1 assert all(0.5 <=\",\n \"assert code == 200 args, _ = client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry\",\n \"self.make_request_stream( method, abs_url, headers, data ) assert code == 200 # Here we\",\n \"1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\", 1)\",\n \"stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1\",\n \"client.request_with_retries(\\\"get\\\", url, {}, None) assert code == 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future\",\n \"json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\" class ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name]\",\n \"'{\\\"foo\\\": \\\"baz\\\"}', 200) for method in VALID_API_METHODS: abs_url = self.valid_url data = \\\"\\\"\",\n \"json import asyncio import stripe import urllib3 from stripe import six, util from\",\n \"def mock_error(mock, error): raise NotImplementedError( \\\"You must implement this in your test subclass\\\"\",\n \"client._add_jitter_time = lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay,\",\n \"four_xx codes.remove(409) # These status codes should not be retried by default. for\",\n \"self.assert_sleep_times(client, expected) def test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t #\",\n \"streamed content\\\", 200) abs_url = self.valid_url data = \\\"\\\" if method != \\\"post\\\":\",\n \"(\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks\",\n \"mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"true\\\"} #\",\n \"= stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) >\",\n \"@pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters def\",\n \"three_xx = list(range(300, 308)) four_xx = list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries =\",\n \"assert client._should_retry((None, 503, None), None, 0) is True def test_should_retry_on_error(self, mocker): client =\",\n \"200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await\",\n \"expected): for lib in none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst,\",\n \"client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"true\\\"} # Ordinarily,\",\n \"code == 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\":\",\n \"a 500, but with the header as false, we would not. assert client._should_retry((None,\",\n \":TODO: Write tests for tornado client pass class TestAPIEncode(StripeClientTestCase): def test_encode_dict(self): body =\",\n \"test subclass\\\" ) return mock_error @pytest.fixture def check_call(self): def check_call( mock, method, abs_url,\",\n \"503, None), None, 0) is True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries\",\n \"StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {} for lib\",\n \"headers, post_data ) @pytest.fixture def mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( \\\"You\",\n \"409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big values assert 1 == client._sleep_time_seconds( 2,\",\n \"data, headers) def test_request_stream( self, mocker, request_mock, mock_response, check_call ): for method in\",\n \"import absolute_import, division, print_function import pytest import json import asyncio import stripe import\",\n \"200) for method in VALID_API_METHODS: abs_url = self.valid_url data = \\\"\\\" if method\",\n \"import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES\",\n \"def mock_error(self): def mock_error(mock, error): raise NotImplementedError( \\\"You must implement this in your\",\n \"client._should_retry((None, 503, None), None, 0) is True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client()\",\n \"\\\"%s?%s\\\" % (abs_url, data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} body,\",\n \"request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def make_request(self, method, url,\",\n \"{\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for t in stripe.api_requestor._api_encode(body)]\",\n \"NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return mock_error @pytest.fixture\",\n \"409, None), None, max_test_retries + 1) is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def\",\n \"assert code == 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200,\",\n \"in dir(self)) stream, code, _ = self.make_request_stream( method, abs_url, headers, data ) assert\",\n \"% (path,) def make_request(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method,\",\n \"headers = {\\\"stripe-should-retry\\\": \\\"true\\\"} # Ordinarily, we would not retry a 400, but\",\n \"in your test subclass\\\" ) return mock_error @pytest.fixture def check_call(self): def check_call( mock,\",\n \"max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay, max_delay] self.assert_sleep_times(client, expected) def\",\n \"0) is False assert client._should_retry((None, 400, headers), None, 0) is True def test_should_retry_on_stripe_should_retry_false(self,\",\n \"headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in dir(self)) stream, code, _ =\",\n \"response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _,\",\n \"def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200, 209)) three_xx = list(range(300,\",\n \"six, util from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\",\",\n \"expected == actual @contextmanager def mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value\",\n \"be retried by default. for code in codes: assert client._should_retry((None, code, None), None,\",\n \"True assert client._should_retry((None, 503, None), None, 0) is True def test_should_retry_on_error(self, mocker): client\",\n \"False def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda:\",\n \"url, headers, post_data) async def make_request_stream(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True)\",\n \"[0.5, 1.0, 2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda\",\n \"url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data\",\n \"stripe import six, util from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS =\",\n \"jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200, 209))\",\n \"class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield\",\n \"lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay, max_delay]\",\n \"!= \\\"post\\\": abs_url = \\\"%s?%s\\\" % (abs_url, data) data = None headers =\",\n \"code): raise NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return\",\n \"method, abs_url, headers, params, is_streaming=False ): raise NotImplementedError( \\\"You must implement this in\",\n \"TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES =\",\n \"jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1 assert\",\n \"def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"]\",\n \"t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1) in values assert (\\\"foo[name]\\\", \\\"bat\\\") in values\",\n \"must implement this in your test subclass\\\" ) return mock_error @pytest.fixture def check_call(self):\",\n \"stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters def check_default(self, none_libs, expected): for lib in\",\n \"> 1 assert all(0.5 <= val <= 1 for val in jittered_ones) class\",\n \"await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def mock_response(self): def mock_response(mock, body,\",\n \"Write tests for tornado client pass class TestAPIEncode(StripeClientTestCase): def test_encode_dict(self): body = {\\\"foo\\\":\",\n \"import pytest import json import asyncio import stripe import urllib3 from stripe import\",\n \"test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries api_connection_error\",\n \"orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def\",\n \"None) response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request = mocker.MagicMock( return_value=response_future )\",\n \"@pytest.fixture def mock_error(self): def mock_error(mock, error): raise NotImplementedError( \\\"You must implement this in\",\n \"1}, \\\"name\\\": \\\"bat\\\"}} values = [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1)\",\n \"list(range(100, 104)) two_xx = list(range(200, 209)) three_xx = list(range(300, 308)) four_xx = list(range(400,\",\n \"(abs_url, data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} body, code, _\",\n \"check_call( mock, method, abs_url, headers, params, is_streaming=False ): raise NotImplementedError( \\\"You must implement\",\n \"def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters def check_default(self,\",\n \"None, 0) is True assert client._should_retry((None, 503, None), None, 0) is True def\",\n \"with the header as true, we would. assert client._should_retry((None, 400, {}), None, 0)\",\n \"test_encode_dict(self): body = {\\\"foo\\\": {\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values = [t for\",\n \"1 ) is False ) assert ( client._should_retry((None, 409, None), None, max_test_retries +\",\n \"headers, data ) assert code == 200 # Here we need to convert\",\n \"stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t\",\n \"TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry\",\n \"test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"false\\\"}\",\n \"client._should_retry(None, api_connection_error, 0) is False def test_should_retry_on_stripe_should_retry_true(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries =\",\n \"assert client._should_retry(None, api_connection_error, 0) is False def test_should_retry_on_stripe_should_retry_true(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries\",\n \"# These status codes should be retried by default. assert client._should_retry((None, 409, None),\",\n \"client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 api_connection_error = mocker.Mock() api_connection_error.should_retry = True\",\n \"= lambda t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY * random_value with self.mock_max_delay(10):\",\n \"False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry =\",\n \"1) in values assert (\\\"foo[name]\\\", \\\"bat\\\") in values def test_encode_array(self): body = {\\\"foo\\\":\",\n \"raise NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return check_call\",\n \"{\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries(\\\"get\\\",\",\n \"for code in codes: assert client._should_retry((None, code, None), None, 0) is False #\",\n \"== 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}])\",\n \"t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client =\",\n \"409, {\\\"retry-after\\\": \\\"300\\\"}) ) def test_randomness_added(self): client = stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time\",\n \"async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object):\",\n \"code == 200 assert body == '{\\\"foo\\\": \\\"baz\\\"}' check_call(request_mock, method, abs_url, data, headers)\",\n \"self.assert_sleep_times(client, [initial_delay] * 5) def test_maximum_delay(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t:\",\n \"test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t # Prefer retry-after if\",\n \"method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method, url, headers, post_data) async\",\n \"TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado client pass class TestAPIEncode(StripeClientTestCase): def test_encode_dict(self):\",\n \"pytest.raises(stripe.error.APIConnectionError): self.make_request(\\\"get\\\", self.valid_url, {}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado\",\n \"= None headers = {\\\"my-header\\\": \\\"header val\\\"} body, code, _ = self.make_request(method, abs_url,\",\n \"original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value\",\n \") is False ) assert ( client._should_retry((None, 409, None), None, max_test_retries + 1)\",\n \"Here we need to convert and align all content on one type (string)\",\n \"= {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for t in\",\n \"# Prefer default if it's bigger assert 2 == client._sleep_time_seconds( 3, (None, 409,\",\n \"import asyncio import stripe import urllib3 from stripe import six, util from async_stripe.http_client\",\n \"] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _:\",\n \"method in VALID_API_METHODS: mock_response(request_mock, \\\"some streamed content\\\", 200) abs_url = self.valid_url data =\",\n \"assert_sleep_times(self, client, expected): until = len(expected) actual = list( map(lambda i: client._sleep_time_seconds(i +\",\n \"209)) three_xx = list(range(300, 308)) four_xx = list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries\",\n \"stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0,\",\n \"convert and align all content on one type (string) # as some clients\",\n \"{}, None) assert code == 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future = asyncio.Future()\",\n \"retry-after if it's bigger assert 30 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"})\",\n \"request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return\",\n \"assert len(jittered_ones) > 1 assert all(0.5 <= val <= 1 for val in\",\n \"valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def make_request(self, method, url, headers, post_data): client\",\n \"we need to convert and align all content on one type (string) #\",\n \"method != \\\"post\\\": abs_url = \\\"%s?%s\\\" % (abs_url, data) data = None headers\",\n \"def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t * 0.001 initial_delay\",\n \"400, but with the header as true, we would. assert client._should_retry((None, 400, {}),\",\n \"headers, post_data) async def make_request_stream(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return\",\n \"for method in VALID_API_METHODS: mock_response(request_mock, \\\"some streamed content\\\", 200) abs_url = self.valid_url data\",\n \"= body_content.decode(\\\"utf-8\\\") assert body_content == \\\"some streamed content\\\" mocker.resetall() def test_exception(self, request_mock, mock_error):\",\n \"16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda\",\n \"request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def\",\n \"0) is False # These status codes should be retried by default. assert\",\n \"+ 1), range(until)) ) assert expected == actual @contextmanager def mock_max_delay(self, new_value): original_value\",\n \"check_default(self, none_libs, expected): for lib in none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client()\",\n \"client._add_jitter_time = lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0, 8.0])\",\n \"map(lambda i: client._sleep_time_seconds(i + 1), range(until)) ) assert expected == actual @contextmanager def\",\n \"= (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker):\",\n \"orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url =\",\n \"some clients return a string stream others a byte stream. body_content = stream.read()\",\n \"expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value * 4, base_value * 8,\",\n \"0) is True assert client._should_retry((None, 500, None), None, 0) is True assert client._should_retry((None,\",\n \"check_call def test_request(self, request_mock, mock_response, check_call): mock_response(request_mock, '{\\\"foo\\\": \\\"baz\\\"}', 200) for method in\",\n \"= lambda: 1 api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0)\",\n \"client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer default if it's bigger\",\n \"len(jittered_ones) > 1 assert all(0.5 <= val <= 1 for val in jittered_ones)\",\n \"url = \\\"http://fake.url\\\" client = TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}])\",\n \"error): raise NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return\",\n \"\\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks =\",\n \"== 200 # Here we need to convert and align all content on\",\n \"headers = {\\\"stripe-should-retry\\\": \\\"false\\\"} # Ordinarily, we would retry a 500, but with\",\n \"+ two_xx + three_xx + four_xx codes.remove(409) # These status codes should not\",\n \"None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase):\",\n \"test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for\",\n \"def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def make_request(self, method, url, headers, post_data):\",\n \"client = TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock(\",\n \"[t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\", 1) in values assert (\\\"foo[0][name]\\\", \\\"bat\\\")\",\n \"= stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t # Prefer retry-after if it's bigger\",\n \"= list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes = one_xx\",\n \"500, {}), None, 0) is True assert client._should_retry((None, 500, headers), None, 0) is\",\n \"TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters\",\n \"mock_response(request_mock, '{\\\"foo\\\": \\\"baz\\\"}', 200) for method in VALID_API_METHODS: abs_url = self.valid_url data =\",\n \"yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry =\",\n \"should be retried by default. assert client._should_retry((None, 409, None), None, 0) is True\",\n \"self.valid_url data = \\\"\\\" if method != \\\"post\\\": abs_url = \\\"%s?%s\\\" % (abs_url,\",\n \"import contextmanager def assert_sleep_times(self, client, expected): until = len(expected) actual = list( map(lambda\",\n \"return check_call def test_request(self, request_mock, mock_response, check_call): mock_response(request_mock, '{\\\"foo\\\": \\\"baz\\\"}', 200) for method\",\n \"== client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer default if it's\",\n \"= mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry =\",\n \"assert 2 == client._sleep_time_seconds( 3, (None, 409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big\",\n \"client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def mock_response(self): def mock_response(mock, body, code):\",\n \"= asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code,\",\n \"is True assert client._should_retry((None, 503, None), None, 0) is True def test_should_retry_on_error(self, mocker):\",\n \"client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry = False assert client._should_retry(None, api_connection_error, 0) is\",\n \"values assert 1 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"300\\\"}) ) def test_randomness_added(self):\",\n \"None, 0) is False def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10\",\n \"url, {}, None) assert code == 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future =\",\n \"\\\"bat\\\") in values def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]}\",\n \"= {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self,\",\n \"stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes = one_xx + two_xx + three_xx +\",\n \"\\\"You must implement this in your test subclass\\\" ) return mock_error @pytest.fixture def\",\n \"be retried by default. assert client._should_retry((None, 409, None), None, 0) is True assert\",\n \"return mock_response @pytest.fixture def mock_error(self): def mock_error(mock, error): raise NotImplementedError( \\\"You must implement\",\n \"stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class\",\n \"method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers,\",\n \"post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method, url, headers, post_data) async def make_request_stream(self, method,\",\n \"two_xx + three_xx + four_xx codes.remove(409) # These status codes should not be\",\n \"def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,)\",\n \"NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return check_call def\",\n \"stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import\",\n \"431)) client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes = one_xx + two_xx\",\n \"random_value = 0.8 client._add_jitter_time = lambda t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY\",\n \"codes.remove(409) # These status codes should not be retried by default. for code\",\n \"in none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self):\",\n \"retry a 500, but with the header as false, we would not. assert\",\n \"map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1 assert all(0.5 <= val\",\n \"# Prefer retry-after if it's bigger assert 30 == client._sleep_time_seconds( 2, (None, 409,\",\n \"two_xx = list(range(200, 209)) three_xx = list(range(300, 308)) four_xx = list(range(400, 431)) client\",\n \"= one_xx + two_xx + three_xx + four_xx codes.remove(409) # These status codes\",\n \"we would. assert client._should_retry((None, 400, {}), None, 0) is False assert client._should_retry((None, 400,\",\n \"we would not retry a 400, but with the header as true, we\",\n \"stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"true\\\"} # Ordinarily, we would\",\n \"headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data )\",\n \"400, {}), None, 0) is False assert client._should_retry((None, 400, headers), None, 0) is\",\n \"import json import asyncio import stripe import urllib3 from stripe import six, util\",\n \"code == 200 # Here we need to convert and align all content\",\n \"default. for code in codes: assert client._should_retry((None, code, None), None, 0) is False\",\n \"stream others a byte stream. body_content = stream.read() if hasattr(body_content, \\\"decode\\\"): body_content =\",\n \"if hasattr(body_content, \\\"decode\\\"): body_content = body_content.decode(\\\"utf-8\\\") assert body_content == \\\"some streamed content\\\" mocker.resetall()\",\n \"\\\"some streamed content\\\", 200) abs_url = self.valid_url data = \\\"\\\" if method !=\",\n \"try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time\",\n \"return a string stream others a byte stream. body_content = stream.read() if hasattr(body_content,\",\n \"* 16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set(\",\n \"None, 0) is True assert client._should_retry((None, 500, None), None, 0) is True assert\",\n \"True url = \\\"http://fake.url\\\" client = TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\":\",\n \"codes should be retried by default. assert client._should_retry((None, 409, None), None, 0) is\",\n \"false, we would not. assert client._should_retry((None, 500, {}), None, 0) is True assert\",\n \"none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((),\",\n \"expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100)))\",\n \"max_test_retries api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert ( client._should_retry( None, api_connection_error, max_test_retries\",\n \"bigger assert 2 == client._sleep_time_seconds( 3, (None, 409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore\",\n \"= len(expected) actual = list( map(lambda i: client._sleep_time_seconds(i + 1), range(until)) ) assert\",\n \"client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1 assert all(0.5 <= val <= 1\",\n \"is False # These status codes should be retried by default. assert client._should_retry((None,\",\n \"\\\"true\\\"} # Ordinarily, we would not retry a 400, but with the header\",\n \"= None headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in dir(self)) stream, code,\",\n \"0) is True api_connection_error.should_retry = False assert client._should_retry(None, api_connection_error, 0) is False def\",\n \"values assert (\\\"foo[name]\\\", \\\"bat\\\") in values def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\":\",\n \"original_value def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t with self.mock_max_delay(10):\",\n \"original_filters def check_default(self, none_libs, expected): for lib in none_libs: setattr(stripe.http_client, lib, None) inst\",\n \"5) def test_maximum_delay(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t max_delay =\",\n \"assert all(0.5 <= val <= 1 for val in jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def\",\n \"client._should_retry((None, 400, {}), None, 0) is False assert client._should_retry((None, 400, headers), None, 0)\",\n \"mock_response, check_call ): for method in VALID_API_METHODS: mock_response(request_mock, \\\"some streamed content\\\", 200) abs_url\",\n \"False assert client._should_retry(None, api_connection_error, 0) is False def test_should_retry_on_stripe_should_retry_true(self, mocker): client = stripe.http_client.new_default_http_client()\",\n \"# These status codes should not be retried by default. for code in\",\n \"client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def\",\n \"{} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class\",\n \"with self.mock_max_delay(10): expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value * 4, base_value\",\n \"async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url = \\\"http://fake.url\\\"\",\n \"(abs_url, data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in\",\n \"def test_request_stream( self, mocker, request_mock, mock_response, check_call ): for method in VALID_API_METHODS: mock_response(request_mock,\",\n \"pass stripe.enable_telemetry = True url = \\\"http://fake.url\\\" client = TestClient() response_future = asyncio.Future()\",\n \"REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {} for lib in\",\n \"2, base_value * 4, base_value * 8, base_value * 16, ] self.assert_sleep_times(client, expected)\",\n \"api_connection_error.should_retry = False assert client._should_retry(None, api_connection_error, 0) is False def test_should_retry_on_stripe_should_retry_true(self, mocker): client\",\n \"\\\"http://fake.url\\\" client = TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request =\",\n \"assert body == '{\\\"foo\\\": \\\"baz\\\"}' check_call(request_mock, method, abs_url, data, headers) def test_request_stream( self,\",\n \"in values assert (\\\"foo[name]\\\", \\\"bat\\\") in values def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\":\",\n \"client._should_retry((None, 500, None), None, 0) is True assert client._should_retry((None, 503, None), None, 0)\",\n \"def mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( \\\"You must implement this in\",\n \"mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries api_connection_error =\",\n \"= json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\" class ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return\",\n \"= new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client =\",\n \"* 2, base_value * 4, base_value * 8, base_value * 16, ] self.assert_sleep_times(client,\",\n \"stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"false\\\"} # Ordinarily, we would\",\n \"= lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0, 8.0]) def\",\n \"_ = self.make_request(method, abs_url, headers, data) assert code == 200 assert body ==\",\n \"= {\\\"stripe-should-retry\\\": \\\"true\\\"} # Ordinarily, we would not retry a 400, but with\",\n \"val in jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx =\",\n \"in jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200,\",\n \"def mock_response(mock, body, code): raise NotImplementedError( \\\"You must implement this in your test\",\n \"* 4, base_value * 8, base_value * 16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self):\",\n \"a byte stream. body_content = stream.read() if hasattr(body_content, \\\"decode\\\"): body_content = body_content.decode(\\\"utf-8\\\") assert\",\n \"stripe.http_client.HTTPClient.MAX_DELAY = new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client\",\n \"the header as true, we would. assert client._should_retry((None, 400, {}), None, 0) is\",\n \"False ) assert ( client._should_retry((None, 409, None), None, max_test_retries + 1) is False\",\n \"max_delay, max_delay, max_delay] self.assert_sleep_times(client, expected) def test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda\",\n \"post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture\",\n \"this in your test subclass\\\" ) return check_call def test_request(self, request_mock, mock_response, check_call):\",\n \"client._sleep_time_seconds(i + 1), range(until)) ) assert expected == actual @contextmanager def mock_max_delay(self, new_value):\",\n \"def assert_sleep_times(self, client, expected): until = len(expected) actual = list( map(lambda i: client._sleep_time_seconds(i\",\n \"True assert client._should_retry((None, 500, headers), None, 0) is False def test_should_retry_on_num_retries(self, mocker): client\",\n \"class ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return\",\n \"t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\", 1) in values assert (\\\"foo[0][name]\\\", \\\"bat\\\") in values\",\n \"in your test subclass\\\" ) return mock_response @pytest.fixture def mock_error(self): def mock_error(mock, error):\",\n \"if it's bigger assert 30 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) )\",\n \"body_content = stream.read() if hasattr(body_content, \\\"decode\\\"): body_content = body_content.decode(\\\"utf-8\\\") assert body_content == \\\"some\",\n \"True def test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers =\",\n \"def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t\",\n \"assert client._should_retry((None, 400, {}), None, 0) is False assert client._should_retry((None, 400, headers), None,\",\n \"mock, method, abs_url, headers, params, is_streaming=False ): raise NotImplementedError( \\\"You must implement this\",\n \"client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries api_connection_error = mocker.Mock()\",\n \"= stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t * 0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client,\",\n \"\\\"req_123\\\" class ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"):\",\n \"= stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"true\\\"} # Ordinarily, we\",\n \"by default. assert client._should_retry((None, 409, None), None, 0) is True assert client._should_retry((None, 500,\",\n \"new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client()\",\n \"return await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def mock_response(self): def mock_response(mock,\",\n \"0) is True assert client._should_retry((None, 503, None), None, 0) is True def test_should_retry_on_error(self,\",\n \"t: t * 0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client, [initial_delay] * 5) def test_maximum_delay(self):\",\n \"== 200 args, _ = client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"])\",\n \"1.0, 2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t:\",\n \"2 == client._sleep_time_seconds( 3, (None, 409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big values\",\n \"Ordinarily, we would not retry a 400, but with the header as true,\",\n \"= list(range(100, 104)) two_xx = list(range(200, 209)) three_xx = list(range(300, 308)) four_xx =\",\n \"class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters =\",\n \"len(expected) actual = list( map(lambda i: client._sleep_time_seconds(i + 1), range(until)) ) assert expected\",\n \"t # Prefer retry-after if it's bigger assert 30 == client._sleep_time_seconds( 2, (None,\",\n \"stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time = lambda t: t * random_value base_value =\",\n \"True assert client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry = False assert client._should_retry(None, api_connection_error,\",\n \"in your test subclass\\\" ) return check_call def test_request(self, request_mock, mock_response, check_call): mock_response(request_mock,\",\n \"= lambda: 1 codes = one_xx + two_xx + three_xx + four_xx codes.remove(409)\",\n \"{\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values = [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\",\",\n \"_, code, _ = await client.request_with_retries(\\\"get\\\", url, {}, None) assert code == 200\",\n \"{}), None, 0) is True assert client._should_retry((None, 500, headers), None, 0) is False\",\n \"client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones)\",\n \"4.0, 8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t *\",\n \"in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\" class ClientTestBase(object): @pytest.fixture def\",\n \"from stripe import six, util from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS\",\n \"self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1),\",\n \"for lib in none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected)\",\n \"Ignore crazy-big values assert 1 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"300\\\"}) )\",\n \"# Ordinarily, we would not retry a 400, but with the header as\",\n \"would not retry a 400, but with the header as true, we would.\",\n \"\\\"300\\\"}) ) def test_randomness_added(self): client = stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time = lambda\",\n \"+ 1 ) is False ) assert ( client._should_retry((None, 409, None), None, max_test_retries\",\n \"response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ =\",\n \"code, _ = await client.request_with_retries(\\\"get\\\", url, {}, None) assert code == 200 client.request.assert_called_with(\\\"get\\\",\",\n \"retry a 400, but with the header as true, we would. assert client._should_retry((None,\",\n \"body == '{\\\"foo\\\": \\\"baz\\\"}' check_call(request_mock, method, abs_url, data, headers) def test_request_stream( self, mocker,\",\n \"self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def mock_response(self): def\",\n \"with the header as false, we would not. assert client._should_retry((None, 500, {}), None,\",\n \"codes: assert client._should_retry((None, code, None), None, 0) is False # These status codes\",\n \"= set( map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1 assert all(0.5\",\n \"False # These status codes should be retried by default. assert client._should_retry((None, 409,\",\n \"[initial_delay] * 5) def test_maximum_delay(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t\",\n \"def make_request_stream(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method,\",\n \"@pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry =\",\n \"{\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries(\\\"get\\\",\",\n \"None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado client pass class TestAPIEncode(StripeClientTestCase):\",\n \"+ four_xx codes.remove(409) # These status codes should not be retried by default.\",\n \"(\\\"foo[name]\\\", \\\"bat\\\") in values def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\":\",\n \"None), None, 0) is True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries =\",\n \") _, code, _ = await client.request_with_retries(\\\"get\\\", url, {}, None) assert code ==\",\n \"client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected =\",\n \"string stream others a byte stream. body_content = stream.read() if hasattr(body_content, \\\"decode\\\"): body_content\",\n \"headers), None, 0) is False def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries =\",\n \"= pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture\",\n \"\\\"false\\\"} # Ordinarily, we would retry a 500, but with the header as\",\n \"stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1) in values assert (\\\"foo[name]\\\", \\\"bat\\\") in values def test_encode_array(self):\",\n \"These status codes should be retried by default. assert client._should_retry((None, 409, None), None,\",\n \"= client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\"\",\n \"make_request(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method, url, headers, post_data)\",\n \"mocker, request_mock, mock_response, check_call ): for method in VALID_API_METHODS: mock_response(request_mock, \\\"some streamed content\\\",\",\n \"stream, code, _ = self.make_request_stream( method, abs_url, headers, data ) assert code ==\",\n \"import urllib3 from stripe import six, util from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark =\",\n \"None), None, 0) is False # These status codes should be retried by\",\n \"1 api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0) is True\",\n \"header as false, we would not. assert client._should_retry((None, 500, {}), None, 0) is\",\n \"mock_error(self): def mock_error(mock, error): raise NotImplementedError( \\\"You must implement this in your test\",\n \"body_content.decode(\\\"utf-8\\\") assert body_content == \\\"some streamed content\\\" mocker.resetall() def test_exception(self, request_mock, mock_error): mock_error(request_mock)\",\n \"api_connection_error.should_retry = True assert ( client._should_retry( None, api_connection_error, max_test_retries + 1 ) is\",\n \"pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"]\",\n \"api_connection_error, 0) is True api_connection_error.should_retry = False assert client._should_retry(None, api_connection_error, 0) is False\",\n \"client._should_retry((None, 500, {}), None, 0) is True assert client._should_retry((None, 500, headers), None, 0)\",\n \"ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\"\",\n \") @pytest.fixture def mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( \\\"You must implement\",\n \"print(dir(self)) print(\\\"make_request_stream\\\" in dir(self)) stream, code, _ = self.make_request_stream( method, abs_url, headers, data\",\n \"def test_encode_dict(self): body = {\\\"foo\\\": {\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values = [t\",\n \"headers) def test_request_stream( self, mocker, request_mock, mock_response, check_call ): for method in VALID_API_METHODS:\",\n \"mock_error(mock, error): raise NotImplementedError( \\\"You must implement this in your test subclass\\\" )\",\n \"[ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value * 4, base_value * 8, base_value *\",\n \"= orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url\",\n \"base_value * 2, base_value * 4, base_value * 8, base_value * 16, ]\",\n \"% (abs_url, data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\"\",\n \"true, we would. assert client._should_retry((None, 400, {}), None, 0) is False assert client._should_retry((None,\",\n \"(None, 409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big values assert 1 == client._sleep_time_seconds(\",\n \"\\\"baz\\\"}' check_call(request_mock, method, abs_url, data, headers) def test_request_stream( self, mocker, request_mock, mock_response, check_call\",\n \"method, abs_url, headers, data ) assert code == 200 # Here we need\",\n \") class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False\",\n \"pytest import json import asyncio import stripe import urllib3 from stripe import six,\",\n \"setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient)\",\n \"None, 0) is True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda:\",\n \"def check_call( mock, method, abs_url, headers, params, is_streaming=False ): raise NotImplementedError( \\\"You must\",\n \"\\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in dir(self)) stream, code, _ = self.make_request_stream( method, abs_url,\",\n \"is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry\",\n \"test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t * 0.001 initial_delay =\",\n \"lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"true\\\"} # Ordinarily, we would not retry a\",\n \"mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters =\",\n \"it's bigger assert 30 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) ) #\",\n \"False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry\",\n \"class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {} for\",\n \"@pytest.fixture def request_mocks(self, mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] =\",\n \"def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100))) )\",\n \"\\\"name\\\": \\\"bat\\\"}} values = [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1) in\",\n \"class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200, 209)) three_xx\",\n \"assert client._should_retry((None, 409, None), None, 0) is True assert client._should_retry((None, 500, None), None,\",\n \"{\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker):\",\n \"lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True)\",\n \"client._sleep_time_seconds( 3, (None, 409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big values assert 1\",\n \"\\\"%s?%s\\\" % (abs_url, data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self))\",\n \"{\\\"my-header\\\": \\\"header val\\\"} body, code, _ = self.make_request(method, abs_url, headers, data) assert code\",\n \"class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager def assert_sleep_times(self, client, expected): until = len(expected)\",\n \"values = [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\", 1) in values assert\",\n \"body_content == \\\"some streamed content\\\" mocker.resetall() def test_exception(self, request_mock, mock_error): mock_error(request_mock) with pytest.raises(stripe.error.APIConnectionError):\",\n \"mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,))\",\n \"yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time =\",\n \"self.make_request(\\\"get\\\", self.valid_url, {}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado client\",\n \"type (string) # as some clients return a string stream others a byte\",\n \"base_value * 16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones =\",\n \"def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 api_connection_error = mocker.Mock()\",\n \"post_data) async def make_request_stream(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await\",\n \"response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _,\",\n \"t: t # Prefer retry-after if it's bigger assert 30 == client._sleep_time_seconds( 2,\",\n \"def mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value try: yield self finally:\",\n \"[{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for t in stripe.api_requestor._api_encode(body)] assert\",\n \"@pytest.fixture def check_call(self): def check_call( mock, method, abs_url, headers, params, is_streaming=False ): raise\",\n \"from contextlib import contextmanager def assert_sleep_times(self, client, expected): until = len(expected) actual =\",\n \"{\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big values assert 1 == client._sleep_time_seconds( 2, (None,\",\n \"request_mocks(self, mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" %\",\n \"== actual @contextmanager def mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value try:\",\n \"stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t * 0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client, [initial_delay]\",\n \"url, {}, None) assert code == 200 args, _ = client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\"\",\n \"this in your test subclass\\\" ) return mock_response @pytest.fixture def mock_error(self): def mock_error(mock,\",\n \"* 5) def test_maximum_delay(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t max_delay\",\n \"\\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {}\",\n \"def check_call(self): def check_call( mock, method, abs_url, headers, params, is_streaming=False ): raise NotImplementedError(\",\n \"data = None headers = {\\\"my-header\\\": \\\"header val\\\"} body, code, _ = self.make_request(method,\",\n \"\\\"You must implement this in your test subclass\\\" ) return mock_response @pytest.fixture def\",\n \"= lambda t: t # Prefer retry-after if it's bigger assert 30 ==\",\n \"= stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0,\",\n \"bigger assert 30 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer\",\n \"[\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib]\",\n \"t * 0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client, [initial_delay] * 5) def test_maximum_delay(self): client\",\n \"test_maximum_delay(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected\",\n \"client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"false\\\"} # Ordinarily,\",\n \"request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters\",\n \"\\\"You must implement this in your test subclass\\\" ) return check_call def test_request(self,\",\n \"VALID_API_METHODS: abs_url = self.valid_url data = \\\"\\\" if method != \\\"post\\\": abs_url =\",\n \"0) is False def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries\",\n \"stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries api_connection_error = mocker.Mock() api_connection_error.should_retry =\",\n \"abs_url, headers, data ) assert code == 200 # Here we need to\",\n \"= stripe.http_client.HTTPClient.INITIAL_DELAY * random_value with self.mock_max_delay(10): expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2,\",\n \"asyncio import stripe import urllib3 from stripe import six, util from async_stripe.http_client import\",\n \"NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return mock_response @pytest.fixture\",\n \"def request_mocks(self, mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\"\",\n \"client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries(\\\"get\\\", url, {},\",\n \"_: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1 assert all(0.5 <= val <=\",\n \"must implement this in your test subclass\\\" ) return mock_response @pytest.fixture def mock_error(self):\",\n \"\\\"header val\\\"} body, code, _ = self.make_request(method, abs_url, headers, data) assert code ==\",\n \"body, code, _ = self.make_request(method, abs_url, headers, data) assert code == 200 assert\",\n \"implement this in your test subclass\\\" ) return mock_error @pytest.fixture def check_call(self): def\",\n \"client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0,\",\n \"from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class\",\n \"status codes should not be retried by default. for code in codes: assert\",\n \"self.valid_url, {}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado client pass\",\n \"Ordinarily, we would retry a 500, but with the header as false, we\",\n \"2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer default if it's bigger assert\",\n \"= asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code,\",\n \"1 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"300\\\"}) ) def test_randomness_added(self): client =\",\n \"def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries\",\n \"response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ =\",\n \"args, _ = client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"]\",\n \") def test_randomness_added(self): client = stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time = lambda t:\",\n \"104)) two_xx = list(range(200, 209)) three_xx = list(range(300, 308)) four_xx = list(range(400, 431))\",\n \"is False assert client._should_retry((None, 400, headers), None, 0) is True def test_should_retry_on_stripe_should_retry_false(self, mocker):\",\n \"assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager\",\n \"None) assert code == 200 client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future = asyncio.Future() response_future.set_result([\\\"\\\",\",\n \"with pytest.raises(stripe.error.APIConnectionError): self.make_request(\\\"get\\\", self.valid_url, {}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for\",\n \"stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value * 4, base_value * 8, base_value * 16,\",\n \"url, {}, None) response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request = mocker.MagicMock(\",\n \"max_test_retries + 1) is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs =\",\n \"_ = client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] ==\",\n \"self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager def assert_sleep_times(self, client, expected): until\",\n \") assert len(jittered_ones) > 1 assert all(0.5 <= val <= 1 for val\",\n \"subclass\\\" ) return mock_response @pytest.fixture def mock_error(self): def mock_error(mock, error): raise NotImplementedError( \\\"You\",\n \"assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\" class ClientTestBase(object):\",\n \"body = {\\\"foo\\\": {\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values = [t for t\",\n \"* random_value with self.mock_max_delay(10): expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value *\",\n \"{}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado client pass class\",\n \"True assert ( client._should_retry( None, api_connection_error, max_test_retries + 1 ) is False )\",\n \"we would not. assert client._should_retry((None, 500, {}), None, 0) is True assert client._should_retry((None,\",\n \"False def test_should_retry_on_stripe_should_retry_true(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers =\",\n \"post_data ) @pytest.fixture def mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( \\\"You must\",\n \"= stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"false\\\"} # Ordinarily, we\",\n \"= [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1) in values assert (\\\"foo[name]\\\",\",\n \"def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client,\",\n \"assert client._should_retry((None, code, None), None, 0) is False # These status codes should\",\n \"mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url = \\\"http://fake.url\\\" client = TestClient()\",\n \"\\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\" class ClientTestBase(object): @pytest.fixture\",\n \"{\\\"foo\\\": {\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values = [t for t in stripe.api_requestor._api_encode(body)]\",\n \"max_delay] self.assert_sleep_times(client, expected) def test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t\",\n \"contextlib import contextmanager def assert_sleep_times(self, client, expected): until = len(expected) actual = list(\",\n \"implement this in your test subclass\\\" ) return check_call def test_request(self, request_mock, mock_response,\",\n \"500, headers), None, 0) is False def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries\",\n \"# Ordinarily, we would retry a 500, but with the header as false,\",\n \"not be retried by default. for code in codes: assert client._should_retry((None, code, None),\",\n \"= \\\"\\\" if method != \\\"post\\\": abs_url = \\\"%s?%s\\\" % (abs_url, data) data\",\n \"is True assert client._should_retry((None, 500, None), None, 0) is True assert client._should_retry((None, 503,\",\n \"none_libs, expected): for lib in none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert\",\n \"for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\", 1) in values assert (\\\"foo[0][name]\\\", \\\"bat\\\") in\",\n \"4, base_value * 8, base_value * 16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client\",\n \"val <= 1 for val in jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx =\",\n \"in values def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values\",\n \"one_xx = list(range(100, 104)) two_xx = list(range(200, 209)) three_xx = list(range(300, 308)) four_xx\",\n \"return client.request_with_retries(method, url, headers, post_data) async def make_request_stream(self, method, url, headers, post_data): client\",\n \"stream.read() if hasattr(body_content, \\\"decode\\\"): body_content = body_content.decode(\\\"utf-8\\\") assert body_content == \\\"some streamed content\\\"\",\n \"client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"300\\\"}) ) def test_randomness_added(self): client = stripe.http_client.new_default_http_client() random_value\",\n \"four_xx = list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes =\",\n \"header as true, we would. assert client._should_retry((None, 400, {}), None, 0) is False\",\n \"mocker.Mock() api_connection_error.should_retry = True assert ( client._should_retry( None, api_connection_error, max_test_retries + 1 )\",\n \"request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def make_request(self,\",\n \"500, but with the header as false, we would not. assert client._should_retry((None, 500,\",\n \"request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks):\",\n \"TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200, 209)) three_xx =\",\n \"1) is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry}\",\n \"method, url, headers, post_data ) @pytest.fixture def mock_response(self): def mock_response(mock, body, code): raise\",\n \"= TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future\",\n \"= self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method, url, headers, post_data) async def make_request_stream(self, method, url, headers,\",\n \"body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for t\",\n \"= stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay, max_delay] self.assert_sleep_times(client, expected) def test_retry_after_header(self):\",\n \"data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in dir(self))\",\n \"would not. assert client._should_retry((None, 500, {}), None, 0) is True assert client._should_retry((None, 500,\",\n \"None, 0) is False # These status codes should be retried by default.\",\n \"def test_randomness_added(self): client = stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time = lambda t: t\",\n \"stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0,\",\n \"max_test_retries + 1 ) is False ) assert ( client._should_retry((None, 409, None), None,\",\n \"return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def make_request(self, method,\",\n \"three_xx + four_xx codes.remove(409) # These status codes should not be retried by\",\n \"= \\\"%s?%s\\\" % (abs_url, data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"}\",\n \"8, base_value * 16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones\",\n \"test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200, 209)) three_xx = list(range(300, 308))\",\n \"\\\"req_234\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries(\\\"get\\\", url,\",\n \"original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters def check_default(self, none_libs, expected): for\",\n \"client._should_retry((None, 409, None), None, max_test_retries + 1) is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True)\",\n \"mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY\",\n \"byte stream. body_content = stream.read() if hasattr(body_content, \\\"decode\\\"): body_content = body_content.decode(\\\"utf-8\\\") assert body_content\",\n \"None), None, 0) is True assert client._should_retry((None, 503, None), None, 0) is True\",\n \"These status codes should not be retried by default. for code in codes:\",\n \"assert client._should_retry((None, 400, headers), None, 0) is True def test_should_retry_on_stripe_should_retry_false(self, mocker): client =\",\n \"t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY * random_value with self.mock_max_delay(10): expected =\",\n \"check_call): mock_response(request_mock, '{\\\"foo\\\": \\\"baz\\\"}', 200) for method in VALID_API_METHODS: abs_url = self.valid_url data\",\n \"should not be retried by default. for code in codes: assert client._should_retry((None, code,\",\n \"= stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries api_connection_error = mocker.Mock() api_connection_error.should_retry\",\n \"crazy-big values assert 1 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"300\\\"}) ) def\",\n \"in VALID_API_METHODS: abs_url = self.valid_url data = \\\"\\\" if method != \\\"post\\\": abs_url\",\n \"@pytest.fixture def mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( \\\"You must implement this\",\n \"client, expected): until = len(expected) actual = list( map(lambda i: client._sleep_time_seconds(i + 1),\",\n \"check_call(request_mock, method, abs_url, data, headers) def test_request_stream( self, mocker, request_mock, mock_response, check_call ):\",\n \") # Ignore crazy-big values assert 1 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\":\",\n \"must implement this in your test subclass\\\" ) return check_call def test_request(self, request_mock,\",\n \"= self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def mock_response(self):\",\n \"need to convert and align all content on one type (string) # as\",\n \"in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def\",\n \"= mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters\",\n \"client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in args[2] telemetry = json.loads(args[2][\\\"X-Stripe-Client-Telemetry\\\"]) assert telemetry[\\\"last_request_metrics\\\"][\\\"request_id\\\"] == \\\"req_123\\\" class\",\n \"308)) four_xx = list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes\",\n \"headers, data) assert code == 200 assert body == '{\\\"foo\\\": \\\"baz\\\"}' check_call(request_mock, method,\",\n \"VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\") class StripeClientTestCase(object): REQUEST_LIBRARIES = [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self,\",\n \"= lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay,\",\n \"mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( \\\"You must implement this in your\",\n \"None, 0) is True def test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda:\",\n \"TestAPIEncode(StripeClientTestCase): def test_encode_dict(self): body = {\\\"foo\\\": {\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values =\",\n \"(string) # as some clients return a string stream others a byte stream.\",\n \"list( map(lambda i: client._sleep_time_seconds(i + 1), range(until)) ) assert expected == actual @contextmanager\",\n \"\\\"https://api.stripe.com%s\\\" % (path,) def make_request(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return\",\n \"util from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = (\\\"get\\\", \\\"post\\\", \\\"delete\\\")\",\n \"code, None), None, 0) is False # These status codes should be retried\",\n \"client._max_network_retries = lambda: max_test_retries api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert ( client._should_retry(\",\n \"data ) assert code == 200 # Here we need to convert and\",\n \"1), range(until)) ) assert expected == actual @contextmanager def mock_max_delay(self, new_value): original_value =\",\n \"all content on one type (string) # as some clients return a string\",\n \"assert body_content == \\\"some streamed content\\\" mocker.resetall() def test_exception(self, request_mock, mock_error): mock_error(request_mock) with\",\n \"= False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass\",\n \"= self.make_request_stream( method, abs_url, headers, data ) assert code == 200 # Here\",\n \"one_xx + two_xx + three_xx + four_xx codes.remove(409) # These status codes should\",\n \"self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time\",\n \"values def test_encode_array(self): body = {\\\"foo\\\": [{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values =\",\n \"setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters def check_default(self, none_libs,\",\n \"client.request.assert_called_with(\\\"get\\\", url, {}, None) response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_234\\\"}]) client.request =\",\n \"data) data = None headers = {\\\"my-header\\\": \\\"header val\\\"} body, code, _ =\",\n \"t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client\",\n \"8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t * 0.001\",\n \"mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {\\\"stripe-should-retry\\\": \\\"false\\\"} #\",\n \"TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager def assert_sleep_times(self, client, expected): until =\",\n \"url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method, url, headers, post_data) async def\",\n \"test subclass\\\" ) return mock_response @pytest.fixture def mock_error(self): def mock_error(mock, error): raise NotImplementedError(\",\n \"as some clients return a string stream others a byte stream. body_content =\",\n \"= stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time = lambda t: t * random_value base_value\",\n \"{\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values = [t for t in stripe.api_requestor._api_encode(body)] assert\",\n \"{\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}]} values = [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\",\",\n \"= {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks\",\n \"): for method in VALID_API_METHODS: mock_response(request_mock, \\\"some streamed content\\\", 200) abs_url = self.valid_url\",\n \"contextmanager def assert_sleep_times(self, client, expected): until = len(expected) actual = list( map(lambda i:\",\n \"class TestAPIEncode(StripeClientTestCase): def test_encode_dict(self): body = {\\\"foo\\\": {\\\"dob\\\": {\\\"month\\\": 1}, \\\"name\\\": \\\"bat\\\"}} values\",\n \"= False assert client._should_retry(None, api_connection_error, 0) is False def test_should_retry_on_stripe_should_retry_true(self, mocker): client =\",\n \"2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t\",\n \"= lambda t: t * 0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client, [initial_delay] * 5)\",\n \"0) is True def test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1\",\n \"tests for tornado client pass class TestAPIEncode(StripeClientTestCase): def test_encode_dict(self): body = {\\\"foo\\\": {\\\"dob\\\":\",\n \"to convert and align all content on one type (string) # as some\",\n \"all(0.5 <= val <= 1 for val in jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self):\",\n \"streamed content\\\" mocker.resetall() def test_exception(self, request_mock, mock_error): mock_error(request_mock) with pytest.raises(stripe.error.APIConnectionError): self.make_request(\\\"get\\\", self.valid_url, {},\",\n \"= 0.8 client._add_jitter_time = lambda t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY *\",\n \"None headers = {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in dir(self)) stream, code, _\",\n \"TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager def assert_sleep_times(self, client, expected): until = len(expected) actual\",\n \"= [\\\"AsyncHTTPClient\\\"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES:\",\n \"(lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\")\",\n \"{\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer default if it's bigger assert 2 == client._sleep_time_seconds(\",\n \"by default. for code in codes: assert client._should_retry((None, code, None), None, 0) is\",\n \"api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry = False assert\",\n \"expected) def test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t # Prefer\",\n \"{}), None, 0) is False assert client._should_retry((None, 400, headers), None, 0) is True\",\n \") return mock_error @pytest.fixture def check_call(self): def check_call( mock, method, abs_url, headers, params,\",\n \"client.request_with_retries(\\\"get\\\", url, {}, None) assert code == 200 args, _ = client.request.call_args assert\",\n \"mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry = False\",\n \") return check_call def test_request(self, request_mock, mock_response, check_call): mock_response(request_mock, '{\\\"foo\\\": \\\"baz\\\"}', 200) for\",\n \"inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from\",\n \"None, max_test_retries + 1) is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs\",\n \"{}, None) assert code == 200 args, _ = client.request.call_args assert \\\"X-Stripe-Client-Telemetry\\\" in\",\n \"list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes = one_xx +\",\n \"400, headers), None, 0) is True def test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries\",\n \"raise NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return mock_error\",\n \"if method != \\\"post\\\": abs_url = \\\"%s?%s\\\" % (abs_url, data) data = None\",\n \"= 10 client._max_network_retries = lambda: max_test_retries api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert\",\n \"implement this in your test subclass\\\" ) return mock_response @pytest.fixture def mock_error(self): def\",\n \"= {\\\"stripe-should-retry\\\": \\\"false\\\"} # Ordinarily, we would retry a 500, but with the\",\n \"others a byte stream. body_content = stream.read() if hasattr(body_content, \\\"decode\\\"): body_content = body_content.decode(\\\"utf-8\\\")\",\n \"stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self):\",\n \"True assert client._should_retry((None, 500, None), None, 0) is True assert client._should_retry((None, 503, None),\",\n \"code in codes: assert client._should_retry((None, code, None), None, 0) is False # These\",\n \"( client._should_retry( None, api_connection_error, max_test_retries + 1 ) is False ) assert (\",\n \") # Prefer default if it's bigger assert 2 == client._sleep_time_seconds( 3, (None,\",\n \"\\\"baz\\\"}', 200) for method in VALID_API_METHODS: abs_url = self.valid_url data = \\\"\\\" if\",\n \"== client._sleep_time_seconds( 3, (None, 409, {\\\"retry-after\\\": \\\"1\\\"}) ) # Ignore crazy-big values assert\",\n \"= True url = \\\"http://fake.url\\\" client = TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200,\",\n \"absolute_import, division, print_function import pytest import json import asyncio import stripe import urllib3\",\n \"mock_error): mock_error(request_mock) with pytest.raises(stripe.error.APIConnectionError): self.make_request(\\\"get\\\", self.valid_url, {}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write\",\n \"stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True\",\n \"self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch(\\\"async_stripe.http_client.%s\\\" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self,\",\n \"None headers = {\\\"my-header\\\": \\\"header val\\\"} body, code, _ = self.make_request(method, abs_url, headers,\",\n \"409, {\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer default if it's bigger assert 2 ==\",\n \"random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY * random_value with self.mock_max_delay(10): expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value\",\n \"0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client, [initial_delay] * 5) def test_maximum_delay(self): client = stripe.http_client.new_default_http_client()\",\n \"range(until)) ) assert expected == actual @contextmanager def mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY\",\n \"lambda t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY * random_value with self.mock_max_delay(10): expected\",\n \"= [t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[0][dob][month]\\\", 1) in values assert (\\\"foo[0][name]\\\",\",\n \"assert code == 200 # Here we need to convert and align all\",\n \"your test subclass\\\" ) return mock_error @pytest.fixture def check_call(self): def check_call( mock, method,\",\n \"client._should_retry((None, code, None), None, 0) is False # These status codes should be\",\n \"0.8 client._add_jitter_time = lambda t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY * random_value\",\n \"assert ( client._should_retry( None, api_connection_error, max_test_retries + 1 ) is False ) assert\",\n \"_ = await client.request_with_retries(\\\"get\\\", url, {}, None) assert code == 200 client.request.assert_called_with(\\\"get\\\", url,\",\n \"\\\"req_123\\\"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries(\\\"get\\\", url,\",\n \"is True def test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers\",\n \"409, None), None, 0) is True assert client._should_retry((None, 500, None), None, 0) is\",\n \"% (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:]\",\n \"== \\\"req_123\\\" class ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self,\",\n \"in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1) in values assert (\\\"foo[name]\\\", \\\"bat\\\") in values def\",\n \"but with the header as true, we would. assert client._should_retry((None, 400, {}), None,\",\n \"__future__ import absolute_import, division, print_function import pytest import json import asyncio import stripe\",\n \"def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url = \\\"http://fake.url\\\" client\",\n \"assert 30 == client._sleep_time_seconds( 2, (None, 409, {\\\"retry-after\\\": \\\"30\\\"}) ) # Prefer default\",\n \"list(range(100))) ) assert len(jittered_ones) > 1 assert all(0.5 <= val <= 1 for\",\n \"test_request_stream( self, mocker, request_mock, mock_response, check_call ): for method in VALID_API_METHODS: mock_response(request_mock, \\\"some\",\n \"= {\\\"my-header\\\": \\\"header val\\\"} print(dir(self)) print(\\\"make_request_stream\\\" in dir(self)) stream, code, _ = self.make_request_stream(\",\n \"class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url = \\\"http://fake.url\\\" client = TestClient() response_future\",\n \"0) is True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1\",\n \"None, 0) is True assert client._should_retry((None, 500, headers), None, 0) is False def\",\n \"\\\"30\\\"}) ) # Prefer default if it's bigger assert 2 == client._sleep_time_seconds( 3,\",\n \"on one type (string) # as some clients return a string stream others\",\n \"1 codes = one_xx + two_xx + three_xx + four_xx codes.remove(409) # These\",\n \"mock_response @pytest.fixture def mock_error(self): def mock_error(mock, error): raise NotImplementedError( \\\"You must implement this\",\n \") assert expected == actual @contextmanager def mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY\",\n \"t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay, max_delay] self.assert_sleep_times(client, expected)\",\n \"[t for t in stripe.api_requestor._api_encode(body)] assert (\\\"foo[dob][month]\\\", 1) in values assert (\\\"foo[name]\\\", \\\"bat\\\")\",\n \"expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager def assert_sleep_times(self,\",\n \"): raise NotImplementedError( \\\"You must implement this in your test subclass\\\" ) return\",\n \"stripe.http_client.warnings.filters = original_filters def check_default(self, none_libs, expected): for lib in none_libs: setattr(stripe.http_client, lib,\",\n \"1 headers = {\\\"stripe-should-retry\\\": \\\"false\\\"} # Ordinarily, we would retry a 500, but\",\n \"max_delay, max_delay] self.assert_sleep_times(client, expected) def test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t:\",\n \"= stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib\",\n \"setup_stripe(self): orig_attrs = {\\\"enable_telemetry\\\": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs[\\\"enable_telemetry\\\"] async\",\n \"code, _ = self.make_request_stream( method, abs_url, headers, data ) assert code == 200\",\n \"client._max_network_retries = lambda: 1 api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error,\",\n \"= \\\"http://fake.url\\\" client = TestClient() response_future = asyncio.Future() response_future.set_result([\\\"\\\", 200, {\\\"Request-Id\\\": \\\"req_123\\\"}]) client.request\",\n \"self.mock_max_delay(10): expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value * 4, base_value *\",\n \"path=\\\"/foo\\\"): return \\\"https://api.stripe.com%s\\\" % (path,) def make_request(self, method, url, headers, post_data): client =\",\n \"_ = await client.request_with_retries(\\\"get\\\", url, {}, None) assert code == 200 args, _\",\n \"stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay, max_delay] self.assert_sleep_times(client, expected) def test_retry_after_header(self): client\",\n \"def test_request(self, request_mock, mock_response, check_call): mock_response(request_mock, '{\\\"foo\\\": \\\"baz\\\"}', 200) for method in VALID_API_METHODS:\",\n \"= stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter(\\\"ignore\\\") yield stripe.http_client.warnings.filters = original_filters def check_default(self, none_libs, expected): for lib\",\n \"api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry\",\n \"== '{\\\"foo\\\": \\\"baz\\\"}' check_call(request_mock, method, abs_url, data, headers) def test_request_stream( self, mocker, request_mock,\"\n]","isConversational":false}}},{"rowIdx":585,"cells":{"ngram":{"kind":"list like","value":["def download(self, url: str): \"\"\" Store a python dictionary generated from json data","data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data:","\"\"\" Store a python dictionary generated from json data at <url> in self.data.","self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \"\"\" Store a python dictionary","def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if","stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \"\"\" Take self.data","file visualization of a json dataset \"\"\" import json import subprocess class JsonVis:","json dataset \"\"\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None))","required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return","JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule',","self.data. Returns self. \"\"\" data = subprocess.run( f\"curl '{url}'\", # Quotes required around","URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \"\"\"","value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item","Provides html file visualization of a json dataset \"\"\" import json import subprocess","return self def make_instructions(self): \"\"\" Take self.data and return a list of instructions","url: str): \"\"\" Store a python dictionary generated from json data at <url>","a list of instructions about its html visualization that is parsed by json.html.","and return a list of instructions about its html visualization that is parsed","self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url:","data at <url> in self.data. Returns self. \"\"\" data = subprocess.run( f\"curl '{url}'\",","elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data)","return a list of instructions about its html visualization that is parsed by","data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value)","in self.data. Returns self. \"\"\" data = subprocess.run( f\"curl '{url}'\", # Quotes required","of a json dataset \"\"\" import json import subprocess class JsonVis: def _open_list(self):","is parsed by json.html. \"\"\" self.instructions = [] self._open_list() self._iterate(self.data) self._close_list() return self.instructions","\"\"\" data = subprocess.run( f\"curl '{url}'\", # Quotes required around url for URL","url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def","class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self):","self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \"\"\" Store a python","download(self, url: str): \"\"\" Store a python dictionary generated from json data at","self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self):","# Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data =","dictionary generated from json data at <url> in self.data. Returns self. \"\"\" data","self def make_instructions(self): \"\"\" Take self.data and return a list of instructions about","json data at <url> in self.data. Returns self. \"\"\" data = subprocess.run( f\"curl","python dictionary generated from json data at <url> in self.data. Returns self. \"\"\"","self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else:","subprocess.run( f\"curl '{url}'\", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True","if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif","None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for","around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self","from json data at <url> in self.data. Returns self. \"\"\" data = subprocess.run(","str): \"\"\" Store a python dictionary generated from json data at <url> in","\"\"\"\\ Provides html file visualization of a json dataset \"\"\" import json import","isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def","_iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list()","f\"curl '{url}'\", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout","item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \"\"\"","import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data)))","self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data:","Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data)","of instructions about its html visualization that is parsed by json.html. \"\"\" self.instructions","subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def","else: self._list_item(data) def download(self, url: str): \"\"\" Store a python dictionary generated from","that is parsed by json.html. \"\"\" self.instructions = [] self._open_list() self._iterate(self.data) self._close_list() return","'{url}'\", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data","in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in",").stdout self.data = json.loads(data) return self def make_instructions(self): \"\"\" Take self.data and return","dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list):","about its html visualization that is parsed by json.html. \"\"\" self.instructions = []","def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None))","in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \"\"\" Store","parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \"\"\" Take","self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule()","self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item)","\"\"\" Take self.data and return a list of instructions about its html visualization","self.data and return a list of instructions about its html visualization that is","key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for","list of instructions about its html visualization that is parsed by json.html. \"\"\"","\"\"\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self,","visualization that is parsed by json.html. \"\"\" self.instructions = [] self._open_list() self._iterate(self.data) self._close_list()","list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self,","None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list',","_close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value","def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key,","self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict):","_horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data,","visualization of a json dataset \"\"\" import json import subprocess class JsonVis: def","<filename>http/static/jsonvis.py \"\"\"\\ Provides html file visualization of a json dataset \"\"\" import json","isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data,","for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list()","self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value in","json.loads(data) return self def make_instructions(self): \"\"\" Take self.data and return a list of","instructions about its html visualization that is parsed by json.html. \"\"\" self.instructions =","str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter):","import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data):","data = subprocess.run( f\"curl '{url}'\", # Quotes required around url for URL parameters","dataset \"\"\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def","def make_instructions(self): \"\"\" Take self.data and return a list of instructions about its","_list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def","<url> in self.data. Returns self. \"\"\" data = subprocess.run( f\"curl '{url}'\", # Quotes","make_instructions(self): \"\"\" Take self.data and return a list of instructions about its html","at <url> in self.data. Returns self. \"\"\" data = subprocess.run( f\"curl '{url}'\", #","html file visualization of a json dataset \"\"\" import json import subprocess class","def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None))","json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item',","self. \"\"\" data = subprocess.run( f\"curl '{url}'\", # Quotes required around url for","self.data = json.loads(data) return self def make_instructions(self): \"\"\" Take self.data and return a","for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str):","shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \"\"\" Take self.data and","Take self.data and return a list of instructions about its html visualization that","a python dictionary generated from json data at <url> in self.data. Returns self.","generated from json data at <url> in self.data. Returns self. \"\"\" data =","data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \"\"\" Store a","_open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def","for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self):","= subprocess.run( f\"curl '{url}'\", # Quotes required around url for URL parameters stdout=subprocess.PIPE,","self._list_item(data) def download(self, url: str): \"\"\" Store a python dictionary generated from json","self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list()","a json dataset \"\"\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list',","self._close_list() else: self._list_item(data) def download(self, url: str): \"\"\" Store a python dictionary generated","def _iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key)","Store a python dictionary generated from json data at <url> in self.data. Returns","its html visualization that is parsed by json.html. \"\"\" self.instructions = [] self._open_list()","html visualization that is parsed by json.html. \"\"\" self.instructions = [] self._open_list() self._iterate(self.data)","Returns self. \"\"\" data = subprocess.run( f\"curl '{url}'\", # Quotes required around url","data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self,","= json.loads(data) return self def make_instructions(self): \"\"\" Take self.data and return a list","None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items():","iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list()"],"string":"[\n \"def download(self, url: str): \\\"\\\"\\\" Store a python dictionary generated from json data\",\n \"data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data:\",\n \"\\\"\\\"\\\" Store a python dictionary generated from json data at <url> in self.data.\",\n \"self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store a python dictionary\",\n \"def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if\",\n \"stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take self.data\",\n \"file visualization of a json dataset \\\"\\\"\\\" import json import subprocess class JsonVis:\",\n \"json dataset \\\"\\\"\\\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None))\",\n \"required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return\",\n \"JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule',\",\n \"self.data. Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around\",\n \"URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \\\"\\\"\\\"\",\n \"value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item\",\n \"Provides html file visualization of a json dataset \\\"\\\"\\\" import json import subprocess\",\n \"return self def make_instructions(self): \\\"\\\"\\\" Take self.data and return a list of instructions\",\n \"url: str): \\\"\\\"\\\" Store a python dictionary generated from json data at <url>\",\n \"a list of instructions about its html visualization that is parsed by json.html.\",\n \"and return a list of instructions about its html visualization that is parsed\",\n \"self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url:\",\n \"data at <url> in self.data. Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\",\",\n \"elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data)\",\n \"return a list of instructions about its html visualization that is parsed by\",\n \"data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value)\",\n \"in self.data. Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes required\",\n \"of a json dataset \\\"\\\"\\\" import json import subprocess class JsonVis: def _open_list(self):\",\n \"is parsed by json.html. \\\"\\\"\\\" self.instructions = [] self._open_list() self._iterate(self.data) self._close_list() return self.instructions\",\n \"\\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around url for URL\",\n \"url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def\",\n \"class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self):\",\n \"self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store a python\",\n \"download(self, url: str): \\\"\\\"\\\" Store a python dictionary generated from json data at\",\n \"self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self):\",\n \"# Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data =\",\n \"dictionary generated from json data at <url> in self.data. Returns self. \\\"\\\"\\\" data\",\n \"self def make_instructions(self): \\\"\\\"\\\" Take self.data and return a list of instructions about\",\n \"json data at <url> in self.data. Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl\",\n \"python dictionary generated from json data at <url> in self.data. Returns self. \\\"\\\"\\\"\",\n \"self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else:\",\n \"subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True\",\n \"if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif\",\n \"None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for\",\n \"around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self\",\n \"from json data at <url> in self.data. Returns self. \\\"\\\"\\\" data = subprocess.run(\",\n \"str): \\\"\\\"\\\" Store a python dictionary generated from json data at <url> in\",\n \"\\\"\\\"\\\"\\\\ Provides html file visualization of a json dataset \\\"\\\"\\\" import json import\",\n \"isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def\",\n \"_iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list()\",\n \"f\\\"curl '{url}'\\\", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout\",\n \"item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\"\",\n \"import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data)))\",\n \"self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data:\",\n \"Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data)\",\n \"of instructions about its html visualization that is parsed by json.html. \\\"\\\"\\\" self.instructions\",\n \"subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def\",\n \"else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store a python dictionary generated from\",\n \"that is parsed by json.html. \\\"\\\"\\\" self.instructions = [] self._open_list() self._iterate(self.data) self._close_list() return\",\n \"'{url}'\\\", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data\",\n \"in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in\",\n \").stdout self.data = json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take self.data and return\",\n \"dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list):\",\n \"about its html visualization that is parsed by json.html. \\\"\\\"\\\" self.instructions = []\",\n \"def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None))\",\n \"in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store\",\n \"parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take\",\n \"self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule()\",\n \"self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item)\",\n \"\\\"\\\"\\\" Take self.data and return a list of instructions about its html visualization\",\n \"self.data and return a list of instructions about its html visualization that is\",\n \"key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for\",\n \"list of instructions about its html visualization that is parsed by json.html. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self,\",\n \"visualization that is parsed by json.html. \\\"\\\"\\\" self.instructions = [] self._open_list() self._iterate(self.data) self._close_list()\",\n \"list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self,\",\n \"None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list',\",\n \"_close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value\",\n \"def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key,\",\n \"self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict):\",\n \"_horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data,\",\n \"visualization of a json dataset \\\"\\\"\\\" import json import subprocess class JsonVis: def\",\n \"<filename>http/static/jsonvis.py \\\"\\\"\\\"\\\\ Provides html file visualization of a json dataset \\\"\\\"\\\" import json\",\n \"isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data,\",\n \"for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list()\",\n \"self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value in\",\n \"json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take self.data and return a list of\",\n \"instructions about its html visualization that is parsed by json.html. \\\"\\\"\\\" self.instructions =\",\n \"str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter):\",\n \"import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data):\",\n \"data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around url for URL parameters\",\n \"dataset \\\"\\\"\\\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def\",\n \"def make_instructions(self): \\\"\\\"\\\" Take self.data and return a list of instructions about its\",\n \"_list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def\",\n \"<url> in self.data. Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes\",\n \"make_instructions(self): \\\"\\\"\\\" Take self.data and return a list of instructions about its html\",\n \"at <url> in self.data. Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", #\",\n \"html file visualization of a json dataset \\\"\\\"\\\" import json import subprocess class\",\n \"def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None))\",\n \"json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item',\",\n \"self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around url for\",\n \"self.data = json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take self.data and return a\",\n \"for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str):\",\n \"shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take self.data and\",\n \"Take self.data and return a list of instructions about its html visualization that\",\n \"a python dictionary generated from json data at <url> in self.data. Returns self.\",\n \"generated from json data at <url> in self.data. Returns self. \\\"\\\"\\\" data =\",\n \"data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store a\",\n \"_open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def\",\n \"for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self):\",\n \"= subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around url for URL parameters stdout=subprocess.PIPE,\",\n \"self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store a python dictionary generated from json\",\n \"self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list()\",\n \"a json dataset \\\"\\\"\\\" import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list',\",\n \"self._close_list() else: self._list_item(data) def download(self, url: str): \\\"\\\"\\\" Store a python dictionary generated\",\n \"def _iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key)\",\n \"Store a python dictionary generated from json data at <url> in self.data. Returns\",\n \"its html visualization that is parsed by json.html. \\\"\\\"\\\" self.instructions = [] self._open_list()\",\n \"html visualization that is parsed by json.html. \\\"\\\"\\\" self.instructions = [] self._open_list() self._iterate(self.data)\",\n \"Returns self. \\\"\\\"\\\" data = subprocess.run( f\\\"curl '{url}'\\\", # Quotes required around url\",\n \"data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self,\",\n \"= json.loads(data) return self def make_instructions(self): \\\"\\\"\\\" Take self.data and return a list\",\n \"None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items():\",\n \"iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list()\"\n]","isConversational":false}}},{"rowIdx":586,"cells":{"ngram":{"kind":"list like","value":["FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app =","QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox,","= 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title)","70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout()","= QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app = QApplication(sys.argv) mainwindow =","QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app = QApplication(sys.argv) mainwindow = MainWindow()","left = 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry())","class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top = 500 left = 100","130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget()","self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__","wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app = QApplication(sys.argv) mainwindow","from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout,","import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from","QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid)","title = \"Sim2d Game Analyzer\" top = 500 left = 100 width =","from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import","sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top = 500","PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget)","= QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget)","import sys from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from","QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab","import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from","sys from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui","import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class","QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game","MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top = 500 left = 100 width","from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top =","tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app","= \"Sim2d Game Analyzer\" top = 500 left = 100 width = 70*4","top = 500 left = 100 width = 70*4 height = 130*4 def","self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app = QApplication(sys.argv) mainwindow = MainWindow() sys.exit(app.exec())","height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget","\"Sim2d Game Analyzer\" top = 500 left = 100 width = 70*4 height","tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox)","import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog,","= QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self)","QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title","QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\"","QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab","vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME)","QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top","QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d","PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication,","import FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top = 500 left","QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets","PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab","PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon","QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title =","12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if","FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\":","from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import","= 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget =","self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab =","Game Analyzer\" top = 500 left = 100 width = 70*4 height =","QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid","= 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox =","def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\",","tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid =","= 500 left = 100 width = 70*4 height = 130*4 def __init__(self):","Analyzer\" top = 500 left = 100 width = 70*4 height = 130*4","= FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ ==","vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app = QApplication(sys.argv)","(QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow):","500 left = 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self)","Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget,","FMDBTab class MainWindow(QMainWindow): title = \"Sim2d Game Analyzer\" top = 500 left =","100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\"))","__init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12))","self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab,","width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox","from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import","QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow,","self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\"sim2d_game_analyzer/figures/icon.png\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\"Sanserif\", 12)) self.fmdb_tab = FMDBTab()"],"string":"[\n \"FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app =\",\n \"QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox,\",\n \"= 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title)\",\n \"70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout()\",\n \"= QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app = QApplication(sys.argv) mainwindow =\",\n \"QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app = QApplication(sys.argv) mainwindow = MainWindow()\",\n \"left = 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry())\",\n \"class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top = 500 left = 100\",\n \"130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget()\",\n \"self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__\",\n \"wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app = QApplication(sys.argv) mainwindow\",\n \"from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout,\",\n \"import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from\",\n \"QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid)\",\n \"title = \\\"Sim2d Game Analyzer\\\" top = 500 left = 100 width =\",\n \"from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import\",\n \"sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top = 500\",\n \"PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget)\",\n \"= QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget)\",\n \"import sys from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from\",\n \"QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab\",\n \"import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from\",\n \"sys from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui\",\n \"import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class\",\n \"QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game\",\n \"MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top = 500 left = 100 width\",\n \"from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top =\",\n \"tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app\",\n \"= \\\"Sim2d Game Analyzer\\\" top = 500 left = 100 width = 70*4\",\n \"top = 500 left = 100 width = 70*4 height = 130*4 def\",\n \"self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app = QApplication(sys.argv) mainwindow = MainWindow() sys.exit(app.exec())\",\n \"height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget\",\n \"\\\"Sim2d Game Analyzer\\\" top = 500 left = 100 width = 70*4 height\",\n \"tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox)\",\n \"import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog,\",\n \"= QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self)\",\n \"QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title\",\n \"QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\"\",\n \"QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab\",\n \"vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME)\",\n \"QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top\",\n \"QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d\",\n \"PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication,\",\n \"import FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top = 500 left\",\n \"QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets\",\n \"PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab\",\n \"PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon\",\n \"QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title =\",\n \"12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if\",\n \"FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\":\",\n \"from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import\",\n \"= 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget =\",\n \"self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab =\",\n \"Game Analyzer\\\" top = 500 left = 100 width = 70*4 height =\",\n \"QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid\",\n \"= 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox =\",\n \"def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\",\",\n \"tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid =\",\n \"= 500 left = 100 width = 70*4 height = 130*4 def __init__(self):\",\n \"Analyzer\\\" top = 500 left = 100 width = 70*4 height = 130*4\",\n \"= FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ ==\",\n \"vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \\\"__main__\\\": app = QApplication(sys.argv)\",\n \"(QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow):\",\n \"500 left = 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self)\",\n \"Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget,\",\n \"FMDBTab class MainWindow(QMainWindow): title = \\\"Sim2d Game Analyzer\\\" top = 500 left =\",\n \"100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\"))\",\n \"__init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12))\",\n \"self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab,\",\n \"width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox\",\n \"from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import\",\n \"QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow,\",\n \"self.setWindowTitle(self.title) self.setWindowIcon(QIcon(\\\"sim2d_game_analyzer/figures/icon.png\\\")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont(\\\"Sanserif\\\", 12)) self.fmdb_tab = FMDBTab()\"\n]","isConversational":false}}},{"rowIdx":587,"cells":{"ngram":{"kind":"list like","value":["similar-sized chunks from l.\"\"\" chunk_size = 1 + len(l) // n for i","LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name","\"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"]","self.rows = rows or [] def convert_row_to_dict(titles, row): data = {} for (i,","exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes = {","= [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS =","school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\",","\"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\":","self.name = name self.titles = titles or [] self.rows = rows or []","return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\":","[ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS])","locations, teachers, students): # Build school -> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"]","org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in schools] # Build location ->","f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive similar-sized chunks from l.\"\"\" chunk_size =","+ str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as","str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f:","list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive similar-sized chunks from l.\"\"\"","= \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\",","= {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\",","\"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive similar-sized","[make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers for schoolId in","that for being related to a different school. # We have to collect","graphcool import required\" for x in dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid()","l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools),","school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"],","\"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\",","of Sheet objects, in which row has been converted into a dictionary\"\"\" work_book","for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs =","\"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS =","in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES,","yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students if student[\"schoolId\"]","multiple records, # each of which is identitical except that for being related","camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if i == 0 else","x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations)","\"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\",","\"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\":","injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like locations, the same","= [x for x in sheet.iter_rows()] if rows: titles = [cell.value for cell","yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in schools] # Build","\"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\",","[\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\",","add the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list())","Sheet objects, in which row has been converted into a dictionary\"\"\" work_book =","\"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\",","enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\")","return injected def prepare_teachers(teachers): # Like locations, the same teacher can have multiple","get an existing teacher with that id, or add the new teacher record","EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\",","{\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations, schools, locations, teachers, students):","yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers for schoolId","data = {} for (i, cell) in enumerate(row): if cell.Value is not None:","on microseconds, hence the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected =","\"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\",","unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques)","import required\" for x in dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"]","LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\":","\"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\",","from l.