a0a909a6b932f3c

refs/heads/master

2020-08-03T11:28:08.448505

2019-09-29T23:00:14

211,736,220

0

null

UTF-8

Python

false

297

py

x = input("<escribe tu edad: ") if int(x) > 26: print("Por mayor") elif int(x) == 26: print("En el limite") else: print("Por menor") y = input("Escribe un Color:") if y == 'red' or y == 'blue' or y == 'yellow': print("Colores primarios") else: print("Colores Secundarios")

[ "joel96terce@gmail.com" ]

joel96terce@gmail.com

e4a1d05206a0a5d258b62ecda07b2e6abc7290b0

dd5e7feb8fd98f77c6a5597e37c666cf97b71ee3

/py/cnn_miracle_check.py

1066651cc740b90cf87689b119953f30fbdb5c6a

[]

no_license

N-31V/robotic-software

1d6706baa746cabf404cd0c17fc5a14ad03f1088

7aa1933394978cf07f8d62b915088c1b7c8cc940

refs/heads/master

2021-07-21T22:47:01.740444

2020-05-30T19:06:37

173,922,016

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py

#!/usr/bin/env python3 from cnn_miracle import * import cv2 as cv import sys import argparse def createParser (): parser = argparse.ArgumentParser() parser.add_argument ('-o', '--old', action='store_true', default=False) return parser if __name__ == '__main__': parser = createParser() namespace = parser.parse_args(sys.argv[1:]) model = make_model() if namespace.old: test_data = np.load('test_data.npy', allow_pickle=True) else: test_data = process_test_data() for elem in test_data[:30]: img_num = elem[1] img_data = elem[0] img_np = np.array(elem[0]) data = img_np.reshape(IMG_WIDTH,IMG_HEIGHT,3) model_out = model.predict([data])[0] if np.argmax(model_out) == 1: str_label="Isn't miracle" else: str_label='Miracle' img = elem[0] cv.imshow ( str_label , img) cv.waitKey (0) cv.destroyAllWindows()

[ "litvintseva.ann@gmail.com" ]

litvintseva.ann@gmail.com

643b2ad8db2c458d77f96dff2374d2efa0c66723

a00ed711e3e08b50ad6e91cc07a2cddc4a1de5ea

/airflow/api_connexion/schemas/dag_warning_schema.py

9531eb6b36bc3833a39d24bcef895f01444f9bb6

[ "Apache-2.0", "BSD-3-Clause", "MIT" ]

permissive

ishiis/airflow

4305794e36b611d01f49e3f2401be3dc49782670

292440d54f4db84aaf0c5a98cf5fcf34303f2fa8

refs/heads/master

2022-07-30T00:51:28.806940

2022-07-14T12:07:11

209,801,072

1

0

Apache-2.0

2019-09-20T13:47:26

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UTF-8

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py

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from typing import List, NamedTuple from marshmallow import Schema, fields from marshmallow_sqlalchemy import SQLAlchemySchema, auto_field from airflow.models.dagwarning import DagWarning class DagWarningSchema(SQLAlchemySchema): """Import error schema""" class Meta: """Meta""" model = DagWarning dag_id = auto_field(data_key="dag_id", dump_only=True) warning_type = auto_field() message = auto_field() timestamp = auto_field(format="iso") class DagWarningCollection(NamedTuple): """List of dag warnings with metadata""" dag_warnings: List[DagWarning] total_entries: int class DagWarningCollectionSchema(Schema): """Import error collection schema""" dag_warnings = fields.List(fields.Nested(DagWarningSchema)) total_entries = fields.Int() dag_warning_schema = DagWarningSchema() dag_warning_collection_schema = DagWarningCollectionSchema()

[ "noreply@github.com" ]

noreply@github.com

f3b97341a44d4fe1f94ed1617edef2d9b577cc13

e6fb8de1ecfafa787d826d3255029b4ad7776adf

/Class2/1181.py

e0189a35f15ce2c5fd3870c1f99820626382d7a9

[]

no_license

minari1505/AlgorithmForH-M

2318e743558b21ea6295f100075d99e2bd6e2c58

e751acf0897f96640e0c3d85d8a7c37ed9202c44

refs/heads/main

2023-08-30T00:31:58.426096

2021-10-27T08:37:46

386,270,328

0

1

null

2021-10-06T12:55:09

2021-07-15T11:48:28

Python

UTF-8

Python

false

300

py

import sys n=int(input()) line = [sys.stdin.readline().strip() for i in range(n)] set_line=set(line) line = list(set_line) line.sort() line.sort(key=len) for i in line: print(i) #dic = {} #print(dic.items()) #print(sorted(dic.items(),key=lambda x : x[1])) #for key in dic.keys(): # print(key)

[ "threeyearr@gmail.com" ]

threeyearr@gmail.com

acd65c46ffa5dd3f4fa612a415887f694e67e27f

9a6c5607ae6f6305f1427fe5ee37ab8a0aa9b710

/0 Python Fundamental/25.c.filter.py

bf848a8ad7d8b4c95625bf195a090ed00fc3af2e

[]

no_license

raviitsoft/Python_Fundamental_DataScience

3796b957751a6d9125452bcf2aa409e64d7c8d8a

6f99fdd187646f0d28ffd4ddbe3ace4597c47967

refs/heads/master

2020-12-22T19:39:46.814043

2020-01-28T09:04:55

null

0

null

UTF-8

Python

false

560

py

ages = [5, 12, 17, 18, 24, 32] def myFunc(x): if x < 18: return False else: return True adults = filter(myFunc, ages) # print(adults) # print(list(adults)) ############################# z = filter(lambda a: True if a >= 18 else False, ages) print(list(z)) z = filter(lambda a: a >= 18, ages) print(list(z)) ############################ x = [1, 2, 3, 4, 5, 99] y = [1, 2, 6, 7, 8, 99] z = list(filter(lambda a: a in x, y)) # print(z) z = list(filter(lambda x: True if x<3 else False, x)) print(z) z = list(filter(lambda x: x<3, x)) print(z)

[ "lintangwisesa@ymail.com" ]

lintangwisesa@ymail.com

3402c917b6adeac02e37b98b2e7fb951b691c3b0

7797913bc78cfa0cfdb30813d4268e02770722e0

/Section 1/Q13.py

6c52afab4b9f16447181fdd6bccb59c92347316f

[]

no_license

pkoi5088/CodeFestival

ecdf5dbdbc78793fd093a614f60d2639a68bffb3

cdc3065f6a2557936432f09344d5bfa09ff8b651

refs/heads/main

2023-03-19T14:49:02.488625

2021-03-14T03:20:15

345,632,059

0

null

UTF-8

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false

134

py

planet={1:'수성',2:'금성',3:'지구',4:'화성',5:'목성',6:'토성',7:'천왕성',8:'해왕성'} N=int(input()) print(planet[N])

[ "pkoi5088@gmail.com" ]

pkoi5088@gmail.com

b4648464a36f3eb658eb2eb905a4d93fc19c34f4

d0f0cad6484b70838078efc01f6ec4122b489be4

/dz7/3.py

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[]

no_license

w2kzx80/py

9543f6e3d89ee546adfd0e8d48a1accbefb18409

0f54a95a2e72eb5cba0442d3be233f23722529f6

refs/heads/main

2023-01-28T17:54:47.662471

2020-12-02T18:05:00

308,981,748

0

null

2020-12-02T18:05:02

2020-10-31T22:20:07

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UTF-8

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py

class Cell: def __init__(self, ccnt:int): self.ccnt=ccnt def __str__(self): return f"{self.ccnt}" def __add__(self, other): if type(self) == Cell and type(other) == Cell: return Cell(self.ccnt + other.ccnt) raise TypeError def __sub__(self, other): if type(self) == Cell and type(other) == Cell: if self.ccnt > other.ccnt: return Cell(self.ccnt - other.ccnt) print("second cell too big") return self raise TypeError def __mul__(self, other): if type(self) == Cell and type(other) == Cell: return Cell(self.ccnt * other.ccnt) raise TypeError def __truediv__(self, other): if type(self) == Cell and type(other) == Cell: return Cell(self.ccnt // other.ccnt) raise TypeError def make_order(self, rlen): rows = [] rsum = self.ccnt while rsum > rlen: rows.append("".join(["*" for ri in range(0,rlen)])) rsum -= rlen rows.append("".join(["*" for ri in range(0, rsum)])) return "\n".join(rows) myCell1 = Cell(12) myCell2 = Cell(5) myCell3 = Cell(7) print(myCell1 + myCell2 + myCell3) print(myCell1 / myCell2) print(myCell2 * myCell3) print(myCell3 - myCell2) print(myCell2 - myCell3) print(f"ORDER c1:\n{myCell1.make_order(6)}\n\n") print(f"ORDER c2:\n{myCell2.make_order(6)}\n\n") print(f"ORDER c3:\n{myCell3.make_order(6)}\n\n") print(myCell1 + 12)

[ "w2kzx80@gmail.com" ]

w2kzx80@gmail.com

b47a4a5a30e4c6a69d65b10c5957b45ee232cebb

f1a313e07e512649d513de1cbf43a680222a2a08

/classes/fan.py

debb1a32ab2a6e57fed56479d558dcc74e6158a9

[]

no_license

ambrogio-galbusera/gr0g

04ba61ad8c33ed563e4dc8cac3fdd5c57a05f537

78658513c052e89f7823ff4027f7ae7e6fbd8f39

refs/heads/master

2023-02-04T04:16:13.656160

2020-12-20T08:37:38

309,800,223

0

null

UTF-8

Python

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513

py

import gertutil as gu class Fan: fan_channel = 2 fan_freq = 1000 def __init__ (self) : print("[FAN ] PWM brushed on board {}, channel {}".format(gu.board, self.fan_channel)) gu.pwm_init(self.fan_channel) gu.pwm_set(self.fan_channel,self.fan_freq,0) def set (self,dc) : print("[FAN ] Setting PWM to {}".format(dc)) gu.pwm_set(self.fan_channel,self.fan_freq,dc) def off (self) : print("[FAN ] Power off") gu.pwm_off(self.fan_channel)

[ "amgalbu@gmail.com" ]

amgalbu@gmail.com

ea192e4b23bffcdc8beec6046e91bd58a61df8fb

168dfe4755fe68bc3ec43a70d87197645b5ed98c

/Crossin/test7.py

9ce0228e86d593ec9c9fefe674a21485d47e5c65

[]

no_license

cjmking2010/python_study

e6364109be508dbbf34508999a72dd714b0c3986

48c365aece079218f0f78b1f0f90f0c2d5a12eec

refs/heads/master

2021-01-01T16:15:14.012244

2018-01-11T08:53:55

97,796,484

0

null

UTF-8

Python

false

171

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Filename: test7.py # Author: peter.chen from random import randint a = randint(5,10) b = randint(1,100) print a print b

[ "cjm20008@126.com" ]

cjm20008@126.com

a429a7f8ebb10dd8c7961a52873a18b1be3fc2e5

0156c5bb165ee4017c46f62db2a70455b7dca63a

/dir_for_bind/counter.py

94f08a89e9fb52da153538483f943e0dcbc0db64

[]

no_license

KotovVitaliy/LearnQA_Docker

ba8206b14a7ab2d130d49bddf763a6cd5374d1c5

e6afc4c7bf11bf6dea579a2777c9aeb70c4e1a18

refs/heads/master

2022-06-19T18:12:51.577052

2020-05-11T12:02:58

253,278,981

0

1

null

UTF-8

Python

false

30

py

for i in range(1, 6): print(i)

[ "v.kotov@corp.badoo.com" ]

v.kotov@corp.badoo.com

bf1e15e32502c294cb2398b0ca3de70499a04222

8b5d58fc22888d2fb051a3c59936659ca347043a

/NtupleAnalysis/src/Hplus2tbAnalysis/work/plotting/plotHistograms.py

35ff6c7ca74f8f56b0ceca013609826b3d9da715

[]

no_license

attikis/HplusHW

c54f4429dd48e99b7e597043fa6d442d7a3573ba

e62ce79c914c6b5bfd1faa44ff94356fb55fe561

refs/heads/master

2020-06-15T22:00:43.733407

2019-07-05T10:30:07

195,402,507

1

0

null

2019-07-05T12:02:22

null

UTF-8

Python

false

9,600

py

#!/usr/bin/env python ''' Usage (single plot): ./plotHistograms.py -m <pseudo_mcrab_directory> <jsonfile> Usage (multiple plots): ./plotHistograms.py -m <pseudo_mcrab_directory> json/AfterAllSelections/*.json or ./plotHistograms.py -m <pseudo_mcrab_directory> json/AfterAllSelections/*.json json/AfterStandardSelections/*.json Last Used: ./plotHistograms.py -m Hplus2tbAnalysis_161128_082955/ json/AfterAllSelections/BjetPt.json or ./plotHistograms.py -m Hplus2tbAnalysis_161128_082955/ json/AfterAllSelections/*.json or ./plotHistograms.py -m Hplus2tbAnalysis_161128_082955/ json/AfterAllSelections/*/*.json ''' #================================================================================================ # Imports #================================================================================================ import os import sys from optparse import OptionParser import getpass import socket import json import HiggsAnalysis.NtupleAnalysis.tools.dataset as dataset import HiggsAnalysis.NtupleAnalysis.tools.tdrstyle as tdrstyle import HiggsAnalysis.NtupleAnalysis.tools.styles as styles import HiggsAnalysis.NtupleAnalysis.tools.plots as plots import HiggsAnalysis.NtupleAnalysis.tools.histograms as histograms import HiggsAnalysis.NtupleAnalysis.tools.aux as aux import ROOT #================================================================================================ # Main #================================================================================================ def Print(msg, printHeader=False): fName = __file__.split("/")[-1] if printHeader==True: print "=== ", fName print "\t", msg else: print "\t", msg return def Verbose(msg, printHeader=True, verbose=False): if not opts.verbose: return Print(msg, printHeader) return def GetLumi(datasetsMgr): Verbose("Determininig Integrated Luminosity") lumi = 0.0 for d in datasetsMgr.getAllDatasets(): if d.isMC(): continue else: lumi += d.getLuminosity() Verbose("Luminosity = %s (pb)" % (lumi), True ) return lumi def GetDatasetsFromDir(opts, json): Verbose("Getting datasets") if len(json["samples"])<1: Print("No samples defined in the JSON file. Exit", True) print __doc__ sys.exit() else: return dataset.getDatasetsFromMulticrabDirs([opts.mcrab], dataEra=json["dataEra"], searchMode=json["searchMode"], analysisName=json["analysis"], includeOnlyTasks="|".join(json["samples"]), optimizationMode=json["optMode"]) def Plot(jsonfile, opts): Verbose("Plotting") with open(os.path.abspath(jsonfile)) as jfile: j = json.load(jfile) Print("Plotting %s in %s" % (j["title"], j["saveDir"]), True) # Setup the style style = tdrstyle.TDRStyle() style.setGridX(j["gridX"]=="True") style.setGridY(j["gridY"]=="True") # Set ROOT batch mode boolean ROOT.gROOT.SetBatch(opts.batchMode) # Setup & configure the dataset manager datasetsMgr = GetDatasetsFromDir(opts, j) #datasetsMgr.loadLuminosities() datasetsMgr.updateNAllEventsToPUWeighted() if opts.verbose: datasetsMgr.PrintCrossSections() datasetsMgr.PrintLuminosities() # Set/Overwrite cross-sections for d in datasetsMgr.getAllDatasets(): if "ChargedHiggs" in d.getName(): datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) plots.mergeRenameReorderForDataMC(datasetsMgr) # Print dataset information datasetsMgr.PrintInfo() # Get Integrated Luminosity lumi = GetLumi(datasetsMgr) # Plot the histogram DataMCPlot(datasetsMgr, j) return def DataMCPlot(datasetsMgr, json): Verbose("Creating Data-MC plot") # Create the Data-MC Plot p = plots.DataMCPlot(datasetsMgr, json["histogram"]) # Customise histograms before drawing (before being converted to TGraphs) if "drawStyle" in json: p.histoMgr.setHistoDrawStyleAll(json["drawStyle"]) if "rebinx" in json: p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(json["rebinX"])) if "rebiny" in json: if json["rebinY"] != "None": p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(json["rebinY"])) # Label size (optional. Commonly Used in counters) xlabelSize = None if "xlabelsize" in json: xlabelSize = json["xlabelsize"] ylabelSize = None if "ylabelsize" in json: ylabelSize = json["ylabelsize"] # Draw a customised plot saveName = os.path.join(json["saveDir"], json["title"]) plots.drawPlot(p, saveName, xlabel = json["xlabel"], ylabel = json["ylabel"], rebinX = json["rebinX"], rebinY = json["rebinY"], ratioYlabel = json["ratioYlabel"], ratio = json["ratio"]=="True", stackMCHistograms = json["stackMCHistograms"]=="True", ratioInvert = json["ratioInvert"]=="True", addMCUncertainty = json["addMCUncertainty"]=="True", addLuminosityText = json["addLuminosityText"]=="True", addCmsText = json["addCmsText"]=="True", cmsExtraText = json["cmsExtraText"], opts = json["opts"], opts2 = json["ratioOpts"], log = json["logY"]=="True", errorBarsX = json["errorBarsX"]=="True", moveLegend = json["moveLegend"], # cutLine = json["cutValue"], #cannot have this and "cutBox" defined cutBox = {"cutValue": json["cutValue"], "fillColor": json["cutFillColour"], "box": json["cutBox"]=="True", "line": json["cutLine"]=="True", "greaterThan": json["cutGreaterThan"]=="True"}, xlabelsize = xlabelSize, ylabelsize = ylabelSize, ) # Remove legend? if json["removeLegend"] == "True": p.removeLegend() # Additional text histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"), json["extraText"].get("text"), json["extraText"].get("size") ) # Save in all formats chosen by user saveFormats = json["saveFormats"] for i, ext in enumerate(saveFormats): Print("%s" % saveName + ext, i==0) p.saveAs(saveName, formats=saveFormats) return def main(opts): Verbose("main function") jsonFiles = [] # For-loop: All system script arguments for arg in sys.argv[1:]: # Skip if not a json file if ".json" not in arg: continue # Sanity check - File exists if not os.path.exists(arg): Print("The JSON file \"%s\" does not seem to be a valid path.. Please check that the file exists. Exit" % (arg), True) sys.exit() # Load & append json file with open(os.path.abspath(arg)) as jsonFile: try: json.load(jsonFile) jsonFiles.append(arg) except ValueError, e: Print("Problem loading JSON file %s. Please check the file" % (arg)) sys.exit() # Sanity check - At least 1 json file found if len(jsonFiles) == 0: Print("No JSON files found. Please read the script instructions. Exit", True) print __doc__ sys.exit() # For-loop: All json files for j in jsonFiles: Print("Processing JSON file \"%s\"" % (j), True) Plot(j, opts) return #================================================================================================ # Main #================================================================================================ if __name__ == "__main__": # Default Settings global opts BATCHMODE = True VERBOSE = False parser = OptionParser(usage="Usage: %prog [options]" , add_help_option=False,conflict_handler="resolve") parser.add_option("-m", "--mcrab", dest="mcrab", action="store", help="Path to the multicrab directory for input") parser.add_option("-b", "--batchMode", dest="batchMode", action="store_false", default=BATCHMODE, help="Enables batch mode (canvas creation NOT generates a window) [default: %s]" % BATCHMODE) parser.add_option("-v", "--verbose", dest="verbose", action="store_true", default=VERBOSE, help="Enables verbose mode (for debugging purposes) [default: %s]" % VERBOSE) (opts, parseArgs) = parser.parse_args() # Require at least two arguments (script-name, path to multicrab) if opts.mcrab == None: Print("Not enough arguments passed to script execution. Printing docstring & EXIT.") print __doc__ sys.exit(0) # Call the main function main(opts) if not opts.batchMode: raw_input("=== plotHistograms.py: Press any key to quit ROOT ...")

[ "attikis@cern.ch" ]

attikis@cern.ch

4381790eeb2b2b365378271185ea7d4b8453afa4

0ddf984c1e64c937bb2864968002b86e4e4dd77a

/IE517_F20_HW4/regression housing2.py

1bd8949c029d2fc2b5ab3e44db59c9f9e00435d4

[]

no_license

kanonlove/UIUC-IE-517

7ee3774c127f33a28d33e5eb8aadbf4e3dc1ebfe

853c537fbea5faf3b034f2f6c17dc4b01e8940d7

refs/heads/master

2022-12-25T14:04:40.094655

2020-10-10T03:31:08

290,069,342

0

null

UTF-8

Python

false

11,540

py

import pandas as pd import seaborn as sns import numpy as np from pandas import DataFrame from sklearn.linear_model import LinearRegression from sklearn.linear_model import Ridge from sklearn.linear_model import Lasso import matplotlib.pyplot as plt; """ Part 1: Exploratory Data Analysis Describe the data sufficiently using the methods and visualizations that we used previously in Module 3 and again this week. Include any output, graphs, tables, heatmaps, box plots, etc. Label your figures and axes. DO NOT INCLUDE CODE! Split data into training and test sets. Use random_state = 42. Use 80% of the data for the training set. Use the same split for all models. Part 2: Linear regression Fit a linear model using SKlearn to all of the features of the dataset. Describe the model (coefficients and y intercept), plot the residual errors, calculate performance metrics: MSE and R2. Part 3.1: Ridge regression Fit a Ridge model using SKlearn to all of the features of the dataset. Test some settings for alpha. Describe the model (coefficients and y intercept), plot the residual errors, calculate performance metrics: MSE and R2. Which alpha gives the best performing model? Part 3.2: LASSO regression Fit a LASSO model using SKlearn to all of the features of the dataset. Test some settings for alpha. Describe the model (coefficients and y intercept), plot the residual errors, calculate performance metrics: MSE and R2. Which alpha gives the best performing model? Part 4: Conclusions Write a short paragraph summarizing your findings. Part 5: Appendix Link to github repo """ df=pd.read_csv("HW4_housing2.csv") print(df.head()) #print(len(df)) print("\n") """ • CRIM: Per capita crime rate by town • ZN: Proportion of residential land zoned for lots over 25,000 sq. ft. • INDUS: Proportion of non-retail business acres per town • CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) • NOX: Nitric oxide concentration (parts per 10 million) • RM: Average number of rooms per dwelling • AGE: Proportion of owner-occupied units built prior to 1940 • DIS: Weighted distances to five Boston employment centers • RAD: Index of accessibility to radial highways • TAX: Full-value property tax rate per $10,000 • PTRATIO: Pupil-teacher ratio by town • B: 1000(Bk - 0.63)^2, where Bk is the proportion of [people of African American descent] by town • LSTAT: Percentage of lower status of the population • MEDV: Median value of owner-occupied homes in $1000s (TARGET) """ # heat map cormat = DataFrame(df.corr()) #visualize correlations using heatmap fig, ax = plt.subplots(figsize=(10,10)) sns.heatmap(cormat,linewidth=1,square=True,ax=ax,annot=True) ax.set_ylim([27, 0]) plt.xlabel("Heat Map of Boston House Price") plt.show() print("\n") #CRIM plt.hist(df["CRIM"],edgecolor="black",bins=10) plt.xlabel("per capita crime rate by town") plt.ylabel("numbers") plt.show() #ZN plt.hist(df["ZN"],edgecolor="black",bins=10) plt.xlabel("ZProportion of residential land zoned for lots over 25,000 sq. ft") plt.ylabel("numbers") plt.show() #INDUS plt.hist(df["INDUS"],edgecolor="black",bins=[0,2.5,5,7.5,10,12.5,15,17.5,20,22.5,25,27.5,30]) plt.xlabel("Proportion of non-retail business acres per town") plt.ylabel("numbers") plt.show() #CHAS false_number=0 true_number=0 for value in df["CHAS"]: if value== 0: false_number=false_number+1 else: true_number=true_number+1 plt.bar(["tract bounding river","tract not bounding river"],height=[true_number,false_number],width=0.4,edgecolor="black") plt.xlabel("Charles River dummy variable") plt.ylabel("Number") plt.show() #NOX plt.hist(df["NOX"],edgecolor="black",bins=[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]) plt.xlabel("Nitric oxide concentration (parts per 10 million)") plt.ylabel("numbers") plt.show() #RM plt.hist(df["RM"],edgecolor="black",bins=[0,1,2,3,4,5,6,7,8,9,10]) plt.xlabel("Average number of rooms per dwelling") plt.ylabel("numbers") plt.show() #AGE plt.hist(df["AGE"],edgecolor="black",bins=[0,10,20,30,40,50,60,70,80,90,100,110]) plt.xlabel("Proportion of owner-occupied units built prior to 1940") plt.ylabel("numbers") plt.show() #DIS plt.hist(df["DIS"],edgecolor="black",bins=[0,2,4,6,8,10,12,14]) plt.xlabel("Weighted distances to five Boston employment centers") plt.ylabel("numbers") plt.show() #RAD #this one is more like binary data #I am not sure what the "data" are #I might also do a hist for the data below 10 below_10_number=0 index_24_number=0 for value in df["RAD"]: if value == 24: index_24_number=index_24_number+1 else: below_10_number=below_10_number+1 plt.bar(["index below 10","index is 24"],height=[below_10_number,index_24_number],width=0.4,edgecolor="black") plt.xlabel("Index of accessibility to radial highways") plt.ylabel("Number") plt.show() plt.hist(df["RAD"],edgecolor="black",bins=[0,2,4,6,8,10,12,14,16,18,20,24,26]) plt.xlabel("Index of accessibility to radial highways") plt.ylabel("numbers") plt.show() #TAX plt.hist(df["TAX"],edgecolor="black",bins=[0,100,200,300,400,500,600,700,800]) plt.xlabel("Full-value property tax rate per $10,000") plt.ylabel("numbers") plt.show() #PTRATIO plt.hist(df["PTRATIO"],edgecolor="black",bins=[0,2.5,5,7.5,10,12.5,15,17.5,20,22.5,25]) plt.xlabel("Pupil-teacher ratio by town") plt.ylabel("numbers") plt.show() #B plt.hist(df["B"],edgecolor="black",bins=[0,50,100,150,200,250,300,350,400]) plt.xlabel("1000(Bk - 0.63)^2,Bk is the proportion of people of African American descent by town") plt.ylabel("numbers") plt.show() #LSTAT plt.hist(df["LSTAT"],edgecolor="black",bins=[0,5,10,15,20,25,30,35,40]) plt.xlabel("Percentage of lower status of the population") plt.ylabel("numbers") plt.show() #MEDV plt.hist(df["MEDV"],edgecolor="black",bins=[0,5,10,15,20,25,30,35,40,45,50,55,60]) plt.xlabel("Median value of owner-occupied homes in $1000s (TARGET)") plt.ylabel("numbers") plt.show() #scatter plots for two most correlated features RM and LSAT plt.scatter(df["LSTAT"],df["MEDV"]) plt.xlabel("LSTAT") plt.ylabel("MEDV") plt.show() plt.scatter(df["RM"],df["MEDV"]) plt.xlabel("RM") plt.ylabel("MEDV") plt.xlim(0,10) plt.show() #exploratory ends here print("\n") print("Linear Regression without penalty") print("\n") #split the data #standardize the data from sklearn.model_selection import train_test_split from sklearn import preprocessing X,y=df.iloc[:,0:26],df.iloc[:,26] X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=42) #print( X_train.shape, y_train.shape) scaler = preprocessing.StandardScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score # linear regression linear_reg=LinearRegression() linear_reg.fit(X_train,y_train) y_test_linear_predict=linear_reg.predict(X_test) y_train_linear_predict=linear_reg.predict(X_train) #coefficient and intercept for each column counter=0 for value in linear_reg.coef_: print(df.columns[counter],"coefficient:",value) counter=counter+1 print("\n") print("intercept is",linear_reg.intercept_) #residual plot of linear regression plt.scatter(y_train_linear_predict,y_train_linear_predict-y_train,color="red",edgecolor="white",label="Training Data") plt.scatter(y_test_linear_predict,y_test_linear_predict-y_test,edgecolor="white",label="Testing Data") plt.legend(loc="upper left") plt.hlines(y=0,xmin=-50,xmax=100,lw=2) plt.xlim(-10,50) plt.xlabel("predicted values") plt.ylabel("residuals") plt.show() #MSE and R^2 print("MSE Train:") print (mean_squared_error(y_train_linear_predict,y_train)) print("MSE Test:") print(mean_squared_error(y_test_linear_predict,y_test)) print("\n") print("R^2 Train:") print (r2_score(y_train_linear_predict,y_train)) print("R^2 Test:") print (r2_score(y_test_linear_predict,y_test)) print("\n") #Ridge Regression print("Ridge Regression") # use cross validation to pick the best alpha for Ridge Regression from sklearn.linear_model import RidgeCV ridgecv = RidgeCV(alphas=range(1,100),cv=5) ridgecv.fit(X_train, y_train) print("\n") print("under cross validation") print("the best alpha is",ridgecv.alpha_) print("\n") ridge_reg=Ridge(alpha=ridgecv.alpha_) ridge_reg.fit(X_train,y_train) y_test_ridge_predict=ridge_reg.predict(X_test) y_train_ridge_predict=ridge_reg.predict(X_train) #coefficient and intercept for each column counter=0 for value in ridge_reg.coef_: print(df.columns[counter],"coefficient:",value) counter=counter+1 print("\n") print("intercept is",ridge_reg.intercept_) #residual plot of ridge regression plt.scatter(y_train_ridge_predict,y_train_ridge_predict-y_train,color="red",edgecolor="white",label="Training Data") plt.scatter(y_test_ridge_predict,y_test_ridge_predict-y_test,edgecolor="white",label="Testing Data") plt.legend(loc="upper left") plt.hlines(y=0,xmin=-50,xmax=100,lw=2) plt.xlim(-10,50) plt.xlabel("predicted values") plt.ylabel("residuals") plt.show() #MSE and R^2 print("MSE Train:") print (mean_squared_error(y_train_ridge_predict,y_train)) print("MSE Test:") print(mean_squared_error(y_test_ridge_predict,y_test)) print("\n") print("R^2 Train:") print (r2_score(y_train,y_train_ridge_predict)) print("R^2 Test:") print (r2_score(y_test,y_test_ridge_predict)) print("\n") #Lasso Regression print("Lasso Regression") # use cross validation to pick the best alpha for Lasso Regression from sklearn.linear_model import LassoCV lassocv = LassoCV(alphas=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],cv=5) lassocv.fit(X_train, y_train) print("\n") print("under cross validation") print("the best alpha is",lassocv.alpha_) print("\n") lasso_reg=Lasso(alpha=lassocv.alpha_) lasso_reg.fit(X_train,y_train) y_test_lasso_predict=lasso_reg.predict(X_test) y_train_lasso_predict=lasso_reg.predict(X_train) #coefficient and intercept for each column counter=0 for value in lasso_reg.coef_: print(df.columns[counter],"coefficient:",value) counter=counter+1 print("\n") print("intercept is",lasso_reg.intercept_) #residual plot of ridge regression plt.scatter(y_train_lasso_predict,y_train_lasso_predict-y_train,color="red",edgecolor="white",label="Training Data") plt.scatter(y_test_lasso_predict,y_test_lasso_predict-y_test,edgecolor="white",label="Testing Data") plt.legend(loc="upper left") plt.hlines(y=0,xmin=-50,xmax=100,lw=2) plt.xlim(-10,50) plt.xlabel("predicted values") plt.ylabel("residuals") plt.show() #MSE and R^2 print("MSE Train:") print (mean_squared_error(y_train_lasso_predict,y_train)) print("MSE Test:") print(mean_squared_error(y_test_lasso_predict,y_test)) print("\n") print("R^2 Train:") print (r2_score(y_train_lasso_predict,y_train)) print("R^2 Test:") print (r2_score(y_test_lasso_predict,y_test)) print("\n") print("My name is Yi Zhou") print("My NetID is: yizhou16") print("I hereby certify that I have read the University policy on Academic Integrity and that I am not in violation.")

