paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
Project3/Demo_use_q2_point.py	yunchen-yang/CV_classes	0	163994	<filename>Project3/Demo_use_q2_point.py import cv2 import numpy as np import copy import sys path=sys.path[0]+'/' img_input=cv2.imread(path+"original_imgs/point.jpg",0) filter_kernel=np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]) size_img_input=img_input.shape kernel_size=filter_kernel.shape decenter=int((kernel_size[1]-1)/2) new_img=np.zeros(img_input.shape) for i in range(0,size_img_input[0]): for j in range(0,size_img_input[1]): H=np.zeros((kernel_size[0],kernel_size[1])).astype('int') G=np.zeros((kernel_size[0],kernel_size[1])).astype('int') for n in range(-decenter,decenter+1): for m in range(-decenter,decenter+1): if i+n in range(0,size_img_input[0]) and j+m in range(0,size_img_input[1]): H[decenter+n,decenter+m]=img_input[i+n,j+m] else: H[decenter+n,decenter+m]=0 for x in range(-decenter,decenter+1): for y in range(-decenter,decenter+1): G[decenter+x,decenter+y]=H[decenter+x,decenter+y]*filter_kernel[decenter+x,decenter+y] new_img[i,j]=sum(sum(G)) #img_new_adjusted=new_img.clip(0, 255) #img_new=np.rint(img_new_adjusted).astype('uint8') #cv2.imshow('img_mask',img_new)	[ 1, 529, 9507, 29958, 7653, 29941, 29914, 23444, 29918, 1509, 29918, 29939, 29906, 29918, 3149, 29889, 2272, 13, 5215, 13850, 29906, 13, 5215, 12655, 408, 7442, 13, 5215, 3509, 13, 5215, 10876, 13, 13, 2084, 29922, 9675, 29889, 2084, 29961, 29900, 10062, 29915, 22208, 29871, 13, 2492, 29918, 2080, 29922, 11023, 29906, 29889, 326, 949, 29898, 2084, 13578, 13492, 29918, 2492, 29879, 29914, 3149, 29889, 6173, 613, 29900, 29897, 13, 4572, 29918, 17460, 29922, 9302, 29889, 2378, 4197, 14352, 29896, 6653, 29896, 6653, 29896, 1402, 14352, 29896, 29892, 29947, 6653, 29896, 1402, 14352, 29896, 6653, 29896, 6653, 29896, 24960, 13, 2311, 29918, 2492, 29918, 2080, 29922, 2492, 29918, 2080, 29889, 12181, 13, 17460, 29918, 2311, 29922, 4572, 29918, 17460, 29889, 12181, 13, 311, 5064, 29922, 524, 3552, 17460, 29918, 2311, 29961, 29896, 29962, 29899, 29896, 6802, 29906, 29897, 13, 1482, 29918, 2492, 29922, 9302, 29889, 3298, 359, 29898, 2492, 29918, 2080, 29889, 12181, 29897, 13, 1454, 474, 297, 3464, 29898, 29900, 29892, 2311, 29918, 2492, 29918, 2080, 29961, 29900, 29962, 1125, 13, 4706, 363, 432, 297, 3464, 29898, 29900, 29892, 2311, 29918, 2492, 29918, 2080, 29961, 29896, 29962, 1125, 13, 9651, 379, 29922, 9302, 29889, 3298, 359, 3552, 17460, 29918, 2311, 29961, 29900, 1402, 17460, 29918, 2311, 29961, 29896, 2314, 467, 579, 668, 877, 524, 1495, 13, 9651, 402, 29922, 9302, 29889, 3298, 359, 3552, 17460, 29918, 2311, 29961, 29900, 1402, 17460, 29918, 2311, 29961, 29896, 2314, 467, 579, 668, 877, 524, 1495, 13, 9651, 363, 302, 297, 3464, 6278, 311, 5064, 29892, 311, 5064, 29974, 29896, 1125, 13, 18884, 363, 286, 297, 3464, 6278, 311, 5064, 29892, 311, 5064, 29974, 29896, 1125, 13, 462, 1678, 565, 474, 29974, 29876, 297, 3464, 29898, 29900, 29892, 2311, 29918, 2492, 29918, 2080, 29961, 29900, 2314, 322, 432, 29974, 29885, 297, 3464, 29898, 29900, 29892, 2311, 29918, 2492, 29918, 2080, 29961, 29896, 29962, 1125, 13, 462, 4706, 379, 29961, 311, 5064, 29974, 29876, 29892, 311, 5064, 29974, 29885, 13192, 2492, 29918, 2080, 29961, 29875, 29974, 29876, 29892, 29926, 29974, 29885, 29962, 13, 462, 1678, 1683, 29901, 13, 462, 4706, 379, 29961, 311, 5064, 29974, 29876, 29892, 311, 5064, 29974, 29885, 13192, 29900, 13, 9651, 363, 921, 297, 3464, 6278, 311, 5064, 29892, 