\"\"\" chunk_size = 1 + len(l) // n for i in range(0,","return data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to iterable of Sheet","SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\",","\"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\",","that meet at the same location. uniques = {} for x in locations:","uniques = {} for x in teachers: # get an existing teacher with","one sheet within an excel spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name =","list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs","unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques","fields that graphcool import required\" for x in dicts: x[\"_typeName\"] = type_name x[\"id\"]","\"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students))","\"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\",","prepare_teachers(teachers): # Like locations, the same teacher can have multiple records, # each","\"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive similar-sized chunks","return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2,","\"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\",","\"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container object to hold","in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir,","def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations org_keys","school. # We have to collect all the schools that the same teacher","[] structs = [[extract(defn, x) for x in sheet.rows] for defn in field_defns]","an existing teacher with that id, or add the new teacher record teacher","with all the given keys removed\" copies = [x.copy() for x in dicts]","titles:\", [(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return","open(path, \"w\") as f: nodes = { \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes))","identitical except that for being related to a different school. # We have","[(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return []","row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert","\"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\":","if rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row)","\"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\":","entity2, \"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): #","[make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in schools] # Build location","if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in","x in teachers: # get an existing teacher with that id, or add","\"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS","prepare_students(students): uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for x in injected:","sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable that","not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX","\"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\",","x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS","= os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\": \"nodes\",","field2): return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2,","\"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\",","= {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def","teacher can have multiple records, # each of which is identitical except that","x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for","different school. # We have to collect all the schools that meet at","Sheet: \"Data container object to hold the contents of one sheet within an","for x in schools] # Build location -> school relations school_keys = {x[\"clpSchoolId\"]:","def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to iterable of Sheet objects, in","or add the new location location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list())","write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i),","for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def","same teacher is teaching at. uniques = {} for x in teachers: #","(i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path","\"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\":","nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with","graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\",","location in locations for schoolId in location.get(\"schools\", [])] # Build teacher -> school","openpyxl # pip install cuid import os.path import json import datetime from openpyxl","the format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES =","\"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\":","dictionary with all the given keys removed\" copies = [x.copy() for x in","make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\":","\"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\":","to_camel(s): \"\"\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits =","\"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\",","an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if","\"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir","inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current extract doesn't include the CLP location","\"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in schools] # Build location -> school","\"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\",","in sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t =","[\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\",","x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in","in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data def","fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected","class Sheet: \"Data container object to hold the contents of one sheet within","a different school. # We have to collect all the schools that meet","in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in schools]","\"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\",","to iterable of Sheet objects, in which row has been converted into a","except that for being related to a different school. # We have to","# GraphCool import insists on microseconds, hence the \".0\" def prepare_students(students): uniques =","in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists):","\"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir =","field_defns): if titles != sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i, x) for","\"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\":","dt.isoformat() + \".0Z\" # GraphCool import insists on microseconds, hence the \".0\" def","convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS,","which is identitical except that for being related to a different school. #","= relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\")","in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def","{x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\")","import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's","related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current extract doesn't include","keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir","\"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\",","teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values())","x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path)","for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x","return injected def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to a ISO date\"","x in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None)","-> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\",","\"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers for schoolId in teacher.get(\"schools\", [])]","a different school. # We have to collect all the schools that the","= load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for x","convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to iterable of Sheet objects, in which","\"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\",","return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return","all the schools that meet at the same location. uniques = {} for","\"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"]","in locations: # get an existing location with the given name, or add","[] def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if","row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def","structs = [[extract(defn, x) for x in sheet.rows] for defn in field_defns] return","dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool import insists on","+ chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations),","schools] # Build location -> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x","for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return","install cuid import os.path import json import datetime from openpyxl import load_workbook import","\"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\",","ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\":","in work_book: rows = [x for x in sheet.iter_rows()] if rows: titles =","injected def prepare_locations(locations): # There are multiple locations, each of which is identitical","Build student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for","\"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\":","with open(path, \"w\") as f: nodes = { \"valueType\": \"relations\", \"values\": list(one_list) }","school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students","related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS -","\"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\",","return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR +","def chunks(n, l): \"\"\"Yield n successive similar-sized chunks from l.\"\"\" chunk_size = 1","\"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\",","= { \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list)","injected = inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): # There are multiple locations,","\"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\":","for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield","\"Z\" def inject_required(type_name, dicts): \"Inject the required fields that graphcool import required\" for","\"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES","THIS - Current extract doesn't include the CLP location id :( Make one","expected titles:\", [(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]])","dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return","sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\":","\"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\",","row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return","prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return","STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\":","os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\": \"relations\", \"values\":","for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools,","[convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title)","in the format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES","= str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to iterable","locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\",","schools that the same teacher is teaching at. uniques = {} for x","\"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\",","\"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\",","locations: # get an existing location with the given name, or add the","uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): #","uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected","format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\",","for x in sheet.rows] for defn in field_defns] return structs def unique(key, dicts):","from 99/MON/YY to a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() +","can have multiple records, # each of which is identitical except that for","\"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool import insists on microseconds, hence the","copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations,","the contents of one sheet within an excel spreadsheet\" def __init__(self, name, titles=None,","\"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\",","[make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations for schoolId in","x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to","Build school -> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations}","\"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\",","def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students)","process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i,","\"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\",","inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name ==","copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i),","dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies","is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the given","os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in fields.items():","if i == 0 else x.title()) for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits)","all the schools that the same teacher is teaching at. uniques = {}","\"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", }","create uuid's in the format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR =","\"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\":","meet at the same location. uniques = {} for x in locations: #","\"w\") as f: nodes = { \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def","n successive similar-sized chunks from l.\"\"\" chunk_size = 1 + len(l) // n","to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in","x[\"id\"], \"organisation\") for x in schools] # Build location -> school relations school_keys","process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations, teachers, students)","(i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py,","spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or","load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for x in","read_only=True, data_only=True) for sheet in work_book: rows = [x for x in sheet.iter_rows()]","import insists on microseconds, hence the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students)","multiple locations, each of which is identitical except that for being related to","= prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"),","copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\",","school -> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield","n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations,","the schools that the same teacher is teaching at. uniques = {} for","= location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current","location -> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield","in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles","generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations org_keys =","\"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\",","teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher","1 + len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i","= [(x.lower() if i == 0 else x.title()) for (i, x) in enumerate(s.split(\"_\"))]","= [[extract(defn, x) for x in sheet.rows] for defn in field_defns] return structs","sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet,","{} for x in teachers: # get an existing teacher with that id,","= process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, )","students if student[\"schoolId\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path)","hold the contents of one sheet within an excel spreadsheet\" def __init__(self, name,","\"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\",","x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs =","\"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\",","extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations,","= {} for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]]","\"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\":","\"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class","keys removed\" copies = [x.copy() for x in dicts] for d in copies:","=> 'parentOrganisationId'\"\"\" bits = [(x.lower() if i == 0 else x.title()) for (i,","= {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\",","for x in injected: x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the","FIX THIS - Current extract doesn't include the CLP location id :( Make","d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir =","# https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX =","one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path =","entity2, id2, field2): return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2,","\"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\":","\"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\",","= { \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n","def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" +","SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\":","= teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for","\"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\",","'parentOrganisationId'\"\"\" bits = [(x.lower() if i == 0 else x.title()) for (i, x)","\"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\",","[ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2}","\"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\",","removed\" copies = [x.copy() for x in dicts] for d in copies: for","schoolId in teacher.get(\"schools\", [])] # Build student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"],","data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to","rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts)","\"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools,","collect all the schools that the same teacher is teaching at. uniques =","- create uuid's in the format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR","in teachers: # get an existing teacher with that id, or add the","\"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\",","\"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\",","type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations):","structs def unique(key, dicts): t = {x[key]: x for x in dicts} return","sheet:\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable","as f: nodes = { \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists):","for x in teachers: # get an existing teacher with that id, or","entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ] def","id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations, schools,","\"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\":","x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in","for location in locations for schoolId in location.get(\"schools\", [])] # Build teacher ->","titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title into camel","\"ClpSchool\", x[\"id\"], \"organisation\") for x in schools] # Build location -> school relations","in locations for schoolId in location.get(\"schools\", [])] # Build teacher -> school relations","\"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\",","else x.title()) for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py =","iterable of Sheet objects, in which row has been converted into a dictionary\"\"\"",") def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\"_typeName\": entity1, \"id\":","x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts)","if student[\"schoolId\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations,","cuid import os.path import json import datetime from openpyxl import load_workbook import cuid","\"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\",","for defn in field_defns] return structs def unique(key, dicts): t = {x[key]: x","\"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build","pip install cuid import os.path import json import datetime from openpyxl import load_workbook","name self.titles = titles or [] self.rows = rows or [] def convert_row_to_dict(titles,","given keys removed\" copies = [x.copy() for x in dicts] for d in","\"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\":","\"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes","yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations for schoolId","SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES,","data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to iterable of Sheet objects,","write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir,","which row has been converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True)","= extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"),","hence the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques)","cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX","\"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\",","\"locations\") for location in locations for schoolId in location.get(\"schools\", [])] # Build teacher","= {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\",","0 else x.title()) for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py","# FIX THIS - Current extract doesn't include the CLP location id :(","x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name,","*chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ ==","\"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\":","schools) injected = inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): # There are multiple","\"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\":","# There are multiple locations, each of which is identitical except that for","[make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students if student[\"schoolId\"] in","one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR","\"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\")))","= inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name","def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\"Sheet doesn't have expected titles:\",","\"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if","the time being for x in injected: x[\"clpLocationId\"] = cuid.cuid() return injected def","in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts):","\"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\",","titles=None, rows=None): self.name = name self.titles = titles or [] self.rows = rows","uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\":","in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations","\"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\",","= inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): # There are multiple locations, each","records, # each of which is identitical except that for being related to","TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\",","dicts): \"Inject the required fields that graphcool import required\" for x in dicts:","copies = [x.copy() for x in dicts] for d in copies: for to_remove","def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\"_typeName\": entity1, \"id\": id1,","row): data = {} for (i, cell) in enumerate(row): if cell.Value is not","to hold the contents of one sheet within an excel spreadsheet\" def __init__(self,","\"teachers\") for teacher in teachers for schoolId in teacher.get(\"schools\", [])] # Build student","else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID'","\"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\",","# Build location -> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in","for the time being for x in injected: x[\"clpLocationId\"] = cuid.cuid() return injected","def inject_required(type_name, dicts): \"Inject the required fields that graphcool import required\" for x","\"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\",","each of which is identitical except that for being related to a different","convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to a ISO date\" dt = datetime.datetime.strptime(s,","return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations,","= 1 + len(l) // n for i in range(0, len(l), chunk_size): yield","{x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\")","copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ == \"__main__\":","uniques = {} for x in locations: # get an existing location with","\"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\":","in schools] # Build location -> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for","\"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\",","\"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\":","= cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to a","yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations),","\"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\",","id2, field2): return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\":","\"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\",","yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title","[TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring","underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if i","at. uniques = {} for x in teachers: # get an existing teacher","= uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) #","the same teacher is teaching at. uniques = {} for x in teachers:","unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"])","chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return (","rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for","99/MON/YY to a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\"","students) def copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies of the given dictionary","uniques) for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): #","sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i, x) for (i, x) in enumerate(titles)","id1, field1, entity2, id2, field2): return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1},","def prepare_teachers(teachers): # Like locations, the same teacher can have multiple records, #","\"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__","= inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected =","= {x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat()","if cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert","# get an existing teacher with that id, or add the new teacher","\".0Z\" # GraphCool import insists on microseconds, hence the \".0\" def prepare_students(students): uniques","\"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes","i == 0 else x.title()) for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def","(k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns):","# get an existing location with the given name, or add the new","= os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k,","\".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for x","unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): # There are","return structs def unique(key, dicts): t = {x[key]: x for x in dicts}","\"fieldName\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school ->","!= sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows] for","Build teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for","\"w\") as f: nodes = { \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def","as f: nodes = { \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n,","\"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\",","injected def prepare_teachers(teachers): # Like locations, the same teacher can have multiple records,",") = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, ) = process_sheet(sheet,","id :( Make one up for the time being for x in injected:","return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable that contains","contains copies of the given dictionary with all the given keys removed\" copies","the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\"))","def extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] =","x in sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t","bits = [(x.lower() if i == 0 else x.title()) for (i, x) in","\"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\",","{ \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive","location id :( Make one up for the time being for x in","= process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations, teachers,","sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles,","id2, \"fieldName\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school","for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def","= now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\",","enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path =","nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir,","location.get(\"schools\", [])] # Build teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\",","{} for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] =","teacher.get(\"schools\", [])] # Build student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\",","case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if i == 0 else x.title())","ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool import","prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations):","print(\"Sheet doesn't have expected titles:\", [(i, x) for (i, x) in enumerate(titles) if","inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers):","in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like locations, the","\"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\",","doesn't include the CLP location id :( Make one up for the time","contents of one sheet within an excel spreadsheet\" def __init__(self, name, titles=None, rows=None):","def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data =","location with the given name, or add the new location location = uniques.setdefault(x[\"name\"],","x in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts","\"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container object to hold the","pip install openpyxl # pip install cuid import os.path import json import datetime","convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher can have","the same teacher can have multiple records, # each of which is identitical","cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\",","with that id, or add the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x)","\"Convert the string from 99/MON/YY to a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\")","of which is identitical except that for being related to a different school.","being related to a different school. # We have to collect all the","def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students)","organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for x in schools] #","cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to a ISO","= datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool import insists on microseconds,","elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name ==","{x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() +","prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) )","\"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\",","def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations)","Like locations, the same teacher can have multiple records, # each of which","import json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py -","{ \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in","\"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\",","uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"]","\"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\":","spreadsheet to iterable of Sheet objects, in which row has been converted into","injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools,","\"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ == \"__main__\": main()","TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else:","load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool","one up for the time being for x in injected: x[\"clpLocationId\"] = cuid.cuid()","all the given keys removed\" copies = [x.copy() for x in dicts] for","extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) =","sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t = {x[key]:","\"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS =","Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title into","There are multiple locations, each of which is identitical except that for being","raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def","string from 99/MON/YY to a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat()","\"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data","data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\"Sheet doesn't have expected","= cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs =","collect all the schools that meet at the same location. uniques = {}","{\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ]","= rows or [] def convert_row_to_dict(titles, row): data = {} for (i, cell)","\"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations,","for sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet(","convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if cell.Value is","in teachers for schoolId in teacher.get(\"schools\", [])] # Build student -> school relations","= row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\"Sheet","= [x.copy() for x in dicts] for d in copies: for to_remove in","# We have to collect all the schools that the same teacher is","dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else:","= unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): # There","raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1,","organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools)","} f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR +","for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\"))","\"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container object","relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {}","v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if","has been converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet","x.title()) for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__))","def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to a ISO date\" dt =","== 0 else x.title()) for (i, x) in enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path):","SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\",","\"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\",","same teacher can have multiple records, # each of which is identitical except","sheet in convert_xlsx(file_path): if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet,","up for the time being for x in injected: x[\"clpLocationId\"] = cuid.cuid() return","field_defns] return structs def unique(key, dicts): t = {x[key]: x for x in","return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in","\"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\",","\"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\",","the string from 99/MON/YY to a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return","\"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, )","\"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES =","into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows","schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations for","schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers,","title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if i ==","+ \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path,","[[extract(defn, x) for x in sheet.rows] for defn in field_defns] return structs def","# Build school -> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in","\"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\",","\"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\",","} STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\",","have multiple records, # each of which is identitical except that for being","relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\",","an excel spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles =","i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations,","(organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"),","x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def","in location.get(\"schools\", [])] # Build teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\",","titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row","schools, locations, teachers, students): # Build school -> organisation relations org_keys = {x[\"clpOrganisationId\"]:","teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\",","that id, or add the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools","os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v)","\"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", }","in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return","students): # Build school -> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x","new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected","def unique(key, dicts): t = {x[key]: x for x in dicts} return t.values()","\"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\":","\"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\",","the given XLSX spreadsheet to iterable of Sheet objects, in which row has","injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current extract doesn't include the","STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations, teachers, students) def","d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists):","\"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\",","\"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\",","related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path):","f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\"","location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current extract","\"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\":","\"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet:","return [] structs = [[extract(defn, x) for x in sheet.rows] for defn in","def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject the required fields","datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject the required fields that graphcool import","\"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\",","= process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES,","successive similar-sized chunks from l.\"\"\" chunk_size = 1 + len(l) // n for","( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2,","student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students if student[\"schoolId\"] in school_keys]","\"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\",","relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\",","list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current extract doesn't","work_book: rows = [x for x in sheet.iter_rows()] if rows: titles = [cell.value","\"Inject the required fields that graphcool import required\" for x in dicts: x[\"_typeName\"]","in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for","self.titles = titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row):","time being for x in injected: x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s):","= uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return","organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]],","\"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students if student[\"schoolId\"] in school_keys] def main():","\"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\",","container object to hold the contents of one sheet within an excel spreadsheet\"","sheet.name == \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name)","\"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\",","\"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations for schoolId in location.get(\"schools\", [])]","the schools that meet at the same location. uniques = {} for x","{\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\":","data_only=True) for sheet in work_book: rows = [x for x in sheet.iter_rows()] if","field1, entity2, id2, field2): return [ {\"_typeName\": entity1, \"id\": id1, \"fieldName\": field1}, {\"_typeName\":","\"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\",","TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\":","range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students):","return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict):","] def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations","t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject the required","= [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\",","\"\"\"Yield n successive similar-sized chunks from l.\"\"\" chunk_size = 1 + len(l) //","for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location","} SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\",","for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True)","the given name, or add the new location location = uniques.setdefault(x[\"name\"], x) related_schools","# Build teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\")","\"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\",","record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\",","related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path):","name, or add the new location location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\",","\"\"\"Convert the given XLSX spreadsheet to iterable of Sheet objects, in which row","= convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher can","cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data","\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\":","the given dictionary with all the given keys removed\" copies = [x.copy() for","\"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS","\"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\":","\"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container object to hold the contents","Current extract doesn't include the CLP location id :( Make one up for","\"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\": \"relations\", \"values\": list(one_list)","work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for","\"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students if student[\"schoolId\"] in school_keys] def","main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) =","open(path, \"w\") as f: nodes = { \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes))","for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles,","dicts): t = {x[key]: x for x in dicts} return t.values() def now_as_iso8601():","uuid's in the format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\"","the new location location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected","uniques.values()) # FIX THIS - Current extract doesn't include the CLP location id","row has been converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for","x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows]","\"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\",","injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if","x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs","teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies of the given","teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet","chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers),","teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers for schoolId in teacher.get(\"schools\",","in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts =","\"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\":","\"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\",","None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet","of one sheet within an excel spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name","expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\",","relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers for","= relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes(","nodes = { \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i,","import os.path import json import datetime from openpyxl import load_workbook import cuid #","a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows =","\"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\",","given name, or add the new location location = uniques.setdefault(x[\"name\"], x) related_schools =","teacher with that id, or add the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"],","\"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations, schools, locations,","locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies of the","is teaching at. uniques = {} for x in teachers: # get an","rows=None): self.name = name self.titles = titles or [] self.rows = rows or","return dt.isoformat() + \".0Z\" # GraphCool import insists on microseconds, hence the \".0\"","We have to collect all the schools that the same teacher is teaching","dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x","import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that","existing teacher with that id, or add the new teacher record teacher =","= \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\",","str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the given XLSX spreadsheet to iterable of","new location location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected =","prepare_locations(locations): # There are multiple locations, each of which is identitical except that","= inject_required(\"ClpLocation\", uniques.values()) # FIX THIS - Current extract doesn't include the CLP","!= sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i, x) for (i, x) in","str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f:","in students if student[\"schoolId\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections =","= titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data","iterable that contains copies of the given dictionary with all the given keys","for x in dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"]","for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s):","Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\"","for teacher in teachers for schoolId in teacher.get(\"schools\", [])] # Build student ->","in teacher.get(\"schools\", [])] # Build student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\",","dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject","= [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield","one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True)","for student in students if student[\"schoolId\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX)","\"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\":","\"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data","== \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return","None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR","extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k]","relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with","xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections)","\"DAY_SCHOOL\", } class Sheet: \"Data container object to hold the contents of one","converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book:","copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies of the given dictionary with all","\"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\",","same location. uniques = {} for x in locations: # get an existing","yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' =>","\"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\",","len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size]","the same location. uniques = {} for x in locations: # get an","\"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS","x in locations: # get an existing location with the given name, or","= inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def","that contains copies of the given dictionary with all the given keys removed\"","+ len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i +","the required fields that graphcool import required\" for x in dicts: x[\"_typeName\"] =","existing location with the given name, or add the new location location =","\"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES =","json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create",":( Make one up for the time being for x in injected: x[\"clpLocationId\"]","sheet in work_book: rows = [x for x in sheet.iter_rows()] if rows: titles","location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations for schoolId in location.get(\"schools\",","{} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet,","t = {x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return","\"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\", \"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\",","being for x in injected: x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert","to collect all the schools that the same teacher is teaching at. uniques","org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\",","+ str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as","students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\",","[x for x in sheet.iter_rows()] if rows: titles = [cell.value for cell in","locations, the same teacher can have multiple records, # each of which is","} f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive similar-sized chunks from l.\"\"\" chunk_size","\"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\",","XLSX spreadsheet to iterable of Sheet objects, in which row has been converted","relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\")","l.\"\"\" chunk_size = 1 + len(l) // n for i in range(0, len(l),","elif sheet.name == \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\",","*keys_to_remove): \"Return iterable that contains copies of the given dictionary with all the","return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject the required fields that graphcool","-> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in","\"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\",","\"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\":","\"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield n successive similar-sized chunks from","STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS","os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes =","for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value)","\"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\",","required\" for x in dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] =","return injected def prepare_locations(locations): # There are multiple locations, each of which is","// n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def","\"Return iterable that contains copies of the given dictionary with all the given","microseconds, hence the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\",","unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\",","\"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\",","fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles !=","teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\", list()) related_schools.append(x.pop(\"schoolId\")) injected =","\"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"}","in which row has been converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True,","x in schools] # Build location -> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"]","that the same teacher is teaching at. uniques = {} for x in","os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\": \"nodes\", \"values\":","process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) =","relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student in students if","insists on microseconds, hence the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected","the given keys removed\" copies = [x.copy() for x in dicts] for d","= {x[\"clpSchoolId\"]: x[\"id\"] for x in schools} yield [make_relation(\"ClpLocation\", location[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId],","injected: x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY","\"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\":","within an excel spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles","objects, in which row has been converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file,","\"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\": \"nodes\", \"values\": one_list","# Build student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\")","to a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" #","in dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601()","raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools,","school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers","{\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\":","[])] # Build teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId],","\"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\",","prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3,","\"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\", \"SCH_ELECTORATE\", \"SCHOOL_ID\",","location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values())","\"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\", \"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\",","schools that meet at the same location. uniques = {} for x in","= name self.titles = titles or [] self.rows = rows or [] def","uniques) return injected def prepare_locations(locations): # There are multiple locations, each of which","# each of which is identitical except that for being related to a","x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\"","x in dicts: x[\"_typeName\"] = type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] =","fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return injected","} LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\",","inject_required(type_name, dicts): \"Inject the required fields that graphcool import required\" for x in","\"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\":","teaching at. uniques = {} for x in teachers: # get an existing","for x in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]]","sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows] for defn","(organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies","relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations,","row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\"Sheet doesn't","prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2):","= [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\", \"S_POSTCODE\", \"SCHOOL_NAME\",","the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for","# We have to collect all the schools that meet at the same","an existing location with the given name, or add the new location location","\"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\":","\"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\":","school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers,","include the CLP location id :( Make one up for the time being","\"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\",","def prepare_students(students): uniques = unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for x in","\"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\":","def copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies of the given dictionary with","sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS])","\"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\",","location. uniques = {} for x in locations: # get an existing location","[STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts,","teachers for schoolId in teacher.get(\"schools\", [])] # Build student -> school relations yield","= type_name x[\"id\"] = cuid.cuid() x[\"createdAt\"] = x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def","(i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn,","that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\" EXTRACT_OUTPUT_DIR = \"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\",","have expected titles:\", [(i, x) for (i, x) in enumerate(titles) if x !=","copies of the given dictionary with all the given keys removed\" copies =","\"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\", \"MOBILE\":","\"STUDENT_FNAME\": \"GIVEN_NAMES\", \"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container","teacher is teaching at. uniques = {} for x in teachers: # get","[] self.rows = rows or [] def convert_row_to_dict(titles, row): data = {} for","\"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\",","copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i,","chunks(n, l): \"\"\"Yield n successive similar-sized chunks from l.\"\"\" chunk_size = 1 +","data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data","\"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\":","[x.copy() for x in dicts] for d in copies: for to_remove in keys_to_remove:","l): \"\"\"Yield n successive similar-sized chunks from l.\"\"\" chunk_size = 1 + len(l)","\"../server/extract\" SCHOOL_TITLES = [\"ORGANISATION_ID\", \"ORGANISATION_NAME\", \"ORG_ELECTORATE\", \"P_ADDRESS1\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"S_ADDRESS1\", \"S_SUBURB\", \"S_STATE\",","\"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists):","(teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, ) =","a ISO date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool","in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations,","for schoolId in teacher.get(\"schools\", [])] # Build student -> school relations yield [make_relation(\"ClpStudent\",","(i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return","date\" dt = datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool import insists","teacher in teachers for schoolId in teacher.get(\"schools\", [])] # Build student -> school","locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers,","\"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container object to hold the contents of","titles != sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i, x) for (i, x)","'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if i == 0 else x.title()) for","openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format","sheet within an excel spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name = name","defn in field_defns] return structs def unique(key, dicts): t = {x[key]: x for","if sheet.name == \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS,","else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove):","raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1,","-> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\",","or add the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools = teacher.setdefault(\"schools\",","{\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\":","(organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name ==","student[\"schoolId\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools,","\"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\":","in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path","\"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\",","organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\",","locations, each of which is identitical except that for being related to a","return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\"Sheet doesn't have","datetime.datetime.strptime(s, \"%d/%b/%y\") return dt.isoformat() + \".0Z\" # GraphCool import insists on microseconds, hence","# pip install openpyxl # pip install cuid import os.path import json import","been converted into a dictionary\"\"\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in","\"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", }","\"EMAIL\", \"MOBILE\", \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\", \"FIELD_OF_EDUCATION\", \"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\",","= {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\": \"SCHOOL_ID\", \"STUDENT_ID\": \"CLP_STUDENT_ID\", \"STUDENT_SRN\": \"SRN\", \"LOCATION_NAME\": \"LOCATION\", \"STUDENT_LNAME\": \"FAMILY_NAME\",","nodes = { \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \"\"\"Yield","\"\"\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower()","\"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\": \"DAY_PHONE\", \"TEL_EVENING\": \"EVENING_PHONE\", \"EMAIL\": \"EMAIL\",","field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations, schools, locations, teachers,","required fields that graphcool import required\" for x in dicts: x[\"_typeName\"] = type_name","or [] def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row):","to a different school. # We have to collect all the schools that","\"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\":","from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the","https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX = \"./data/CLP_combined.xlsx\"","cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \"\"\"Convert the","for schoolId in location.get(\"schools\", [])] # Build teacher -> school relations yield [make_relation(\"ClpTeacher\",","Make one up for the time being for x in injected: x[\"clpLocationId\"] =","have to collect all the schools that the same teacher is teaching at.","\"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\": \"ORGANISATION_ID\", \"SCHOOL_ID\": \"SCHOOL_ID\", } STUDENT_FIELDS = {\"SCHOOL_NAME\": \"SCHOOL_NAME\", \"SCHOOL_ID\":","\"LOC_S_POSTCODE\"] ORGANISATION_FIELDS = {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\",","\"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations, teachers,","\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"SCHOOL_S_ADDRESS1\": \"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS =","\"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", }","school_keys[student[\"schoolId\"]], \"students\") for student in students if student[\"schoolId\"] in school_keys] def main(): xlsx_path","schoolId in location.get(\"schools\", [])] # Build teacher -> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"],","for being related to a different school. # We have to collect all","CLP location id :( Make one up for the time being for x","\"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\",","= unique(\"clpStudentId\", students) injected = inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"] =","chunk_size = 1 + len(l) // n for i in range(0, len(l), chunk_size):","= [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in","dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored title into camel case.","import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects","titles, field_defns): if titles != sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i, x)","enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x","f: nodes = { \"valueType\": \"nodes\", \"values\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for","\"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"] ORGANISATION_FIELDS =","\"DOB\": \"DATE_OF_BIRTH\", \"TEL\": \"PHONE\", \"LOCATION_NAME_1\": \"DAY_SCHOOL\", } class Sheet: \"Data container object to","\"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\":","raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1,","\"Data container object to hold the contents of one sheet within an excel","name, titles=None, rows=None): self.name = name self.titles = titles or [] self.rows =","unique(key, dicts): t = {x[key]: x for x in dicts} return t.values() def","related to a different school. # We have to collect all the schools","now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs)","\"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\", \"LOC_S_SUBURB\", \"LOC_S_STATE\", \"LOC_S_POSTCODE\"]","def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if cell.Value","list()) related_schools.append(x.pop(\"schoolId\")) injected = inject_required(\"ClpTeacher\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in","schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\":","students) injected = inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return","{\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\":","= relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path,","= os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\": \"relations\",","teachers, students): # Build school -> organisation relations org_keys = {x[\"clpOrganisationId\"]: x[\"id\"] for","[])] # Build student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]],","cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title,","in field_defns] return structs def unique(key, dicts): t = {x[key]: x for x","\"organisation\") for x in schools] # Build location -> school relations school_keys =","for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like","path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\") as f: nodes = { \"valueType\":","enumerate(s.split(\"_\"))] return \"\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields,","def prepare_locations(locations): # There are multiple locations, each of which is identitical except","teachers: # get an existing teacher with that id, or add the new","+ \".0Z\" # GraphCool import insists on microseconds, hence the \".0\" def prepare_students(students):","x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like locations,","[(x.lower() if i == 0 else x.title()) for (i, x) in enumerate(s.split(\"_\"))] return","different school. # We have to collect all the schools that the same","\"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name","We have to collect all the schools that meet at the same location.","school. # We have to collect all the schools that meet at the","of the given dictionary with all the given keys removed\" copies = [x.copy()","x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs","== \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students,","def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or []","= x[\"updatedAt\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs","school_keys[schoolId], \"teachers\") for teacher in teachers for schoolId in teacher.get(\"schools\", [])] # Build","} class Sheet: \"Data container object to hold the contents of one sheet","GraphCool import insists on microseconds, hence the \".0\" def prepare_students(students): uniques = unique(\"clpStudentId\",","chunks from l.\"\"\" chunk_size = 1 + len(l) // n for i in","the CLP location id :( Make one up for the time being for","injected = inject_required(\"ClpStudent\", uniques) for x in injected: x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected","titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data =","= {} for x in teachers: # get an existing teacher with that","given XLSX spreadsheet to iterable of Sheet objects, in which row has been","def to_camel(s): \"\"\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits","extract doesn't include the CLP location id :( Make one up for the","- Current extract doesn't include the CLP location id :( Make one up","or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data = {}","rows or [] def convert_row_to_dict(titles, row): data = {} for (i, cell) in","if titles != sheet.titles: print(\"Sheet doesn't have expected titles:\", [(i, x) for (i,","prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\"_typeName\":","\"id\": id1, \"fieldName\": field1}, {\"_typeName\": entity2, \"id\": id2, \"fieldName\": field2} ] def generate_relations(organisations,","with the given name, or add the new location location = uniques.setdefault(x[\"name\"], x)","= {\"ORGANISATION_ID\": \"CLP_ORGANISATION_ID\", \"ORGANISATION_NAME\": \"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\",","x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x)","add the new location location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\"))",") = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations,","in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an","== \"SCHOOL-ORG\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif","location location = uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\",","install openpyxl # pip install cuid import os.path import json import datetime from","# Like locations, the same teacher can have multiple records, # each of","prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\"_typeName\": entity1,","\"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\":","\"DEGREE_COUNTRY\", \"DEGREE_YEAR\", \"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\",","to collect all the schools that meet at the same location. uniques =","= {} for x in locations: # get an existing location with the","relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)]","-> school relations yield [make_relation(\"ClpTeacher\", teacher[\"id\"], \"schools\", \"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in","inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\",","school_keys[schoolId], \"locations\") for location in locations for schoolId in location.get(\"schools\", [])] # Build","for x in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove,","[\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\", \"LOCATION_NAME_1\"] TEACHER_FIELDS = {\"TEACHER_ID\":","prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools):","in injected: x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the string from","student in students if student[\"schoolId\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections","for sheet in work_book: rows = [x for x in sheet.iter_rows()] if rows:","id, or add the new teacher record teacher = uniques.setdefault(x[\"clpTeacherId\"], x) related_schools =","with open(path, \"w\") as f: nodes = { \"valueType\": \"nodes\", \"values\": one_list }","\"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\",","student -> school relations yield [make_relation(\"ClpStudent\", student[\"id\"], \"school\", \"ClpSchool\", school_keys[student[\"schoolId\"]], \"students\") for student","\"ORGANISATION_ID\", \"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \"LOCATION_NAME\", \"STUDENT_LNAME\", \"STUDENT_FNAME\", \"DOB\", \"TEL\",","def prepare_organisations(organisations): unique_orgs = unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def","SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif","\"ClpSchool\", school_keys[schoolId], \"locations\") for location in locations for schoolId in location.get(\"schools\", [])] #","object to hold the contents of one sheet within an excel spreadsheet\" def","(i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir,","{\"TEACHER_ID\": \"CLP_TEACHER_ID\", \"ORGANISATION_NAME\": \"ORGANISATION_NAME\", \"SCHOOL_NAME\": \"SCHOOL_NAME\", \"TITLE\": \"TITLE\", \"LNAME\": \"FAMILY_NAME\", \"FNAME\": \"GIVEN_NAMES\", \"TEACHER_LANGUAGES\":","datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in","\"TEACHER_LANGUAGES\": \"LANGUAGES\", \"P_ADDRESS1\": \"ADDRESS1\", \"P_ADDRESS2\": \"ADDRESS2\", \"P_SUBURB\": \"SUBURB\", \"P_STATE\": \"STATE\", \"P_POSTCODE\": \"POSTCODE\", \"TELEPHONE\":","get an existing location with the given name, or add the new location","for x in locations: # get an existing location with the given name,","(students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\"Ignoring sheet:\", sheet.name) return (organisations, schools,","into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\"\"\" bits = [(x.lower() if i == 0","return fat_orgs def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return","enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file):","write_nodes( organisations, copy_without(schools, \"clpOrganisationId\"), copy_without(locations, \"schools\"), copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students,","{} for x in locations: # get an existing location with the given","x) for x in sheet.rows] for defn in field_defns] return structs def unique(key,","\"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\", \"P_ADDRESS2\", \"P_SUBURB\", \"P_STATE\", \"P_POSTCODE\", \"TELEPHONE\", \"TEL_EVENING\",","copy_without(teachers, \"organisationId\", \"organisationName\", \"schools\", \"schoolName\"), *chunks(3, copy_without(students, \"schoolId\", \"schoolName\", \"location\"))) write_relations(generate_relations(organisations, schools, locations,","that graphcool import required\" for x in dicts: x[\"_typeName\"] = type_name x[\"id\"] =","= unique(\"clpOrganisationId\", organisations) fat_orgs = inject_required(\"ClpOrganisation\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques =","return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject the","now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \"Z\" def inject_required(type_name, dicts): \"Inject the required fields that","path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for","+ \"Z\" def inject_required(type_name, dicts): \"Inject the required fields that graphcool import required\"","field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation","Build location -> school relations school_keys = {x[\"clpSchoolId\"]: x[\"id\"] for x in schools}","\"ADDRESS\", \"SCHOOL_S_SUBURB\": \"SUBURB\", \"SCHOOL_S_STATE\": \"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\":","are multiple locations, each of which is identitical except that for being related","injected def convert_dob_to_datetime(s): \"Convert the string from 99/MON/YY to a ISO date\" dt","def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \"nodes\"))","x in injected: x[\"clpLocationId\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \"Convert the string","\"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\": \"ADDRESS\", \"LOC_S_SUBURB\": \"SUBURB\", \"LOC_S_STATE\": \"STATE\", \"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES","def prepare_schools(schools): uniques = unique(\"clpSchoolId\", schools) injected = inject_required(\"ClpSchool\", uniques) return injected def","x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"], \"organisation\") for","in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles,","\"NAME\", \"ORG_ELECTORATE\": \"ELECTORATE\", \"S_ADDRESS1\": \"ADDRESS\", \"S_SUBURB\": \"SUBURB\", \"S_STATE\": \"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS","at the same location. uniques = {} for x in locations: # get","given dictionary with all the given keys removed\" copies = [x.copy() for x","f: nodes = { \"valueType\": \"relations\", \"values\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l):","for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list)","\"STATE\", \"S_POSTCODE\": \"POSTCODE\", } SCHOOL_FIELDS = {\"SCHOOL_NAME\": \"NAME\", \"SCH_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"ORGANISATION_ID\":","rows = [x for x in sheet.iter_rows()] if rows: titles = [cell.value for","doesn't have expected titles:\", [(i, x) for (i, x) in enumerate(titles) if x","process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \"Student\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS])","# pip install cuid import os.path import json import datetime from openpyxl import","= {x[\"clpOrganisationId\"]: x[\"id\"] for x in organisations} yield [make_relation(\"ClpOrganisation\", org_keys[x[\"clpOrganisationId\"]], \"schools\", \"ClpSchool\", x[\"id\"],","locations for schoolId in location.get(\"schools\", [])] # Build teacher -> school relations yield","schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return iterable that contains copies of","data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles:","have to collect all the schools that meet at the same location. uniques","\"-relations\" + str(i), \"relations\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \"1.json\") with open(path, \"w\")","is identitical except that for being related to a different school. # We","__init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or [] self.rows","in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for","in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers,","\"LOC_S_POSTCODE\": \"POSTCODE\"} TEACHER_TITLES = [\"TEACHER_ID\", \"ORGANISATION_NAME\", \"SCHOOL_NAME\", \"TEACHER_NAME\", \"TITLE\", \"LNAME\", \"FNAME\", \"TEACHER_LANGUAGES\", \"P_ADDRESS1\",","rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \"\"\"Convert an underscored","\"MOBILE\": \"MOBILE\", \"LEVEL_TAUGHT\": \"LEVEL_TAUGHT\", \"LEVEL_OF_EDUCATION\": \"EDUCATION_LEVEL\", \"FIELD_OF_EDUCATION\": \"EDUCATION_FIELD\", \"DEGREE_COUNTRY\": \"EDUCATION_COUNTRY\", \"DEGREE_YEAR\": \"EDUCATION_YEAR\", \"ORGANISATION_ID\":","len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return","\"STATE\", \"SCHOOL_S_POSTCODE\": \"POSTCODE\", } LOCATION_FIELDS = {\"LOCATION_NAME\": \"NAME\", \"LOC_ELECTORATE\": \"ELECTORATE\", \"SCHOOL_ID\": \"CLP_SCHOOL_ID\", \"LOC_S_ADDRESS1\":","\"ClpSchool\", school_keys[schoolId], \"teachers\") for teacher in teachers for schoolId in teacher.get(\"schools\", [])] #","print(\"Ignoring sheet:\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \"Return","excel spreadsheet\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles","inject_required(\"ClpSchool\", uniques) return injected def prepare_locations(locations): # There are multiple locations, each of","\"students\") for student in students if student[\"schoolId\"] in school_keys] def main(): xlsx_path =","prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [","x[\"dateOfBirth\"] = convert_dob_to_datetime(x[\"dateOfBirth\"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher","uniques.setdefault(x[\"name\"], x) related_schools = location.setdefault(\"schools\", list()) related_schools.append(x.pop(\"clpSchoolId\")) injected = inject_required(\"ClpLocation\", uniques.values()) # FIX","[cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]]","os.path import json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py","\"SCH_ELECTORATE\", \"SCHOOL_ID\", \"SCHOOL_P_ADDRESS1\", \"SCHOOL_P_SUBURB\", \"SCHOOL_P_STATE\", \"SCHOOL_P_POSTCODE\", \"SCHOOL_S_ADDRESS1\", \"SCHOOL_S_SUBURB\", \"SCHOOL_S_STATE\", \"SCHOOL_S_POSTCODE\", \"LOCATION_NAME\", \"LOC_ELECTORATE\", \"LOC_S_ADDRESS1\","],"string":"[\n \"similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1 + len(l) // n for i\",\n \"LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name\",\n \"\\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"]\",\n \"self.rows = rows or [] def convert_row_to_dict(titles, row): data = {} for (i,\",\n \"exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = {\",\n \"= [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS =\",\n \"school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\",\",\n \"\\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\":\",\n \"self.name = name self.titles = titles or [] self.rows = rows or []\",\n \"return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\":\",\n \"[ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS])\",\n \"locations, teachers, students): # Build school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"]\",\n \"org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools] # Build location ->\",\n \"f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size =\",\n \"+ str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as\",\n \"str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f:\",\n \"list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\"\",\n \"= \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\",\",\n \"= {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\",\",\n \"\\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized\",\n \"[make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for schoolId in\",\n \"that for being related to a different school. # We have to collect\",\n \"graphcool import required\\\" for x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid()\",\n \"l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools),\",\n \"school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"],\",\n \"\\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\",\",\n \"of Sheet objects, in which row has been converted into a dictionary\\\"\\\"\\\" work_book\",\n \"for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs =\",\n \"\\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS =\",\n \"in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES,\",\n \"yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if student[\\\"schoolId\\\"]\",\n \"multiple records, # each of which is identitical except that for being related\",\n \"camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i == 0 else\",\n \"x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations)\",\n \"\\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\",\",\n \"\\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\":\",\n \"injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like locations, the same\",\n \"= [x for x in sheet.iter_rows()] if rows: titles = [cell.value for cell\",\n \"yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools] # Build\",\n \"\\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\",\",\n \"[\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\",\",\n \"add the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list())\",\n \"Sheet objects, in which row has been converted into a dictionary\\\"\\\"\\\" work_book =\",\n \"\\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\",\",\n \"enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\")\",\n \"return injected def prepare_teachers(teachers): # Like locations, the same teacher can have multiple\",\n \"get an existing teacher with that id, or add the new teacher record\",\n \"EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\",\",\n \"{\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations, teachers, students):\",\n \"yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for schoolId\",\n \"data = {} for (i, cell) in enumerate(row): if cell.Value is not None:\",\n \"on microseconds, hence the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected =\",\n \"\\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\",\",\n \"unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques)\",\n \"import required\\\" for x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"]\",\n \"LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\":\",\n \"\\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\",\",\n \"from l.\\\"\\\"\\\" chunk_size = 1 + len(l) // n for i in range(0,\",\n \"return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable of Sheet\",\n \"SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\",\",\n \"\\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\",\",\n \"that meet at the same location. uniques = {} for x in locations:\",\n \"uniques = {} for x in teachers: # get an existing teacher with\",\n \"one sheet within an excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name =\",\n \"list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs\",\n \"unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques\",\n \"fields that graphcool import required\\\" for x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"]\",\n \"\\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students))\",\n \"\\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\",\",\n \"prepare_teachers(teachers): # Like locations, the same teacher can have multiple records, # each\",\n \"\\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks\",\n \"return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2,\",\n \"\\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\",\",\n \"\\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object to hold\",\n \"in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir,\",\n \"def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations org_keys\",\n \"school. # We have to collect all the schools that the same teacher\",\n \"[] structs = [[extract(defn, x) for x in sheet.rows] for defn in field_defns]\",\n \"an existing teacher with that id, or add the new teacher record teacher\",\n \"with all the given keys removed\\\" copies = [x.copy() for x in dicts]\",\n \"titles:\\\", [(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return\",\n \"open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes))\",\n \"identitical except that for being related to a different school. # We have\",\n \"[(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return []\",\n \"row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert\",\n \"\\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\":\",\n \"if rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row)\",\n \"\\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\":\",\n \"entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): #\",\n \"[make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools] # Build location\",\n \"if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in\",\n \"x in teachers: # get an existing teacher with that id, or add\",\n \"\\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS\",\n \"prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x in injected:\",\n \"sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that\",\n \"not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX\",\n \"\\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\",\",\n \"x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS\",\n \"= os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"nodes\\\",\",\n \"field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2,\",\n \"\\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\",\",\n \"= {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def\",\n \"teacher can have multiple records, # each of which is identitical except that\",\n \"x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for\",\n \"different school. # We have to collect all the schools that meet at\",\n \"Sheet: \\\"Data container object to hold the contents of one sheet within an\",\n \"for x in schools] # Build location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]:\",\n \"def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable of Sheet objects, in\",\n \"or add the new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list())\",\n \"write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i),\",\n \"for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def\",\n \"same teacher is teaching at. uniques = {} for x in teachers: #\",\n \"(i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path\",\n \"\\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\":\",\n \"nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with\",\n \"graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\",\",\n \"location in locations for schoolId in location.get(\\\"schools\\\", [])] # Build teacher -> school\",\n \"openpyxl # pip install cuid import os.path import json import datetime from openpyxl\",\n \"the format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES =\",\n \"\\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\":\",\n \"dictionary with all the given keys removed\\\" copies = [x.copy() for x in\",\n \"make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\":\",\n \"\\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\":\",\n \"to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits =\",\n \"\\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\",\",\n \"an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if\",\n \"\\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir\",\n \"inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current extract doesn't include the CLP location\",\n \"\\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools] # Build location -> school\",\n \"\\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\",\",\n \"in sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t =\",\n \"[\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\",\",\n \"x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in\",\n \"in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data def\",\n \"fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected\",\n \"class Sheet: \\\"Data container object to hold the contents of one sheet within\",\n \"a different school. # We have to collect all the schools that meet\",\n \"in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools]\",\n \"\\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\",\",\n \"to iterable of Sheet objects, in which row has been converted into a\",\n \"except that for being related to a different school. # We have to\",\n \"# GraphCool import insists on microseconds, hence the \\\".0\\\" def prepare_students(students): uniques =\",\n \"in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists):\",\n \"\\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir =\",\n \"field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i, x) for\",\n \"\\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\":\",\n \"dt.isoformat() + \\\".0Z\\\" # GraphCool import insists on microseconds, hence the \\\".0\\\" def\",\n \"convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS,\",\n \"which is identitical except that for being related to a different school. #\",\n \"= relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\")\",\n \"in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def\",\n \"{x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\")\",\n \"import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's\",\n \"related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current extract doesn't include\",\n \"keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir\",\n \"\\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\",\",\n \"teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values())\",\n \"x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path)\",\n \"for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x\",\n \"return injected def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to a ISO date\\\"\",\n \"x in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None)\",\n \"-> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\",\",\n \"\\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for schoolId in teacher.get(\\\"schools\\\", [])]\",\n \"a different school. # We have to collect all the schools that the\",\n \"= load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for x\",\n \"convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable of Sheet objects, in which\",\n \"\\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\",\",\n \"return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return\",\n \"all the schools that meet at the same location. uniques = {} for\",\n \"\\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"]\",\n \"in locations: # get an existing location with the given name, or add\",\n \"[] def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if\",\n \"row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def\",\n \"structs = [[extract(defn, x) for x in sheet.rows] for defn in field_defns] return\",\n \"dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool import insists on\",\n \"+ chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations),\",\n \"schools] # Build location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x\",\n \"for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return\",\n \"install cuid import os.path import json import datetime from openpyxl import load_workbook import\",\n \"\\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\",\",\n \"ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\":\",\n \"in work_book: rows = [x for x in sheet.iter_rows()] if rows: titles =\",\n \"injected def prepare_locations(locations): # There are multiple locations, each of which is identitical\",\n \"Build student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for\",\n \"\\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\":\",\n \"with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) }\",\n \"school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students\",\n \"related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS -\",\n \"\\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\",\",\n \"return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR +\",\n \"def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1\",\n \"\\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\",\",\n \"= { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list)\",\n \"injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): # There are multiple locations,\",\n \"\\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\":\",\n \"for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield\",\n \"\\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required fields that graphcool import required\\\" for\",\n \"\\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES\",\n \"THIS - Current extract doesn't include the CLP location id :( Make one\",\n \"expected titles:\\\", [(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]])\",\n \"dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return\",\n \"sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\":\",\n \"\\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\",\",\n \"row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return\",\n \"prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return\",\n \"STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\":\",\n \"os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\":\",\n \"for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools,\",\n \"[convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title)\",\n \"in the format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES\",\n \"= str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable\",\n \"locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\",\",\n \"schools that the same teacher is teaching at. uniques = {} for x\",\n \"\\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\",\",\n \"\\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\",\",\n \"locations: # get an existing location with the given name, or add the\",\n \"uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): #\",\n \"uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected\",\n \"format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\",\",\n \"for x in sheet.rows] for defn in field_defns] return structs def unique(key, dicts):\",\n \"from 99/MON/YY to a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() +\",\n \"can have multiple records, # each of which is identitical except that for\",\n \"\\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool import insists on microseconds, hence the\",\n \"copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations,\",\n \"the contents of one sheet within an excel spreadsheet\\\" def __init__(self, name, titles=None,\",\n \"\\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\",\",\n \"[make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations for schoolId in\",\n \"x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to\",\n \"Build school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations}\",\n \"\\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\",\",\n \"def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students)\",\n \"process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i,\",\n \"\\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\",\",\n \"inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name ==\",\n \"copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i),\",\n \"dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies\",\n \"is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given\",\n \"os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in fields.items():\",\n \"if i == 0 else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits)\",\n \"all the schools that the same teacher is teaching at. uniques = {}\",\n \"\\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", }\",\n \"create uuid's in the format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR =\",\n \"\\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\":\",\n \"meet at the same location. uniques = {} for x in locations: #\",\n \"\\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def\",\n \"n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1 + len(l) // n\",\n \"to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in\",\n \"x[\\\"id\\\"], \\\"organisation\\\") for x in schools] # Build location -> school relations school_keys\",\n \"process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations, teachers, students)\",\n \"(i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py,\",\n \"spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or\",\n \"load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for x in\",\n \"read_only=True, data_only=True) for sheet in work_book: rows = [x for x in sheet.iter_rows()]\",\n \"import insists on microseconds, hence the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students)\",\n \"multiple locations, each of which is identitical except that for being related to\",\n \"= prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"),\",\n \"copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\",\",\n \"school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield\",\n \"n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations,\",\n \"the schools that the same teacher is teaching at. uniques = {} for\",\n \"= location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current\",\n \"location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield\",\n \"in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles\",\n \"generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations org_keys =\",\n \"\\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\",\",\n \"teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher\",\n \"1 + len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i\",\n \"= [(x.lower() if i == 0 else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))]\",\n \"= [[extract(defn, x) for x in sheet.rows] for defn in field_defns] return structs\",\n \"sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet,\",\n \"{} for x in teachers: # get an existing teacher with that id,\",\n \"= process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, )\",\n \"students if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path)\",\n \"hold the contents of one sheet within an excel spreadsheet\\\" def __init__(self, name,\",\n \"\\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\",\",\n \"x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs =\",\n \"\\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\",\",\n \"extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations,\",\n \"= {} for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]]\",\n \"\\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\":\",\n \"\\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class\",\n \"keys removed\\\" copies = [x.copy() for x in dicts] for d in copies:\",\n \"=> 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i == 0 else x.title()) for (i,\",\n \"= {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\",\",\n \"for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the\",\n \"FIX THIS - Current extract doesn't include the CLP location id :( Make\",\n \"d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir =\",\n \"# https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX =\",\n \"one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path =\",\n \"entity2, id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2,\",\n \"\\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\":\",\n \"\\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\",\",\n \"= { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n\",\n \"def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" +\",\n \"SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\":\",\n \"= teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for\",\n \"\\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\",\",\n \"'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i == 0 else x.title()) for (i, x)\",\n \"\\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\",\",\n \"[ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2}\",\n \"\\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\",\",\n \"removed\\\" copies = [x.copy() for x in dicts] for d in copies: for\",\n \"schoolId in teacher.get(\\\"schools\\\", [])] # Build student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"],\",\n \"data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to\",\n \"rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts)\",\n \"\\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools,\",\n \"collect all the schools that the same teacher is teaching at. uniques =\",\n \"- create uuid's in the format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR\",\n \"in teachers: # get an existing teacher with that id, or add the\",\n \"\\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\",\",\n \"\\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\",\",\n \"type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations):\",\n \"structs def unique(key, dicts): t = {x[key]: x for x in dicts} return\",\n \"sheet:\\\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable\",\n \"as f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists):\",\n \"for x in teachers: # get an existing teacher with that id, or\",\n \"entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def\",\n \"id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools,\",\n \"\\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\":\",\n \"x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in\",\n \"for location in locations for schoolId in location.get(\\\"schools\\\", [])] # Build teacher ->\",\n \"titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel\",\n \"\\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools] # Build location -> school relations\",\n \"in locations for schoolId in location.get(\\\"schools\\\", [])] # Build teacher -> school relations\",\n \"\\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\",\",\n \"else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py =\",\n \"iterable of Sheet objects, in which row has been converted into a dictionary\\\"\\\"\\\"\",\n \") def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\":\",\n \"x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts)\",\n \"if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations,\",\n \"cuid import os.path import json import datetime from openpyxl import load_workbook import cuid\",\n \"\\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\",\",\n \"for defn in field_defns] return structs def unique(key, dicts): t = {x[key]: x\",\n \"\\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build\",\n \"pip install cuid import os.path import json import datetime from openpyxl import load_workbook\",\n \"name self.titles = titles or [] self.rows = rows or [] def convert_row_to_dict(titles,\",\n \"given keys removed\\\" copies = [x.copy() for x in dicts] for d in\",\n \"\\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\":\",\n \"\\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes\",\n \"yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations for schoolId\",\n \"SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES,\",\n \"data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable of Sheet objects,\",\n \"write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir,\",\n \"which row has been converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True)\",\n \"= extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"),\",\n \"hence the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques)\",\n \"cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX\",\n \"\\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\",\",\n \"\\\"locations\\\") for location in locations for schoolId in location.get(\\\"schools\\\", [])] # Build teacher\",\n \"= {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\",\",\n \"0 else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py\",\n \"# FIX THIS - Current extract doesn't include the CLP location id :(\",\n \"x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name,\",\n \"*chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ ==\",\n \"\\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\":\",\n \"schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): # There are multiple\",\n \"\\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\":\",\n \"# There are multiple locations, each of which is identitical except that for\",\n \"[make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if student[\\\"schoolId\\\"] in\",\n \"one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR\",\n \"\\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\")))\",\n \"= inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name\",\n \"def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\",\",\n \"\\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if\",\n \"the time being for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def\",\n \"in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts):\",\n \"\\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\",\",\n \"titles=None, rows=None): self.name = name self.titles = titles or [] self.rows = rows\",\n \"uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\":\",\n \"in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations\",\n \"\\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\",\",\n \"= inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): # There are multiple locations, each\",\n \"records, # each of which is identitical except that for being related to\",\n \"TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\",\",\n \"dicts): \\\"Inject the required fields that graphcool import required\\\" for x in dicts:\",\n \"copies = [x.copy() for x in dicts] for d in copies: for to_remove\",\n \"def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1,\",\n \"row): data = {} for (i, cell) in enumerate(row): if cell.Value is not\",\n \"to hold the contents of one sheet within an excel spreadsheet\\\" def __init__(self,\",\n \"\\\"teachers\\\") for teacher in teachers for schoolId in teacher.get(\\\"schools\\\", [])] # Build student\",\n \"else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID'\",\n \"\\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\",\",\n \"# Build location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in\",\n \"for the time being for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected\",\n \"def inject_required(type_name, dicts): \\\"Inject the required fields that graphcool import required\\\" for x\",\n \"\\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\",\",\n \"each of which is identitical except that for being related to a different\",\n \"convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to a ISO date\\\" dt = datetime.datetime.strptime(s,\",\n \"return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations,\",\n \"= 1 + len(l) // n for i in range(0, len(l), chunk_size): yield\",\n \"{x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\")\",\n \"copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ == \\\"__main__\\\":\",\n \"uniques = {} for x in locations: # get an existing location with\",\n \"\\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\":\",\n \"in schools] # Build location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for\",\n \"\\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\",\",\n \"\\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\":\",\n \"= cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to a\",\n \"yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations),\",\n \"\\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\",\",\n \"id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\":\",\n \"\\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\",\",\n \"yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title\",\n \"[TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring\",\n \"underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i\",\n \"at. uniques = {} for x in teachers: # get an existing teacher\",\n \"= uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) #\",\n \"the same teacher is teaching at. uniques = {} for x in teachers:\",\n \"unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"])\",\n \"chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return (\",\n \"rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for\",\n \"99/MON/YY to a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\"\",\n \"students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies of the given dictionary\",\n \"uniques) for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): #\",\n \"sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i, x) for (i, x) in enumerate(titles)\",\n \"id1, field1, entity2, id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1},\",\n \"def prepare_teachers(teachers): # Like locations, the same teacher can have multiple records, #\",\n \"\\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__\",\n \"= inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected =\",\n \"= {x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat()\",\n \"if cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert\",\n \"# get an existing teacher with that id, or add the new teacher\",\n \"\\\".0Z\\\" # GraphCool import insists on microseconds, hence the \\\".0\\\" def prepare_students(students): uniques\",\n \"\\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes\",\n \"i == 0 else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def\",\n \"(k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns):\",\n \"# get an existing location with the given name, or add the new\",\n \"= os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k,\",\n \"\\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x\",\n \"unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): # There are\",\n \"return structs def unique(key, dicts): t = {x[key]: x for x in dicts}\",\n \"\\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school ->\",\n \"!= sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows] for\",\n \"Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for\",\n \"\\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def\",\n \"as f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n,\",\n \"\\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\",\",\n \"injected def prepare_teachers(teachers): # Like locations, the same teacher can have multiple records,\",\n \") = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet,\",\n \"id :( Make one up for the time being for x in injected:\",\n \"return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains\",\n \"contains copies of the given dictionary with all the given keys removed\\\" copies\",\n \"the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\"))\",\n \"def extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] =\",\n \"x in sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t\",\n \"bits = [(x.lower() if i == 0 else x.title()) for (i, x) in\",\n \"\\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\",\",\n \"{ \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive\",\n \"location id :( Make one up for the time being for x in\",\n \"= process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations, teachers,\",\n \"sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles,\",\n \"id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school\",\n \"for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def\",\n \"= now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\",\",\n \"enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path =\",\n \"nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir,\",\n \"location.get(\\\"schools\\\", [])] # Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\",\",\n \"{} for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] =\",\n \"teacher.get(\\\"schools\\\", [])] # Build student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\",\",\n \"case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i == 0 else x.title())\",\n \"ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool import\",\n \"prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations):\",\n \"print(\\\"Sheet doesn't have expected titles:\\\", [(i, x) for (i, x) in enumerate(titles) if\",\n \"inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers):\",\n \"in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like locations, the\",\n \"\\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\",\",\n \"doesn't include the CLP location id :( Make one up for the time\",\n \"contents of one sheet within an excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None):\",\n \"def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data =\",\n \"location with the given name, or add the new location location = uniques.setdefault(x[\\\"name\\\"],\",\n \"x in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts\",\n \"\\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object to hold the\",\n \"pip install openpyxl # pip install cuid import os.path import json import datetime\",\n \"convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher can have\",\n \"the same teacher can have multiple records, # each of which is identitical\",\n \"cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\",\",\n \"with that id, or add the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x)\",\n \"\\\"Convert the string from 99/MON/YY to a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\")\",\n \"of which is identitical except that for being related to a different school.