[ "noreply@github.com" ]

noreply@github.com

4d3bc7d3165b2029c9fb1ec9a5e3389bde0e2870

4aee52a4375c4792f5f79c8d44376d21ee24a485

/basics/hello.py

4d976f1ba7ff8dadc2aada3a19cbad03fe305f88

[]

no_license

python-practice-04-2019/repo-1.1.1

4e5450f046653eb7faea5e3bce109ce7496a3326

937885fe9ad65754c11cd09adfa6dfa561d5d3a8

refs/heads/master

2020-05-15T15:56:17.279287

2019-04-20T19:55:11

182,381,027

0

null

UTF-8

Python

false

57

py

print("hello world") x = 2 if(x == 2): print("test")

[ "RabindraPatra@corp.fairisaac.com" ]

RabindraPatra@corp.fairisaac.com

09ce754a548f68f3ade391cfe3187a6d981e4cb8

af8d5f01fb4d77d997f1c7fc3a677261a9f16763

/ML/folder_wise/Decision tree/decision.py

1eb83d10275d90a37e59b223af025b42abb35ae3

[]

no_license

ashishsalunkhe/BE-Comp-Sem-8

e6660ac09553dc927e6ff6edf006598f42beb442

1fe3df171cd3dcd732baa010aac59c8cd6abe1db

refs/heads/master

2022-09-30T19:31:59.440717

2020-06-05T08:21:30

269,570,770

0

1

null

UTF-8

Python

false

838

py

import pandas as pd import numpy as np #reading Dataset dataset=pd.read_csv("dt.csv") X=dataset.iloc[:,:-1] y=dataset.iloc[:,5] #Perform Label encoding from sklearn.preprocessing import LabelEncoder le=LabelEncoder() X=X.apply(le.fit_transform) print("X") from sklearn.tree import DecisionTreeClassifier regressor=DecisionTreeClassifier() regressor.fit(X.iloc[:,1:5],y) #Predict value for the given Expression X_in=np.array([1,1,0,0]) y_pred=regressor.predict([X_in]) print("Prediction:", y_pred) from sklearn.externals.six import StringIO from IPython.display import Image from sklearn.tree import export_graphviz import pydotplus dot_data=StringIO() export_graphviz(regressor,out_file=dot_data,filled=True,rounded=True,special_characters=True) graph=pydotplus.graph_from_dot_data(dot_data.getvalue()) graph.write_png('tree.png')

[ "avsalunkhe98@gmail.com" ]

avsalunkhe98@gmail.com

6f7ccfa4d4f3f222e1b912cf39b3a94c834653d2

04b07d92102301b14aaed0b1dbef85dd0c8ed59d

/code/tinyos/apps/RxDecoder/Decoded.py

6625f3ec8ae823d72f8a42554e10ac925cf4c7d4

[]

no_license

ab39826/IndexCoding

8fb03bb6152b7e47905f7b463c201182bc04bfb1

f8e36a9baefef46d7c7be858eabc35b0384cb0cc

refs/heads/master

2016-09-03T07:29:56.648775

2015-09-04T14:03:01

41,918,815

0

null

UTF-8

Python

false

2,776

py

#!/usr/bin/python import time import numpy as np import sys #tos stuff from DecodedMsg import * from tinyos.message import MoteIF class MyClass: def __init__(self,N): self.prevtime = time.time() self.N = N self.A = make_A_matrix(self.N) self.counter = 0; self.current_row = 0; # Create a MoteIF self.mif = MoteIF.MoteIF() # Attach a source to it self.source = self.mif.addSource("sf@localhost:9002") # SomeMessageClass.py would be generated by MIG self.mif.addListener(self, DecodedMsg) # Called by the MoteIF's receive thread when a new message # is received def receive(self, src, msg): time.sleep(1) m = DecodedMsg(msg.dataGet()) self.counter = m.get_counter() timeformat = '%Y/%d/%m %H:%M:%S' print 'Received message %s: counter: %d' % (time.strftime(timeformat), self.counter) print ' current row: ', m.get_current_row() print ' true current row: ', self.current_row z = np.array(m.get_V_row()) z = z[0:self.current_row+1] print z V = self.A[:m.get_current_row()+1] #U, S, W = np.linalg.svd(V.T) #print S Vnull = V[ :, [1,3,5,7] ] #U,S,V = np.linalg.svd(Vnull.T) #print S print np.matrix(Vnull).T*np.matrix(z).T #U, s, W = np.linalg.svd(Vnull.T) #print W.T #print self.A[m.get_current_row()][:] #print m.get_current_row() #print S #V_null = self.A[0:self.current_row+1,[1,3, 9, 14]] #U, S, W = np.linalg.svd(V_null) #print S #if m.get_perform_svd() == self.N: ##print ' svd received:' #Svals = m.get_W() #print 'Rx svd: ', Svals #U,S,V = np.linalg.svd(self.A) ##S = [s**2 for s in S] ##print ' svd check:' #print 'PC svd: ', S #self.perform_svd = 0 #self.A = make_A_matrix(self.N) #print 'MSE: ', np.linalg.norm(np.array(S)-np.array(Svals),2) #proctime = time.time() - self.prevtime #print 'Elapsed time: %f seconds' % proctime #else: #self.prevtime = time.time() #self.perform_svd += 1 self.counter += 1 self.current_row = (self.current_row + 1) % self.N #if self.current_row == 0: #self.A = make_A_matrix(self.N) self.send() def send(self): smsg = DecodedMsg() smsg.set_counter(self.counter) smsg.set_current_row(self.current_row) smsg.set_V_row(self.A[self.current_row]) self.mif.sendMsg(self.source, 0xFFFF, smsg.get_amType(), 0, smsg) def make_A_matrix(N): A = np.random.randn(N,N) B = np.matrix(np.random.randn(4,4)) U, s, W = np.linalg.svd(B) s[-1] = 0 B = np.array(U*np.diag(s)*W) A[0:4,1] = B[:,0] A[0:4,3] = B[:,1] A[0:4,5] = B[:,2] A[0:4,7] = B[:,0] print A return A if __name__ == "__main__": print "Running" np.set_printoptions(precision=3) np.set_printoptions(suppress=True) if len(sys.argv) > 1: N = int(sys.argv[1]) else: N = 6 m = MyClass(N) time.sleep(1) m.send()

[ "Anurag.Banerjee@utexas.edu" ]

Anurag.Banerjee@utexas.edu

98c23c12da7c87e54e9d2ca110846a9658f7421f

c28df1b0bf09c1ce1cb6d9de219676827f899e8d

/person/models.py

15cd98731d76acb568c797b28b4da1531700cac6

[]

no_license

frozyum/split-expenses-app-backend

36fc501c432b6508a7c34bc08b6ab27a6bff245d

55e23ea222396814a80512df3fab0d5710c88fdf

refs/heads/master

2023-01-01T08:56:16.145140

2020-10-27T09:17:14

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null

2020-09-28T16:49:28

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false

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py

from django.db import models # Create your models here. from group.models import Group class Person(models.Model): group = models.ForeignKey(to=Group, on_delete=models.CASCADE) name = models.CharField(max_length=12) def __str__(self): return str(self.name) + "in " + str(self.group)

[ "smamu13@freeuni.edu.ge" ]

smamu13@freeuni.edu.ge

162eb2ee34fdecebf7be87ac009e79c0a715e25f

77077a391973d1f8c05647d08fc135facd04fc5e

/xlsxwriter/test/app/test_app02.py

fa347d734560186995daf0fad3e57c79c5129178

[ "BSD-2-Clause-Views" ]

permissive

DeltaEpsilon7787/XlsxWriter

28fb1012eaa42ea0f82e063f28c0c548ca016c5e

550b9c5bd678c861dcc9f6f4072b33a69566e065

refs/heads/main

2023-08-02T09:14:10.657395

2021-09-06T10:51:56

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0

NOASSERTION

2021-07-11T12:57:26

2021-07-11T12:57:25

null

UTF-8

Python

false

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py

############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013-2021, John McNamara, jmcnamara@cpan.org # import unittest from io import StringIO from ..helperfunctions import _xml_to_list from ...app import App class TestAssembleApp(unittest.TestCase): """ Test assembling a complete App file. """ def test_assemble_xml_file(self): """Test writing an App file.""" self.maxDiff = None fh = StringIO() app = App() app._set_filehandle(fh) app._add_part_name('Sheet1') app._add_part_name('Sheet2') app._add_heading_pair(('Worksheets', 2)) app._assemble_xml_file() exp = _xml_to_list(""" <?xml version="1.0" encoding="UTF-8" standalone="yes"?> <Properties xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"> <Application>Microsoft Excel</Application> <DocSecurity>0</DocSecurity> <ScaleCrop>false</ScaleCrop> <HeadingPairs> <vt:vector size="2" baseType="variant"> <vt:variant> <vt:lpstr>Worksheets</vt:lpstr> </vt:variant> <vt:variant> <vt:i4>2</vt:i4> </vt:variant> </vt:vector> </HeadingPairs> <TitlesOfParts> <vt:vector size="2" baseType="lpstr"> <vt:lpstr>Sheet1</vt:lpstr> <vt:lpstr>Sheet2</vt:lpstr> </vt:vector> </TitlesOfParts> <Company> </Company> <LinksUpToDate>false</LinksUpToDate> <SharedDoc>false</SharedDoc> <HyperlinksChanged>false</HyperlinksChanged> <AppVersion>12.0000</AppVersion> </Properties> """) got = _xml_to_list(fh.getvalue()) self.assertEqual(got, exp)

[ "jmcnamara@cpan.org" ]

jmcnamara@cpan.org

f84fa5685ebf64f3dc5afa3a2688e45e99336d77

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/profile_project/accounts/migrations/0003_auto_20190606_1432.py

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DavidJMcGarvey/User-Profile-with-Django

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refs/heads/master

2022-11-26T19:25:38.394320

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# -*- coding: utf-8 -*- # Generated by Django 1.9.9 on 2019-06-06 21:32 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('accounts', '0002_auto_20190604_1438'), ] operations = [ migrations.AlterField( model_name='userprofile', name='avatar', field=models.ImageField(default=None, upload_to=''), ), migrations.AlterField( model_name='userprofile', name='bio', field=models.TextField(default='I am a nerd for love.'), ), migrations.AlterField( model_name='userprofile', name='birthday', field=models.DateField(default='1990-01-15', max_length=255), ), migrations.AlterField( model_name='userprofile', name='confirm_email', field=models.EmailField(default='dave@email.com', max_length=254), ), migrations.AlterField( model_name='userprofile', name='email', field=models.EmailField(default='dave@email.com', max_length=254), ), migrations.AlterField( model_name='userprofile', name='favorite_animal', field=models.CharField(default='Gianni', max_length=255), ), migrations.AlterField( model_name='userprofile', name='favorite_color', field=models.CharField(default='Blue', max_length=255), ), migrations.AlterField( model_name='userprofile', name='first_name', field=models.CharField(default='Dave', max_length=255), ), migrations.AlterField( model_name='userprofile', name='hobby', field=models.CharField(default='Buckets', max_length=255), ), migrations.AlterField( model_name='userprofile', name='hometown', field=models.CharField(default='Denver', max_length=255), ), migrations.AlterField( model_name='userprofile', name='last_name', field=models.CharField(default='McGarvey', max_length=255), ), ]

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davidjmcgarvey@gmail.com

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/venv/Scripts/pip3-script.py

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netanelm7/Work_Hours_Project

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refs/heads/master

2020-04-02T11:09:44.258428

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#!L:\Pycharm\Work_Hours_Project\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3' __requires__ = 'pip==10.0.1' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==10.0.1', 'console_scripts', 'pip3')() )

[ "39493783+netanelm7@users.noreply.github.com" ]

39493783+netanelm7@users.noreply.github.com

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/apps/users/models.py

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[]

no_license

Anton-Karpenko/test4

a6429dd0632bdd53511ee533d2314196270b0d01

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refs/heads/master

2020-06-22T17:11:36.484175

2019-07-21T21:52:29

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from django.contrib.auth.models import AbstractUser from django.db import models from django.utils.translation import ugettext_lazy as _ from django_extensions.db.models import TimeStampedModel from apps.base.utils import custom_uuid class User(AbstractUser, TimeStampedModel): id = models.CharField( max_length=11, primary_key=True, default=custom_uuid, editable=False, ) # First Name and Last Name do not cover name patterns around the globe. name = models.CharField( _("Name of User"), blank=True, max_length=255 ) class Meta: db_table = 'users'

[ "antykkarp@gmail.com" ]

antykkarp@gmail.com

86a0bc8d228e9bc61e282dd6bd5b4e84a35754b0

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/fruit_store/fruit_store/settings.py

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[]

no_license

nguyenvanhuybk99/django_rest_mongo

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refs/heads/master

2023-04-28T15:30:54.441411

2021-05-22T01:42:38

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py

""" Django settings for fruit_store project. Generated by 'django-admin startproject' using Django 3.0.5. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ij8sa@@26^2(7^uy3=7a$k54yq45o6o7gnkcu8+!-4&rlq$y4*' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ["0.0.0.0", "127.0.0.1"] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'shop', 'customer', 'manager', 'cart' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'fruit_store.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'fruit_store.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default': { 'ENGINE': 'djongo', 'NAME': 'fruit_store', } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' REST_FRAMEWORK = { # Use Django's standard `django.contrib.auth` permissions, # or allow read-only access for unauthenticated users. 'DEFAULT_PERMISSION_CLASSES': [], 'TEST_REQUEST_DEFAULT_FORMAT': 'json' } APPEND_SLASH=False

[ "nguyenvanhuybk99@gmail.com" ]

nguyenvanhuybk99@gmail.com

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/app.py

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[]

no_license

gdeepakdeepuml/pomodoro-app

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bc78b3c78c77c54315d826f76aa515a45e91431e

refs/heads/main

2023-02-26T19:08:32.188016

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py

# App created by Data Professor http://youtube.com/dataprofessor # GitHub repo of this app # Demo of this app import streamlit as st import time # CSS by andfanilo # Source: https://discuss.streamlit.io/t/creating-a-nicely-formatted-search-field/1804 def local_css(file_name): with open(file_name) as f: st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True) #def remote_css(url): # st.markdown(f'<link href="{url}" rel="stylesheet">', unsafe_allow_html=True) #def icon(icon_name): # st.markdown(f'<i class="material-icons">{icon_name}</i>', unsafe_allow_html=True) local_css("style.css") #remote_css('https://fonts.googleapis.com/icon?family=Material+Icons') #---------------------------------# st.write(""" # The Pomodoro App Let's do some focus work in data science with this app. Developed by: [Data Professor](http://youtube.com/dataprofessor) """) # Timer # Created by adapting from: # https://www.geeksforgeeks.org/how-to-create-a-countdown-timer-using-python/ # https://docs.streamlit.io/en/latest/api.html#lay-out-your-app button_clicked = st.button("Start") t1 = 1500 t2 = 300 if button_clicked: with st.empty(): while t1: mins, secs = divmod(t1, 60) timer = '{:02d}:{:02d}'.format(mins, secs) st.header(f"⏳ {timer}") time.sleep(1) t1 -= 1 st.success("🔔 25 minutes is over! Time for a break!") with st.empty(): while t2: # Start the break mins2, secs2 = divmod(t2, 60) timer2 = '{:02d}:{:02d}'.format(mins2, secs2) st.header(f"⏳ {timer2}") time.sleep(1) t2 -= 1 st.error("⏰ 5 minute break is over!")

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noreply@github.com

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/request_sign/signature.py

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[]

no_license

yongzhengw/django-request-sign

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refs/heads/master

2023-07-17T05:17:24.863434

2021-08-17T09:40:14

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""" @author: liyao @contact: liyao2598330@126.com @time: 2020/8/14 4:39 下午 """ import json.decoder import re import hashlib import logging from datetime import datetime from urllib.parse import unquote from django.core.cache import cache from request_sign.settings import ( SIGNATURE_METHOD, SIGNATURE_SECRET, SIGNATURE_ALLOW_TIME_ERROR, NONCE_CACHE_KEY, SIGNATURE_PASS_URL, SIGNATURE_DEBUG, SIGNATURE_PASS_URL_REGULAR ) DELETE_KEY_MAP = [[], {}, None, ''] logger = logging.getLogger("default") def signature_parameters(nonce: str, parameters: list): parameters_str = ''.join(re.findall(r"[A-Za-z0-9]", "".join(parameters) + SIGNATURE_SECRET)) + \ nonce # 参数名ASCII码从小到大排序 parameters_sort = "".join(sorted(list(parameters_str))).split("_") parameters_sort[0], parameters_sort[1] = parameters_sort[1], parameters_sort[0] # MD5加密 m = hashlib.md5() m.update("".join(parameters_sort).encode('UTF-8')) return m.hexdigest() def check_pass_url_regular(path): for r in SIGNATURE_PASS_URL_REGULAR: if re.search(r, path): return True return False class Log: def __init__(self, debug=False): self.debug = debug self.prefix = "#MIDDLEWARE# request_sign -> " def info(self, message): if self.debug: logger.info(self.prefix + message) def error(self, message): if self.debug: logger.error(self.prefix + message) def check_signature(request): """ 检查签名是否符合 """ if request.method.lower() not in SIGNATURE_METHOD or \ request.path in SIGNATURE_PASS_URL or \ check_pass_url_regular(request.path): return True timestamp = request.META.get("HTTP_T") nonce = request.META.get("HTTP_N") sign = request.META.get("HTTP_S") log = Log(debug=SIGNATURE_DEBUG) log.info(("timestamp, nonce, sign=[%s, %s, %s]" % (timestamp, nonce, sign))) if not all([timestamp, nonce, sign]): log.error("required parameter missing, no pass") return False # 判断cache是否正常 if hasattr(cache, 'get') and hasattr(cache, 'set'): if cache.get(NONCE_CACHE_KEY.format(nonce=nonce)): log.error("nonce:%s repeat, no pass" % nonce) return False else: cache.set(NONCE_CACHE_KEY.format(nonce=nonce), True, 300) try: timestamp = int(timestamp) except: log.error("timestamp format error, no pass") return False now_timestamp = datetime.now().timestamp() if (now_timestamp - SIGNATURE_ALLOW_TIME_ERROR) > timestamp or timestamp > ( now_timestamp + SIGNATURE_ALLOW_TIME_ERROR): log.error("request timestamp expired, not pass") return False get_parameters = request.GET.dict() post_parameters = request.POST.dict() try: body_parameters = json.loads(request.body.decode("utf-8")) if request.body else None except json.decoder.JSONDecodeError: body_parameters = None log.info( "get_parameters, post_parameters, body_parameters=[%s, %s, %s]" % ( get_parameters, post_parameters, body_parameters ) ) parameters = handle_parameter(get_parameters, post_parameters, body_parameters, str(timestamp)) log.info("after parameters process: %s" % parameters) result = signature_parameters(nonce, parameters) log.info("get sign:%s, origin:%s -> %s" % ( result, sign, result == sign )) return sign == result def handle_parameter(get_parameters, post_parameters, body_parameters, timestamp) -> list: parameter_list = [] for p in [get_parameters, post_parameters, body_parameters, timestamp]: if isinstance(p, dict): t = {} for key, value in p.items(): if value not in DELETE_KEY_MAP: t[key] = value parameter_list.append(json.dumps(t)) elif isinstance(p, str): parameter_list.append(unquote(p)) elif isinstance(p, bytes): parameter_list.append(str(p, encoding="utf-8")) return parameter_list

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liyao2598330@126.com

e477eb40de85d06949607669ae61fe6137e832d2

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/front_sy.py

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[]

no_license

chlrhalsgur/2021_1_py_project

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refs/heads/master

2023-05-28T17:14:24.482214

2021-06-10T09:54:12

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UTF-8

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py

from tkinter import * #from tkhtmlview import HTMLLabel //html사용해서 네이버영화, 네이버영화랭킹링크 사용할려고 했음 but 안됨 #https://remixicon.com/ 사용할려고 했던 아이콘 win=Tk() #Show Movie 클릭했을 경우 def Naver_movie_Ranking(): window_naver=Toplevel(win) window_naver.geometry("1000x600") #두 번째 페이지 def create_window(): window = Toplevel(win) #새로운 창 열기 window.geometry("1000x600") window.configure(bg='#4682B4') btn = Button(window, text = "Show Movie", command=Naver_movie_Ranking) btn1 = Button(window, text = "Show Movie Ranking") btn2 = Button(window, text = "Our New Movie Ranking") btn.pack(pady=40) btn1.pack(pady=20) btn2.pack(pady=20) win.geometry("1000x600") #사이즈 가로x세로(픽셀) win.title("Movie Ranking") #타이틀 win.option_add("*Font","Courier 40") #기본 폰트와 글자크기 설정 #첫 번째 페이지 lab=Label(win, text = "Pop Corn Movie") lab.pack(side=TOP, pady=60) lab.configure(font=("Courier", 70, "italic")) #첫 번째 페이지 버튼 btn = Button(win, text="Let's start", command=create_window) btn.pack(side=BOTTOM, pady=50) #배경색 win.configure(bg='#49A') win.mainloop() #창 열기

[ "sy91031@naver.com" ]

sy91031@naver.com

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/gps_logger.py

369058ceb00efe2a56e224099eb751253dc01d99

[ "CC-BY-4.0" ]

permissive

bortek/gps_tracker

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51c36e26dfeacacbbe8c9d7c45a9e048df7b6656

refs/heads/master

2020-04-26T00:26:46.575836

2019-05-01T03:54:20

173,178,275

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null

2019-02-28T19:55:43

null

UTF-8

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py

""" gps_logger.py logs received gps points in a local file. """ __author__ = "Konstantinos Kagiampakis" __license__ = """ Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/legalcode """ import gpsd import json import time import sys STEP = 3 # Connect to the local gpsd while True: try: print("Connecting on GPSD...") gpsd.connect() except: print("Could not connect to GPSD.\nThis script is persistent and will try to reconnect to GPSD in 10 sec.",sys.exc_info()[0]) time.sleep(10) else: print("GPSD connected!") break filename = "/home/pi/gps_route.log" try: f = open(filename, 'a') except: raise while True: try: try: packet = gpsd.get_current() if packet.mode > 1: if packet.mode >= 2: print("Latitude: " + str(packet.lat)) print("Longitude: " + str(packet.lon)) print("Track: " + str(packet.track)) print("Horizontal Speed: " + str(packet.hspeed)) print("Time: " + str(packet.time)) print("Error: " + str(packet.error)) if packet.mode == 3: print("Altitude: " + str(packet.alt)) print("Climb: " + str(packet.climb)) point = {'lat': str(packet.lat), 'lon': str(packet.lon), 'track': str(packet.track), 'hspeed': str(packet.hspeed), 'time': str(packet.time)} if packet.mode == 3: point['alt'] = str(packet.alt) point['climb'] = str(packet.climb) str_point = json.dumps(point) print("storing point to file:#"+str_point+"# str len:"+str(len(str_point))) f.write(str_point+',\n') else: print("There is no GPS FIX yet. Packet mode 0.") time.sleep(10) except (NameError, KeyError): print("There is no GPS FIX yet. Key or Name exception.") time.sleep(3) except: print (sys.exc_info()[0]) time.sleep(10) time.sleep(STEP) except KeyboardInterrupt: print(" Received KeyboardInterrupt") try: print("Closing file.") f.close() except: raise else: print("File closed.") break except: print(sys.exc_info()[0])

[ "kostiskag@gmail.com" ]

kostiskag@gmail.com

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/tests/tests/meta_add_lazy.py

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[ "MIT" ]

permissive

yigarashi-9/mini_python

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refs/heads/master

2021-04-09T11:30:05.430724

2018-10-26T06:53:21

null

0

null

UTF-8

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353

py

class A: def __init__(self, x): self.x = x class B(A): def __add__(self, other): return self.x + other.x + 1 class C(A): pass = 1 def myadd(self, other): return self.x + other.x + 100 a1 = A(0) a2 = A(0) b1 = B(0) b2 = B(0) c1 = C(0) c2 = C(0) A.__add__ = myadd assert (a1 + a2) + (b1 + b2) + (c1 + c2) == 201

[ "yuu.igarashi.9@gmail.com" ]

yuu.igarashi.9@gmail.com

4ecd369a2ed6d3164513285595b15ebc98e9de89

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/prac_04/litst_comprehentions.py