311, 5064, 29974, 29896, 1125, 13, 18884, 363, 343, 297, 3464, 6278, 311, 5064, 29892, 311, 5064, 29974, 29896, 1125, 13, 462, 1678, 402, 29961, 311, 5064, 29974, 29916, 29892, 311, 5064, 29974, 29891, 13192, 29950, 29961, 311, 5064, 29974, 29916, 29892, 311, 5064, 29974, 29891, 14178, 4572, 29918, 17460, 29961, 311, 5064, 29974, 29916, 29892, 311, 5064, 29974, 29891, 29962, 13, 9651, 716, 29918, 2492, 29961, 29875, 29892, 29926, 13192, 2083, 29898, 2083, 29898, 29954, 876, 13, 268, 13, 29937, 2492, 29918, 1482, 29918, 328, 5143, 287, 29922, 1482, 29918, 2492, 29889, 24049, 29898, 29900, 29892, 29871, 29906, 29945, 29945, 29897, 13, 29937, 2492, 29918, 1482, 29922, 9302, 29889, 29878, 524, 29898, 2492, 29918, 1482, 29918, 328, 5143, 287, 467, 579, 668, 877, 13470, 29947, 1495, 13, 13, 13, 29937, 11023, 29906, 29889, 326, 4294, 877, 2492, 29918, 13168, 742, 2492, 29918, 1482, 29897, 13, 2 ]
slippy/core/tests/test_materials.py	KDriesen/slippy	12	17278	import numpy as np import numpy.testing as npt import slippy import slippy.core as core """ If you add a material you need to add the properties that it will be tested with to the material_parameters dict, the key should be the name of the class (what ever it is declared as after the class key word). The value should be a tuple of dicts: The first dict in the tuple will be unpacked to instantiate the class, The second will be used with the displacement from loads method The third will be used with the loads from displacement method to ensure that the methods are inverses of each other If there is a limit the applicability of the displacements from loads method (such as for a perfectly plastic material the _max_load key word should be set in the second dict. For more complex behaviour please also implement your own tests """ material_parameters = { 'Elastic': ({'name': 'steel_5', 'properties': {'E': 200e9, 'v': 0.3}}, {'grid_spacing': 0.01, 'simple': True}, {'grid_spacing': 0.01, 'simple': True, 'tol': 1e-9}), 'Rigid': ({}, {}, {}) } exceptions = [core.Rigid] def test_materials_basic(): # check that one of influence matrix or displacement from loading is given for material in core.materials._IMMaterial._subclass_registry: if material in exceptions: continue try: mat_params = material_parameters[material.material_type] except KeyError: raise AssertionError(f"Material test parameters are not specified, for material {material.material_type}") mat_instance = material(*mat_params[0]) max_load = mat_params[1].pop('_max_load', 1) np.random.seed(0) loads = np.random.rand(16, 16) max_load # check that the loads and displacement functions are inverse of each other for direction in {'x', 'y', 'z'}: load_in_direction = {direction: loads} displacement = mat_instance.displacement_from_surface_loads(load_in_direction, mat_params[1]) set_disp = displacement[direction] loads_calc = mat_instance.loads_from_surface_displacement(displacements={direction: set_disp}, mat_params[2]) npt.assert_allclose(loads, slippy.asnumpy(loads_calc[direction]), atol=max_load * 0.02) def test_elastic_coupled(): mat = core.Elastic('steel_6', {'E': 200e9, 'v': 0.3}) np.random.seed(0) loads1 = np.random.rand(16, 16) loads2 = np.random.rand(16, 16) directions = 'xyzx' for i in range(3): dir_1 = directions[i] dir_2 = directions[i+1] loads_in_direction = {dir_1: loads1, dir_2: loads2} displacement = mat.displacement_from_surface_loads(loads_in_direction, grid_spacing=0.01, simple=True) loads_calc = mat.loads_from_surface_displacement(displacements=displacement, grid_spacing=0.01, simple=True) for direction in [dir_1, dir_2]: npt.assert_allclose(loads_in_direction[direction], slippy.asnumpy(loads_calc[direction]), atol=0.02) displacement = 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pandaserver/workflow/pcwl_utils.py	virthead/panda-server	0	156696	<reponame>virthead/panda-server<filename>pandaserver/workflow/pcwl_utils.py import copy import re import six import os.path from pathlib import Path from ruamel import yaml from urllib.parse import urlparse from .workflow_utils import Node, ConditionItem WORKFLOW_NAMES = ['prun', 'phpo', 'junction', 'reana'] # extract id def extract_id(id_str): if not id_str: return id_str if not isinstance(id_str, list): id_str = [id_str] not_list = True else: not_list = False items = [re.search(r'[^/]+#.+$', s).group(0) for s in id_str] if not_list: return items[0] return items # topological sorting def top_sort(list_data, visited): if not list_data: return [] new_list = [] new_visited = [] for node in list_data: isOK = True for p in node.parents: if p not in visited: isOK = False break if isOK: new_visited.append(node) visited.add(node.id) else: new_list.append(node) return new_visited + top_sort(new_list, visited) # parse CWL file def parse_workflow_file(workflow_file, log_stream, in_loop=False): # read the file from yaml cwl_file = Path(os.path.abspath(workflow_file)) with open(cwl_file, "r") as cwl_h: yaml_obj = yaml.main.round_trip_load(cwl_h, preserve_quotes=True) # Check CWLVersion if 'cwlVersion' not in list(yaml_obj.keys()): log_stream.error("could not get the cwlVersion in {}".format(cwl_file.as_uri())) return False, None # Import parser based on CWL Version if yaml_obj['cwlVersion'] == 'v1.0': from cwl_utils import parser_v1_0 as parser elif yaml_obj['cwlVersion'] == 'v1.1': from cwl_utils import parser_v1_1 as parser elif yaml_obj['cwlVersion'] == 'v1.2': from cwl_utils import parser_v1_2 as parser else: log_stream.error("Version error. Did not recognise {} as a CWL version".format(yaml_obj["CWLVersion"])) return False, None # Import CWL Object root_obj = parser.load_document_by_yaml(yaml_obj, cwl_file.as_uri()) # root inputs root_inputs = {extract_id(s.id): s.default for s in root_obj.inputs} # root outputs root_outputs = set([extract_id(s.id.split('#')[0] + '#' + re.sub(s.id+'/', '', s.outputSource)) for s in root_obj.outputs]) # loop over steps node_list = [] output_map = {} serial_id = 0 for step in root_obj.steps: cwl_name = os.path.basename(step.run) # check cwl command if not cwl_name.endswith('.cwl') and cwl_name not in WORKFLOW_NAMES: log_stream.error("Unknown workflow {}".format(step.run)) return False, None serial_id += 1 workflow_name = step.id.split('#')[-1] # leaf workflow and sub-workflow if cwl_name == 'prun.cwl': node = Node(serial_id, 'prun', None, True, workflow_name) elif cwl_name == 'phpo.cwl': node = Node(serial_id, 'phpo', None, True, workflow_name) elif cwl_name in WORKFLOW_NAMES: node = Node(serial_id, cwl_name, None, True, workflow_name) else: node = Node(serial_id, 'workflow', None, False, workflow_name) node.inputs = {extract_id(s.id): {'default': s.default, 'source': extract_id(s.source)} for s in step.in_} node.outputs = {extract_id(s): {} for s in step.out} # add outDS if no output is defined if not node.outputs: node.outputs = {extract_id(step.id + '/outDS'): {}} output_map.update({name: serial_id for name in node.outputs}) if step.scatter: node.scatter = [extract_id(s) for s in step.scatter] if