\",\n \"being related to a different school. # We have to collect all the\",\n \"def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students)\",\n \"organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for x in schools] #\",\n \"cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to a ISO\",\n \"= datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool import insists on microseconds,\",\n \"elif sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name ==\",\n \"{x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() +\",\n \"prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) )\",\n \"\\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\",\",\n \"def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations)\",\n \"Like locations, the same teacher can have multiple records, # each of which\",\n \"import json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py -\",\n \"{ \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in\",\n \"\\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\",\",\n \"uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"]\",\n \"\\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\":\",\n \"spreadsheet to iterable of Sheet objects, in which row has been converted into\",\n \"injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools,\",\n \"\\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ == \\\"__main__\\\": main()\",\n \"TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else:\",\n \"load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool\",\n \"one up for the time being for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid()\",\n \"all the given keys removed\\\" copies = [x.copy() for x in dicts] for\",\n \"extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) =\",\n \"sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t = {x[key]:\",\n \"\\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS =\",\n \"Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title into\",\n \"There are multiple locations, each of which is identitical except that for being\",\n \"raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def\",\n \"string from 99/MON/YY to a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat()\",\n \"\\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data\",\n \"data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't have expected\",\n \"= cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs =\",\n \"collect all the schools that meet at the same location. uniques = {}\",\n \"{\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ]\",\n \"= rows or [] def convert_row_to_dict(titles, row): data = {} for (i, cell)\",\n \"\\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations,\",\n \"for sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet(\",\n \"convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if cell.Value is\",\n \"in teachers for schoolId in teacher.get(\\\"schools\\\", [])] # Build student -> school relations\",\n \"= row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\\\"Sheet\",\n \"= [x.copy() for x in dicts] for d in copies: for to_remove in\",\n \"# We have to collect all the schools that the same teacher is\",\n \"dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else:\",\n \"= unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): # There\",\n \"raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1,\",\n \"organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools)\",\n \"} f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR +\",\n \"for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\"))\",\n \"\\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object\",\n \"relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {}\",\n \"v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if\",\n \"has been converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet\",\n \"x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__))\",\n \"def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to a ISO date\\\" dt =\",\n \"== 0 else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path):\",\n \"SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\",\",\n \"\\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\",\",\n \"same teacher can have multiple records, # each of which is identitical except\",\n \"sheet in convert_xlsx(file_path): if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet,\",\n \"up for the time being for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return\",\n \"field_defns] return structs def unique(key, dicts): t = {x[key]: x for x in\",\n \"return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in\",\n \"\\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\",\",\n \"\\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\",\",\n \"the string from 99/MON/YY to a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return\",\n \"\\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, )\",\n \"\\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES =\",\n \"into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows\",\n \"schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations for\",\n \"schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers,\",\n \"title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i ==\",\n \"+ \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path,\",\n \"[[extract(defn, x) for x in sheet.rows] for defn in field_defns] return structs def\",\n \"# Build school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in\",\n \"\\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\",\",\n \"\\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\",\",\n \"} STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\",\",\n \"have multiple records, # each of which is identitical except that for being\",\n \"relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\",\",\n \"an excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles =\",\n \"i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations,\",\n \"(organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"),\",\n \"x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def\",\n \"in location.get(\\\"schools\\\", [])] # Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\",\",\n \"titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row\",\n \"schools, locations, teachers, students): # Build school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]:\",\n \"teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\",\",\n \"that id, or add the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools\",\n \"os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v)\",\n \"\\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", }\",\n \"in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return\",\n \"students): # Build school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x\",\n \"new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected\",\n \"def unique(key, dicts): t = {x[key]: x for x in dicts} return t.values()\",\n \"\\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\":\",\n \"\\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\",\",\n \"the given XLSX spreadsheet to iterable of Sheet objects, in which row has\",\n \"injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current extract doesn't include the\",\n \"STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations, teachers, students) def\",\n \"d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists):\",\n \"\\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\",\",\n \"\\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\",\",\n \"related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path):\",\n \"f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\"\",\n \"location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current extract\",\n \"\\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\":\",\n \"\\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet:\",\n \"return [] structs = [[extract(defn, x) for x in sheet.rows] for defn in\",\n \"def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required fields\",\n \"datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required fields that graphcool import\",\n \"\\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\",\",\n \"= process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES,\",\n \"successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1 + len(l) // n for\",\n \"( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2,\",\n \"student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if student[\\\"schoolId\\\"] in school_keys]\",\n \"\\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\",\",\n \"relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\",\",\n \"list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current extract doesn't\",\n \"work_book: rows = [x for x in sheet.iter_rows()] if rows: titles = [cell.value\",\n \"\\\"Inject the required fields that graphcool import required\\\" for x in dicts: x[\\\"_typeName\\\"]\",\n \"in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for\",\n \"self.titles = titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row):\",\n \"time being for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s):\",\n \"= uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return\",\n \"organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]],\",\n \"\\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if student[\\\"schoolId\\\"] in school_keys] def main():\",\n \"\\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\",\",\n \"container object to hold the contents of one sheet within an excel spreadsheet\\\"\",\n \"sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name)\",\n \"\\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\",\",\n \"\\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations for schoolId in location.get(\\\"schools\\\", [])]\",\n \"the schools that meet at the same location. uniques = {} for x\",\n \"{\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\":\",\n \"data_only=True) for sheet in work_book: rows = [x for x in sheet.iter_rows()] if\",\n \"field1, entity2, id2, field2): return [ {\\\"_typeName\\\": entity1, \\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\":\",\n \"\\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\",\",\n \"TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\":\",\n \"range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students):\",\n \"return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict):\",\n \"] def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations\",\n \"t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required\",\n \"= [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\",\",\n \"\\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1 + len(l) //\",\n \"for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location\",\n \"} SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\",\",\n \"for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True)\",\n \"the given name, or add the new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools\",\n \"# Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\")\",\n \"\\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\",\",\n \"record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\",\",\n \"related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path):\",\n \"name, or add the new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\",\",\n \"\\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable of Sheet objects, in which row\",\n \"= convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher can\",\n \"cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data\",\n \"\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\":\",\n \"the given dictionary with all the given keys removed\\\" copies = [x.copy() for\",\n \"\\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS\",\n \"\\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\":\",\n \"\\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object to hold the contents\",\n \"Current extract doesn't include the CLP location id :( Make one up for\",\n \"\\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list)\",\n \"work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for\",\n \"\\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if student[\\\"schoolId\\\"] in school_keys] def\",\n \"main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) =\",\n \"open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes))\",\n \"for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles,\",\n \"dicts): t = {x[key]: x for x in dicts} return t.values() def now_as_iso8601():\",\n \"uuid's in the format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\"\",\n \"the new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected\",\n \"uniques.values()) # FIX THIS - Current extract doesn't include the CLP location id\",\n \"row has been converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for\",\n \"x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows]\",\n \"\\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\",\",\n \"injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if\",\n \"x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs\",\n \"teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies of the given\",\n \"teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet\",\n \"chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers),\",\n \"teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for schoolId in teacher.get(\\\"schools\\\",\",\n \"in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts =\",\n \"\\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\":\",\n \"\\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\",\",\n \"None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet\",\n \"of one sheet within an excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name\",\n \"expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\",\",\n \"relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for\",\n \"= relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes(\",\n \"nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i,\",\n \"import os.path import json import datetime from openpyxl import load_workbook import cuid #\",\n \"a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows =\",\n \"\\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\",\",\n \"given name, or add the new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools =\",\n \"teacher with that id, or add the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"],\",\n \"\\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations,\",\n \"locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies of the\",\n \"is teaching at. uniques = {} for x in teachers: # get an\",\n \"rows=None): self.name = name self.titles = titles or [] self.rows = rows or\",\n \"return dt.isoformat() + \\\".0Z\\\" # GraphCool import insists on microseconds, hence the \\\".0\\\"\",\n \"We have to collect all the schools that the same teacher is teaching\",\n \"dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x\",\n \"import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that\",\n \"existing teacher with that id, or add the new teacher record teacher =\",\n \"= \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\",\",\n \"str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the given XLSX spreadsheet to iterable of\",\n \"new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected =\",\n \"prepare_locations(locations): # There are multiple locations, each of which is identitical except that\",\n \"= inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX THIS - Current extract doesn't include the CLP\",\n \"!= sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i, x) for (i, x) in\",\n \"str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f:\",\n \"in students if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections =\",\n \"= titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data\",\n \"iterable that contains copies of the given dictionary with all the given keys\",\n \"for x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"]\",\n \"for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s):\",\n \"Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\"\",\n \"for teacher in teachers for schoolId in teacher.get(\\\"schools\\\", [])] # Build student ->\",\n \"in teacher.get(\\\"schools\\\", [])] # Build student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\",\",\n \"dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject\",\n \"= [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield\",\n \"one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True)\",\n \"for student in students if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX)\",\n \"\\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\":\",\n \"\\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data\",\n \"== \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return\",\n \"None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR\",\n \"extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k]\",\n \"relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with\",\n \"xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections)\",\n \"\\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object to hold the contents of one\",\n \"converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book:\",\n \"copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies of the given dictionary with all\",\n \"\\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\",\",\n \"same location. uniques = {} for x in locations: # get an existing\",\n \"yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' =>\",\n \"\\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\",\",\n \"len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size]\",\n \"the same location. uniques = {} for x in locations: # get an\",\n \"\\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS\",\n \"x in locations: # get an existing location with the given name, or\",\n \"= inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def\",\n \"that contains copies of the given dictionary with all the given keys removed\\\"\",\n \"+ len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i +\",\n \"the required fields that graphcool import required\\\" for x in dicts: x[\\\"_typeName\\\"] =\",\n \"existing location with the given name, or add the new location location =\",\n \"\\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES =\",\n \"json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create\",\n \":( Make one up for the time being for x in injected: x[\\\"clpLocationId\\\"]\",\n \"sheet in work_book: rows = [x for x in sheet.iter_rows()] if rows: titles\",\n \"location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations for schoolId in location.get(\\\"schools\\\",\",\n \"{} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet,\",\n \"t = {x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return\",\n \"\\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\", \\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\",\",\n \"being for x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert\",\n \"to collect all the schools that the same teacher is teaching at. uniques\",\n \"org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\",\",\n \"+ str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as\",\n \"students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\",\",\n \"[x for x in sheet.iter_rows()] if rows: titles = [cell.value for cell in\",\n \"locations, the same teacher can have multiple records, # each of which is\",\n \"} f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size\",\n \"\\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\",\",\n \"XLSX spreadsheet to iterable of Sheet objects, in which row has been converted\",\n \"relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\")\",\n \"l.\\\"\\\"\\\" chunk_size = 1 + len(l) // n for i in range(0, len(l),\",\n \"elif sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\",\",\n \"*keys_to_remove): \\\"Return iterable that contains copies of the given dictionary with all the\",\n \"return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required fields that graphcool\",\n \"-> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in\",\n \"\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\",\",\n \"\\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\":\",\n \"\\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks from\",\n \"STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS\",\n \"os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes =\",\n \"for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value)\",\n \"\\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\",\",\n \"required\\\" for x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] =\",\n \"return injected def prepare_locations(locations): # There are multiple locations, each of which is\",\n \"// n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def\",\n \"\\\"Return iterable that contains copies of the given dictionary with all the given\",\n \"microseconds, hence the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\",\",\n \"unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\",\",\n \"\\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\",\",\n \"fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles !=\",\n \"teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected =\",\n \"\\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"}\",\n \"in which row has been converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True,\",\n \"x in schools] # Build location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"]\",\n \"that the same teacher is teaching at. uniques = {} for x in\",\n \"os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\":\",\n \"process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, ) =\",\n \"relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if\",\n \"insists on microseconds, hence the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected\",\n \"the given keys removed\\\" copies = [x.copy() for x in dicts] for d\",\n \"= {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools} yield [make_relation(\\\"ClpLocation\\\", location[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId],\",\n \"injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY\",\n \"\\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\":\",\n \"within an excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles\",\n \"objects, in which row has been converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file,\",\n \"\\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list\",\n \"# Build student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\")\",\n \"to a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" #\",\n \"in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601()\",\n \"raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools,\",\n \"school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers\",\n \"{\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\":\",\n \"[])] # Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId],\",\n \"\\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\",\",\n \"prepare(*raw_collections) write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3,\",\n \"\\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\", \\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\",\",\n \"location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values())\",\n \"\\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\", \\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\",\",\n \"schools that meet at the same location. uniques = {} for x in\",\n \"= name self.titles = titles or [] self.rows = rows or [] def\",\n \"uniques) return injected def prepare_locations(locations): # There are multiple locations, each of which\",\n \"# each of which is identitical except that for being related to a\",\n \"x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\"\",\n \"x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] =\",\n \"fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected\",\n \"} LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\",\",\n \"inject_required(type_name, dicts): \\\"Inject the required fields that graphcool import required\\\" for x in\",\n \"\\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\":\",\n \"teaching at. uniques = {} for x in teachers: # get an existing\",\n \"for x in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]]\",\n \"sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows] for defn\",\n \"(organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies\",\n \"relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations,\",\n \"row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't\",\n \"prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2):\",\n \"= [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\", \\\"S_POSTCODE\\\", \\\"SCHOOL_NAME\\\",\",\n \"the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for\",\n \"# We have to collect all the schools that meet at the same\",\n \"an existing location with the given name, or add the new location location\",\n \"\\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\":\",\n \"\\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\":\",\n \"school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers,\",\n \"include the CLP location id :( Make one up for the time being\",\n \"\\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\",\",\n \"def prepare_students(students): uniques = unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x in\",\n \"\\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\":\",\n \"def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies of the given dictionary with\",\n \"sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS])\",\n \"\\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\",\",\n \"location. uniques = {} for x in locations: # get an existing location\",\n \"[STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts,\",\n \"teachers for schoolId in teacher.get(\\\"schools\\\", [])] # Build student -> school relations yield\",\n \"= type_name x[\\\"id\\\"] = cuid.cuid() x[\\\"createdAt\\\"] = x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def\",\n \"(i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn,\",\n \"that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\" EXTRACT_OUTPUT_DIR = \\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\",\",\n \"have expected titles:\\\", [(i, x) for (i, x) in enumerate(titles) if x !=\",\n \"copies of the given dictionary with all the given keys removed\\\" copies =\",\n \"\\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\", \\\"MOBILE\\\":\",\n \"\\\"STUDENT_FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container\",\n \"teacher is teaching at. uniques = {} for x in teachers: # get\",\n \"[] self.rows = rows or [] def convert_row_to_dict(titles, row): data = {} for\",\n \"\\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\",\",\n \"copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i,\",\n \"chunks(n, l): \\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1 +\",\n \"data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data\",\n \"\\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\":\",\n \"[x.copy() for x in dicts] for d in copies: for to_remove in keys_to_remove:\",\n \"l): \\\"\\\"\\\"Yield n successive similar-sized chunks from l.\\\"\\\"\\\" chunk_size = 1 + len(l)\",\n \"\\\"../server/extract\\\" SCHOOL_TITLES = [\\\"ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"ORG_ELECTORATE\\\", \\\"P_ADDRESS1\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"S_ADDRESS1\\\", \\\"S_SUBURB\\\", \\\"S_STATE\\\",\",\n \"\\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists):\",\n \"(teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, ) =\",\n \"a ISO date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool\",\n \"in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations,\",\n \"for schoolId in teacher.get(\\\"schools\\\", [])] # Build student -> school relations yield [make_relation(\\\"ClpStudent\\\",\",\n \"(i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return\",\n \"date\\\" dt = datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool import insists\",\n \"teacher in teachers for schoolId in teacher.get(\\\"schools\\\", [])] # Build student -> school\",\n \"locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers,\",\n \"\\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object to hold the contents of\",\n \"titles != sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i, x) for (i, x)\",\n \"'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i == 0 else x.title()) for\",\n \"openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format\",\n \"sheet within an excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name = name\",\n \"defn in field_defns] return structs def unique(key, dicts): t = {x[key]: x for\",\n \"if sheet.name == \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS,\",\n \"else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove):\",\n \"raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1,\",\n \"-> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\",\",\n \"or add the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools = teacher.setdefault(\\\"schools\\\",\",\n \"{\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\":\",\n \"(organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name ==\",\n \"student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools,\",\n \"\\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\":\",\n \"in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path\",\n \"\\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\",\",\n \"organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\",\",\n \"locations, each of which is identitical except that for being related to a\",\n \"return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't have\",\n \"datetime.datetime.strptime(s, \\\"%d/%b/%y\\\") return dt.isoformat() + \\\".0Z\\\" # GraphCool import insists on microseconds, hence\",\n \"# pip install openpyxl # pip install cuid import os.path import json import\",\n \"been converted into a dictionary\\\"\\\"\\\" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in\",\n \"\\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", }\",\n \"\\\"EMAIL\\\", \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\", \\\"FIELD_OF_EDUCATION\\\", \\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\",\",\n \"= {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\": \\\"CLP_STUDENT_ID\\\", \\\"STUDENT_SRN\\\": \\\"SRN\\\", \\\"LOCATION_NAME\\\": \\\"LOCATION\\\", \\\"STUDENT_LNAME\\\": \\\"FAMILY_NAME\\\",\",\n \"nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): \\\"\\\"\\\"Yield\",\n \"\\\"\\\"\\\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower()\",\n \"\\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\": \\\"DAY_PHONE\\\", \\\"TEL_EVENING\\\": \\\"EVENING_PHONE\\\", \\\"EMAIL\\\": \\\"EMAIL\\\",\",\n \"field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations, schools, locations, teachers,\",\n \"required fields that graphcool import required\\\" for x in dicts: x[\\\"_typeName\\\"] = type_name\",\n \"or [] def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row):\",\n \"to a different school. # We have to collect all the schools that\",\n \"\\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\":\",\n \"from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the\",\n \"https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX = \\\"./data/CLP_combined.xlsx\\\"\",\n \"cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): \\\"\\\"\\\"Convert the\",\n \"for schoolId in location.get(\\\"schools\\\", [])] # Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\",\",\n \"Make one up for the time being for x in injected: x[\\\"clpLocationId\\\"] =\",\n \"have to collect all the schools that the same teacher is teaching at.\",\n \"\\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\": \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\": \\\"SCHOOL_ID\\\", } STUDENT_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\":\",\n \"\\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS = {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\",\",\n \"\\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations, teachers,\",\n \"\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"SCHOOL_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS =\",\n \"\\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", }\",\n \"school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student in students if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path\",\n \"schoolId in location.get(\\\"schools\\\", [])] # Build teacher -> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"],\",\n \"for being related to a different school. # We have to collect all\",\n \"CLP location id :( Make one up for the time being for x\",\n \"\\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\",\",\n \"= unique(\\\"clpStudentId\\\", students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"] =\",\n \"chunk_size = 1 + len(l) // n for i in range(0, len(l), chunk_size):\",\n \"= [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in\",\n \"dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel case.\",\n \"import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects\",\n \"titles, field_defns): if titles != sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i, x)\",\n \"enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x\",\n \"f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for\",\n \"\\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"] ORGANISATION_FIELDS =\",\n \"\\\"DOB\\\": \\\"DATE_OF_BIRTH\\\", \\\"TEL\\\": \\\"PHONE\\\", \\\"LOCATION_NAME_1\\\": \\\"DAY_SCHOOL\\\", } class Sheet: \\\"Data container object to\",\n \"\\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\":\",\n \"raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1,\",\n \"\\\"Data container object to hold the contents of one sheet within an excel\",\n \"name, titles=None, rows=None): self.name = name self.titles = titles or [] self.rows =\",\n \"unique(key, dicts): t = {x[key]: x for x in dicts} return t.values() def\",\n \"related to a different school. # We have to collect all the schools\",\n \"now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs)\",\n \"\\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\", \\\"LOC_S_SUBURB\\\", \\\"LOC_S_STATE\\\", \\\"LOC_S_POSTCODE\\\"]\",\n \"def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if cell.Value\",\n \"list()) related_schools.append(x.pop(\\\"schoolId\\\")) injected = inject_required(\\\"ClpTeacher\\\", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in\",\n \"schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\":\",\n \"students) injected = inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return\",\n \"{\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\":\",\n \"= relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path,\",\n \"= os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"relations\\\",\",\n \"teachers, students): # Build school -> organisation relations org_keys = {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for\",\n \"[])] # Build student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]],\",\n \"cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title,\",\n \"in field_defns] return structs def unique(key, dicts): t = {x[key]: x for x\",\n \"\\\"organisation\\\") for x in schools] # Build location -> school relations school_keys =\",\n \"for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like\",\n \"path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\":\",\n \"enumerate(s.split(\\\"_\\\"))] return \\\"\\\".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields,\",\n \"def prepare_locations(locations): # There are multiple locations, each of which is identitical except\",\n \"teachers: # get an existing teacher with that id, or add the new\",\n \"+ \\\".0Z\\\" # GraphCool import insists on microseconds, hence the \\\".0\\\" def prepare_students(students):\",\n \"x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like locations,\",\n \"[(x.lower() if i == 0 else x.title()) for (i, x) in enumerate(s.split(\\\"_\\\"))] return\",\n \"different school. # We have to collect all the schools that the same\",\n \"\\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name\",\n \"We have to collect all the schools that meet at the same location.\",\n \"school. # We have to collect all the schools that meet at the\",\n \"of the given dictionary with all the given keys removed\\\" copies = [x.copy()\",\n \"x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs\",\n \"== \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students,\",\n \"def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or []\",\n \"= x[\\\"updatedAt\\\"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs\",\n \"school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for schoolId in teacher.get(\\\"schools\\\", [])] # Build\",\n \"} class Sheet: \\\"Data container object to hold the contents of one sheet\",\n \"GraphCool import insists on microseconds, hence the \\\".0\\\" def prepare_students(students): uniques = unique(\\\"clpStudentId\\\",\",\n \"chunks from l.\\\"\\\"\\\" chunk_size = 1 + len(l) // n for i in\",\n \"the CLP location id :( Make one up for the time being for\",\n \"injected = inject_required(\\\"ClpStudent\\\", uniques) for x in injected: x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected\",\n \"titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data =\",\n \"= {} for x in teachers: # get an existing teacher with that\",\n \"given XLSX spreadsheet to iterable of Sheet objects, in which row has been\",\n \"def to_camel(s): \\\"\\\"\\\"Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits\",\n \"extract doesn't include the CLP location id :( Make one up for the\",\n \"- Current extract doesn't include the CLP location id :( Make one up\",\n \"or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data = {}\",\n \"rows or [] def convert_row_to_dict(titles, row): data = {} for (i, cell) in\",\n \"if titles != sheet.titles: print(\\\"Sheet doesn't have expected titles:\\\", [(i, x) for (i,\",\n \"prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\\\"_typeName\\\":\",\n \"\\\"id\\\": id1, \\\"fieldName\\\": field1}, {\\\"_typeName\\\": entity2, \\\"id\\\": id2, \\\"fieldName\\\": field2} ] def generate_relations(organisations,\",\n \"with the given name, or add the new location location = uniques.setdefault(x[\\\"name\\\"], x)\",\n \"= {\\\"ORGANISATION_ID\\\": \\\"CLP_ORGANISATION_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\",\",\n \"x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x)\",\n \"add the new location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\"))\",\n \") = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations,\",\n \"in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an\",\n \"== \\\"SCHOOL-ORG\\\": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif\",\n \"location location = uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\",\",\n \"install openpyxl # pip install cuid import os.path import json import datetime from\",\n \"# Like locations, the same teacher can have multiple records, # each of\",\n \"prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {\\\"_typeName\\\": entity1,\",\n \"\\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\":\",\n \"\\\"DEGREE_COUNTRY\\\", \\\"DEGREE_YEAR\\\", \\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\",\",\n \"to collect all the schools that meet at the same location. uniques =\",\n \"= {} for x in locations: # get an existing location with the\",\n \"relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)]\",\n \"-> school relations yield [make_relation(\\\"ClpTeacher\\\", teacher[\\\"id\\\"], \\\"schools\\\", \\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in\",\n \"inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\",\",\n \"school_keys[schoolId], \\\"locations\\\") for location in locations for schoolId in location.get(\\\"schools\\\", [])] # Build\",\n \"for x in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove,\",\n \"[\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\", \\\"LOCATION_NAME_1\\\"] TEACHER_FIELDS = {\\\"TEACHER_ID\\\":\",\n \"prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools):\",\n \"in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the string from\",\n \"student in students if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections\",\n \"for sheet in work_book: rows = [x for x in sheet.iter_rows()] if rows:\",\n \"id, or add the new teacher record teacher = uniques.setdefault(x[\\\"clpTeacherId\\\"], x) related_schools =\",\n \"with open(path, \\\"w\\\") as f: nodes = { \\\"valueType\\\": \\\"nodes\\\", \\\"values\\\": one_list }\",\n \"\\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\",\",\n \"student -> school relations yield [make_relation(\\\"ClpStudent\\\", student[\\\"id\\\"], \\\"school\\\", \\\"ClpSchool\\\", school_keys[student[\\\"schoolId\\\"]], \\\"students\\\") for student\",\n \"\\\"ORGANISATION_ID\\\", \\\"SCHOOL_ID\\\"] STUDENT_TITLES = [\\\"SCHOOL_NAME\\\", \\\"SCHOOL_ID\\\", \\\"STUDENT_ID\\\", \\\"STUDENT_SRN\\\", \\\"LOCATION_NAME\\\", \\\"STUDENT_LNAME\\\", \\\"STUDENT_FNAME\\\", \\\"DOB\\\", \\\"TEL\\\",\",\n \"def prepare_organisations(organisations): unique_orgs = unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def\",\n \"SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == \\\"Teacher\\\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif\",\n \"\\\"ClpSchool\\\", school_keys[schoolId], \\\"locations\\\") for location in locations for schoolId in location.get(\\\"schools\\\", [])] #\",\n \"object to hold the contents of one sheet within an excel spreadsheet\\\" def\",\n \"(i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + \\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir,\",\n \"{\\\"TEACHER_ID\\\": \\\"CLP_TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\": \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\": \\\"SCHOOL_NAME\\\", \\\"TITLE\\\": \\\"TITLE\\\", \\\"LNAME\\\": \\\"FAMILY_NAME\\\", \\\"FNAME\\\": \\\"GIVEN_NAMES\\\", \\\"TEACHER_LANGUAGES\\\":\",\n \"datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in\",\n \"\\\"TEACHER_LANGUAGES\\\": \\\"LANGUAGES\\\", \\\"P_ADDRESS1\\\": \\\"ADDRESS1\\\", \\\"P_ADDRESS2\\\": \\\"ADDRESS2\\\", \\\"P_SUBURB\\\": \\\"SUBURB\\\", \\\"P_STATE\\\": \\\"STATE\\\", \\\"P_POSTCODE\\\": \\\"POSTCODE\\\", \\\"TELEPHONE\\\":\",\n \"get an existing location with the given name, or add the new location\",\n \"for x in locations: # get an existing location with the given name,\",\n \"(students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools,\",\n \"into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'\\\"\\\"\\\" bits = [(x.lower() if i == 0\",\n \"return fat_orgs def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return\",\n \"enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file):\",\n \"write_nodes( organisations, copy_without(schools, \\\"clpOrganisationId\\\"), copy_without(locations, \\\"schools\\\"), copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students,\",\n \"{} for x in locations: # get an existing location with the given\",\n \"x) for x in sheet.rows] for defn in field_defns] return structs def unique(key,\",\n \"\\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\", \\\"P_ADDRESS2\\\", \\\"P_SUBURB\\\", \\\"P_STATE\\\", \\\"P_POSTCODE\\\", \\\"TELEPHONE\\\", \\\"TEL_EVENING\\\",\",\n \"copy_without(teachers, \\\"organisationId\\\", \\\"organisationName\\\", \\\"schools\\\", \\\"schoolName\\\"), *chunks(3, copy_without(students, \\\"schoolId\\\", \\\"schoolName\\\", \\\"location\\\"))) write_relations(generate_relations(organisations, schools, locations,\",\n \"that graphcool import required\\\" for x in dicts: x[\\\"_typeName\\\"] = type_name x[\\\"id\\\"] =\",\n \"= unique(\\\"clpOrganisationId\\\", organisations) fat_orgs = inject_required(\\\"ClpOrganisation\\\", unique_orgs) return fat_orgs def prepare_schools(schools): uniques =\",\n \"return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the\",\n \"now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required fields that\",\n \"path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for\",\n \"+ \\\"Z\\\" def inject_required(type_name, dicts): \\\"Inject the required fields that graphcool import required\\\"\",\n \"field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation\",\n \"Build location -> school relations school_keys = {x[\\\"clpSchoolId\\\"]: x[\\\"id\\\"] for x in schools}\",\n \"\\\"ADDRESS\\\", \\\"SCHOOL_S_SUBURB\\\": \\\"SUBURB\\\", \\\"SCHOOL_S_STATE\\\": \\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\":\",\n \"are multiple locations, each of which is identitical except that for being related\",\n \"injected def convert_dob_to_datetime(s): \\\"Convert the string from 99/MON/YY to a ISO date\\\" dt\",\n \"def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), \\\"nodes\\\"))\",\n \"x in injected: x[\\\"clpLocationId\\\"] = cuid.cuid() return injected def convert_dob_to_datetime(s): \\\"Convert the string\",\n \"\\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"LOC_S_SUBURB\\\": \\\"SUBURB\\\", \\\"LOC_S_STATE\\\": \\\"STATE\\\", \\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES\",\n \"def prepare_schools(schools): uniques = unique(\\\"clpSchoolId\\\", schools) injected = inject_required(\\\"ClpSchool\\\", uniques) return injected def\",\n \"x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"], \\\"organisation\\\") for\",\n \"in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles,\",\n \"\\\"NAME\\\", \\\"ORG_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"S_ADDRESS1\\\": \\\"ADDRESS\\\", \\\"S_SUBURB\\\": \\\"SUBURB\\\", \\\"S_STATE\\\": \\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS\",\n \"at the same location. uniques = {} for x in locations: # get\",\n \"given dictionary with all the given keys removed\\\" copies = [x.copy() for x\",\n \"f: nodes = { \\\"valueType\\\": \\\"relations\\\", \\\"values\\\": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l):\",\n \"for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list)\",\n \"\\\"STATE\\\", \\\"S_POSTCODE\\\": \\\"POSTCODE\\\", } SCHOOL_FIELDS = {\\\"SCHOOL_NAME\\\": \\\"NAME\\\", \\\"SCH_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"ORGANISATION_ID\\\":\",\n \"rows = [x for x in sheet.iter_rows()] if rows: titles = [cell.value for\",\n \"doesn't have expected titles:\\\", [(i, x) for (i, x) in enumerate(titles) if x\",\n \"process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == \\\"Student\\\": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS])\",\n \"# pip install cuid import os.path import json import datetime from openpyxl import\",\n \"= {x[\\\"clpOrganisationId\\\"]: x[\\\"id\\\"] for x in organisations} yield [make_relation(\\\"ClpOrganisation\\\", org_keys[x[\\\"clpOrganisationId\\\"]], \\\"schools\\\", \\\"ClpSchool\\\", x[\\\"id\\\"],\",\n \"locations for schoolId in location.get(\\\"schools\\\", [])] # Build teacher -> school relations yield\",\n \"schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return iterable that contains copies of\",\n \"data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles:\",\n \"have to collect all the schools that meet at the same location. uniques\",\n \"\\\"-relations\\\" + str(i), \\\"relations\\\")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, \\\"1.json\\\") with open(path, \\\"w\\\")\",\n \"is identitical except that for being related to a different school. # We\",\n \"__init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or [] self.rows\",\n \"in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for\",\n \"in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers,\",\n \"\\\"LOC_S_POSTCODE\\\": \\\"POSTCODE\\\"} TEACHER_TITLES = [\\\"TEACHER_ID\\\", \\\"ORGANISATION_NAME\\\", \\\"SCHOOL_NAME\\\", \\\"TEACHER_NAME\\\", \\\"TITLE\\\", \\\"LNAME\\\", \\\"FNAME\\\", \\\"TEACHER_LANGUAGES\\\", \\\"P_ADDRESS1\\\",\",\n \"rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): \\\"\\\"\\\"Convert an underscored\",\n \"\\\"MOBILE\\\": \\\"MOBILE\\\", \\\"LEVEL_TAUGHT\\\": \\\"LEVEL_TAUGHT\\\", \\\"LEVEL_OF_EDUCATION\\\": \\\"EDUCATION_LEVEL\\\", \\\"FIELD_OF_EDUCATION\\\": \\\"EDUCATION_FIELD\\\", \\\"DEGREE_COUNTRY\\\": \\\"EDUCATION_COUNTRY\\\", \\\"DEGREE_YEAR\\\": \\\"EDUCATION_YEAR\\\", \\\"ORGANISATION_ID\\\":\",\n \"len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return\",\n \"\\\"STATE\\\", \\\"SCHOOL_S_POSTCODE\\\": \\\"POSTCODE\\\", } LOCATION_FIELDS = {\\\"LOCATION_NAME\\\": \\\"NAME\\\", \\\"LOC_ELECTORATE\\\": \\\"ELECTORATE\\\", \\\"SCHOOL_ID\\\": \\\"CLP_SCHOOL_ID\\\", \\\"LOC_S_ADDRESS1\\\":\",\n \"\\\"ClpSchool\\\", school_keys[schoolId], \\\"teachers\\\") for teacher in teachers for schoolId in teacher.get(\\\"schools\\\", [])] #\",\n \"print(\\\"Ignoring sheet:\\\", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): \\\"Return\",\n \"excel spreadsheet\\\" def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles\",\n \"inject_required(\\\"ClpSchool\\\", uniques) return injected def prepare_locations(locations): # There are multiple locations, each of\",\n \"\\\"students\\\") for student in students if student[\\\"schoolId\\\"] in school_keys] def main(): xlsx_path =\",\n \"prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [\",\n \"x[\\\"dateOfBirth\\\"] = convert_dob_to_datetime(x[\\\"dateOfBirth\\\"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher\",\n \"uniques.setdefault(x[\\\"name\\\"], x) related_schools = location.setdefault(\\\"schools\\\", list()) related_schools.append(x.pop(\\\"clpSchoolId\\\")) injected = inject_required(\\\"ClpLocation\\\", uniques.values()) # FIX\",\n \"[cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]]\",\n \"os.path import json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py\",\n \"\\\"SCH_ELECTORATE\\\", \\\"SCHOOL_ID\\\", \\\"SCHOOL_P_ADDRESS1\\\", \\\"SCHOOL_P_SUBURB\\\", \\\"SCHOOL_P_STATE\\\", \\\"SCHOOL_P_POSTCODE\\\", \\\"SCHOOL_S_ADDRESS1\\\", \\\"SCHOOL_S_SUBURB\\\", \\\"SCHOOL_S_STATE\\\", \\\"SCHOOL_S_POSTCODE\\\", \\\"LOCATION_NAME\\\", \\\"LOC_ELECTORATE\\\", \\\"LOC_S_ADDRESS1\\\",\"\n]","isConversational":false}}},{"rowIdx":588,"cells":{"ngram":{"kind":"list like","value":["= [] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else:","format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming',","driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\") need_vpn = True except NoSuchElementException: need_vpn","playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl","def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4))","driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist)","self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image',","Saved Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self):","'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl':","driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe iframe = None try: iframe =","class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir","if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return","enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr","= videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return","from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR =","self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading","to watch!']\") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if","self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache, 'r')","= \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self,","def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"]","self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect VPN-required channels","= None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in","return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN","streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url):","ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True)","zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict)","FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False)","chandict, allchandict): caching = True streamlist = [] streamdict = {} if chandict[\"callsign\"]","driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\") need_vpn = True except NoSuchElementException:","{ \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return","chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict,","m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel","self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl}","iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return","Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\") need_vpn","(url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def","driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless')","grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException:","for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth >","= tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live',","+= 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist =","firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True","with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names,","TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable","firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False,","streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl =","firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver","json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u,","streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl)","firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0')","FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') #","Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame","chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign","self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u","cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = []","self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe iframe = None try:","bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream:","= fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u =","url in zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url),","channel requires our VPN to watch!']\") need_vpn = True except NoSuchElementException: need_vpn =","Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr","streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self):","import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions","= m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not","else: return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree =","firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None,","def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false')","= {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with","False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True }","m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w')","chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None","from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint():","for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires","Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull,","= 1 for name, url in zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(),","for name, url in zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index,","get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe","= [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in","2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout':","[] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl","= self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache):","open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self,","driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions()","self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self):","channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True","import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' #","VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\") need_vpn =","be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except","= { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1","= True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs","self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath)","= self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe iframe = None","def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict = {}","return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict","streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"],","= self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\":","None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8","with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u","return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url)","streamlist = [] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]]","self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict =","except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit()","m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import","in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl","= pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved","Previously Saved Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def","tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls","blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe iframe = None try: iframe","import html import pathlib import json import m3u8 from seleniumwire import webdriver from","our VPN to watch!']\") need_vpn = True except NoSuchElementException: need_vpn = False finally:","get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload',","os import sys from lxml import html import pathlib import json import m3u8","= self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict =","self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously","need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel","m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index =","streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching","bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def","if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\"","m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl","chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]:","pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel","\"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self,","'') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver","if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream","OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir =","m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream:","= self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def","caching = True streamlist = [] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u):","chandict[\"callsign\"]) enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except","fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {}","set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options,","= origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def","allchandict): caching = True streamlist = [] streamdict = {} if chandict[\"callsign\"] in","\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url):","def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache, 'r') as","origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self):","webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR","found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel",".replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict):","= '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def","in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream","= driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl =","sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin","streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u =","chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict = { \"name\":","Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout =","# Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin):","def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for","streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not","import os import sys from lxml import html import pathlib import json import","if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream","= webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options","\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls","None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def","requires our VPN to watch!']\") need_vpn = True except NoSuchElementException: need_vpn = False","iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return","'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr,","frame is not found for channel') return None # Detect VPN-required channels try:","found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return","def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr =","{\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream =","return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content)","callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return","chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names","{} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) #","bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self):","ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale',","IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore","'') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\"","def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '')","chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free',","NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def","caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant:","# Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not","need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.')","as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w')","self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] =","import sys from lxml import html import pathlib import json import m3u8 from","lxml import html import pathlib import json import m3u8 from seleniumwire import webdriver","} channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching =","False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None","= html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls =","m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url","True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors':","not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if","to be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10)","NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be","try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None","if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if","> bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url","return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel',","channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def","import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options","import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException","self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile)","'') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver()","scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\"","= driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None","channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url","try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\") need_vpn = True except","streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url)",".split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self,","VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8',","streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile()","in zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), }","= str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options","html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath)","None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to","self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w') as","fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache =","timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close()","__init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache","chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe iframe","os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u =","channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our","Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.')","sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels():","m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath","sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin","finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None #","chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist","NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect VPN-required","None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists:","channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict =","self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist,","= tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign =","except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to","Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving","channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\") need_vpn = True","webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options =","load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile:","streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so',","= \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath =","TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]]","return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if","html import pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions","bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream","+ chandict[\"callsign\"]) enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR)","not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream =","chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2]",".replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" +","= (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign","videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream","return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2)","True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN","def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath =","= None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found","videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url =","= json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w') as m3ufile:","def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__","= sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin =","{} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache,","tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '')","return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl","\"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict):","watch!']\") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn:","save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def","import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout =","channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return","driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None #","chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '')","'0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver =","videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth:","= True streamlist = [] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl","pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException,","enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException:","1 for name, url in zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\":","None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for","except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect","origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json')","chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index +=","get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name,","{ 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile)","sys from lxml import html import pathlib import json import m3u8 from seleniumwire",".replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint()","needs VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist =","= {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict)",".replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver =","Channel m3u cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list","m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index","\"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list","chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index","tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url","= \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names,","def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get","videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri","return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif","m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for","else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\":","indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1","import pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import","need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click() try:","get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict = {} if","bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not","1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = []","= videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url","self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile","= { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options,","'.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint():","iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not","# if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict)","'')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"])","bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree","True streamlist = [] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl =","is not found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe)","self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching","m3u cache.\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list =","seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as","list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl =","chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls):","self.m3u8_beststream(streamurl) streamdict = {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self,","return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint()","from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options","str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options =","'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls =","elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else:","= False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return","self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict = {","json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from","selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe'","name, url in zip(chan_names, chan_urls): chan_dict = { \"name\": name.rstrip(), \"number\": chan_number_index, \"callsign\":","selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout","return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel","bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage =","as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels()","'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile) return","= {\"number\": chandict[\"number\"], \"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream","videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if","= open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def","[] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names,","\"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\"","driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay","try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel')","not found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This","\"stream_url\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u","chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict","channels_url = \"https://ustvgo.tv/\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath","if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u","# Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\"//*[text()='This channel requires our VPN to watch!']\")","VPN to watch!']\") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content()","driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() # Get iframe iframe =","self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if","'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with","as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\") with open(self.m3ucache,","= self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\" channel_urls_xpath = \"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\" chan_names =","from lxml import html import pathlib import json import m3u8 from seleniumwire import","not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \"https://ustvgo.tv/\" chanpage","videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return","# Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout","= streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile =","name.rstrip(), \"number\": chan_number_index, \"callsign\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def","# Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video","blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class","def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return","self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\"Loading Previously Saved Channel m3u.\")","url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', ''))","not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\"callsign\"]] = streamurl self.save_m3u_cache()","open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls","iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is","= FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose',","callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\"https://ustvgo.tv/\" + chandict[\"callsign\"]) enablePrint() #","None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile) return driver","= self.cached_m3u[chandict[\"callsign\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\"origin\"][\"force_best\"]: streamurl = self.m3u8_beststream(streamurl) streamdict","self.origin = origin self.cache_dir = self.fhdhr.config.dict[\"filedir\"][\"epg_cache\"][\"origin\"][\"top\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache()","m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth","self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist","open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\"Saving Channel m3u cache.\")","if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click()","firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = {","videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream ="],"string":"[\n \"= [] streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else:\",\n \"format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming',\",\n \"driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn\",\n \"playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl\",\n \"def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4))\",\n \"driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist)\",\n \"self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image',\",\n \"Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self):\",\n \"'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl':\",\n \"driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe = None try: iframe =\",\n \"class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir\",\n \"if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return\",\n \"enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr\",\n \"= videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return\",\n \"from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR =\",\n \"self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading\",\n \"to watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if\",\n \"self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r')\",\n \"= \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self,\",\n \"def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"]\",\n \"self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect VPN-required channels\",\n \"= None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in\",\n \"return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN\",\n \"streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url):\",\n \"ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True)\",\n \"zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict)\",\n \"FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False)\",\n \"chandict, allchandict): caching = True streamlist = [] streamdict = {} if chandict[\\\"callsign\\\"]\",\n \"driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\") need_vpn = True except NoSuchElementException:\",\n \"{ \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return\",\n \"chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict,\",\n \"m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel\",\n \"self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl}\",\n \"iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return\",\n \"Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\") need_vpn\",\n \"(url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def\",\n \"driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless')\",\n \"grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException:\",\n \"for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth >\",\n \"= tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live',\",\n \"+= 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist =\",\n \"firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True\",\n \"with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names,\",\n \"TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable\",\n \"firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False,\",\n \"streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl =\",\n \"firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver\",\n \"json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u,\",\n \"streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl)\",\n \"firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0')\",\n \"FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') #\",\n \"Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame\",\n \"chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign\",\n \"self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u\",\n \"cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = []\",\n \"self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe = None try:\",\n \"bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream:\",\n \"= fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u =\",\n \"url in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url),\",\n \"channel requires our VPN to watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn =\",\n \"Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr\",\n \"streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self):\",\n \"import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions\",\n \"= m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not\",\n \"else: return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree =\",\n \"firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None,\",\n \"def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false')\",\n \"= {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with\",\n \"False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True }\",\n \"m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w')\",\n \"chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None\",\n \"from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint():\",\n \"for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires\",\n \"Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull,\",\n \"= 1 for name, url in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(),\",\n \"for name, url in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index,\",\n \"get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe\",\n \"= [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in\",\n \"2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout':\",\n \"[] streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl\",\n \"= self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache):\",\n \"open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self,\",\n \"driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions()\",\n \"self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self):\",\n \"channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True\",\n \"import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' #\",\n \"VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\") need_vpn =\",\n \"be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except\",\n \"= { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1\",\n \"= True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs\",\n \"self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath)\",\n \"= self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe = None\",\n \"def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict = {}\",\n \"return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict\",\n \"streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"],\",\n \"= self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\":\",\n \"None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8\",\n \"with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u\",\n \"return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url)\",\n \"streamlist = [] streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]]\",\n \"self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict =\",\n \"except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit()\",\n \"m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import\",\n \"in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl\",\n \"= pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved\",\n \"Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def\",\n \"tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls\",\n \"blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe = None try: iframe\",\n \"import html import pathlib import json import m3u8 from seleniumwire import webdriver from\",\n \"our VPN to watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn = False finally:\",\n \"get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload',\",\n \"os import sys from lxml import html import pathlib import json import m3u8\",\n \"= self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict =\",\n \"self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously\",\n \"need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel\",\n \"m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index =\",\n \"streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching\",\n \"bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def\",\n \"if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\"\",\n \"m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl\",\n \"chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]:\",\n \"pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel\",\n \"\\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self,\",\n \"'') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver\",\n \"if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream\",\n \"OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir =\",\n \"m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream:\",\n \"= self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def\",\n \"caching = True streamlist = [] streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u):\",\n \"chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except\",\n \"fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {}\",\n \"set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options,\",\n \"= origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def\",\n \"allchandict): caching = True streamlist = [] streamdict = {} if chandict[\\\"callsign\\\"] in\",\n \"\\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url):\",\n \"def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as\",\n \"origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self):\",\n \"webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR\",\n \"found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel\",\n \".replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict):\",\n \"= '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def\",\n \"in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream\",\n \"= driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl =\",\n \"sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin\",\n \"streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u =\",\n \"chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\":\",\n \"Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout =\",\n \"# Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin):\",\n \"def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for\",\n \"streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not\",\n \"import os import sys from lxml import html import pathlib import json import\",\n \"if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream\",\n \"= webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options\",\n \"\\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls\",\n \"None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def\",\n \"requires our VPN to watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn = False\",\n \"iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return\",\n \"'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr,\",\n \"frame is not found for channel') return None # Detect VPN-required channels try:\",\n \"found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return\",\n \"def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr =\",\n \"{\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream =\",\n \"return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content)\",\n \"callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return\",\n \"chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names\",\n \"{} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) #\",\n \"bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self):\",\n \"ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale',\",\n \"IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore\",\n \"'') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\"\",\n \"def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '')\",\n \"chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free',\",\n \"NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def\",\n \"caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant:\",\n \"# Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not\",\n \"need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.')\",\n \"as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w')\",\n \"self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] =\",\n \"import sys from lxml import html import pathlib import json import m3u8 from\",\n \"lxml import html import pathlib import json import m3u8 from seleniumwire import webdriver\",\n \"} channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching =\",\n \"False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None\",\n \"= html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls =\",\n \"m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url\",\n \"True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors':\",\n \"not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if\",\n \"to be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10)\",\n \"NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be\",\n \"try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None\",\n \"if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if\",\n \"> bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url\",\n \"return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel',\",\n \"channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def\",\n \"import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options\",\n \"import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException\",\n \"self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile)\",\n \"'') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver()\",\n \"scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\"\",\n \"= driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None\",\n \"channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url\",\n \"try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\") need_vpn = True except\",\n \"streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url)\",\n \".split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self,\",\n \"VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8',\",\n \"streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile()\",\n \"in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), }\",\n \"= str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options\",\n \"html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath)\",\n \"None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to\",\n \"self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w') as\",\n \"fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache =\",\n \"timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close()\",\n \"__init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache\",\n \"chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe\",\n \"os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u =\",\n \"channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our\",\n \"Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.')\",\n \"sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels():\",\n \"m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath\",\n \"sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin\",\n \"finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None #\",\n \"chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist\",\n \"NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect VPN-required\",\n \"None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists:\",\n \"channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict =\",\n \"self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist,\",\n \"= tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign =\",\n \"except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to\",\n \"Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving\",\n \"channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\") need_vpn = True\",\n \"webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options =\",\n \"load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile:\",\n \"streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so',\",\n \"= \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath =\",\n \"TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]]\",\n \"return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if\",\n \"html import pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions\",\n \"bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream\",\n \"+ chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR)\",\n \"not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream =\",\n \"chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2]\",\n \".replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" +\",\n \"= (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign\",\n \"videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream\",\n \"return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2)\",\n \"True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN\",\n \"def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath =\",\n \"= None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found\",\n \"videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url =\",\n \"= json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile:\",\n \"def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__\",\n \"= sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin =\",\n \"{} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache,\",\n \"tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '')\",\n \"return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl\",\n \"\\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict):\",\n \"watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn:\",\n \"save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def\",\n \"import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout =\",\n \"channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return\",\n \"driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None #\",\n \"chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '')\",\n \"'0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver =\",\n \"videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth:\",\n \"= True streamlist = [] streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl\",\n \"pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException,\",\n \"enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException:\",\n \"1 for name, url in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\":\",\n \"None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for\",\n \"except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect\",\n \"origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json')\",\n \"chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index +=\",\n \"get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name,\",\n \"{ 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile)\",\n \"sys from lxml import html import pathlib import json import m3u8 from seleniumwire\",\n \".replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint()\",\n \"needs VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist =\",\n \"= {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict)\",\n \".replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver =\",\n \"Channel m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list\",\n \"m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index\",\n \"\\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list\",\n \"chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index\",\n \"tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url\",\n \"= \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names,\",\n \"def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get\",\n \"videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri\",\n \"return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif\",\n \"m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for\",\n \"else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\":\",\n \"indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1\",\n \"import pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import\",\n \"need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click() try:\",\n \"get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict = {} if\",\n \"bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not\",\n \"1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = []\",\n \"= videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url\",\n \"self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile\",\n \"= { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options,\",\n \"'.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint():\",\n \"iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not\",\n \"# if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict)\",\n \"'')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"])\",\n \"bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree\",\n \"True streamlist = [] streamdict = {} if chandict[\\\"callsign\\\"] in list(self.cached_m3u): streamurl =\",\n \"is not found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe)\",\n \"self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching\",\n \"m3u cache.\\\") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list =\",\n \"seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as\",\n \"list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl =\",\n \"chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls):\",\n \"self.m3u8_beststream(streamurl) streamdict = {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self,\",\n \"return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint()\",\n \"from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options\",\n \"str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options =\",\n \"'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls =\",\n \"elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else:\",\n \"= False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return\",\n \"self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict = {\",\n \"json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from\",\n \"selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe'\",\n \"name, url in zip(chan_names, chan_urls): chan_dict = { \\\"name\\\": name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\":\",\n \"selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout\",\n \"return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel\",\n \"bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage =\",\n \"as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels()\",\n \"'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile) return\",\n \"= {\\\"number\\\": chandict[\\\"number\\\"], \\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream\",\n \"videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if\",\n \"= open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def\",\n \"[] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names,\",\n \"\\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\"\",\n \"driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay\",\n \"try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel')\",\n \"not found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This\",\n \"\\\"stream_url\\\": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u\",\n \"chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict\",\n \"channels_url = \\\"https://ustvgo.tv/\\\" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath\",\n \"if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u\",\n \"# Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath(\\\"//*[text()='This channel requires our VPN to watch!']\\\")\",\n \"VPN to watch!']\\\") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content()\",\n \"driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() # Get iframe iframe =\",\n \"self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if\",\n \"'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with\",\n \"as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\") with open(self.m3ucache,\",\n \"= self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()\\\" channel_urls_xpath = \\\"/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href\\\" chan_names =\",\n \"from lxml import html import pathlib import json import m3u8 from seleniumwire import\",\n \"not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = \\\"https://ustvgo.tv/\\\" chanpage\",\n \"videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return\",\n \"# Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout\",\n \"= streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile =\",\n \"name.rstrip(), \\\"number\\\": chan_number_index, \\\"callsign\\\": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def\",\n \"# Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video\",\n \"blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class\",\n \"def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return\",\n \"self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info(\\\"Loading Previously Saved Channel m3u.\\\")\",\n \"url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', ''))\",\n \"not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict[\\\"callsign\\\"]] = streamurl self.save_m3u_cache()\",\n \"open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls\",\n \"iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is\",\n \"= FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose',\",\n \"callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get(\\\"https://ustvgo.tv/\\\" + chandict[\\\"callsign\\\"]) enablePrint() #\",\n \"None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile) return driver\",\n \"= self.cached_m3u[chandict[\\\"callsign\\\"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict[\\\"origin\\\"][\\\"force_best\\\"]: streamurl = self.m3u8_beststream(streamurl) streamdict\",\n \"self.origin = origin self.cache_dir = self.fhdhr.config.dict[\\\"filedir\\\"][\\\"epg_cache\\\"][\\\"origin\\\"][\\\"top\\\"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache()\",\n \"m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth\",\n \"self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist\",\n \"open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info(\\\"Saving Channel m3u cache.