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[]

no_license

micah-clarke/Practicals

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2020-05-04T21:43:20.857199

2019-04-11T04:16:03

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null

2019-04-11T04:36:55

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Python

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1,529

py

""" CP1404/CP5632 Practical List comprehensions """ names = ["Bob", "Angel", "Jimi", "Alan", "Ada"] full_names = ["Bob Martin", "Angel Harlem", "Jimi Hendrix", "Alan Turing", "Ada Lovelace"] # for loop that creates a new list containing the first letter of each name first_initials = [] for name in names: first_initials.append(name[0]) print(first_initials) # list comprehension that does the same thing as the loop above first_initials = [name[0] for name in names] print(first_initials) # list comprehension that creates a list containing the initials # splits each name and adds the first letters of each part to a string full_initials = [name.split()[0][0] + name.split()[1][0] for name in full_names] print(full_initials) # one more example, using filtering to select only the names that start with A a_names = [name for name in names if name.startswith('A')] print(a_names) # List comprehension to create a list of all of the full_names # in lowercase format lowercase_full_names = [name.lower() for name in full_names] print(lowercase_full_names) almost_numbers = ['0', '10', '21', '3', '-7', '88', '9'] # List comprehension to create a list of integers # from the above list of strings numbers = [int(almost_number) for almost_number in almost_numbers] print(numbers) # List comprehension to create a list of only the numbers that are # greater than 9 from the numbers (not strings) you just created big_numbers = [number for number in numbers if number > 9] print(big_numbers)

[ "48541196+micah-clarke@users.noreply.github.com" ]

48541196+micah-clarke@users.noreply.github.com

25e9604e708758b56d016d3c384898a1c74f8d6d

e2752da08870dc935d21d2157786aa919766c296

/zemiao/129.py

c1bf0b0cdc9d496e1e042931b7ba65dc6c27c091

[]

no_license

Zichuanyun/go-shuati

87dc192da65a9f08ebe8530f623e473a2f3c2865

7c443f85217ab96ceac717ece7fc472271e1d3ab

refs/heads/master

2020-03-19T11:03:12.855384

2018-09-27T17:20:25

136,425,571

9

6

null

2018-06-21T04:51:56

2018-06-07T05:22:35

Java

UTF-8

Python

false

742

py

# Definition for a binary tree node. # class TreeNode(object): # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution(object): def sumNumbers(self, root): """ :type root: TreeNode :rtype: int """ ans = 0 if not root: return ans stack = [(root, root.val)] while stack: node, temp = stack.pop() if node.left: stack += [(node.left, temp*10+node.left.val)] if node.right: stack += [(node.right, temp*10+node.right.val)] if not (node.left or node.right): ans += temp return ans

[ "zemiaozhu@outlook.com" ]

zemiaozhu@outlook.com

6345ca5f9e91bd0fbd6aebb9ade41315802962db

f6bebd025f62621478401dbb75e0cf852171058c

/twitter-reply-notification.py

e53654e47062e878817a783d7d2ae4f68c280286

[]

no_license

chmouel/twitter-reply-notification

9a0e683e35e9156b6a291d1f092cf6a509274732

6bc8d32b20f8daabe963eef825e48b17275820e7

refs/heads/master

2020-05-29T10:19:26.559288

2010-01-07T15:13:04

null

0

null

UTF-8

Python

false

3,515

py

#!/usr/bin/python # -*- encoding: utf-8 -*- # # Chmouel Boudjnah <chmouel@chmouel.com> import twitter import rfc822 import datetime import os import ConfigParser import stat import sys import urllib2 CONFIG_FILE = os.path.expanduser("~/.config/twitter-reply-notification/config.ini") CACHE_FILE = os.path.expanduser("~/.cache/twitter-reply-notification/cache") def setup_dir(): if not os.path.exists(os.path.dirname(CONFIG_FILE)): os.mkdir(os.path.dirname(CONFIG_FILE), 0755) if not os.path.exists(os.path.dirname(CACHE_FILE)): os.mkdir(os.path.dirname(CACHE_FILE), 0755) def send_mail(config, text): p = os.popen("%s -t" % config['sendmail_location'], "w") p.write("From: %s\n" % config['mail_from']) p.write("To: %s\n" % config['mail_to']) p.write("Subject: New Twit (%d)\n" % (len(text))) p.write("\n") # p.write("\n".join(text)) status = p.close() if status == 256: sys.exit(1) def parse_config(config_file): if not os.path.exists(config_file): config = ConfigParser.ConfigParser() config.add_section("auth") config.set("auth", "username", "") config.set("auth", "password", "") config.add_section("mail") config.set("mail", "from", "") config.set("mail", "to", "") config.set("mail", "sendmail_location", "/usr/sbin/sendmail") config.write(open(config_file, 'w')) return filemode = stat.S_IMODE(os.stat(config_file).st_mode) & 0777 if filemode != 384: os.chmod(config_file, 0600) config = ConfigParser.ConfigParser() cfh = config.read(config_file) if not cfh: return username = config.get('auth', 'username').strip() password = config.get('auth', 'password').strip() mail_from = config.get('mail', 'from').strip() mail_to = config.get('mail', 'to').strip() sendmail_location = config.get('mail', 'sendmail_location').strip() if not all([username, password, mail_from, mail_to]): return return { 'username' : username, 'password' : password, 'mail_from' : mail_from, 'mail_to' : mail_to, 'sendmail_location' : sendmail_location, } def get_replies(config): ret = [] api = twitter.Api(config['username'], config['password']) replies = api.GetReplies(since_id=int(config['last_seen_id'])) if not replies: return (None, None) last = replies[0].id for reply in replies: fdate = datetime.datetime(*rfc822.parsedate( reply.created_at)[:-2]).strftime("%H:%M") ret.append("%s - %s: %s" % (fdate, reply.user.name, reply.text)) return (ret, last) def parse_last_seen_id(): if not os.path.exists(CACHE_FILE): return last_seen_id = open(CACHE_FILE, 'r').read().strip() if last_seen_id: return last_seen_id return None def main(): setup_dir() config = parse_config(CONFIG_FILE) if not config: print "Configuration is missing" sys.exit(1) return config['last_seen_id'] = parse_last_seen_id() try: text, last_seen_id = get_replies(config) except(urllib2.URLError): return if not text or not last_seen_id: return if config['last_seen_id'] and \ (int(last_seen_id) == int(config['last_seen_id'])): return open(CACHE_FILE, "w").write(str(last_seen_id)) send_mail(config, text) if __name__ == '__main__': main()

[ "chmouel.boudjnah@rackspace.co.uk" ]

chmouel.boudjnah@rackspace.co.uk

6f714648f18ce1ce8143128be377d7d28c4191de

c54e219c257e5b81489cda16268e17137b0ca9b4

/fake_script.py

c8e09215684366fe70b2ad2efc016809fa5304a6

[]

no_license

ericearl/playground

259c33e3878f5907f55bd612dda771d206a8b5af

3e88d37a2db0c0081d5a770afa792b38736a5641

refs/heads/master

2020-07-08T12:16:10.453382

2019-08-21T22:18:30

203,669,587

0

null

2019-08-21T22:18:32

2019-08-21T21:51:37

Python

UTF-8

Python

false

32

py

import script print('new one')

[ "eric.earl@gmail.com" ]

eric.earl@gmail.com

884954af9fd64a0f3d0508d1272327e2ed3bedf5

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03013/s813541273.py

bdbeb4a2fbfb0352ad56b6b9937305511b1f8a7c

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

379

py

N, M = map(int, input().split(' ')) broken_list = [] if M > 0: for i in range(M): broken_list.append(int(input())) broken_set =set(broken_list) nums = [0] * (N + 1) nums[0] = 1 if 1 not in broken_set: nums[1] = 1 for i in range(2, N + 1): nums[i] = nums[i - 1] + nums[i - 2] if i in broken_set: nums[i] = 0 print(nums[N] % 1000000007)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

80fde7ee8b5e1fdbb6283256a002d905055f43f7

d007f0fceaaad349b7b8cb9eda5382919621e644

/core/helpers.py

2493da2b4a8abb732fe2e371edfa043a366f1da7

[]

no_license

dsarrut/badminapp

fddcc1b8f79b536e9ca0e5e952dcd8584fcb292e

de1ffe0879b443227c6d7cca972664c26e133e4f

refs/heads/master

2022-04-29T01:47:08.397626

2020-05-01T09:45:38

254,719,003

0

null

UTF-8

Python

false

48

py

def raise_except(s): raise Exception(s)

[ "david.sarrut@gmail.com" ]

david.sarrut@gmail.com

276a93f98115025f9db6a4c5e6df42b82e9feccc

db274b14aa63f4cf40b1e496ffeef918d8654f69

/manage.py

d6262ef4e1dd3c3abaf89b085aa2ffe2f3d672f2

[]

no_license

mahmudgithub/demo_pactics_project_seven

02f98d8373dfa3e9b5d8e06d2e5f01a030d48291

4a8aa330a6abfb5e12916c368bd849190788127a

refs/heads/master

2022-03-28T11:58:34.185598

2020-01-29T06:35:22

236,919,480

2

0

null

UTF-8

Python

false

629

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project_g.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "mahmudhossain838@gmail.com" ]

mahmudhossain838@gmail.com

ad75ec26cf69addbf6442ccd275abe67170dd61d

c0b0b50d52cb0d4f10f91595709a1e1815637e60

/ephr_module/migrations/0019_auto_20170402_0942.py

5d6ddc377bb8a61a6db3b8ecc8028e79b2970bdb

[]

no_license

namkan/patientvault

50bdcea0c2f746533a1c11794fc14f76e838fa6a

5617a3686fe48325a5a77372c7bd29f30fe8a175

refs/heads/master

2022-11-25T20:10:56.884810

2017-05-01T12:18:23

89,921,210

0

null

2022-11-22T01:26:28

2017-05-01T12:13:03

HTML

UTF-8

Python

false

411

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('ephr_module', '0018_pvradorders_order_name'), ] operations = [ migrations.RenameField( model_name='pvradorders', old_name='shared_yesno', new_name='is_sharable', ), ]

[ "naman.kansal@vyalatech.com" ]

naman.kansal@vyalatech.com

8faf2068a458cf72abd7903f35a6d54f9f5fb74c

cdd878d50c423a8e6b282d6b1fb177ac35210880

/config.py

aeecf863e06726014ab4cbc8756829a475e1ad53

[]

no_license

alexmochu/WeConnect-API-oop-

83dbb66656342ecf1aa9dd74b702fe0bbdb873bc

bfe48150f05b447d432819e90bc385d870b31ee2

refs/heads/master

2021-01-24T02:23:33.609665

2018-05-18T12:44:19

122,846,674

1

null

2019-10-18T16:37:33

2018-02-25T15:06:06

Python

UTF-8

Python

false

383

py

# config.py class Config(object): """ Common or Parent configuration class.""" DEBUG = True class DevelopmentConfig(Config): """ Development Configurations""" DEBUG = True class TestingConfig(Config): """ Testing Configurations""" TESTING = True DEBUG = True class ProductionConfig(Config): """ Configurations for Production.""" DEBUG = False

[ "Mochualex4@gmail.com" ]

Mochualex4@gmail.com

6a09c9dd986fd82c23206712f58db29e1d2ab891

2dfad29c6c93e420ebe20168ef18c402f9a5c636

/tests/test_media_utils.py

51508d0cb0e0d4f4e4654c6ec23629e48c1eb516

[ "MIT", "LicenseRef-scancode-ubuntu-font-1.0" ]

permissive

susantabiswas/realtime-facial-emotion-analyzer

b9c3c80342df7c79ad5297407e2eb0214a0fa307

e9d5ee19aebe7602de91ba57ade15a6655944ed6

refs/heads/master

2021-12-15T00:42:51.190038

2021-12-10T15:50:10

129,482,642

93

29

MIT

2021-12-05T17:20:50

2018-04-14T04:47:12

Jupyter Notebook

UTF-8

Python

false

1,470

py

# ---- coding: utf-8 ---- # =================================================== # Author: Susanta Biswas # =================================================== """Description: Tests for media utils.""" # =================================================== from emotion_analyzer.exceptions import InvalidImage import pytest from emotion_analyzer.media_utils import ( convert_to_dlib_rectangle, convert_to_rgb, load_image_path, ) import numpy as np import cv2 import dlib def test_convert_to_dlib_rectangle(): """ Check if dlib rectangle is created properly""" bbox = [1, 2, 3, 4] dlib_box = dlib.rectangle(bbox[0], bbox[1], bbox[2], bbox[3]) assert convert_to_dlib_rectangle(bbox) == dlib_box def test_load_image_path(): """ Check if exception is thrown when an invalid array is given""" path = "data/sample/1.jpg" img = cv2.imread(path) img = convert_to_rgb(img) loaded_img = load_image_path(path) assert np.all(loaded_img == img) == True def test_convert_to_rgb_exception(): """ Check if exception is thrown when an invalid array is given""" # create a dummy image img = np.zeros((100, 100, 5)) with pytest.raises(InvalidImage): convert_to_rgb(img) def test_convert_to_rgb(img1_data): """ Check if RGB conversion happens correctly""" rgb = cv2.cvtColor(img1_data, cv2.COLOR_BGR2RGB) converted_img = convert_to_rgb(img1_data) assert np.all(rgb == converted_img) == True

[ "17782600+susantabiswas@users.noreply.github.com" ]

17782600+susantabiswas@users.noreply.github.com

3c82d59c15b5c2b692af3bef9c303e878b6a2a8a

af2dee4ab4df778801dbe16346c76b295bcad6be

/Projects/bom_words/test.py

afe55c0eac6c0861d4f768825bc9743cad2833e5

[]

no_license

cpalm9/PythonDataStructures

7420881c11bc6003fc97359653260ee028e1d555

ee40fe85d3ac56a7e448ba5e598ea1c55cff0233

refs/heads/master

2021-08-31T09:37:02.925765

2017-12-15T02:21:59

102,979,775

0

null

UTF-8

Python

false

1,238

py

from sort_api import SortMethods from worddata import WordData import unittest class UnitTest(unittest.TestCase): test_list = [] def test_bubble_sort(self): s = SortMethods() test_list = [5,4,2,7,6,9,1] s.bubble_sort_test(test_list) self.assertEqual(test_list[6], 9) test_list = ['a', 'c', 'd', 'b', 'f', 'g', 'i', 'h', 'm', 'j'] s.bubble_sort_test(test_list) self.assertEqual(test_list[2], 'c') self.assertEqual(test_list[5], 'g') def test_insert_sort(self): s = SortMethods() test_list = [5,4,2,7,6,9,1, 100, -1, 55] s.insert_sort_test(test_list) self.assertEqual(test_list[0], -1) self.assertEqual(test_list[3], 4) test_list = ['a', 'c', 'd', 'b', 'f', 'g', 'i', 'h', 'm', 'j'] s.insert_sort_test(test_list) self.assertEqual(test_list[9], 'm') def test_select_sort(self): s = SortMethods() test_list = [5,4,2,7,6,9,1] s.select_sort(test_list) self.assertEqual(test_list[1], 2) test_list = ['a', 'c', 'd', 'b'] s.select_sort(test_list) self.assertEqual(test_list[2], 'c') if __name__ == '__main__': unittest.main()

[ "christian_palmer@byu.edu" ]

christian_palmer@byu.edu

66ac40d9cca324cb59d106f29f489141d55e0c6b

fe52adcccf82ea5e81ec882eafb9fa11d7312e52

/main.py

9bd7ddaa888c538f473569acdb5383d8cf7b608c

[]

no_license

f18-os/producer-consumer-lab-alexmelz95

15f53a1187ab701b0dfc1f0dd374fdf9e784d9ad

a1f98ccfe76697e825185a63857731dfa2c8cc4d

refs/heads/master

2020-04-07T06:00:51.482106

2018-11-24T22:01:41

null

0

null

UTF-8

Python

false

414

py

import ConsProdThread import queue fileName = 'clip.mp4' # shared queues extractionQueue = queue.Queue(10) grayscaleQueue = queue.Queue(10) theHelp.finished1 = False theHelp.finished2 = False t1 = ConsProdThread('extractFrames',params1=fileName,params2=extractionQueue) t2 = ConsProdThread('grayScale',params1=extractionQueue, params2=grayscaleQueue) t3 = ConsProdThread('displayFrames', params1=grayscaleQueue)

[ "alex.melz95@gmail.com" ]

alex.melz95@gmail.com

4159cf0257ad3d20a29b9c1d3308026f6be5c1cf

1925c535d439d2d47e27ace779f08be0b2a75750

/leetcode/best_time_to_buy_and_sell_stock_4.py

1d58d8730fa45eba6ecf813ee448ef105a05236d

[]

no_license

arthurDz/algorithm-studies

ee77d716041671c4b8bb757d8d96f3d10b6589f7

1e4d23dd0c40df34f58d71c7ca3e6491be732075

refs/heads/master

2023-04-27T12:17:06.209278

2021-04-30T20:16:18

null

0

null

UTF-8

Python

false

2,462

py

# Say you have an array for which the ith element is the price of a given stock on day i. # Design an algorithm to find the maximum profit. You may complete at most k transactions. # Note: # You may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again). # Example 1: # Input: [2,4,1], k = 2 # Output: 2 # Explanation: Buy on day 1 (price = 2) and sell on day 2 (price = 4), profit = 4-2 = 2. # Example 2: # Input: [3,2,6,5,0,3], k = 2 # Output: 7 # Explanation: Buy on day 2 (price = 2) and sell on day 3 (price = 6), profit = 6-2 = 4. # Then buy on day 5 (price = 0) and sell on day 6 (price = 3), profit = 3-0 = 3. def maxProfit(k, prices): if not prices or k < 0: return 0 minimum = prices[0] profit = 0 for key, v in enumerate(prices): minimum = min(minimum, v) if k == 1: profit = max(profit, v - minimum) else: profit = max(profit, v - minimum + maxProfit(k - 1, prices[key + 1:])) return profit def maxProfit(k, prices): n = len(prices) if n < 2: return 0 # k is big enougth to cover all ramps. if k >= n / 2: return sum(i - j for i, j in zip(prices[1:], prices[:-1]) if i - j > 0) globalMax = [[0] * n for _ in xrange(k + 1)] for i in xrange(1, k + 1): # The max profit with i transations and selling stock on day j. localMax = [0] * n for j in xrange(1, n): profit = prices[j] - prices[j - 1] localMax[j] = max( # We have made max profit with (i - 1) transations in # (j - 1) days. # For the last transation, we buy stock on day (j - 1) # and sell it on day j. globalMax[i - 1][j - 1] + profit, # We have made max profit with (i - 1) transations in # (j - 1) days. # For the last transation, we buy stock on day j and # sell it on the same day, so we have 0 profit, apparently # we do not have to add it. globalMax[i - 1][j - 1], # + 0, # We have made profit in (j - 1) days. # We want to cancel the day (j - 1) sale and sell it on # day j. localMax[j - 1] + profit) globalMax[i][j] = max(globalMax[i][j - 1], localMax[j]) return globalMax[k][-1]

[ "yunfan.yang@minerva.kgi.edu" ]

yunfan.yang@minerva.kgi.edu

d1ea3793a74e4332420872c9502e50e122bf9b35

481389bd83c40bf153d9fbad904926282ebd0cfe

/pylib/_mypkg_foo/__init__.py

494a71d652ce909f16585dab7b756602d8d0552b

[]

no_license

leisquare/study_bigdata

e1cd952df484c4595d8892fef779226a31ec2ea2

f0a048e8b3a6ffc4507efe7ee2df40cb1ac4bb34

refs/heads/main

2023-05-06T16:09:45.903354

2021-05-29T07:23:36

371,617,971

0

null

UTF-8

Python

false

112

py

# -*- coding: utf-8 -*- """ Created on Thu Feb 25 16:46:19 2021 @author: tjoeun """ print("_mypkg_foo loaded")

[ "75233145+leisquare@users.noreply.github.com" ]

75233145+leisquare@users.noreply.github.com

70ef858cdea40eb5fbf28ecf09e21e2cda639394

0499444beef10cefa5f071d652f8ac73f5436bd1

/simplemooc/courses/admin.py

aa6c08ebee37ae82a0fa6aa01ca730831fe593c7

[]

no_license

iagoid/Django-SimpleMooc

42fc239a12a11f143fa86e765e3936ebdc47170b

0fe810230fb55af0bc41e56bae5849fea5710769

refs/heads/master

2022-11-16T11:13:45.187485

2020-07-11T13:28:26

null

0

null

UTF-8

Python

false

755

py

from django.contrib import admin from .models import Course, Enrollment, Announcement, Comment, Lesson, Material class CourseAdmin(admin.ModelAdmin): list_display = ['name', 'slug', 'start_date', 'created_at'] search_fields = ['name', 'slug'] prepopulated_fields = {'slug': ('name',)} class MaterialInlineAdmin(admin.TabularInline): model = Material class LessonAdmin(admin.ModelAdmin): list_display = ['name', 'number', 'course', 'release_date'] search_fields = ['name', 'description'] list_filter = ['created_at'] inlines = [ MaterialInlineAdmin ] admin.site.register(Course, CourseAdmin) admin.site.register([Enrollment, Announcement, Comment, Material]) admin.site.register(Lesson, LessonAdmin)

[ "iagoid01@gmail.com" ]

iagoid01@gmail.com

d6405350eccf438965119c55bf9bfb85bebcba7f

f709a86ee1eff46e84d00ef1820f27d0d9641aba

/8,9일차/random_실습2.py

2e6edda394afd4668117db2dd7cb99c3ecc95b33

[]

no_license

limsehui/likelionstudy

a697e3a306edb49fbc09700d4390f56109e4f7a5

10a77a2f503283eb48a314652debfa57ed2d2a78

refs/heads/master

2020-04-13T05:50:49.106004

2019-01-18T09:45:43

163,004,861

0

null

UTF-8

Python

false

73

py

import random random_number = random.randint(2, 5) print(random_number)

[ "tpgml944@gmail.com" ]

tpgml944@gmail.com

f2af3503bf7206c6d28a8f29b727061a682f9706

3bafaed1d12e4e1fb221a11998a7b9a858b04644

/App/migrations/0013_auto_20201230_1553.py

fb1ff2ce8bdd568a36fb4d395ecb6cc782160ba0

[]

no_license

nian-20/AtroBOM

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refs/heads/master

2023-08-15T09:13:10.042024

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# Generated by Django 3.1.4 on 2020-12-30 12:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('App', '0012_auto_20201230_1544'), ] operations = [ migrations.AlterField( model_name='rate', name='rate', field=models.CharField(blank=True, max_length=10, null=True, verbose_name=' ضریب مصرف '), ), ]

[ "nimadorostkar97@gmail.com" ]

nimadorostkar97@gmail.com

17c7afc8245e0de1833dc9c9650a56e655b84873

1dc210fbb89aa82ce16e27756f4c6bda624024ab

/blog/migrations/0002_post_insert_date.py

a885948673607242952b80e2729b4e762b8ad64c

[ "LicenseRef-scancode-warranty-disclaimer" ]

no_license

aabele/kasmanzvana.lv

68f3a118a9081639578fe29842abf50ab8c8af6c

409df7a776d17ea7fff5519fe270f73c02428a41

refs/heads/master

2022-11-28T00:07:24.900787

2018-03-21T07:23:51

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# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-08-30 21:25 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0001_initial'), ] operations = [ migrations.AddField( model_name='post', name='insert_date', field=models.DateTimeField(auto_now_add=True, null=True), ), ]

[ "aabele@gmail.com" ]

aabele@gmail.com

babc707cd3d0eba6a38dddd6d813e56e3a003465

044c3ad600995b80aee0a8733cc518f0e5177f85

/Accounts1/scheduler/migrations/0002_auto_20160214_0355.py

fab102ae95cf5a54239757e332c6d6ab84102643

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connieli4/WeddingApp

83c6fdb96c22be33b69f2c874df914fe22ee4ab6

b87a1905ec2268a405bae2d949922a3f190d89a0

refs/heads/master

2020-04-01T12:17:31.522920

2018-12-14T23:36:45

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from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('scheduler', '0001_initial'), ] operations = [ migrations.AlterField( model_name='activity', name='owner', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.DeleteModel( name='User', ), ]

[ "noreply@github.com" ]

noreply@github.com

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32647bb5265c91f68dcb92cdf298c4b38d6b60ec