hasattr(step, 'when') and step.when: # parse condition node.condition = parse_condition_string(step.when) # suppress inputs based on condition suppress_inputs_based_on_condition(node.condition, node.inputs) if step.hints and 'loop' in step.hints: node.loop = True if node.loop or in_loop: node.in_loop = True # expand sub-workflow if not node.is_leaf: p = urlparse(step.run) tmp_path = os.path.abspath(os.path.join(p.netloc, p.path)) node.sub_nodes, node.root_inputs = parse_workflow_file(tmp_path, log_stream, node.in_loop) # check if tail if root_outputs & set(node.outputs): node.is_tail = True node_list.append(node) # look for parents for node in node_list: for tmp_name, tmp_data in six.iteritems(node.inputs): if not tmp_data['source']: continue if isinstance(tmp_data['source'], list): sources = tmp_data['source'] is_str = False else: sources = [tmp_data['source']] is_str = True parent_ids = [] for tmp_source in sources: if tmp_source in output_map: parent_id = output_map[tmp_source] node.add_parent(parent_id) parent_ids.append(parent_id) if parent_ids: if is_str: parent_ids = parent_ids[0] tmp_data['parent_id'] = parent_ids # sort node_list = top_sort(node_list, set()) return node_list, root_inputs # resolve nodes def resolve_nodes(node_list, root_inputs, data, serial_id, parent_ids, out_ds_name, log_stream): for k in root_inputs: kk = k.split('#')[-1] if kk in data: root_inputs[k] = data[kk] tmp_to_real_id_map = {} resolved_map = {} all_nodes = [] for node in node_list: # resolve input for tmp_name, tmp_data in six.iteritems(node.inputs): if not tmp_data['source']: continue if isinstance(tmp_data['source'], list): tmp_sources = tmp_data['source'] if 'parent_id' in tmp_data: tmp_parent_ids = tmp_data['parent_id'] else: tmp_parent_ids = [None] * len(tmp_sources) else: tmp_sources = [tmp_data['source']] if 'parent_id' in tmp_data: tmp_parent_ids = [tmp_data['parent_id']] else: tmp_parent_ids = [None] * len(tmp_sources) for tmp_source, tmp_parent_id in zip(tmp_sources, tmp_parent_ids): isOK = False # check root input if tmp_source in root_inputs: node.is_head = True node.set_input_value(tmp_name, tmp_source, root_inputs[tmp_source]) continue # check parent output for i in node.parents: for r_node in resolved_map[i]: if tmp_source in r_node.outputs: node.set_input_value(tmp_name, tmp_source, r_node.outputs[tmp_source]['value']) isOK = True break if isOK: break if isOK: continue # check resolved parent outputs if tmp_parent_id is not None: values = [list(r_node.outputs.values())[0]['value'] for r_node in resolved_map[tmp_parent_id]] if len(values) == 1: values = values[0] node.set_input_value(tmp_name, tmp_source, values) continue # scatter if node.scatter: # resolve scattered parameters scatters = None sc_nodes = [] for item in node.scatter: if scatters is None: scatters = [{item: v} for v in node.inputs[item]['value']] else: [i.update({item: v}) for i, v in zip(scatters, node.inputs[item]['value'])] for idx, item in enumerate(scatters): sc_node = copy.deepcopy(node) for k, v in six.iteritems(item): sc_node.inputs[k]['value'] = v for tmp_node in sc_node.sub_nodes: tmp_node.scatter_index = idx tmp_node.upper_root_inputs = sc_node.root_inputs sc_nodes.append(sc_node) else: sc_nodes = [node] # loop over scattered nodes for sc_node in sc_nodes: all_nodes.append(sc_node) # set real node ID resolved_map.setdefault(sc_node.id, []) tmp_to_real_id_map.setdefault(sc_node.id, set()) # resolve parents real_parens = set() for i in sc_node.parents: real_parens \|= tmp_to_real_id_map[i] sc_node.parents = real_parens if sc_node.is_head: sc_node.parents \|= parent_ids if sc_node.is_leaf: resolved_map[sc_node.id].append(sc_node) tmp_to_real_id_map[sc_node.id].add(serial_id) sc_node.id = serial_id serial_id += 1 else: serial_id, sub_tail_nodes, sc_node.sub_nodes = \ resolve_nodes(sc_node.sub_nodes, sc_node.root_inputs, sc_node.convert_dict_inputs(), serial_id, sc_node.parents, out_ds_name, log_stream) resolved_map[sc_node.id] += sub_tail_nodes tmp_to_real_id_map[sc_node.id] \|= set([n.id for n in sub_tail_nodes]) sc_node.id = serial_id serial_id += 1 # convert parameters to parent IDs in conditions if sc_node.condition: convert_params_in_condition_to_parent_ids(sc_node.condition, sc_node.inputs, tmp_to_real_id_map) # resolve outputs if sc_node.is_leaf: for tmp_name, tmp_data in six.iteritems(sc_node.outputs): tmp_data['value'] = "{}_{:03d}_{}".format(out_ds_name, sc_node.id, sc_node.name) # add loop count for nodes in a loop if sc_node.in_loop: tmp_data['value'] += '.___idds___num_run___' # return tails tail_nodes = [] for node in all_nodes: if node.is_tail: if node.is_tail: tail_nodes.append(node) else: tail_nodes += resolved_map[node.id] return serial_id, tail_nodes, all_nodes # parse condition string def parse_condition_string(cond_string): # remove $() cond_string = re.sub(r'\$\((?P<aaa>.+)\)', r'\g<aaa>', cond_string) cond_map = {} id = 0 while True: # look for the most inner parentheses item_list = re.findall(r'\(([^\(\)]+)\)', cond_string) if not item_list: return convert_plain_condition_string(cond_string, cond_map) else: for item in item_list: cond = convert_plain_condition_string(item, cond_map) key = '___{}___'.format(id) id += 1 cond_map[key] = cond cond_string = cond_string.replace('('+item+')', key) # extract parameter from token def extract_parameter(token): m = re.search(r'self\.([^!=]+)', token) return m.group(1) # convert plain condition string def convert_plain_condition_string(cond_string, cond_map): cond_string = re.sub(r' ! ', r'!', cond_string) cond_string = re.sub(r'\\|\\|', r' \|\| ', cond_string) cond_string = re.sub(r'&&', r' && ', cond_string) tokens = cond_string.split() left = None operator = None for token in tokens: token = token.strip() if token == '\|\|': operator = 'or' continue elif token == '&&': operator = 'and' continue elif token.startswith('self.'): param = extract_parameter(token) right = ConditionItem(param) if not left: left = right continue elif token.startswith('!self.'): param = extract_parameter(token) right = ConditionItem(param, operator='not') if not left: left = right continue elif re.search(r'^___\d+___$', token) and token in cond_map: right = cond_map[token] if not left: left = right continue elif re.search(r'^!___\d+___$', token) and token[1:] in cond_map: right = ConditionItem(cond_map[token[1:]], operator='not') if not left: left = right continue else: raise TypeError('unknown token "{}"'.format(token)) left = ConditionItem(left, right, operator) return left # convert parameter names to parent IDs def convert_params_in_condition_to_parent_ids(condition_item, input_data, id_map): for item in ['left', 'right']: param = getattr(condition_item, item) if isinstance(param, str): m = re.search(r'^[^\[]+\[(\d+)\]', param) if m: param = param.split('[')[0] idx = int(m.group(1)) else: idx = None isOK = False for tmp_name, tmp_data in six.iteritems(input_data): if param == tmp_name.split('/')[-1]: isOK = True if isinstance(tmp_data['parent_id'], list): if idx is not