\\\")\",\n \"if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click()\",\n \"firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = {\",\n \"videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream =\"\n]","isConversational":false}}},{"rowIdx":589,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":590,"cells":{"ngram":{"kind":"list like","value":["def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg',","size self.rect.center = self.centre # set centre for all frames self.frame = 0","= 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if","= self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to parameter","self.centre = centre # use for all frames self.key = key # key","self.image.get_rect() # define rectangle from image size self.rect.center = self.centre # set centre","0.1 seconds has passed self.time_passed = 0 # reset timer self.frame += 1","self.rect.center = self.centre # set centre for all frames self.frame = 0 #","1 # increase frame number if self.frame >= len(self.images[self.key]): # check if end","define rectangle from image size self.rect.center = self.centre # set centre for all","= centre # use for all frames self.key = key # key used","self.rect = self.image.get_rect() # define rectangle from image size self.rect.center = self.centre #","8 images played at 1 frame per 0.1 secs = 0.8 seconds self.active","centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw current","import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class","passed self.time_passed = 0 # reset timer self.frame += 1 # increase frame","timer self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]): #","self.active = False # animation finished else: self.image = self.images[self.key][self.frame] # next frame","Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm',","'player ''' self.images = images # list of 8 images self.centre = centre","= self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center =","else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle","self.images = images # list of 8 images self.centre = centre # use","end of list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame]","pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: '''","rectangle from image size self.rect.center = self.centre # set centre for all frames","+= dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed =","at 1 frame per 0.1 secs = 0.8 seconds self.active = True def","played at 1 frame per 0.1 secs = 0.8 seconds self.active = True","# list of 8 images self.centre = centre # use for all frames","key used later self.image = images[key][0] # set to first image in the","dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0","number if self.frame >= len(self.images[self.key]): # check if end of list? self.active =","centre # use for all frames self.key = key # key used later","''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images # list","variables. key: 'sm', 'lg', 'player ''' self.images = images # list of 8","has passed self.time_passed = 0 # reset timer self.frame += 1 # increase","# check if end of list? self.active = False # animation finished else:","= self.centre # set centre for all frames self.frame = 0 # no","key # key used later self.image = images[key][0] # set to first image","1 frame per 0.1 secs = 0.8 seconds self.active = True def update(self,","increase frame number if self.frame >= len(self.images[self.key]): # check if end of list?","per 0.1 secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed","True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1","Class variables. key: 'sm', 'lg', 'player ''' self.images = images # list of","<gh_stars>1-10 import pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) ->","key: 'sm', 'lg', 'player ''' self.images = images # list of 8 images","0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if self.time_passed","0 # no of first frame self.time_passed = 0 # set timer to","of first frame self.time_passed = 0 # set timer to 0 self.frame_rate =","self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to parameter value","frame per 0.1 secs = 0.8 seconds self.active = True def update(self, dt):","key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images =","use for all frames self.key = key # key used later self.image =","self.centre # set centre for all frames self.frame = 0 # no of","for all frames self.frame = 0 # no of first frame self.time_passed =","self.image = images[key][0] # set to first image in the sequence self.rect =","list of 8 images self.centre = centre # use for all frames self.key","list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame] # next","first image in the sequence self.rect = self.image.get_rect() # define rectangle from image","def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds","if end of list? self.active = False # animation finished else: self.image =","# new rectangle self.rect.center = self.centre # set centre to parameter value return","# set centre for all frames self.frame = 0 # no of first","images played at 1 frame per 0.1 secs = 0.8 seconds self.active =","len(self.images[self.key]): # check if end of list? self.active = False # animation finished","self.time_passed = 0 # reset timer self.frame += 1 # increase frame number","seconds self.active = True def update(self, dt): self.time_passed += dt if self.time_passed >=","to 0 self.frame_rate = 0.1 # 8 images played at 1 frame per","0 # set timer to 0 self.frame_rate = 0.1 # 8 images played","# use for all frames self.key = key # key used later self.image","''' self.images = images # list of 8 images self.centre = centre #","frame number if self.frame >= len(self.images[self.key]): # check if end of list? self.active","self.frame >= len(self.images[self.key]): # check if end of list? self.active = False #","secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt","dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed","# define rectangle from image size self.rect.center = self.centre # set centre for","# set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) #","# animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect()","centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images","self.centre # set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect)","rectangle self.rect.center = self.centre # set centre to parameter value return self.active def","# reset timer self.frame += 1 # increase frame number if self.frame >=","None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images #","'sm', 'lg', 'player ''' self.images = images # list of 8 images self.centre","later self.image = images[key][0] # set to first image in the sequence self.rect","__init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player","in the sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center","# set to first image in the sequence self.rect = self.image.get_rect() # define","sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center = self.centre","# no of first frame self.time_passed = 0 # set timer to 0","self.frame_rate = 0.1 # 8 images played at 1 frame per 0.1 secs","# next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre #","images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player '''","= images # list of 8 images self.centre = centre # use for","of 8 images self.centre = centre # use for all frames self.key =","update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has","image size self.rect.center = self.centre # set centre for all frames self.frame =","# 8 images played at 1 frame per 0.1 secs = 0.8 seconds","# 0.1 seconds has passed self.time_passed = 0 # reset timer self.frame +=","set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw","self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed","to first image in the sequence self.rect = self.image.get_rect() # define rectangle from","= self.image.get_rect() # define rectangle from image size self.rect.center = self.centre # set","class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key:","timer to 0 self.frame_rate = 0.1 # 8 images played at 1 frame","= 0 # reset timer self.frame += 1 # increase frame number if","animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() #","new rectangle self.rect.center = self.centre # set centre to parameter value return self.active","no of first frame self.time_passed = 0 # set timer to 0 self.frame_rate","set to first image in the sequence self.rect = self.image.get_rect() # define rectangle","self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1 # 8","+= 1 # increase frame number if self.frame >= len(self.images[self.key]): # check if","self.active = True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate:","self.rect.center = self.centre # set centre to parameter value return self.active def draw(self):","next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set","reset timer self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]):","images[key][0] # set to first image in the sequence self.rect = self.image.get_rect() #","shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables.","finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new","self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center","# increase frame number if self.frame >= len(self.images[self.key]): # check if end of","seconds has passed self.time_passed = 0 # reset timer self.frame += 1 #","frames self.frame = 0 # no of first frame self.time_passed = 0 #","to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw current frame","self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]): # check","import pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None:","= False # animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect","all frames self.key = key # key used later self.image = images[key][0] #","# key used later self.image = images[key][0] # set to first image in","images self.centre = centre # use for all frames self.key = key #","of list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame] #","= self.centre # set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image,","used later self.image = images[key][0] # set to first image in the sequence","-> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images","0 # reset timer self.frame += 1 # increase frame number if self.frame","frames self.key = key # key used later self.image = images[key][0] # set","= 0 # no of first frame self.time_passed = 0 # set timer","= 0.1 # 8 images played at 1 frame per 0.1 secs =","False # animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect =","set centre for all frames self.frame = 0 # no of first frame","self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset timer self.frame","frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre","= key # key used later self.image = images[key][0] # set to first","frame self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1 #","self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to","check if end of list? self.active = False # animation finished else: self.image","# set timer to 0 self.frame_rate = 0.1 # 8 images played at","self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset","0.1 secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed +=","self.frame = 0 # no of first frame self.time_passed = 0 # set","= 0 # set timer to 0 self.frame_rate = 0.1 # 8 images","self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre","for all frames self.key = key # key used later self.image = images[key][0]","if self.frame >= len(self.images[self.key]): # check if end of list? self.active = False","image in the sequence self.rect = self.image.get_rect() # define rectangle from image size","set timer to 0 self.frame_rate = 0.1 # 8 images played at 1","= images[key][0] # set to first image in the sequence self.rect = self.image.get_rect()","images # list of 8 images self.centre = centre # use for all","from image size self.rect.center = self.centre # set centre for all frames self.frame","0.1 # 8 images played at 1 frame per 0.1 secs = 0.8","self.key = key # key used later self.image = images[key][0] # set to",">= len(self.images[self.key]): # check if end of list? self.active = False # animation","'lg', 'player ''' self.images = images # list of 8 images self.centre =","if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 #","the sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center =",">= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset timer","0 self.frame_rate = 0.1 # 8 images played at 1 frame per 0.1","centre for all frames self.frame = 0 # no of first frame self.time_passed","all frames self.frame = 0 # no of first frame self.time_passed = 0","= True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: #","8 images self.centre = centre # use for all frames self.key = key","first frame self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1"],"string":"[\n \"def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg',\",\n \"size self.rect.center = self.centre # set centre for all frames self.frame = 0\",\n \"= 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if\",\n \"= self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to parameter\",\n \"self.centre = centre # use for all frames self.key = key # key\",\n \"self.image.get_rect() # define rectangle from image size self.rect.center = self.centre # set centre\",\n \"0.1 seconds has passed self.time_passed = 0 # reset timer self.frame += 1\",\n \"self.rect.center = self.centre # set centre for all frames self.frame = 0 #\",\n \"1 # increase frame number if self.frame >= len(self.images[self.key]): # check if end\",\n \"define rectangle from image size self.rect.center = self.centre # set centre for all\",\n \"= centre # use for all frames self.key = key # key used\",\n \"self.rect = self.image.get_rect() # define rectangle from image size self.rect.center = self.centre #\",\n \"8 images played at 1 frame per 0.1 secs = 0.8 seconds self.active\",\n \"centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw current\",\n \"import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class\",\n \"passed self.time_passed = 0 # reset timer self.frame += 1 # increase frame\",\n \"timer self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]): #\",\n \"self.active = False # animation finished else: self.image = self.images[self.key][self.frame] # next frame\",\n \"Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm',\",\n \"'player ''' self.images = images # list of 8 images self.centre = centre\",\n \"= self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center =\",\n \"else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle\",\n \"self.images = images # list of 8 images self.centre = centre # use\",\n \"end of list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame]\",\n \"pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: '''\",\n \"rectangle from image size self.rect.center = self.centre # set centre for all frames\",\n \"+= dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed =\",\n \"at 1 frame per 0.1 secs = 0.8 seconds self.active = True def\",\n \"played at 1 frame per 0.1 secs = 0.8 seconds self.active = True\",\n \"# list of 8 images self.centre = centre # use for all frames\",\n \"key used later self.image = images[key][0] # set to first image in the\",\n \"dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0\",\n \"number if self.frame >= len(self.images[self.key]): # check if end of list? self.active =\",\n \"centre # use for all frames self.key = key # key used later\",\n \"''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images # list\",\n \"variables. key: 'sm', 'lg', 'player ''' self.images = images # list of 8\",\n \"has passed self.time_passed = 0 # reset timer self.frame += 1 # increase\",\n \"# check if end of list? self.active = False # animation finished else:\",\n \"= self.centre # set centre for all frames self.frame = 0 # no\",\n \"key # key used later self.image = images[key][0] # set to first image\",\n \"1 frame per 0.1 secs = 0.8 seconds self.active = True def update(self,\",\n \"increase frame number if self.frame >= len(self.images[self.key]): # check if end of list?\",\n \"per 0.1 secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed\",\n \"True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1\",\n \"Class variables. key: 'sm', 'lg', 'player ''' self.images = images # list of\",\n \"<gh_stars>1-10 import pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) ->\",\n \"key: 'sm', 'lg', 'player ''' self.images = images # list of 8 images\",\n \"0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if self.time_passed\",\n \"0 # no of first frame self.time_passed = 0 # set timer to\",\n \"of first frame self.time_passed = 0 # set timer to 0 self.frame_rate =\",\n \"self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to parameter value\",\n \"frame per 0.1 secs = 0.8 seconds self.active = True def update(self, dt):\",\n \"key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images =\",\n \"use for all frames self.key = key # key used later self.image =\",\n \"self.centre # set centre for all frames self.frame = 0 # no of\",\n \"for all frames self.frame = 0 # no of first frame self.time_passed =\",\n \"self.image = images[key][0] # set to first image in the sequence self.rect =\",\n \"list of 8 images self.centre = centre # use for all frames self.key\",\n \"list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame] # next\",\n \"first image in the sequence self.rect = self.image.get_rect() # define rectangle from image\",\n \"def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds\",\n \"if end of list? self.active = False # animation finished else: self.image =\",\n \"# new rectangle self.rect.center = self.centre # set centre to parameter value return\",\n \"# set centre for all frames self.frame = 0 # no of first\",\n \"images played at 1 frame per 0.1 secs = 0.8 seconds self.active =\",\n \"len(self.images[self.key]): # check if end of list? self.active = False # animation finished\",\n \"self.time_passed = 0 # reset timer self.frame += 1 # increase frame number\",\n \"seconds self.active = True def update(self, dt): self.time_passed += dt if self.time_passed >=\",\n \"to 0 self.frame_rate = 0.1 # 8 images played at 1 frame per\",\n \"0 # set timer to 0 self.frame_rate = 0.1 # 8 images played\",\n \"# use for all frames self.key = key # key used later self.image\",\n \"''' self.images = images # list of 8 images self.centre = centre #\",\n \"frame number if self.frame >= len(self.images[self.key]): # check if end of list? self.active\",\n \"self.frame >= len(self.images[self.key]): # check if end of list? self.active = False #\",\n \"secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt\",\n \"dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed\",\n \"# define rectangle from image size self.rect.center = self.centre # set centre for\",\n \"# set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) #\",\n \"# animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect()\",\n \"centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images\",\n \"self.centre # set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect)\",\n \"rectangle self.rect.center = self.centre # set centre to parameter value return self.active def\",\n \"# reset timer self.frame += 1 # increase frame number if self.frame >=\",\n \"None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images #\",\n \"'sm', 'lg', 'player ''' self.images = images # list of 8 images self.centre\",\n \"later self.image = images[key][0] # set to first image in the sequence self.rect\",\n \"__init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player\",\n \"in the sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center\",\n \"# set to first image in the sequence self.rect = self.image.get_rect() # define\",\n \"sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center = self.centre\",\n \"# no of first frame self.time_passed = 0 # set timer to 0\",\n \"self.frame_rate = 0.1 # 8 images played at 1 frame per 0.1 secs\",\n \"# next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre #\",\n \"images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player '''\",\n \"= images # list of 8 images self.centre = centre # use for\",\n \"of 8 images self.centre = centre # use for all frames self.key =\",\n \"update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has\",\n \"image size self.rect.center = self.centre # set centre for all frames self.frame =\",\n \"# 8 images played at 1 frame per 0.1 secs = 0.8 seconds\",\n \"# 0.1 seconds has passed self.time_passed = 0 # reset timer self.frame +=\",\n \"set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw\",\n \"self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed\",\n \"to first image in the sequence self.rect = self.image.get_rect() # define rectangle from\",\n \"= self.image.get_rect() # define rectangle from image size self.rect.center = self.centre # set\",\n \"class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key:\",\n \"timer to 0 self.frame_rate = 0.1 # 8 images played at 1 frame\",\n \"= 0 # reset timer self.frame += 1 # increase frame number if\",\n \"animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() #\",\n \"new rectangle self.rect.center = self.centre # set centre to parameter value return self.active\",\n \"no of first frame self.time_passed = 0 # set timer to 0 self.frame_rate\",\n \"set to first image in the sequence self.rect = self.image.get_rect() # define rectangle\",\n \"self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1 # 8\",\n \"+= 1 # increase frame number if self.frame >= len(self.images[self.key]): # check if\",\n \"self.active = True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate:\",\n \"self.rect.center = self.centre # set centre to parameter value return self.active def draw(self):\",\n \"next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set\",\n \"reset timer self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]):\",\n \"images[key][0] # set to first image in the sequence self.rect = self.image.get_rect() #\",\n \"shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables.\",\n \"finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new\",\n \"self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center\",\n \"# increase frame number if self.frame >= len(self.images[self.key]): # check if end of\",\n \"seconds has passed self.time_passed = 0 # reset timer self.frame += 1 #\",\n \"frames self.frame = 0 # no of first frame self.time_passed = 0 #\",\n \"to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw current frame\",\n \"self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]): # check\",\n \"import pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None:\",\n \"= False # animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect\",\n \"all frames self.key = key # key used later self.image = images[key][0] #\",\n \"# key used later self.image = images[key][0] # set to first image in\",\n \"images self.centre = centre # use for all frames self.key = key #\",\n \"of list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame] #\",\n \"= self.centre # set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image,\",\n \"used later self.image = images[key][0] # set to first image in the sequence\",\n \"-> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images\",\n \"0 # reset timer self.frame += 1 # increase frame number if self.frame\",\n \"frames self.key = key # key used later self.image = images[key][0] # set\",\n \"= 0 # no of first frame self.time_passed = 0 # set timer\",\n \"= 0.1 # 8 images played at 1 frame per 0.1 secs =\",\n \"False # animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect =\",\n \"set centre for all frames self.frame = 0 # no of first frame\",\n \"self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset timer self.frame\",\n \"frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre\",\n \"= key # key used later self.image = images[key][0] # set to first\",\n \"frame self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1 #\",\n \"self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to\",\n \"check if end of list? self.active = False # animation finished else: self.image\",\n \"# set timer to 0 self.frame_rate = 0.1 # 8 images played at\",\n \"self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset\",\n \"0.1 secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed +=\",\n \"self.frame = 0 # no of first frame self.time_passed = 0 # set\",\n \"= 0 # set timer to 0 self.frame_rate = 0.1 # 8 images\",\n \"self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre\",\n \"for all frames self.key = key # key used later self.image = images[key][0]\",\n \"if self.frame >= len(self.images[self.key]): # check if end of list? self.active = False\",\n \"image in the sequence self.rect = self.image.get_rect() # define rectangle from image size\",\n \"set timer to 0 self.frame_rate = 0.1 # 8 images played at 1\",\n \"= images[key][0] # set to first image in the sequence self.rect = self.image.get_rect()\",\n \"images # list of 8 images self.centre = centre # use for all\",\n \"from image size self.rect.center = self.centre # set centre for all frames self.frame\",\n \"0.1 # 8 images played at 1 frame per 0.1 secs = 0.8\",\n \"self.key = key # key used later self.image = images[key][0] # set to\",\n \">= len(self.images[self.key]): # check if end of list? self.active = False # animation\",\n \"'lg', 'player ''' self.images = images # list of 8 images self.centre =\",\n \"if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 #\",\n \"the sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center =\",\n \">= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset timer\",\n \"0 self.frame_rate = 0.1 # 8 images played at 1 frame per 0.1\",\n \"centre for all frames self.frame = 0 # no of first frame self.time_passed\",\n \"all frames self.frame = 0 # no of first frame self.time_passed = 0\",\n \"= True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: #\",\n \"8 images self.centre = centre # use for all frames self.key = key\",\n \"first frame self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1\"\n]","isConversational":false}}},{"rowIdx":591,"cells":{"ngram":{"kind":"list like","value":["= args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event()","the appropriate executors for processing. This is based on the following logic: ..","bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. \"\"\"","is started, so this could be from a duplicate logger being added by",">= THRESHOLD : break callback() This logic ensures that the callbacks are activated","self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon","= self._wake_up_time # Waiting for the event returns True only when the event","INTERVAL) : wait() break callback() \"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize the","interval (int) : seconds after which timer expires \"\"\" self.interval = interval self.cb_args","logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The overall goal","the timer thread is started, so this could be from a duplicate logger","return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def make_callback(self,","the timer. KWargs: - kind (str): Default=None, used to pass information on what","DFK object to track parsl progress KWargs: - threshold (int) : Tasks after","following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time()","= time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon =","= time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the threads and terminate. \"\"\"","the thread can make a quick exit when close() is called Args: -","notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount","kind=None): \"\"\"Makes the callback and resets the timer. \"\"\" self._wake_up_time = time.time() +","be from a duplicate logger being added by the thread. \"\"\" def __init__(self,","# Sleep till time to wake up while True: prev = self._wake_up_time #","and redirect it the appropriate executors for processing. This is based on the","function that the thread will execute. waits on an event so that the","timer expires \"\"\" self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time","more debugging. What I've learnt so far is that the duplicate messages are","This method is to be called from the Interchange to notify the flowcontrol","= time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge the","interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start()","threshold-interval based flow control. The overall goal is to trap the flow of","self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread =","logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while","flowcontrol object We start the timer thread here Args: - dfk (DataFlowKernel) :","- interval (int) : seconds after which timer expires \"\"\" self.interval = interval","the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >=","kind=None): \"\"\"Makes the callback and resets the timer. KWargs: - kind (str): Default=None,","(INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : wait()","the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start =","threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the","if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\")","the flowcontrol object. We start the timer thread here Parameters ---------- - threshold","- kind (str): Default=None, used to pass information on what triggered the callback","Sleep till time to wake up while True: prev = self._wake_up_time # Waiting","when the event # is set, usually by the parent thread time_to_die =","for processing. This is based on the following logic: .. code-block:: none BEGIN","module emits duplicate messages. This issue needs more debugging. What I've learnt so","sys import threading import logging import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements","start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to","from a duplicate logger being added by the thread. \"\"\" def __init__(self, *args,","available as well asqeuque TODO: When the debug logs are enabled this module","+ self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge the threads and","threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \"\"\"Actually start the strategy Parameters","an event so that the thread can make a quick exit when close()","logic ensures that the callbacks are activated with a maximum delay of `interval`","threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. KWargs:","terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is a simplified version of the","(DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold (int) :","resets the timer. \"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge","time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True","time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a","time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def","the event # is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev","self.interchange = None self.threshold = threshold self.interval = interval self.cb_args = args self.callback","self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback","threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \"\"\"Actually start","callback \"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def","called with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function","threading import logging import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based","logs are enabled this module emits duplicate messages. This issue needs more debugging.","the thread will execute. waits on an event so that the thread can","to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold:","callback generally runs a strategy method on the sites available as well asqeuque","= threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \"\"\"Actually","so far is that the duplicate messages are present only when the timer","= threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is","Args: - dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: -","print(\"Sleeping a bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the","logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the","more\") def notify(self, event_id): \"\"\"Let the FlowControl system know that there is an","threshold or the interval is hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs))","is a simplified version of the FlowControl timer. This timer does not employ","= True def start(self, interchange): \"\"\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange","kind (str): Default=None, used to pass information on what triggered the callback \"\"\"",">= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback and","not employ notify events. This is based on the following logic : ..","self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self,","= self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time =","self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that","def strategize(self, *args, **kwargs): \"\"\" Strategize is called everytime the threshold or the","execute. waits on an event so that the thread can make a quick","with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that","(current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break","callback) : start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start)","control. The overall goal is to trap the flow of apps from the","while True: prev = self._wake_up_time # Waiting for the event returns True only","if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def notify(self, event_id):","track parsl progress KWargs: - threshold (int) : Tasks after which the callback","logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the","flowcontrol object. We start the timer thread here Parameters ---------- - threshold (int)","True only when the event # is set, usually by the parent thread","prev = self._wake_up_time # Waiting for the event returns True only when the","the sites available as well asqeuque TODO: When the debug logs are enabled","on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start","messages are present only when the timer thread is started, so this could","prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def notify(self, event_id): \"\"\"Let","called from the Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1","*args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We start the timer thread here","callback and resets the timer. \"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def","True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the thread","= 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event()","measure it and redirect it the appropriate executors for processing. This is based","employ notify events. This is based on the following logic : .. code-block::","Event to wait on \"\"\" while True: prev = self._wake_up_time # Waiting for","\"\"\"Internal. This is the function that the thread will execute. waits on an","== self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def make_callback(self, kind=None): \"\"\"Makes the","return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def notify(self,","def __init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol object We start the timer","that there is an event. This method is to be called from the","the callback is triggered - interval (int) : seconds after which timer expires","triggered, the callback generally runs a strategy method on the sites available as","event returns True only when the event # is set, usually by the","quick exit when close() is called Args: - kill_event (threading.Event) : Event to","KWargs: - threshold (int) : Tasks after which the callback is triggered -","None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event","self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() +","close() is called Args: - kill_event (threading.Event) : Event to wait on \"\"\"","object We start the timer thread here Args: - dfk (DataFlowKernel) : DFK","duplicate messages. This issue needs more debugging. What I've learnt so far is","< INTERVAL) : wait() break callback() \"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize","is called Args: - kill_event (threading.Event) : Event to wait on \"\"\" #","\"\"\"Let the FlowControl system know that there is an event. This method is","to trap the flow of apps from the workflow, measure it and redirect","the strategy to \"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{},","logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time()","the flowcontrol object We start the timer thread here Args: - dfk (DataFlowKernel)","This is the function that the thread will execute. waits on an event","callback is triggered, the callback generally runs a strategy method on the sites","= interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks))","the function that the thread will execute. waits on an event so that","workflow, measure it and redirect it the appropriate executors for processing. This is","Waiting for the event returns True only when the event # is set,","INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break callback() This","the interval is hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def _wake_up_timer(self,","else: print(\"Sleeping a bit more\") def notify(self, event_id): \"\"\"Let the FlowControl system know","= current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >=","print(\"Sleeping a bit more\") def notify(self, event_id): \"\"\"Let the FlowControl system know that","= callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer,",": start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if","kill_event (threading.Event) : Event to wait on \"\"\" while True: prev = self._wake_up_time","time to wake up while True: prev = self._wake_up_time # Waiting for the","class Timer(object): \"\"\"This timer is a simplified version of the FlowControl timer. This","set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if","def notify(self, event_id): \"\"\"Let the FlowControl system know that there is an event.","only when the timer thread is started, so this could be from a","self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def","here Args: - dfk (DataFlowKernel) : DFK object to track parsl progress KWargs:","The overall goal is to trap the flow of apps from the workflow,","if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def make_callback(self, kind=None):","init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize","args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer = []","1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes","self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() +","as well asqeuque TODO: When the debug logs are enabled this module emits","break callback() This logic ensures that the callbacks are activated with a maximum","the event returns True only when the event # is set, usually by","the timer thread here Parameters ---------- - threshold (int) : Tasks after which","thread here Parameters ---------- - threshold (int) : Tasks after which the callback","\"\"\"Initialize the flowcontrol object. We start the timer thread here Parameters ---------- -","a duplicate logger being added by the thread. \"\"\" def __init__(self, *args, threshold=20,","up while True: prev = self._wake_up_time # Waiting for the event returns True","and resets the timer. KWargs: - kind (str): Default=None, used to pass information","close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is","infrequent events as well as systems which would generate large bursts of events.","the callback and resets the timer. KWargs: - kind (str): Default=None, used to","enabled this module emits duplicate messages. This issue needs more debugging. What I've","system know that there is an event. This method is to be called","self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def notify(self, event_id): \"\"\"Let the FlowControl","events as well as systems which would generate large bursts of events. Once","= [] def close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object):","there is an event. This method is to be called from the Interchange",": seconds after which timer expires \"\"\" self.interchange = None self.threshold = threshold","\"\"\"Makes the callback and resets the timer. \"\"\" self._wake_up_time = time.time() + self.interval","\"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We start the","that the duplicate messages are present only when the timer thread is started,","self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback","would generate large bursts of events. Once a callback is triggered, the callback","self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \"\"\"Actually start the","runs a strategy method on the sites available as well asqeuque TODO: When","interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize is called everytime the threshold","self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the threads and terminate.","could be from a duplicate logger being added by the thread. \"\"\" def","the callback \"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = []","---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \"\"\" self.interchange = interchange","callback and resets the timer. KWargs: - kind (str): Default=None, used to pass","[] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,))","self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks,","systems with infrequent events as well as systems which would generate large bursts","Default=None, used to pass information on what triggered the callback \"\"\" self._wake_up_time =","= args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer =","hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self,","that the callbacks are activated with a maximum delay of `interval` for systems","asqeuque TODO: When the debug logs are enabled this module emits duplicate messages.","the callbacks are activated with a maximum delay of `interval` for systems with","to wait on \"\"\" # Sleep till time to wake up while True:","\"\"\" # Sleep till time to wake up while True: prev = self._wake_up_time","delay of `interval` for systems with infrequent events as well as systems which","appropriate executors for processing. This is based on the following logic: .. code-block::","of `interval` for systems with infrequent events as well as systems which would","emits duplicate messages. This issue needs more debugging. What I've learnt so far","timer thread is started, so this could be from a duplicate logger being","self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange):","based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) :","activated with a maximum delay of `interval` for systems with infrequent events as","Once a callback is triggered, the callback generally runs a strategy method on",": Event to wait on \"\"\" while True: prev = self._wake_up_time # Waiting","\"\"\"Implements threshold-interval based flow control. The overall goal is to trap the flow","here Parameters ---------- - threshold (int) : Tasks after which the callback is","def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. KWargs: - kind","class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The overall goal is to trap","THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : wait() break","0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit","This logic ensures that the callbacks are activated with a maximum delay of","THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : count =","Event to wait on \"\"\" # Sleep till time to wake up while","well asqeuque TODO: When the debug logs are enabled this module emits duplicate","threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle = None","*args, interval=5): \"\"\"Initialize the flowcontrol object We start the timer thread here Args:","get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures that the","self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the thread will",": start = current_time() while (current_time()-start < INTERVAL) : wait() break callback() \"\"\"","is based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback)","from the workflow, measure it and redirect it the appropriate executors for processing.","event_id): \"\"\"Let the FlowControl system know that there is an event. This method","time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge the threads","Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \"\"\" self.interchange =","wait on \"\"\" while True: prev = self._wake_up_time # Waiting for the event","self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is a simplified version of the FlowControl",": Tasks after which the callback is triggered - interval (int) : seconds","current_time() while (current_time()-start < INTERVAL) : wait() break callback() \"\"\" def __init__(self, callback,","be called from the Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count +=","Parameters ---------- - threshold (int) : Tasks after which the callback is triggered","(int) : seconds after which timer expires \"\"\" self.interchange = None self.threshold =","Tasks after which the callback is triggered - interval (int) : seconds after","on what triggered the callback \"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind)","large bursts of events. Once a callback is triggered, the callback generally runs","are activated with a maximum delay of `interval` for systems with infrequent events",": count = get_events_since(start) if count >= THRESHOLD : break callback() This logic","a maximum delay of `interval` for systems with infrequent events as well as","notify events. This is based on the following logic : .. code-block:: none","logging import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control.","dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold (int)","a strategy method on the sites available as well asqeuque TODO: When the","BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) :","= interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count =","timer. This timer does not employ notify events. This is based on the","+ 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start()","Interchange to bind the strategy to \"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'):","a bit more\") def notify(self, event_id): \"\"\"Let the FlowControl system know that there","issue needs more debugging. What I've learnt so far is that the duplicate","triggered - interval (int) : seconds after which timer expires \"\"\" self.interchange =","is called Args: - kill_event (threading.Event) : Event to wait on \"\"\" while","or the interval is hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def","= True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the","progress KWargs: - threshold (int) : Tasks after which the callback is triggered","processing. This is based on the following logic: .. code-block:: none BEGIN (INTERVAL,","(int) : seconds after which timer expires \"\"\" self.interval = interval self.cb_args =","the FlowControl timer. This timer does not employ notify events. This is based","is an event. This method is to be called from the Interchange to","object to track parsl progress KWargs: - threshold (int) : Tasks after which","interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event","timer expires \"\"\" self.interchange = None self.threshold = threshold self.interval = interval self.cb_args","**kwargs): \"\"\" Strategize is called everytime the threshold or the interval is hit","when the timer thread is started, so this could be from a duplicate","(threading.Event) : Event to wait on \"\"\" while True: prev = self._wake_up_time #","exit when close() is called Args: - kill_event (threading.Event) : Event to wait","\"\"\" while True: prev = self._wake_up_time # Waiting for the event returns True","make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. KWargs: - kind (str):","= logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The overall goal is","\"\"\" self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time = time.time()","\"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is a","self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge","events. Once a callback is triggered, the callback generally runs a strategy method","THRESHOLD : break callback() This logic ensures that the callbacks are activated with","strategy method on the sites available as well asqeuque TODO: When the debug","expires \"\"\" self.interchange = None self.threshold = threshold self.interval = interval self.cb_args =","(threading.Event) : Event to wait on \"\"\" # Sleep till time to wake","the callback generally runs a strategy method on the sites available as well","threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is a simplified version","added by the thread. \"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol","FlowControl timer. This timer does not employ notify events. This is based on","following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start =","information on what triggered the callback \"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer,","interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \"\"\" self.interchange = interchange if","the duplicate messages are present only when the timer thread is started, so","called everytime the threshold or the interval is hit \"\"\" logger.debug(\"Strategize called with","kill_event): \"\"\"Internal. This is the function that the thread will execute. waits on",": seconds after which timer expires \"\"\" self.interval = interval self.cb_args = args","\"\"\"Initialize the flowcontrol object We start the timer thread here Args: - dfk","triggered the callback \"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer =","self._event_buffer = [] def close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class","everytime the threshold or the interval is hit \"\"\" logger.debug(\"Strategize called with {}","on the sites available as well asqeuque TODO: When the debug logs are","duplicate logger being added by the thread. \"\"\" def __init__(self, *args, threshold=20, interval=5):","on \"\"\" # Sleep till time to wake up while True: prev =","wait on \"\"\" # Sleep till time to wake up while True: prev","does not employ notify events. This is based on the following logic :","= current_time() while (current_time()-start < INTERVAL) : wait() break callback() \"\"\" def __init__(self,","def close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer","notify(self, event_id): \"\"\"Let the FlowControl system know that there is an event. This","event. This method is to be called from the Interchange to notify the","kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the thread will","method is to be called from the Interchange to notify the flowcontrol \"\"\"","which timer expires \"\"\" self.interval = interval self.cb_args = args self.callback = callback","logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The overall goal is to","= get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures that","started, so this could be from a duplicate logger being added by the","code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start <","thread will execute. waits on an event so that the thread can make","and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is a simplified version of","'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args,","if count >= THRESHOLD : break callback() This logic ensures that the callbacks","are present only when the timer thread is started, so this could be","the threshold or the interval is hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args,","a callback is triggered, the callback generally runs a strategy method on the","after which the callback is triggered - interval (int) : seconds after which","(str): Default=None, used to pass information on what triggered the callback \"\"\" self._wake_up_time","so this could be from a duplicate logger being added by the thread.","TODO: When the debug logs are enabled this module emits duplicate messages. This","\"\"\"This timer is a simplified version of the FlowControl timer. This timer does","method on the sites available as well asqeuque TODO: When the debug logs","the debug logs are enabled this module emits duplicate messages. This issue needs","else: print(\"Sleeping a bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets","self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize is called everytime the threshold or","Timer(object): \"\"\"This timer is a simplified version of the FlowControl timer. This timer","can make a quick exit when close() is called Args: - kill_event (threading.Event)","= threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \"\"\"Actually start the strategy","What I've learnt so far is that the duplicate messages are present only","0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread","# Waiting for the event returns True only when the event # is","kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join()","logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs):","the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if","which would generate large bursts of events. Once a callback is triggered, the","True: prev = self._wake_up_time # Waiting for the event returns True only when","it the appropriate executors for processing. This is based on the following logic:","wait() break callback() \"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol object","callback) : start = current_time() while (current_time()-start < INTERVAL) : wait() break callback()","callback() \"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol object We start","and resets the timer. \"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self):","= threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle =","strategy to \"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{},","the workflow, measure it and redirect it the appropriate executors for processing. This","This issue needs more debugging. What I've learnt so far is that the","which timer expires \"\"\" self.interchange = None self.threshold = threshold self.interval = interval","to bind the strategy to \"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy","so that the thread can make a quick exit when close() is called","threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal.","self.cb_args = args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer","import threading import logging import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval","is triggered, the callback generally runs a strategy method on the sites available","callbacks are activated with a maximum delay of `interval` for systems with infrequent","maximum delay of `interval` for systems with infrequent events as well as systems","def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We start the timer","self._thread.join() class Timer(object): \"\"\"This timer is a simplified version of the FlowControl timer.","- dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold","\"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self):","what triggered the callback \"\"\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer","\"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the threads and","redirect it the appropriate executors for processing. This is based on the following","strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \"\"\" self.interchange","a quick exit when close() is called Args: - kill_event (threading.Event) : Event","events. This is based on the following logic : .. code-block:: none BEGIN","= None self.threshold = threshold self.interval = interval self.cb_args = args self.callback =","Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >=","args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \"\"\"Actually start the strategy Parameters ----------","thread is started, so this could be from a duplicate logger being added","== self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def notify(self, event_id): \"\"\"Let the","time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The overall","(int) : Tasks after which the callback is triggered - interval (int) :","thread here Args: - dfk (DataFlowKernel) : DFK object to track parsl progress","goal is to trap the flow of apps from the workflow, measure it","self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set()","know that there is an event. This method is to be called from","self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. KWargs: -","self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets","interval is hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event):","time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the threads and terminate. \"\"\" self._kill_event.set()","timer thread here Args: - dfk (DataFlowKernel) : DFK object to track parsl","debug logs are enabled this module emits duplicate messages. This issue needs more","the timer. \"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the","event # is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev -","ensures that the callbacks are activated with a maximum delay of `interval` for","being added by the thread. \"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the","if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the","\"\"\" self.interchange = None self.threshold = threshold self.interval = interval self.cb_args = args","thread can make a quick exit when close() is called Args: - kill_event","\"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is","to \"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format(","- time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping","import logging import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow","on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) :","version of the FlowControl timer. This timer does not employ notify events. This","by the thread. \"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object.","called Args: - kill_event (threading.Event) : Event to wait on \"\"\" while True:","the thread. \"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We","a simplified version of the FlowControl timer. This timer does not employ notify","is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0)))","that the thread will execute. waits on an event so that the thread","the callback and resets the timer. \"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args)","if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def","def start(self, interchange): \"\"\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to","+= 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None):","none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL)","import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The","is hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal.","We start the timer thread here Args: - dfk (DataFlowKernel) : DFK object","= interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1","\"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol object We start the","is the function that the thread will execute. waits on an event so","thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev ==","\"\"\"Makes the callback and resets the timer. KWargs: - kind (str): Default=None, used","self.threshold = threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle","is called everytime the threshold or the interval is hit \"\"\" logger.debug(\"Strategize called","start = current_time() while (current_time()-start < INTERVAL) : wait() break callback() \"\"\" def","called Args: - kill_event (threading.Event) : Event to wait on \"\"\" # Sleep","current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD","BaseStrategy(object): \"\"\"Implements threshold-interval based flow control. The overall goal is to trap the","flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\")","start the timer thread here Parameters ---------- - threshold (int) : Tasks after","of apps from the workflow, measure it and redirect it the appropriate executors","self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread =","[] def close(self): \"\"\"Merge the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This","used to pass information on what triggered the callback \"\"\" self._wake_up_time = time.time()","KWargs: - kind (str): Default=None, used to pass information on what triggered the","more\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. \"\"\" self._wake_up_time","flow control. The overall goal is to trap the flow of apps from","wake up while True: prev = self._wake_up_time # Waiting for the event returns","which the callback is triggered - interval (int) : seconds after which timer","break callback() \"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol object We","1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self,","this module emits duplicate messages. This issue needs more debugging. What I've learnt","seconds after which timer expires \"\"\" self.interchange = None self.threshold = threshold self.interval",": .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while","+ 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def","executors for processing. This is based on the following logic: .. code-block:: none","it and redirect it the appropriate executors for processing. This is based on","# is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(),","- kill_event (threading.Event) : Event to wait on \"\"\" while True: prev =","= [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer,","---------- - threshold (int) : Tasks after which the callback is triggered -","interval (int) : seconds after which timer expires \"\"\" self.interchange = None self.threshold","based on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback)","the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \"\"\"","self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback and","count >= THRESHOLD : break callback() This logic ensures that the callbacks are","args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread","interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize is called everytime","start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count","based flow control. The overall goal is to trap the flow of apps","learnt so far is that the duplicate messages are present only when the","self._thread.daemon = True def start(self, interchange): \"\"\"Actually start the strategy Parameters ---------- interchange:","kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else:","interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize is called everytime the","present only when the timer thread is started, so this could be from","Args: - kill_event (threading.Event) : Event to wait on \"\"\" while True: prev","{} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the","= kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer')","+ self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the threads and terminate. \"\"\" self._kill_event.set() self._thread.join()","bursts of events. Once a callback is triggered, the callback generally runs a","by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return","timer. \"\"\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \"\"\"Merge the threads","seconds after which timer expires \"\"\" self.interval = interval self.cb_args = args self.callback","(INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : count",": break callback() This logic ensures that the callbacks are activated with a","None self.threshold = threshold self.interval = interval self.cb_args = args self.callback = self.strategize","self._wake_up_time # Waiting for the event returns True only when the event #","logger being added by the thread. \"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize","threshold (int) : Tasks after which the callback is triggered - interval (int)","event so that the thread can make a quick exit when close() is","This timer does not employ notify events. This is based on the following","This is based on the following logic : .. code-block:: none BEGIN (INTERVAL,","systems which would generate large bursts of events. Once a callback is triggered,","thread. \"\"\" def __init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We start",".. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start","import sys import threading import logging import time logger = logging.getLogger(\"interchange.strategy.base\") class BaseStrategy(object):","bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\"","parsl progress KWargs: - threshold (int) : Tasks after which the callback is","this could be from a duplicate logger being added by the thread. \"\"\"","*args, **kwargs): \"\"\" Strategize is called everytime the threshold or the interval is","\"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds-> init:{}, min:{}, max:{}\".format( interchange.config.provider.init_blocks,","self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time()","callback is triggered - interval (int) : seconds after which timer expires \"\"\"","- kill_event (threading.Event) : Event to wait on \"\"\" # Sleep till time","pass information on what triggered the callback \"\"\" self._wake_up_time = time.time() + self.interval","object. We start the timer thread here Parameters ---------- - threshold (int) :","on an event so that the thread can make a quick exit when","timer. KWargs: - kind (str): Default=None, used to pass information on what triggered","self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This",">= threshold\") self.make_callback(kind=\"event\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer.","timer does not employ notify events. This is based on the following logic","to track parsl progress KWargs: - threshold (int) : Tasks after which the","max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize is called","bit more\") def notify(self, event_id): \"\"\"Let the FlowControl system know that there is","debugging. What I've learnt so far is that the duplicate messages are present","< INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break callback()",": wait() break callback() \"\"\" def __init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol","time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time:","= threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event):","strategize(self, *args, **kwargs): \"\"\" Strategize is called everytime the threshold or the interval","of events. Once a callback is triggered, the callback generally runs a strategy","Strategize is called everytime the threshold or the interval is hit \"\"\" logger.debug(\"Strategize","is to trap the flow of apps from the workflow, measure it and","self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event =","\"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\"Eventcount >= threshold\") self.make_callback(kind=\"event\")","is based on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD,",": Event to wait on \"\"\" # Sleep till time to wake up","timer is a simplified version of the FlowControl timer. This timer does not","with a maximum delay of `interval` for systems with infrequent events as well","waits on an event so that the thread can make a quick exit","only when the event # is set, usually by the parent thread time_to_die","the threads and terminate.\"\"\" self._kill_event.set() self._thread.join() class Timer(object): \"\"\"This timer is a simplified","callback() This logic ensures that the callbacks are activated with a maximum delay","the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start","interchange): \"\"\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the","will execute. waits on an event so that the thread can make a","count = get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures","usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die:","for systems with infrequent events as well as systems which would generate large","when close() is called Args: - kill_event (threading.Event) : Event to wait on","trap the flow of apps from the workflow, measure it and redirect it","timer thread here Parameters ---------- - threshold (int) : Tasks after which the","are enabled this module emits duplicate messages. This issue needs more debugging. What","triggered - interval (int) : seconds after which timer expires \"\"\" self.interval =","to pass information on what triggered the callback \"\"\" self._wake_up_time = time.time() +","interval=5): \"\"\"Initialize the flowcontrol object. We start the timer thread here Parameters ----------","\"\"\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy","an event. This method is to be called from the Interchange to notify","self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def notify(self, event_id): \"\"\"Let the FlowControl system","with infrequent events as well as systems which would generate large bursts of","till time to wake up while True: prev = self._wake_up_time # Waiting for","parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev","threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We start the timer thread here Parameters","is triggered - interval (int) : seconds after which timer expires \"\"\" self.interchange","= None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1","__init__(self, *args, threshold=20, interval=5): \"\"\"Initialize the flowcontrol object. We start the timer thread","def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. \"\"\" self._wake_up_time =","funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \"\"\" self.interchange = interchange if hasattr(interchange.config,","is that the duplicate messages are present only when the timer thread is","(current_time()-start < INTERVAL) : wait() break callback() \"\"\" def __init__(self, callback, *args, interval=5):",": DFK object to track parsl progress KWargs: - threshold (int) : Tasks","bind the strategy to \"\"\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\"Strategy bounds->","far is that the duplicate messages are present only when the timer thread","__init__(self, callback, *args, interval=5): \"\"\"Initialize the flowcontrol object We start the timer thread","callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,))","self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time","to be called from the Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count","When the debug logs are enabled this module emits duplicate messages. This issue","prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\"Sleeping a bit more\") def make_callback(self, kind=None): \"\"\"Makes","that the thread can make a quick exit when close() is called Args:","True def start(self, interchange): \"\"\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange","`interval` for systems with infrequent events as well as systems which would generate","- threshold (int) : Tasks after which the callback is triggered - interval","needs more debugging. What I've learnt so far is that the duplicate messages","from the Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if","{}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the thread","\"\"\" Strategize is called everytime the threshold or the interval is hit \"\"\"","args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function","min:{}, max:{}\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \"\"\" Strategize is","returns True only when the event # is set, usually by the parent","of the FlowControl timer. This timer does not employ notify events. This is","for the event returns True only when the event # is set, usually","generate large bursts of events. Once a callback is triggered, the callback generally","interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count = 0","self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \"\"\"Merge the threads and terminate.\"\"\"","as systems which would generate large bursts of events. Once a callback is","start the timer thread here Args: - dfk (DataFlowKernel) : DFK object to","well as systems which would generate large bursts of events. Once a callback","messages. This issue needs more debugging. What I've learnt so far is that","We start the timer thread here Parameters ---------- - threshold (int) : Tasks","self.interval = interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count","self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event =","make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. \"\"\" self._wake_up_time = time.time()","_wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the thread will execute. waits","interval=5): \"\"\"Initialize the flowcontrol object We start the timer thread here Args: -","expires \"\"\" self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time =","to wake up while True: prev = self._wake_up_time # Waiting for the event","to wait on \"\"\" while True: prev = self._wake_up_time # Waiting for the","self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self,","1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def","after which timer expires \"\"\" self.interchange = None self.threshold = threshold self.interval =","start(self, interchange): \"\"\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind","on \"\"\" while True: prev = self._wake_up_time # Waiting for the event returns","while (current_time()-start < INTERVAL) : wait() break callback() \"\"\" def __init__(self, callback, *args,","as well as systems which would generate large bursts of events. Once a","FlowControl system know that there is an event. This method is to be","flow of apps from the workflow, measure it and redirect it the appropriate","duplicate messages are present only when the timer thread is started, so this","resets the timer. KWargs: - kind (str): Default=None, used to pass information on","make a quick exit when close() is called Args: - kill_event (threading.Event) :","is triggered - interval (int) : seconds after which timer expires \"\"\" self.interval","callback, *args, interval=5): \"\"\"Initialize the flowcontrol object We start the timer thread here","generally runs a strategy method on the sites available as well asqeuque TODO:","after which timer expires \"\"\" self.interval = interval self.cb_args = args self.callback =","the FlowControl system know that there is an event. This method is to","simplified version of the FlowControl timer. This timer does not employ notify events.","is to be called from the Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id])","I've learnt so far is that the duplicate messages are present only when","- interval (int) : seconds after which timer expires \"\"\" self.interchange = None","overall goal is to trap the flow of apps from the workflow, measure","a bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer.","the Interchange to notify the flowcontrol \"\"\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count","kill_event (threading.Event) : Event to wait on \"\"\" # Sleep till time to","the flow of apps from the workflow, measure it and redirect it the","hit \"\"\" logger.debug(\"Strategize called with {} {}\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \"\"\"Internal. This","This is based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD,","while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD :","the timer thread here Args: - dfk (DataFlowKernel) : DFK object to track","def _wake_up_timer(self, kill_event): \"\"\"Internal. This is the function that the thread will execute.","Args: - kill_event (threading.Event) : Event to wait on \"\"\" # Sleep till","apps from the workflow, measure it and redirect it the appropriate executors for","sites available as well asqeuque TODO: When the debug logs are enabled this"],"string":"[\n \"= args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event()\",\n \"the appropriate executors for processing. This is based on the following logic: ..\",\n \"bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. \\\"\\\"\\\"\",\n \"is started, so this could be from a duplicate logger being added by\",\n \">= THRESHOLD : break callback() This logic ensures that the callbacks are activated\",\n \"self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon\",\n \"= self._wake_up_time # Waiting for the event returns True only when the event\",\n \"INTERVAL) : wait() break callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the\",\n \"interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interval = interval self.cb_args\",\n \"logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The overall goal\",\n \"the timer thread is started, so this could be from a duplicate logger\",\n \"return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def make_callback(self,\",\n \"the timer. KWargs: - kind (str): Default=None, used to pass information on what\",\n \"DFK object to track parsl progress KWargs: - threshold (int) : Tasks after\",\n \"following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time()\",\n \"= time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon =\",\n \"= time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the threads and terminate. \\\"\\\"\\\"\",\n \"the thread can make a quick exit when close() is called Args: -\",\n \"notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount\",\n \"kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time() +\",\n \"be from a duplicate logger being added by the thread. \\\"\\\"\\\" def __init__(self,\",\n \"# Sleep till time to wake up while True: prev = self._wake_up_time #\",\n \"and redirect it the appropriate executors for processing. This is based on the\",\n \"function that the thread will execute. waits on an event so that the\",\n \"timer expires \\\"\\\"\\\" self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time\",\n \"more debugging. What I've learnt so far is that the duplicate messages are\",\n \"This method is to be called from the Interchange to notify the flowcontrol\",\n \"= time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the\",\n \"interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start()\",\n \"threshold-interval based flow control. The overall goal is to trap the flow of\",\n \"self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread =\",\n \"logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while\",\n \"flowcontrol object We start the timer thread here Args: - dfk (DataFlowKernel) :\",\n \"- interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interval = interval\",\n \"the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >=\",\n \"kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. KWargs: - kind (str): Default=None,\",\n \"(INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : wait()\",\n \"the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start =\",\n \"threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the\",\n \"if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\")\",\n \"the flowcontrol object. We start the timer thread here Parameters ---------- - threshold\",\n \"- kind (str): Default=None, used to pass information on what triggered the callback\",\n \"Sleep till time to wake up while True: prev = self._wake_up_time # Waiting\",\n \"when the event # is set, usually by the parent thread time_to_die =\",\n \"for processing. This is based on the following logic: .. code-block:: none BEGIN\",\n \"module emits duplicate messages. This issue needs more debugging. What I've learnt so\",\n \"sys import threading import logging import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements\",\n \"start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to\",\n \"from a duplicate logger being added by the thread. \\\"\\\"\\\" def __init__(self, *args,\",\n \"available as well asqeuque TODO: When the debug logs are enabled this module\",\n \"+ self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the threads and\",\n \"threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters\",\n \"an event so that the thread can make a quick exit when close()\",\n \"logic ensures that the callbacks are activated with a maximum delay of `interval`\",\n \"threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. KWargs:\",\n \"terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a simplified version of the\",\n \"(DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold (int) :\",\n \"resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge\",\n \"time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True\",\n \"time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a\",\n \"time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def\",\n \"the event # is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev\",\n \"self.interchange = None self.threshold = threshold self.interval = interval self.cb_args = args self.callback\",\n \"self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback\",\n \"threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually start\",\n \"callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def\",\n \"called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function\",\n \"threading import logging import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based\",\n \"logs are enabled this module emits duplicate messages. This issue needs more debugging.\",\n \"the thread will execute. waits on an event so that the thread can\",\n \"to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold:\",\n \"callback generally runs a strategy method on the sites available as well asqeuque\",\n \"= threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually\",\n \"so far is that the duplicate messages are present only when the timer\",\n \"= threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is\",\n \"Args: - dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: -\",\n \"print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the\",\n \"logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the\",\n \"more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know that there is an\",\n \"threshold or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs))\",\n \"is a simplified version of the FlowControl timer. This timer does not employ\",\n \"= True def start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange\",\n \"kind (str): Default=None, used to pass information on what triggered the callback \\\"\\\"\\\"\",\n \">= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and\",\n \"not employ notify events. This is based on the following logic : ..\",\n \"self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self,\",\n \"= self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time =\",\n \"self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that\",\n \"def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called everytime the threshold or the\",\n \"execute. waits on an event so that the thread can make a quick\",\n \"with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that\",\n \"(current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break\",\n \"callback) : start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start)\",\n \"control. The overall goal is to trap the flow of apps from the\",\n \"while True: prev = self._wake_up_time # Waiting for the event returns True only\",\n \"if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def notify(self, event_id):\",\n \"track parsl progress KWargs: - threshold (int) : Tasks after which the callback\",\n \"logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the\",\n \"flowcontrol object. We start the timer thread here Parameters ---------- - threshold (int)\",\n \"True only when the event # is set, usually by the parent thread\",\n \"prev = self._wake_up_time # Waiting for the event returns True only when the\",\n \"the sites available as well asqeuque TODO: When the debug logs are enabled\",\n \"on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start\",\n \"messages are present only when the timer thread is started, so this could\",\n \"prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let\",\n \"called from the Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1\",\n \"*args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start the timer thread here\",\n \"callback and resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def\",\n \"True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the thread\",\n \"= 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event()\",\n \"measure it and redirect it the appropriate executors for processing. This is based\",\n \"employ notify events. This is based on the following logic : .. code-block::\",\n \"Event to wait on \\\"\\\"\\\" while True: prev = self._wake_up_time # Waiting for\",\n \"\\\"\\\"\\\"Internal. This is the function that the thread will execute. waits on an\",\n \"== self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the\",\n \"return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def notify(self,\",\n \"def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start the timer\",\n \"that there is an event. This method is to be called from the\",\n \"the callback is triggered - interval (int) : seconds after which timer expires\",\n \"triggered, the callback generally runs a strategy method on the sites available as\",\n \"event returns True only when the event # is set, usually by the\",\n \"quick exit when close() is called Args: - kill_event (threading.Event) : Event to\",\n \"KWargs: - threshold (int) : Tasks after which the callback is triggered -\",\n \"None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event\",\n \"self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() +\",\n \"close() is called Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\"\",\n \"object We start the timer thread here Args: - dfk (DataFlowKernel) : DFK\",\n \"duplicate messages. This issue needs more debugging. What I've learnt so far is\",\n \"< INTERVAL) : wait() break callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize\",\n \"is called Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" #\",\n \"\\\"\\\"\\\"Let the FlowControl system know that there is an event. This method is\",\n \"to trap the flow of apps from the workflow, measure it and redirect\",\n \"the strategy to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{},\",\n \"logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time()\",\n \"the flowcontrol object We start the timer thread here Args: - dfk (DataFlowKernel)\",\n \"This is the function that the thread will execute. waits on an event\",\n \"callback is triggered, the callback generally runs a strategy method on the sites\",\n \"= interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks))\",\n \"the function that the thread will execute. waits on an event so that\",\n \"workflow, measure it and redirect it the appropriate executors for processing. This is\",\n \"Waiting for the event returns True only when the event # is set,\",\n \"INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break callback() This\",\n \"the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self,\",\n \"else: print(\\\"Sleeping a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know\",\n \"= current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >=\",\n \"print(\\\"Sleeping a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know that\",\n \"= callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer,\",\n \": start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if\",\n \"kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" while True: prev = self._wake_up_time\",\n \"time to wake up while True: prev = self._wake_up_time # Waiting for the\",\n \"class Timer(object): \\\"\\\"\\\"This timer is a simplified version of the FlowControl timer. This\",\n \"set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if\",\n \"def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know that there is an event.\",\n \"only when the timer thread is started, so this could be from a\",\n \"self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def\",\n \"here Args: - dfk (DataFlowKernel) : DFK object to track parsl progress KWargs:\",\n \"The overall goal is to trap the flow of apps from the workflow,\",\n \"if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None):\",\n \"init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize\",\n \"args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer = []\",\n \"1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes\",\n \"self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() +\",\n \"as well asqeuque TODO: When the debug logs are enabled this module emits\",\n \"break callback() This logic ensures that the callbacks are activated with a maximum\",\n \"the event returns True only when the event # is set, usually by\",\n \"the timer thread here Parameters ---------- - threshold (int) : Tasks after which\",\n \"thread here Parameters ---------- - threshold (int) : Tasks after which the callback\",\n \"\\\"\\\"\\\"Initialize the flowcontrol object. We start the timer thread here Parameters ---------- -\",\n \"a duplicate logger being added by the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20,\",\n \"up while True: prev = self._wake_up_time # Waiting for the event returns True\",\n \"and resets the timer. KWargs: - kind (str): Default=None, used to pass information\",\n \"close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is\",\n \"infrequent events as well as systems which would generate large bursts of events.\",\n \"the callback and resets the timer. KWargs: - kind (str): Default=None, used to\",\n \"enabled this module emits duplicate messages. This issue needs more debugging. What I've\",\n \"system know that there is an event. This method is to be called\",\n \"self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl\",\n \"events as well as systems which would generate large bursts of events. Once\",\n \"= [] def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object):\",\n \"there is an event. This method is to be called from the Interchange\",\n \": seconds after which timer expires \\\"\\\"\\\" self.interchange = None self.threshold = threshold\",\n \"\\\"\\\"\\\"Makes the callback and resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval\",\n \"\\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start the\",\n \"that the duplicate messages are present only when the timer thread is started,\",\n \"self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback\",\n \"would generate large bursts of events. Once a callback is triggered, the callback\",\n \"self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually start the\",\n \"runs a strategy method on the sites available as well asqeuque TODO: When\",\n \"interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called everytime the threshold\",\n \"self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the threads and terminate.\",\n \"could be from a duplicate logger being added by the thread. \\\"\\\"\\\" def\",\n \"the callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = []\",\n \"---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \\\"\\\"\\\" self.interchange = interchange\",\n \"callback and resets the timer. KWargs: - kind (str): Default=None, used to pass\",\n \"[] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,))\",\n \"self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks,\",\n \"systems with infrequent events as well as systems which would generate large bursts\",\n \"Default=None, used to pass information on what triggered the callback \\\"\\\"\\\" self._wake_up_time =\",\n \"= args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer =\",\n \"hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self,\",\n \"that the callbacks are activated with a maximum delay of `interval` for systems\",\n \"asqeuque TODO: When the debug logs are enabled this module emits duplicate messages.\",\n \"the callbacks are activated with a maximum delay of `interval` for systems with\",\n \"to wait on \\\"\\\"\\\" # Sleep till time to wake up while True:\",\n \"\\\"\\\"\\\" # Sleep till time to wake up while True: prev = self._wake_up_time\",\n \"delay of `interval` for systems with infrequent events as well as systems which\",\n \"appropriate executors for processing. This is based on the following logic: .. code-block::\",\n \"of `interval` for systems with infrequent events as well as systems which would\",\n \"emits duplicate messages. This issue needs more debugging. What I've learnt so far\",\n \"timer thread is started, so this could be from a duplicate logger being\",\n \"self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange):\",\n \"based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) :\",\n \"activated with a maximum delay of `interval` for systems with infrequent events as\",\n \"Once a callback is triggered, the callback generally runs a strategy method on\",\n \": Event to wait on \\\"\\\"\\\" while True: prev = self._wake_up_time # Waiting\",\n \"\\\"\\\"\\\"Implements threshold-interval based flow control. The overall goal is to trap the flow\",\n \"here Parameters ---------- - threshold (int) : Tasks after which the callback is\",\n \"def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. KWargs: - kind\",\n \"class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The overall goal is to trap\",\n \"THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : wait() break\",\n \"0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit\",\n \"This logic ensures that the callbacks are activated with a maximum delay of\",\n \"THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : count =\",\n \"Event to wait on \\\"\\\"\\\" # Sleep till time to wake up while\",\n \"well asqeuque TODO: When the debug logs are enabled this module emits duplicate\",\n \"threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle = None\",\n \"*args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start the timer thread here Args:\",\n \"get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures that the\",\n \"self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the thread will\",\n \": start = current_time() while (current_time()-start < INTERVAL) : wait() break callback() \\\"\\\"\\\"\",\n \"is based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback)\",\n \"from the workflow, measure it and redirect it the appropriate executors for processing.\",\n \"event_id): \\\"\\\"\\\"Let the FlowControl system know that there is an event. This method\",\n \"time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the threads\",\n \"Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \\\"\\\"\\\" self.interchange =\",\n \"wait on \\\"\\\"\\\" while True: prev = self._wake_up_time # Waiting for the event\",\n \"self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a simplified version of the FlowControl\",\n \": Tasks after which the callback is triggered - interval (int) : seconds\",\n \"current_time() while (current_time()-start < INTERVAL) : wait() break callback() \\\"\\\"\\\" def __init__(self, callback,\",\n \"be called from the Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count +=\",\n \"Parameters ---------- - threshold (int) : Tasks after which the callback is triggered\",\n \"(int) : seconds after which timer expires \\\"\\\"\\\" self.interchange = None self.threshold =\",\n \"Tasks after which the callback is triggered - interval (int) : seconds after\",\n \"on what triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind)\",\n \"large bursts of events. Once a callback is triggered, the callback generally runs\",\n \"are activated with a maximum delay of `interval` for systems with infrequent events\",\n \": count = get_events_since(start) if count >= THRESHOLD : break callback() This logic\",\n \"a maximum delay of `interval` for systems with infrequent events as well as\",\n \"notify events. This is based on the following logic : .. code-block:: none\",\n \"logging import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control.\",\n \"dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold (int)\",\n \"a strategy method on the sites available as well asqeuque TODO: When the\",\n \"BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) :\",\n \"= interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count =\",\n \"timer. This timer does not employ notify events. This is based on the\",\n \"+ 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start()\",\n \"Interchange to bind the strategy to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'):\",\n \"a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know that there\",\n \"issue needs more debugging. What I've learnt so far is that the duplicate\",\n \"triggered - interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interchange =\",\n \"is called Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" while\",\n \"or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def\",\n \"= True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the\",\n \"progress KWargs: - threshold (int) : Tasks after which the callback is triggered\",\n \"processing. This is based on the following logic: .. code-block:: none BEGIN (INTERVAL,\",\n \"(int) : seconds after which timer expires \\\"\\\"\\\" self.interval = interval self.cb_args =\",\n \"the FlowControl timer. This timer does not employ notify events. This is based\",\n \"is an event. This method is to be called from the Interchange to\",\n \"object to track parsl progress KWargs: - threshold (int) : Tasks after which\",\n \"interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event\",\n \"timer expires \\\"\\\"\\\" self.interchange = None self.threshold = threshold self.interval = interval self.cb_args\",\n \"**kwargs): \\\"\\\"\\\" Strategize is called everytime the threshold or the interval is hit\",\n \"when the timer thread is started, so this could be from a duplicate\",\n \"(threading.Event) : Event to wait on \\\"\\\"\\\" while True: prev = self._wake_up_time #\",\n \"exit when close() is called Args: - kill_event (threading.Event) : Event to wait\",\n \"\\\"\\\"\\\" while True: prev = self._wake_up_time # Waiting for the event returns True\",\n \"make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. KWargs: - kind (str):\",\n \"= logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The overall goal is\",\n \"\\\"\\\"\\\" self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time = time.time()\",\n \"\\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a\",\n \"self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge\",\n \"events. Once a callback is triggered, the callback generally runs a strategy method\",\n \"THRESHOLD : break callback() This logic ensures that the callbacks are activated with\",\n \"strategy method on the sites available as well asqeuque TODO: When the debug\",\n \"expires \\\"\\\"\\\" self.interchange = None self.threshold = threshold self.interval = interval self.cb_args =\",\n \"(threading.Event) : Event to wait on \\\"\\\"\\\" # Sleep till time to wake\",\n \"the callback generally runs a strategy method on the sites available as well\",\n \"threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a simplified version\",\n \"added by the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol\",\n \"FlowControl timer. This timer does not employ notify events. This is based on\",\n \"following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start =\",\n \"information on what triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer,\",\n \"interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \\\"\\\"\\\" self.interchange = interchange if\",\n \"the duplicate messages are present only when the timer thread is started, so\",\n \"called everytime the threshold or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with\",\n \"kill_event): \\\"\\\"\\\"Internal. This is the function that the thread will execute. waits on\",\n \": seconds after which timer expires \\\"\\\"\\\" self.interval = interval self.cb_args = args\",\n \"\\\"\\\"\\\"Initialize the flowcontrol object We start the timer thread here Args: - dfk\",\n \"triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer =\",\n \"self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class\",\n \"everytime the threshold or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {}\",\n \"on the sites available as well asqeuque TODO: When the debug logs are\",\n \"duplicate logger being added by the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5):\",\n \"on \\\"\\\"\\\" # Sleep till time to wake up while True: prev =\",\n \"wait on \\\"\\\"\\\" # Sleep till time to wake up while True: prev\",\n \"does not employ notify events. This is based on the following logic :\",\n \"= current_time() while (current_time()-start < INTERVAL) : wait() break callback() \\\"\\\"\\\" def __init__(self,\",\n \"def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer\",\n \"notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know that there is an event. This\",\n \"event. This method is to be called from the Interchange to notify the\",\n \"kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the thread will\",\n \"method is to be called from the Interchange to notify the flowcontrol \\\"\\\"\\\"\",\n \"which timer expires \\\"\\\"\\\" self.interval = interval self.cb_args = args self.callback = callback\",\n \"logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The overall goal is to\",\n \"= get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures that\",\n \"started, so this could be from a duplicate logger being added by the\",\n \"code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start <\",\n \"thread will execute. waits on an event so that the thread can make\",\n \"and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a simplified version of\",\n \"'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args,\",\n \"if count >= THRESHOLD : break callback() This logic ensures that the callbacks\",\n \"are present only when the timer thread is started, so this could be\",\n \"the threshold or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args,\",\n \"a callback is triggered, the callback generally runs a strategy method on the\",\n \"after which the callback is triggered - interval (int) : seconds after which\",\n \"(str): Default=None, used to pass information on what triggered the callback \\\"\\\"\\\" self._wake_up_time\",\n \"so this could be from a duplicate logger being added by the thread.\",\n \"TODO: When the debug logs are enabled this module emits duplicate messages. This\",\n \"\\\"\\\"\\\"This timer is a simplified version of the FlowControl timer. This timer does\",\n \"method on the sites available as well asqeuque TODO: When the debug logs\",\n \"the debug logs are enabled this module emits duplicate messages. This issue needs\",\n \"else: print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets\",\n \"self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called everytime the threshold or\",\n \"Timer(object): \\\"\\\"\\\"This timer is a simplified version of the FlowControl timer. This timer\",\n \"can make a quick exit when close() is called Args: - kill_event (threading.Event)\",\n \"= threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually start the strategy\",\n \"What I've learnt so far is that the duplicate messages are present only\",\n \"0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread\",\n \"# Waiting for the event returns True only when the event # is\",\n \"kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join()\",\n \"logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs):\",\n \"the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if\",\n \"which would generate large bursts of events. Once a callback is triggered, the\",\n \"True: prev = self._wake_up_time # Waiting for the event returns True only when\",\n \"it the appropriate executors for processing. This is based on the following logic:\",\n \"wait() break callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object\",\n \"callback) : start = current_time() while (current_time()-start < INTERVAL) : wait() break callback()\",\n \"callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start\",\n \"and resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self):\",\n \"= threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle =\",\n \"strategy to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{},\",\n \"the workflow, measure it and redirect it the appropriate executors for processing. This\",\n \"This issue needs more debugging. What I've learnt so far is that the\",\n \"which timer expires \\\"\\\"\\\" self.interchange = None self.threshold = threshold self.interval = interval\",\n \"to bind the strategy to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy\",\n \"so that the thread can make a quick exit when close() is called\",\n \"threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal.\",\n \"self.cb_args = args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer\",\n \"import threading import logging import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval\",\n \"is triggered, the callback generally runs a strategy method on the sites available\",\n \"callbacks are activated with a maximum delay of `interval` for systems with infrequent\",\n \"maximum delay of `interval` for systems with infrequent events as well as systems\",\n \"def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start the timer\",\n \"self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a simplified version of the FlowControl timer.\",\n \"- dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold\",\n \"\\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self):\",\n \"what triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer\",\n \"\\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the threads and\",\n \"redirect it the appropriate executors for processing. This is based on the following\",\n \"strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \\\"\\\"\\\" self.interchange\",\n \"a quick exit when close() is called Args: - kill_event (threading.Event) : Event\",\n \"events. This is based on the following logic : .. code-block:: none BEGIN\",\n \"= None self.threshold = threshold self.interval = interval self.cb_args = args self.callback =\",\n \"Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >=\",\n \"args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters ----------\",\n \"thread is started, so this could be from a duplicate logger being added\",\n \"== self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the\",\n \"time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The overall\",\n \"(int) : Tasks after which the callback is triggered - interval (int) :\",\n \"thread here Args: - dfk (DataFlowKernel) : DFK object to track parsl progress\",\n \"goal is to trap the flow of apps from the workflow, measure it\",\n \"self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set()\",\n \"know that there is an event. This method is to be called from\",\n \"self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. KWargs: -\",\n \"self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets\",\n \"interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event):\",\n \"time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the threads and terminate. \\\"\\\"\\\" self._kill_event.set()\",\n \"timer thread here Args: - dfk (DataFlowKernel) : DFK object to track parsl\",\n \"debug logs are enabled this module emits duplicate messages. This issue needs more\",\n \"the timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the\",\n \"event # is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev -\",\n \"ensures that the callbacks are activated with a maximum delay of `interval` for\",\n \"being added by the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the\",\n \"if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the\",\n \"\\\"\\\"\\\" self.interchange = None self.threshold = threshold self.interval = interval self.cb_args = args\",\n \"thread can make a quick exit when close() is called Args: - kill_event\",\n \"\\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is\",\n \"to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format(\",\n \"- time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping\",\n \"import logging import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow\",\n \"on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) :\",\n \"version of the FlowControl timer. This timer does not employ notify events. This\",\n \"by the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object.\",\n \"called Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" while True:\",\n \"the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We\",\n \"a simplified version of the FlowControl timer. This timer does not employ notify\",\n \"is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0)))\",\n \"that the thread will execute. waits on an event so that the thread\",\n \"the callback and resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args)\",\n \"if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def\",\n \"def start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to\",\n \"+= 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None):\",\n \"none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL)\",\n \"import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The\",\n \"is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal.\",\n \"We start the timer thread here Args: - dfk (DataFlowKernel) : DFK object\",\n \"= interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1\",\n \"\\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start the\",\n \"is the function that the thread will execute. waits on an event so\",\n \"thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev ==\",\n \"\\\"\\\"\\\"Makes the callback and resets the timer. KWargs: - kind (str): Default=None, used\",\n \"self.threshold = threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle\",\n \"is called everytime the threshold or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize called\",\n \"start = current_time() while (current_time()-start < INTERVAL) : wait() break callback() \\\"\\\"\\\" def\",\n \"called Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" # Sleep\",\n \"current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD\",\n \"BaseStrategy(object): \\\"\\\"\\\"Implements threshold-interval based flow control. The overall goal is to trap the\",\n \"flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\")\",\n \"start the timer thread here Parameters ---------- - threshold (int) : Tasks after\",\n \"of apps from the workflow, measure it and redirect it the appropriate executors\",\n \"self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread =\",\n \"[] def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This\",\n \"used to pass information on what triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time()\",\n \"KWargs: - kind (str): Default=None, used to pass information on what triggered the\",\n \"more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. \\\"\\\"\\\" self._wake_up_time\",\n \"flow control. The overall goal is to trap the flow of apps from\",\n \"wake up while True: prev = self._wake_up_time # Waiting for the event returns\",\n \"which the callback is triggered - interval (int) : seconds after which timer\",\n \"break callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We\",\n \"1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self,\",\n \"this module emits duplicate messages. This issue needs more debugging. What I've learnt\",\n \"seconds after which timer expires \\\"\\\"\\\" self.interchange = None self.threshold = threshold self.interval\",\n \": .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while\",\n \"+ 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def\",\n \"executors for processing. This is based on the following logic: .. code-block:: none\",\n \"it and redirect it the appropriate executors for processing. This is based on\",\n \"# is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(),\",\n \"- kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" while True: prev =\",\n \"= [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer,\",\n \"---------- - threshold (int) : Tasks after which the callback is triggered -\",\n \"interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interchange = None self.threshold\",\n \"based on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback)\",\n \"the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \\\"\\\"\\\"\",\n \"self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and\",\n \"count >= THRESHOLD : break callback() This logic ensures that the callbacks are\",\n \"args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread\",\n \"interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called everytime\",\n \"start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count\",\n \"based flow control. The overall goal is to trap the flow of apps\",\n \"learnt so far is that the duplicate messages are present only when the\",\n \"self._thread.daemon = True def start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange:\",\n \"kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else:\",\n \"interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called everytime the\",\n \"present only when the timer thread is started, so this could be from\",\n \"Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" while True: prev\",\n \"{} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the\",\n \"= kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer')\",\n \"+ self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the threads and terminate. \\\"\\\"\\\" self._kill_event.set() self._thread.join()\",\n \"bursts of events. Once a callback is triggered, the callback generally runs a\",\n \"by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return\",\n \"timer. \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): \\\"\\\"\\\"Merge the threads\",\n \"seconds after which timer expires \\\"\\\"\\\" self.interval = interval self.cb_args = args self.callback\",\n \"(INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : count\",\n \": break callback() This logic ensures that the callbacks are activated with a\",\n \"None self.threshold = threshold self.interval = interval self.cb_args = args self.callback = self.strategize\",\n \"self._wake_up_time # Waiting for the event returns True only when the event #\",\n \"logger being added by the thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize\",\n \"threshold (int) : Tasks after which the callback is triggered - interval (int)\",\n \"event so that the thread can make a quick exit when close() is\",\n \"This timer does not employ notify events. This is based on the following\",\n \"This is based on the following logic : .. code-block:: none BEGIN (INTERVAL,\",\n \"systems which would generate large bursts of events. Once a callback is triggered,\",\n \"thread. \\\"\\\"\\\" def __init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start\",\n \".. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start\",\n \"import sys import threading import logging import time logger = logging.getLogger(\\\"interchange.strategy.base\\\") class BaseStrategy(object):\",\n \"bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\"\",\n \"parsl progress KWargs: - threshold (int) : Tasks after which the callback is\",\n \"this could be from a duplicate logger being added by the thread. \\\"\\\"\\\"\",\n \"*args, **kwargs): \\\"\\\"\\\" Strategize is called everytime the threshold or the interval is\",\n \"\\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds-> init:{}, min:{}, max:{}\\\".format( interchange.config.provider.init_blocks,\",\n \"self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time()\",\n \"callback is triggered - interval (int) : seconds after which timer expires \\\"\\\"\\\"\",\n \"- kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" # Sleep till time\",\n \"pass information on what triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time() + self.interval\",\n \"object. We start the timer thread here Parameters ---------- - threshold (int) :\",\n \"on an event so that the thread can make a quick exit when\",\n \"timer. KWargs: - kind (str): Default=None, used to pass information on what triggered\",\n \"self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This\",\n \">= threshold\\\") self.make_callback(kind=\\\"event\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer.\",\n \"timer does not employ notify events. This is based on the following logic\",\n \"to track parsl progress KWargs: - threshold (int) : Tasks after which the\",\n \"max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called\",\n \"bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system know that there is\",\n \"debugging. What I've learnt so far is that the duplicate messages are present\",\n \"< INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break callback()\",\n \": wait() break callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol\",\n \"time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time:\",\n \"= threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event):\",\n \"strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is called everytime the threshold or the interval\",\n \"of events. Once a callback is triggered, the callback generally runs a strategy\",\n \"Strategize is called everytime the threshold or the interval is hit \\\"\\\"\\\" logger.debug(\\\"Strategize\",\n \"is to trap the flow of apps from the workflow, measure it and\",\n \"self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event =\",\n \"\\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug(\\\"Eventcount >= threshold\\\") self.make_callback(kind=\\\"event\\\")\",\n \"is based on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD,\",\n \": Event to wait on \\\"\\\"\\\" # Sleep till time to wake up\",\n \"timer is a simplified version of the FlowControl timer. This timer does not\",\n \"with a maximum delay of `interval` for systems with infrequent events as well\",\n \"waits on an event so that the thread can make a quick exit\",\n \"only when the event # is set, usually by the parent thread time_to_die\",\n \"the threads and terminate.