/test/test_parsing.py

7faeaaf57b3f10d9c7f4b164f22786765fb13b31

[]

no_license

kevin-brown/SmokeDetector

a1a7b887640acad3a460c9bb7550f9ccc772b14e

f1c1ad69e715100a2ec64ec8ca00d6010818d9d0

refs/heads/master

2021-01-18T01:33:37.203878

2015-12-27T17:04:46

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from parsing import * import pytest test_data_inputs = [] with open("test/data_test_parsing.txt", "r") as f: test_data_inputs = f.readlines() # Large inputs should go to that file. # Only inputs should go there, not the parsing method and the expected output, # because the input is always a string and `parse_method` and `expected` are not. @pytest.mark.parametrize("input_data, parse_method, expected", [ ('Testing * escaping of ] special [ characters', escape_special_chars_in_title, 'Testing \* escaping of \] special \[ characters'), ('HTML ' unescaping<', unescape_title, 'HTML \' unescaping<'), ('http://physics.stackexchange.com/users/7433/manishearth', get_user_from_url, ('7433', 'physics.stackexchange.com')), ('http://softwarerecs.stackexchange.com/users/46/undo', get_user_from_url, ('46', 'softwarerecs.stackexchange.com')), ('http://earthscience.stackexchange.com/users/20/hichris123', get_user_from_url, ('20', 'earthscience.stackexchange.com')), ('http://codegolf.stackexchange.com/users/9275/programfox', get_user_from_url, ('9275', 'codegolf.stackexchange.com')), ('http://stackoverflow.com/users/1/jeff-atwood', get_user_from_url, ('1', 'stackoverflow.com')), ('http://mathoverflow.net/users/66/ben-webster', get_user_from_url, ('66', 'mathoverflow.net')), ('http://codegolf.stackexchange.com/u/9275', get_user_from_url, ('9275', 'codegolf.stackexchange.com')), ('http://codegolf.stackexchange.com/u/9275/', get_user_from_url, ('9275', 'codegolf.stackexchange.com')), ('http://codegolf.stackexchange.com/users/9275', get_user_from_url, ('9275', 'codegolf.stackexchange.com')), ('http://codegolf.stackexchange.com/users/9275/', get_user_from_url, ('9275', 'codegolf.stackexchange.com')), ('//stackoverflow.com/users/1/jeff-atwood', get_user_from_url, ('1', 'stackoverflow.com')), ('!!/addblu http://stackoverflow.com/users/0/test', get_user_from_list_command, ('0', 'stackoverflow.com')), ('!!/rmblu http://stackoverflow.com/users/0/test', get_user_from_list_command, ('0', 'stackoverflow.com')), ('!!/addwlu http://stackoverflow.com/users/0/test', get_user_from_list_command, ('0', 'stackoverflow.com')), ('!!/rmwlu http://stackoverflow.com/users/0/test', get_user_from_list_command, ('0', 'stackoverflow.com')), ('!!/addwlu http://codegolf.stackexchange.com/users/9275/programfox', get_user_from_list_command, ('9275', 'codegolf.stackexchange.com')), ('!!/addwlu http://mathoverflow.net/users/66/ben-webster', get_user_from_list_command, ('66', 'mathoverflow.net')), ('!!/rmblu 1234 stackoverflow.com', get_user_from_list_command, ('1234', 'stackoverflow.com')), ('!!/rmwlu 4321 communitybuilding.stackexchange.com', get_user_from_list_command, ('4321', 'communitybuilding.stackexchange.com')), ('!!/addblu 1 stackoverflow', get_user_from_list_command, ('1', 'stackoverflow.com')), ('http://stackoverflow.com/questions/1/title-here', url_to_shortlink, 'http://stackoverflow.com/questions/1'), ('http://stackoverflow.com/questions/1/title-here/2#2', url_to_shortlink, 'http://stackoverflow.com/a/2'), ('http://writers.stackexchange.com/questions/1/%2f%2f', url_to_shortlink, 'http://writers.stackexchange.com/questions/1'), ('http://writers.stackexchange.com/questions/1/%2f%2f/2#2', url_to_shortlink, 'http://writers.stackexchange.com/a/2'), ('http://mathoverflow.net/q/1', url_to_shortlink, 'http://mathoverflow.net/questions/1'), ('http://stackexchange.com', to_protocol_relative, '//stackexchange.com'), ('https://stackexchange.com', to_protocol_relative, '//stackexchange.com'), ('//stackexchange.com', to_protocol_relative, '//stackexchange.com'), ('sd 2tpu', preprocess_shortcut_command, 'sd tpu tpu'), ('sd - 3tpu fp', preprocess_shortcut_command, 'sd - tpu tpu tpu fp'), ('sd 3- 2fp', preprocess_shortcut_command, 'sd - - - fp fp'), ('sd tpu fp ignore delete', preprocess_shortcut_command, 'sd tpu fp ignore delete'), ('sd 5-', preprocess_shortcut_command, 'sd - - - - -'), ('sd tpu', preprocess_shortcut_command, 'sd tpu'), ('sd 2 tpu', preprocess_shortcut_command, 'sd tpu tpu'), ('sd fp 3 tpu', preprocess_shortcut_command, 'sd fp tpu tpu tpu'), (test_data_inputs[0], fetch_post_id_and_site_from_msg_content, ('246651', 'meta.stackexchange.com', 'question')), (test_data_inputs[0], fetch_owner_url_from_msg_content, 'http://meta.stackexchange.com/users/279263/lisa-usher'), (test_data_inputs[0], fetch_title_from_msg_content, 'Best Weight Loss Tips For Fast Results'), (test_data_inputs[1], fetch_post_url_from_msg_content, 'http://stackoverflow.com/questions/0/test-test'), (test_data_inputs[1], fetch_post_id_and_site_from_msg_content, ('0', 'stackoverflow.com', 'question')), (test_data_inputs[1], fetch_owner_url_from_msg_content, 'http://stackoverflow.com/users/0/test-test'), (test_data_inputs[1], fetch_title_from_msg_content, 'TEST TEST TEST ]]])))'), (test_data_inputs[2], fetch_post_url_from_msg_content, 'http://stackoverflow.com/questions/0/test-test/42#42'), (test_data_inputs[2], fetch_post_id_and_site_from_msg_content, ('42', 'stackoverflow.com', 'answer')), (test_data_inputs[2], fetch_owner_url_from_msg_content, 'http://stackoverflow.com/users/0/test-test'), (test_data_inputs[3], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[3], fetch_owner_url_from_msg_content, 'http://stackoverflow.com/users/3754535/user3754535'), (test_data_inputs[3], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[4], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[4], fetch_owner_url_from_msg_content, None), (test_data_inputs[4], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[5], fetch_post_id_and_site_from_msg_content, ('246651', 'meta.stackexchange.com', 'question')), (test_data_inputs[5], fetch_owner_url_from_msg_content, 'http://meta.stackexchange.com/users/279263/lisa-usher'), (test_data_inputs[5], fetch_title_from_msg_content, 'Best Weight Loss Tips For Fast Results'), (test_data_inputs[6], fetch_post_url_from_msg_content, 'http://stackoverflow.com/q/0'), (test_data_inputs[6], fetch_post_id_and_site_from_msg_content, ('0', 'stackoverflow.com', 'question')), (test_data_inputs[6], fetch_owner_url_from_msg_content, 'http://stackoverflow.com/users/0/test-test'), (test_data_inputs[6], fetch_title_from_msg_content, 'TEST TEST TEST ]]])))'), (test_data_inputs[7], fetch_post_url_from_msg_content, 'http://stackoverflow.com/a/42'), (test_data_inputs[7], fetch_post_id_and_site_from_msg_content, ('42', 'stackoverflow.com', 'answer')), (test_data_inputs[7], fetch_owner_url_from_msg_content, 'http://stackoverflow.com/users/0/test-test'), (test_data_inputs[8], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[8], fetch_owner_url_from_msg_content, 'http://stackoverflow.com/users/3754535/user3754535'), (test_data_inputs[8], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[9], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[9], fetch_owner_url_from_msg_content, None), (test_data_inputs[9], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[10], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[10], fetch_owner_url_from_msg_content, '//stackoverflow.com/users/3754535/user3754535'), (test_data_inputs[10], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[11], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[11], fetch_owner_url_from_msg_content, '//stackoverflow.com/users/3754535/user3754535'), (test_data_inputs[11], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[12], fetch_post_id_and_site_from_msg_content, ('458053', 'ru.stackoverflow.com', 'question')), (test_data_inputs[12], fetch_owner_url_from_msg_content, '//ru.stackoverflow.com/users/20555/ni55an'), (test_data_inputs[12], fetch_title_from_msg_content, '-----------------------------'), (test_data_inputs[13], fetch_post_id_and_site_from_msg_content, ('458053', 'ru.stackoverflow.com', 'question')), (test_data_inputs[13], fetch_owner_url_from_msg_content, '//ru.stackoverflow.com/users/20555/ni55an'), (test_data_inputs[13], fetch_title_from_msg_content, '-----------------------------'), (test_data_inputs[14], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[14], fetch_owner_url_from_msg_content, '//stackoverflow.com/users/3754535/user3754535'), (test_data_inputs[14], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?"), (test_data_inputs[15], fetch_post_id_and_site_from_msg_content, ('27954020', 'stackoverflow.com', 'question')), (test_data_inputs[15], fetch_owner_url_from_msg_content, '//stackoverflow.com/users/3754535/user3754535'), (test_data_inputs[15], fetch_title_from_msg_content, "Why I can't insert data in a model from a custom controller?") ]) def test_parsing(input_data, parse_method, expected): assert parse_method(input_data.strip()) == expected

[ "programfox@hotmail.be" ]

programfox@hotmail.be

319088b0fb5a6e5458a9fe0225ff0b44d1d38204

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/scripts/EpsilonResnetBase.py

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[]

no_license

yuxwind/epsilonResnet

f8f961251f7f45ff63ed7c5ce14afd7935031e89

eaf5e33b767b31d60dd528b850faed919aadadc5

refs/heads/master

2021-06-26T17:22:41.644909

2020-09-27T22:57:11

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#!/usr/bin/env python # -*- coding: UTF-8 -*- # File: EpsilonResnetBase.py # Author: Xin Yu <yuxwind@gmail.com> import sys sys.path.append('../../tensorpack') from tensorpack import * from tensorpack.tfutils.symbolic_functions import * from tensorpack.tfutils.summary import * import tensorflow as tf """ Implementation of strict identity and side output in the following paper: Learning Strict Identity Mappings in Deep Residual Networks (https://arxiv.org/pdf/1804.01661.pdf) """ # implement sparsity promting function with 4 ReLUs # Usually, l is a 4 dimension tensor: Batch_size X Width X Height X Channel # return 0.0 only if the absolute values of all elemenents in l are smaller than EPSILON def strict_identity(l, EPSILON): add_moving_summary(tf.reduce_max(tf.abs(l), name='response_abs_max')) add_moving_summary(tf.reduce_mean(tf.abs(l), name='response_mean_max')) l = tf.to_float(l) s = tf.reduce_max(tf.nn.relu(l - EPSILON) +\ tf.nn.relu(-l - EPSILON)) identity_w = tf.nn.relu(tf.nn.relu(s * (-1000000) + 1.0) * (-1000000) + 1.0) return identity_w # implement side supervision at the intermediate of the network # get cross entropy loss after layer l def side_output(name, l, label, outdim): prefix = 'side_output/'+name with tf.variable_scope(prefix) as scope: l = BNReLU('bnlast', l) l = GlobalAvgPooling('gap', l) logits = FullyConnected('linear', l, out_dim=outdim, nl=tf.identity) cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label) cost = tf.reduce_mean(cost, name='cross_entropy_loss') wrong = prediction_incorrect(logits, label) # monitor training error add_moving_summary(tf.reduce_mean(wrong, name='train_error')) return cost

[ "yuxwind@gmail.com" ]

yuxwind@gmail.com

ef7c1442a1b18beb9d8abcb18e13e6af8ec14174

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/venv/Lib/site-packages/python_imagesearch/imagesearch.py

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[]

no_license

cmlya/Lumberjack-Bot

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e1abb4a3428bf6a15ebc5b2fc1ae990fb563b48f

refs/heads/master

2023-07-07T17:49:38.545196

2021-08-25T14:37:52

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import math import cv2 import numpy as np import pyautogui import random import time import platform import subprocess import os import mss is_retina = False if platform.system() == "Darwin": is_retina = subprocess.call("system_profiler SPDisplaysDataType | grep 'retina'", shell=True) == 0 ''' grabs a region (topx, topy, bottomx, bottomy) to the tuple (topx, topy, width, height) input : a tuple containing the 4 coordinates of the region to capture output : a PIL image of the area selected. ''' def region_grabber(region): if is_retina: region = [n * 2 for n in region] x1 = region[0] y1 = region[1] width = region[2] - x1 height = region[3] - y1 region = x1, y1, width, height with mss.mss() as sct: return sct.grab(region) ''' Searchs for an image within an area input : image : path to the image file (see opencv imread for supported types) x1 : top left x value y1 : top left y value x2 : bottom right x value y2 : bottom right y value precision : the higher, the lesser tolerant and fewer false positives are found default is 0.8 im : a PIL image, usefull if you intend to search the same unchanging region for several elements returns : the top left corner coordinates of the element if found as an array [x,y] or [-1,-1] if not ''' def imagesearcharea(image, x1, y1, x2, y2, precision=0.8, im=None): if im is None: im = region_grabber(region=(x1, y1, x2, y2)) if is_retina: im.thumbnail((round(im.size[0] * 0.5), round(im.size[1] * 0.5))) # im.save('testarea.png') usefull for debugging purposes, this will save the captured region as "testarea.png" img_rgb = np.array(im) img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) template = cv2.imread(image, 0) if template is None: raise FileNotFoundError('Image file not found: {}'.format(image)) res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) if max_val < precision: return [-1, -1] return max_loc ''' click on the center of an image with a bit of random. eg, if an image is 100*100 with an offset of 5 it may click at 52,50 the first time and then 55,53 etc Usefull to avoid anti-bot monitoring while staying precise. this function doesn't search for the image, it's only ment for easy clicking on the images. input : image : path to the image file (see opencv imread for supported types) pos : array containing the position of the top left corner of the image [x,y] action : button of the mouse to activate : "left" "right" "middle", see pyautogui.click documentation for more info time : time taken for the mouse to move from where it was to the new position ''' def click_image(image, pos, action, timestamp, offset=5): img = cv2.imread(image) if img is None: raise FileNotFoundError('Image file not found: {}'.format(image)) height, width, channels = img.shape pyautogui.moveTo(pos[0] + r(width / 2, offset), pos[1] + r(height / 2, offset), timestamp) pyautogui.click(button=action) ''' Searchs for an image on the screen input : image : path to the image file (see opencv imread for supported types) precision : the higher, the lesser tolerant and fewer false positives are found default is 0.8 im : a PIL image, usefull if you intend to search the same unchanging region for several elements returns : the top left corner coordinates of the element if found as an array [x,y] or [-1,-1] if not ''' def closest_coord(query_coord, coords): return min( math.hypot(c[0] - query_coord[0], c[1] - query_coord[1]) for c in coords ) def imagesearch(image, precision=0.8): with mss.mss() as sct: im = sct.grab(sct.monitors[0]) if is_retina: im.thumbnail((round(im.size[0] * 0.5), round(im.size[1] * 0.5))) # im.save('testarea.png') useful for debugging purposes, this will save the captured region as "testarea.png" img_rgb = np.array(im) img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) template = cv2.imread(image, 0) if template is None: raise FileNotFoundError('Image file not found: {}'.format(image)) template.shape[::-1] res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED) loc = np.where(res >= precision) coords_unfiltered = list(zip(*loc[::-1])) coords = [] for coord in coords_unfiltered: if len(coords) == 0 or closest_coord(coord, coords) > 3**2: coords.append(coord) if len(coords) > 0: print(len(coords)) for coord in coords: cv2.rectangle(img_rgb, coord, (coord[0]+18,coord[1]+85), (0,0,255), 2) cv2.imwrite("debug_output.png", img_rgb) return coords # min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) # if max_val < precision: # return [-1, -1] # return max_loc ''' Searchs for an image on screen continuously until it's found. input : image : path to the image file (see opencv imread for supported types) time : Waiting time after failing to find the image precision : the higher, the lesser tolerant and fewer false positives are found default is 0.8 returns : the top left corner coordinates of the element if found as an array [x,y] ''' def imagesearch_loop(image, timesample, precision=0.8): pos = imagesearch(image, precision) while pos[0] == -1: print(image + " not found, waiting") time.sleep(timesample) pos = imagesearch(image, precision) return pos ''' Searchs for an image on screen continuously until it's found or max number of samples reached. input : image : path to the image file (see opencv imread for supported types) time : Waiting time after failing to find the image maxSamples: maximum number of samples before function times out. precision : the higher, the lesser tolerant and fewer false positives are found default is 0.8 returns : the top left corner coordinates of the element if found as an array [x,y] ''' def imagesearch_numLoop(image, timesample, maxSamples, precision=0.8): pos = imagesearch(image, precision) count = 0 while pos[0] == -1: print(image + " not found, waiting") time.sleep(timesample) pos = imagesearch(image, precision) count = count + 1 if count > maxSamples: break return pos ''' Searchs for an image on a region of the screen continuously until it's found. input : image : path to the image file (see opencv imread for supported types) time : Waiting time after failing to find the image x1 : top left x value y1 : top left y value x2 : bottom right x value y2 : bottom right y value precision : the higher, the lesser tolerant and fewer false positives are found default is 0.8 returns : the top left corner coordinates of the element as an array [x,y] ''' def imagesearch_region_loop(image, timesample, x1, y1, x2, y2, precision=0.8): pos = imagesearcharea(image, x1, y1, x2, y2, precision) while pos[0] == -1: time.sleep(timesample) pos = imagesearcharea(image, x1, y1, x2, y2, precision) return pos ''' Searches for an image on the screen and counts the number of occurrences. input : image : path to the target image file (see opencv imread for supported types) precision : the higher, the lesser tolerant and fewer false positives are found default is 0.9 returns : the number of times a given image appears on the screen. optionally an output image with all the occurances boxed with a red outline. ''' def imagesearch_count(image, precision=0.9): with mss.mss() as sct: img_rgb = sct.grab() if is_retina: img_rgb.thumbnail((round(img_rgb.size[0] * 0.5), round(img_rgb.size[1] * 0.5))) img_rgb = np.array(img_rgb) img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) template = cv2.imread(image, 0) if template is None: raise FileNotFoundError('Image file not found: {}'.format(image)) w, h = template.shape[::-1] res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED) loc = np.where(res >= precision) count = 0 for pt in zip(*loc[::-1]): # Swap columns and rows # cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2) // Uncomment to draw boxes around found occurrences count = count + 1 # cv2.imwrite('result.png', img_rgb) // Uncomment to write output image with boxes drawn around occurrences return count ''' Get all screens on the provided folder and search them on screen. input : path : path of the folder with the images to be searched on screen precision : the higher, the lesser tolerant and fewer false positives are found default is 0.8 returns : A dictionary where the key is the path to image file and the value is the position where was found. ''' def imagesearch_from_folder(path, precision): print(path) imagesPos = {} path = path if path[-1] == '/' or '\\' else path+'/' valid_images = [".jpg", ".gif", ".png", ".jpeg"] files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f)) and os.path.splitext(f)[1].lower() in valid_images] for file in files: pos = imagesearch(path+file, precision) imagesPos[path+file] = pos return imagesPos def r(num, rand): return num + rand * random.random()

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"""Structs for NSM API.""" # Standard library imports import ctypes class IoVec(ctypes.Structure): """ IoVec struct for use in the NsmMessage struct. The IoVec struct has two fields: iov_base, which is a pointer to a buffer, and iov_len, which defines the length of the contents in the buffer. The IoVec is used both to send data to /dev/nsm (in which case the length of the buffer is defined by the sender) and to receive data from /dev/nsm (in which case the length is set by /dev/nsm). """ iov_base: ctypes.c_void_p iov_len: ctypes.c_size_t _fields_ = [ ('iov_base', ctypes.c_void_p), ('iov_len', ctypes.c_size_t) ] class NsmMessage(ctypes.Structure): """ NsmMessage struct to interface with /dev/nsm. The NsmMessage struct has two fields: request, which contains the data sent to /dev/nsm, and response, which contains the data returned by /dev/nsm after the call has completed. """ request: IoVec response: IoVec _fields_ = [ ('request', IoVec), ('response', IoVec) ]

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lucvandonkersgoed@gmail.com

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refs/heads/main

2023-06-01T23:56:39.824175

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from strings_with_arrows import * import string import os import math import constants from error import * from position import * from lexer import * from nodes import * from rtresult import * ######################################################## ## Value ######################################################## ##for storing numbers and operating on them with other numbers. class Value: def __init__(self): self.set_pos() self.set_context() def set_pos(self, pos_start = None, pos_end = None): ## if we face an error we need to know where the error is in self.pos_start = pos_start self.pos_end = pos_end return self def set_context(self, context=None):##Context for error handling self.context = context return self def added_to(self, other): return None, self.illegal_operation(other) def subbed_by(self, other): return None, self.illegal_operation(other) def multed_by(self, other): return None, self.illegal_operation(other) def dived_by(self, other): return None, self.illegal_operation(other) def powed_by(self, other): return None, self.illegal_operation(other) def get_comparison_eq(self, other): return None, self.illegal_operation(other) def get_comparison_ne(self, other): return None, self.illegal_operation(other) def get_comparison_lt(self, other): return None, self.illegal_operation(other) def get_comparison_gt(self, other): return None, self.illegal_operation(other) def get_comparison_lte(self, other): return None, self.illegal_operation(other) def get_comparison_gte(self, other): return None, self.illegal_operation(other) def anded_by(self, other): return None, self.illegal_operation(other) def ored_by(self, other): return None, self.illegal_operation(other) def notted(self): return None, self.illegal_operation(other) def execute(self, args): return RTResult().failure(self.illegal_operation()) def copy(self): raise Exception('No copy method defined') def is_true(self): return False def illegal_operation(self, other=None): if not other: other = self return RTError(self.pos_start, other.pos_end,'Illegal operation',self.context) class String(Value): def __init__(self, value): super().__init__() self.value = value def added_to(self, other): ##concatenate if isinstance(other, String): return String(self.value + other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def multed_by(self, other):##repeat the string other.values number of time if isinstance(other, Number): return String(self.value * other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def is_true(self): return len(self.value) > 0 def copy(self): copy = String(self.value) copy.set_pos(self.pos_start, self.pos_end) copy.set_context(self.context) return copy def __str__(self): return self.value def __repr__(self): return f'"{self.value}"' class Number(Value): def __init__(self,value): self.value = value self.set_pos() self.set_context() def added_to(self,other): if isinstance(other, Number): ##check if the value that we are operating on is a number return Number(self.value + other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def subbed_by(self,other): if isinstance(other, Number): ##check if the value that we are operating on is a number return Number(self.value - other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def multed_by(self,other): if isinstance(other, Number): ##check if the value that we are operating on is a number return Number(self.value * other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def dived_by(self,other): if isinstance(other, Number): ##check if the value that we are operating on is a number if other.value == 0: return None, return Number(self.value / other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def powed_by(self, other): if isinstance(other, Number): ##return poer return Number(self.value ** other.value).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def get_comparison_eq(self, other): if isinstance(other, Number): ## comparison operator == return Number(int(self.value == other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def get_comparison_ne(self, other): if isinstance(other, Number): ## comparison for != return Number(int(self.value != other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def get_comparison_lt(self, other): if isinstance(other, Number): ##compairon for < return Number(int(self.value < other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def get_comparison_gt(self, other): if isinstance(other, Number): ##comparion for > return Number(int(self.value > other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def get_comparison_lte(self, other): if isinstance(other, Number): ##comparison for less than or equal to <= return Number(int(self.value <= other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def get_comparison_gte(self, other): if isinstance(other, Number): ##comparison for greater than or equal to >= return Number(int(self.value >= other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def anded_by(self, other): if isinstance(other, Number): ##comparison for and return Number(int(self.value and other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def ored_by(self, other): if isinstance(other, Number): ##comparison for or return Number(int(self.value or other.value)).set_context(self.context), None else: return None, Value.illegal_operation(self, other) def notted(self): ##comparison for not function if self.value == 0: return Number(1).set_context(self.context), None else : return Number(0).set_context(self.context), None def is_true(self): return self.value != 0 def copy(self): copy = Number(self.value) copy.set_pos(self.set_pos,self.pos_end) copy.set_context(self.context) return copy def __repr__(self): return str(self.value) Number.null = Number(0) Number.true = Number(1) Number.false = Number(0) Number.math_PI = Number(math.pi) class BaseFunction(Value): def __init__(self, name): super().__init__() self.name = name or "<anonymous>" ##anonymous if it doesnt have a name def generate_new_context(self): ##new context for new function new_context = Context(self.name, self.context, self.pos_start) new_context.symbol_table = SymbolTable(new_context.parent.symbol_table) return new_context def check_args(self, arg_names, args): ##check if there are the correct numbr of arguments res = RTResult() if len(args) > len(arg_names): return res.failure(RTError(self.pos_start, self.pos_end,f"{len(args) - len(arg_names)} too many args passed into {self}",self.context)) if len(args) < len(arg_names): return res.failure(RTError(self.pos_start, self.pos_end,f"{len(arg_names) - len(args)} too few args passed into {self}",self.context)) return res.success(None) def populate_args(self, arg_names, args, exec_ctx): ##put all arguments to symbol table for i in range(len(args)): arg_name = arg_names[i] arg_value = args[i] arg_value.set_context(exec_ctx) exec_ctx.symbol_table.set(arg_name, arg_value) def check_and_populate_args(self, arg_names, args, exec_ctx): ##check the args and populate them res = RTResult() res.register(self.check_args(arg_names, args)) if res.error: return res self.populate_args(arg_names, args, exec_ctx) return res.success(None) class Function(BaseFunction): def __init__(self, name, body_node, arg_names, should_auto_return): super().__init__(name) self.body_node = body_node self.arg_names = arg_names self.should_auto_return = should_auto_return def execute(self, args): ##execute functions res = RTResult() interpreter = Interpreter() exec_ctx = self.generate_new_context() res.register(self.check_and_populate_args(self.arg_names, args, exec_ctx)) if res.error: return res value = res.register(interpreter.visit(self.body_node, exec_ctx)) if res.should_return() and res.func_return_value == None: return res ret_value = (value if self.should_auto_return else None) or res.func_return_value or Number.null return res.success(ret_value) def copy(self): copy = Function(self.name, self.body_node, self.arg_names, self.should_auto_return) copy.set_context(self.context) copy.set_pos(self.pos_start, self.pos_end) return copy def __repr__(self): return f"<function {self.name}>" class BuiltInFunction(BaseFunction): def __init__(self, name): super().__init__(name) def execute(self, args): res = RTResult() exec_ctx = self.generate_new_context() ##create new exec context method_name = f'execute_{self.name}' ##create seperate function for every method = getattr(self, method_name, self.no_visit_method) res.register(self.check_and_populate_args(method.arg_names, args, exec_ctx)) if res.should_return(): return res return_value = res.register(method(exec_ctx)) if res.should_return(): return res return res.success(return_value) def no_visit_method(self, node, context): ## if method ist defined raise Exception(f'No execute_{self.name} method defined') def copy(self): copy = BuiltInFunction(self.name) copy.set_context(self.context) copy.set_pos(self.pos_start, self.pos_end) return copy def __repr__(self): return f"<built-in function {self.name}>" ##################################### def execute_print(self, exec_ctx): print(str(exec_ctx.symbol_table.get('value'))) ##print from symbol table return RTResult().success(Number.null) execute_print.arg_names = ['value'] ## we get the arg_name methods from the method def execute_print_ret(self, exec_ctx): return RTResult().success(String(str(exec_ctx.symbol_table.get('value')))) ##return value that should be printed execute_print_ret.arg_names = ['value'] def execute_input(self, exec_ctx): ##take inpute text = input() return RTResult().success(String(text)) execute_input.arg_names = [] def execute_input_int(self, exec_ctx): while True: text = input() try: number = int(text) break except ValueError: print(f"'{text}' must be an integer. Try again!") return RTResult().success(Number(number)) execute_input_int.arg_names = [] def execute_clear(self, exec_ctx): os.system('cls' if os.name == 'nt' else 'cls') ##clear the terminal return RTResult().success(Number.null) execute_clear.arg_names = [] def execute_is_number(self, exec_ctx): is_number = isinstance(exec_ctx.symbol_table.get("value"), Number) return RTResult().success(Number.true if is_number else Number.false) execute_is_number.arg_names = ["value"] def execute_is_string(self, exec_ctx): is_number = isinstance(exec_ctx.symbol_table.get("value"), String) return RTResult().success(Number.true if is_number else Number.false) execute_is_string.arg_names = ["value"] def execute_is_list(self, exec_ctx): is_number = isinstance(exec_ctx.symbol_table.get("value"), List) return RTResult().success(Number.true if is_number else Number.false) execute_is_list.arg_names = ["value"] def execute_is_function(self, exec_ctx): is_number = isinstance(exec_ctx.symbol_table.get("value"), BaseFunction) return RTResult().success(Number.true if is_number else Number.false) execute_is_function.arg_names = ["value"] def execute_append(self, exec_ctx): list_ = exec_ctx.symbol_table.get("list") value = exec_ctx.symbol_table.get("value") if not isinstance(list_, List): return RTResult().failure(RTError(self.pos_start, self.pos_end,"First argument must be list",exec_ctx)) list_.elements.append(value) return RTResult().success(Number.null) execute_append.arg_names = ["list", "value"] def execute_pop(self, exec_ctx): list_ = exec_ctx.symbol_table.get("list") index = exec_ctx.symbol_table.get("index") if not isinstance(list_, List): return RTResult().failure(RTError( self.pos_start, self.pos_end, "First argument must be list", exec_ctx )) if not isinstance(index, Number): return RTResult().failure(RTError( self.pos_start, self.pos_end, "Second argument must be number", exec_ctx )) try: element = list_.elements.pop(index.value) except: return RTResult().failure(RTError( self.pos_start, self.pos_end, 'Element at this index could not be removed from list because index is out of bounds', exec_ctx )) return RTResult().success(element) execute_pop.arg_names = ["list", "index"] def execute_extend(self, exec_ctx): listA = exec_ctx.symbol_table.get("listA") listB = exec_ctx.symbol_table.get("listB") if not isinstance(listA, List): return RTResult().failure(RTError( self.pos_start, self.pos_end, "First argument must be list", exec_ctx )) if not isinstance(listB, List): return RTResult().failure(RTError( self.pos_start, self.pos_end, "Second argument must be list", exec_ctx )) listA.elements.extend(listB.elements) return RTResult().success(Number.null) execute_extend.arg_names = ["listA", "listB"] def execute_len(self, exec_ctx): ##length of a list list_ = exec_ctx.symbol_table.get("list") if not isinstance(list_, List): return RTResult().failure(RTError( self.pos_start, self.pos_end, "Argument must be list", exec_ctx )) return RTResult().success(Number(len(list_.elements))) execute_len.arg_names = ["list"] def execute_run(self, exec_ctx): fn = exec_ctx.symbol_table.get("fn") ##get file name from symbol table if not isinstance(fn, String):##raise error if it isnt a string return RTResult().failure(RTError(self.pos_start, self.pos_end,"Second argument must be string",exec_ctx)) fn = fn.value try: with open(fn, "r") as f: ##open file in readmode and assign it to variable f script = f.read() ##script content of faile except Exception as e: return RTResult().failure(RTError(self.pos_start, self.pos_end,f"Failed to load script \"{fn}\"\n" + str(e),exec_ctx)) _, error = run(fn, script) if error: return RTResult().failure(RTError(self.pos_start, self.pos_end,f"Failed to finish executing script \"{fn}\"\n" +error.as_string(),exec_ctx)) return RTResult().success(Number.null) execute_run.arg_names = ["fn"] BuiltInFunction.print = BuiltInFunction("print") BuiltInFunction.print_ret = BuiltInFunction("print_ret") BuiltInFunction.input = BuiltInFunction("input") BuiltInFunction.input_int = BuiltInFunction("input_int") BuiltInFunction.clear = BuiltInFunction("clear") BuiltInFunction.is_number = BuiltInFunction("is_number") BuiltInFunction.is_string = BuiltInFunction("is_string") BuiltInFunction.is_list = BuiltInFunction("is_list") BuiltInFunction.is_function = BuiltInFunction("is_function") BuiltInFunction.append = BuiltInFunction("append") BuiltInFunction.pop = BuiltInFunction("pop") BuiltInFunction.extend = BuiltInFunction("extend") BuiltInFunction.len = BuiltInFunction("len") BuiltInFunction.run = BuiltInFunction("run") class List(Value): def __init__(self, elements): super().__init__() self.elements = elements def added_to(self, other): new_list = self.copy() new_list.elements.append(other) return new_list, None def subbed_by(self, other): if isinstance(other, Number): new_list = self.copy() try: ##if the element doesnt exist new_list.elements.pop(other.value) return new_list, None except: return None, RTError(other.pos_start, other.pos_end,'Element at this index could not be removed from list because index is out of bounds',self.context) else: return None, Value.illegal_operation(self, other) def dived_by(self, other): if isinstance(other, Number): try: return self.elements[other.value], None except: return None, RTError(other.pos_start, other.pos_end,'Element at this index could not be retrieved from list because index is out of bounds',self.context) else: return None, Value.illegal_operation(self, other) def copy(self): copy = List(self.elements) copy.set_pos(self.pos_start, self.pos_end) copy.set_context(self.context) return copy def __repr__(self): return f'[{", ".join([str(x) for x in self.elements])}]'

[ "sachinjose16@gmail.com" ]

sachinjose16@gmail.com

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/BTS.py

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d4nielix/BTS_simulator

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2023-08-14T17:32:50.518556

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py

from ResourceBlock import ResourceBlock as rb from User import User from typing import List import logging # from numpy.random import randint, exponential import time import matplotlib.pyplot as plt from Generator import Generator gnrt = Generator() def nonzero(p: int): tmp = round(gnrt.rndexp(p)) while tmp <= 0: tmp = round(gnrt.rndexp(p)) return tmp class BTS: def __init__(self, epsilon_: float, k_: int, s_: int, l_: int, logs: logging, iterations: bool, runtime: int, clock: int): self.log: logging.Logger = logs.getChild(__name__) self.iterations = iterations self.epsilon: float = epsilon_ self.k: int = k_ self.taken_ks: int = 0 self.s: int = s_ self.l: int = l_ self.tau: float = gnrt.randminmax(1, 10) self.lambda1 = 2 self.lambda2 = 2 self.t: float = nonzero(self.lambda1) self.t2: float = nonzero(self.lambda2) self.cycles: int = 0 self.user_list: List[User] = list() self.iterations: bool self.users_served: int = 0 self.users_counter: int = 0 self.runtime = runtime * 1000 self.clock = clock self.user_avg_time = 0 self.transmitted_data = 0 self.wait_list = [] self.user_throughput = 0 self.throughput_list = [] self.retransmissions_time = 0 self.retransmissions_time_list = [] self.retransmitted_data = 0 self.retransmissions_counter = 0 self.log.log(msg='BTS has been created', level=1) def iteration(self): self.log.log(msg='Number of users in user list: ' + str(len(self.user_list)), level=1) if not self.cycles % self.t: self.user_add() if not self.cycles % self.t2: self.user_add() if not self.cycles % self.s and self.user_list: self.distribute_resources() self.log.log(msg='Resources has been distributed.', level=1) if not self.cycles % self.tau and self.user_list: self.users_update_bitrate() self.log.log(msg='Bitrate has been updated.', level=1) for user in self.user_list: for rb in user.user_rb_list: if rb.issent: user.data -= rb.bitrate self.transmitted_data += rb.bitrate self.user_throughput += rb.bitrate self.log.log(msg='Data has been transmitted.', level=1) else: self.retransmissions_counter += 1 self.retransmissions_time = time.time() rb.rb_issent() self.retransmitted_data += rb.bitrate self.retransmissions_time_list.append(time.time() - self.retransmissions_time) self.retransmissions_time = 0 self.log.log(msg='Updating "issent"', level=1) if user.data <= 0: self.users_served += 1 self.taken_ks -= len(user.user_rb_list) self.user_avg_time += time.time() - user.start_time self.wait_list.append(time.time() - user.start_time) self.throughput_list.append(self.user_throughput) self.user_throughput = 0 self.user_remove(user) self.cycles += self.clock def run(self): logging.getLogger('matplotlib.font_manager').disabled = True now = time.localtime() sim_start = time.strftime("%H:%M:%S", now) start_time = time.time() while self.cycles < self.runtime: self.iteration() if self.iterations: c = input("Press ENTER for next step, or type 'quit' to quit the simulator.") if c == "quit": break print("Simulation started: " + sim_start) sim_time = round((time.time() - start_time), 2) print("Simulation took: %s seconds." % sim_time) print("Users at all: " + str(self.users_counter)) print("Users served: " + str(self.users_served)) print("Ratio of served users to users at all: " + str( round(self.users_served / self.users_counter * 100, 2)) + "%") print("Finished cycles: " + str(self.cycles)) print("Average wait time for being served: %s ms." % round(self.user_avg_time / self.users_served * 1000, 2)) print("Average system throughput: " + str(round(self.transmitted_data / sim_time, 2))) print("Average user throughput: " + str(round(self.transmitted_data / sim_time / self.users_served, 2))) print("Average retransmissions counter: " + str(round(self.retransmissions_counter / self.users_counter, 2))) print("Average user retransmitted data: " + str(round(self.retransmitted_data / self.users_counter, 2))) plt.title('Average wait time for being served') plt.xlabel('Average wait time') plt.ylabel('Count') plt.hist(self.wait_list, 10) plt.show() plt.close() plt.title('Average user throughput') plt.xlabel('Average user throughput') plt.ylabel('Count') plt.hist(self.throughput_list, bins=10) plt.show() plt.close() plt.title('Average retransmissions time') plt.xlabel('Average retransmissions time') plt.ylabel('Count') plt.plot(self.retransmissions_time_list) # plt.show() # plt.close() with open("simdata.csv", "a", newline='') as simdata: simdata.write(str(sim_start) + ";" + str(sim_time) + ";" + str(self.users_counter) + ";" + str( self.users_served) + ";" + str( round(self.users_served / self.users_counter * 100, 2)) + "%;" + str(self.cycles) + ";" + str( round(self.user_avg_time / self.users_served * 1000, 2)) + ";" + str( round(self.transmitted_data / sim_time, 2)) + ";" + str( round(self.transmitted_data / sim_time / self.users_served, 2)) + ";" + str( round(self.retransmissions_counter / self.users_counter, 2)) + ";" + str( round(self.retransmitted_data / self.users_counter, 2)) + "\r\n") # with open("lambdas.csv", "a", newline='') as lambdas: lambdas.write(str(self.lambda1) + ";" + str( # self.lambda2) + ";" + str(round(self.user_avg_time / self.users_served * 1000, 2)) + "\r\n") with open("throughput.csv", "a", newline='') as throughputfile: throughputfile.write(str(self.throughput_list) + "\r\n") with open("waittime.csv", "a", newline='') as waittime: waittime.write(str(self.wait_list) + "\r\n") def user_add(self): self.user_list.append(User(logs=self.log, _epsilon=self.epsilon)) self.log.log(msg='New user has appeared', level=1) self.users_counter += 1 def users_update_bitrate(self): for user in self.user_list: user.rb_update() def user_remove(self, user: User): self.user_list.remove(user) self.log.log(msg='User has been deleted', level=1) def distribute_resources(self): for user in self.user_list: if not user.has_rb(): for _ in range(self.l): if self.taken_ks < self.k: user.rb_add_to_list(rb(_epsilon=self.epsilon, logs=user.log)) self.taken_ks += 1

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corutopi/AtCorder_python

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import sys sys.setrecursionlimit(10 ** 6) # from decorator import stop_watch # # # @stop_watch def solve(N, M, K, ABs, CDs): friend_map = [[] for _ in range(N + 1)] for a, b in ABs: friend_map[a].append(b) friend_map[b].append(a) block_map = [[] for _ in range(N + 1)] for c, d in CDs: block_map[c].append(d) block_map[d].append(c) def dfs(group_num, members, now_n): belongs[now_n] = group_num members.append(now_n) for f in friend_map[now_n]: if belongs[f] == -1: members = dfs(group_num, members, f) return members friend_groups = [] belongs = [-1] * (N + 1) for i in range(1, N + 1): if belongs[i] == -1: m = dfs(len(friend_groups), [], i) m.sort() friend_groups.append(m) ans = '' for n in range(1, N + 1): block = 0 group = friend_groups[belongs[n]] for b in block_map[n]: if belongs[n] == belongs[b]: block += 1 ans += ' ' + str(len(group) - len(friend_map[n]) - block - 1) print(ans[1:]) if __name__ == '__main__': # # handmade test # N, M, K = 2 * 10 ** 5, 10 ** 5, 10 ** 5 # ABs = [[1, i] for i in range(2, 10 ** 5 + 2)] # CDs = [[i, i + 1] for i in range(2, 10 ** 5 + 2)] # # handmade random # import random # N, M, K = 20, 10, 10 # ABs = [] # while True: # if len(ABs) == M: # break # a = random.randint(1, N - 1) # b = random.randint(a + 1, N) # if not [a, b] in ABs: # ABs.append([a, b]) # CDs = [] # while True: # if len(CDs) == K: # break # c = random.randint(1, N - 1) # d = random.randint(c + 1, N) # if not [c, d] in ABs and not [c, d] in CDs: # CDs.append([c, d]) # print(N, M, K) # print(ABs) # print(CDs) N, M, K = map(int, input().split()) ABs = [[int(i) for i in input().split()] for _ in range(M)] CDs = [[int(i) for i in input().split()] for _ in range(K)] solve(N, M, K, ABs, CDs)