\\\"\\\"\\\" self._kill_event.set() self._thread.join() class Timer(object): \\\"\\\"\\\"This timer is a simplified\",\n \"callback() This logic ensures that the callbacks are activated with a maximum delay\",\n \"the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start\",\n \"interchange): \\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the\",\n \"will execute. waits on an event so that the thread can make a\",\n \"count = get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures\",\n \"usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die:\",\n \"for systems with infrequent events as well as systems which would generate large\",\n \"when close() is called Args: - kill_event (threading.Event) : Event to wait on\",\n \"trap the flow of apps from the workflow, measure it and redirect it\",\n \"timer thread here Parameters ---------- - threshold (int) : Tasks after which the\",\n \"are enabled this module emits duplicate messages. This issue needs more debugging. What\",\n \"triggered - interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interval =\",\n \"to pass information on what triggered the callback \\\"\\\"\\\" self._wake_up_time = time.time() +\",\n \"interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start the timer thread here Parameters ----------\",\n \"\\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy\",\n \"an event. This method is to be called from the Interchange to notify\",\n \"self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def notify(self, event_id): \\\"\\\"\\\"Let the FlowControl system\",\n \"with infrequent events as well as systems which would generate large bursts of\",\n \"till time to wake up while True: prev = self._wake_up_time # Waiting for\",\n \"parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev\",\n \"threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start the timer thread here Parameters\",\n \"is triggered - interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interchange\",\n \"= None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1\",\n \"__init__(self, *args, threshold=20, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object. We start the timer thread\",\n \"def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. \\\"\\\"\\\" self._wake_up_time =\",\n \"funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config,\",\n \"is that the duplicate messages are present only when the timer thread is\",\n \"(current_time()-start < INTERVAL) : wait() break callback() \\\"\\\"\\\" def __init__(self, callback, *args, interval=5):\",\n \": DFK object to track parsl progress KWargs: - threshold (int) : Tasks\",\n \"bind the strategy to \\\"\\\"\\\" self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug(\\\"Strategy bounds->\",\n \"far is that the duplicate messages are present only when the timer thread\",\n \"__init__(self, callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start the timer thread\",\n \"callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,))\",\n \"self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time\",\n \"to be called from the Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count\",\n \"When the debug logs are enabled this module emits duplicate messages. This issue\",\n \"prev == self._wake_up_time: self.make_callback(kind='timer') else: print(\\\"Sleeping a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes\",\n \"that the thread can make a quick exit when close() is called Args:\",\n \"True def start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange\",\n \"`interval` for systems with infrequent events as well as systems which would generate\",\n \"- threshold (int) : Tasks after which the callback is triggered - interval\",\n \"needs more debugging. What I've learnt so far is that the duplicate messages\",\n \"from the Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if\",\n \"{}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the thread\",\n \"\\\"\\\"\\\" Strategize is called everytime the threshold or the interval is hit \\\"\\\"\\\"\",\n \"args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function\",\n \"min:{}, max:{}\\\".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): \\\"\\\"\\\" Strategize is\",\n \"returns True only when the event # is set, usually by the parent\",\n \"of the FlowControl timer. This timer does not employ notify events. This is\",\n \"for the event returns True only when the event # is set, usually\",\n \"generate large bursts of events. Once a callback is triggered, the callback generally\",\n \"interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count = 0\",\n \"self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): \\\"\\\"\\\"Merge the threads and terminate.\\\"\\\"\\\"\",\n \"as systems which would generate large bursts of events. Once a callback is\",\n \"start the timer thread here Args: - dfk (DataFlowKernel) : DFK object to\",\n \"well as systems which would generate large bursts of events. Once a callback\",\n \"messages. This issue needs more debugging. What I've learnt so far is that\",\n \"We start the timer thread here Parameters ---------- - threshold (int) : Tasks\",\n \"self.interval = interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count\",\n \"self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event =\",\n \"make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer. \\\"\\\"\\\" self._wake_up_time = time.time()\",\n \"_wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the thread will execute. waits\",\n \"interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start the timer thread here Args: -\",\n \"expires \\\"\\\"\\\" self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time =\",\n \"to wake up while True: prev = self._wake_up_time # Waiting for the event\",\n \"to wait on \\\"\\\"\\\" while True: prev = self._wake_up_time # Waiting for the\",\n \"self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self,\",\n \"1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def\",\n \"after which timer expires \\\"\\\"\\\" self.interchange = None self.threshold = threshold self.interval =\",\n \"start(self, interchange): \\\"\\\"\\\"Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind\",\n \"on \\\"\\\"\\\" while True: prev = self._wake_up_time # Waiting for the event returns\",\n \"while (current_time()-start < INTERVAL) : wait() break callback() \\\"\\\"\\\" def __init__(self, callback, *args,\",\n \"as well as systems which would generate large bursts of events. Once a\",\n \"FlowControl system know that there is an event. This method is to be\",\n \"flow of apps from the workflow, measure it and redirect it the appropriate\",\n \"duplicate messages are present only when the timer thread is started, so this\",\n \"resets the timer. KWargs: - kind (str): Default=None, used to pass information on\",\n \"make a quick exit when close() is called Args: - kill_event (threading.Event) :\",\n \"is triggered - interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interval\",\n \"callback, *args, interval=5): \\\"\\\"\\\"Initialize the flowcontrol object We start the timer thread here\",\n \"generally runs a strategy method on the sites available as well asqeuque TODO:\",\n \"after which timer expires \\\"\\\"\\\" self.interval = interval self.cb_args = args self.callback =\",\n \"the FlowControl system know that there is an event. This method is to\",\n \"simplified version of the FlowControl timer. This timer does not employ notify events.\",\n \"is to be called from the Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id])\",\n \"I've learnt so far is that the duplicate messages are present only when\",\n \"- interval (int) : seconds after which timer expires \\\"\\\"\\\" self.interchange = None\",\n \"overall goal is to trap the flow of apps from the workflow, measure\",\n \"a bit more\\\") def make_callback(self, kind=None): \\\"\\\"\\\"Makes the callback and resets the timer.\",\n \"the Interchange to notify the flowcontrol \\\"\\\"\\\" self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count\",\n \"kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" # Sleep till time to\",\n \"the flow of apps from the workflow, measure it and redirect it the\",\n \"hit \\\"\\\"\\\" logger.debug(\\\"Strategize called with {} {}\\\".format(args, kwargs)) def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This\",\n \"This is based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD,\",\n \"while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD :\",\n \"the timer thread here Args: - dfk (DataFlowKernel) : DFK object to track\",\n \"def _wake_up_timer(self, kill_event): \\\"\\\"\\\"Internal. This is the function that the thread will execute.\",\n \"Args: - kill_event (threading.Event) : Event to wait on \\\"\\\"\\\" # Sleep till\",\n \"apps from the workflow, measure it and redirect it the appropriate executors for\",\n \"sites available as well asqeuque TODO: When the debug logs are enabled this\"\n]","isConversational":false}}},{"rowIdx":592,"cells":{"ngram":{"kind":"list like","value":["[attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor,","= True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)","guided backprop results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2,","# For layer gradcam look at the usage of the parameter relu_attributions #","target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int =","= torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True)","the other captum sections, using our helper methods), # # but with some","import matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr import","W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target =","import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import * from","torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x","# # but with some preprocessing calculations. # # # # You can","= True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps","torch import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image","# set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X,","= compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual","keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1)","other captum sections, using our helper methods), # # but with some preprocessing","import * from image_utils import * from captum_utils import * import numpy as","torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val","from captum_utils.py is useful for # # visualizing captum outputs # # Use","X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam","Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module","captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import","with respect to model parameters. for param in model.parameters(): param.requires_grad = False #","5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final","out different layers and see observe how the attributions change layer = model.features[3]","model we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc","X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module =","i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img =","target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int =","from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of","dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i","= np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1)","plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** #","= torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell PyTorch","Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1,","= y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum,","GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) #","LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## #","plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) #","compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True)","y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W))","Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr","layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize","import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr","plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and Guided","= gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i]","normalization of the gradcam and guided backprop results (2 lines) img = gradcam_val","LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC","some preprocessing calculations. # # # # You can refer to the LayerActivation","sections, using our helper methods), # # but with some preprocessing calculations. #","numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set","need to customize your 'attr_preprocess' # # parameter that you send along to","plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a","= model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. #","gradcam look at the usage of the parameter relu_attributions # ############################################################################## # Layer","gradcam aggregates across all channels from captum.attr import LayerAttribution N, C, H, W","attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X,","but with some preprocessing calculations. # # # # You can refer to","target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF","numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0)","layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## #","set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y,","plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True)","24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i]","= X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor)","gradcam and guided backprop results (2 lines) img = gradcam_val * gbp_val img","for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img","usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all","you which module(=layer) that we need to capture gradients from, which you can","for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise","plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) #","compute gradients # with respect to model parameters. for param in model.parameters(): param.requires_grad","############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # #","CODE # ############################################################################## # Try out different layers and see observe how the","LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names,","as the default # # 'attr_preprocess' is written to only to handle multi","i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum","gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of","gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y,","conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png',","and you should be # # using 'layer' given above for this section","'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need","to the LayerActivation example above and you should be # # using 'layer'","be # # using 'layer' given above for this section # # #","gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i],","variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor","visualizing captum outputs # # Use conv_module as the convolution layer for gradcam","X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int","= load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients. We","['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try out different","rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given","from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],","# Use conv_module as the convolution layer for gradcam # ############################################################################## # Computing",") LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance'])","= 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS","GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for","'attr_preprocess' is written to only to handle multi channel attributions. # # #","# TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function","FOR THIS SECTION ONLY, we need to use gradients. We introduce a new","helper methods), # # but with some preprocessing calculations. # # # #","dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and","torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ##############################################################################","dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor,","= model.features[3] # Example visualization for using layer visualizations # layer_act = LayerActivation(model,","* from captum_utils import * import numpy as np from visualizers import GradCam","YOUR CODE # ############################################################################## # Try out different layers and see observe how","= 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3,","# FOR THIS SECTION ONLY, we need to use gradients. We introduce a","parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from captum.attr","we need to use gradients. We introduce a new model we will use","# ############################################################################## # Try out different layers and see observe how the attributions","8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray'","lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img","Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y)","for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img","# print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided","1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr =","visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots","= y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) #","= LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True)","preprocessing calculations. # # # # You can refer to the LayerActivation example","using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor)","layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer","to model parameters. for param in model.parameters(): param.requires_grad = False # Convert X","LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ##############################################################################","# # using 'layer' given above for this section # # # #","(H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int],","you should be # # using 'layer' given above for this section #","GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result","# ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc,","# Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N, C,","parameters. for param in model.parameters(): param.requires_grad = False # Convert X and y","captum_utils import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] =","= False # Convert X and y from numpy arrays to Torch Tensors","gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val =","backprop results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0,","**************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train","LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum =","plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY,","# # # # Also note that, you would need to customize your","y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W)","you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written","in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result =","'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as the default","i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step,","the default # # 'attr_preprocess' is written to only to handle multi channel","target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam'])","gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1)","for param in model.parameters(): param.requires_grad = False # Convert X and y from","X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor,","parameter that you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess'","LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim","(10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] =","= compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim =","at the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates","plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We","attributions change layer = model.features[3] # Example visualization for using layer visualizations #","= gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i]","all channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC","for # # visualizing captum outputs # # Use conv_module as the convolution","layers and see observe how the attributions change layer = model.features[3] # Example","given you which module(=layer) that we need to capture gradients from, which you","for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## #","what we did for the other captum sections, using our helper methods), #","did for the other captum sections, using our helper methods), # # but","results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1),","dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24))","LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis =","need to capture gradients from, which you can see in conv_module variable below","above for this section # # # # Also note that, you would","captum outputs # # Use conv_module as the convolution layer for gradcam #","size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names =","# Also note that, you would need to customize your 'attr_preprocess' # #","np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As","THIS SECTION ONLY, we need to use gradients. We introduce a new model","a final step, we can combine GradCam and Guided Backprop to get Guided","will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam()","= gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the","in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize","the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc =","# GradCam # GradCAM. We have given you which module(=layer) that we need","X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided","OF YOUR CODE # ############################################################################## # Try out different layers and see observe","X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc],","and Layer Conductance (similar # # to what we did for the other","we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc =","gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor =","False # Convert X and y from numpy arrays to Torch Tensors X_tensor","combine GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for","model.features[3] # Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer)","SECTION ONLY, we need to use gradients. We introduce a new model we","= torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i","matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from","conv_module as the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam","and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful","Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so","# Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target =","plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer) that we","y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target","data_utils import * from image_utils import * from captum_utils import * import numpy","import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam","attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam'])","y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in","Convert X and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x))","look at the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam","the model, so tell PyTorch not to compute gradients # with respect to","is useful for # # visualizing captum outputs # # Use conv_module as","+ 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png')","import matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop","captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer)","# # # You can refer to the LayerActivation example above and you","GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) #","print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp","the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across","capture gradients from, which you can see in conv_module variable below gc_model =","not to compute gradients # with respect to model parameters. for param in","respect to model parameters. for param in model.parameters(): param.requires_grad = False # Convert","captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import *","torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as","5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # #","# but with some preprocessing calculations. # # # # You can refer","model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell","LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim","using our helper methods), # # but with some preprocessing calculations. # #","= img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout()","outputs # # Use conv_module as the convolution layer for gradcam # ##############################################################################","captum_utils.py is useful for # # visualizing captum outputs # # Use conv_module","compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## #","as well. # # visualize_attr_maps function from captum_utils.py is useful for # #","= torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]):","= LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis","x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24,","Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)","a new model we will use explicitly for GradCAM for this. gc_model =","np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img)","= torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off')","new model we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True)","1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr","need to use gradients. We introduce a new model we will use explicitly","= y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum,","3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END","import torch import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import","gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img)","plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import","in model.parameters(): param.requires_grad = False # Convert X and y from numpy arrays","import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import *","keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar #","from, which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for","you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in","* from image_utils import * from captum_utils import * import numpy as np","= torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM","use gradients. We introduce a new model we will use explicitly for GradCAM","should be # # using 'layer' given above for this section # #","channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC =","= gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i +","X and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for","layer = model.features[3] # Example visualization for using layer visualizations # layer_act =","for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x))","= LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC =","layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar","torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr","is written to only to handle multi channel attributions. # # # #","use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor","img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i","convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model,","can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x))","LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X,","gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],","Layer Conductance (similar # # to what we did for the other captum","arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor","# Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target","(matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]])","gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img","gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for","visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum outputs #","= gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1,","# TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # # to","# using 'layer' given above for this section # # # # Also","layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1,","END OF YOUR CODE # ############################################################################## # Try out different layers and see","plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't","You can refer to the LayerActivation example above and you should be #","Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],","and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in","# # visualizing captum outputs # # Use conv_module as the convolution layer","# # 'attr_preprocess' is written to only to handle multi channel attributions. #","Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N, C, H,","send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to","visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum","GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from","which module(=layer) that we need to capture gradients from, which you can see","Conductance (similar # # to what we did for the other captum sections,","Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target =","GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp],","this section # # # # Also note that, you would need to","gradients # with respect to model parameters. for param in model.parameters(): param.requires_grad =","img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off')","in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model)","= True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1)","in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True)","X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24,","[LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor)","True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result","that you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is","H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target","for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x","ONLY, we need to use gradients. We introduce a new model we will","plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and Guided Backprop","customize your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as","layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1,","= LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int =","param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],","and see observe how the attributions change layer = model.features[3] # Example visualization","y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp =","gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc =","(2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0)","to customize your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps'","above and you should be # # using 'layer' given above for this","= gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam #","relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from captum.attr import","which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param","torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout()","given above for this section # # # # Also note that, you","X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ##############################################################################","x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor,","# # For layer gradcam look at the usage of the parameter relu_attributions","Try out different layers and see observe how the attributions change layer =","# You can refer to the LayerActivation example above and you should be","we did for the other captum sections, using our helper methods), # #","1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5,","We don't want to train the model, so tell PyTorch not to compute","# Pointwise multiplication and normalization of the gradcam and guided backprop results (2","We have given you which module(=layer) that we need to capture gradients from,","gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val","different layers and see observe how the attributions change layer = model.features[3] #","gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam","'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only to handle","torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result","np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size","model, so tell PyTorch not to compute gradients # with respect to model","LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam'])","compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names,","X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1)","img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img =","############################################################################## # Try out different layers and see observe how the attributions change","# END OF YOUR CODE # ############################################################################## # Try out different layers and","note that, you would need to customize your 'attr_preprocess' # # parameter that","model parameters. for param in model.parameters(): param.requires_grad = False # Convert X and","y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img =","the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from","module(=layer) that we need to capture gradients from, which you can see in","img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i +","LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) )","= X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i +","# # Use conv_module as the convolution layer for gradcam # ############################################################################## #","the LayerActivation example above and you should be # # using 'layer' given","LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int","multiplication and normalization of the gradcam and guided backprop results (2 lines) img","'layer' given above for this section # # # # Also note that,","Also note that, you would need to customize your 'attr_preprocess' # # parameter","gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5,","= gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i","to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor =","import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0,","Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor)","(H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int],","# layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and","from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils","# Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]):","Pointwise multiplication and normalization of the gradcam and guided backprop results (2 lines)","GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is","X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int","across all channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape","= compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ##############################################################################","layer gradcam look at the usage of the parameter relu_attributions # ############################################################################## #","in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img)","# Convert X and y from numpy arrays to Torch Tensors X_tensor =","# Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model,","############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor,","class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try","from image_utils import * from captum_utils import * import numpy as np from","visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model)","1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1,","function from captum_utils.py is useful for # # visualizing captum outputs # #","class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target =","torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in","# # to what we did for the other captum sections, using our","= LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ##############################################################################","multi channel attributions. # # # # For layer gradcam look at the","['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target","Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png',","x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result","plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2)","introduce a new model we will use explicitly for GradCAM for this. gc_model","np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img)","i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication","to what we did for the other captum sections, using our helper methods),","X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor,","Visualize Individual Layer Gradcam and Layer Conductance (similar # # to what we","of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels","# to what we did for the other captum sections, using our helper","1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF","as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr","Individual Layer Gradcam and Layer Conductance (similar # # to what we did","Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from","useful for # # visualizing captum outputs # # Use conv_module as the","# As a final step, we can combine GradCam and Guided Backprop to","GradCAM. We have given you which module(=layer) that we need to capture gradients","# # parameter that you send along to 'visualize_attr_maps' as the default #","img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam","compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer","don't want to train the model, so tell PyTorch not to compute gradients","# # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the","# **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to","axis=2) # Pointwise multiplication and normalization of the gradcam and guided backprop results","1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3,","as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default","* gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img =","y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for","/ np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') #","= deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') #","= compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim =","= np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and guided","# layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO:","in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and","torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24))","gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and","PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance,","LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names,","visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE #","for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc","to use gradients. We introduce a new model we will use explicitly for","attributions. # # # # For layer gradcam look at the usage of","from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import","= torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor,","GradCam # GradCAM. We have given you which module(=layer) that we need to","range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization","LayerGradCam from data_utils import * from image_utils import * from captum_utils import *","gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM.","Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful for","0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1,","TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # # to what","gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x))","Layer Gradcam and Layer Conductance (similar # # to what we did for","plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer) that","= torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12]","y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided","observe how the attributions change layer = model.features[3] # Example visualization for using","with some preprocessing calculations. # # # # You can refer to the","image_utils import * from captum_utils import * import numpy as np from visualizers","+ 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we","X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result =","= compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y,","X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to","Gradcam and Layer Conductance (similar # # to what we did for the","= 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1,","True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png',","3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer)","model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # #","calculations. # # # # You can refer to the LayerActivation example above","train the model, so tell PyTorch not to compute gradients # with respect","gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in","final step, we can combine GradCam and Guided Backprop to get Guided GradCam.","# layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum =","conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True","default # # 'attr_preprocess' is written to only to handle multi channel attributions.","from captum_utils import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize']","in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad =","conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well.","* import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0)","gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img)","y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val =","from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation,","axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i","channel attributions. # # # # For layer gradcam look at the usage","We introduce a new model we will use explicitly for GradCAM for this.","LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X,","C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor,","[attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try out","x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO:","= LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y,","from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model,","to capture gradients from, which you can see in conv_module variable below gc_model","############################################################################## # END OF YOUR CODE # ############################################################################## # Try out different layers","Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor)","along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only","= torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24,","img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We","can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters():","= LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int","get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor =","methods), # # but with some preprocessing calculations. # # # # You","LayerActivation example above and you should be # # using 'layer' given above","= gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img)","deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # ****************************************************************************************","refer to the LayerActivation example above and you should be # # using","example above and you should be # # using 'layer' given above for","that we need to capture gradients from, which you can see in conv_module","= GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names,","the gradcam and guided backprop results (2 lines) img = gradcam_val * gbp_val","plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which","below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor =","i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img =","= LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis","N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC,","import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation']","plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img =","well. # # visualize_attr_maps function from captum_utils.py is useful for # # visualizing","for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model)","y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing","to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only to","# # visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum","for the other captum sections, using our helper methods), # # but with","model.parameters(): param.requires_grad = False # Convert X and y from numpy arrays to","that, you would need to customize your 'attr_preprocess' # # parameter that you","1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR","plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam","5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off')","gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor","you would need to customize your 'attr_preprocess' # # parameter that you send","explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor =","and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x","matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam,","# 'attr_preprocess' is written to only to handle multi channel attributions. # #","# visualizing captum outputs # # Use conv_module as the convolution layer for","plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you","24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) #","gradients. We introduce a new model we will use explicitly for GradCAM for","visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE","to compute gradients # with respect to model parameters. for param in model.parameters():","############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps","keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1,","want to train the model, so tell PyTorch not to compute gradients #","and normalization of the gradcam and guided backprop results (2 lines) img =","y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in","GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] =","only to handle multi channel attributions. # # # # For layer gradcam","visualization for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr =","have given you which module(=layer) that we need to capture gradients from, which","TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from","LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr =","img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png')","X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for","gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i","= np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img =","img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]])","= GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape,","section # # # # Also note that, you would need to customize","from data_utils import * from image_utils import * from captum_utils import * import","param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor =","plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] +","X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp","y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE","24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255)","y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]):","for this section # # # # Also note that, you would need","change layer = model.features[3] # Example visualization for using layer visualizations # layer_act","torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell PyTorch not","this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in","in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24))","layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) #","import * from captum_utils import * import numpy as np from visualizers import","in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img","class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients.","= GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)","# parameter that you send along to 'visualize_attr_maps' as the default # #","y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp","torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor,","GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape)","attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp =","# # # For layer gradcam look at the usage of the parameter","# Try out different layers and see observe how the attributions change layer","# GradCAM. We have given you which module(=layer) that we need to capture","can refer to the LayerActivation example above and you should be # #","using 'layer' given above for this section # # # # Also note","PyTorch not to compute gradients # with respect to model parameters. for param","plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer)","= 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we","range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img)","written to only to handle multi channel attributions. # # # # For","we need to capture gradients from, which you can see in conv_module variable","# # # # You can refer to the LayerActivation example above and","the attributions change layer = model.features[3] # Example visualization for using layer visualizations","LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum =","############################################################################## # Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N,","our helper methods), # # but with some preprocessing calculations. # # #","1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can","For layer gradcam look at the usage of the parameter relu_attributions # ##############################################################################","# visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum outputs","we can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor =","layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1,","gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val","range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img /","X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum","# # # Also note that, you would need to customize your 'attr_preprocess'","param in model.parameters(): param.requires_grad = False # Convert X and y from numpy","X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE # ##############################################################################","how the attributions change layer = model.features[3] # Example visualization for using layer","import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) #","gradients from, which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True)","= layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance","layer for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module)","= y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR","compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True)","to train the model, so tell PyTorch not to compute gradients # with","plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR","as the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc","plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine","LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1,","param.requires_grad = False # Convert X and y from numpy arrays to Torch","plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and","np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and guided backprop","see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad","y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model)","visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC,","('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE #","see observe how the attributions change layer = model.features[3] # Example visualization for","# with respect to model parameters. for param in model.parameters(): param.requires_grad = False","i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout()","tell PyTorch not to compute gradients # with respect to model parameters. for","# Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer) #","LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis =","1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') #","LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model,","y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use","X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop","= torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation","aggregates across all channels from captum.attr import LayerAttribution N, C, H, W =","1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model =","gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i]","gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img","plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want","for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in","of the gradcam and guided backprop results (2 lines) img = gradcam_val *","to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor","plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest'","to only to handle multi channel attributions. # # # # For layer","BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X,","of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5)","gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img","LayerConductance, LayerGradCam from data_utils import * from image_utils import * from captum_utils import","for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result =","['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum","plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]])","GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x","LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int =","# # Also note that, you would need to customize your 'attr_preprocess' #","and guided backprop results (2 lines) img = gradcam_val * gbp_val img =","= LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y,","'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION","GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful for #","would need to customize your 'attr_preprocess' # # parameter that you send along","img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img","= torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for","As a final step, we can combine GradCam and Guided Backprop to get","to handle multi channel attributions. # # # # For layer gradcam look","(similar # # to what we did for the other captum sections, using","= rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have","y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## #","+ (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img)","img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png')","Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True)","X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END","so tell PyTorch not to compute gradients # with respect to model parameters.","torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor","ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape,","Use conv_module as the convolution layer for gradcam # ############################################################################## # Computing Guided","LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import * from captum_utils","class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp,","# # # # For layer gradcam look at the usage of the","True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png',","# ############################################################################## # Layer gradcam aggregates across all channels from captum.attr import LayerAttribution","gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val =","+ 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model","default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names","captum sections, using our helper methods), # # but with some preprocessing calculations.","for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act,","# # You can refer to the LayerActivation example above and you should","handle multi channel attributions. # # # # For layer gradcam look at","GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils","Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5,","# We don't want to train the model, so tell PyTorch not to","Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py","load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients. We introduce","step, we can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor","= (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap']","your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as the","import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image from"],"string":"[\n \"[attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor,\",\n \"= True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)\",\n \"guided backprop results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2,\",\n \"# For layer gradcam look at the usage of the parameter relu_attributions #\",\n \"target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int =\",\n \"= torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True)\",\n \"the other captum sections, using our helper methods), # # but with some\",\n \"import matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr import\",\n \"W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target =\",\n \"import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import * from\",\n \"torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x\",\n \"# # but with some preprocessing calculations. # # # # You can\",\n \"= True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps\",\n \"torch import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image\",\n \"# set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X,\",\n \"= compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual\",\n \"keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1)\",\n \"other captum sections, using our helper methods), # # but with some preprocessing\",\n \"import * from image_utils import * from captum_utils import * import numpy as\",\n \"torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val\",\n \"from captum_utils.py is useful for # # visualizing captum outputs # # Use\",\n \"X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam\",\n \"Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module\",\n \"captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import\",\n \"with respect to model parameters. for param in model.parameters(): param.requires_grad = False #\",\n \"5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final\",\n \"out different layers and see observe how the attributions change layer = model.features[3]\",\n \"model we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc\",\n \"X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module =\",\n \"i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img =\",\n \"target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int =\",\n \"from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of\",\n \"dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i\",\n \"= np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1)\",\n \"plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** #\",\n \"= torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell PyTorch\",\n \"Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1,\",\n \"= y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum,\",\n \"GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) #\",\n \"LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## #\",\n \"plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) #\",\n \"compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True)\",\n \"y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W))\",\n \"Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr\",\n \"layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize\",\n \"import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr\",\n \"plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and Guided\",\n \"= gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i]\",\n \"normalization of the gradcam and guided backprop results (2 lines) img = gradcam_val\",\n \"LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC\",\n \"some preprocessing calculations. # # # # You can refer to the LayerActivation\",\n \"sections, using our helper methods), # # but with some preprocessing calculations. #\",\n \"numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set\",\n \"need to customize your 'attr_preprocess' # # parameter that you send along to\",\n \"plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a\",\n \"= model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. #\",\n \"gradcam look at the usage of the parameter relu_attributions # ############################################################################## # Layer\",\n \"gradcam aggregates across all channels from captum.attr import LayerAttribution N, C, H, W\",\n \"attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X,\",\n \"but with some preprocessing calculations. # # # # You can refer to\",\n \"target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF\",\n \"numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0)\",\n \"layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## #\",\n \"set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y,\",\n \"plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True)\",\n \"24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i]\",\n \"= X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor)\",\n \"gradcam and guided backprop results (2 lines) img = gradcam_val * gbp_val img\",\n \"for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img\",\n \"usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all\",\n \"you which module(=layer) that we need to capture gradients from, which you can\",\n \"for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise\",\n \"plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) #\",\n \"compute gradients # with respect to model parameters. for param in model.parameters(): param.requires_grad\",\n \"############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # #\",\n \"CODE # ############################################################################## # Try out different layers and see observe how the\",\n \"LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names,\",\n \"as the default # # 'attr_preprocess' is written to only to handle multi\",\n \"i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum\",\n \"gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of\",\n \"gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y,\",\n \"conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png',\",\n \"and you should be # # using 'layer' given above for this section\",\n \"'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need\",\n \"to the LayerActivation example above and you should be # # using 'layer'\",\n \"be # # using 'layer' given above for this section # # #\",\n \"gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i],\",\n \"variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor\",\n \"visualizing captum outputs # # Use conv_module as the convolution layer for gradcam\",\n \"X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int\",\n \"= load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients. We\",\n \"['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try out different\",\n \"rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given\",\n \"from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],\",\n \"# Use conv_module as the convolution layer for gradcam # ############################################################################## # Computing\",\n \") LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance'])\",\n \"= 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS\",\n \"GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for\",\n \"'attr_preprocess' is written to only to handle multi channel attributions. # # #\",\n \"# TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function\",\n \"FOR THIS SECTION ONLY, we need to use gradients. We introduce a new\",\n \"helper methods), # # but with some preprocessing calculations. # # # #\",\n \"dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and\",\n \"torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ##############################################################################\",\n \"dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor,\",\n \"= model.features[3] # Example visualization for using layer visualizations # layer_act = LayerActivation(model,\",\n \"* from captum_utils import * import numpy as np from visualizers import GradCam\",\n \"YOUR CODE # ############################################################################## # Try out different layers and see observe how\",\n \"= 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3,\",\n \"# FOR THIS SECTION ONLY, we need to use gradients. We introduce a\",\n \"parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from captum.attr\",\n \"we need to use gradients. We introduce a new model we will use\",\n \"# ############################################################################## # Try out different layers and see observe how the attributions\",\n \"8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray'\",\n \"lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img\",\n \"Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y)\",\n \"for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img\",\n \"# print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided\",\n \"1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr =\",\n \"visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\",\n \"= y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) #\",\n \"= LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True)\",\n \"preprocessing calculations. # # # # You can refer to the LayerActivation example\",\n \"using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor)\",\n \"layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer\",\n \"to model parameters. for param in model.parameters(): param.requires_grad = False # Convert X\",\n \"LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ##############################################################################\",\n \"# # using 'layer' given above for this section # # # #\",\n \"(H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int],\",\n \"you should be # # using 'layer' given above for this section #\",\n \"GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result\",\n \"# ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc,\",\n \"# Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N, C,\",\n \"parameters. for param in model.parameters(): param.requires_grad = False # Convert X and y\",\n \"captum_utils import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] =\",\n \"= False # Convert X and y from numpy arrays to Torch Tensors\",\n \"gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val =\",\n \"backprop results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0,\",\n \"**************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train\",\n \"LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum =\",\n \"plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY,\",\n \"# # # # Also note that, you would need to customize your\",\n \"y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W)\",\n \"you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written\",\n \"in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result =\",\n \"'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as the default\",\n \"i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step,\",\n \"the default # # 'attr_preprocess' is written to only to handle multi channel\",\n \"target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam'])\",\n \"gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1)\",\n \"for param in model.parameters(): param.requires_grad = False # Convert X and y from\",\n \"X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor,\",\n \"parameter that you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess'\",\n \"LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim\",\n \"(10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] =\",\n \"= compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim =\",\n \"at the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates\",\n \"plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We\",\n \"attributions change layer = model.features[3] # Example visualization for using layer visualizations #\",\n \"= gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i]\",\n \"all channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC\",\n \"for # # visualizing captum outputs # # Use conv_module as the convolution\",\n \"layers and see observe how the attributions change layer = model.features[3] # Example\",\n \"given you which module(=layer) that we need to capture gradients from, which you\",\n \"for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## #\",\n \"what we did for the other captum sections, using our helper methods), #\",\n \"did for the other captum sections, using our helper methods), # # but\",\n \"results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1),\",\n \"dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24))\",\n \"LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis =\",\n \"need to capture gradients from, which you can see in conv_module variable below\",\n \"above for this section # # # # Also note that, you would\",\n \"captum outputs # # Use conv_module as the convolution layer for gradcam #\",\n \"size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names =\",\n \"# Also note that, you would need to customize your 'attr_preprocess' # #\",\n \"np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As\",\n \"THIS SECTION ONLY, we need to use gradients. We introduce a new model\",\n \"a final step, we can combine GradCam and Guided Backprop to get Guided\",\n \"will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam()\",\n \"= gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the\",\n \"in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize\",\n \"the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc =\",\n \"# GradCam # GradCAM. We have given you which module(=layer) that we need\",\n \"X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided\",\n \"OF YOUR CODE # ############################################################################## # Try out different layers and see observe\",\n \"X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc],\",\n \"and Layer Conductance (similar # # to what we did for the other\",\n \"we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc =\",\n \"gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor =\",\n \"False # Convert X and y from numpy arrays to Torch Tensors X_tensor\",\n \"combine GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for\",\n \"model.features[3] # Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer)\",\n \"SECTION ONLY, we need to use gradients. We introduce a new model we\",\n \"= torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i\",\n \"matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from\",\n \"conv_module as the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam\",\n \"and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful\",\n \"Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so\",\n \"# Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target =\",\n \"plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer) that we\",\n \"y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target\",\n \"data_utils import * from image_utils import * from captum_utils import * import numpy\",\n \"import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam\",\n \"attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam'])\",\n \"y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in\",\n \"Convert X and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x))\",\n \"look at the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam\",\n \"the model, so tell PyTorch not to compute gradients # with respect to\",\n \"is useful for # # visualizing captum outputs # # Use conv_module as\",\n \"+ 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png')\",\n \"import matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop\",\n \"captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer)\",\n \"# # # You can refer to the LayerActivation example above and you\",\n \"GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) #\",\n \"print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp\",\n \"the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across\",\n \"capture gradients from, which you can see in conv_module variable below gc_model =\",\n \"not to compute gradients # with respect to model parameters. for param in\",\n \"respect to model parameters. for param in model.parameters(): param.requires_grad = False # Convert\",\n \"captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import *\",\n \"torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as\",\n \"5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # #\",\n \"# but with some preprocessing calculations. # # # # You can refer\",\n \"model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell\",\n \"LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim\",\n \"using our helper methods), # # but with some preprocessing calculations. # #\",\n \"= img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout()\",\n \"outputs # # Use conv_module as the convolution layer for gradcam # ##############################################################################\",\n \"captum_utils.py is useful for # # visualizing captum outputs # # Use conv_module\",\n \"compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## #\",\n \"as well. # # visualize_attr_maps function from captum_utils.py is useful for # #\",\n \"= torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]):\",\n \"= LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis\",\n \"x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24,\",\n \"Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)\",\n \"a new model we will use explicitly for GradCAM for this. gc_model =\",\n \"np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img)\",\n \"= torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off')\",\n \"new model we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True)\",\n \"1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr\",\n \"need to use gradients. We introduce a new model we will use explicitly\",\n \"= y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum,\",\n \"3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END\",\n \"import torch import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import\",\n \"gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img)\",\n \"plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import\",\n \"in model.parameters(): param.requires_grad = False # Convert X and y from numpy arrays\",\n \"import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import *\",\n \"keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar #\",\n \"from, which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for\",\n \"you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in\",\n \"* from image_utils import * from captum_utils import * import numpy as np\",\n \"= torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM\",\n \"use gradients. We introduce a new model we will use explicitly for GradCAM\",\n \"should be # # using 'layer' given above for this section # #\",\n \"channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC =\",\n \"= gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i +\",\n \"X and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for\",\n \"layer = model.features[3] # Example visualization for using layer visualizations # layer_act =\",\n \"for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x))\",\n \"= LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC =\",\n \"layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar\",\n \"torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr\",\n \"is written to only to handle multi channel attributions. # # # #\",\n \"use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor\",\n \"img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i\",\n \"convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model,\",\n \"can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x))\",\n \"LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X,\",\n \"gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],\",\n \"Layer Conductance (similar # # to what we did for the other captum\",\n \"arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor\",\n \"# Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target\",\n \"(matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]])\",\n \"gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img\",\n \"gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for\",\n \"visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum outputs #\",\n \"= gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1,\",\n \"# TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # # to\",\n \"# using 'layer' given above for this section # # # # Also\",\n \"layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1,\",\n \"END OF YOUR CODE # ############################################################################## # Try out different layers and see\",\n \"plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't\",\n \"You can refer to the LayerActivation example above and you should be #\",\n \"Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],\",\n \"and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in\",\n \"# # visualizing captum outputs # # Use conv_module as the convolution layer\",\n \"# # 'attr_preprocess' is written to only to handle multi channel attributions. #\",\n \"Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N, C, H,\",\n \"send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to\",\n \"visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum\",\n \"GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from\",\n \"which module(=layer) that we need to capture gradients from, which you can see\",\n \"Conductance (similar # # to what we did for the other captum sections,\",\n \"Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target =\",\n \"GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp],\",\n \"this section # # # # Also note that, you would need to\",\n \"gradients # with respect to model parameters. for param in model.parameters(): param.requires_grad =\",\n \"img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off')\",\n \"in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model)\",\n \"= True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1)\",\n \"in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True)\",\n \"X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24,\",\n \"[LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor)\",\n \"True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result\",\n \"that you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is\",\n \"H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target\",\n \"for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x\",\n \"ONLY, we need to use gradients. We introduce a new model we will\",\n \"plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and Guided Backprop\",\n \"customize your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as\",\n \"layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1,\",\n \"= LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int =\",\n \"param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],\",\n \"and see observe how the attributions change layer = model.features[3] # Example visualization\",\n \"y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp =\",\n \"gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc =\",\n \"(2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0)\",\n \"to customize your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps'\",\n \"above and you should be # # using 'layer' given above for this\",\n \"= gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam #\",\n \"relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from captum.attr import\",\n \"which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param\",\n \"torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout()\",\n \"given above for this section # # # # Also note that, you\",\n \"X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ##############################################################################\",\n \"x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor,\",\n \"# # For layer gradcam look at the usage of the parameter relu_attributions\",\n \"Try out different layers and see observe how the attributions change layer =\",\n \"# You can refer to the LayerActivation example above and you should be\",\n \"we did for the other captum sections, using our helper methods), # #\",\n \"1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5,\",\n \"We don't want to train the model, so tell PyTorch not to compute\",\n \"# Pointwise multiplication and normalization of the gradcam and guided backprop results (2\",\n \"We have given you which module(=layer) that we need to capture gradients from,\",\n \"gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val\",\n \"different layers and see observe how the attributions change layer = model.features[3] #\",\n \"gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam\",\n \"'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only to handle\",\n \"torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result\",\n \"np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size\",\n \"model, so tell PyTorch not to compute gradients # with respect to model\",\n \"LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam'])\",\n \"compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names,\",\n \"X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1)\",\n \"img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img =\",\n \"############################################################################## # Try out different layers and see observe how the attributions change\",\n \"# END OF YOUR CODE # ############################################################################## # Try out different layers and\",\n \"note that, you would need to customize your 'attr_preprocess' # # parameter that\",\n \"model parameters. for param in model.parameters(): param.requires_grad = False # Convert X and\",\n \"y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img =\",\n \"the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from\",\n \"module(=layer) that we need to capture gradients from, which you can see in\",\n \"img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i +\",\n \"LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) )\",\n \"= X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i +\",\n \"# # Use conv_module as the convolution layer for gradcam # ############################################################################## #\",\n \"the LayerActivation example above and you should be # # using 'layer' given\",\n \"LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int\",\n \"multiplication and normalization of the gradcam and guided backprop results (2 lines) img\",\n \"'layer' given above for this section # # # # Also note that,\",\n \"Also note that, you would need to customize your 'attr_preprocess' # # parameter\",\n \"gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5,\",\n \"= gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i\",\n \"to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor =\",\n \"import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0,\",\n \"Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor)\",\n \"(H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int],\",\n \"# layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and\",\n \"from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils\",\n \"# Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]):\",\n \"Pointwise multiplication and normalization of the gradcam and guided backprop results (2 lines)\",\n \"GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is\",\n \"X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int\",\n \"across all channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape\",\n \"= compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ##############################################################################\",\n \"layer gradcam look at the usage of the parameter relu_attributions # ############################################################################## #\",\n \"in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img)\",\n \"# Convert X and y from numpy arrays to Torch Tensors X_tensor =\",\n \"# Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model,\",\n \"############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor,\",\n \"class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try\",\n \"from image_utils import * from captum_utils import * import numpy as np from\",\n \"visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model)\",\n \"1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1,\",\n \"function from captum_utils.py is useful for # # visualizing captum outputs # #\",\n \"class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target =\",\n \"torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in\",\n \"# # to what we did for the other captum sections, using our\",\n \"= LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ##############################################################################\",\n \"multi channel attributions. # # # # For layer gradcam look at the\",\n \"['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target\",\n \"Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png',\",\n \"x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result\",\n \"plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2)\",\n \"introduce a new model we will use explicitly for GradCAM for this. gc_model\",\n \"np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img)\",\n \"i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication\",\n \"to what we did for the other captum sections, using our helper methods),\",\n \"X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor,\",\n \"Visualize Individual Layer Gradcam and Layer Conductance (similar # # to what we\",\n \"of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels\",\n \"# to what we did for the other captum sections, using our helper\",\n \"1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF\",\n \"as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr\",\n \"Individual Layer Gradcam and Layer Conductance (similar # # to what we did\",\n \"Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from\",\n \"useful for # # visualizing captum outputs # # Use conv_module as the\",\n \"# As a final step, we can combine GradCam and Guided Backprop to\",\n \"GradCAM. We have given you which module(=layer) that we need to capture gradients\",\n \"# # parameter that you send along to 'visualize_attr_maps' as the default #\",\n \"img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam\",\n \"compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer\",\n \"don't want to train the model, so tell PyTorch not to compute gradients\",\n \"# # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the\",\n \"# **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to\",\n \"axis=2) # Pointwise multiplication and normalization of the gradcam and guided backprop results\",\n \"1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3,\",\n \"as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default\",\n \"* gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img =\",\n \"y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for\",\n \"/ np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') #\",\n \"= deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') #\",\n \"= compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim =\",\n \"= np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and guided\",\n \"# layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO:\",\n \"in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and\",\n \"torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24))\",\n \"gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and\",\n \"PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance,\",\n \"LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names,\",\n \"visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE #\",\n \"for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc\",\n \"to use gradients. We introduce a new model we will use explicitly for\",\n \"attributions. # # # # For layer gradcam look at the usage of\",\n \"from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import\",\n \"= torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor,\",\n \"GradCam # GradCAM. We have given you which module(=layer) that we need to\",\n \"range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization\",\n \"LayerGradCam from data_utils import * from image_utils import * from captum_utils import *\",\n \"gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM.\",\n \"Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful for\",\n \"0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1,\",\n \"TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # # to what\",\n \"gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x))\",\n \"Layer Gradcam and Layer Conductance (similar # # to what we did for\",\n \"plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer) that\",\n \"= torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12]\",\n \"y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided\",\n \"observe how the attributions change layer = model.features[3] # Example visualization for using\",\n \"with some preprocessing calculations. # # # # You can refer to the\",\n \"image_utils import * from captum_utils import * import numpy as np from visualizers\",\n \"+ 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we\",\n \"X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result =\",\n \"= compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y,\",\n \"X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to\",\n \"Gradcam and Layer Conductance (similar # # to what we did for the\",\n \"= 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1,\",\n \"True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png',\",\n \"3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer)\",\n \"model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # #\",\n \"calculations. # # # # You can refer to the LayerActivation example above\",\n \"train the model, so tell PyTorch not to compute gradients # with respect\",\n \"gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in\",\n \"final step, we can combine GradCam and Guided Backprop to get Guided GradCam.\",\n \"# layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum =\",\n \"conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True\",\n \"default # # 'attr_preprocess' is written to only to handle multi channel attributions.\",\n \"from captum_utils import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize']\",\n \"in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad =\",\n \"conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well.\",\n \"* import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0)\",\n \"gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img)\",\n \"y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val =\",\n \"from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation,\",\n \"axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i\",\n \"channel attributions. # # # # For layer gradcam look at the usage\",\n \"We introduce a new model we will use explicitly for GradCAM for this.\",\n \"LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X,\",\n \"C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor,\",\n \"[attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try out\",\n \"x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO:\",\n \"= LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y,\",\n \"from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model,\",\n \"to capture gradients from, which you can see in conv_module variable below gc_model\",\n \"############################################################################## # END OF YOUR CODE # ############################################################################## # Try out different layers\",\n \"Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor)\",\n \"along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only\",\n \"= torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24,\",\n \"img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We\",\n \"can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters():\",\n \"= LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int\",\n \"get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor =\",\n \"methods), # # but with some preprocessing calculations. # # # # You\",\n \"LayerActivation example above and you should be # # using 'layer' given above\",\n \"= gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img)\",\n \"deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # ****************************************************************************************\",\n \"refer to the LayerActivation example above and you should be # # using\",\n \"example above and you should be # # using 'layer' given above for\",\n \"that we need to capture gradients from, which you can see in conv_module\",\n \"= GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names,\",\n \"the gradcam and guided backprop results (2 lines) img = gradcam_val * gbp_val\",\n \"plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which\",\n \"below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor =\",\n \"i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img =\",\n \"= LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis\",\n \"N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC,\",\n \"import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation']\",\n \"plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img =\",\n \"well. # # visualize_attr_maps function from captum_utils.py is useful for # # visualizing\",\n \"for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model)\",\n \"y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing\",\n \"to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only to\",\n \"# # visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum\",\n \"for the other captum sections, using our helper methods), # # but with\",\n \"model.parameters(): param.requires_grad = False # Convert X and y from numpy arrays to\",\n \"that, you would need to customize your 'attr_preprocess' # # parameter that you\",\n \"1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR\",\n \"plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam\",\n \"5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off')\",\n \"gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor\",\n \"you would need to customize your 'attr_preprocess' # # parameter that you send\",\n \"explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor =\",\n \"and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x\",\n \"matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam,\",\n \"# 'attr_preprocess' is written to only to handle multi channel attributions. # #\",\n \"# visualizing captum outputs # # Use conv_module as the convolution layer for\",\n \"plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you\",\n \"24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) #\",\n \"gradients. We introduce a new model we will use explicitly for GradCAM for\",\n \"visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE\",\n \"to compute gradients # with respect to model parameters. for param in model.parameters():\",\n \"############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps\",\n \"keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1,\",\n \"want to train the model, so tell PyTorch not to compute gradients #\",\n \"and normalization of the gradcam and guided backprop results (2 lines) img =\",\n \"y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in\",\n \"GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] =\",\n \"only to handle multi channel attributions. # # # # For layer gradcam\",\n \"visualization for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr =\",\n \"have given you which module(=layer) that we need to capture gradients from, which\",\n \"TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from\",\n \"LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr =\",\n \"img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png')\",\n \"X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for\",\n \"gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i\",\n \"= np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img =\",\n \"img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]])\",\n \"= GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape,\",\n \"section # # # # Also note that, you would need to customize\",\n \"from data_utils import * from image_utils import * from captum_utils import * import\",\n \"param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor =\",\n \"plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] +\",\n \"X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp\",\n \"y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE\",\n \"24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255)\",\n \"y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]):\",\n \"for this section # # # # Also note that, you would need\",\n \"change layer = model.features[3] # Example visualization for using layer visualizations # layer_act\",\n \"torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell PyTorch not\",\n \"this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in\",\n \"in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24))\",\n \"layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) #\",\n \"import * from captum_utils import * import numpy as np from visualizers import\",\n \"in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img\",\n \"class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients.\",\n \"= GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)\",\n \"# parameter that you send along to 'visualize_attr_maps' as the default # #\",\n \"y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp\",\n \"torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor,\",\n \"GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape)\",\n \"attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp =\",\n \"# # # For layer gradcam look at the usage of the parameter\",\n \"# Try out different layers and see observe how the attributions change layer\",\n \"# GradCAM. We have given you which module(=layer) that we need to capture\",\n \"can refer to the LayerActivation example above and you should be # #\",\n \"using 'layer' given above for this section # # # # Also note\",\n \"PyTorch not to compute gradients # with respect to model parameters. for param\",\n \"plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer)\",\n \"= 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we\",\n \"range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img)\",\n \"written to only to handle multi channel attributions. # # # # For\",\n \"we need to capture gradients from, which you can see in conv_module variable\",\n \"# # # # You can refer to the LayerActivation example above and\",\n \"the attributions change layer = model.features[3] # Example visualization for using layer visualizations\",\n \"LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum =\",\n \"############################################################################## # Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N,\",\n \"our helper methods), # # but with some preprocessing calculations. # # #\",\n \"1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can\",\n \"For layer gradcam look at the usage of the parameter relu_attributions # ##############################################################################\",\n \"# visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum outputs\",\n \"we can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor =\",\n \"layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1,\",\n \"gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val\",\n \"range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img /\",\n \"X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum\",\n \"# # # Also note that, you would need to customize your 'attr_preprocess'\",\n \"param in model.parameters(): param.requires_grad = False # Convert X and y from numpy\",\n \"X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE # ##############################################################################\",\n \"how the attributions change layer = model.features[3] # Example visualization for using layer\",\n \"import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) #\",\n \"gradients from, which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True)\",\n \"= layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance\",\n \"layer for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module)\",\n \"= y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR\",\n \"compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True)\",\n \"to train the model, so tell PyTorch not to compute gradients # with\",\n \"plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR\",\n \"as the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc\",\n \"plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine\",\n \"LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1,\",\n \"param.requires_grad = False # Convert X and y from numpy arrays to Torch\",\n \"plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and\",\n \"np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and guided backprop\",\n \"see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad\",\n \"y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model)\",\n \"visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC,\",\n \"('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE #\",\n \"see observe how the attributions change layer = model.features[3] # Example visualization for\",\n \"# with respect to model parameters. for param in model.parameters(): param.requires_grad = False\",\n \"i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout()\",\n \"tell PyTorch not to compute gradients # with respect to model parameters. for\",\n \"# Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer) #\",\n \"LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis =\",\n \"1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') #\",\n \"LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model,\",\n \"y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use\",\n \"X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop\",\n \"= torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation\",\n \"aggregates across all channels from captum.attr import LayerAttribution N, C, H, W =\",\n \"1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model =\",\n \"gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i]\",\n \"gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img\",\n \"plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want\",\n \"for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in\",\n \"of the gradcam and guided backprop results (2 lines) img = gradcam_val *\",\n \"to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor\",\n \"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest'\",\n \"to only to handle multi channel attributions. # # # # For layer\",\n \"BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X,\",\n \"of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5)\",\n \"gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img\",\n \"LayerConductance, LayerGradCam from data_utils import * from image_utils import * from captum_utils import\",\n \"for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result =\",\n \"['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum\",\n \"plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]])\",\n \"GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x\",\n \"LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int =\",\n \"# # Also note that, you would need to customize your 'attr_preprocess' #\",\n \"and guided backprop results (2 lines) img = gradcam_val * gbp_val img =\",\n \"= LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y,\",\n \"'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION\",\n \"GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful for #\",\n \"would need to customize your 'attr_preprocess' # # parameter that you send along\",\n \"img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img\",\n \"= torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for\",\n \"As a final step, we can combine GradCam and Guided Backprop to get\",\n \"to handle multi channel attributions. # # # # For layer gradcam look\",\n \"(similar # # to what we did for the other captum sections, using\",\n \"= rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have\",\n \"y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## #\",\n \"+ (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img)\",\n \"img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png')\",\n \"Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True)\",\n \"X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END\",\n \"so tell PyTorch not to compute gradients # with respect to model parameters.\",\n \"torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor\",\n \"ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape,\",\n \"Use conv_module as the convolution layer for gradcam # ############################################################################## # Computing Guided\",\n \"LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import * from captum_utils\",\n \"class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp,\",\n \"# # # # For layer gradcam look at the usage of the\",\n \"True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png',\",\n \"# ############################################################################## # Layer gradcam aggregates across all channels from captum.attr import LayerAttribution\",\n \"gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val =\",\n \"+ 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model\",\n \"default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names\",\n \"captum sections, using our helper methods), # # but with some preprocessing calculations.