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from collections import defaultdict import concurrent import copy from datetime import datetime import functools import gym import importlib import json import logging import math import numpy as np import os from packaging import version import pkg_resources import tempfile import time from typing import ( Callable, Container, DefaultDict, Dict, List, Optional, Set, Tuple, Type, Union, ) from ray.rllib.offline.offline_evaluator import OfflineEvaluator import tree import ray from ray._private.usage.usage_lib import TagKey, record_extra_usage_tag from ray.actor import ActorHandle from ray.air.checkpoint import Checkpoint import ray.cloudpickle as pickle from ray.exceptions import GetTimeoutError, RayActorError, RayError from ray.rllib.algorithms.algorithm_config import AlgorithmConfig from ray.rllib.algorithms.callbacks import DefaultCallbacks from ray.rllib.algorithms.registry import ALGORITHMS as ALL_ALGORITHMS from ray.rllib.env.env_context import EnvContext from ray.rllib.env.utils import _gym_env_creator from ray.rllib.evaluation.episode import Episode from ray.rllib.evaluation.metrics import ( collect_episodes, collect_metrics, summarize_episodes, ) from ray.rllib.evaluation.rollout_worker import RolloutWorker from ray.rllib.evaluation.worker_set import WorkerSet from ray.rllib.execution.common import ( STEPS_TRAINED_THIS_ITER_COUNTER, # TODO: Backward compatibility. ) from ray.rllib.execution.parallel_requests import AsyncRequestsManager from ray.rllib.execution.rollout_ops import synchronous_parallel_sample from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step from ray.rllib.offline import get_offline_io_resource_bundles from ray.rllib.offline.estimators import ( OffPolicyEstimator, ImportanceSampling, WeightedImportanceSampling, DirectMethod, DoublyRobust, ) from ray.rllib.policy.policy import Policy from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch, concat_samples from ray.rllib.utils import deep_update, FilterManager, merge_dicts from ray.rllib.utils.annotations import ( DeveloperAPI, ExperimentalAPI, OverrideToImplementCustomLogic, OverrideToImplementCustomLogic_CallToSuperRecommended, PublicAPI, override, ) from ray.rllib.utils.checkpoints import CHECKPOINT_VERSION, get_checkpoint_info from ray.rllib.utils.debug import update_global_seed_if_necessary from ray.rllib.utils.deprecation import ( DEPRECATED_VALUE, Deprecated, deprecation_warning, ) from ray.rllib.utils.error import ERR_MSG_INVALID_ENV_DESCRIPTOR, EnvError from ray.rllib.utils.framework import try_import_tf, try_import_torch from ray.rllib.utils.from_config import from_config from ray.rllib.utils.metrics import ( NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_SAMPLED_THIS_ITER, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED_THIS_ITER, NUM_ENV_STEPS_TRAINED, SYNCH_WORKER_WEIGHTS_TIMER, TRAINING_ITERATION_TIMER, ) from ray.rllib.utils.metrics.learner_info import LEARNER_INFO from ray.rllib.utils.policy import validate_policy_id from ray.rllib.utils.pre_checks.multi_agent import check_multi_agent from ray.rllib.utils.replay_buffers import MultiAgentReplayBuffer from ray.rllib.utils.spaces import space_utils from ray.rllib.utils.typing import ( AgentID, AlgorithmConfigDict, EnvCreator, EnvInfoDict, EnvType, EpisodeID, PartialAlgorithmConfigDict, PolicyID, PolicyState, ResultDict, SampleBatchType, TensorStructType, TensorType, ) from ray.tune.execution.placement_groups import PlacementGroupFactory from ray.tune.experiment.trial import ExportFormat from ray.tune.logger import Logger, UnifiedLogger from ray.tune.registry import ENV_CREATOR, _global_registry from ray.tune.resources import Resources from ray.tune.result import DEFAULT_RESULTS_DIR from ray.tune.trainable import Trainable from ray.util import log_once from ray.util.timer import _Timer tf1, tf, tfv = try_import_tf() logger = logging.getLogger(__name__) @DeveloperAPI def with_common_config(extra_config: PartialAlgorithmConfigDict) -> AlgorithmConfigDict: """Returns the given config dict merged with common agent confs. Args: extra_config: A user defined partial config which will get merged with a default AlgorithmConfig() object and returned as plain python dict. Returns: AlgorithmConfigDict: The merged config dict resulting from AlgorithmConfig() plus `extra_config`. """ return Algorithm.merge_trainer_configs( AlgorithmConfig().to_dict(), extra_config, _allow_unknown_configs=True ) @PublicAPI class Algorithm(Trainable): """An RLlib algorithm responsible for optimizing one or more Policies. Algorithms contain a WorkerSet under `self.workers`. A WorkerSet is normally composed of a single local worker (self.workers.local_worker()), used to compute and apply learning updates, and optionally one or more remote workers (self.workers.remote_workers()), used to generate environment samples in parallel. Each worker (remotes or local) contains a PolicyMap, which itself may contain either one policy for single-agent training or one or more policies for multi-agent training. Policies are synchronized automatically from time to time using ray.remote calls. The exact synchronization logic depends on the specific algorithm used, but this usually happens from local worker to all remote workers and after each training update. You can write your own Algorithm classes by sub-classing from `Algorithm` or any of its built-in sub-classes. This allows you to override the `execution_plan` method to implement your own algorithm logic. You can find the different built-in algorithms' execution plans in their respective main py files, e.g. rllib.algorithms.dqn.dqn.py or rllib.algorithms.impala.impala.py. The most important API methods a Algorithm exposes are `train()`, `evaluate()`, `save()` and `restore()`. """ # Whether to allow unknown top-level config keys. _allow_unknown_configs = False # List of top-level keys with value=dict, for which new sub-keys are # allowed to be added to the value dict. _allow_unknown_subkeys = [ "tf_session_args", "local_tf_session_args", "env_config", "model", "optimizer", "multiagent", "custom_resources_per_worker", "evaluation_config", "exploration_config", "replay_buffer_config", "extra_python_environs_for_worker", "input_config", "output_config", ] # List of top level keys with value=dict, for which we always override the # entire value (dict), iff the "type" key in that value dict changes. _override_all_subkeys_if_type_changes = [ "exploration_config", "replay_buffer_config", ] # List of keys that are always fully overridden if present in any dict or sub-dict _override_all_key_list = ["off_policy_estimation_methods"] _progress_metrics = [ "episode_reward_mean", "evaluation/episode_reward_mean", "num_env_steps_sampled", "num_env_steps_trained", ] @staticmethod def from_checkpoint( checkpoint: Union[str, Checkpoint], policy_ids: Optional[Container[PolicyID]] = None, policy_mapping_fn: Optional[Callable[[AgentID, EpisodeID], PolicyID]] = None, policies_to_train: Optional[ Union[ Container[PolicyID], Callable[[PolicyID, Optional[SampleBatchType]], bool], ] ] = None, ) -> "Algorithm": """Creates a new algorithm instance from a given checkpoint. Note: This method must remain backward compatible from 2.0.0 on. Args: checkpoint: The path (str) to the checkpoint directory to use or an AIR Checkpoint instance to restore from. policy_ids: Optional list of PolicyIDs to recover. This allows users to restore an Algorithm with only a subset of the originally present Policies. policy_mapping_fn: An optional (updated) policy mapping function to use from here on. policies_to_train: An optional list of policy IDs to be trained or a callable taking PolicyID and SampleBatchType and returning a bool (trainable or not?). If None, will keep the existing setup in place. Policies, whose IDs are not in the list (or for which the callable returns False) will not be updated. Returns: The instantiated Algorithm. """ checkpoint_info = get_checkpoint_info(checkpoint) # Not possible for (v0.1) (algo class and config information missing # or very hard to retrieve). if checkpoint_info["checkpoint_version"] == version.Version("0.1"): raise ValueError( "Cannot restore a v0 checkpoint using `Algorithm.from_checkpoint()`!" "In this case, do the following:\n" "1) Create a new Algorithm object using your original config.\n" "2) Call the `restore()` method of this algo object passing it" " your checkpoint dir or AIR Checkpoint object." ) if checkpoint_info["checkpoint_version"] < version.Version("1.0"): raise ValueError( "`checkpoint_info['checkpoint_version']` in `Algorithm.from_checkpoint" "()` must be 1.0 or later! You are using a checkpoint with " f"version v{checkpoint_info['checkpoint_version']}." ) state = Algorithm._checkpoint_info_to_algorithm_state( checkpoint_info=checkpoint_info, policy_ids=policy_ids, policy_mapping_fn=policy_mapping_fn, policies_to_train=policies_to_train, ) return Algorithm.from_state(state) @staticmethod def from_state(state: Dict) -> "Algorithm": """Recovers an Algorithm from a state object. The `state` of an instantiated Algorithm can be retrieved by calling its `get_state` method. It contains all information necessary to create the Algorithm from scratch. No access to the original code (e.g. configs, knowledge of the Algorithm's class, etc..) is needed. Args: state: The state to recover a new Algorithm instance from. Returns: A new Algorithm instance. """ algorithm_class: Type[Algorithm] = state.get("algorithm_class") if algorithm_class is None: raise ValueError( "No `algorithm_class` key was found in given `state`! " "Cannot create new Algorithm." ) # algo_class = get_algorithm_class(algo_class_name) # Create the new algo. config = state.get("config") if not config: raise ValueError("No `config` found in given Algorithm state!") new_algo = algorithm_class(config=config) # Set the new algo's state. new_algo.__setstate__(state) # Return the new algo. return new_algo @PublicAPI def __init__( self, config: Optional[Union[PartialAlgorithmConfigDict, AlgorithmConfig]] = None, env: Optional[Union[str, EnvType]] = None, logger_creator: Optional[Callable[[], Logger]] = None, **kwargs, ): """Initializes an Algorithm instance. Args: config: Algorithm-specific configuration dict. env: Name of the environment to use (e.g. a gym-registered str), a full class path (e.g. "ray.rllib.examples.env.random_env.RandomEnv"), or an Env class directly. Note that this arg can also be specified via the "env" key in `config`. logger_creator: Callable that creates a ray.tune.Logger object. If unspecified, a default logger is created. **kwargs: Arguments passed to the Trainable base class. """ # User provided (partial) config (this may be w/o the default # Algorithm's Config object). Will get merged with AlgorithmConfig() # in self.setup(). config = config or {} # Resolve AlgorithmConfig into a plain dict. # TODO: In the future, only support AlgorithmConfig objects here. if isinstance(config, AlgorithmConfig): config = config.to_dict() # Convert `env` provided in config into a concrete env creator callable, which # takes an EnvContext (config dict) as arg and returning an RLlib supported Env # type (e.g. a gym.Env). self._env_id, self.env_creator = self._get_env_id_and_creator( env or config.get("env"), config ) env_descr = ( self._env_id.__name__ if isinstance(self._env_id, type) else self._env_id ) # Placeholder for a local replay buffer instance. self.local_replay_buffer = None # Create a default logger creator if no logger_creator is specified if logger_creator is None: # Default logdir prefix containing the agent's name and the # env id. timestr = datetime.today().strftime("%Y-%m-%d_%H-%M-%S") logdir_prefix = "{}_{}_{}".format(str(self), env_descr, timestr) if not os.path.exists(DEFAULT_RESULTS_DIR): # Possible race condition if dir is created several times on # rollout workers os.makedirs(DEFAULT_RESULTS_DIR, exist_ok=True) logdir = tempfile.mkdtemp(prefix=logdir_prefix, dir=DEFAULT_RESULTS_DIR) # Allow users to more precisely configure the created logger # via "logger_config.type". if config.get("logger_config") and "type" in config["logger_config"]: def default_logger_creator(config): """Creates a custom logger with the default prefix.""" cfg = config["logger_config"].copy() cls = cfg.pop("type") # Provide default for logdir, in case the user does # not specify this in the "logger_config" dict. logdir_ = cfg.pop("logdir", logdir) return from_config(cls=cls, _args=[cfg], logdir=logdir_) # If no `type` given, use tune's UnifiedLogger as last resort. else: def default_logger_creator(config): """Creates a Unified logger with the default prefix.""" return UnifiedLogger(config, logdir, loggers=None) logger_creator = default_logger_creator # Metrics-related properties. self._timers = defaultdict(_Timer) self._counters = defaultdict(int) self._episode_history = [] self._episodes_to_be_collected = [] self._remote_workers_for_metrics = [] # Evaluation WorkerSet and metrics last returned by `self.evaluate()`. self.evaluation_workers: Optional[WorkerSet] = None # If evaluation duration is "auto", use a AsyncRequestsManager to be more # robust against eval worker failures. self._evaluation_async_req_manager: Optional[AsyncRequestsManager] = None # Initialize common evaluation_metrics to nan, before they become # available. We want to make sure the metrics are always present # (although their values may be nan), so that Tune does not complain # when we use these as stopping criteria. self.evaluation_metrics = { "evaluation": { "episode_reward_max": np.nan, "episode_reward_min": np.nan, "episode_reward_mean": np.nan, } } super().__init__(config=config, logger_creator=logger_creator, **kwargs) # Check, whether `training_iteration` is still a tune.Trainable property # and has not been overridden by the user in the attempt to implement the # algos logic (this should be done now inside `training_step`). try: assert isinstance(self.training_iteration, int) except AssertionError: raise AssertionError( "Your Algorithm's `training_iteration` seems to be overridden by your " "custom training logic! To solve this problem, simply rename your " "`self.training_iteration()` method into `self.training_step`." ) @OverrideToImplementCustomLogic @classmethod def get_default_config(cls) -> AlgorithmConfigDict: return AlgorithmConfig().to_dict() @OverrideToImplementCustomLogic_CallToSuperRecommended @override(Trainable) def setup(self, config: PartialAlgorithmConfigDict): # Setup our config: Merge the user-supplied config (which could # be a partial config dict with the class' default). self.config = self.merge_trainer_configs( self.get_default_config(), config, self._allow_unknown_configs ) self.config["env"] = self._env_id # Validate the framework settings in config. self.validate_framework(self.config) # Set Algorithm's seed after we have - if necessary - enabled # tf eager-execution. update_global_seed_if_necessary(self.config["framework"], self.config["seed"]) self.validate_config(self.config) self._record_usage(self.config) self.callbacks = self.config["callbacks"]() log_level = self.config.get("log_level") if log_level in ["WARN", "ERROR"]: logger.info( "Current log_level is {}. For more information, " "set 'log_level': 'INFO' / 'DEBUG' or use the -v and " "-vv flags.".format(log_level) ) if self.config.get("log_level"): logging.getLogger("ray.rllib").setLevel(self.config["log_level"]) # Create local replay buffer if necessary. self.local_replay_buffer = self._create_local_replay_buffer_if_necessary( self.config ) # Create a dict, mapping ActorHandles to sets of open remote # requests (object refs). This way, we keep track, of which actors # inside this Algorithm (e.g. a remote RolloutWorker) have # already been sent how many (e.g. `sample()`) requests. self.remote_requests_in_flight: DefaultDict[ ActorHandle, Set[ray.ObjectRef] ] = defaultdict(set) self.workers: Optional[WorkerSet] = None self.train_exec_impl = None # Offline RL settings. input_evaluation = self.config.get("input_evaluation") if input_evaluation is not None and input_evaluation is not DEPRECATED_VALUE: ope_dict = {str(ope): {"type": ope} for ope in input_evaluation} deprecation_warning( old="config.input_evaluation={}".format(input_evaluation), new='config["evaluation_config"]' '["off_policy_estimation_methods"]={}'.format( ope_dict, ), error=True, help="Running OPE during training is not recommended.", ) self.config["off_policy_estimation_methods"] = ope_dict # Deprecated way of implementing Trainer sub-classes (or "templates" # via the `build_trainer` utility function). # Instead, sub-classes should override the Trainable's `setup()` # method and call super().setup() from within that override at some # point. # Old design: Override `Trainer._init`. _init = False try: self._init(self.config, self.env_creator) _init = True # New design: Override `Trainable.setup()` (as indented by tune.Trainable) # and do or don't call `super().setup()` from within your override. # By default, `super().setup()` will create both worker sets: # "rollout workers" for collecting samples for training and - if # applicable - "evaluation workers" for evaluation runs in between or # parallel to training. # TODO: Deprecate `_init()` and remove this try/except block. except NotImplementedError: pass # Only if user did not override `_init()`: if _init is False: # - Create rollout workers here automatically. # - Run the execution plan to create the local iterator to `next()` # in each training iteration. # This matches the behavior of using `build_trainer()`, which # has been deprecated. try: self.workers = WorkerSet( env_creator=self.env_creator, validate_env=self.validate_env, policy_class=self.get_default_policy_class(self.config), trainer_config=self.config, num_workers=self.config["num_workers"], local_worker=True, logdir=self.logdir, ) # WorkerSet creation possibly fails, if some (remote) workers cannot # be initialized properly (due to some errors in the RolloutWorker's # constructor). except RayActorError as e: # In case of an actor (remote worker) init failure, the remote worker # may still exist and will be accessible, however, e.g. calling # its `sample.remote()` would result in strange "property not found" # errors. if e.actor_init_failed: # Raise the original error here that the RolloutWorker raised # during its construction process. This is to enforce transparency # for the user (better to understand the real reason behind the # failure). # - e.args[0]: The RayTaskError (inside the caught RayActorError). # - e.args[0].args[2]: The original Exception (e.g. a ValueError due # to a config mismatch) thrown inside the actor. raise e.args[0].args[2] # In any other case, raise the RayActorError as-is. else: raise e # By default, collect metrics for all remote workers. self._remote_workers_for_metrics = self.workers.remote_workers() # TODO (avnishn): Remove the execution plan API by q1 2023 # Function defining one single training iteration's behavior. if self.config["_disable_execution_plan_api"]: # Ensure remote workers are initially in sync with the local worker. self.workers.sync_weights() # LocalIterator-creating "execution plan". # Only call this once here to create `self.train_exec_impl`, # which is a ray.util.iter.LocalIterator that will be `next`'d # on each training iteration. else: self.train_exec_impl = self.execution_plan( self.workers, self.config, **self._kwargs_for_execution_plan() ) # Now that workers have been created, update our policies # dict in config[multiagent] (with the correct original/ # unpreprocessed spaces). self.config["multiagent"][ "policies" ] = self.workers.local_worker().policy_dict # Evaluation WorkerSet setup. # User would like to setup a separate evaluation worker set. # Update with evaluation settings: user_eval_config = copy.deepcopy(self.config["evaluation_config"]) # Merge user-provided eval config with the base config. This makes sure # the eval config is always complete, no matter whether we have eval # workers or perform evaluation on the (non-eval) local worker. eval_config = merge_dicts(self.config, user_eval_config) self.config["evaluation_config"] = eval_config if self.config.get("evaluation_num_workers", 0) > 0 or self.config.get( "evaluation_interval" ): logger.debug(f"Using evaluation_config: {user_eval_config}.") # Validate evaluation config. self.validate_config(eval_config) # Set the `in_evaluation` flag. eval_config["in_evaluation"] = True # Evaluation duration unit: episodes. # Switch on `complete_episode` rollouts. Also, make sure # rollout fragments are short so we never have more than one # episode in one rollout. if eval_config["evaluation_duration_unit"] == "episodes": eval_config.update( { "batch_mode": "complete_episodes", "rollout_fragment_length": 1, } ) # Evaluation duration unit: timesteps. # - Set `batch_mode=truncate_episodes` so we don't perform rollouts # strictly along episode borders. # Set `rollout_fragment_length` such that desired steps are divided # equally amongst workers or - in "auto" duration mode - set it # to a reasonably small number (10), such that a single `sample()` # call doesn't take too much time and we can stop evaluation as soon # as possible after the train step is completed. else: eval_config.update( { "batch_mode": "truncate_episodes", "rollout_fragment_length": 10 if self.config["evaluation_duration"] == "auto" else int( math.ceil( self.config["evaluation_duration"] / (self.config["evaluation_num_workers"] or 1) ) ), } ) self.config["evaluation_config"] = eval_config _, env_creator = self._get_env_id_and_creator( eval_config.get("env"), eval_config ) # Create a separate evaluation worker set for evaluation. # If evaluation_num_workers=0, use the evaluation set's local # worker for evaluation, otherwise, use its remote workers # (parallelized evaluation). self.evaluation_workers: WorkerSet = WorkerSet( env_creator=env_creator, validate_env=None, policy_class=self.get_default_policy_class(self.config), trainer_config=eval_config, num_workers=self.config["evaluation_num_workers"], # Don't even create a local worker if num_workers > 0. local_worker=False, logdir=self.logdir, ) if self.config["enable_async_evaluation"]: self._evaluation_async_req_manager = AsyncRequestsManager( workers=self.evaluation_workers.remote_workers(), max_remote_requests_in_flight_per_worker=1, return_object_refs=True, ) self._evaluation_weights_seq_number = 0 self.reward_estimators: Dict[str, OffPolicyEstimator] = {} ope_types = { "is": ImportanceSampling, "wis": WeightedImportanceSampling, "dm": DirectMethod, "dr": DoublyRobust, } for name, method_config in self.config["off_policy_estimation_methods"].items(): method_type = method_config.pop("type") if method_type in ope_types: deprecation_warning( old=method_type, new=str(ope_types[method_type]), error=True, ) method_type = ope_types[method_type] elif isinstance(method_type, str): logger.log(0, "Trying to import from string: " + method_type) mod, obj = method_type.rsplit(".", 1) mod = importlib.import_module(mod) method_type = getattr(mod, obj) if isinstance(method_type, type) and issubclass( method_type, OfflineEvaluator ): # TODO(kourosh) : Add an integration test for all these # offline evaluators. policy = self.get_policy() if issubclass(method_type, OffPolicyEstimator): method_config["gamma"] = self.config["gamma"] self.reward_estimators[name] = method_type(policy, **method_config) else: raise ValueError( f"Unknown off_policy_estimation type: {method_type}! Must be " "either a class path or a sub-class of ray.rllib." "offline.estimators.off_policy_estimator::OffPolicyEstimator" ) # Run `on_algorithm_init` callback after initialization is done. self.callbacks.on_algorithm_init(algorithm=self) # TODO: Deprecated: In your sub-classes of Trainer, override `setup()` # directly and call super().setup() from within it if you would like the # default setup behavior plus some own setup logic. # If you don't need the env/workers/config/etc.. setup for you by super, # simply do not call super().setup() from your overridden method. def _init(self, config: AlgorithmConfigDict, env_creator: EnvCreator) -> None: raise NotImplementedError @OverrideToImplementCustomLogic def get_default_policy_class(self, config: AlgorithmConfigDict) -> Type[Policy]: """Returns a default Policy class to use, given a config. This class will be used inside RolloutWorkers' PolicyMaps in case the policy class is not provided by the user in any single- or multi-agent PolicySpec. This method is experimental and currently only used, iff the Trainer class was not created using the `build_trainer` utility and if the Trainer sub-class does not override `_init()` and create it's own WorkerSet in `_init()`. """ return getattr(self, "_policy_class", None) @override(Trainable) def step(self) -> ResultDict: """Implements the main `Trainer.train()` logic. Takes n attempts to perform a single training step. Thereby catches RayErrors resulting from worker failures. After n attempts, fails gracefully. Override this method in your Trainer sub-classes if you would like to handle worker failures yourself. Otherwise, override only `training_step()` to implement the core algorithm logic. Returns: The results dict with stats/infos on sampling, training, and - if required - evaluation. """ # Do we have to run `self.evaluate()` this iteration? # `self.iteration` gets incremented after this function returns, # meaning that e. g. the first time this function is called, # self.iteration will be 0. evaluate_this_iter = ( self.config["evaluation_interval"] is not None and (self.iteration + 1) % self.config["evaluation_interval"] == 0 ) # Results dict for training (and if appolicable: evaluation). results: ResultDict = {} local_worker = ( self.workers.local_worker() if hasattr(self.workers, "local_worker") else None ) # Parallel eval + training: Kick off evaluation-loop and parallel train() call. if evaluate_this_iter and self.config["evaluation_parallel_to_training"]: ( results, train_iter_ctx, ) = self._run_one_training_iteration_and_evaluation_in_parallel() # - No evaluation necessary, just run the next training iteration. # - We have to evaluate in this training iteration, but no parallelism -> # evaluate after the training iteration is entirely done. else: results, train_iter_ctx = self._run_one_training_iteration() # Sequential: Train (already done above), then evaluate. if evaluate_this_iter and not self.config["evaluation_parallel_to_training"]: results.update(self._run_one_evaluation(train_future=None)) # Attach latest available evaluation results to train results, # if necessary. if not evaluate_this_iter and self.config["always_attach_evaluation_results"]: assert isinstance( self.evaluation_metrics, dict ), "Trainer.evaluate() needs to return a dict." results.update(self.evaluation_metrics) if hasattr(self, "workers") and isinstance(self.workers, WorkerSet): # Sync filters on workers. self._sync_filters_if_needed( from_worker=self.workers.local_worker(), workers=self.workers, timeout_seconds=self.config[ "sync_filters_on_rollout_workers_timeout_s" ], ) # TODO (avnishn): Remove the execution plan API by q1 2023 # Collect worker metrics and add combine them with `results`. if self.config["_disable_execution_plan_api"]: episodes_this_iter, self._episodes_to_be_collected = collect_episodes( local_worker, self._remote_workers_for_metrics, self._episodes_to_be_collected, timeout_seconds=self.config["metrics_episode_collection_timeout_s"], ) results = self._compile_iteration_results( episodes_this_iter=episodes_this_iter, step_ctx=train_iter_ctx, iteration_results=results, ) # Check `env_task_fn` for possible update of the env's task. if self.config["env_task_fn"] is not None: if not callable(self.config["env_task_fn"]): raise ValueError( "`env_task_fn` must be None or a callable taking " "[train_results, env, env_ctx] as args!" ) def fn(env, env_context, task_fn): new_task = task_fn(results, env, env_context) cur_task = env.get_task() if cur_task != new_task: env.set_task(new_task) fn = functools.partial(fn, task_fn=self.config["env_task_fn"]) self.workers.foreach_env_with_context(fn) return results @PublicAPI def evaluate( self, duration_fn: Optional[Callable[[int], int]] = None, ) -> dict: """Evaluates current policy under `evaluation_config` settings. Note that this default implementation does not do anything beyond merging evaluation_config with the normal trainer config. Args: duration_fn: An optional callable taking the already run num episodes as only arg and returning the number of episodes left to run. It's used to find out whether evaluation should continue. """ # Call the `_before_evaluate` hook. self._before_evaluate() # Sync weights to the evaluation WorkerSet. if self.evaluation_workers is not None: self.evaluation_workers.sync_weights( from_worker=self.workers.local_worker() ) self._sync_filters_if_needed( from_worker=self.workers.local_worker(), workers=self.evaluation_workers, timeout_seconds=self.config[ "sync_filters_on_rollout_workers_timeout_s" ], ) self.callbacks.on_evaluate_start(algorithm=self) if self.config["custom_eval_function"]: logger.info( "Running custom eval function {}".format( self.config["custom_eval_function"] ) ) metrics = self.config["custom_eval_function"](self, self.evaluation_workers) if not metrics or not isinstance(metrics, dict): raise ValueError( "Custom eval function must return " "dict of metrics, got {}.".format(metrics) ) else: if ( self.evaluation_workers is None and self.workers.local_worker().input_reader is None ): raise ValueError( "Cannot evaluate w/o an evaluation worker set in " "the Trainer or w/o an env on the local worker!\n" "Try one of the following:\n1) Set " "`evaluation_interval` >= 0 to force creating a " "separate evaluation worker set.\n2) Set " "`create_env_on_driver=True` to force the local " "(non-eval) worker to have an environment to " "evaluate on." ) # How many episodes/timesteps do we need to run? # In "auto" mode (only for parallel eval + training): Run as long # as training lasts. unit = self.config["evaluation_duration_unit"] eval_cfg = self.config["evaluation_config"] rollout = eval_cfg["rollout_fragment_length"] num_envs = eval_cfg["num_envs_per_worker"] auto = self.config["evaluation_duration"] == "auto" duration = ( self.config["evaluation_duration"] if not auto else (self.config["evaluation_num_workers"] or 1) * (1 if unit == "episodes" else rollout) ) agent_steps_this_iter = 0 env_steps_this_iter = 0 # Default done-function returns True, whenever num episodes # have been completed. if duration_fn is None: def duration_fn(num_units_done): return duration - num_units_done logger.info(f"Evaluating current policy for {duration} {unit}.") metrics = None all_batches = [] # No evaluation worker set -> # Do evaluation using the local worker. Expect error due to the # local worker not having an env. if self.evaluation_workers is None: # If unit=episodes -> Run n times `sample()` (each sample # produces exactly 1 episode). # If unit=ts -> Run 1 `sample()` b/c the # `rollout_fragment_length` is exactly the desired ts. iters = duration if unit == "episodes" else 1 for _ in range(iters): batch = self.workers.local_worker().sample() agent_steps_this_iter += batch.agent_steps() env_steps_this_iter += batch.env_steps() if self.reward_estimators: all_batches.append(batch) metrics = collect_metrics( self.workers.local_worker(), keep_custom_metrics=eval_cfg["keep_per_episode_custom_metrics"], timeout_seconds=eval_cfg["metrics_episode_collection_timeout_s"], ) # Evaluation worker set only has local worker. elif self.config["evaluation_num_workers"] == 0: # If unit=episodes -> Run n times `sample()` (each sample # produces exactly 1 episode). # If unit=ts -> Run 1 `sample()` b/c the # `rollout_fragment_length` is exactly the desired ts. iters = duration if unit == "episodes" else 1 for _ in range(iters): batch = self.evaluation_workers.local_worker().sample() agent_steps_this_iter += batch.agent_steps() env_steps_this_iter += batch.env_steps() if self.reward_estimators: all_batches.append(batch) # Evaluation worker set has n remote workers. else: # How many episodes have we run (across all eval workers)? num_units_done = 0 _round = 0 while True: units_left_to_do = duration_fn(num_units_done) if units_left_to_do <= 0: break _round += 1 try: batches = ray.get( [ w.sample.remote() for i, w in enumerate( self.evaluation_workers.remote_workers() ) if i * (1 if unit == "episodes" else rollout * num_envs) < units_left_to_do ], timeout=self.config["evaluation_sample_timeout_s"], ) except GetTimeoutError: logger.warning( "Calling `sample()` on your remote evaluation worker(s) " "resulted in a timeout (after the configured " f"{self.config['evaluation_sample_timeout_s']} seconds)! " "Try to set `evaluation_sample_timeout_s` in your config" " to a larger value." + ( " If your episodes don't terminate easily, you may " "also want to set `evaluation_duration_unit` to " "'timesteps' (instead of 'episodes')." if unit == "episodes" else "" ) ) break _agent_steps = sum(b.agent_steps() for b in batches) _env_steps = sum(b.env_steps() for b in batches) # 1 episode per returned batch. if unit == "episodes": num_units_done += len(batches) # Make sure all batches are exactly one episode. for ma_batch in batches: ma_batch = ma_batch.as_multi_agent() for batch in ma_batch.policy_batches.values(): assert np.sum(batch[SampleBatch.DONES]) # n timesteps per returned batch. else: num_units_done += ( _agent_steps if self._by_agent_steps else _env_steps ) if self.reward_estimators: # TODO: (kourosh) This approach will cause an OOM issue when # the dataset gets huge (should be ok for now). all_batches.extend(batches) agent_steps_this_iter += _agent_steps env_steps_this_iter += _env_steps logger.info( f"Ran round {_round} of parallel evaluation " f"({num_units_done}/{duration if not auto else '?'} " f"{unit} done)" ) if metrics is None: metrics = collect_metrics( self.evaluation_workers.local_worker(), self.evaluation_workers.remote_workers(), keep_custom_metrics=self.config["keep_per_episode_custom_metrics"], timeout_seconds=eval_cfg["metrics_episode_collection_timeout_s"], ) metrics[NUM_AGENT_STEPS_SAMPLED_THIS_ITER] = agent_steps_this_iter metrics[NUM_ENV_STEPS_SAMPLED_THIS_ITER] = env_steps_this_iter # TODO: Remove this key at some point. Here for backward compatibility. metrics["timesteps_this_iter"] = env_steps_this_iter # Compute off-policy estimates estimates = defaultdict(list) # for each batch run the estimator's fwd pass for name, estimator in self.reward_estimators.items(): for batch in all_batches: estimate_result = estimator.estimate( batch, split_batch_by_episode=self.config[ "ope_split_batch_by_episode" ], ) estimates[name].append(estimate_result) # collate estimates from all batches if estimates: metrics["off_policy_estimator"] = {} for name, estimate_list in estimates.items(): avg_estimate = tree.map_structure( lambda *x: np.mean(x, axis=0), *estimate_list ) metrics["off_policy_estimator"][name] = avg_estimate # Evaluation does not run for every step. # Save evaluation metrics on trainer, so it can be attached to # subsequent step results as latest evaluation result. self.evaluation_metrics = {"evaluation": metrics} # Trigger `on_evaluate_end` callback. self.callbacks.on_evaluate_end( algorithm=self, evaluation_metrics=self.evaluation_metrics ) # Also return the results here for convenience. return self.evaluation_metrics @ExperimentalAPI def _evaluate_async( self, duration_fn: Optional[Callable[[int], int]] = None, ) -> dict: """Evaluates current policy under `evaluation_config` settings. Uses the AsyncParallelRequests manager to send frequent `sample.remote()` requests to the evaluation RolloutWorkers and collect the results of these calls. Handles worker failures (or slowdowns) gracefully due to the asynch'ness and the fact that other eval RolloutWorkers can thus cover the workload. Important Note: This will replace the current `self.evaluate()` method as the default in the future. Args: duration_fn: An optional callable taking the already run num episodes as only arg and returning the number of episodes left to run. It's used to find out whether evaluation should continue. """ # How many episodes/timesteps do we need to run? # In "auto" mode (only for parallel eval + training): Run as long # as training lasts. unit = self.config["evaluation_duration_unit"] eval_cfg = self.config["evaluation_config"] rollout = eval_cfg["rollout_fragment_length"] num_envs = eval_cfg["num_envs_per_worker"] auto = self.config["evaluation_duration"] == "auto" duration = ( self.config["evaluation_duration"] if not auto else (self.config["evaluation_num_workers"] or 1) * (1 if unit == "episodes" else rollout) ) # Call the `_before_evaluate` hook. self._before_evaluate() # Put weights only once into object store and use same object # ref to synch to all workers. self._evaluation_weights_seq_number += 1 weights_ref = ray.put(self.workers.local_worker().get_weights()) # TODO(Jun): Make sure this cannot block for e.g. 1h. Implement solution via # connectors. self._sync_filters_if_needed( from_worker=self.workers.local_worker(), workers=self.evaluation_workers, timeout_seconds=eval_cfg.get("sync_filters_on_rollout_workers_timeout_s"), ) if self.config["custom_eval_function"]: raise ValueError( "`custom_eval_function` not supported in combination " "with `enable_async_evaluation=True` config setting!" ) if self.evaluation_workers is None and ( self.workers.local_worker().input_reader is None or self.config["evaluation_num_workers"] == 0 ): raise ValueError( "Evaluation w/o eval workers (calling Algorithm.evaluate() w/o " "evaluation specifically set up) OR evaluation without input reader " "OR evaluation with only a local evaluation worker " "(`evaluation_num_workers=0`) not supported in combination " "with `enable_async_evaluation=True` config setting!" ) agent_steps_this_iter = 0 env_steps_this_iter = 0 logger.info(f"Evaluating current policy for {duration} {unit}.") all_batches = [] # Default done-function returns True, whenever num episodes # have been completed. if duration_fn is None: def duration_fn(num_units_done): return duration - num_units_done def remote_fn(worker, w_ref, w_seq_no): # Pass in seq-no so that eval workers may ignore this call if no update has # happened since the last call to `remote_fn` (sample). worker.set_weights(weights=w_ref, weights_seq_no=w_seq_no) batch = worker.sample() metrics = worker.get_metrics() return batch, metrics, w_seq_no rollout_metrics = [] # How many episodes have we run (across all eval workers)? num_units_done = 0 _round = 0 errors = [] while len(self._evaluation_async_req_manager.workers) > 0: units_left_to_do = duration_fn(num_units_done) if units_left_to_do <= 0: break _round += 1 # Use the AsyncRequestsManager to get ready evaluation results and # metrics. self._evaluation_async_req_manager.call_on_all_available( remote_fn=remote_fn, fn_args=[weights_ref, self._evaluation_weights_seq_number], ) ready_requests = self._evaluation_async_req_manager.get_ready() batches = [] i = 0 for actor, requests in ready_requests.items(): for req in requests: try: batch, metrics, seq_no = ray.get(req) # Ignore results, if the weights seq-number does not match (is # from a previous evaluation step) OR if we have already reached # the configured duration (e.g. number of episodes to evaluate # for). if seq_no == self._evaluation_weights_seq_number and ( i * (1 if unit == "episodes" else rollout * num_envs) < units_left_to_do ): batches.append(batch) rollout_metrics.extend(metrics) except RayError as e: errors.append(e) self._evaluation_async_req_manager.remove_workers(actor) i += 1 _agent_steps = sum(b.agent_steps() for b in batches) _env_steps = sum(b.env_steps() for b in batches) # 1 episode per returned batch. if unit == "episodes": num_units_done += len(batches) # Make sure all batches are exactly one episode. for ma_batch in batches: ma_batch = ma_batch.as_multi_agent() for batch in ma_batch.policy_batches.values(): assert np.sum(batch[SampleBatch.DONES]) # n timesteps per returned batch. else: num_units_done += _agent_steps if self._by_agent_steps else _env_steps if self.reward_estimators: all_batches.extend(batches) agent_steps_this_iter += _agent_steps env_steps_this_iter += _env_steps logger.info( f"Ran round {_round} of parallel evaluation " f"({num_units_done}/{duration if not auto else '?'} " f"{unit} done)" ) num_recreated_workers = 0 if errors: num_recreated_workers = self.try_recover_from_step_attempt( error=errors[0], worker_set=self.evaluation_workers, ignore=eval_cfg.get("ignore_worker_failures"), recreate=eval_cfg.get("recreate_failed_workers"), ) metrics = summarize_episodes( rollout_metrics, keep_custom_metrics=eval_cfg["keep_per_episode_custom_metrics"], ) metrics["num_recreated_workers"] = num_recreated_workers metrics[NUM_AGENT_STEPS_SAMPLED_THIS_ITER] = agent_steps_this_iter metrics[NUM_ENV_STEPS_SAMPLED_THIS_ITER] = env_steps_this_iter # TODO: Remove this key at some point. Here for backward compatibility. metrics["timesteps_this_iter"] = env_steps_this_iter if self.reward_estimators: # Compute off-policy estimates metrics["off_policy_estimator"] = {} total_batch = concat_samples(all_batches) for name, estimator in self.reward_estimators.items(): estimates = estimator.estimate(total_batch) metrics["off_policy_estimator"][name] = estimates # Evaluation does not run for every step. # Save evaluation metrics on trainer, so it can be attached to # subsequent step results as latest evaluation result. self.evaluation_metrics = {"evaluation": metrics} # Trigger `on_evaluate_end` callback. self.callbacks.on_evaluate_end( algorithm=self, evaluation_metrics=self.evaluation_metrics ) # Return evaluation results. return self.evaluation_metrics @OverrideToImplementCustomLogic @DeveloperAPI def training_step(self) -> ResultDict: """Default single iteration logic of an algorithm. - Collect on-policy samples (SampleBatches) in parallel using the Trainer's RolloutWorkers (@ray.remote). - Concatenate collected SampleBatches into one train batch. - Note that we may have more than one policy in the multi-agent case: Call the different policies' `learn_on_batch` (simple optimizer) OR `load_batch_into_buffer` + `learn_on_loaded_batch` (multi-GPU optimizer) methods to calculate loss and update the model(s). - Return all collected metrics for the iteration. Returns: The results dict from executing the training iteration. """ # Collect SampleBatches from sample workers until we have a full batch. if self._by_agent_steps: train_batch = synchronous_parallel_sample( worker_set=self.workers, max_agent_steps=self.config["train_batch_size"] ) else: train_batch = synchronous_parallel_sample( worker_set=self.workers, max_env_steps=self.config["train_batch_size"] ) train_batch = train_batch.as_multi_agent() self._counters[NUM_AGENT_STEPS_SAMPLED] += train_batch.agent_steps() self._counters[NUM_ENV_STEPS_SAMPLED] += train_batch.env_steps() # Use simple optimizer (only for multi-agent or tf-eager; all other # cases should use the multi-GPU optimizer, even if only using 1 GPU). # TODO: (sven) rename MultiGPUOptimizer into something more # meaningful. if self.config.get("simple_optimizer") is True: train_results = train_one_step(self, train_batch) else: train_results = multi_gpu_train_one_step(self, train_batch) # Update weights and global_vars - after learning on the local worker - on all # remote workers. global_vars = { "timestep": self._counters[NUM_ENV_STEPS_SAMPLED], } with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]: self.workers.sync_weights(global_vars=global_vars) return train_results @staticmethod def execution_plan(workers, config, **kwargs): raise NotImplementedError( "It is not longer recommended to use Trainer's `execution_plan` method/API." " Set `_disable_execution_plan_api=True` in your config and override the " "`Trainer.training_step()` method with your algo's custom " "execution logic." ) @PublicAPI def compute_single_action( self, observation: Optional[TensorStructType] = None, state: Optional[List[TensorStructType]] = None, *, prev_action: Optional[TensorStructType] = None, prev_reward: Optional[float] = None, info: Optional[EnvInfoDict] = None, input_dict: Optional[SampleBatch] = None, policy_id: PolicyID = DEFAULT_POLICY_ID, full_fetch: bool = False, explore: Optional[bool] = None, timestep: Optional[int] = None, episode: Optional[Episode] = None, unsquash_action: Optional[bool] = None, clip_action: Optional[bool] = None, # Deprecated args. unsquash_actions=DEPRECATED_VALUE, clip_actions=DEPRECATED_VALUE, # Kwargs placeholder for future compatibility. **kwargs, ) -> Union[ TensorStructType, Tuple[TensorStructType, List[TensorType], Dict[str, TensorType]], ]: """Computes an action for the specified policy on the local worker. Note that you can also access the policy object through self.get_policy(policy_id) and call compute_single_action() on it directly. Args: observation: Single (unbatched) observation from the environment. state: List of all RNN hidden (single, unbatched) state tensors. prev_action: Single (unbatched) previous action value. prev_reward: Single (unbatched) previous reward value. info: Env info dict, if any. input_dict: An optional SampleBatch that holds all the values for: obs, state, prev_action, and prev_reward, plus maybe custom defined views of the current env trajectory. Note that only one of `obs` or `input_dict` must be non-None. policy_id: Policy to query (only applies to multi-agent). Default: "default_policy". full_fetch: Whether to return extra action fetch results. This is always set to True if `state` is specified. explore: Whether to apply exploration to the action. Default: None -> use self.config["explore"]. timestep: The current (sampling) time step. episode: This provides access to all of the internal episodes' state, which may be useful for model-based or multi-agent algorithms. unsquash_action: Should actions be unsquashed according to the env's/Policy's action space? If None, use the value of self.config["normalize_actions"]. clip_action: Should actions be clipped according to the env's/Policy's action space? If None, use the value of self.config["clip_actions"]. Keyword Args: kwargs: forward compatibility placeholder Returns: The computed action if full_fetch=False, or a tuple of a) the full output of policy.compute_actions() if full_fetch=True or we have an RNN-based Policy. Raises: KeyError: If the `policy_id` cannot be found in this Trainer's local worker. """ if clip_actions != DEPRECATED_VALUE: deprecation_warning( old="Trainer.compute_single_action(`clip_actions`=...)", new="Trainer.compute_single_action(`clip_action`=...)", error=True, ) clip_action = clip_actions if unsquash_actions != DEPRECATED_VALUE: deprecation_warning( old="Trainer.compute_single_action(`unsquash_actions`=...)", new="Trainer.compute_single_action(`unsquash_action`=...)", error=True, ) unsquash_action = unsquash_actions # `unsquash_action` is None: Use value of config['normalize_actions']. if unsquash_action is None: unsquash_action = self.config["normalize_actions"] # `clip_action` is None: Use value of config['clip_actions']. elif clip_action is None: clip_action = self.config["clip_actions"] # User provided an input-dict: Assert that `obs`, `prev_a|r`, `state` # are all None. err_msg = ( "Provide either `input_dict` OR [`observation`, ...] as " "args to Trainer.compute_single_action!" ) if input_dict is not None: assert ( observation is None and prev_action is None and prev_reward is None and state is None ), err_msg observation = input_dict[SampleBatch.OBS] else: assert observation is not None, err_msg # Get the policy to compute the action for (in the multi-agent case, # Trainer may hold >1 policies). policy = self.get_policy(policy_id) if policy is None: raise KeyError( f"PolicyID '{policy_id}' not found in PolicyMap of the " f"Trainer's local worker!" ) local_worker = self.workers.local_worker() # Check the preprocessor and preprocess, if necessary. pp = local_worker.preprocessors[policy_id] if pp and type(pp).__name__ != "NoPreprocessor": observation = pp.transform(observation) observation = local_worker.filters[policy_id](observation, update=False) # Input-dict. if input_dict is not None: input_dict[SampleBatch.OBS] = observation action, state, extra = policy.compute_single_action( input_dict=input_dict, explore=explore, timestep=timestep, episode=episode, ) # Individual args. else: action, state, extra = policy.compute_single_action( obs=observation, state=state, prev_action=prev_action, prev_reward=prev_reward, info=info, explore=explore, timestep=timestep, episode=episode, ) # If we work in normalized action space (normalize_actions=True), # we re-translate here into the env's action space. if unsquash_action: action = space_utils.unsquash_action(action, policy.action_space_struct) # Clip, according to env's action space. elif clip_action: action = space_utils.clip_action(action, policy.action_space_struct) # Return 3-Tuple: Action, states, and extra-action fetches. if state or full_fetch: return action, state, extra # Ensure backward compatibility. else: return action @PublicAPI def compute_actions( self, observations: TensorStructType, state: Optional[List[TensorStructType]] = None, *, prev_action: Optional[TensorStructType] = None, prev_reward: Optional[TensorStructType] = None, info: Optional[EnvInfoDict] = None, policy_id: PolicyID = DEFAULT_POLICY_ID, full_fetch: bool = False, explore: Optional[bool] = None, timestep: Optional[int] = None, episodes: Optional[List[Episode]] = None, unsquash_actions: Optional[bool] = None, clip_actions: Optional[bool] = None, # Deprecated. normalize_actions=None, **kwargs, ): """Computes an action for the specified policy on the local Worker. Note that you can also access the policy object through self.get_policy(policy_id) and call compute_actions() on it directly. Args: observation: Observation from the environment. state: RNN hidden state, if any. If state is not None, then all of compute_single_action(...) is returned (computed action, rnn state(s), logits dictionary). Otherwise compute_single_action(...)[0] is returned (computed action). prev_action: Previous action value, if any. prev_reward: Previous reward, if any. info: Env info dict, if any. policy_id: Policy to query (only applies to multi-agent). full_fetch: Whether to return extra action fetch results. This is always set to True if RNN state is specified. explore: Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep: The current (sampling) time step. episodes: This provides access to all of the internal episodes' state, which may be useful for model-based or multi-agent algorithms. unsquash_actions: Should actions be unsquashed according to the env's/Policy's action space? If None, use self.config["normalize_actions"]. clip_actions: Should actions be clipped according to the env's/Policy's action space? If None, use self.config["clip_actions"]. Keyword Args: kwargs: forward compatibility placeholder Returns: The computed action if full_fetch=False, or a tuple consisting of the full output of policy.compute_actions_from_input_dict() if full_fetch=True or we have an RNN-based Policy. """ if normalize_actions is not None: deprecation_warning( old="Trainer.compute_actions(`normalize_actions`=...)", new="Trainer.compute_actions(`unsquash_actions`=...)", error=True, ) unsquash_actions = normalize_actions # `unsquash_actions` is None: Use value of config['normalize_actions']. if unsquash_actions is None: unsquash_actions = self.config["normalize_actions"] # `clip_actions` is None: Use value of config['clip_actions']. elif clip_actions is None: clip_actions = self.config["clip_actions"] # Preprocess obs and states. state_defined = state is not None policy = self.get_policy(policy_id) filtered_obs, filtered_state = [], [] for agent_id, ob in observations.items(): worker = self.workers.local_worker() preprocessed = worker.preprocessors[policy_id].transform(ob) filtered = worker.filters[policy_id](preprocessed, update=False) filtered_obs.append(filtered) if state is None: continue elif agent_id in state: filtered_state.append(state[agent_id]) else: filtered_state.append(policy.get_initial_state()) # Batch obs and states obs_batch = np.stack(filtered_obs) if state is None: state = [] else: state = list(zip(*filtered_state)) state = [np.stack(s) for s in state] input_dict = {SampleBatch.OBS: obs_batch} # prev_action and prev_reward can be None, np.ndarray, or tensor-like structure. # Explicitly check for None here to avoid the error message "The truth value of # an array with more than one element is ambiguous.", when np arrays are passed # as arguments. if prev_action is not None: input_dict[SampleBatch.PREV_ACTIONS] = prev_action if prev_reward is not None: input_dict[SampleBatch.PREV_REWARDS] = prev_reward if info: input_dict[SampleBatch.INFOS] = info for i, s in enumerate(state): input_dict[f"state_in_{i}"] = s # Batch compute actions actions, states, infos = policy.compute_actions_from_input_dict( input_dict=input_dict, explore=explore, timestep=timestep, episodes=episodes, ) # Unbatch actions for the environment into a multi-agent dict. single_actions = space_utils.unbatch(actions) actions = {} for key, a in zip(observations, single_actions): # If we work in normalized action space (normalize_actions=True), # we re-translate here into the env's action space. if unsquash_actions: a = space_utils.unsquash_action(a, policy.action_space_struct) # Clip, according to env's action space. elif clip_actions: a = space_utils.clip_action(a, policy.action_space_struct) actions[key] = a # Unbatch states into a multi-agent dict. unbatched_states = {} for idx, agent_id in enumerate(observations): unbatched_states[agent_id] = [s[idx] for s in states] # Return only actions or full tuple if state_defined or full_fetch: return actions, unbatched_states, infos else: return actions @PublicAPI def get_policy(self, policy_id: PolicyID = DEFAULT_POLICY_ID) -> Policy: """Return policy for the specified id, or None. Args: policy_id: ID of the policy to return. """ return self.workers.local_worker().get_policy(policy_id) @PublicAPI def get_weights(self, policies: Optional[List[PolicyID]] = None) -> dict: """Return a dictionary of policy ids to weights. Args: policies: Optional list of policies to return weights for, or None for all policies. """ return self.workers.local_worker().get_weights(policies) @PublicAPI def set_weights(self, weights: Dict[PolicyID, dict]): """Set policy weights by policy id. Args: weights: Map of policy ids to weights to set. """ self.workers.local_worker().set_weights(weights) @PublicAPI def add_policy( self, policy_id: PolicyID, policy_cls: Optional[Type[Policy]] = None, policy: Optional[Policy] = None, *, observation_space: Optional[gym.spaces.Space] = None, action_space: Optional[gym.spaces.Space] = None, config: Optional[PartialAlgorithmConfigDict] = None, policy_state: Optional[PolicyState] = None, policy_mapping_fn: Optional[Callable[[AgentID, EpisodeID], PolicyID]] = None, policies_to_train: Optional[ Union[ Container[PolicyID], Callable[[PolicyID, Optional[SampleBatchType]], bool], ] ] = None, evaluation_workers: bool = True, workers: Optional[List[Union[RolloutWorker, ActorHandle]]] = None, ) -> Optional[Policy]: """Adds a new policy to this Algorithm. Args: policy_id: ID of the policy to add. IMPORTANT: Must not contain characters that are also not allowed in Unix/Win filesystems, such as: `<>:"/\|?*` or a dot `.` or space ` ` at the end of the ID. policy_cls: The Policy class to use for constructing the new Policy. Note: Only one of `policy_cls` or `policy` must be provided. policy: The Policy instance to add to this algorithm. If not None, the given Policy object will be directly inserted into the Algorithm's local worker and clones of that Policy will be created on all remote workers as well as all evaluation workers. Note: Only one of `policy_cls` or `policy` must be provided. observation_space: The observation space of the policy to add. If None, try to infer this space from the environment. action_space: The action space of the policy to add. If None, try to infer this space from the environment. config: The config overrides for the policy to add. policy_state: Optional state dict to apply to the new policy instance, right after its construction. policy_mapping_fn: An optional (updated) policy mapping function to use from here on. Note that already ongoing episodes will not change their mapping but will use the old mapping till the end of the episode. policies_to_train: An optional list of policy IDs to be trained or a callable taking PolicyID and SampleBatchType and returning a bool (trainable or not?). If None, will keep the existing setup in place. Policies, whose IDs are not in the list (or for which the callable returns False) will not be updated. evaluation_workers: Whether to add the new policy also to the evaluation WorkerSet. workers: A list of RolloutWorker/ActorHandles (remote RolloutWorkers) to add this policy to. If defined, will only add the given policy to these workers. Returns: The newly added policy (the copy that got added to the local worker). If `workers` was provided, None is returned. """ validate_policy_id(policy_id, error=True) # Worker list is explicitly provided -> Use only those workers (local or remote) # specified. if workers is not None: # Call static utility method. WorkerSet.add_policy_to_workers( workers, policy_id, policy_cls, policy, observation_space=observation_space, action_space=action_space, config=config, policy_state=policy_state, policy_mapping_fn=policy_mapping_fn, policies_to_train=policies_to_train, ) # Add to all our regular RolloutWorkers and maybe also all evaluation workers. else: self.workers.add_policy( policy_id, policy_cls, policy, observation_space=observation_space, action_space=action_space, config=config, policy_state=policy_state, policy_mapping_fn=policy_mapping_fn, policies_to_train=policies_to_train, ) # Add to evaluation workers, if necessary. if evaluation_workers is True and self.evaluation_workers is not None: self.evaluation_workers.add_policy( policy_id, policy_cls, policy, observation_space=observation_space, action_space=action_space, config=config, policy_state=policy_state, policy_mapping_fn=policy_mapping_fn, policies_to_train=policies_to_train, ) # Return newly added policy (from the local rollout worker). return self.get_policy(policy_id) @PublicAPI def remove_policy( self, policy_id: PolicyID = DEFAULT_POLICY_ID, *, policy_mapping_fn: Optional[Callable[[AgentID], PolicyID]] = None, policies_to_train: Optional[ Union[ Container[PolicyID], Callable[[PolicyID, Optional[SampleBatchType]], bool], ] ] = None, evaluation_workers: bool = True, ) -> None: """Removes a new policy from this Algorithm. Args: policy_id: ID of the policy to be removed. policy_mapping_fn: An optional (updated) policy mapping function to use from here on. Note that already ongoing episodes will not change their mapping but will use the old mapping till the end of the episode. policies_to_train: An optional list of policy IDs to be trained or a callable taking PolicyID and SampleBatchType and returning a bool (trainable or not?). If None, will keep the existing setup in place. Policies, whose IDs are not in the list (or for which the callable returns False) will not be updated. evaluation_workers: Whether to also remove the policy from the evaluation WorkerSet. """ def fn(worker): worker.remove_policy( policy_id=policy_id, policy_mapping_fn=policy_mapping_fn, policies_to_train=policies_to_train, ) self.workers.foreach_worker(fn) if evaluation_workers and self.evaluation_workers is not None: self.evaluation_workers.foreach_worker(fn) @DeveloperAPI def export_policy_model( self, export_dir: str, policy_id: PolicyID = DEFAULT_POLICY_ID, onnx: Optional[int] = None, ) -> None: """Exports policy model with given policy_id to a local directory. Args: export_dir: Writable local directory. policy_id: Optional policy id to export. onnx: If given, will export model in ONNX format. The value of this parameter set the ONNX OpSet version to use. If None, the output format will be DL framework specific. Example: >>> from ray.rllib.algorithms.ppo import PPO >>> # Use an Algorithm from RLlib or define your own. >>> algo = PPO(...) # doctest: +SKIP >>> for _ in range(10): # doctest: +SKIP >>> algo.train() # doctest: +SKIP >>> algo.export_policy_model("/tmp/dir") # doctest: +SKIP >>> algo.export_policy_model("/tmp/dir/onnx", onnx=1) # doctest: +SKIP """ self.get_policy(policy_id).export_model(export_dir, onnx) @DeveloperAPI def export_policy_checkpoint( self, export_dir: str, filename_prefix=DEPRECATED_VALUE, # deprecated arg, do not use anymore policy_id: PolicyID = DEFAULT_POLICY_ID, ) -> None: """Exports Policy checkpoint to a local directory and returns an AIR Checkpoint. Args: export_dir: Writable local directory to store the AIR Checkpoint information into. policy_id: Optional policy ID to export. If not provided, will export "default_policy". If `policy_id` does not exist in this Algorithm, will raise a KeyError. Raises: KeyError if `policy_id` cannot be found in this Algorithm. Example: >>> from ray.rllib.algorithms.ppo import PPO >>> # Use an Algorithm from RLlib or define your own. >>> algo = PPO(...) # doctest: +SKIP >>> for _ in range(10): # doctest: +SKIP >>> algo.train() # doctest: +SKIP >>> algo.export_policy_checkpoint("/tmp/export_dir") # doctest: +SKIP """ # `filename_prefix` should not longer be used as new Policy checkpoints # contain more than one file with a fixed filename structure. if filename_prefix != DEPRECATED_VALUE: deprecation_warning( old="Algorithm.export_policy_checkpoint(filename_prefix=...)", error=True, ) policy = self.get_policy(policy_id) if policy is None: raise KeyError(f"Policy with ID {policy_id} not found in Algorithm!") policy.export_checkpoint(export_dir) @DeveloperAPI def import_policy_model_from_h5( self, import_file: str, policy_id: PolicyID = DEFAULT_POLICY_ID, ) -> None: """Imports a policy's model with given policy_id from a local h5 file. Args: import_file: The h5 file to import from. policy_id: Optional policy id to import into. Example: >>> from ray.rllib.algorithms.ppo import PPO >>> algo = PPO(...) # doctest: +SKIP >>> algo.import_policy_model_from_h5("/tmp/weights.h5") # doctest: +SKIP >>> for _ in range(10): # doctest: +SKIP >>> algo.train() # doctest: +SKIP """ self.get_policy(policy_id).import_model_from_h5(import_file) # Sync new weights to remote workers. self._sync_weights_to_workers(worker_set=self.workers) @override(Trainable) def save_checkpoint(self, checkpoint_dir: str) -> str: """Exports AIR Checkpoint to a local directory and returns its directory path. The structure of an Algorithm checkpoint dir will be as follows:: policies/ pol_1/ policy_state.pkl pol_2/ policy_state.pkl rllib_checkpoint.json algorithm_state.pkl Note: `rllib_checkpoint.json` contains a "version" key (e.g. with value 0.1) helping RLlib to remain backward compatible wrt. restoring from checkpoints from Ray 2.0 onwards. Args: checkpoint_dir: The directory where the checkpoint files will be stored. Returns: The path to the created AIR Checkpoint directory. """ state = self.__getstate__() # Extract policy states from worker state (Policies get their own # checkpoint sub-dirs). policy_states = {} if "worker" in state and "policy_states" in state["worker"]: policy_states = state["worker"].pop("policy_states", {}) # Add RLlib checkpoint version. state["checkpoint_version"] = CHECKPOINT_VERSION # Write state (w/o policies) to disk. state_file = os.path.join(checkpoint_dir, "algorithm_state.pkl") with open(state_file, "wb") as f: pickle.dump(state, f) # Write rllib_checkpoint.json. with open(os.path.join(checkpoint_dir, "rllib_checkpoint.json"), "w") as f: json.dump( { "type": "Algorithm", "checkpoint_version": str(state["checkpoint_version"]), "ray_version": ray.__version__, "ray_commit": ray.__commit__, }, f, ) # Write individual policies to disk, each in their own sub-directory. for pid, policy_state in policy_states.items(): # From here on, disallow policyIDs that would not work as directory names. validate_policy_id(pid, error=True) policy_dir = os.path.join(checkpoint_dir, "policies", pid) os.makedirs(policy_dir, exist_ok=True) policy = self.get_policy(pid) policy.export_checkpoint(policy_dir, policy_state=policy_state) return checkpoint_dir @override(Trainable) def load_checkpoint(self, checkpoint: Union[Dict, str]) -> None: # Checkpoint is provided as a directory name. # Restore from the checkpoint file or dir. if isinstance(checkpoint, str): checkpoint_info = get_checkpoint_info(checkpoint) checkpoint_data = Algorithm._checkpoint_info_to_algorithm_state( checkpoint_info ) # Checkpoint is a checkpoint-as-dict -> Restore state from it as-is. else: checkpoint_data = checkpoint self.__setstate__(checkpoint_data) @override(Trainable) def log_result(self, result: ResultDict) -> None: # Log after the callback is invoked, so that the user has a chance # to mutate the result. # TODO: Remove `trainer` arg at some point to fully deprecate the old signature. self.callbacks.on_train_result(algorithm=self, result=result) # Then log according to Trainable's logging logic. Trainable.log_result(self, result) @override(Trainable) def cleanup(self) -> None: # Stop all workers. if hasattr(self, "workers") and self.workers is not None: self.workers.stop() if hasattr(self, "evaluation_workers") and self.evaluation_workers is not None: self.evaluation_workers.stop() @OverrideToImplementCustomLogic @classmethod @override(Trainable) def default_resource_request( cls, config: PartialAlgorithmConfigDict ) -> Union[Resources, PlacementGroupFactory]: # Default logic for RLlib Algorithms: # Create one bundle per individual worker (local or remote). # Use `num_cpus_for_driver` and `num_gpus` for the local worker and # `num_cpus_per_worker` and `num_gpus_per_worker` for the remote # workers to determine their CPU/GPU resource needs. # Convenience config handles. cf = dict(cls.get_default_config(), **config) eval_cf = cf["evaluation_config"] local_worker = { "CPU": cf["num_cpus_for_driver"], "GPU": 0 if cf["_fake_gpus"] else cf["num_gpus"], } rollout_workers = [ { "CPU": cf["num_cpus_per_worker"], "GPU": cf["num_gpus_per_worker"], **cf["custom_resources_per_worker"], } for _ in range(cf["num_workers"]) ] bundles = [local_worker] + rollout_workers if cf["evaluation_interval"]: # Evaluation workers. # Note: The local eval worker is located on the driver CPU. bundles += [ { "CPU": eval_cf.get( "num_cpus_per_worker", cf["num_cpus_per_worker"] ), "GPU": eval_cf.get( "num_gpus_per_worker", cf["num_gpus_per_worker"] ), **eval_cf.get( "custom_resources_per_worker", cf["custom_resources_per_worker"] ), } for _ in range(cf["evaluation_num_workers"]) ] # In case our I/O reader/writer requires conmpute resources. bundles += get_offline_io_resource_bundles(cf) # Return PlacementGroupFactory containing all needed resources # (already properly defined as device bundles). return PlacementGroupFactory( bundles=bundles, strategy=config.get("placement_strategy", "PACK"), ) @DeveloperAPI def _before_evaluate(self): """Pre-evaluation callback.""" pass @staticmethod def _get_env_id_and_creator( env_specifier: Union[str, EnvType, None], config: PartialAlgorithmConfigDict ) -> Tuple[Optional[str], EnvCreator]: """Returns env_id and creator callable given original env id from config. Args: env_specifier: An env class, an already tune registered env ID, a known gym env name, or None (if no env is used). config: The Algorithm's (maybe partial) config dict. Returns: Tuple consisting of a) env ID string and b) env creator callable. """ # Environment is specified via a string. if isinstance(env_specifier, str): # An already registered env. if _global_registry.contains(ENV_CREATOR, env_specifier): return env_specifier, _global_registry.get(ENV_CREATOR, env_specifier) # A class path specifier. elif "." in env_specifier: def env_creator_from_classpath(env_context): try: env_obj = from_config(env_specifier, env_context) except ValueError: raise EnvError( ERR_MSG_INVALID_ENV_DESCRIPTOR.format(env_specifier) ) return env_obj return env_specifier, env_creator_from_classpath # Try gym/PyBullet/Vizdoom. else: return env_specifier, functools.partial( _gym_env_creator, env_descriptor=env_specifier ) elif isinstance(env_specifier, type): env_id = env_specifier # .__name__ if config.get("remote_worker_envs"): # Check gym version (0.22 or higher?). # If > 0.21, can't perform auto-wrapping of the given class as this # would lead to a pickle error. gym_version = pkg_resources.get_distribution("gym").version if version.parse(gym_version) >= version.parse("0.22"): raise ValueError( "Cannot specify a gym.Env class via `config.env` while setting " "`config.remote_worker_env=True` AND your gym version is >= " "0.22! Try installing an older version of gym or set `config." "remote_worker_env=False`." ) @ray.remote(num_cpus=1) class _wrapper(env_specifier): # Add convenience `_get_spaces` and `_is_multi_agent` # methods: def _get_spaces(self): return self.observation_space, self.action_space def _is_multi_agent(self): from ray.rllib.env.multi_agent_env import MultiAgentEnv return isinstance(self, MultiAgentEnv) return env_id, lambda cfg: _wrapper.remote(cfg) else: return env_id, lambda cfg: env_specifier(cfg) # No env -> Env creator always returns None. elif env_specifier is None: return None, lambda env_config: None else: raise ValueError( "{} is an invalid env specifier. ".format(env_specifier) + "You can specify a custom env as either a class " '(e.g., YourEnvCls) or a registered env id (e.g., "your_env").' ) def _sync_filters_if_needed( self, from_worker: RolloutWorker, workers: WorkerSet, timeout_seconds: Optional[float] = None, ): if ( from_worker and self.config.get("observation_filter", "NoFilter") != "NoFilter" ): FilterManager.synchronize( from_worker.filters, workers.remote_workers(), update_remote=self.config["synchronize_filters"], timeout_seconds=timeout_seconds, ) logger.debug("synchronized filters: {}".format(from_worker.filters)) @DeveloperAPI def _sync_weights_to_workers( self, *, worker_set: Optional[WorkerSet] = None, workers: Optional[List[RolloutWorker]] = None, ) -> None: """Sync "main" weights to given WorkerSet or list of workers.""" assert worker_set is not None # Broadcast the new policy weights to all evaluation workers. logger.info("Synchronizing weights to workers.") weights = ray.put(self.workers.local_worker().get_state()) worker_set.foreach_worker(lambda w: w.set_state(ray.get(weights))) @classmethod @override(Trainable) def resource_help(cls, config: AlgorithmConfigDict) -> str: return ( "\n\nYou can adjust the resource requests of RLlib agents by " "setting `num_workers`, `num_gpus`, and other configs. See " "the DEFAULT_CONFIG defined by each agent for more info.