\",\n \"for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act,\",\n \"# # You can refer to the LayerActivation example above and you should\",\n \"handle multi channel attributions. # # # # For layer gradcam look at\",\n \"GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils\",\n \"Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5,\",\n \"# We don't want to train the model, so tell PyTorch not to\",\n \"Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py\",\n \"load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients. We introduce\",\n \"step, we can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor\",\n \"= (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap']\",\n \"your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as the\",\n \"import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image from\"\n]","isConversational":false}}},{"rowIdx":593,"cells":{"ngram":{"kind":"list like","value":["reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ]","0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2),","NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3',","reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0),","= namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect',","('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2","1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2,","('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet =","1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7',","('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ],","], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2),","2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 =","1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3',","('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7',","('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3',","'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ]","1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5],","1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)],","3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6],","0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1),","('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS","('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3',","('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0),","0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4,","6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5',","0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5',","1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)],","('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect',","0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS =","('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 =","('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3',","('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4,","2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH","('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0),","('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2),","0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5])","Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2),","3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1),","normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7',","('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3',","5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3',","reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3',","reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect',","normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3',","4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3',","1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)],","1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5],","2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0),","BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1),","('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3',","1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)],","] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet","Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect',","('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] )","reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3',","1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype(","('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3',","= Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3',","], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1),","0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3),","('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1),","= Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0),","reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5',","collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [","('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect',","1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1),","0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3',","2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2),","('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2,","('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], )","3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet","normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3',","('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4,","4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect',","('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3',","('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3',","1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3',","2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1),","'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0),","DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0),","Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2),","('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3',","('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4,","('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2,","('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2,","2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1),","4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect',","('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3',","'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5',","Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3',",") AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2),","0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3',","6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect',","PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES","1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0),","('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3,","6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5',","import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none',","1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0),","('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype(","2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0),","2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3,","('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3',","'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5',","6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3',","0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0),","('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[","('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[","reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0),","2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3,","= DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2),","1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1),","('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect',","('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3',","2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3',","1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)],","normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect',","BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0),","0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0),","('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3',","('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5',","1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2),","from collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES =","1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0),","5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1),","reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect',","DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5',","0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3,","normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7',","1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6))","0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1),","5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2),","'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3',","('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3',","2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5),","CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet =","0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2),","], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1),","], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect',","('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect',","= Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect',","2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1),","0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3,","= Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3',","('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3',","('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0),","('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[","1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3',","'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1),","('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3',","('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3',","('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3',","2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype(","<gh_stars>0 from collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES","= [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype(","('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2,","2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1),","1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4,","normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3',","namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3',","2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3',","0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3,","('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ],","('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3',","0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0),","('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect',","'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3',","= Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1),","('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3',","0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2),","('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3',","normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect',","0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)],","0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0),","'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5',","0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0),","1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1),","('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3',","('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2,","('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect',","0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1),","'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3',","('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3',","1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6]","('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3',","'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3',","('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5,","normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3',","1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3),","('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2","Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1),","1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3',","0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6),","1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7',","0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0),","reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'","0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1),","0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2),","('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype(","('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2,","0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2),","5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0),","0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4,","0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2, 6))","('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0),","('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1),","('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5,","0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1),","1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1),","('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3',","1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5])","5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0),","0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)],","3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3',","= Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect',","('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5',","('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1),","normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3',","AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3',","6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1),","5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2),","1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4),","normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3',","reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0),","0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0),","1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1),","('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1),","('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3',","0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6],","'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3',","[ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [","0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)],","('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2","reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1),","'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0),","('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3',","DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1),","3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0),","namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3',","('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5,","('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ],","6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0),","1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0),","'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3',","('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH =","2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3',","0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1),","'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'","normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3),","0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1),","normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3',","('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ],","1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3',","('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0),","0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1","('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect',","6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2),","4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3',","('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2,","2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6),","('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3',","1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0),","('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3',","('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4,","0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2),","('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect',","5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0),","Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3',","('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3',","2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect',","DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5',","3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3',",") DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect',","'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect',","reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3',","0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0),","1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1),","4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1),","0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6],","('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect',","('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0),","('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2,","5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0),","3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0),","] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0),","reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1),","3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0),","('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3',","Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0),","= [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES =","2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype(","0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 =","[ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[","('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4,","normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0),","'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5',","('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect',","4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3',","('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3',","4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect',"],"string":"[\n \"reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ]\",\n \"0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2),\",\n \"NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3',\",\n \"reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0),\",\n \"= namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect',\",\n \"('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2\",\n \"1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2,\",\n \"('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet =\",\n \"1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7',\",\n \"('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ],\",\n \"], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2),\",\n \"2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 =\",\n \"1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3',\",\n \"('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7',\",\n \"('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ]\",\n \"1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5],\",\n \"1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)],\",\n \"3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6],\",\n \"0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS\",\n \"('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3',\",\n \"('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0),\",\n \"0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4,\",\n \"6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5',\",\n \"0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5',\",\n \"1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)],\",\n \"('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect',\",\n \"0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS =\",\n \"('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 =\",\n \"('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3',\",\n \"('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4,\",\n \"2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH\",\n \"('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0),\",\n \"('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2),\",\n \"0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5])\",\n \"Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2),\",\n \"3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7',\",\n \"('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3',\",\n \"reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3',\",\n \"reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect',\",\n \"normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3',\",\n \"4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3',\",\n \"1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)],\",\n \"1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5],\",\n \"2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0),\",\n \"BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1),\",\n \"('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3',\",\n \"1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)],\",\n \"] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet\",\n \"Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect',\",\n \"('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] )\",\n \"reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype(\",\n \"('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3',\",\n \"= Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3',\",\n \"], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1),\",\n \"0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3),\",\n \"('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1),\",\n \"= Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0),\",\n \"reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5',\",\n \"collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [\",\n \"('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect',\",\n \"1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1),\",\n \"0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3',\",\n \"2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2),\",\n \"('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2,\",\n \"('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], )\",\n \"3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet\",\n \"normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3',\",\n \"('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4,\",\n \"4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect',\",\n \"('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3',\",\n \"('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3',\",\n \"1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3',\",\n \"2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1),\",\n \"'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0),\",\n \"DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0),\",\n \"Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2),\",\n \"('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3',\",\n \"('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4,\",\n \"('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2,\",\n \"('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2,\",\n \"2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1),\",\n \"4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect',\",\n \"('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3',\",\n \"'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5',\",\n \"Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3',\",\n \") AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2),\",\n \"0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3',\",\n \"6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect',\",\n \"PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES\",\n \"1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0),\",\n \"('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3,\",\n \"6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5',\",\n \"import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none',\",\n \"1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0),\",\n \"('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype(\",\n \"2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0),\",\n \"2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3,\",\n \"('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3',\",\n \"'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5',\",\n \"6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3',\",\n \"0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0),\",\n \"('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[\",\n \"('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[\",\n \"reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0),\",\n \"2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3,\",\n \"= DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2),\",\n \"1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect',\",\n \"('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3',\",\n \"2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3',\",\n \"1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)],\",\n \"normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect',\",\n \"BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0),\",\n \"0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0),\",\n \"('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3',\",\n \"('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5',\",\n \"1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2),\",\n \"from collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES =\",\n \"1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0),\",\n \"5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1),\",\n \"reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect',\",\n \"DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5',\",\n \"0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3,\",\n \"normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7',\",\n \"1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6))\",\n \"0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1),\",\n \"5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2),\",\n \"'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3',\",\n \"('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3',\",\n \"2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5),\",\n \"CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet =\",\n \"0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2),\",\n \"], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect',\",\n \"('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect',\",\n \"= Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect',\",\n \"2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1),\",\n \"0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3,\",\n \"= Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3',\",\n \"('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3',\",\n \"('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0),\",\n \"('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[\",\n \"1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3',\",\n \"'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1),\",\n \"('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3',\",\n \"('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3',\",\n \"2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype(\",\n \"<gh_stars>0 from collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES\",\n \"= [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype(\",\n \"('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2,\",\n \"2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4,\",\n \"normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3',\",\n \"namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3',\",\n \"2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3',\",\n \"0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3,\",\n \"('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ],\",\n \"('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3',\",\n \"0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0),\",\n \"('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect',\",\n \"'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3',\",\n \"= Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1),\",\n \"('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2),\",\n \"('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3',\",\n \"normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect',\",\n \"0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)],\",\n \"0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0),\",\n \"'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5',\",\n \"0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0),\",\n \"1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3',\",\n \"('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2,\",\n \"('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect',\",\n \"0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1),\",\n \"'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3',\",\n \"('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3',\",\n \"1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6]\",\n \"('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3',\",\n \"'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3',\",\n \"('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5,\",\n \"normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3',\",\n \"1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3),\",\n \"('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2\",\n \"Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1),\",\n \"1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3',\",\n \"0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6),\",\n \"1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7',\",\n \"0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0),\",\n \"reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'\",\n \"0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1),\",\n \"0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2),\",\n \"('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype(\",\n \"('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2,\",\n \"0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2),\",\n \"5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0),\",\n \"0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4,\",\n \"0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2, 6))\",\n \"('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0),\",\n \"('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1),\",\n \"('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5,\",\n \"0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1),\",\n \"1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1),\",\n \"('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3',\",\n \"1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5])\",\n \"5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0),\",\n \"0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)],\",\n \"3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3',\",\n \"= Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect',\",\n \"('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5',\",\n \"('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1),\",\n \"normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3',\",\n \"AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3',\",\n \"6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1),\",\n \"5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2),\",\n \"1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4),\",\n \"normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3',\",\n \"reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0),\",\n \"0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0),\",\n \"1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1),\",\n \"('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1),\",\n \"('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3',\",\n \"0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6],\",\n \"'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3',\",\n \"[ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [\",\n \"0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)],\",\n \"('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2\",\n \"reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1),\",\n \"'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0),\",\n \"('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3',\",\n \"DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1),\",\n \"3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0),\",\n \"namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3',\",\n \"('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5,\",\n \"('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ],\",\n \"6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0),\",\n \"1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0),\",\n \"'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3',\",\n \"('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH =\",\n \"2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3',\",\n \"0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1),\",\n \"'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'\",\n \"normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3),\",\n \"0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1),\",\n \"normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ],\",\n \"1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3',\",\n \"('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0),\",\n \"0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1\",\n \"('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect',\",\n \"6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2),\",\n \"4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3',\",\n \"('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2,\",\n \"2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6),\",\n \"('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3',\",\n \"1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0),\",\n \"('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3',\",\n \"('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4,\",\n \"0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2),\",\n \"('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect',\",\n \"5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0),\",\n \"Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3',\",\n \"('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3',\",\n \"2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect',\",\n \"DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5',\",\n \"3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3',\",\n \") DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect',\",\n \"'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect',\",\n \"reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3',\",\n \"0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0),\",\n \"1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1),\",\n \"4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1),\",\n \"0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6],\",\n \"('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect',\",\n \"('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0),\",\n \"('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2,\",\n \"5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0),\",\n \"3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0),\",\n \"] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0),\",\n \"reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1),\",\n \"3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0),\",\n \"('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3',\",\n \"Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0),\",\n \"= [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES =\",\n \"2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype(\",\n \"0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 =\",\n \"[ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[\",\n \"('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4,\",\n \"normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0),\",\n \"'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5',\",\n \"('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect',\",\n \"4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3',\",\n \"('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3',\",\n \"4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect',\"\n]","isConversational":false}}},{"rowIdx":594,"cells":{"ngram":{"kind":"list like","value":["use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions =","KIND, either express or implied. # See the License for the specific language","Unless required by applicable law or agreed to in writing, software # distributed","IPython.display import display import os import pandas from .parser_py import parse from .common","PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection):","PostgreSQL database.\") result = 0 result += os.system('sudo apt-get -y -qq update') result","return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return","= bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection)","SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection","sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with","% color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner()","from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect();","if run_query: print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.'","run_query=True) def ParseList(line): line = line.strip() if not line: predicates = [] else:","language governing permissions and # limitations under the License. \"\"\"Library for using Logica","logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;'","\\n' 'It is the easiest to run BigQuery requests from Google CoLab:\\n' '","logica WITH SUPERUSER\" !sudo -u postgres psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\"","True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project):","with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored","print('Installation succeeded. Connecting...') # Connect to the database. from logica import colab_logica from","# Install Logica. !pip install logica # Install postgresql server. !sudo apt-get -y","queries.\") PROJECT = input() print(\"project_id is set to %s\" % PROJECT) print(\"You can","= PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica","' 'variable.' % predicate)) display(t) else: print('The query was not run.') print(' ')","from .common import concertina_lib from .compiler import functors from .compiler import rule_translate from","this file except in compliance with the License. # You may obtain a","input() print(\"project_id is set to %s\" % PROJECT) print(\"You can change it with","else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite","'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates):","= ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program =","bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as","Logica. !pip install logica # Install postgresql server. !sudo apt-get -y -qq update","] ' + error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica predicates and storing","program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates)","+= os.system( 'sudo -u postgres psql -U postgres -c \\'CREATE DATABASE logica;\\'') if","pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar()","elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif","PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres psql -U postgres -c \\'CREATE","= PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final):","cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print(","ANY KIND, either express or implied. # See the License for the specific","run_query): \"\"\"Running Logica predicates and storing results.\"\"\" predicates = ParseList(line) if not predicates:","an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class","def Logica(line, cell, run_query): \"\"\"Running Logica predicates and storing results.\"\"\" predicates = ParseList(line)","Connecting...') # Connect to the database. from logica import colab_logica from sqlalchemy import","if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program","'_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine ==","predicate)) display(t) else: print('The query was not run.') print(' ') # To activate","support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self):","2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the","import register_cell_magic from IPython.display import display import os import pandas from .parser_py import","running Logica on SQLite requires no installation.\\n' 'This could be a good fit","for BigQuery queries.\") PROJECT = input() print(\"project_id is set to %s\" % PROJECT)","import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os import","postgres -c \\'CREATE DATABASE logica;\\'') if result != 0: print(\"\"\"Installation failed. Please try","'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner","failed. Please try the following manually: # Install Logica. !pip install logica #","result += 0 * os.system( 'sudo -u postgres psql -c \"CREATE USER logica","self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION =","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError(","False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True","PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect()","engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line,","pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x):","SUPERUSER\" !sudo -u postgres psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u","if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def","-y -qq install postgresql !sudo service postgresql start # Prepare database for Logica.","-y -qq update') result += os.system('sudo apt-get -y -qq install postgresql') result +=","SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args):","def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line,","parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError","\\'CREATE DATABASE logica;\\'') if result != 0: print(\"\"\"Installation failed. Please try the following","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","#!/usr/bin/python # # Copyright 2020 Google LLC # # Licensed under the Apache","postgres psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u","== 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL","stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query","run BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica on","for working with small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your","if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is","DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return","global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED:","'variable.' % predicate)) display(t) else: print('The query was not run.') print(' ') #","is the easiest to run BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note","= True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None","OF ANY KIND, either express or implied. # See the License for the","CoLab.\"\"\" from .common import color from .common import concertina_lib from .compiler import functors","result += os.system( 'sudo -u postgres psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"')","PROJECT) print(\"You can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output):","default. try: from google.cloud import bigquery except: BQ_READY = False print('Could not import","!sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to","= TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = []","logs_idx = len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for","% predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar","USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE","following manually: # Install Logica. !pip install logica # Install postgresql server. !sudo","use for BigQuery queries.\") PROJECT = input() print(\"project_id is set to %s\" %","result += os.system('sudo service postgresql start') # Ignoring user creation error, as they","postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation error, as","in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql})","and storing results.\"\"\" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.')","governing permissions and # limitations under the License. \"\"\"Library for using Logica in","except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None # TODO:","logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line:","runner should not be accepting an engine. def __call__(self, sql, engine, is_final): return","= DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine","with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException","= None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE =","is not installed. \\n' 'It is the easiest to run BigQuery requests from","from google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try:","# Prepare database for Logica. !sudo -u postgres psql -c \"CREATE USER logica","print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except:","enter project_id to use for BigQuery queries.\") PROJECT = input() print(\"project_id is set","ShowError('No predicates to run.') return try: program = ';\\n'.join(s for s in [PREAMBLE,","supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection","USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\"Please enter project_id to use for","logica # Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y","psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U postgres","predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if","{warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored","postgres psql -U postgres -c \\'CREATE DATABASE logica;\\'') if result != 0: print(\"\"\"Installation","program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if","connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica","import concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler import","Install Logica. !pip install logica # Install postgresql server. !sudo apt-get -y -qq","import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return","import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection","result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq","LLC # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","BigQuery queries.\") PROJECT = input() print(\"project_id is set to %s\" % PROJECT) print(\"You","pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';')","try the following manually: # Install Logica. !pip install logica # Install postgresql","s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program =","and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\n' 'It","os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install postgresql')","elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1],","+= os.system('sudo service postgresql start') # Ignoring user creation error, as they may","-c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres psql","= universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine","WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar or a mock. Useful for","USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY","software # distributed under the License is distributed on an \"AS IS\" BASIS,","update !sudo apt-get -y -qq install postgresql !sudo service postgresql start # Prepare","True # By default. try: from google.cloud import bigquery except: BQ_READY = False","TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION =","colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection =","PROJECT is None: print(\"Please enter project_id to use for BigQuery queries.\") PROJECT =","google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could not import","line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client","Ignoring user creation error, as they may already exist. result += 0 *","is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements =","import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could not","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if","self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine","( predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.' %","== 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif",".common import concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler","DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final)","sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite '","\"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres psql -U","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import auth","[] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql","print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica predicates","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","+ ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip =","except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return","DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def","only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self):","+= os.system( 'sudo -u postgres psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result","print(\"Installing and configuring an empty PostgreSQL database.\") result = 0 result += os.system('sudo","'sudo -u postgres psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system(","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","predicates: ShowError('No predicates to run.') return try: program = ';\\n'.join(s for s in","postgresql start') # Ignoring user creation error, as they may already exist. result","-u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the","print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' % (","'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine","TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns","required by applicable law or agreed to in writing, software # distributed under","self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell,","def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection =","% predicate)) display(t) else: print('The query was not run.') print(' ') # To","be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED =","applicable law or agreed to in writing, software # distributed under the License","-u postgres psql -U postgres -c \\'CREATE DATABASE logica;\\'') if result != 0:","# By default. try: from google.cloud import bigquery except: BQ_READY = False print('Could","__init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION","<gh_stars>0 #!/usr/bin/python # # Copyright 2020 Google LLC # # Licensed under the","else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map","line = line.strip() if not line: predicates = [] else: predicates = [p.strip()","-U postgres -c \\'CREATE DATABASE logica;\\'') if result != 0: print(\"\"\"Installation failed. Please","= SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery':","-u postgres psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql","import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED =","auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import","def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def","import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.') return","os import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY =","or agreed to in writing, software # distributed under the License is distributed","None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT","else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self,","DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) =","'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite':","import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE,","CONDITIONS OF ANY KIND, either express or implied. # See the License for","print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with","'CREATE DATABASE logica;' # Connect to the database. from logica import colab_logica from","EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite","pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...')","Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install","SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar or a mock. Useful for UIs","@register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if","self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine.","google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from","when compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab cell.","not line: predicates = [] else: predicates = [p.strip() for p in line.split(',')]","executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling","database. from logica import colab_logica from sqlalchemy import create_engine import pandas engine =","parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules)","# TODO: Sqlite runner should not be accepting an engine. def __call__(self, sql,","under the Apache License, Version 2.0 (the \"License\"); # you may not use","functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and","writing, software # distributed under the License is distributed on an \"AS IS\"","import sqlite3_logica BQ_READY = True # By default. try: from google.cloud import bigquery","executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t})","return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for","from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect()","with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT","Logica predicates and storing results.\"\"\" predicates = ParseList(line) if not predicates: ShowError('No predicates","run.') return try: program = ';\\n'.join(s for s in [PREAMBLE, cell] if s)","try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine()","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","display import os import pandas from .parser_py import parse from .common import sqlite3_logica","is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine ==","raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map =","None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.')","exist. result += 0 * os.system( 'sudo -u postgres psql -c \"CREATE USER","License. # You may obtain a copy of the License at # #","connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT","DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user()","for the specific language governing permissions and # limitations under the License. \"\"\"Library","to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions","Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None","the specific language governing permissions and # limitations under the License. \"\"\"Library for","query is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql)","command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output","global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\"Please enter project_id","\"CREATE USER logica WITH SUPERUSER\"') result += os.system( 'sudo -u postgres psql -c","else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx):","installation.\\n' 'This could be a good fit for working with small data or","SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project","sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage()","!sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo service","and # limitations under the License. \"\"\"Library for using Logica in CoLab.\"\"\" from","compliance with the License. # You may obtain a copy of the License","PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT =","tabbar. def PostgresJumpStart(): # Install postgresql server. print(\"Installing and configuring an empty PostgreSQL","{warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not run.') print('","global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection)","import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.') return engine, connection","client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql)","-u postgres psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo","to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if","color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif","def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell,","from google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try:","return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final:","def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return","in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage()","= line.strip() if not line: predicates = [] else: predicates = [p.strip() for","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect to the database.","%s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner","' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored at","'_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate)","global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\"Please","with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format(","configuring an empty PostgreSQL database.\") result = 0 result += os.system('sudo apt-get -y","manually: # Install Logica. !pip install logica # Install postgresql server. !sudo apt-get","= input() print(\"project_id is set to %s\" % PROJECT) print(\"You can change it","['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython()","only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions,","return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine =","line: predicates = [] else: predicates = [p.strip() for p in line.split(',')] return","Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def","t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format(","def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is","ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql =","the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\"Installing and configuring an empty","DATABASE logica;\\'') if result != 0: print(\"\"\"Installation failed. Please try the following manually:","create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation","'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to use SQLite.') return bar = TabBar(predicates","RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.')","-qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation","tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real","!pip install logica # Install postgresql server. !sudo apt-get -y -qq update !sudo","def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting","not use this file except in compliance with the License. # You may","sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx):","program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification","a good fit for working with small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end}","psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres","not predicates: ShowError('No predicates to run.') return try: program = ';\\n'.join(s for s","pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite '","is None: print(\"Please enter project_id to use for BigQuery queries.\") PROJECT = input()","cell, run_query): \"\"\"Running Logica predicates and storing results.\"\"\" predicates = ParseList(line) if not","logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600)","predicates to run.') return try: program = ';\\n'.join(s for s in [PREAMBLE, cell]","License, Version 2.0 (the \"License\"); # you may not use this file except","SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be","postgres psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U","is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was","print(\"project_id is set to %s\" % PROJECT) print(\"You can change it with logica.colab_logica.SetProject","installed. \\n' 'It is the easiest to run BigQuery requests from Google CoLab:\\n'","now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should","'Note that running Logica on SQLite requires no installation.\\n' 'This could be a","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","auth.authenticate_user() if PROJECT is None: print(\"Please enter project_id to use for BigQuery queries.\")","sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine ==","and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() #","= result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The","0: print(\"\"\"Installation failed. Please try the following manually: # Install Logica. !pip install","engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif","if not predicates: ShowError('No predicates to run.') return try: program = ';\\n'.join(s for","= True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY =","or a mock. Useful for UIs that don't support JS.\"\"\" if TABULATED_OUTPUT: return","# you may not use this file except in compliance with the License.","import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection","google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from","MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line","for using Logica in CoLab.\"\"\" from .common import color from .common import concertina_lib","RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def","agreed to in writing, software # distributed under the License is distributed on","None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True","try: from google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.')","class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not","following query is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql')))","engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client","except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery'","PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global","connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return","sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} '","== 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL","(the \"License\"); # you may not use this file except in compliance with","= create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect","= [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False):","requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite requires","a mock. Useful for UIs that don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args)","-qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql start #","= False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED =","not installed. \\n' 'It is the easiest to run BigQuery requests from Google","creation error, as they may already exist. result += 0 * os.system( 'sudo","logica;' # Connect to the database. from logica import colab_logica from sqlalchemy import","PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for","EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None:","class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class","'_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql'))","be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection,",".compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display","= [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates):","result = 0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo","apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring","'sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\"') result += os.system(","# Unless required by applicable law or agreed to in writing, software #","sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\")","= None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY =","by applicable law or agreed to in writing, software # distributed under the","result != 0: print(\"\"\"Installation failed. Please try the following manually: # Install Logica.","return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client =","colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection =","IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection,","self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text):","to run BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica","could be a good fit for working with small data or learning Logica.\\n'","authentification is not installed. \\n' 'It is the easiest to run BigQuery requests","engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION:","database for Logica. !sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\"","in CoLab.\"\"\" from .common import color from .common import concertina_lib from .compiler import","real TabBar or a mock. Useful for UIs that don't support JS.\"\"\" if","display(t) else: print('The query was not run.') print(' ') # To activate the","') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\"Installing","else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class","# Ignoring user creation error, as they may already exist. result += 0","to the database. from logica import colab_logica from sqlalchemy import create_engine import pandas","'The following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else:","for UIs that don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def","bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import","from .compiler import functors from .compiler import rule_translate from .compiler import universe import","import color from .common import concertina_lib from .compiler import functors from .compiler import","-y -qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring user","apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql","file except in compliance with the License. # You may obtain a copy","__enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self,","== 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else:","USER logica WITH SUPERUSER\" !sudo -u postgres psql -c \"ALTER USER logica PASSWORD","os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start') #","cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates","pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL","if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements","License for the specific language governing permissions and # limitations under the License.","from .common import color from .common import concertina_lib from .compiler import functors from","WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should","= True # By default. try: from google.cloud import bigquery except: BQ_READY =","run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql","+= os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start')","run_query: print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' %","Please try the following manually: # Install Logica. !pip install logica # Install","start # Prepare database for Logica. !sudo -u postgres psql -c \"CREATE USER","to in writing, software # distributed under the License is distributed on an","__call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end}","predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program","USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def","implied. # See the License for the specific language governing permissions and #","predicates = [] else: predicates = [p.strip() for p in line.split(',')] return predicates","\"License\"); # you may not use this file except in compliance with the","idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate +","% ( predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.'","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass","= self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine,","# Connect to the database. from logica import colab_logica from sqlalchemy import create_engine","sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered","= program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or","Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with","= False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY","0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y","e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish output","not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar or a mock. Useful","is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine","sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise","%s\" % PROJECT) print(\"You can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True","pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass","= engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect to the database. from","specific language governing permissions and # limitations under the License. \"\"\"Library for using",".compiler import functors from .compiler import rule_translate from .compiler import universe import IPython","good fit for working with small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation","program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when","or implied. # See the License for the specific language governing permissions and","import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded.","statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return","global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED:","PostgresJumpStart(): # Install postgresql server. print(\"Installing and configuring an empty PostgreSQL database.\") result","# Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar)","= False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this","Apache License, Version 2.0 (the \"License\"); # you may not use this file","engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') #","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else:","idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1):","from .parser_py import parse from .common import sqlite3_logica BQ_READY = True # By","= project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","__init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell):","mock. Useful for UIs that don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class","print(\"Please enter project_id to use for BigQuery queries.\") PROJECT = input() print(\"project_id is","in writing, software # distributed under the License is distributed on an \"AS","'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass","ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program = ';\\n'.join(s","__init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self):","error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica predicates and storing results.\"\"\" predicates =","don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def","SUPERUSER\"') result += os.system( 'sudo -u postgres psql -c \"ALTER USER logica PASSWORD","the following manually: # Install Logica. !pip install logica # Install postgresql server.","an empty PostgreSQL database.\") result = 0 result += os.system('sudo apt-get -y -qq","def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION =","with small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to","False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this be","USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\"Please enter","elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports","'<PASSWORD>';\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect","import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.')","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' % ( predicate","BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine)","logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres psql -U postgres -c","else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if","True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT","Install postgresql server. print(\"Installing and configuring an empty PostgreSQL database.\") result = 0","TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar or a","if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.'","PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return","Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram(","data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to use SQLite.')","start') # Ignoring user creation error, as they may already exist. result +=","the License. \"\"\"Library for using Logica in CoLab.\"\"\" from .common import color from","not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED","WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT =","def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def","'This could be a good fit for working with small data or learning","variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner =","update') result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service","engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect to the database. from logica","os.system( 'sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\"') result +=","DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql,","small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to use","a real TabBar or a mock. Useful for UIs that don't support JS.\"\"\"","len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate","predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar =","return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text))","as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage()","= False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble):","Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY:","sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.')","None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None","== 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final:","the Apache License, Version 2.0 (the \"License\"); # you may not use this","UIs that don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self):","e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return","= None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT","PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True","you may not use this file except in compliance with the License. #","if not line: predicates = [] else: predicates = [p.strip() for p in","By default. try: from google.cloud import bigquery except: BQ_READY = False print('Could not","enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print(","self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE =","accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final)","color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t)","PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE","pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line,","SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if","logica;\\'') if result != 0: print(\"\"\"Installation failed. Please try the following manually: #","Google LLC # # Licensed under the Apache License, Version 2.0 (the \"License\");","def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if","# Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq","not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could","in your program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx","# Copyright 2020 Google LLC # # Licensed under the Apache License, Version","engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object):","result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t =","parse from .common import sqlite3_logica BQ_READY = True # By default. try: from","None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None","use this file except in compliance with the License. # You may obtain","Logica in CoLab.\"\"\" from .common import color from .common import concertina_lib from .compiler","if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\"Please enter project_id to use","is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else:","self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine,","was not run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): #","google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import","-c \"CREATE USER logica WITH SUPERUSER\"') result += os.system( 'sudo -u postgres psql","from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import","import os import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY","as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not","postgres psql -c \"CREATE USER logica WITH SUPERUSER\"') result += os.system( 'sudo -u","DATABASE logica;' # Connect to the database. from logica import colab_logica from sqlalchemy","on SQLite requires no installation.\\n' 'This could be a good fit for working","TabBar(*args): \"\"\"Returns a real TabBar or a mock. Useful for UIs that don't","at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not run.')","install postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation error,","pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect to the","%s.' % predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx):","not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\n' 'It is","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE","BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED","enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except","client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql,","BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite","is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' +","easiest to run BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running","os.system( 'sudo -u postgres psql -c \"ALTER USER logica PASSWORD \\'<PASSWORD>\\';\"') result +=","SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = []","database.\") result = 0 result += os.system('sudo apt-get -y -qq update') result +=","engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not","def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT =","2.0 (the \"License\"); # you may not use this file except in compliance","SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' %","empty PostgreSQL database.\") result = 0 result += os.system('sudo apt-get -y -qq update')","!= 0: print(\"\"\"Installation failed. Please try the following manually: # Install Logica. !pip","register_cell_magic from IPython.display import display import os import pandas from .parser_py import parse","\"\"\"Returns a real TabBar or a mock. Useful for UIs that don't support","learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to use SQLite.') return bar","\"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U postgres -c 'CREATE","may already exist. result += 0 * os.system( 'sudo -u postgres psql -c","or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to use SQLite.') return","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","is set to %s\" % PROJECT) print(\"You can change it with logica.colab_logica.SetProject command.\")","apt-get -y -qq install postgresql !sudo service postgresql start # Prepare database for","= sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine. def","pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY = True #","False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY =","postgres psql -c \"CREATE USER logica WITH SUPERUSER\" !sudo -u postgres psql -c","WITH SUPERUSER\"') result += os.system( 'sudo -u postgres psql -c \"ALTER USER logica","= preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION","';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise","# # Unless required by applicable law or agreed to in writing, software","sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine. def __call__(self,","engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner =","os.system('sudo service postgresql start') # Ignoring user creation error, as they may already","try: from google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.')","project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global","{error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica predicates and","error, as they may already exist. result += 0 * os.system( 'sudo -u","should not be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql,","express or implied. # See the License for the specific language governing permissions","Useful for UIs that don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab:","return try: program = ';\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules","widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT","USER logica PASSWORD \\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres psql -U postgres","__call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self):","connection) else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and","def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT","e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY:","ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica","from .common import sqlite3_logica BQ_READY = True # By default. try: from google.cloud","from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not","universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os","succeeded. Connecting...') # Connect to the database. from logica import colab_logica from sqlalchemy","+ error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica predicates and storing results.\"\"\" predicates","either express or implied. # See the License for the specific language governing","TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip","+ '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' %","= RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for","Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite requires no installation.\\n'","TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is","PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only","= TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar or","else: predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine,","raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object):","-c 'CREATE DATABASE logica;' # Connect to the database. from logica import colab_logica","' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite","except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as","for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global","SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble","from IPython.display import display import os import pandas from .parser_py import parse from","output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0):","IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas from .parser_py","service postgresql start') # Ignoring user creation error, as they may already exist.","os.system( 'sudo -u postgres psql -U postgres -c \\'CREATE DATABASE logica;\\'') if result","rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return #","engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection)","return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line =","= None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE =","PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser():","True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE =","\"CREATE USER logica WITH SUPERUSER\" !sudo -u postgres psql -c \"ALTER USER logica","no installation.\\n' 'This could be a good fit for working with small data","+= os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install","change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global","sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ]","cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try:","apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install postgresql') result","== 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine ==","bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars =","cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates = []","stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not","CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite requires no installation.\\n' 'This","error when compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab","sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at {warning}%s{end} '","the License. # You may obtain a copy of the License at #","'The following query is stored at {warning}%s{end} ' 'variable.' % ( predicate +","else: print('The query was not run.') print(' ') # To activate the tabbar.","import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import","PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\"Please enter project_id to","Sqlite runner should not be accepting an engine. def __call__(self, sql, engine, is_final):","at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite':","SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser()","annotation in your program to use SQLite.') return bar = TabBar(predicates + ['(Log)'])","# distributed under the License is distributed on an \"AS IS\" BASIS, #","https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite requires no installation.\\n' 'This could be","if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner =","import display import os import pandas from .parser_py import parse from .common import","# limitations under the License. \"\"\"Library for using Logica in CoLab.\"\"\" from .common","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","% PROJECT) print(\"You can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def","SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not","results.\"\"\" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return try:","it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY","sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx):","sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following","def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return","return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql,","except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import widgets","= 0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get","concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate:","BQ_READY = True # By default. try: from google.cloud import bigquery except: BQ_READY","= IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate)","!sudo apt-get -y -qq install postgresql !sudo service postgresql start # Prepare database","Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0","project_id to use for BigQuery queries.\") PROJECT = input() print(\"project_id is set to","(self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql,","engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery,","= parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except","create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.') return engine,","= program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error","TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self,","your program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx =","if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart()","import functors from .compiler import rule_translate from .compiler import universe import IPython from","not run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install","to run.') return try: program = ';\\n'.join(s for s in [PREAMBLE, cell] if","following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The","output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True)","be a good fit for working with small data or learning Logica.\\n' 'Use","bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The","colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect to the database. from logica import","DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION","TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE","print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False","logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT =","google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False","+ '_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate +","not be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine,","# TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION","* os.system( 'sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\"') result","with the License. # You may obtain a copy of the License at","e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine","create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\"\"\") return print('Installation succeeded. Connecting...') # Connect to","t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is","+ '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine","postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the database.","-U postgres -c 'CREATE DATABASE logica;' # Connect to the database. from logica","requires no installation.\\n' 'This could be a good fit for working with small","sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at","postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql","= TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query","install postgresql !sudo service postgresql start # Prepare database for Logica. !sudo -u","logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600);","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global","TODO: Sqlite runner should not be accepting an engine. def __call__(self, sql, engine,","predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql':","ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.'","try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e:","user creation error, as they may already exist. result += 0 * os.system(","return print('Installation succeeded. Connecting...') # Connect to the database. from logica import colab_logica","install logica # Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get","SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar","'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored at %s","functors from .compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic","Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program to use SQLite.') return bar =","law or agreed to in writing, software # distributed under the License is","return auth.authenticate_user() if PROJECT is None: print(\"Please enter project_id to use for BigQuery","the License for the specific language governing permissions and # limitations under the","to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT","for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine","def TabBar(*args): \"\"\"Returns a real TabBar or a mock. Useful for UIs that","ParseList(line): line = line.strip() if not line: predicates = [] else: predicates =","for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate","postgresql !sudo service postgresql start # Prepare database for Logica. !sudo -u postgres","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","!sudo -u postgres psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres","= True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def","SQLite requires no installation.\\n' 'This could be a good fit for working with","in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query:","class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic","e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL',","__init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an","Logica on SQLite requires no installation.\\n' 'This could be a good fit for","TabBar or a mock. Useful for UIs that don't support JS.\"\"\" if TABULATED_OUTPUT:","universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine ==","postgresql start # Prepare database for Logica. !sudo -u postgres psql -c \"CREATE","RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe()","psql -c \"CREATE USER logica WITH SUPERUSER\" !sudo -u postgres psql -c \"ALTER","global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else:","x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def","is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query):","Connect to the database. from logica import colab_logica from sqlalchemy import create_engine import","'BigQuery client and/or authentification is not installed. \\n' 'It is the easiest to","== 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed.","*x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return","predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate","'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else:","server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo","MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def","sqlite3_logica BQ_READY = True # By default. try: from google.cloud import bigquery except:","parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e:","WITH SUPERUSER\" !sudo -u postgres psql -c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo","= True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE","from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite requires no","google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION?","USER logica WITH SUPERUSER\"') result += os.system( 'sudo -u postgres psql -c \"ALTER","permissions and # limitations under the License. \"\"\"Library for using Logica in CoLab.\"\"\"","MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar:","result += os.system( 'sudo -u postgres psql -U postgres -c \\'CREATE DATABASE logica;\\'')","working with small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in your program","try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could","preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION","!sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\" !sudo -u postgres","in compliance with the License. # You may obtain a copy of the","= ';\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except",".parser_py import parse from .common import sqlite3_logica BQ_READY = True # By default.","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global","PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection","connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def","if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a real TabBar or a mock.","to %s\" % PROJECT) print(\"You can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED =","DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine,","Logica(line, cell, run_query): \"\"\"Running Logica predicates and storing results.\"\"\" predicates = ParseList(line) if","DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if","elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner","server. print(\"Installing and configuring an empty PostgreSQL database.\") result = 0 result +=","-c \"ALTER USER logica PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U postgres -c","[PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return","return # Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result'])","def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def","-qq update') result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo","' https://colab.research.google.com/.\\n' 'Note that running Logica on SQLite requires no installation.\\n' 'This could","print('The query was not run.') print(' ') # To activate the tabbar. def","color from .common import concertina_lib from .compiler import functors from .compiler import rule_translate","See the License for the specific language governing permissions and # limitations under","BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection =","[] else: predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql,","client and/or authentification is not installed. \\n' 'It is the easiest to run","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end}","compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab cell. with","at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is","bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner","\\'<PASSWORD>\\';\"') result += os.system( 'sudo -u postgres psql -U postgres -c \\'CREATE DATABASE","psql -U postgres -c \\'CREATE DATABASE logica;\\'') if result != 0: print(\"\"\"Installation failed.","TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False)","def PostgresJumpStart(): # Install postgresql server. print(\"Installing and configuring an empty PostgreSQL database.\")","engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner()","is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO:","and/or authentification is not installed. \\n' 'It is the easiest to run BigQuery",".common import sqlite3_logica BQ_READY = True # By default. try: from google.cloud import","this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED","set to %s\" % PROJECT) print(\"You can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED",".common import color from .common import concertina_lib from .compiler import functors from .compiler","print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed","engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] '","+= 0 * os.system( 'sudo -u postgres psql -c \"CREATE USER logica WITH","def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[","for Logica. !sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\" !sudo","# To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\"Installing and","predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT)","def ParseList(line): line = line.strip() if not line: predicates = [] else: predicates","-qq install postgresql !sudo service postgresql start # Prepare database for Logica. !sudo","def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not","with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql':","and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx,","return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery","line.strip() if not line: predicates = [] else: predicates = [p.strip() for p","' + error_text)) def Logica(line, cell, run_query): \"\"\"Running Logica predicates and storing results.\"\"\"","import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY = True","False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global","is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine =","as they may already exist. result += 0 * os.system( 'sudo -u postgres","-y -qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql start","postgres -c 'CREATE DATABASE logica;' # Connect to the database. from logica import","engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION","True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None #","result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following","def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \"\"\"Running","[] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with","print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' % predicate))","Logica. !sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\" !sudo -u","global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def","limitations under the License. \"\"\"Library for using Logica in CoLab.\"\"\" from .common import","s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e:","'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\n'","Version 2.0 (the \"License\"); # you may not use this file except in","except in compliance with the License. # You may obtain a copy of","with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at {warning}%s{end}","'It is the easiest to run BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n'","psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the database. from","Prepare database for Logica. !sudo -u postgres psql -c \"CREATE USER logica WITH",".compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic","that running Logica on SQLite requires no installation.\\n' 'This could be a good","program = ';\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule']","PROJECT = input() print(\"project_id is set to %s\" % PROJECT) print(\"You can change","'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored","0 * os.system( 'sudo -u postgres psql -c \"CREATE USER logica WITH SUPERUSER\"')","= tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \"\"\"Returns a","-c \"CREATE USER logica WITH SUPERUSER\" !sudo -u postgres psql -c \"ALTER USER","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates =","' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in","table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query","result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql","return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE","try: program = ';\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules =","import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab","BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except:","import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab","= self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def","fit for working with small data or learning Logica.\\n' 'Use {warning}@Engine(\"sqlite\");{end} annotation in","if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql':","that don't support JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass","class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE","now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t","rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display","query was not run.') print(' ') # To activate the tabbar. def PostgresJumpStart():","= connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if","DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY","# Install postgresql server. print(\"Installing and configuring an empty PostgreSQL database.\") result =","= DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection","To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\"Installing and configuring","!sudo service postgresql start # Prepare database for Logica. !sudo -u postgres psql","under the License. \"\"\"Library for using Logica in CoLab.\"\"\" from .common import color","connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql)","return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars","they may already exist. result += 0 * os.system( 'sudo -u postgres psql","print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql server.","MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip()","import parse from .common import sqlite3_logica BQ_READY = True # By default. try:","IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution)","already exist. result += 0 * os.system( 'sudo -u postgres psql -c \"CREATE","activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\"Installing and configuring an","not import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to","= [] else: predicates = [p.strip() for p in line.split(',')] return predicates def","executions = [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in","from IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas from","= concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate]","if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only","using Logica in CoLab.\"\"\" from .common import color from .common import concertina_lib from","can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT","ShowError( 'BigQuery client and/or authentification is not installed. \\n' 'It is the easiest","return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x):","return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports BigQuery,","as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish","postgresql server. print(\"Installing and configuring an empty PostgreSQL database.\") result = 0 result","License. \"\"\"Library for using Logica in CoLab.\"\"\" from .common import color from .common","= len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for idx,","service postgresql start # Prepare database for Logica. !sudo -u postgres psql -c","= parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None","bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else:","= [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try:","-u postgres psql -c \"CREATE USER logica WITH SUPERUSER\" !sudo -u postgres psql","the database. from logica import colab_logica from sqlalchemy import create_engine import pandas engine","SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate","ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table","[p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if","to use for BigQuery queries.\") PROJECT = input() print(\"project_id is set to %s\"","'sudo -u postgres psql -U postgres -c \\'CREATE DATABASE logica;\\'') if result !=","\"\"\"Library for using Logica in CoLab.\"\"\" from .common import color from .common import","print(\"You can change it with logica.colab_logica.SetProject command.\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global","'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and","supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner,","JS.\"\"\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass","import rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from","print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish output to","engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine","from logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1',","__exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab()","-u postgres psql -c \"CREATE USER logica WITH SUPERUSER\"') result += os.system( 'sudo","RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if","';\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException","if result != 0: print(\"\"\"Installation failed. Please try the following manually: # Install","bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at","from .compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic import","return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line):","\"\"\"Running Logica predicates and storing results.\"\"\" predicates = ParseList(line) if not predicates: ShowError('No","concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler import universe","{warning}@Engine(\"sqlite\");{end} annotation in your program to use SQLite.') return bar = TabBar(predicates +","psql -c \"CREATE USER logica WITH SUPERUSER\"') result += os.system( 'sudo -u postgres","-c \\'CREATE DATABASE logica;\\'') if result != 0: print(\"\"\"Installation failed. Please try the","storing results.\"\"\" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","and configuring an empty PostgreSQL database.\") result = 0 result += os.system('sudo apt-get","BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\n' 'It is the","the easiest to run BigQuery requests from Google CoLab:\\n' ' https://colab.research.google.com/.\\n' 'Note that","print(\"\"\"Installation failed. Please try the following manually: # Install Logica. !pip install logica","# # Copyright 2020 Google LLC # # Licensed under the Apache License,","for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as","PASSWORD '<PASSWORD>';\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' #","predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None,","p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine ==","if PROJECT is None: print(\"Please enter project_id to use for BigQuery queries.\") PROJECT","predicates and storing results.\"\"\" predicates = ParseList(line) if not predicates: ShowError('No predicates to","PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global","'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return","global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def","in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery':","logica WITH SUPERUSER\"') result += os.system( 'sudo -u postgres psql -c \"ALTER USER","None: print(\"Please enter project_id to use for BigQuery queries.\") PROJECT = input() print(\"project_id","renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False","stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine =="],"string":"[\n \"use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions =\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"IPython.display import display import os import pandas from .parser_py import parse from .common\",\n \"PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection):\",\n \"PostgreSQL database.\\\") result = 0 result += os.system('sudo apt-get -y -qq update') result\",\n \"return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return\",\n \"= bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection)\",\n \"SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection\",\n \"sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with\",\n \"% color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner()\",\n \"from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect();\",\n \"if run_query: print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.'\",\n \"run_query=True) def ParseList(line): line = line.strip() if not line: predicates = [] else:\",\n \"language governing permissions and # limitations under the License. \\\"\\\"\\\"Library for using Logica\",\n \"logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;'\",\n \"\\\\n' 'It is the easiest to run BigQuery requests from Google CoLab:\\\\n' '\",\n \"logica WITH SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\"\",\n \"True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project):\",\n \"with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored\",\n \"print('Installation succeeded. Connecting...') # Connect to the database. from logica import colab_logica from\",\n \"# Install Logica. !pip install logica # Install postgresql server. !sudo apt-get -y\",\n \"queries.\\\") PROJECT = input() print(\\\"project_id is set to %s\\\" % PROJECT) print(\\\"You can\",\n \"= PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica\",\n \"' 'variable.' % predicate)) display(t) else: print('The query was not run.') print(' ')\",\n \"from .common import concertina_lib from .compiler import functors from .compiler import rule_translate from\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"input() print(\\\"project_id is set to %s\\\" % PROJECT) print(\\\"You can change it with\",\n \"else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite\",\n \"'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates):\",\n \"= ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program =\",\n \"bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as\",\n \"Logica. !pip install logica # Install postgresql server. !sudo apt-get -y -qq update\",\n \"] ' + error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing\",\n \"program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates)\",\n \"+= os.system( 'sudo -u postgres psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if\",\n \"pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar()\",\n \"elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif\",\n \"PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres psql -U postgres -c \\\\'CREATE\",\n \"= PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final):\",\n \"cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print(\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates:\",\n \"an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class\",\n \"def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line)\",\n \"Connecting...') # Connect to the database. from logica import colab_logica from sqlalchemy import\",\n \"if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program\",\n \"'_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine ==\",\n \"predicate)) display(t) else: print('The query was not run.') print(' ') # To activate\",\n \"support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self):\",\n \"2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the\",\n \"import register_cell_magic from IPython.display import display import os import pandas from .parser_py import\",\n \"running Logica on SQLite requires no installation.