\n\n" "The config of this agent is: {}".format(config) ) @classmethod def merge_trainer_configs( cls, config1: AlgorithmConfigDict, config2: PartialAlgorithmConfigDict, _allow_unknown_configs: Optional[bool] = None, ) -> AlgorithmConfigDict: """Merges a complete Algorithm config dict with a partial override dict. Respects nested structures within the config dicts. The values in the partial override dict take priority. Args: config1: The complete Algorithm's dict to be merged (overridden) with `config2`. config2: The partial override config dict to merge on top of `config1`. _allow_unknown_configs: If True, keys in `config2` that don't exist in `config1` are allowed and will be added to the final config. Returns: The merged full algorithm config dict. """ config1 = copy.deepcopy(config1) if "callbacks" in config2 and type(config2["callbacks"]) is dict: deprecation_warning( "callbacks dict interface", "a class extending rllib.algorithms.callbacks.DefaultCallbacks; " "see `rllib/examples/custom_metrics_and_callbacks.py` for an example.", error=True, ) if _allow_unknown_configs is None: _allow_unknown_configs = cls._allow_unknown_configs return deep_update( config1, config2, _allow_unknown_configs, cls._allow_unknown_subkeys, cls._override_all_subkeys_if_type_changes, cls._override_all_key_list, ) @staticmethod def validate_framework(config: PartialAlgorithmConfigDict) -> None: """Validates the config dictionary wrt the framework settings. Args: config: The config dictionary to be validated. """ _tf1, _tf, _tfv = None, None, None _torch = None framework = config["framework"] tf_valid_frameworks = {"tf", "tf2", "tfe"} if framework not in tf_valid_frameworks and framework != "torch": return elif framework in tf_valid_frameworks: _tf1, _tf, _tfv = try_import_tf() else: _torch, _ = try_import_torch() def check_if_correct_nn_framework_installed(): """Check if tf/torch experiment is running and tf/torch installed.""" if framework in tf_valid_frameworks: if not (_tf1 or _tf): raise ImportError( ( "TensorFlow was specified as the 'framework' " "inside of your config dictionary. However, there was " "no installation found. You can install TensorFlow " "via `pip install tensorflow`" ) ) elif framework == "torch": if not _torch: raise ImportError( ( "PyTorch was specified as the 'framework' inside " "of your config dictionary. However, there was no " "installation found. You can install PyTorch via " "`pip install torch`" ) ) def resolve_tf_settings(): """Check and resolve tf settings.""" if _tf1 and config["framework"] in ["tf2", "tfe"]: if config["framework"] == "tf2" and _tfv < 2: raise ValueError( "You configured `framework`=tf2, but your installed " "pip tf-version is < 2.0! Make sure your TensorFlow " "version is >= 2.x." ) if not _tf1.executing_eagerly(): _tf1.enable_eager_execution() # Recommend setting tracing to True for speedups. logger.info( f"Executing eagerly (framework='{config['framework']}')," f" with eager_tracing={config['eager_tracing']}. For " "production workloads, make sure to set eager_tracing=True" " in order to match the speed of tf-static-graph " "(framework='tf'). For debugging purposes, " "`eager_tracing=False` is the best choice." ) # Tf-static-graph (framework=tf): Recommend upgrading to tf2 and # enabling eager tracing for similar speed. elif _tf1 and config["framework"] == "tf": logger.info( "Your framework setting is 'tf', meaning you are using " "static-graph mode. Set framework='tf2' to enable eager " "execution with tf2.x. You may also then want to set " "eager_tracing=True in order to reach similar execution " "speed as with static-graph mode." ) check_if_correct_nn_framework_installed() resolve_tf_settings() @OverrideToImplementCustomLogic_CallToSuperRecommended @DeveloperAPI def validate_config(self, config: AlgorithmConfigDict) -> None: """Validates a given config dict for this Algorithm. Users should override this method to implement custom validation behavior. It is recommended to call `super().validate_config()` in this override. Args: config: The given config dict to check. Raises: ValueError: If there is something wrong with the config. """ model_config = config.get("model") if model_config is None: config["model"] = model_config = {} # Use DefaultCallbacks class, if callbacks is None. if config["callbacks"] is None: config["callbacks"] = DefaultCallbacks # Check, whether given `callbacks` is a callable. if not callable(config["callbacks"]): raise ValueError( "`callbacks` must be a callable method that " "returns a subclass of DefaultCallbacks, got " f"{config['callbacks']}!" ) # Multi-GPU settings. simple_optim_setting = config.get("simple_optimizer", DEPRECATED_VALUE) if simple_optim_setting != DEPRECATED_VALUE: deprecation_warning(old="simple_optimizer", error=False) # Validate "multiagent" sub-dict and convert policy 4-tuples to # PolicySpec objects. policies, is_multi_agent = check_multi_agent(config) framework = config.get("framework") # Multi-GPU setting: Must use MultiGPUTrainOneStep. if config.get("num_gpus", 0) > 1: if framework in ["tfe", "tf2"]: raise ValueError( "`num_gpus` > 1 not supported yet for " "framework={}!".format(framework) ) elif simple_optim_setting is True: raise ValueError( "Cannot use `simple_optimizer` if `num_gpus` > 1! " "Consider not setting `simple_optimizer` in your config." ) config["simple_optimizer"] = False # Auto-setting: Use simple-optimizer for tf-eager or multiagent, # otherwise: MultiGPUTrainOneStep (if supported by the algo's execution # plan). elif simple_optim_setting == DEPRECATED_VALUE: # tf-eager: Must use simple optimizer. if framework not in ["tf", "torch"]: config["simple_optimizer"] = True # Multi-agent case: Try using MultiGPU optimizer (only # if all policies used are DynamicTFPolicies or TorchPolicies). elif is_multi_agent: from ray.rllib.policy.dynamic_tf_policy import DynamicTFPolicy from ray.rllib.policy.torch_policy import TorchPolicy default_policy_cls = self.get_default_policy_class(config) if any( (p.policy_class or default_policy_cls) is None or not issubclass( p.policy_class or default_policy_cls, (DynamicTFPolicy, TorchPolicy), ) for p in config["multiagent"]["policies"].values() ): config["simple_optimizer"] = True else: config["simple_optimizer"] = False else: config["simple_optimizer"] = False # User manually set simple-optimizer to False -> Error if tf-eager. elif simple_optim_setting is False: if framework in ["tfe", "tf2"]: raise ValueError( "`simple_optimizer=False` not supported for " "framework={}!".format(framework) ) # Check model config. # If no preprocessing, propagate into model's config as well # (so model will know, whether inputs are preprocessed or not). if config["_disable_preprocessor_api"] is True: model_config["_disable_preprocessor_api"] = True # If no action flattening, propagate into model's config as well # (so model will know, whether action inputs are already flattened or # not). if config["_disable_action_flattening"] is True: model_config["_disable_action_flattening"] = True # Prev_a/r settings. prev_a_r = model_config.get("lstm_use_prev_action_reward", DEPRECATED_VALUE) if prev_a_r != DEPRECATED_VALUE: deprecation_warning( "model.lstm_use_prev_action_reward", "model.lstm_use_prev_action and model.lstm_use_prev_reward", error=True, ) model_config["lstm_use_prev_action"] = prev_a_r model_config["lstm_use_prev_reward"] = prev_a_r # Check batching/sample collection settings. if config["batch_mode"] not in ["truncate_episodes", "complete_episodes"]: raise ValueError( "`batch_mode` must be one of [truncate_episodes|" "complete_episodes]! Got {}".format(config["batch_mode"]) ) # Store multi-agent batch count mode. self._by_agent_steps = ( self.config["multiagent"].get("count_steps_by") == "agent_steps" ) # Metrics settings. if ( config.get("metrics_smoothing_episodes", DEPRECATED_VALUE) != DEPRECATED_VALUE ): deprecation_warning( old="metrics_smoothing_episodes", new="metrics_num_episodes_for_smoothing", error=True, ) config["metrics_num_episodes_for_smoothing"] = config[ "metrics_smoothing_episodes" ] if config.get("min_iter_time_s", DEPRECATED_VALUE) != DEPRECATED_VALUE: deprecation_warning( old="min_iter_time_s", new="min_time_s_per_iteration", error=True, ) config["min_time_s_per_iteration"] = config["min_iter_time_s"] or 0 if config.get("min_time_s_per_reporting", DEPRECATED_VALUE) != DEPRECATED_VALUE: deprecation_warning( old="min_time_s_per_reporting", new="min_time_s_per_iteration", error=True, ) config["min_time_s_per_iteration"] = config["min_time_s_per_reporting"] or 0 if ( config.get("min_sample_timesteps_per_reporting", DEPRECATED_VALUE) != DEPRECATED_VALUE ): deprecation_warning( old="min_sample_timesteps_per_reporting", new="min_sample_timesteps_per_iteration", error=True, ) config["min_sample_timesteps_per_iteration"] = ( config["min_sample_timesteps_per_reporting"] or 0 ) if ( config.get("min_train_timesteps_per_reporting", DEPRECATED_VALUE) != DEPRECATED_VALUE ): deprecation_warning( old="min_train_timesteps_per_reporting", new="min_train_timesteps_per_iteration", error=True, ) config["min_train_timesteps_per_iteration"] = ( config["min_train_timesteps_per_reporting"] or 0 ) if config.get("collect_metrics_timeout", DEPRECATED_VALUE) != DEPRECATED_VALUE: # TODO: Warn once all algos use the `training_iteration` method. # deprecation_warning( # old="collect_metrics_timeout", # new="metrics_episode_collection_timeout_s", # error=False, # ) config["metrics_episode_collection_timeout_s"] = config[ "collect_metrics_timeout" ] if config.get("timesteps_per_iteration", DEPRECATED_VALUE) != DEPRECATED_VALUE: deprecation_warning( old="timesteps_per_iteration", new="`min_sample_timesteps_per_iteration` OR " "`min_train_timesteps_per_iteration`", error=True, ) config["min_sample_timesteps_per_iteration"] = ( config["timesteps_per_iteration"] or 0 ) config["timesteps_per_iteration"] = DEPRECATED_VALUE # Evaluation settings. # Deprecated setting: `evaluation_num_episodes`. if config.get("evaluation_num_episodes", DEPRECATED_VALUE) != DEPRECATED_VALUE: deprecation_warning( old="evaluation_num_episodes", new="`evaluation_duration` and `evaluation_duration_unit=episodes`", error=True, ) config["evaluation_duration"] = config["evaluation_num_episodes"] config["evaluation_duration_unit"] = "episodes" config["evaluation_num_episodes"] = DEPRECATED_VALUE # If `evaluation_num_workers` > 0, warn if `evaluation_interval` is # None (also set `evaluation_interval` to 1). if config["evaluation_num_workers"] > 0 and not config["evaluation_interval"]: logger.warning( f"You have specified {config['evaluation_num_workers']} " "evaluation workers, but your `evaluation_interval` is None! " "Therefore, evaluation will not occur automatically with each" " call to `Algorithm.train()`. Instead, you will have to call " "`Algorithm.evaluate()` manually in order to trigger an " "evaluation run." ) # If `evaluation_num_workers=0` and # `evaluation_parallel_to_training=True`, warn that you need # at least one remote eval worker for parallel training and # evaluation, and set `evaluation_parallel_to_training` to False. elif config["evaluation_num_workers"] == 0 and config.get( "evaluation_parallel_to_training", False ): logger.warning( "`evaluation_parallel_to_training` can only be done if " "`evaluation_num_workers` > 0! Setting " "`evaluation_parallel_to_training` to False." ) config["evaluation_parallel_to_training"] = False # If `evaluation_duration=auto`, error if # `evaluation_parallel_to_training=False`. if config["evaluation_duration"] == "auto": if not config["evaluation_parallel_to_training"]: raise ValueError( "`evaluation_duration=auto` not supported for " "`evaluation_parallel_to_training=False`!" ) # Make sure, it's an int otherwise. elif ( not isinstance(config["evaluation_duration"], int) or config["evaluation_duration"] <= 0 ): raise ValueError( "`evaluation_duration` ({}) must be an int and " ">0!".format(config["evaluation_duration"]) ) @staticmethod @ExperimentalAPI def validate_env(env: EnvType, env_context: EnvContext) -> None: """Env validator function for this Algorithm class. Override this in child classes to define custom validation behavior. Args: env: The (sub-)environment to validate. This is normally a single sub-environment (e.g. a gym.Env) within a vectorized setup. env_context: The EnvContext to configure the environment. Raises: Exception in case something is wrong with the given environment. """ pass def try_recover_from_step_attempt(self, error, worker_set, ignore, recreate) -> int: """Try to identify and remove any unhealthy workers (incl. eval workers). This method is called after an unexpected remote error is encountered from a worker during the call to `self.step()`. It issues check requests to all current workers and removes any that respond with error. If no healthy workers remain, an error is raised. Returns: The number of remote workers recreated. """ # @ray.remote RolloutWorker failure. if isinstance(error, RayError): # Try to recover w/o the failed worker. if ignore or recreate: logger.exception( "Error in training or evaluation attempt! Trying to recover." ) # Error out. else: logger.warning( "Worker crashed during training or evaluation! " "To try to continue without failed " "worker(s), set `ignore_worker_failures=True`. " "To try to recover the failed worker(s), set " "`recreate_failed_workers=True`." ) raise error # Any other exception. else: # Allow logs messages to propagate. time.sleep(0.5) raise error removed_workers, new_workers = [], [] # Search for failed workers and try to recover (restart) them. if recreate: removed_workers, new_workers = worker_set.recreate_failed_workers( local_worker_for_synching=self.workers.local_worker() ) elif ignore: removed_workers = worker_set.remove_failed_workers() # If `worker_set` is the main training WorkerSet: `self.workers`. if worker_set is getattr(self, "workers", None): # Call the `on_worker_failures` callback. self.on_worker_failures(removed_workers, new_workers) # Recreate execution_plan iterator. if not self.config.get("_disable_execution_plan_api") and callable( self.execution_plan ): logger.warning("Recreating execution plan after failure") self.train_exec_impl = self.execution_plan( worker_set, self.config, **self._kwargs_for_execution_plan() ) elif self._evaluation_async_req_manager is not None and worker_set is getattr( self, "evaluation_workers", None ): self._evaluation_async_req_manager.remove_workers(removed_workers) self._evaluation_async_req_manager.add_workers(new_workers) return len(new_workers) def on_worker_failures( self, removed_workers: List[ActorHandle], new_workers: List[ActorHandle] ): """Called after a worker failure is detected. Args: removed_workers: List of removed workers. new_workers: List of new workers. """ pass @override(Trainable) def _export_model( self, export_formats: List[str], export_dir: str ) -> Dict[str, str]: ExportFormat.validate(export_formats) exported = {} if ExportFormat.CHECKPOINT in export_formats: path = os.path.join(export_dir, ExportFormat.CHECKPOINT) self.export_policy_checkpoint(path) exported[ExportFormat.CHECKPOINT] = path if ExportFormat.MODEL in export_formats: path = os.path.join(export_dir, ExportFormat.MODEL) self.export_policy_model(path) exported[ExportFormat.MODEL] = path if ExportFormat.ONNX in export_formats: path = os.path.join(export_dir, ExportFormat.ONNX) self.export_policy_model(path, onnx=int(os.getenv("ONNX_OPSET", "11"))) exported[ExportFormat.ONNX] = path return exported def import_model(self, import_file: str): """Imports a model from import_file. Note: Currently, only h5 files are supported. Args: import_file: The file to import the model from. Returns: A dict that maps ExportFormats to successfully exported models. """ # Check for existence. if not os.path.exists(import_file): raise FileNotFoundError( "`import_file` '{}' does not exist! Can't import Model.".format( import_file ) ) # Get the format of the given file. import_format = "h5" # TODO(sven): Support checkpoint loading. ExportFormat.validate([import_format]) if import_format != ExportFormat.H5: raise NotImplementedError else: return self.import_policy_model_from_h5(import_file) @PublicAPI def __getstate__(self) -> Dict: """Returns current state of Algorithm, sufficient to restore it from scratch. Returns: The current state dict of this Algorithm, which can be used to sufficiently restore the algorithm from scratch without any other information. """ # Add config to state so complete Algorithm can be reproduced w/o it. state = { "algorithm_class": type(self), "config": self.config, } if hasattr(self, "workers"): state["worker"] = self.workers.local_worker().get_state() # TODO: Experimental functionality: Store contents of replay buffer # to checkpoint, only if user has configured this. if self.local_replay_buffer is not None and self.config.get( "store_buffer_in_checkpoints" ): state["local_replay_buffer"] = self.local_replay_buffer.get_state() if self.train_exec_impl is not None: state["train_exec_impl"] = self.train_exec_impl.shared_metrics.get().save() else: state["counters"] = self._counters return state @PublicAPI def __setstate__(self, state) -> None: """Sets the algorithm to the provided state. Args: state: The state dict to restore this Algorithm instance to. `state` may have been returned by a call to an Algorithm's `__getstate__()` method. """ # TODO (sven): Validate that our config and the config in state are compatible. # For example, the model architectures may differ. # Also, what should the behavior be if e.g. some training parameter # (e.g. lr) changed? if hasattr(self, "workers") and "worker" in state: self.workers.local_worker().set_state(state["worker"]) remote_state = ray.put(state["worker"]) for r in self.workers.remote_workers(): r.set_state.remote(remote_state) if self.evaluation_workers: # If evaluation workers are used, also restore the policies # there in case they are used for evaluation purpose. for r in self.evaluation_workers.remote_workers(): r.set_state.remote(remote_state) # If necessary, restore replay data as well. if self.local_replay_buffer is not None: # TODO: Experimental functionality: Restore contents of replay # buffer from checkpoint, only if user has configured this. if self.config.get("store_buffer_in_checkpoints"): if "local_replay_buffer" in state: self.local_replay_buffer.set_state(state["local_replay_buffer"]) else: logger.warning( "`store_buffer_in_checkpoints` is True, but no replay " "data found in state!" ) elif "local_replay_buffer" in state and log_once( "no_store_buffer_in_checkpoints_but_data_found" ): logger.warning( "`store_buffer_in_checkpoints` is False, but some replay " "data found in state!" ) if self.train_exec_impl is not None: self.train_exec_impl.shared_metrics.get().restore(state["train_exec_impl"]) elif "counters" in state: self._counters = state["counters"] @staticmethod def _checkpoint_info_to_algorithm_state( checkpoint_info: dict, policy_ids: Optional[Container[PolicyID]] = None, policy_mapping_fn: Optional[Callable[[AgentID, EpisodeID], PolicyID]] = None, policies_to_train: Optional[ Union[ Container[PolicyID], Callable[[PolicyID, Optional[SampleBatchType]], bool], ] ] = None, ) -> Dict: """Converts a checkpoint info or object to a proper Algorithm state dict. The returned state dict can be used inside self.__setstate__(). Args: checkpoint_info: A checkpoint info dict as returned by `ray.rllib.utils.checkpoints.get_checkpoint_info( [checkpoint dir or AIR Checkpoint])`. policy_ids: Optional list/set of PolicyIDs. If not None, only those policies listed here will be included in the returned state. Note that state items such as filters, the `is_policy_to_train` function, as well as the multi-agent `policy_ids` dict will be adjusted as well, based on this arg. policy_mapping_fn: An optional (updated) policy mapping function to include in the returned state. policies_to_train: An optional list of policy IDs to be trained or a callable taking PolicyID and SampleBatchType and returning a bool (trainable or not?) to include in the returned state. Returns: The state dict usable within the `self.__setstate__()` method. """ if checkpoint_info["type"] != "Algorithm": raise ValueError( "`checkpoint` arg passed to " "`Algorithm._checkpoint_info_to_algorithm_state()` must be an " f"Algorithm checkpoint (but is {checkpoint_info['type']})!" ) with open(checkpoint_info["state_file"], "rb") as f: state = pickle.load(f) # New checkpoint format: Policies are in separate sub-dirs. # Note: Algorithms like ES/ARS don't have a WorkerSet, so we just return # the plain state here. if ( checkpoint_info["checkpoint_version"] > version.Version("0.1") and state.get("worker") is not None ): worker_state = state["worker"] # Retrieve the set of all required policy IDs. policy_ids = set( policy_ids if policy_ids is not None else worker_state["policy_ids"] ) # Remove those policies entirely from filters that are not in # `policy_ids`. worker_state["filters"] = { pid: filter for pid, filter in worker_state["filters"].items() if pid in policy_ids } # Remove policies from multiagent dict that are not in `policy_ids`. policies_dict = state["config"]["multiagent"]["policies"] policies_dict = { pid: spec for pid, spec in policies_dict.items() if pid in policy_ids } state["config"]["multiagent"]["policies"] = policies_dict # Prepare local `worker` state to add policies' states into it, # read from separate policy checkpoint files. worker_state["policy_states"] = {} for pid in policy_ids: policy_state_file = os.path.join( checkpoint_info["checkpoint_dir"], "policies", pid, "policy_state.pkl", ) if not os.path.isfile(policy_state_file): raise ValueError( "Given checkpoint does not seem to be valid! No policy " f"state file found for PID={pid}. " f"The file not found is: {policy_state_file}." ) with open(policy_state_file, "rb") as f: worker_state["policy_states"][pid] = pickle.load(f) if policy_mapping_fn is not None: worker_state["policy_mapping_fn"] = policy_mapping_fn if policies_to_train is not None: worker_state["is_policy_to_train"] = policies_to_train return state @DeveloperAPI def _create_local_replay_buffer_if_necessary( self, config: PartialAlgorithmConfigDict ) -> Optional[MultiAgentReplayBuffer]: """Create a MultiAgentReplayBuffer instance if necessary. Args: config: Algorithm-specific configuration data. Returns: MultiAgentReplayBuffer instance based on algorithm config. None, if local replay buffer is not needed. """ if not config.get("replay_buffer_config") or config["replay_buffer_config"].get( "no_local_replay_buffer" or config.get("no_local_replay_buffer") ): return buffer_type = config["replay_buffer_config"]["type"] return from_config(buffer_type, config["replay_buffer_config"]) @DeveloperAPI def _kwargs_for_execution_plan(self): kwargs = {} if self.local_replay_buffer is not None: kwargs["local_replay_buffer"] = self.local_replay_buffer return kwargs def _run_one_training_iteration(self) -> Tuple[ResultDict, "TrainIterCtx"]: """Runs one training iteration (self.iteration will be +1 after this). Calls `self.training_step()` repeatedly until the minimum time (sec), sample- or training steps have been reached. Returns: The results dict from the training iteration. """ # In case we are training (in a thread) parallel to evaluation, # we may have to re-enable eager mode here (gets disabled in the # thread). if ( self.config.get("framework") in ["tf2", "tfe"] and not tf.executing_eagerly() ): tf1.enable_eager_execution() results = None # Create a step context ... with TrainIterCtx(algo=self) as train_iter_ctx: # .. so we can query it whether we should stop the iteration loop (e.g. # when we have reached `min_time_s_per_iteration`). num_recreated = 0 while not train_iter_ctx.should_stop(results): # Try to train one step. try: # TODO (avnishn): Remove the execution plan API by q1 2023 with self._timers[TRAINING_ITERATION_TIMER]: if self.config["_disable_execution_plan_api"]: results = self.training_step() else: results = next(self.train_exec_impl) # In case of any failures, try to ignore/recover the failed workers. except Exception as e: num_recreated += self.try_recover_from_step_attempt( error=e, worker_set=self.workers, ignore=self.config["ignore_worker_failures"], recreate=self.config["recreate_failed_workers"], ) results["num_recreated_workers"] = num_recreated return results, train_iter_ctx def _run_one_evaluation( self, train_future: Optional[concurrent.futures.ThreadPoolExecutor] = None, ) -> ResultDict: """Runs evaluation step via `self.evaluate()` and handling worker failures. Args: train_future: In case, we are training and avaluating in parallel, this arg carries the currently running ThreadPoolExecutor object that runs the training iteration Returns: The results dict from the evaluation call. """ eval_results = { "evaluation": { "episode_reward_max": np.nan, "episode_reward_min": np.nan, "episode_reward_mean": np.nan, } } eval_func_to_use = ( self._evaluate_async if self.config["enable_async_evaluation"] else self.evaluate ) num_recreated = 0 try: if self.config["evaluation_duration"] == "auto": assert ( train_future is not None and self.config["evaluation_parallel_to_training"] ) unit = self.config["evaluation_duration_unit"] eval_results = eval_func_to_use( duration_fn=functools.partial( self._automatic_evaluation_duration_fn, unit, self.config["evaluation_num_workers"], self.config["evaluation_config"], train_future, ) ) # Run `self.evaluate()` only once per training iteration. else: eval_results = eval_func_to_use() # In case of any failures, try to ignore/recover the failed evaluation workers. except Exception as e: num_recreated = self.try_recover_from_step_attempt( error=e, worker_set=self.evaluation_workers, ignore=self.config["evaluation_config"].get("ignore_worker_failures"), recreate=self.config["evaluation_config"].get( "recreate_failed_workers" ), ) # `self._evaluate_async` handles its own worker failures and already adds # this metric, but `self.evaluate` doesn't. if "num_recreated_workers" not in eval_results["evaluation"]: eval_results["evaluation"]["num_recreated_workers"] = num_recreated # Add number of healthy evaluation workers after this iteration. eval_results["evaluation"]["num_healthy_workers"] = ( len(self.evaluation_workers.remote_workers()) if self.evaluation_workers is not None else 0 ) return eval_results def _run_one_training_iteration_and_evaluation_in_parallel( self, ) -> Tuple[ResultDict, "TrainIterCtx"]: """Runs one training iteration and one evaluation step in parallel. First starts the training iteration (via `self._run_one_training_iteration()`) within a ThreadPoolExecutor, then runs the evaluation step in parallel. In auto-duration mode (config.evaluation_duration=auto), makes sure the evaluation step takes roughly the same time as the training iteration. Returns: The accumulated training and evaluation results. """ with concurrent.futures.ThreadPoolExecutor() as executor: train_future = executor.submit(lambda: self._run_one_training_iteration()) # Pass the train_future into `self._run_one_evaluation()` to allow it # to run exactly as long as the training iteration takes in case # evaluation_duration=auto. results = self._run_one_evaluation(train_future) # Collect the training results from the future. train_results, train_iter_ctx = train_future.result() results.update(train_results) return results, train_iter_ctx @staticmethod def _automatic_evaluation_duration_fn( unit, num_eval_workers, eval_cfg, train_future, num_units_done ): # Training is done and we already ran at least one # evaluation -> Nothing left to run. if num_units_done > 0 and train_future.done(): return 0 # Count by episodes. -> Run n more # (n=num eval workers). elif unit == "episodes": return num_eval_workers # Count by timesteps. -> Run n*m*p more # (n=num eval workers; m=rollout fragment length; # p=num-envs-per-worker). else: return ( num_eval_workers * eval_cfg["rollout_fragment_length"] * eval_cfg["num_envs_per_worker"] ) def _compile_iteration_results( self, *, episodes_this_iter, step_ctx, iteration_results=None ): # Return dict. results: ResultDict = {} iteration_results = iteration_results or {} # Evaluation results. if "evaluation" in iteration_results: results["evaluation"] = iteration_results.pop("evaluation") # Custom metrics and episode media. results["custom_metrics"] = iteration_results.pop("custom_metrics", {}) results["episode_media"] = iteration_results.pop("episode_media", {}) results["num_recreated_workers"] = iteration_results.pop( "num_recreated_workers", 0 ) # Learner info. results["info"] = {LEARNER_INFO: iteration_results} # Calculate how many (if any) of older, historical episodes we have to add to # `episodes_this_iter` in order to reach the required smoothing window. episodes_for_metrics = episodes_this_iter[:] missing = self.config["metrics_num_episodes_for_smoothing"] - len( episodes_this_iter ) # We have to add some older episodes to reach the smoothing window size. if missing > 0: episodes_for_metrics = self._episode_history[-missing:] + episodes_this_iter assert ( len(episodes_for_metrics) <= self.config["metrics_num_episodes_for_smoothing"] ) # Note that when there are more than `metrics_num_episodes_for_smoothing` # episodes in `episodes_for_metrics`, leave them as-is. In this case, we'll # compute the stats over that larger number. # Add new episodes to our history and make sure it doesn't grow larger than # needed. self._episode_history.extend(episodes_this_iter) self._episode_history = self._episode_history[ -self.config["metrics_num_episodes_for_smoothing"] : ] results["sampler_results"] = summarize_episodes( episodes_for_metrics, episodes_this_iter, self.config["keep_per_episode_custom_metrics"], ) # TODO: Don't dump sampler results into top-level. results.update(results["sampler_results"]) results["num_healthy_workers"] = len(self.workers.remote_workers()) # Train-steps- and env/agent-steps this iteration. for c in [ NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, ]: results[c] = self._counters[c] if self._by_agent_steps: results[NUM_AGENT_STEPS_SAMPLED + "_this_iter"] = step_ctx.sampled results[NUM_AGENT_STEPS_TRAINED + "_this_iter"] = step_ctx.trained # TODO: For CQL and other algos, count by trained steps. results["timesteps_total"] = self._counters[NUM_AGENT_STEPS_SAMPLED] # TODO: Backward compatibility. results[STEPS_TRAINED_THIS_ITER_COUNTER] = step_ctx.trained else: results[NUM_ENV_STEPS_SAMPLED + "_this_iter"] = step_ctx.sampled results[NUM_ENV_STEPS_TRAINED + "_this_iter"] = step_ctx.trained # TODO: For CQL and other algos, count by trained steps. results["timesteps_total"] = self._counters[NUM_ENV_STEPS_SAMPLED] # TODO: Backward compatibility. results[STEPS_TRAINED_THIS_ITER_COUNTER] = step_ctx.trained # TODO: Backward compatibility. results["agent_timesteps_total"] = self._counters[NUM_AGENT_STEPS_SAMPLED] # Process timer results. timers = {} for k, timer in self._timers.items(): timers["{}_time_ms".format(k)] = round(timer.mean * 1000, 3) if timer.has_units_processed(): timers["{}_throughput".format(k)] = round(timer.mean_throughput, 3) results["timers"] = timers # Process counter results. counters = {} for k, counter in self._counters.items(): counters[k] = counter results["counters"] = counters # TODO: Backward compatibility. results["info"].update(counters) return results def __repr__(self): return type(self).__name__ def _record_usage(self, config): """Record the framework and algorithm used. Args: config: Algorithm config dict. """ record_extra_usage_tag(TagKey.RLLIB_FRAMEWORK, config["framework"]) record_extra_usage_tag(TagKey.RLLIB_NUM_WORKERS, str(config["num_workers"])) alg = self.__class__.__name__ # We do not want to collect user defined algorithm names. if alg not in ALL_ALGORITHMS: alg = "USER_DEFINED" record_extra_usage_tag(TagKey.RLLIB_ALGORITHM, alg) @Deprecated(new="Algorithm.compute_single_action()", error=True) def compute_action(self, *args, **kwargs): return self.compute_single_action(*args, **kwargs) @Deprecated(new="construct WorkerSet(...) instance directly", error=False) def _make_workers( self, *, env_creator: EnvCreator, validate_env: Optional[Callable[[EnvType, EnvContext], None]], policy_class: Type[Policy], config: AlgorithmConfigDict, num_workers: int, local_worker: bool = True, ) -> WorkerSet: return WorkerSet( env_creator=env_creator, validate_env=validate_env, policy_class=policy_class, trainer_config=config, num_workers=num_workers, local_worker=local_worker, logdir=self.logdir, ) @staticmethod @Deprecated(new="Algorithm.validate_config()", error=True) def _validate_config(config, trainer_or_none): assert trainer_or_none is not None return trainer_or_none.validate_config(config) # TODO: Create a dict that throw a deprecation warning once we have fully moved # to AlgorithmConfig() objects (some algos still missing). COMMON_CONFIG: AlgorithmConfigDict = AlgorithmConfig(Algorithm).to_dict() class TrainIterCtx: def __init__(self, algo: Algorithm): self.algo = algo def __enter__(self): # Before first call to `step()`, `results` is expected to be None -> # Start with self.failures=-1 -> set to 0 before the very first call # to `self.step()`. self.failures = -1 self.time_start = time.time() self.sampled = 0 self.trained = 0 self.init_env_steps_sampled = self.algo._counters[NUM_ENV_STEPS_SAMPLED] self.init_env_steps_trained = self.algo._counters[NUM_ENV_STEPS_TRAINED] self.init_agent_steps_sampled = self.algo._counters[NUM_AGENT_STEPS_SAMPLED] self.init_agent_steps_trained = self.algo._counters[NUM_AGENT_STEPS_TRAINED] self.failure_tolerance = self.algo.config[ "num_consecutive_worker_failures_tolerance" ] return self def __exit__(self, *args): pass def should_stop(self, results): # Before first call to `step()`. if results is None: # Fail after n retries. self.failures += 1 if self.failures > self.failure_tolerance: raise RuntimeError( "More than `num_consecutive_worker_failures_tolerance=" f"{self.failure_tolerance}` consecutive worker failures! " "Exiting." ) # Continue to very first `step()` call or retry `step()` after # a (tolerable) failure. return False # Stopping criteria: Only when using the `training_iteration` # API, b/c for the `exec_plan` API, the logic to stop is # already built into the execution plans via the # `StandardMetricsReporting` op. elif self.algo.config["_disable_execution_plan_api"]: if self.algo._by_agent_steps: self.sampled = ( self.algo._counters[NUM_AGENT_STEPS_SAMPLED] - self.init_agent_steps_sampled ) self.trained = ( self.algo._counters[NUM_AGENT_STEPS_TRAINED] - self.init_agent_steps_trained ) else: self.sampled = ( self.algo._counters[NUM_ENV_STEPS_SAMPLED] - self.init_env_steps_sampled ) self.trained = ( self.algo._counters[NUM_ENV_STEPS_TRAINED] - self.init_env_steps_trained ) min_t = self.algo.config["min_time_s_per_iteration"] min_sample_ts = self.algo.config["min_sample_timesteps_per_iteration"] min_train_ts = self.algo.config["min_train_timesteps_per_iteration"] # Repeat if not enough time has passed or if not enough # env|train timesteps have been processed (or these min # values are not provided by the user). if ( (not min_t or time.time() - self.time_start >= min_t) and (not min_sample_ts or self.sampled >= min_sample_ts) and (not min_train_ts or self.trained >= min_train_ts) ): return True else: return False # No errors (we got results != None) -> Return True # (meaning: yes, should stop -> no further step attempts). else: return True

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2023-04-22T10:22:44.763102

2021-05-13T17:27:22

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N = int(input()) #入力する整数 A = list(map(int,input().split())) #入力する数列A SUMA = sum(A) #数列の和 MOD = 10**9 + 7 # mod C = [0] * (N-1) #累積和数列 for i in range(N-1): #\sum_{j = i+1}^{N}を求めて数列に代入する SUMA -= A[i] C[i] = SUMA ans = 0 #求める答え for i in range(N-1): ans += A[i]*C[i] ans %= MOD #その都度modで割った余りにする print(ans) #答えを出力する

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66529651+Aastha2104@users.noreply.github.com