\\\\n' 'This could be a good fit\",\n \"for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is set to %s\\\" % PROJECT)\",\n \"import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os import\",\n \"postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed. Please try\",\n \"'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner\",\n \"failed. Please try the following manually: # Install Logica. !pip install logica #\",\n \"result += 0 * os.system( 'sudo -u postgres psql -c \\\"CREATE USER logica\",\n \"self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION =\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError(\",\n \"False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True\",\n \"PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect()\",\n \"engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line,\",\n \"pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x):\",\n \"SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u\",\n \"if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def\",\n \"-y -qq install postgresql !sudo service postgresql start # Prepare database for Logica.\",\n \"-y -qq update') result += os.system('sudo apt-get -y -qq install postgresql') result +=\",\n \"SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args):\",\n \"def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line,\",\n \"parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError\",\n \"\\\\'CREATE DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed. Please try the following\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"#!/usr/bin/python # # Copyright 2020 Google LLC # # Licensed under the Apache\",\n \"postgres psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u\",\n \"== 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL\",\n \"stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query\",\n \"run BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica on\",\n \"for working with small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your\",\n \"if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is\",\n \"DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return\",\n \"global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED:\",\n \"'variable.' % predicate)) display(t) else: print('The query was not run.') print(' ') #\",\n \"is the easiest to run BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note\",\n \"= True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"CoLab.\\\"\\\"\\\" from .common import color from .common import concertina_lib from .compiler import functors\",\n \"result += os.system( 'sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"')\",\n \"PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output):\",\n \"default. try: from google.cloud import bigquery except: BQ_READY = False print('Could not import\",\n \"!sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to\",\n \"= TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = []\",\n \"logs_idx = len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for\",\n \"% predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar\",\n \"USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE\",\n \"following manually: # Install Logica. !pip install logica # Install postgresql server. !sudo\",\n \"use for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is set to %s\\\" %\",\n \"result += os.system('sudo service postgresql start') # Ignoring user creation error, as they\",\n \"postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation error, as\",\n \"in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql})\",\n \"and storing results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.')\",\n \"governing permissions and # limitations under the License. \\\"\\\"\\\"Library for using Logica in\",\n \"except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None # TODO:\",\n \"logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line:\",\n \"runner should not be accepting an engine. def __call__(self, sql, engine, is_final): return\",\n \"= DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine\",\n \"with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException\",\n \"= None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE =\",\n \"is not installed. \\\\n' 'It is the easiest to run BigQuery requests from\",\n \"from google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try:\",\n \"# Prepare database for Logica. !sudo -u postgres psql -c \\\"CREATE USER logica\",\n \"print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except:\",\n \"enter project_id to use for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is set\",\n \"ShowError('No predicates to run.') return try: program = ';\\\\n'.join(s for s in [PREAMBLE,\",\n \"supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection\",\n \"USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\\\"Please enter project_id to use for\",\n \"logica # Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y\",\n \"psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U postgres\",\n \"predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if\",\n \"{warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored\",\n \"postgres psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation\",\n \"program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if\",\n \"connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica\",\n \"import concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler import\",\n \"Install Logica. !pip install logica # Install postgresql server. !sudo apt-get -y -qq\",\n \"import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return\",\n \"import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection\",\n \"result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq\",\n \"LLC # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is set to %s\\\" % PROJECT) print(\\\"You\",\n \"pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';')\",\n \"try the following manually: # Install Logica. !pip install logica # Install postgresql\",\n \"s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program =\",\n \"and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\\\n' 'It\",\n \"os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install postgresql')\",\n \"elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1],\",\n \"+= os.system('sudo service postgresql start') # Ignoring user creation error, as they may\",\n \"-c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres psql\",\n \"= universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine\",\n \"WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a mock. Useful for\",\n \"USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"update !sudo apt-get -y -qq install postgresql !sudo service postgresql start # Prepare\",\n \"True # By default. try: from google.cloud import bigquery except: BQ_READY = False\",\n \"TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION =\",\n \"colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection =\",\n \"PROJECT is None: print(\\\"Please enter project_id to use for BigQuery queries.\\\") PROJECT =\",\n \"google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could not import\",\n \"line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client\",\n \"Ignoring user creation error, as they may already exist. result += 0 *\",\n \"is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements =\",\n \"import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could not\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if\",\n \"self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine\",\n \"( predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.' %\",\n \"== 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif\",\n \".common import concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler\",\n \"DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final)\",\n \"sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite '\",\n \"\\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres psql -U\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import auth\",\n \"[] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql\",\n \"print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"+ ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip =\",\n \"except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return\",\n \"DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def\",\n \"only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self):\",\n \"+= os.system( 'sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result\",\n \"print(\\\"Installing and configuring an empty PostgreSQL database.\\\") result = 0 result += os.system('sudo\",\n \"'sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system(\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"predicates: ShowError('No predicates to run.') return try: program = ';\\\\n'.join(s for s in\",\n \"postgresql start') # Ignoring user creation error, as they may already exist. result\",\n \"-u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the\",\n \"print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' % (\",\n \"'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine\",\n \"TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell,\",\n \"def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection =\",\n \"% predicate)) display(t) else: print('The query was not run.') print(' ') # To\",\n \"be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED =\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"-u postgres psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result != 0:\",\n \"# By default. try: from google.cloud import bigquery except: BQ_READY = False print('Could\",\n \"__init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION\",\n \"<gh_stars>0 #!/usr/bin/python # # Copyright 2020 Google LLC # # Licensed under the\",\n \"else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map\",\n \"line = line.strip() if not line: predicates = [] else: predicates = [p.strip()\",\n \"-U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed. Please\",\n \"= SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery':\",\n \"-u postgres psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql\",\n \"import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED =\",\n \"auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import\",\n \"def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def\",\n \"import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.') return\",\n \"os import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY =\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT\",\n \"else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self,\",\n \"DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) =\",\n \"'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite':\",\n \"import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE,\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with\",\n \"'CREATE DATABASE logica;' # Connect to the database. from logica import colab_logica from\",\n \"EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite\",\n \"pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...')\",\n \"Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install\",\n \"SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a mock. Useful for UIs\",\n \"@register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if\",\n \"self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine.\",\n \"google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from\",\n \"when compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab cell.\",\n \"not line: predicates = [] else: predicates = [p.strip() for p in line.split(',')]\",\n \"executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling\",\n \"database. from logica import colab_logica from sqlalchemy import create_engine import pandas engine =\",\n \"parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules)\",\n \"# TODO: Sqlite runner should not be accepting an engine. def __call__(self, sql,\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"import sqlite3_logica BQ_READY = True # By default. try: from google.cloud import bigquery\",\n \"executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t})\",\n \"return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for\",\n \"from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect()\",\n \"with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT\",\n \"Logica predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates: ShowError('No predicates\",\n \"run.') return try: program = ';\\\\n'.join(s for s in [PREAMBLE, cell] if s)\",\n \"try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine()\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"display import os import pandas from .parser_py import parse from .common import sqlite3_logica\",\n \"is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine ==\",\n \"raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map =\",\n \"None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.')\",\n \"exist. result += 0 * os.system( 'sudo -u postgres psql -c \\\"CREATE USER\",\n \"License. # You may obtain a copy of the License at # #\",\n \"connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT\",\n \"DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user()\",\n \"for the specific language governing permissions and # limitations under the License. \\\"\\\"\\\"Library\",\n \"to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions\",\n \"Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None\",\n \"the specific language governing permissions and # limitations under the License. \\\"\\\"\\\"Library for\",\n \"query is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql)\",\n \"command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output\",\n \"global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\\\"Please enter project_id\",\n \"\\\"CREATE USER logica WITH SUPERUSER\\\"') result += os.system( 'sudo -u postgres psql -c\",\n \"else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx):\",\n \"installation.\\\\n' 'This could be a good fit for working with small data or\",\n \"SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project\",\n \"sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage()\",\n \"!sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo service\",\n \"and # limitations under the License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT =\",\n \"tabbar. def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and configuring an empty PostgreSQL\",\n \"{warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not run.') print('\",\n \"global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection)\",\n \"import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.') return engine, connection\",\n \"client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql)\",\n \"-u postgres psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo\",\n \"to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if\",\n \"color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif\",\n \"def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell,\",\n \"from google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try:\",\n \"return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final:\",\n \"def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return\",\n \"in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage()\",\n \"= line.strip() if not line: predicates = [] else: predicates = [p.strip() for\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect to the database.\",\n \"%s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner\",\n \"' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored at\",\n \"'_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate)\",\n \"global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\\\"Please\",\n \"with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format(\",\n \"configuring an empty PostgreSQL database.\\\") result = 0 result += os.system('sudo apt-get -y\",\n \"manually: # Install Logica. !pip install logica # Install postgresql server. !sudo apt-get\",\n \"= input() print(\\\"project_id is set to %s\\\" % PROJECT) print(\\\"You can change it\",\n \"['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython()\",\n \"only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions,\",\n \"return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine =\",\n \"line: predicates = [] else: predicates = [p.strip() for p in line.split(',')] return\",\n \"Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def\",\n \"t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format(\",\n \"def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is\",\n \"ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql =\",\n \"the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and configuring an empty\",\n \"DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed. Please try the following manually:\",\n \"create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation\",\n \"'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use SQLite.') return bar = TabBar(predicates\",\n \"RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.')\",\n \"-qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation\",\n \"tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real\",\n \"!pip install logica # Install postgresql server. !sudo apt-get -y -qq update !sudo\",\n \"def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting\",\n \"not use this file except in compliance with the License. # You may\",\n \"sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx):\",\n \"program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification\",\n \"a good fit for working with small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end}\",\n \"psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres\",\n \"not predicates: ShowError('No predicates to run.') return try: program = ';\\\\n'.join(s for s\",\n \"pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite '\",\n \"is None: print(\\\"Please enter project_id to use for BigQuery queries.\\\") PROJECT = input()\",\n \"cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line) if not\",\n \"logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600)\",\n \"predicates to run.') return try: program = ';\\\\n'.join(s for s in [PREAMBLE, cell]\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be\",\n \"postgres psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U\",\n \"is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was\",\n \"print(\\\"project_id is set to %s\\\" % PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject\",\n \"installed. \\\\n' 'It is the easiest to run BigQuery requests from Google CoLab:\\\\n'\",\n \"now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should\",\n \"'Note that running Logica on SQLite requires no installation.\\\\n' 'This could be a\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"auth.authenticate_user() if PROJECT is None: print(\\\"Please enter project_id to use for BigQuery queries.\\\")\",\n \"sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine ==\",\n \"and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() #\",\n \"= result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The\",\n \"0: print(\\\"\\\"\\\"Installation failed. Please try the following manually: # Install Logica. !pip install\",\n \"engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif\",\n \"if not predicates: ShowError('No predicates to run.') return try: program = ';\\\\n'.join(s for\",\n \"= True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY =\",\n \"or a mock. Useful for UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return\",\n \"# you may not use this file except in compliance with the License.\",\n \"import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection\",\n \"google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from\",\n \"MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line\",\n \"for using Logica in CoLab.\\\"\\\"\\\" from .common import color from .common import concertina_lib\",\n \"RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True\",\n \"try: from google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.')\",\n \"class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not\",\n \"following query is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql')))\",\n \"engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client\",\n \"except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery'\",\n \"PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global\",\n \"connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return\",\n \"sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} '\",\n \"== 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"= create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect\",\n \"= [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False):\",\n \"requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite requires\",\n \"a mock. Useful for UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args)\",\n \"-qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql start #\",\n \"= False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED =\",\n \"not installed. \\\\n' 'It is the easiest to run BigQuery requests from Google\",\n \"creation error, as they may already exist. result += 0 * os.system( 'sudo\",\n \"logica;' # Connect to the database. from logica import colab_logica from sqlalchemy import\",\n \"PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for\",\n \"EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None:\",\n \"class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class\",\n \"'_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql'))\",\n \"be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection,\",\n \".compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display\",\n \"= [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates):\",\n \"result = 0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo\",\n \"apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring\",\n \"'sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"') result += os.system(\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\")\",\n \"= None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY =\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"result != 0: print(\\\"\\\"\\\"Installation failed. Please try the following manually: # Install Logica.\",\n \"return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client =\",\n \"colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection =\",\n \"IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection,\",\n \"self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text):\",\n \"to run BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica\",\n \"could be a good fit for working with small data or learning Logica.\\\\n'\",\n \"authentification is not installed. \\\\n' 'It is the easiest to run BigQuery requests\",\n \"engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION:\",\n \"database for Logica. !sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"\",\n \"in CoLab.\\\"\\\"\\\" from .common import color from .common import concertina_lib from .compiler import\",\n \"real TabBar or a mock. Useful for UIs that don't support JS.\\\"\\\"\\\" if\",\n \"display(t) else: print('The query was not run.') print(' ') # To activate the\",\n \"') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing\",\n \"else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class\",\n \"# Ignoring user creation error, as they may already exist. result += 0\",\n \"to the database. from logica import colab_logica from sqlalchemy import create_engine import pandas\",\n \"'The following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else:\",\n \"for UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def\",\n \"bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import\",\n \"from .compiler import functors from .compiler import rule_translate from .compiler import universe import\",\n \"import color from .common import concertina_lib from .compiler import functors from .compiler import\",\n \"-y -qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring user\",\n \"apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"__enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self,\",\n \"== 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else:\",\n \"USER logica WITH SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD\",\n \"os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start') #\",\n \"cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates\",\n \"pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL\",\n \"if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"from .common import color from .common import concertina_lib from .compiler import functors from\",\n \"WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should\",\n \"= True # By default. try: from google.cloud import bigquery except: BQ_READY =\",\n \"run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql\",\n \"+= os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start')\",\n \"run_query: print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' %\",\n \"Please try the following manually: # Install Logica. !pip install logica # Install\",\n \"start # Prepare database for Logica. !sudo -u postgres psql -c \\\"CREATE USER\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"__call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end}\",\n \"predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program\",\n \"USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"predicates = [] else: predicates = [p.strip() for p in line.split(',')] return predicates\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate +\",\n \"% ( predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.'\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass\",\n \"= self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine,\",\n \"# Connect to the database. from logica import colab_logica from sqlalchemy import create_engine\",\n \"sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered\",\n \"= program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or\",\n \"Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with\",\n \"= False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY\",\n \"0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y\",\n \"e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish output\",\n \"not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a mock. Useful\",\n \"is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine\",\n \"sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise\",\n \"%s\\\" % PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True\",\n \"pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass\",\n \"= engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect to the database. from\",\n \"specific language governing permissions and # limitations under the License. \\\"\\\"\\\"Library for using\",\n \".compiler import functors from .compiler import rule_translate from .compiler import universe import IPython\",\n \"good fit for working with small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation\",\n \"program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded.\",\n \"statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return\",\n \"global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED:\",\n \"PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and configuring an empty PostgreSQL database.\\\") result\",\n \"# Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar)\",\n \"= False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') #\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else:\",\n \"idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1):\",\n \"from .parser_py import parse from .common import sqlite3_logica BQ_READY = True # By\",\n \"= project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"__init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell):\",\n \"mock. Useful for UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class\",\n \"print(\\\"Please enter project_id to use for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass\",\n \"ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program = ';\\\\n'.join(s\",\n \"__init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self):\",\n \"error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates =\",\n \"don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def\",\n \"SUPERUSER\\\"') result += os.system( 'sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD\",\n \"the following manually: # Install Logica. !pip install logica # Install postgresql server.\",\n \"an empty PostgreSQL database.\\\") result = 0 result += os.system('sudo apt-get -y -qq\",\n \"def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION =\",\n \"with small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to\",\n \"False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this be\",\n \"USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\\\"Please enter\",\n \"elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports\",\n \"'<PASSWORD>';\\\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect\",\n \"import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.')\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' % ( predicate\",\n \"BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine)\",\n \"logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres psql -U postgres -c\",\n \"else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if\",\n \"True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT\",\n \"Install postgresql server. print(\\\"Installing and configuring an empty PostgreSQL database.\\\") result = 0\",\n \"TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a\",\n \"if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.'\",\n \"PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return\",\n \"Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram(\",\n \"data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use SQLite.')\",\n \"start') # Ignoring user creation error, as they may already exist. result +=\",\n \"the License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from .common import color from\",\n \"not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED\",\n \"WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT =\",\n \"def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def\",\n \"'This could be a good fit for working with small data or learning\",\n \"variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner =\",\n \"update') result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service\",\n \"engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect to the database. from logica\",\n \"os.system( 'sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"') result +=\",\n \"DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql,\",\n \"small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use\",\n \"a real TabBar or a mock. Useful for UIs that don't support JS.\\\"\\\"\\\"\",\n \"len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate\",\n \"predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar =\",\n \"return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text))\",\n \"as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage()\",\n \"= False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble):\",\n \"Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY:\",\n \"sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.')\",\n \"None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None\",\n \"== 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final:\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self):\",\n \"e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return\",\n \"= None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT\",\n \"PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True\",\n \"you may not use this file except in compliance with the License. #\",\n \"if not line: predicates = [] else: predicates = [p.strip() for p in\",\n \"By default. try: from google.cloud import bigquery except: BQ_READY = False print('Could not\",\n \"enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print(\",\n \"self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE =\",\n \"accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final)\",\n \"color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t)\",\n \"PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE\",\n \"pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line,\",\n \"SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if\",\n \"logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed. Please try the following manually: #\",\n \"Google LLC # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if\",\n \"# Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq\",\n \"not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could\",\n \"in your program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx\",\n \"# Copyright 2020 Google LLC # # Licensed under the Apache License, Version\",\n \"engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object):\",\n \"result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t =\",\n \"parse from .common import sqlite3_logica BQ_READY = True # By default. try: from\",\n \"None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"Logica in CoLab.\\\"\\\"\\\" from .common import color from .common import concertina_lib from .compiler\",\n \"if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\\\"Please enter project_id to use\",\n \"is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else:\",\n \"self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine,\",\n \"was not run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): #\",\n \"google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import\",\n \"-c \\\"CREATE USER logica WITH SUPERUSER\\\"') result += os.system( 'sudo -u postgres psql\",\n \"from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import\",\n \"import os import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY\",\n \"as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not\",\n \"postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"') result += os.system( 'sudo -u\",\n \"DATABASE logica;' # Connect to the database. from logica import colab_logica from sqlalchemy\",\n \"on SQLite requires no installation.\\\\n' 'This could be a good fit for working\",\n \"TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a mock. Useful for UIs that don't\",\n \"at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not run.')\",\n \"install postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation error,\",\n \"pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect to the\",\n \"%s.' % predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx):\",\n \"not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\\\n' 'It is\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE\",\n \"BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED\",\n \"enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except\",\n \"client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql,\",\n \"BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite\",\n \"is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' +\",\n \"easiest to run BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running\",\n \"os.system( 'sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result +=\",\n \"SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = []\",\n \"database.\\\") result = 0 result += os.system('sudo apt-get -y -qq update') result +=\",\n \"engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not\",\n \"def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT =\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' %\",\n \"empty PostgreSQL database.\\\") result = 0 result += os.system('sudo apt-get -y -qq update')\",\n \"!= 0: print(\\\"\\\"\\\"Installation failed. Please try the following manually: # Install Logica. !pip\",\n \"register_cell_magic from IPython.display import display import os import pandas from .parser_py import parse\",\n \"\\\"\\\"\\\"Returns a real TabBar or a mock. Useful for UIs that don't support\",\n \"learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use SQLite.') return bar\",\n \"\\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U postgres -c 'CREATE\",\n \"may already exist. result += 0 * os.system( 'sudo -u postgres psql -c\",\n \"or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use SQLite.') return\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"is set to %s\\\" % PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject command.\\\")\",\n \"apt-get -y -qq install postgresql !sudo service postgresql start # Prepare database for\",\n \"= sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine. def\",\n \"pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY = True #\",\n \"False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY =\",\n \"postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u postgres psql -c\",\n \"WITH SUPERUSER\\\"') result += os.system( 'sudo -u postgres psql -c \\\"ALTER USER logica\",\n \"= preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION\",\n \"';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine. def __call__(self,\",\n \"engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner =\",\n \"os.system('sudo service postgresql start') # Ignoring user creation error, as they may already\",\n \"try: from google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.')\",\n \"project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global\",\n \"{error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and\",\n \"error, as they may already exist. result += 0 * os.system( 'sudo -u\",\n \"should not be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql,\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"Useful for UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab:\",\n \"return try: program = ';\\\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules\",\n \"widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT\",\n \"USER logica PASSWORD \\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres psql -U postgres\",\n \"__call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self):\",\n \"connection) else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and\",\n \"def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT\",\n \"e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY:\",\n \"ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica\",\n \"from .common import sqlite3_logica BQ_READY = True # By default. try: from google.cloud\",\n \"from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not\",\n \"universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os\",\n \"succeeded. Connecting...') # Connect to the database. from logica import colab_logica from sqlalchemy\",\n \"+ error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates\",\n \"either express or implied. # See the License for the specific language governing\",\n \"TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip\",\n \"+ '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' %\",\n \"= RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for\",\n \"Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite requires no installation.\\\\n'\",\n \"TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is\",\n \"PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only\",\n \"= TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or\",\n \"else: predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine,\",\n \"raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object):\",\n \"-c 'CREATE DATABASE logica;' # Connect to the database. from logica import colab_logica\",\n \"' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite\",\n \"except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as\",\n \"for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global\",\n \"SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble\",\n \"from IPython.display import display import os import pandas from .parser_py import parse from\",\n \"output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0):\",\n \"IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas from .parser_py\",\n \"service postgresql start') # Ignoring user creation error, as they may already exist.\",\n \"os.system( 'sudo -u postgres psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result\",\n \"rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return #\",\n \"engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection)\",\n \"return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line =\",\n \"= None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE =\",\n \"PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser():\",\n \"True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE =\",\n \"\\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER USER logica\",\n \"no installation.\\\\n' 'This could be a good fit for working with small data\",\n \"+= os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install\",\n \"change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global\",\n \"sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ]\",\n \"cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try:\",\n \"apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install postgresql') result\",\n \"== 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine ==\",\n \"bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars =\",\n \"cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates = []\",\n \"stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not\",\n \"CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite requires no installation.\\\\n' 'This\",\n \"error when compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab\",\n \"sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at {warning}%s{end} '\",\n \"the License. # You may obtain a copy of the License at #\",\n \"'The following query is stored at {warning}%s{end} ' 'variable.' % ( predicate +\",\n \"else: print('The query was not run.') print(' ') # To activate the tabbar.\",\n \"import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import\",\n \"PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print(\\\"Please enter project_id to\",\n \"Sqlite runner should not be accepting an engine. def __call__(self, sql, engine, is_final):\",\n \"at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite':\",\n \"SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser()\",\n \"annotation in your program to use SQLite.') return bar = TabBar(predicates + ['(Log)'])\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite requires no installation.\\\\n' 'This could be\",\n \"if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner =\",\n \"import display import os import pandas from .parser_py import parse from .common import\",\n \"# limitations under the License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from .common\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"% PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def\",\n \"SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not\",\n \"results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return try:\",\n \"it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY\",\n \"sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx):\",\n \"sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following\",\n \"def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return\",\n \"return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql,\",\n \"except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import widgets\",\n \"= 0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get\",\n \"concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate:\",\n \"BQ_READY = True # By default. try: from google.cloud import bigquery except: BQ_READY\",\n \"= IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate)\",\n \"!sudo apt-get -y -qq install postgresql !sudo service postgresql start # Prepare database\",\n \"Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0\",\n \"project_id to use for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is set to\",\n \"(self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql,\",\n \"engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery,\",\n \"= parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except\",\n \"create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600) connection = engine.connect() print('Connected.') return engine,\",\n \"= program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error\",\n \"TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self,\",\n \"your program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx =\",\n \"if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart()\",\n \"import functors from .compiler import rule_translate from .compiler import universe import IPython from\",\n \"not run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install\",\n \"to run.') return try: program = ';\\\\n'.join(s for s in [PREAMBLE, cell] if\",\n \"following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The\",\n \"output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True)\",\n \"be a good fit for working with small data or learning Logica.\\\\n' 'Use\",\n \"bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The\",\n \"colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect to the database. from logica import\",\n \"DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION\",\n \"TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE\",\n \"print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False\",\n \"logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT =\",\n \"google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False\",\n \"+ '_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate +\",\n \"not be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine,\",\n \"# TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION\",\n \"* os.system( 'sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"') result\",\n \"with the License. # You may obtain a copy of the License at\",\n \"e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine\",\n \"create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)\\\"\\\"\\\") return print('Installation succeeded. Connecting...') # Connect to\",\n \"t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is\",\n \"+ '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine\",\n \"postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the database.\",\n \"-U postgres -c 'CREATE DATABASE logica;' # Connect to the database. from logica\",\n \"requires no installation.\\\\n' 'This could be a good fit for working with small\",\n \"sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at\",\n \"postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql\",\n \"= TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query\",\n \"install postgresql !sudo service postgresql start # Prepare database for Logica. !sudo -u\",\n \"logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1', pool_recycle=3600);\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global\",\n \"TODO: Sqlite runner should not be accepting an engine. def __call__(self, sql, engine,\",\n \"predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql':\",\n \"ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.'\",\n \"try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e:\",\n \"user creation error, as they may already exist. result += 0 * os.system(\",\n \"return print('Installation succeeded. Connecting...') # Connect to the database. from logica import colab_logica\",\n \"install logica # Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get\",\n \"SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar\",\n \"'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored at %s\",\n \"functors from .compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic\",\n \"Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use SQLite.') return bar =\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"return auth.authenticate_user() if PROJECT is None: print(\\\"Please enter project_id to use for BigQuery\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT\",\n \"for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine\",\n \"def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a mock. Useful for UIs that\",\n \"ParseList(line): line = line.strip() if not line: predicates = [] else: predicates =\",\n \"for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate\",\n \"postgresql !sudo service postgresql start # Prepare database for Logica. !sudo -u postgres\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"!sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres\",\n \"= True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def\",\n \"SQLite requires no installation.\\\\n' 'This could be a good fit for working with\",\n \"in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query:\",\n \"class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic\",\n \"e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL',\",\n \"__init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an\",\n \"Logica on SQLite requires no installation.\\\\n' 'This could be a good fit for\",\n \"TabBar or a mock. Useful for UIs that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT:\",\n \"universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine ==\",\n \"postgresql start # Prepare database for Logica. !sudo -u postgres psql -c \\\"CREATE\",\n \"RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe()\",\n \"psql -c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER\",\n \"global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else:\",\n \"x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def\",\n \"is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query):\",\n \"Connect to the database. from logica import colab_logica from sqlalchemy import create_engine import\",\n \"'BigQuery client and/or authentification is not installed. \\\\n' 'It is the easiest to\",\n \"== 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed.\",\n \"*x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return\",\n \"predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate\",\n \"'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else:\",\n \"server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo\",\n \"MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def\",\n \"sqlite3_logica BQ_READY = True # By default. try: from google.cloud import bigquery except:\",\n \"parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e:\",\n \"WITH SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo\",\n \"= True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE\",\n \"from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite requires no\",\n \"google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION?\",\n \"USER logica WITH SUPERUSER\\\"') result += os.system( 'sudo -u postgres psql -c \\\"ALTER\",\n \"permissions and # limitations under the License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\"\",\n \"MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar:\",\n \"result += os.system( 'sudo -u postgres psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'')\",\n \"working with small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in your program\",\n \"try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could\",\n \"preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION\",\n \"!sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u postgres\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"= ';\\\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except\",\n \".parser_py import parse from .common import sqlite3_logica BQ_READY = True # By default.\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global\",\n \"PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection\",\n \"connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def\",\n \"if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a real TabBar or a mock.\",\n \"to %s\\\" % PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED =\",\n \"DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine,\",\n \"Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line) if\",\n \"DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if\",\n \"elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner\",\n \"server. print(\\\"Installing and configuring an empty PostgreSQL database.\\\") result = 0 result +=\",\n \"-c \\\"ALTER USER logica PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U postgres -c\",\n \"[PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return\",\n \"return # Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result'])\",\n \"def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def\",\n \"-qq update') result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo\",\n \"' https://colab.research.google.com/.\\\\n' 'Note that running Logica on SQLite requires no installation.\\\\n' 'This could\",\n \"print('The query was not run.') print(' ') # To activate the tabbar. def\",\n \"color from .common import concertina_lib from .compiler import functors from .compiler import rule_translate\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection =\",\n \"[] else: predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql,\",\n \"client and/or authentification is not installed. \\\\n' 'It is the easiest to run\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end}\",\n \"compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab cell. with\",\n \"at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is\",\n \"bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner\",\n \"\\\\'<PASSWORD>\\\\';\\\"') result += os.system( 'sudo -u postgres psql -U postgres -c \\\\'CREATE DATABASE\",\n \"psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed.\",\n \"TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False)\",\n \"def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and configuring an empty PostgreSQL database.\\\")\",\n \"engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner()\",\n \"is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO:\",\n \"and/or authentification is not installed. \\\\n' 'It is the easiest to run BigQuery\",\n \".common import sqlite3_logica BQ_READY = True # By default. try: from google.cloud import\",\n \"this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED\",\n \"set to %s\\\" % PROJECT) print(\\\"You can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED\",\n \".common import color from .common import concertina_lib from .compiler import functors from .compiler\",\n \"print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed\",\n \"engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] '\",\n \"+= 0 * os.system( 'sudo -u postgres psql -c \\\"CREATE USER logica WITH\",\n \"def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[\",\n \"for Logica. !sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo\",\n \"# To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and\",\n \"predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT)\",\n \"def ParseList(line): line = line.strip() if not line: predicates = [] else: predicates\",\n \"-qq install postgresql !sudo service postgresql start # Prepare database for Logica. !sudo\",\n \"def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not\",\n \"with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql':\",\n \"and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx,\",\n \"return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery\",\n \"line.strip() if not line: predicates = [] else: predicates = [p.strip() for p\",\n \"' + error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\"\",\n \"import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY = True\",\n \"False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global\",\n \"is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine =\",\n \"as they may already exist. result += 0 * os.system( 'sudo -u postgres\",\n \"-y -qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql start\",\n \"postgres -c 'CREATE DATABASE logica;' # Connect to the database. from logica import\",\n \"engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION\",\n \"True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None #\",\n \"result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following\",\n \"def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): \\\"\\\"\\\"Running\",\n \"[] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with\",\n \"print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' % predicate))\",\n \"Logica. !sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u\",\n \"global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def\",\n \"limitations under the License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from .common import\",\n \"s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e:\",\n \"'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\\\n'\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at {warning}%s{end}\",\n \"'It is the easiest to run BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n'\",\n \"psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the database. from\",\n \"Prepare database for Logica. !sudo -u postgres psql -c \\\"CREATE USER logica WITH\",\n \".compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic\",\n \"that running Logica on SQLite requires no installation.\\\\n' 'This could be a good\",\n \"program = ';\\\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule']\",\n \"PROJECT = input() print(\\\"project_id is set to %s\\\" % PROJECT) print(\\\"You can change\",\n \"'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored\",\n \"0 * os.system( 'sudo -u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"')\",\n \"= tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): \\\"\\\"\\\"Returns a\",\n \"-c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u postgres psql -c \\\"ALTER USER\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates =\",\n \"' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in\",\n \"table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query\",\n \"result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql\",\n \"return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE\",\n \"try: program = ';\\\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules =\",\n \"import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab\",\n \"BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except:\",\n \"import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab\",\n \"= self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def\",\n \"fit for working with small data or learning Logica.\\\\n' 'Use {warning}@Engine(\\\"sqlite\\\");{end} annotation in\",\n \"if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql':\",\n \"that don't support JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass\",\n \"class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE\",\n \"now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t\",\n \"rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display\",\n \"query was not run.') print(' ') # To activate the tabbar. def PostgresJumpStart():\",\n \"= connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if\",\n \"DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY\",\n \"# Install postgresql server. print(\\\"Installing and configuring an empty PostgreSQL database.\\\") result =\",\n \"= DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection\",\n \"To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and configuring\",\n \"!sudo service postgresql start # Prepare database for Logica. !sudo -u postgres psql\",\n \"under the License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from .common import color\",\n \"connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql)\",\n \"return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars\",\n \"they may already exist. result += 0 * os.system( 'sudo -u postgres psql\",\n \"print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql server.\",\n \"MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip()\",\n \"import parse from .common import sqlite3_logica BQ_READY = True # By default. try:\",\n \"IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution)\",\n \"already exist. result += 0 * os.system( 'sudo -u postgres psql -c \\\"CREATE\",\n \"activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print(\\\"Installing and configuring an\",\n \"not import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to\",\n \"= [] else: predicates = [p.strip() for p in line.split(',')] return predicates def\",\n \"executions = [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in\",\n \"from IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas from\",\n \"= concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate]\",\n \"if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only\",\n \"using Logica in CoLab.\\\"\\\"\\\" from .common import color from .common import concertina_lib from\",\n \"can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT\",\n \"ShowError( 'BigQuery client and/or authentification is not installed. \\\\n' 'It is the easiest\",\n \"return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x):\",\n \"return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports BigQuery,\",\n \"as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish\",\n \"postgresql server. print(\\\"Installing and configuring an empty PostgreSQL database.\\\") result = 0 result\",\n \"License. \\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from .common import color from .common\",\n \"= len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for idx,\",\n \"service postgresql start # Prepare database for Logica. !sudo -u postgres psql -c\",\n \"= parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None\",\n \"bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else:\",\n \"= [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try:\",\n \"-u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\" !sudo -u postgres psql\",\n \"the database. from logica import colab_logica from sqlalchemy import create_engine import pandas engine\",\n \"SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate\",\n \"ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table\",\n \"[p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if\",\n \"to use for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id is set to %s\\\"\",\n \"'sudo -u postgres psql -U postgres -c \\\\'CREATE DATABASE logica;\\\\'') if result !=\",\n \"\\\"\\\"\\\"Library for using Logica in CoLab.\\\"\\\"\\\" from .common import color from .common import\",\n \"print(\\\"You can change it with logica.colab_logica.SetProject command.\\\") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global\",\n \"'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and\",\n \"supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner,\",\n \"JS.\\\"\\\"\\\" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass\",\n \"import rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from\",\n \"print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish output to\",\n \"engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine\",\n \"from logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:logica@127.0.0.1',\",\n \"__exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab()\",\n \"-u postgres psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"') result += os.system( 'sudo\",\n \"RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if\",\n \"';\\\\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException\",\n \"if result != 0: print(\\\"\\\"\\\"Installation failed. Please try the following manually: # Install\",\n \"bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at\",\n \"from .compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic import\",\n \"return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line):\",\n \"\\\"\\\"\\\"Running Logica predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates: ShowError('No\",\n \"concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler import universe\",\n \"{warning}@Engine(\\\"sqlite\\\");{end} annotation in your program to use SQLite.') return bar = TabBar(predicates +\",\n \"psql -c \\\"CREATE USER logica WITH SUPERUSER\\\"') result += os.system( 'sudo -u postgres\",\n \"-c \\\\'CREATE DATABASE logica;\\\\'') if result != 0: print(\\\"\\\"\\\"Installation failed. Please try the\",\n \"storing results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"and configuring an empty PostgreSQL database.\\\") result = 0 result += os.system('sudo apt-get\",\n \"BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \\\\n' 'It is the\",\n \"the easiest to run BigQuery requests from Google CoLab:\\\\n' ' https://colab.research.google.com/.\\\\n' 'Note that\",\n \"print(\\\"\\\"\\\"Installation failed. Please try the following manually: # Install Logica. !pip install logica\",\n \"# # Copyright 2020 Google LLC # # Licensed under the Apache License,\",\n \"for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as\",\n \"PASSWORD '<PASSWORD>';\\\" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' #\",\n \"predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None,\",\n \"p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine ==\",\n \"if PROJECT is None: print(\\\"Please enter project_id to use for BigQuery queries.\\\") PROJECT\",\n \"predicates and storing results.\\\"\\\"\\\" predicates = ParseList(line) if not predicates: ShowError('No predicates to\",\n \"PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global\",\n \"'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return\",\n \"global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def\",\n \"in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery':\",\n \"logica WITH SUPERUSER\\\"') result += os.system( 'sudo -u postgres psql -c \\\"ALTER USER\",\n \"None: print(\\\"Please enter project_id to use for BigQuery queries.\\\") PROJECT = input() print(\\\"project_id\",\n \"renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False\",\n \"stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine ==\"\n]","isConversational":false}}},{"rowIdx":595,"cells":{"ngram":{"kind":"list like","value":["import Basic as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst =","as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def","sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def set_fst(self,","from sympy.core.basic import Basic as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\"","class for Pyccel AST.\"\"\" _fst = None def set_fst(self, fst): \"\"\"Sets the redbaron","sympy.core.basic import Basic as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst","fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst = fst @property def fst(self): return self._fst","for Pyccel AST.\"\"\" _fst = None def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\"","_fst = None def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst = fst","None def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst = fst @property def","set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst = fst @property def fst(self): return","Basic as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None","class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def set_fst(self, fst):","def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst = fst @property def fst(self):","Pyccel AST.\"\"\" _fst = None def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst","= None def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst = fst @property","AST.\"\"\" _fst = None def set_fst(self, fst): \"\"\"Sets the redbaron fst.\"\"\" self._fst =","\"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def set_fst(self, fst): \"\"\"Sets the","Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def set_fst(self, fst): \"\"\"Sets"],"string":"[\n \"import Basic as sp_Basic class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst =\",\n \"as sp_Basic class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst = None def\",\n \"sp_Basic class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self,\",\n \"from sympy.core.basic import Basic as sp_Basic class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\"\",\n \"class for Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron\",\n \"sympy.core.basic import Basic as sp_Basic class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst\",\n \"fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst = fst @property def fst(self): return self._fst\",\n \"for Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\"\",\n \"_fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst = fst\",\n \"None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst = fst @property def\",\n \"set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst = fst @property def fst(self): return\",\n \"Basic as sp_Basic class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst = None\",\n \"class Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst):\",\n \"def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst = fst @property def fst(self):\",\n \"Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst\",\n \"= None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst = fst @property\",\n \"AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets the redbaron fst.\\\"\\\"\\\" self._fst =\",\n \"\\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets the\",\n \"Basic(sp_Basic): \\\"\\\"\\\"Basic class for Pyccel AST.\\\"\\\"\\\" _fst = None def set_fst(self, fst): \\\"\\\"\\\"Sets\"\n]","isConversational":false}}},{"rowIdx":596,"cells":{"ngram":{"kind":"list like","value":["for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100,","indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc)","n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc","= disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) + 1","[1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features,","[[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests =","cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat,","def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def","[str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25,","len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items():","to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for","disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() ==","from itertools import product import numpy as np import pytest from alibi_detect.utils.discretizer import","x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in","3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles))","return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc","perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert","= [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles =","list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return","assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in","len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert","perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x,","cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) ==","def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc","assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all()","cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc =","len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc)","feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles","callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i","percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests","len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)),","= x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1,","<= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:,","disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1]","itertools import product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer","tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles","perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x)","25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def","Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat))","- len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <=","= [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests","= cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc)","100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat,","import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1]","range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100,","tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc =","np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features =","alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_)","cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys())","import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10,","feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc =","== 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i","@pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat,","assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in","categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))]","in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10,","== (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert","1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all()","= Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] -","= tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc =","in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min()","for k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k])","cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles):","= list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k,","test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc =","+ 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() ==","_ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)),","np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features","100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture","perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc","== len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:,","(x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in to_disc:","cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc","10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc","list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request):","= [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests =","assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in","import product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x","4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features =","product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x =","n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[],","len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max()","= np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)]","list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names,","[list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests)","disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert","@pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True)","percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat,","x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) +","numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4)","in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] == x[:, i]).all()","(x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]):","x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]]","import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for","v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:,","from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names =","pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names","k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if","k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert","Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _","i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] == x[:,","assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for","(x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v)","0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not","as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features","len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i]","for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] ==","= len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles',","= list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param]","list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v","k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert"],"string":"[\n \"for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100,\",\n \"indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc)\",\n \"n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc\",\n \"= disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) + 1\",\n \"[1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features,\",\n \"[[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests =\",\n \"cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat,\",\n \"def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def\",\n \"[str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25,\",\n \"len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items():\",\n \"to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for\",\n \"disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() ==\",\n \"from itertools import product import numpy as np import pytest from alibi_detect.utils.discretizer import\",\n \"x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in\",\n \"3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles))\",\n \"return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc\",\n \"perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert\",\n \"= [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles =\",\n \"list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return\",\n \"assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in\",\n \"len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert\",\n \"perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x,\",\n \"cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) ==\",\n \"def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc\",\n \"assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all()\",\n \"cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc =\",\n \"len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc)\",\n \"feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles\",\n \"callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i\",\n \"percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests\",\n \"len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)),\",\n \"= x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1,\",\n \"<= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:,\",\n \"disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1]\",\n \"itertools import product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer\",\n \"tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles\",\n \"perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x)\",\n \"25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def\",\n \"Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat))\",\n \"- len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <=\",\n \"= [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests\",\n \"= cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc)\",\n \"100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat,\",\n \"import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1]\",\n \"range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100,\",\n \"tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc =\",\n \"np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features =\",\n \"alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_)\",\n \"cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys())\",\n \"import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10,\",\n \"feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc =\",\n \"== 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i\",\n \"@pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat,\",\n \"assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in\",\n \"categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))]\",\n \"in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10,\",\n \"== (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert\",\n \"1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all()\",\n \"= Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] -\",\n \"= tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc =\",\n \"in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min()\",\n \"for k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k])\",\n \"cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles):\",\n \"= list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k,\",\n \"test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc =\",\n \"+ 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() ==\",\n \"_ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)),\",\n \"np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features\",\n \"100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture\",\n \"perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc\",\n \"== len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:,\",\n \"(x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in to_disc:\",\n \"cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc\",\n \"10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc\",\n \"list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request):\",\n \"= [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests =\",\n \"assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in\",\n \"import product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x\",\n \"4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features =\",\n \"product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x =\",\n \"n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[],\",\n \"len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max()\",\n \"= np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)]\",\n \"list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names,\",\n \"[list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests)\",\n \"disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert\",\n \"@pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True)\",\n \"percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat,\",\n \"x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) +\",\n \"numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4)\",\n \"in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] == x[:, i]).all()\",\n \"(x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]):\",\n \"x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]]\",\n \"import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for\",\n \"v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:,\",\n \"from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names =\",\n \"pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names\",\n \"k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if\",\n \"k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert\",\n \"Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _\",\n \"i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] == x[:,\",\n \"assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for\",\n \"(x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v)\",\n \"0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not\",\n \"as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features\",\n \"len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i]\",\n \"for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] ==\",\n \"= len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles',\",\n \"= list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param]\",\n \"list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v\",\n \"k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert\"\n]","isConversational":false}}},{"rowIdx":597,"cells":{"ngram":{"kind":"list like","value":["= 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),","[-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return","1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\")","= [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 =","= 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0,","param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5]","def test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2])","\"0.1_2\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3","dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1,","parameter space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2,","2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\")","self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for parameter space","= -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 =","\"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1","def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value *","p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1,","ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\")","param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 =","= -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value","param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value","* class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps =","= [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\")","[0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"]","ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 =","dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value","dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1,","= Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def","dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2])","dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1,","ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]),","Jun 14 11:49:43 2021 @author: Andres \"\"\" import sys,time import unittest from tinc","dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps","Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values =","= 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]),","def test_process(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values","dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids =","0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 =","# -*- coding: utf-8 -*- \"\"\" Created on Mon Jun 14 11:49:43 2021","ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values = [0,","= 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value =","ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self):","-0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 *","= Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values =","ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 * param2 ps.sweep(func) def","dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5]","= Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3])","ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2","= [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2):","p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2])","p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\")","p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\")","0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 *","11:49:43 2021 @author: Andres \"\"\" import sys,time import unittest from tinc import *","0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\")","= ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result =","test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values =","Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0]","[0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\")","\"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value","test_process(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values =","self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func)","dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for","ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func)","Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\")","self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value","= ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML","return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value =","= Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\")","tests for parameter space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1,","= [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps =","= ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def","p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self):","= 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value =","# TODO ML complete tests see C++ tests for parameter space def test_common_id(self):","= Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\"","ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result =","from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2 =","0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2","[0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\")","\"\"\" import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self):","dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value =","= 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),","= 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value","= [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return","p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 =","def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX)","Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1,","dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2","self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1","return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4,","ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 * param2","utf-8 -*- \"\"\" Created on Mon Jun 14 11:49:43 2021 @author: Andres \"\"\"","dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1,","ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value =","0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID)","@author: Andres \"\"\" import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase):","dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3","= 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),","= ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 * param2 ps.sweep(func)","dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++","[\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids","dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values =","ML complete tests see C++ tests for parameter space def test_common_id(self): dim1 =","dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values","\"0.1_1\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1","Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"]","14 11:49:43 2021 @author: Andres \"\"\" import sys,time import unittest from tinc import","dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2,","= Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values","3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value =","ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests","= 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value =","dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value","0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX)","-0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1","ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result,","dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.3_2\") if __name__ == '__main__':","dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2","dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value","for parameter space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2,","= Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1,","import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\")","dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\",","ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1,","0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value = 2","0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2","p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2,","p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3","-0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\")","0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value = 2","[0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\")","param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value =","0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1,","dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for parameter","Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 =","param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3","= [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value","[\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value =","p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2","-*- coding: utf-8 -*- \"\"\" Created on Mon Jun 14 11:49:43 2021 @author:","Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def","see C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values =","self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.3_2\") if","self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value","param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values =","unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2","ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 =","= 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.3_2\") if __name__ == '__main__': unittest.main()","def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\")","\"\"\" Created on Mon Jun 14 11:49:43 2021 @author: Andres \"\"\" import sys,time","\"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value =","= Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\",","Created on Mon Jun 14 11:49:43 2021 @author: Andres \"\"\" import sys,time import","\"0.2_2\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.3_2\") if __name__ ==","Andres \"\"\" import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def","0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2","= [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2]","= ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result","ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete","\"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\") dim1.value = 0.2","'file_0.2_1') # TODO ML complete tests see C++ tests for parameter space def","\"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\",","= ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\")","ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value","dim2]), \"0.1_2\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value =","ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests","dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see","dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.3_2\") if __name__","ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO","1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1,",",\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\",","0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values","p1.value * p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result,","= Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1","def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3]","dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\",","p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value)","p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value","dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values =","tests see C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values","C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,","self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value","test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values","ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result","2021 @author: Andres \"\"\" import sys,time import unittest from tinc import * class","class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\")","dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps =","test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids","on Mon Jun 14 11:49:43 2021 @author: Andres \"\"\" import sys,time import unittest","Mon Jun 14 11:49:43 2021 @author: Andres \"\"\" import sys,time import unittest from","* p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value","Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\"","Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4,","space def test_common_id(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3,","[1,1,1,2,2,2] dim2.ids = [\"0.1_1\", \"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1,",",\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids =","= 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value =","* p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\")","3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4]","func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value)","[-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1","import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1","dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2","= [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(),","p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1","1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_2\")","func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values","0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') #","complete tests see C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\"dim1\")","-*- \"\"\" Created on Mon Jun 14 11:49:43 2021 @author: Andres \"\"\" import","test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def","tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\")","dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps","dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value =","def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values","* param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2","= Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4,",",\"0.2_2\", \"0.3_1\" ,\"0.3_2\"] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\"0.1_1\",","0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.1_1\") dim1.value = 0.2 dim2.value = 1","* param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value","ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self):","p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values","= ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\"param1\") p1.values = [0, 1,2,3,4]","sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 =","import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\"param1\") p2 = Parameter(\"param2\") ps","p1.values = [0, 1,2,3,4] p2 = Parameter(\"param2\") p2.values = [-0.3,-0.2, -0.1, 0] ps","[0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\"file_%%dim1%%_%%dim2:INDEX%%\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1')","p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) def func(param1, param2):","p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value *","Parameter(\"dim1\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\"dim2\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5]","TODO ML complete tests see C++ tests for parameter space def test_common_id(self): dim1","= [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\"0.1_1\" ,\"0.1_2\",\"0.2_1\" ,\"0.2_2\", \"0.3_1\"","p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\"param1\") p1.values =","coding: utf-8 -*- \"\"\" Created on Mon Jun 14 11:49:43 2021 @author: Andres","dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value","\"0.2_1\", \"0.3_1\", \"0.1_2\", \"0.2_2\", \"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1","\"0.3_2\"] ps = ParameterSpace(\"ps\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1,","2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_2\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.3_2\")","result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1","= [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\"dim3\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps =","-0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value ="],"string":"[\n \"= 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),\",\n \"[-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return\",\n \"1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\")\",\n \"= [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 =\",\n \"= 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0,\",\n \"param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5]\",\n \"def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2])\",\n \"\\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3\",\n \"dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1,\",\n \"parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2,\",\n \"2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\")\",\n \"self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for parameter space\",\n \"= -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 =\",\n \"\\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1\",\n \"def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value *\",\n \"p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1,\",\n \"ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\")\",\n \"param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 =\",\n \"= -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value\",\n \"param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value\",\n \"* class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps =\",\n \"= [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\")\",\n \"[0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"]\",\n \"ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 =\",\n \"dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value\",\n \"dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1,\",\n \"= Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def\",\n \"dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2])\",\n \"dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1,\",\n \"ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]),\",\n \"Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time import unittest from tinc\",\n \"dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps\",\n \"Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values =\",\n \"= 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]),\",\n \"def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values\",\n \"dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids =\",\n \"0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 =\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" Created on Mon Jun 14 11:49:43 2021\",\n \"ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0,\",\n \"= 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value =\",\n \"ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self):\",\n \"-0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 *\",\n \"= Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values =\",\n \"ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 * param2 ps.sweep(func) def\",\n \"dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5]\",\n \"= Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3])\",\n \"ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2\",\n \"= [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2):\",\n \"p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2])\",\n \"p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\")\",\n \"p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\")\",\n \"0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 *\",\n \"11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time import unittest from tinc import *\",\n \"0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\")\",\n \"= ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result =\",\n \"test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values =\",\n \"Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0]\",\n \"[0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\")\",\n \"\\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value\",\n \"test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values =\",\n \"self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func)\",\n \"dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for\",\n \"ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func)\",\n \"Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\")\",\n \"self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value\",\n \"= ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML\",\n \"return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value =\",\n \"= Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\")\",\n \"tests for parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1,\",\n \"= [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps =\",\n \"= ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def\",\n \"p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self):\",\n \"= 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value =\",\n \"# TODO ML complete tests see C++ tests for parameter space def test_common_id(self):\",\n \"= Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\"\",\n \"ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result =\",\n \"from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 =\",\n \"0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2\",\n \"[0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\")\",\n \"\\\"\\\"\\\" import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self):\",\n \"dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value =\",\n \"= 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),\",\n \"= 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value\",\n \"= [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return\",\n \"p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 =\",\n \"def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX)\",\n \"Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1,\",\n \"dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2\",\n \"self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1\",\n \"return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4,\",\n \"ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 * param2\",\n \"utf-8 -*- \\\"\\\"\\\" Created on Mon Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\"\",\n \"dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1,\",\n \"ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value =\",\n \"0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID)\",\n \"@author: Andres \\\"\\\"\\\" import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase):\",\n \"dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3\",\n \"= 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),\",\n \"= ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 * param2 ps.sweep(func)\",\n \"dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++\",\n \"[\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids\",\n \"dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values =\",\n \"ML complete tests see C++ tests for parameter space def test_common_id(self): dim1 =\",\n \"dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values\",\n \"\\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1\",\n \"Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"]\",\n \"14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time import unittest from tinc import\",\n \"dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2,\",\n \"= Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values\",\n \"3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value =\",\n \"ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests\",\n \"= 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value =\",\n \"dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value\",\n \"0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX)\",\n \"-0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1\",\n \"ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result,\",\n \"dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.3_2\\\") if __name__ == '__main__':\",\n \"dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2\",\n \"dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value\",\n \"for parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2,\",\n \"= Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1,\",\n \"import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\")\",\n \"dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\",\",\n \"ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1,\",\n \"0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2\",\n \"0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2\",\n \"p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2,\",\n \"p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3\",\n \"-0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\")\",\n \"0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2\",\n \"[0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\")\",\n \"param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value =\",\n \"0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1,\",\n \"dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for parameter\",\n \"Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 =\",\n \"param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3\",\n \"= [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value\",\n \"[\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value =\",\n \"p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" Created on Mon Jun 14 11:49:43 2021 @author:\",\n \"Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def\",\n \"see C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values =\",\n \"self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.3_2\\\") if\",\n \"self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value\",\n \"param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values =\",\n \"unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2\",\n \"ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 =\",\n \"= 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.3_2\\\") if __name__ == '__main__': unittest.main()\",\n \"def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\")\",\n \"\\\"\\\"\\\" Created on Mon Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time\",\n \"\\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value =\",\n \"= Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\",\",\n \"Created on Mon Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time import\",\n \"\\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.3_2\\\") if __name__ ==\",\n \"Andres \\\"\\\"\\\" import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def\",\n \"0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2\",\n \"= [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2]\",\n \"= ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result\",\n \"ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete\",\n \"\\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\") dim1.value = 0.2\",\n \"'file_0.2_1') # TODO ML complete tests see C++ tests for parameter space def\",\n \"\\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\",\",\n \"= ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\")\",\n \"ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value\",\n \"dim2]), \\\"0.1_2\\\") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value =\",\n \"ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests\",\n \"dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see\",\n \"dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.3_2\\\") if __name__\",\n \"ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO\",\n \"1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1,\",\n \",\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\",\",\n \"0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values\",\n \"p1.value * p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result,\",\n \"= Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1\",\n \"def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3]\",\n \"dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\",\",\n \"p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value)\",\n \"p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value\",\n \"dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values =\",\n \"tests see C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values\",\n \"C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,\",\n \"self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value\",\n \"test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values\",\n \"ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result\",\n \"2021 @author: Andres \\\"\\\"\\\" import sys,time import unittest from tinc import * class\",\n \"class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\")\",\n \"dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps =\",\n \"test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids\",\n \"on Mon Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time import unittest\",\n \"Mon Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import sys,time import unittest from\",\n \"* p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value\",\n \"Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\"\",\n \"Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4,\",\n \"space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3,\",\n \"[1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\", \\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1,\",\n \",\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids =\",\n \"= 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value =\",\n \"* p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\")\",\n \"3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4]\",\n \"func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value)\",\n \"[-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2): return param1\",\n \"import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1\",\n \"dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2\",\n \"= [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(),\",\n \"p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1\",\n \"1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_2\\\")\",\n \"func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values\",\n \"0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') #\",\n \"complete tests see C++ tests for parameter space def test_common_id(self): dim1 = Parameter(\\\"dim1\\\")\",\n \"-*- \\\"\\\"\\\" Created on Mon Jun 14 11:49:43 2021 @author: Andres \\\"\\\"\\\" import\",\n \"test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def\",\n \"tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\")\",\n \"dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps\",\n \"dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_1\\\") dim1.value =\",\n \"def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values\",\n \"* param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2\",\n \"= Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4,\",\n \",\\\"0.2_2\\\", \\\"0.3_1\\\" ,\\\"0.3_2\\\"] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = [\\\"0.1_1\\\",\",\n \"0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.1_1\\\") dim1.value = 0.2 dim2.value = 1\",\n \"* param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value\",\n \"ps.register_parameters([p1, p2]) ps.enable_cache(\\\"ps_test\\\") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self):\",\n \"p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values\",\n \"= ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter(\\\"param1\\\") p1.values = [0, 1,2,3,4]\",\n \"sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 =\",\n \"import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter(\\\"param1\\\") p2 = Parameter(\\\"param2\\\") ps\",\n \"p1.values = [0, 1,2,3,4] p2 = Parameter(\\\"param2\\\") p2.values = [-0.3,-0.2, -0.1, 0] ps\",\n \"[0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template(\\\"file_%%dim1%%_%%dim2:INDEX%%\\\") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1')\",\n \"p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([p1, p2]) def func(param1, param2):\",\n \"p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value *\",\n \"Parameter(\\\"dim1\\\") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter(\\\"dim2\\\") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5]\",\n \"TODO ML complete tests see C++ tests for parameter space def test_common_id(self): dim1\",\n \"= [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = [\\\"0.1_1\\\" ,\\\"0.1_2\\\",\\\"0.2_1\\\" ,\\\"0.2_2\\\", \\\"0.3_1\\\"\",\n \"p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter(\\\"param1\\\") p1.values =\",\n \"coding: utf-8 -*- \\\"\\\"\\\" Created on Mon Jun 14 11:49:43 2021 @author: Andres\",\n \"dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value\",\n \"\\\"0.2_1\\\", \\\"0.3_1\\\", \\\"0.1_2\\\", \\\"0.2_2\\\", \\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1\",\n \"\\\"0.3_2\\\"] ps = ParameterSpace(\\\"ps\\\") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1,\",\n \"2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.2_2\\\") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), \\\"0.3_2\\\")\",\n \"result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1\",\n \"= [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter(\\\"dim3\\\") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps =\",\n \"-0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value =\"\n]","isConversational":false}}},{"rowIdx":598,"cells":{"ngram":{"kind":"list like","value":["from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import","bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE ==","reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list,","episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU","\", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run","NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart'","env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No","== 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done =","[] for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME)","reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode","args.gpu # Applies for 'prolo' only. use gpu? Default false if ENV_TYPE ==","8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent","\"--num_hidden\", help=\"number of hidden layers for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment","import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if","if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n","= args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or","print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for","= DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in,","'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected')","torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record","init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on","0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward","% 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\": parser =","main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0","ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else:","parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes","sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last","episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}')","agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is","action = agent.get_action(state) # Step through environment using chosen action state, reward, done,","reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1])","1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu",">= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode %","= gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid","= gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed)","ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif","as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from","import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import","hidden layers for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\",","rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else:","copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME","else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed)","agent.get_action(state) # Step through environment using chosen action state, reward, done, _ =","import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from","ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1')","chosen action state, reward, done, _ = env.step(action) # env.render() # Save reward","<NAME> on 8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent","[reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as e:","'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type","discount_reward import torch.multiprocessing as mp import argparse import copy import random def run_episode(q,","{reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array","= False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment","agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list,","is not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return","run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number","state while not done: action = agent.get_action(state) # Step through environment using chosen","type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on","if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE","== 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\",","return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes):","import discount_reward import torch.multiprocessing as mp import argparse import copy import random def","deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list =","policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent =","= discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list","done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset","init_env.action_space.n else: raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") #","agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0 returned_object","for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name +=","if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in,","type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\",","gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected')","\"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL","done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break","{running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ ==","env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed)","q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent,","'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent","gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0]","not done: action = agent.get_action(state) # Step through environment using chosen action state,","import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import","gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed)","for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for MLP","interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy import random","Applies for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env","input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out,","def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward =","0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\",","environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in","range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE","on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS","if q is not None: try: q.put(to_return) except RuntimeError as e: print(e) return","None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def","on 8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import","of leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers","returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >=","GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS =","Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state","in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0]","if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to","= init_env.action_space.n else: raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \")","else: raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn')","== 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network","as mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0):","import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import","DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp","agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture","help=\"number of hidden layers for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to","'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False","args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for","parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true')","import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent =","if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list)","to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in","== \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\", type=str,","agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env","copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as e: print(e)","interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse","action state, reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward)","AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE ==","Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__","{AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name =","environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset()","DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for MLP \",","num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise","import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME ==","if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward:","by <NAME> on 8/28/19 import gym import numpy as np import torch from","{ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE +","reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done:","Step through environment using chosen action state, reward, done, _ = env.step(action) #","or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use","to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\",","Created by <NAME> on 8/28/19 import gym import numpy as np import torch","agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME ==","to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward","of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000)","episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\": parser","not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return","argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate()","if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\":","in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if","use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in =","using chosen action state, reward, done, _ = env.step(action) # env.render() # Save","Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list =","= rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if","default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\",","agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2')","AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent","Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU =","# env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list,","500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \"__main__\": parser = argparse.ArgumentParser()","env.reset() # Reset environment and record the starting state while not done: action","Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if","mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name","return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode","499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50","agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\",","selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5):","'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only.","import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as","random.seed(seed) state = env.reset() # Reset environment and record the starting state while","raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed)","run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env =","return running_reward_array if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of","output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS, policy_agent, ENV_TYPE)","through environment using chosen action state, reward, done, _ = env.step(action) # env.render()","num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart'","+= returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:])","env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the","bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False,","# Reset environment and record the starting state while not done: action =","valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i","bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS, policy_agent,","MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS,","'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu?","== 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode %","advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list","import torch.multiprocessing as mp import argparse import copy import random def run_episode(q, agent_in,","RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array","= init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out","= init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in","try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def main(episodes,","ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0 returned_object =","Reset environment and record the starting state while not done: action = agent.get_action(state)","help=\"run on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp'","parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt',","advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None:","\"--num_leaves\", help=\"number of leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of","range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0])","'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise","deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except","+= 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves)","action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes","= parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num","torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward","agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 ==","'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type #","= run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499:","'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2')","default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type #","print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 ==","argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how","environment using chosen action state, reward, done, _ = env.step(action) # env.render() #","# 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo'","except RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME):","'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No","= MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array =","torch.multiprocessing as mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME,","init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out =","= init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE}","output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden)","% 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if","if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env =","= np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list","running_reward_array = [] for episode in range(episodes): reward = 0 returned_object = run_episode(None,","dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1')","args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes #","def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env","Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for","init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid","# mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if","= [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as","default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves","= args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000","default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for MLP \", type=int, default=0) parser.add_argument(\"-env\",","type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of","= agent.get_action(state) # Step through environment using chosen action state, reward, done, _","gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent","rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list","rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q","# Created by <NAME> on 8/28/19 import gym import numpy as np import","random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar':","Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th')","ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu #","interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing","DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name,","\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE","on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE =","if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if","dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0]","'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif","elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected')","elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No","while not done: action = agent.get_action(state) # Step through environment using chosen action","returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) /","ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name,","0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500","help=\"architecture of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int,","mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent","len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average","MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\",","for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str, default='cart')","done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list","as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array =","run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th')","== 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE","agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())]","false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out =","state = env.reset() # Reset environment and record the starting state while not","of hidden layers for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run","# Applies for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar':","False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and","env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list,","ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward","mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU:","only. use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in","i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU'","= agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart':","= deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return)","\"--env_type\", help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args","= args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies","layers for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str,","type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type","type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for MLP \", type=int, default=0)","== 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise","\"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\", type=str, default='ddt')","run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args = parser.parse_args() AGENT_TYPE","= args.gpu # Applies for 'prolo' only. use gpu? Default false if ENV_TYPE","= AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt':","gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env =","parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for MLP \", type=int, default=0) parser.add_argument(\"-env\", \"--env_type\",","running_reward_array if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent","agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0:","= gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment","== 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else:","ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif","valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state =","float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward}","= gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env","gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment","parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many","episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL \", type=int, default=8)","agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return =","episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward +=","init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in =","record the starting state while not done: action = agent.get_action(state) # Step through","= sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode}","# Step through environment using chosen action state, reward, done, _ = env.step(action)","selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() #","numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent","q is not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return","for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward","done: action = agent.get_action(state) # Step through environment using chosen action state, reward,","starting state while not done: action = agent.get_action(state) # Step through environment using","np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list =","+ ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent =","dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE}","raise Exception('No valid environment selected') print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system')","'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default false","reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list","MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy","Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return","init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart':","\"--agent_type\", help=\"architecture of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\",","{episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0:","env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list)","parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\",","= env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum =","on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE","args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar'","e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = []","__name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\",","discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return","to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args = parser.parse_args()","for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env =","break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list =","\", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for MLP \", type=int,","Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default","state, reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if","default=0) parser.add_argument(\"-env\", \"--env_type\", help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\",","Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out","from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import","args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE","AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid","many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL \", type=int,","dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\"Agent","agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array)))","= argparse.ArgumentParser() parser.add_argument(\"-a\", \"--agent_type\", help=\"architecture of agent to run\", type=str, default='ddt') parser.add_argument(\"-e\", \"--episodes\",","agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward)","= advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not","50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode","running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode","help=\"environment to run on\", type=str, default='cart') parser.add_argument(\"-gpu\", help=\"run on GPU?\", action='store_true') args =","environment and record the starting state while not done: action = agent.get_action(state) #","seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME","USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default false if","# Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list","help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL \",","help=\"number of leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden","# 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE =","policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array","== 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp':","running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0,","agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try:","agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum,","reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward =","np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the starting","= env.reset() # Reset environment and record the starting state while not done:","leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number of hidden layers for","and record the starting state while not done: action = agent.get_action(state) # Step","to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError","print(f\"Agent {AGENT_TYPE} on {ENV_TYPE} \") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name","/ float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward:","= [] for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent,","# num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default","'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE ==","elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n","USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out,","the starting state while not done: action = agent.get_action(state) # Step through environment","env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the starting state","= 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if","AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default","parser.add_argument(\"-e\", \"--episodes\", help=\"how many episodes\", type=int, default=2000) parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for","ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done","_ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum","from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy import","np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer","parser.add_argument(\"-l\", \"--num_leaves\", help=\"number of leaves for DDT/DRL \", type=int, default=8) parser.add_argument(\"-n\", \"--num_hidden\", help=\"number"],"string":"[\n \"from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import\",\n \"bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE ==\",\n \"reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list,\",\n \"episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU\",\n \"\\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run\",\n \"NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart'\",\n \"env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No\",\n \"== 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done =\",\n \"[] for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME)\",\n \"reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode\",\n \"args.gpu # Applies for 'prolo' only. use gpu? Default false if ENV_TYPE ==\",\n \"8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent\",\n \"\\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment\",\n \"import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if\",\n \"if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n\",\n \"= args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or\",\n \"print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for\",\n \"= DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in,\",\n \"'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected')\",\n \"torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record\",\n \"init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on\",\n \"0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward\",\n \"% 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\": parser =\",\n \"main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0\",\n \"ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else:\",\n \"parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes\",\n \"sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last\",\n \"episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}')\",\n \"agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is\",\n \"action = agent.get_action(state) # Step through environment using chosen action state, reward, done,\",\n \"reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1])\",\n \"1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu\",\n \">= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode %\",\n \"= gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid\",\n \"= gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed)\",\n \"ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif\",\n \"as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from\",\n \"import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import\",\n \"hidden layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\",\",\n \"rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else:\",\n \"copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME\",\n \"else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed)\",\n \"agent.get_action(state) # Step through environment using chosen action state, reward, done, _ =\",\n \"import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from\",\n \"ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1')\",\n \"chosen action state, reward, done, _ = env.step(action) # env.render() # Save reward\",\n \"<NAME> on 8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent\",\n \"[reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as e:\",\n \"'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type\",\n \"discount_reward import torch.multiprocessing as mp import argparse import copy import random def run_episode(q,\",\n \"{reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array\",\n \"= False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment\",\n \"agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list,\",\n \"is not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return\",\n \"run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number\",\n \"state while not done: action = agent.get_action(state) # Step through environment using chosen\",\n \"type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on\",\n \"if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE\",\n \"== 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\",\",\n \"return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes):\",\n \"import discount_reward import torch.multiprocessing as mp import argparse import copy import random def\",\n \"deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list =\",\n \"policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent =\",\n \"= discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list\",\n \"done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset\",\n \"init_env.action_space.n else: raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") #\",\n \"agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0 returned_object\",\n \"for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name +=\",\n \"if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in,\",\n \"type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\",\",\n \"gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected')\",\n \"\\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL\",\n \"done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break\",\n \"{running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ ==\",\n \"env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed)\",\n \"q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent,\",\n \"'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent\",\n \"gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0]\",\n \"not done: action = agent.get_action(state) # Step through environment using chosen action state,\",\n \"import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import\",\n \"gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed)\",\n \"for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP\",\n \"interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy import random\",\n \"Applies for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env\",\n \"input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out,\",\n \"def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward =\",\n \"0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\",\",\n \"environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in\",\n \"range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE\",\n \"on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS\",\n \"if q is not None: try: q.put(to_return) except RuntimeError as e: print(e) return\",\n \"None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def\",\n \"on 8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import\",\n \"of leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers\",\n \"returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >=\",\n \"GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS =\",\n \"Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state\",\n \"in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0]\",\n \"if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to\",\n \"= init_env.action_space.n else: raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\")\",\n \"else: raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn')\",\n \"== 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network\",\n \"as mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0):\",\n \"import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import\",\n \"DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp\",\n \"agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture\",\n \"help=\\\"number of hidden layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to\",\n \"'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False\",\n \"args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for\",\n \"parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true')\",\n \"import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent =\",\n \"if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list)\",\n \"to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in\",\n \"== \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str,\",\n \"agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env\",\n \"copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as e: print(e)\",\n \"interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse\",\n \"action state, reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward)\",\n \"AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE ==\",\n \"Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__\",\n \"{AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name =\",\n \"environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset()\",\n \"DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP \\\",\",\n \"num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise\",\n \"import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME ==\",\n \"if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward:\",\n \"by <NAME> on 8/28/19 import gym import numpy as np import torch from\",\n \"{ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE +\",\n \"reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done:\",\n \"Step through environment using chosen action state, reward, done, _ = env.step(action) #\",\n \"or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use\",\n \"to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\",\",\n \"Created by <NAME> on 8/28/19 import gym import numpy as np import torch\",\n \"agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME ==\",\n \"to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward\",\n \"of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000)\",\n \"episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\": parser\",\n \"not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return\",\n \"argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate()\",\n \"if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\":\",\n \"in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if\",\n \"use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in =\",\n \"using chosen action state, reward, done, _ = env.step(action) # env.render() # Save\",\n \"Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list =\",\n \"= rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if\",\n \"default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\",\",\n \"agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2')\",\n \"AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent\",\n \"Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU =\",\n \"# env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list,\",\n \"500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser()\",\n \"env.reset() # Reset environment and record the starting state while not done: action\",\n \"Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if\",\n \"mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name\",\n \"return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode\",\n \"499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50\",\n \"agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\",\",\n \"selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5):\",\n \"'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only.\",\n \"import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as\",\n \"random.seed(seed) state = env.reset() # Reset environment and record the starting state while\",\n \"raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed)\",\n \"run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env =\",\n \"return running_reward_array if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of\",\n \"output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS, policy_agent, ENV_TYPE)\",\n \"through environment using chosen action state, reward, done, _ = env.step(action) # env.render()\",\n \"num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart'\",\n \"+= returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:])\",\n \"env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the\",\n \"bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False,\",\n \"# Reset environment and record the starting state while not done: action =\",\n \"valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i\",\n \"bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS, policy_agent,\",\n \"MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS,\",\n \"'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu?\",\n \"== 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode %\",\n \"advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list\",\n \"import torch.multiprocessing as mp import argparse import copy import random def run_episode(q, agent_in,\",\n \"RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array\",\n \"= init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out\",\n \"= init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in\",\n \"try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def main(episodes,\",\n \"ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0 returned_object =\",\n \"Reset environment and record the starting state while not done: action = agent.get_action(state)\",\n \"help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp'\",\n \"parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt',\",\n \"advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None:\",\n \"\\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of\",\n \"range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0])\",\n \"'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise\",\n \"deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except\",\n \"+= 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves)\",\n \"action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes\",\n \"= parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num\",\n \"torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward\",\n \"agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 ==\",\n \"'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type #\",\n \"= run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499:\",\n \"'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2')\",\n \"default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type #\",\n \"print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 ==\",\n \"argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how\",\n \"environment using chosen action state, reward, done, _ = env.step(action) # env.render() #\",\n \"# 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo'\",\n \"except RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME):\",\n \"'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No\",\n \"= MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array =\",\n \"torch.multiprocessing as mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME,\",\n \"init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out =\",\n \"= init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE}\",\n \"output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden)\",\n \"% 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if\",\n \"if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env =\",\n \"= np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list\",\n \"running_reward_array = [] for episode in range(episodes): reward = 0 returned_object = run_episode(None,\",\n \"dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1')\",\n \"args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes #\",\n \"def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env\",\n \"Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for\",\n \"init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid\",\n \"# mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if\",\n \"= [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as\",\n \"default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves\",\n \"= args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000\",\n \"default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\",\",\n \"type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of\",\n \"= agent.get_action(state) # Step through environment using chosen action state, reward, done, _\",\n \"gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent\",\n \"rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list\",\n \"rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q\",\n \"# Created by <NAME> on 8/28/19 import gym import numpy as np import\",\n \"random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar':\",\n \"Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th')\",\n \"ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu #\",\n \"interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing\",\n \"DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name,\",\n \"\\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE\",\n \"on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE =\",\n \"if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if\",\n \"dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0]\",\n \"'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif\",\n \"elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected')\",\n \"elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No\",\n \"while not done: action = agent.get_action(state) # Step through environment using chosen action\",\n \"returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) /\",\n \"ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name,\",\n \"0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500\",\n \"help=\\\"architecture of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int,\",\n \"mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent\",\n \"len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average\",\n \"MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\",\",\n \"for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart')\",\n \"done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list\",\n \"as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array =\",\n \"run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th')\",\n \"== 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE\",\n \"agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())]\",\n \"false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out =\",\n \"state = env.reset() # Reset environment and record the starting state while not\",\n \"of hidden layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run\",\n \"# Applies for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar':\",\n \"False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and\",\n \"env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list,\",\n \"ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward\",\n \"mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU:\",\n \"only. use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in\",\n \"i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU'\",\n \"= agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart':\",\n \"= deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return)\",\n \"\\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args\",\n \"= args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies\",\n \"layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str,\",\n \"type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type\",\n \"type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP \\\", type=int, default=0)\",\n \"== 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise\",\n \"\\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str, default='ddt')\",\n \"run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args() AGENT_TYPE\",\n \"= args.gpu # Applies for 'prolo' only. use gpu? Default false if ENV_TYPE\",\n \"= AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt':\",\n \"gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env =\",\n \"parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP \\\", type=int, default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\",\",\n \"running_reward_array if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent\",\n \"agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0:\",\n \"= gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment\",\n \"== 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else:\",\n \"ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif\",\n \"valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state =\",\n \"float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward}\",\n \"= gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env\",\n \"gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment\",\n \"parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many\",\n \"episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\", type=int, default=8)\",\n \"agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return =\",\n \"episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward +=\",\n \"init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in =\",\n \"record the starting state while not done: action = agent.get_action(state) # Step through\",\n \"= sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode}\",\n \"# Step through environment using chosen action state, reward, done, _ = env.step(action)\",\n \"selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() #\",\n \"numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent\",\n \"q is not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return\",\n \"for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward\",\n \"done: action = agent.get_action(state) # Step through environment using chosen action state, reward,\",\n \"starting state while not done: action = agent.get_action(state) # Step through environment using\",\n \"np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list =\",\n \"+ ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent =\",\n \"dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE}\",\n \"raise Exception('No valid environment selected') print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system')\",\n \"'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default false\",\n \"reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list\",\n \"MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy\",\n \"Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return\",\n \"init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart':\",\n \"\\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\",\",\n \"{episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0:\",\n \"env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list)\",\n \"parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\",\",\n \"= env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum =\",\n \"on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE\",\n \"args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar'\",\n \"e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = []\",\n \"__name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\",\",\n \"discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return\",\n \"to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args = parser.parse_args()\",\n \"for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env =\",\n \"break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list =\",\n \"\\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for MLP \\\", type=int,\",\n \"Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default\",\n \"state, reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if\",\n \"default=0) parser.add_argument(\\\"-env\\\", \\\"--env_type\\\", help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\",\",\n \"Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out\",\n \"from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import\",\n \"args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE\",\n \"AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid\",\n \"many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\", type=int,\",\n \"dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f\\\"Agent\",\n \"agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array)))\",\n \"= argparse.ArgumentParser() parser.add_argument(\\\"-a\\\", \\\"--agent_type\\\", help=\\\"architecture of agent to run\\\", type=str, default='ddt') parser.add_argument(\\\"-e\\\", \\\"--episodes\\\",\",\n \"agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward)\",\n \"= advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not\",\n \"50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode\",\n \"running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode\",\n \"help=\\\"environment to run on\\\", type=str, default='cart') parser.add_argument(\\\"-gpu\\\", help=\\\"run on GPU?\\\", action='store_true') args =\",\n \"environment and record the starting state while not done: action = agent.get_action(state) #\",\n \"seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME\",\n \"USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default false if\",\n \"# Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list\",\n \"help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\",\",\n \"help=\\\"number of leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden\",\n \"# 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE =\",\n \"policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array\",\n \"== 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp':\",\n \"running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0,\",\n \"agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try:\",\n \"agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum,\",\n \"reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward =\",\n \"np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the starting\",\n \"= env.reset() # Reset environment and record the starting state while not done:\",\n \"leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number of hidden layers for\",\n \"and record the starting state while not done: action = agent.get_action(state) # Step\",\n \"to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError\",\n \"print(f\\\"Agent {AGENT_TYPE} on {ENV_TYPE} \\\") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name\",\n \"/ float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward:\",\n \"= [] for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent,\",\n \"# num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default\",\n \"'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE ==\",\n \"elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n\",\n \"USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out,\",\n \"the starting state while not done: action = agent.get_action(state) # Step through environment\",\n \"env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the starting state\",\n \"= 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if\",\n \"AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default\",\n \"parser.add_argument(\\\"-e\\\", \\\"--episodes\\\", help=\\\"how many episodes\\\", type=int, default=2000) parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for\",\n \"ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done\",\n \"_ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum\",\n \"from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy import\",\n \"np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer\",\n \"parser.add_argument(\\\"-l\\\", \\\"--num_leaves\\\", help=\\\"number of leaves for DDT/DRL \\\", type=int, default=8) parser.add_argument(\\\"-n\\\", \\\"--num_hidden\\\", help=\\\"number\"\n]","isConversational":false}}},{"rowIdx":599,"cells":{"ngram":{"kind":"list like","value":["my_list: if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter) return","question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for","my_list = [] #count = 0 for key in word: prefix = key[:prefix_length]","key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if letter","0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count","if letter not in count: count[letter] = 0 count[letter] += 1 return count","= [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def","count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list =","For: OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict =","not in count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length,","def question_3(prefix_length, word): my_list = [] for key in word: prefix = key[:prefix_length]","= key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if","for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word):","question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for","'tremendous', 'tree'] my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix)","\"\"\" def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter","<NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word):","for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list","return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key","by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\"","word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key","letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst',","word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = []","key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0","if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] += 1","wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter in word:","word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if","key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list","wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in word: prefix","University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict = {} for key in","prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able',","return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list =","Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict = {} for key in word:","my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if letter not","my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in","for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return","= [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length,","letter in my_list: if letter.isalpha(): if letter not in count: letter[count] = []","= key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count =","in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list:","prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count","Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020","= {} for key in word: for letter in word: prefix_key = letter[:prefix_length]","{} for letter in my_list: if letter.isalpha(): if letter not in count: count[letter]","return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key","count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count =","in word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return","\"\"\" TW10: Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University","my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for","for letter in my_list: if letter.isalpha(): if letter not in count: count[letter] =","letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple',","prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key","if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter) return count","print(my_list) def question_3(prefix_length, word): my_list = [] for key in word: prefix =","for key in word: for letter in word: prefix_key = letter[:prefix_length] letter =","question_3(prefix_length, word): my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix)","count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list","in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list =","'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix =","'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2:","Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\" def","return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree']","key in word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length]","letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = []","count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in","letter in my_list: if letter.isalpha(): if letter not in count: count[letter] = 0","Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict = {} for key","in my_list: if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter)","prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha():","in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2","+= 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0","letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter","= word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial',","[] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word):","my_list: if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] +=","2061, Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict = {} for","= elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in","1 return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for","0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for","word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 =","prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list","'tree'] my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list)","letter not in count: count[letter] = 0 count[letter] += 1 return count def","['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in","= 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {}","letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] += 1 return","Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length,","[] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length,","= ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem","in count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word):","in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = []","TW10: Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date:","OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict = {}","my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list","= 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = []","[] #count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count","elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in word:","#count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count =","for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter","my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in word: prefix","for letter in my_list: if letter.isalpha(): if letter not in count: letter[count] =","key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in","= {} for letter in my_list: if letter.isalpha(): if letter not in count:","word): my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return","in my_list: if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter]","<NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict","word): my_list = [] #count = 0 for key in word: prefix =","elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list =","11/3/2020 \"\"\" def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for","def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in word:","word): my_dict = {} for key in word: for letter in word: prefix_key","def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter in","{} for letter in my_list: if letter.isalpha(): if letter not in count: letter[count]","count = {} for letter in my_list: if letter.isalpha(): if letter not in","= letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability',","word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous',","my_dict = {} for key in word: for letter in word: prefix_key =","{} for key in word: for letter in word: prefix_key = letter[:prefix_length] letter","= [] #count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix)","my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def","'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix","'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix = elem[:2]"],"string":"[\n \"my_list: if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter) return\",\n \"question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for\",\n \"my_list = [] #count = 0 for key in word: prefix = key[:prefix_length]\",\n \"key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if letter\",\n \"0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count\",\n \"if letter not in count: count[letter] = 0 count[letter] += 1 return count\",\n \"= [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def\",\n \"count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list =\",\n \"For: OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict =\",\n \"not in count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length,\",\n \"def question_3(prefix_length, word): my_list = [] for key in word: prefix = key[:prefix_length]\",\n \"= key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if\",\n \"for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word):\",\n \"question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for\",\n \"'tremendous', 'tree'] my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix)\",\n \"\\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter\",\n \"<NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word):\",\n \"for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list\",\n \"return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key\",\n \"by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\"\",\n \"word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key\",\n \"letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst',\",\n \"word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = []\",\n \"key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0\",\n \"if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] += 1\",\n \"wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter in word:\",\n \"word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if\",\n \"key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list\",\n \"wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in word: prefix\",\n \"University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {} for key in\",\n \"prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able',\",\n \"return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list =\",\n \"Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {} for key in word:\",\n \"my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if letter not\",\n \"my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in\",\n \"for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return\",\n \"= [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length,\",\n \"letter in my_list: if letter.isalpha(): if letter not in count: letter[count] = []\",\n \"= key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count =\",\n \"in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list:\",\n \"prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count\",\n \"Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020\",\n \"= {} for key in word: for letter in word: prefix_key = letter[:prefix_length]\",\n \"{} for letter in my_list: if letter.isalpha(): if letter not in count: count[letter]\",\n \"return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key\",\n \"count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count =\",\n \"in word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return\",\n \"\\\"\\\"\\\" TW10: Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University\",\n \"my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for\",\n \"for letter in my_list: if letter.isalpha(): if letter not in count: count[letter] =\",\n \"letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple',\",\n \"prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key\",\n \"if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter) return count\",\n \"print(my_list) def question_3(prefix_length, word): my_list = [] for key in word: prefix =\",\n \"for key in word: for letter in word: prefix_key = letter[:prefix_length] letter =\",\n \"question_3(prefix_length, word): my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix)\",\n \"count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list\",\n \"in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list =\",\n \"'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix =\",\n \"'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2:\",\n \"Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def\",\n \"return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree']\",\n \"key in word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length]\",\n \"letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = []\",\n \"count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in\",\n \"letter in my_list: if letter.isalpha(): if letter not in count: count[letter] = 0\",\n \"Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {} for key\",\n \"in my_list: if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter)\",\n \"prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha():\",\n \"in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2\",\n \"+= 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0\",\n \"letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter\",\n \"= word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial',\",\n \"[] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word):\",\n \"my_list: if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] +=\",\n \"2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {} for\",\n \"= elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in\",\n \"1 return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for\",\n \"0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for\",\n \"word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 =\",\n \"prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list\",\n \"'tree'] my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list)\",\n \"letter not in count: count[letter] = 0 count[letter] += 1 return count def\",\n \"['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in\",\n \"= 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {}\",\n \"letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] += 1 return\",\n \"Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length,\",\n \"[] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length,\",\n \"= ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem\",\n \"in count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word):\",\n \"in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = []\",\n \"TW10: Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date:\",\n \"OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {}\",\n \"my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list\",\n \"= 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = []\",\n \"[] #count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count\",\n \"elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in word:\",\n \"#count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count =\",\n \"for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter\",\n \"my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in word: prefix\",\n \"for letter in my_list: if letter.isalpha(): if letter not in count: letter[count] =\",\n \"key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in\",\n \"= {} for letter in my_list: if letter.isalpha(): if letter not in count:\",\n \"word): my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return\",\n \"in my_list: if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter]\",\n \"<NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict\",\n \"word): my_list = [] #count = 0 for key in word: prefix =\",\n \"elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list =\",\n \"11/3/2020 \\\"\\\"\\\" def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for\",\n \"def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in word:\",\n \"word): my_dict = {} for key in word: for letter in word: prefix_key\",\n \"def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter in\",\n \"{} for letter in my_list: if letter.isalpha(): if letter not in count: letter[count]\",\n \"count = {} for letter in my_list: if letter.isalpha(): if letter not in\",\n \"= letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability',\",\n \"word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous',\",\n \"my_dict = {} for key in word: for letter in word: prefix_key =\",\n \"{} for key in word: for letter in word: prefix_key = letter[:prefix_length] letter\",\n \"= [] #count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix)\",\n \"my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def\",\n \"'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix\",\n \"'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix = elem[:2]\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":5,"numItemsPerPage":100,"numTotalItems":12866649,"offset":500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDM4MTkwNywic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc3NDM4NTUwNywiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.5MDFuG8jP2onbOu_1MgPhcxAaBnDjjC_oil4oAPlCQUpy7xMKZXdpnDkltQ0fP7QDOyAdWhD0B0s3ZTUucRmDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[
"dr_size self.dwr_qty = dwr_qty self.dwr_size = dwr_size self.index = index self.height = height",
"/ 2.0, PAGE_HEIGHT - 34, \"DOOR ORDER FORM\") c.setFont(\"Helvetica\", 12) c.setFillColorRGB(0, 0, 0)",
"c.setFont(\"Helvetica-Bold\", 14) c.setFillColorRGB(BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_CO... |
[
"<reponame>s2t2/tweet-analyzer-py from app.bq_service import BigQueryService if __name__ == \"__main__\": bq_service = BigQueryService() bq_service.migrate_daily_bot_probabilities_table()",
"from app.bq_service import BigQueryService if __name__ == \"__main__\": bq_service = BigQueryService() bq_service.migrate_d... |
[
"2 - 1) self.optimal_distribution = beta(size ** 2 - 1, 1) else: self.optimal_distribution",
"4 if action == MiniGridDoorKeyAction.TurnLeft: newnode_prms[\"Dir\"] = (node.Dir - 1) % 4 if",
"node.IsDoorOpened and next_coord == self.door_position ): newnode_prms[\"X\"], newnode_prms[\"Y\"] = next_coord if action ... |
[
"ALL connections --> should NOT be used for reproducing ticket issue: # #con_admin.execute_immediate('delete",
"Confirmed bug on 3.0.6.33271. # Checked on 3.0.6.33272 (SS/CS) - works fine. #",
"# Confirmed bug on 3.0.6.33271. # Checked on 3.0.6.33272 (SS/CS) - works fine.",
"import fdb # # ATT_CNT=5 # ATT_DEL... |
[
"from bs4 import BeautifulSoup import logging import pandas as pd import csv import",
"bs4 import BeautifulSoup import logging import pandas as pd import csv import re",
"self.base_url = \"https://www25.senado.leg.br/\" self.search_url = self.base_url + \"web/senadores/em-exercicio/-/e/por-nome\" self.senate = ... |
[
"TagFactory from ...users.factories import UserFactory class AdministrativeUnitAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename =",
"factory_class = InstitutionFactory def validate_item(self, item): self.assertEqual(item[\"id\"], self.obj.id) self.assertEqual(item[\"name\"... |
[
"-> LennyBotState: return self._state def source_version(self, application: str) -> str: for action in",
"for action in self._actions: if action.application != application: continue return action.source_version return None",
"@property def actions(self) -> List[IAction]: return self._actions @property def state... |
[
"api_uri = f\"/api/v2/ContainerRegistries/{type}\" else: api_uri = \"/api/v2/ContainerRegistries\" response = self._session.get(api_uri, org=org) return response.json()",
"logging logger = logging.getLogger(__name__) class ContainerRegistriesAPI(object): \"\"\" Lacework Container Registries API. \"\"\" def",
"\... |
[
"# Cell class NewDecoder(nn.Module): def __init__(self, hidden_size, embedding_size, output_size, n_layers=1, dropout_p=0.1): super(NewDecoder, self).__init__()",
"= nn.Dropout(p) self.embedding = nn.Embedding(input_size, embedding_size) self.rnn = nn.LSTM(embedding_size, hidden_size, num_layers, dropout=p,batch_... |
[
"http_resp.status_code == 200 verify_url_re = re.compile(r\"{}/verify/\\w+\".format(api_url)) with responses.RequestsMock() as rsps: # fake no",
"with responses.RequestsMock() as rsps: # fake consent rsps.add(responses.GET, verify_url_re, json.dumps({\"foo\": \"bar\"}), status=200) #",
"Response from satosa.pro... |
[
"Unless required by applicable law or agreed to in writing, software # distributed",
"dim=-1) else: ws, bs = [], [] for i in range(len(self.cutoffs)): if self.div_val",
"labels is not None: if (hasattr(self, \"keep_order\") and self.keep_order) or keep_order: y.index_copy_(0, indices_i,",
"mask.dim() == 3: a ... |
[
"+ 5]] for j in range(5)] warped_face = warp_and_crop_face( np.array(img), facial5points, reference, crop_size=(crop_size,",
"try: # Handle exception bbs, landmarks = detect_faces(img) except Exception: print(\"{} is discarded",
"\"/\".join(image_name.split('/')[-5:]) img_warped.save(image_name) li_meta.append(... |
[
"from srtvoiceext import extract if __name__ == '__main__': ext = extract('video.mkv', 'subtitles.srt', 'outdir')"
] |
[
"def __delitem__(self, key): raise TypeError('Cannot modify ReadOnlyDict') def __iter__(self): return iter(self.store) def __len__(self):",
"__iter__(self): return iter(self.store) def __len__(self): return len(self.store) def __str__(self): return 'ReadOnlyDict(%s)' % self.store",
"value): raise TypeError('Can... |
[
"and summon\"} line = [] for c in self.choices: btn = attr_button(c, self.select_class)",
"def update_header(self): widgets = [] for p in self.header_list: widgets.append(urwid.AttrMap(urwid.AttrMap(urwid.Text(p.status, wrap=\"clip\"), {self.player.id:\"player\"}), {p.id:\"other\"",
"in visible_map] self.map_bo... |
[
"\"Any\", \"XXX TODO XXX\": \"item type\"}, \"XXX TODO XXX\": \"array_like or sparse matrix,",
"= Op(**self._hyperparams) def fit(self, X, y=None): if y is not None: self._wrapped_model.fit(X, y)",
"\"array_like or sparse matrix, shape (n_samples, n_features)\", }, { \"type\": \"array\", \"items\": {\"type\":",... |
[
"= ELU(name='elu_Dx51_{}'.format(dd))(x51) x52 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv52_{}'.format(dd))(x51) if use_bn: x52 =",
"x3 = ELU(name='elu_Dx3_{}'.format(dd))(x3) x51 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv51_{}'.format(dd))(x) if use_bn: x51",
"= BatchNormalizati... |
[
"with schema.\"\"\" from sqlalchemy_utils.functions import database_exists, create_database, drop_database from flunkybot.db import engine, base",
"new database with schema.\"\"\" from sqlalchemy_utils.functions import database_exists, create_database, drop_database from flunkybot.db import",
"database with sch... |
[
"setup( name='webspider', version=__version__, license='MIT', author='heguozhu', author_email='<EMAIL>', description='lagou.com spider', url='<EMAIL>:GuozhuHe/webspider.git', packages=find_packages(exclude=['tests']), package_data={'webspider': ['README.md']}, zip_safe=False,",
"os.path.abspath(os.path.dirname(__... |
[
"data file. refcount_file = 'data/refcounts.dat' # Translation # ----------- gettext_compact = False locale_dirs",
"the index html_split_index = True # Options for LaTeX output # ------------------------ #",
"r''' \\authoraddress{ \\strong{Python Software Foundation}\\\\ Email: \\email{<EMAIL>} } \\let\\Verbati... |
[
"+= 1 l += 1 else: l += 1 dist = k /",
"here. No other modules should be used. # Import plotting library import matplotlib.pyplot",
"graph(my_list): plt.hist(my_list,density=True, facecolor='b') sigma = sd(my_list) #stores standard deviation mu = mean(my_list) #stores",
"1 l += 1 else: l += 1 dist = k / l",
... |
[
"* len(new_label) pattern = '^(' + re.escape(old_label) + ')\\n([' + re.escape(valid_section_characters) + ']+)$'",
"a forthcoming section in their changelog file: %s' % ', '.join(sorted(missing_forthcoming)), file=sys.stderr) if",
"version number to bump? (default: %(default)s)') args = parser.parse_args(sysar... |
[
"function, backend='tcp'): os.environ['MASTER_ADDR'] = '127.0.0.1' os.environ['MASTER_PORT'] = '29500' dist.init_process_group(backend, rank=rank, world_size=world_size) function(rank, world_size)",
"model(torch.ones(10).float()) loss.backward() optimizer.step() processes = [] world_size = 4 for rank in range(wor... |
[
"\"\"\" Squeak model. W_Object W_SmallInteger W_MutableSmallInteger W_AbstractObjectWithIdentityHash W_AbstractFloat W_Float W_MutableFloat W_Character W_PointersObject W_AbstractObjectWithClassReference",
"rsqueakvm.model.display import * from rsqueakvm.model.numeric import * from rsqueakvm.model.pointers import... |
[
"self).__init__() self.size_average = size_average if normalize: self.normalize = Normalize() else: self.normalize = None",
"# Weighting of the loss, default is HED-style if self.pos_weight is None: num_labels_pos",
"mask), torch.masked_select(label, mask), reduction='sum') if self.size_average: if ignore_label... |
[
"DELTA_T, PASSOS+1) z = [] for k in range(PASSOS+1): z_k = np.copy(z_temp) z.append(z_k)",
"y = np.linspace(0.0, DELTA_T, PASSOS+1) z = [] for k in range(PASSOS+1): z_k",
"DELTA_X z_temp = contorno.p_3 TAMANHO_BARRA = 2 x = np.linspace(0.0, TAMANHO_BARRA, INTERVALOS+1) y",
"z = [] for k in range(PASSOS+1): z_... |
[
"0: step = 1 elif index == numRows - 1: step = -1",
"= numRows-1, if we reach bottom (i.e. row = numRows-1) # then we",
"int) -> str: if numRows == 1 or numRows >= len(s): return s",
"move up. Similarly if we reach top, we change direction and move down",
"Similarly if we reach top, we change direction and ... |
[
"embedding_vector_features = 40 model = Sequential() model.add(Embedding(voc_size,embedding_vector_features,input_length=sent_len)) model.add(LSTM(100)) model.add(Dense(1,activation='sigmoid')) model.compile(loss='binary_crossentropy',optimizer = 'adam',metrics =",
"np X_final = np.array(embedded_doc) y_final = n... |
[
"pred = clf.predict(features_test) #### store your predictions in a list named pred from",
"the training features/labels, and to #### make a set of predictions on the",
"on the test data clf.fit(features_train,labels_train) pred = clf.predict(features_test) #### store your predictions in",
"##################... |
[
"Version 2.0 (the \"License\" # you may not use this file except in",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"import numpy as np import unittest import paddle class Net(BaseNet): \"\"\" simple Net",
"KIND, either express or implied. # See the License for the specif... |
[
"arguments) :setter: Sets the task function/method result \"\"\" return self._result @result.setter def result(self,",
"return self._func @property def args(self): \"\"\" Task function/method arguments property - getter only.",
"@property def func(self): \"\"\" Task function/method property - getter only. :gett... |
[
"( parsed.categorized_comments[\"nested\"].get(module, {}).pop(from_import, None) ) if comment: from_imports.remove(from_import) if from_imports: use_comments = []",
"if pending_lines_before or not no_lines_before: output += [\"\"] * config.lines_between_sections output += section_output",
"while from_imports: ... |
[
"type='string' ) def _cleanup_query(q, tag_types): type_map = dict(float=FLOAT.from_bytes, int=INT.from_bytes) for term in q.leaves():",
"not appear to have a header.\" if 'path' not in header: msg =",
"in reader: total += 1 path = record[path_idx] rpath = relpath(path, self.path) index",
"_check_date_range(x... |
[
"be stabilized. class _RefTranslator: \"\"\" Resolve JSON pointers/references in a spec by translation.",
"return ref_url.path.split('/')[-1] + '_' + '_'.join(item_path[1:]) def _local_ref(path): url = '#/' + '/'.join(path)",
"url: self.url = _url.absurl(url) url_key = (_url.urlresource(self.url), self.__strict... |
[
"self.assertEqual(ch, '1') ch = cs.next().ok() self.assertEqual(ch, '1') ch = cs.next().ok() self.assertEqual(ch, '2') r",
"'1') ch = cs.peek().ok() self.assertEqual(ch, '1') ch = cs.next().ok() self.assertEqual(ch, '1') ch =",
"'1') ch = cs.next().ok() self.assertEqual(ch, '2') r = cs.next() self.assertFalse(r... |
[
"rs2 = ql.CReg(2) k.classical(rs1, ql.Operation(3)) k.classical(rs1, ql.Operation(4)) k.classical(rd, ql.Operation(rs1, '+', rs2)) p.add_kernel(k) NCOMPILES=50",
"k.classical(rs1, ql.Operation(3)) k.classical(rs1, ql.Operation(4)) k.classical(rd, ql.Operation(rs1, '+', rs2)) p.add_kernel(k) NCOMPILES=50 QISA_fn =... |
[
"sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=i) addressRead = sensor.read_register(0, functioncode=3) if(i == addressRead): print('FOUND!') return",
"import minimalmodbus import serial from time import sleep parser = argparse.ArgumentParser() parser.add_argument('address', metavar='ADDR',",
"... |
[
"shutil import tempfile import unittest import ray from ray import tune from ray.rllib",
"# Restore the checkpoint config={\"env\": \"CartPole-v0\", \"log_level\": \"DEBUG\"}, ).trials self.assertIsNone(trial.error_file) def testDottedRelativePath(self): local_dir",
"checkpoint_dir): with open(os.path.join(chec... |
[
"20, \"button\": {\"x\": (8, 8), \"y\": (100, 105)}}, \"colours\": { \"primary\": (112, 112,",
"\"Orange\", \"crystal\": [\"Ilum crystal\", \"Ultima Pearl\"], \"type\": \"Sith\", }, \"b9\": { \"colour\": \"Black\",",
"\"tertiary\": (180, 97, 19), # b46113 }, \"length\": 22, \"materials\": \"Alloy metal/Salvaged... |
[
"assert out.shape == (2, 3) assert out.dtype == dt f = theano.function( [],",
") assert self.op == GpuReshape class TestGPUComparison(TestComparison): def setup_method(self): utt.seed_rng() self.mode = mode_with_gpu",
"dtype=\"float32\") f = theano.function( [a, b], tt.join(0, tt.zeros_like(a), tt.ones_like(b))... |
[
") assert b\"Available animations include\" in got assert b\"* dice\" in got assert",
"[]}, ) async it \"can get information\", server, m: # start got =",
"final_future) as server: yield server, m @pytest.fixture() def server(make_server): return make_server[0] @pytest.fixture() def",
"assert got[\"started\"]... |
[
"YouTube def download_video(watch_url): yt = YouTube(watch_url) (yt.streams .filter(progressive=True, file_extension='mp4') .order_by('resolution') .desc() .first() .download())",
"pytube import YouTube def download_video(watch_url): yt = YouTube(watch_url) (yt.streams .filter(progressive=True, file_extension='mp... |
[
"Transform the data_files structure into a dictionary. :param data_files: either a list of",
"function \"\"\" prefix = get_command_prefix( '%s__setup' % build_type, context.build_space, context.build_dependencies) ament_tools_path = os.path.dirname(os.path.dirname(__file__))",
"KIND, either express or implied. ... |
[
"tree. import torch from neuralcompression.functional import soft_round, soft_round_inverse def test_soft_round_inverse(): x = torch.linspace(-2.0,",
"soft_round, soft_round_inverse def test_soft_round_inverse(): x = torch.linspace(-2.0, 2.0, 50) torch.testing.assert_close( x, soft_round_inverse(x, alpha=1e-13),"... |
[
"clip_ratio=0.2, adv_norm=True, value_norm=True, ), collect=dict( n_sample=2048, unroll_len=1, discount_factor=0.99, gae_lambda=0.97, ), eval=dict(evaluator=dict(eval_freq=5000, )), other=dict(replay_buffer=dict(",
"collector_env_num=8, evaluator_env_num=10, use_act_scale=True, n_evaluator_episode=10, stop_value=... |
[
"cls(name,entries,appFamily,id,references,**kwargs) def to_json(self): return { \"name\":self.name, \"description\":\"Desc Not Required\", \"type\":\"app\", \"entries\":[ {\"appFamily\" if",
"\"name\":self.name, \"description\":\"Desc Not Required\", \"type\":\"app\", \"entries\":[ {\"appFamily\" if self.app_fami... |
[
"self._meta = docker_container.attrs _LOGGER.info( \"Starting DNS %s with version %s - %s\", self.image,",
"from ..const import ENV_TIME from ..coresys import CoreSysAttributes from .interface import DockerInterface _LOGGER:",
"\"\"\"Docker Supervisor wrapper for Supervisor DNS.\"\"\" @property def image(self) ... |
[
"and # integrates with CPython, but also works on its own. # #",
"\"Pow\" : \"PyNumber_Power\", # \"IPow\" : \"PyNumber_InPlacePower\", # The others are generic code",
"KIND, either express or implied. # See the License for the specific language",
"\"ILShift\": \"PyNumber_InPlaceLshift\", \"IRShift\": \"PyNum... |
[
"): return graphene_info.context.get_external_execution_plan( external_pipeline=external_pipeline, run_config=run_config, mode=mode, step_keys_to_execute=step_keys_to_execute, known_state=known_state, ) @capture_error def fetch_repositories(graphene_info): from",
"selector.solid_selection: if not full_pipeline.ha... |
[
"parent_dir=d) assert '__init__' in w.modules assert w.has_module_name('__init__') assert w.pkg_path == os.path.join(d, 'foo') w.write()",
"w.get_file_as_string() lns = s.split('\\n') assert 'a' in lns[0] assert 'b' in lns[1] assert",
"import TemporaryDirectory from quickbase_client.utils.pywriting_utils import... |
[
"is True. train_bn : boolean, optional Whether one should normalize the layer input",
": string The ResNet architecture in {'resnet50', 'resnet101'}. stage5 : boolean, optional Whether",
"3 x = conv_block(x, [128,128,512], stage=3, block='a', train_bn=train_bn) x = identity_block(x, [128,128,512], stage=3,",
... |
[
"= 'UTC' return { '@timestamp': self.compose_timestamp(line[0], timezone), 'log_level': log_level, 'process_id': int(pid), 'message': '",
"= line[2].lstrip(\"[\").rstrip(\"]\") timezone = 'UTC' return { '@timestamp': self.compose_timestamp(line[0], timezone), 'log_level': log_level, 'process_id':",
"in line to ... |
[
"in the corpus. Parameters ---------- corpus_context : CorpusContext Context manager for a corpus",
"sequence_type, specifier, max_distance): return phono_edit_distance(getattr(w, sequence_type), getattr(query, sequence_type), sequence_type, specifier) <= max_distance def",
"= corpus_context.sequence_type query... |
[
"import settings from django.db import migrations class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('brokenChains',",
"2018-11-06 17:19 from django.conf import settings from django.db import migrations class Migration(migrations.Migration): depende... |
[
"[[0.2, 0.5], [0.5, 0.2], [0.8, 0.5], [0.5, 0.8]] cmap = plt.cm.RdYlGn norm =",
"'normal')) plt.xticks([]) plt.yticks([]) def choose_abs_greedy_action(state, Q, epsilon): action = None if np.random.uniform(0, 1)",
"epsilon: action = np.random.randint(env.action_space.n) else: action = np.argmax(Q[state,:]) retu... |
[
"Generated by Django 3.0.4 on 2020-07-14 11:00 from django.db import migrations, models import",
"on 2020-07-14 11:00 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies",
"( \"use_case\", models.ForeignKey( on_delete=django.db.models.del... |
[
"for the connection. :type port: string :param connection_username: The new username for the",
"embed_password_flag=None): super().__init__(ts_connection) self._server_address = server_address self._port = port self._connection_username = connection_username self._connection_password =",
"new password for the c... |
[
"name=\"type\" content=\"blog_post\">\"\"\" in html) self.assertTrue(\"\"\"<meta property=\"og:title\" content=\"My Blog\">\"\"\" in html) def tearDown(self): frappe.db.rollback()",
"meta tags for this route doc = frappe.new_doc(\"Website Route Meta\") doc.append(\"meta_tags\", {\"key\": \"type\",",
"test_depen... |
[
"z.InitSnow_0, z.Prec, z.NRur, z.CN, z.AntMoist_0, z.NUrb, z.Grow_0, z.Landuse, z.Area), AgRunoff.AgRunoff(z.NYrs, z.DaysMonth, z.Temp, z.InitSnow_0,",
"z.Grow_0, z.Landuse, z.Area), AgRunoff.AgRunoff(z.NYrs, z.DaysMonth, z.Temp, z.InitSnow_0, z.Prec, z.NRur, z.CN, z.AntMoist_0, z.NUrb, z.Grow_0,",
"from .Varia... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"p, input_shape, expected_shape): batch_size, num_points, _ = input_shape g = tf.Graph() with g.as_default():",
"KIND, either express or implied. #... |
[] |
[
"= str(sniff_path).lower() # Variable in which information will be stored dir_store = {}",
"# Important: it's cross-platform relevant! modified_date = os.path.getmtime(f_path) dir_store[subdir]['file_details'][file] = (modified_date,) # Name",
"modification date # Important: it's cross-platform relevant! modifi... |
[
"and # integrates with CPython, but also works on its own. # #",
"ExpressionBuiltinGlobals(ExpressionBase): kind = \"EXPRESSION_BUILTIN_GLOBALS\" def __init__(self, source_ref): ExpressionBase.__init__(self, source_ref=source_ref) def finalize(self): del self.parent",
"KIND, either express or implied. # See the... |
[
"0x10000), (0x10000, chainerx.int64, 0x10000), (0x10000, chainerx.uint8, 0), (0x100000000, chainerx.bool_, True), (0x100000000, chainerx.int8, 0),",
"# (float('inf'), float('inf')), ]) def test_equality(value1, value2): scalar1 = chainerx.Scalar(value1) scalar2 = chainerx.Scalar(value2)",
"-1.5), (True, True), ... |
[
"for button in self.active_buttons: button.update() def intro_draw(self): for button in self.active_buttons: button.draw() def",
"50, 300, WIDTH - 100, 50, PLAY_BUTTON_COLOUR, hover_colour=(49, 218, 46), function=self.intro_to_play, text=\"PLAY\") self.intro_buttons.append(intro_play_button)",
"self.gameover_ev... |
[
"= {'abstracts': (BillAbstract, 'bill_id', {}), 'other_titles': (BillTitle, 'bill_id', {}), 'other_identifiers': (BillIdentifier, 'bill_id', {}),",
"BillVersion, BillDocumentLink, BillVersionLink) from .base import BaseImporter from ..exceptions import PupaInternalError class BillImporter(BaseImporter):",
"in R... |
[
"i in range(len(test_data_1)): final_labels[i]=np.mean(preds[:,i]) if i%10000==0: print(i ,' out of ',len(test_data_1)) print('making the",
"in range(len(test_data_1)): final_labels[i]=round(np.mean(preds[:,i])) if i%100==0: print(i ,' out of ',len(test_data_1)) print(\"test data accuracy:",
"n_h_features=nlp_f... |
[
"s=20, c=palette[y.astype(np.int)]) ax.axis(\"off\") # Add the labels for each cluster. for i in",
"The vertical line for average silhouette score of all the values ax1.axvline(x=silhouette_avg, color=\"red\",",
"[PathEffects.Stroke(linewidth=5, foreground=\"w\"), PathEffects.Normal()] ) # plt.legend() figure_f... |
[] |
[
"<reponame>jack139/cnnc #!/usr/bin/env python # -*- coding: utf-8 -*- # import web import time",
"= helper.create_render() user_data = web.input(news_id='') if user_data.news_id == '': return render.info('参数错误！') db.index_news.delete_one({'_id':ObjectId(user_data.news_id)}) return",
"url = ('/plat/index_news_re... |
[
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, software # distributed",
"copyvars=None): BaseWindow.__init__(self, **get_args(locals())) # Set the online model for subclasses if type(self).__name__ !=",
"disables it. ... |
[
"this case the email list is needed :return: 3 arrays of outputs \"\"\"",
"Description of breach from API response :return: Description string that altered HTML urls",
"+ ')' breach_description = re.sub(html_link_pattern, link_from_desc, breach_description, count=1) return breach_description def data_to_markdow... |
[
"[self.visit(n.left)], [], lineno=n.lineno, col_offset=n.col_offset ) return self.generic_visit(n) class Pipeline(Extension): identifier = \"pipeline\" def",
"ast.Call( self.visit(n.right), [self.visit(n.left)], [], lineno=n.lineno, col_offset=n.col_offset ) return self.generic_visit(n) class Pipeline(Extension):... |
[
"= struct.pack(flag, patch_value) mm.write(patch_bytes) def get_protected_function_info(r2, function): # find addresses and sizes of",
"names if function in name: mangledName = name match += 1 if match",
"size, expected hash\", required=True) results = parser.parse_args() print(\"python protect program\", resul... |
[
"Django 2.2.15 on 2021-01-29 20:20 from django.db import migrations, models import django.db.models.deletion class",
"migrations.CreateModel( name='auditionRounds', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('roundno', models.IntegerField(default=1... |
[
"is_old_facts = self.module._name == 'azure_rm_availabilityset_facts' if is_old_facts: self.module.deprecate(\"The 'azure_rm_availabilityset_facts' module has been renamed",
"to search for the desired availability set. tags: description: - List of tags",
") self.name = None self.resource_group = None self.tags ... |
[
"nodes = client.list_node(clusterId=cluster.id).data if len(nodes) > 0: if nodes[0].nodeTemplateId is None: return \"Custom\"",
"while not check_function(resource): if time.time() - start > timeout: exceptionMsg = 'Timeout waiting",
"\" http://\" + host_ip + \":\" + \\ str(source_port) + \"/name.html\" validate... |
[
"= {')':'(',']':'[','}':'{'} stack = [] for ch in s: if ch in dict.values():",
"0 or (stack.pop() != dict[ch]): return False return len(stack) == 0 def main():",
"true class Solution(object): def isValid(self, s): \"\"\" :type s: str :rtype: bool \"\"\"",
"empty string is also considered valid. # Example 1: #... |
[
"True except ImportError: IS_CONNECTED = False def get_health(token_info=None, user=None): \"\"\"Get a health report",
"response = ({\"status\": \"Error\", \"message\": \"Incomplete database configuration\"}, 500) else: Result.query.first() response =",
"\"Fetching service status\"}, 200) # Try to connect to th... |
[
"N.uint8, ['CONTIGUOUS', 'ALIGNED']) self._lib.pymodule_bh_tsne( self.embedding, # result self.points, # points self.points.ctypes.shape, # dims",
"Point dumpoing self.dump_file = str(dump_file) self.dump_points = bool(dump_points) self.dump_interval = int(dump_interval) # Viz",
"-- Input array, shape: (n_point... |
[
"any CRUD not related to strictly editing users info and courses info from",
"# contains any CRUD not related to strictly editing users info and courses",
"related to strictly editing users info and courses info from .views import admin",
"CRUD not related to strictly editing users info and courses info from ... |
[
"PATHS_FROM_ECLIPSE_TO_PYTHON). @note: all the paths with breakpoints must be translated (otherwise they won't",
"x in PATHS_FROM_ECLIPSE_TO_PYTHON] # example: # PATHS_FROM_ECLIPSE_TO_PYTHON = [ # (r'd:\\temp\\temp_workspace_2\\test_python\\src\\yyy\\yyy', # r'd:\\temp\\temp_workspace_2\\test_python\\src\\hhh\\xx... |
[
"= self.gen_id() if num != file.read(): file.close() os.remove(Constants.Files.ID) file = self.open_id_file() file.write(num) file.close()",
"== NetworkPackets.NetLogicIncomes.PAIRED.value: Constants.Network.IS_PAIRING = True self.client.send(NetworkPackets.assemble(arr=Actions.COMPUTER.get_specs_as_str_arr())) e... |
[
"EDIT! # source: resource_requirements.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))",
"file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ],",
"ResourceR... |
[
"\"\" if method != \"post\": abs_url = \"%s?%s\" % (abs_url, data) data =",
"True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 api_connection_error =",
"assert telemetry[\"last_request_metrics\"][\"request_id\"] == \"... |
[
"def download(self, url: str): \"\"\" Store a python dictionary generated from json data",
"data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data:",
"\"\"\" Store a python dictionary generated from json data at ... |
[
"FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == \"__main__\": app =",
"QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox,",
"= 100 width = 70*4 height = 130*4 def __init__(self... |
[
"similar-sized chunks from l.\"\"\" chunk_size = 1 + len(l) // n for i",
"LOCATION_FIELDS]) elif sheet.name == \"Teacher\": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name",
"\"SCHOOL_ID\"] STUDENT_TITLES = [\"SCHOOL_NAME\", \"SCHOOL_ID\", \"STUDENT_ID\", \"STUDENT_SRN\", \... |
[
"= [] streamdict = {} if chandict[\"callsign\"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict[\"callsign\"]] else:",
"format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming',",
"driver.find_element_by... |
[] |
[
"def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg',",
"size self.rect.center = self.centre # set centre for all frames self.frame = 0",
"= 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if",
"= self.image.get_rect() # new rectangl... |
[
"= args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event()",
"the appropriate executors for processing. This is based on the following logic: ..",
"bit more\") def make_callback(self, kind=None): \"\"\"Makes the callback and resets the timer. \"\"\"",
"is starte... |
[
"[attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor,",
"= True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y)",
"guided backprop results... |
[
"reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ]",
"0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2),",
"NASNet = Genotype( norm... |
[
"use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions =",
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, software # distributed",
"IPython.display import display import os im... |
[
"import Basic as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst =",
"as sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def",
"sp_Basic class Basic(sp_Basic): \"\"\"Basic class for Pyccel AST.\"\"\" _fst = None def set_fst(self,",
"from sympy.c... |
[
"for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100,",
"indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc)",
"n_tests = len(tests) @pytest.fixture def cats_and_percentiles(... |
[
"= 1 self.assertEqual(ps.get_common_id([dim1, dim2]), \"0.2_1\") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]),",
"[-0.3,-0.2, -0.1, 0] ps = ParameterSpace(\"ps\") ps.register_parameters([p1, p2]) ps.enable_cache(\"ps_test\") def func(param1, param2): return",
"1 self.assertEqu... |
[
"from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import",
"bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE ==",
"reward agent.save_reward(reward... |
[
"my_list: if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter) return",
"question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for",
"my_list = [] #count = 0 for key in word: prefix = key[:prefix_length]",
"key[:prefix_length] my_list.ap... |