None: setattr(condition_item, item, id_map[tmp_data['parent_id'][idx]]) else: setattr(condition_item, item, id_map[tmp_data['parent_id']]) else: setattr(condition_item, item, id_map[tmp_data['parent_id']]) break if not isOK: raise ReferenceError('unresolved paramter {} in the condition string'.format(param)) elif isinstance(param, ConditionItem): convert_params_in_condition_to_parent_ids(param, input_data, id_map) # suppress inputs based on condition def 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h1st/model/ensemble/randomforest_classifier_stack_ensemble.py	Adatao/H1st	796	135115	<reponame>Adatao/H1st from typing import List from sklearn.ensemble import RandomForestClassifier from h1st.model.ensemble.classifier_stack_ensemble import ClassifierStackEnsemble from h1st.model.model import Model class RandomForestClassifierStackEnsemble(ClassifierStackEnsemble): """ A ready to use StackEnsemble for classifier with ensembler is a sklearn's MultiOutputClassifier using RandomForestClassifier Each sub model must be a subclass of h1.Model and its .predict() method will receive an input data as a dictionary that has 'X' key and numeric value and return a dictionary with 'predictions' key and its prediction value .. code-block:: python :caption: Sub model for a StackEnsemble Example class Model1(h1.Model): def predict(self, data): X = data['X'] ... return {'predictions': } .. code-block:: python :caption: RandomForestClassifierStackEnsemble usage Example class Model2(h1.Model): def predict(self, data): X = data['X'] ... return {'predictions': } class RandomForestClassifierStackEnsemble(ClassifierStackEnsemble): def __init__(self): super().__init__([ Model1().load('version_of_model_1'), Model2().load('version_of_model_2') ]) def load_data(self,): ... return loaded_data def prep(self, loaded_data): ... return prepared_data m1 = Model1() m1.load_prep_train_eval() ## Equivalent to # loaded_data = m1.load_data() # prepared_data = m1.prep(loaded_data) # m1.train(prepared_data) # m1.evaluate(prepared_data) print(m1.metrics) m1.persist('version_of_model_1') m2 = Model2() m2.load_prep_train_eval() print(m2.metrics) m2.persist('version_of_model_2') ensemble = RandomForestClassifierStackEnsemble( [Model1().load('version_of_model_1'), Model2().load('version_of_model_2')]) ensemble.load_prep_train_eval() print(ensemble.metrics) ensemble.persist('version_of_model_ensemble') ensemble.predict(...) """ def __init__(self, sub_models: List[Model], kwargs): super().__init__( # MultiOutputClassifier(RandomForestClassifier(n_jobs=-1, max_depth=4, random_state=42)), RandomForestClassifier(n_jobs=-1, max_depth=4, random_state=42), sub_models, kwargs )	[ 1, 529, 276, 1112, 420, 29958, 29909, 1272, 29877, 29914, 29950, 29896, 303, 13, 3166, 19229, 1053, 2391, 13, 13, 3166, 2071, 19668, 29889, 24031, 1053, 16968, 2831, 342, 2385, 3709, 13, 13, 3166, 298, 29896, 303, 29889, 4299, 29889, 24031, 29889, 1990, 3709, 29918, 1429, 29918, 24031, 1053, 4134, 3709, 7264, 29923, 1983, 6967, 13, 3166, 298, 29896, 303, 29889, 4299, 29889, 4299, 1053, 8125, 13, 13, 13, 1990, 16968, 2831, 342, 2385, 3709, 7264, 29923, 1983, 6967, 29898, 2385, 3709, 7264, 29923, 1983, 6967, 1125, 13, 1678, 9995, 13, 1678, 319, 7960, 304, 671, 10292, 29923, 1983, 6967, 363, 770, 3709, 411, 5662, 1590, 1358, 338, 263, 2071, 19668, 29915, 29879, 14974, 6466, 2385, 3709, 773, 16968, 2831, 342, 2385, 3709, 13, 13, 1678, 7806, 1014, 1904, 1818, 367, 263, 19481, 310, 298, 29896, 29889, 3195, 322, 967, 869, 27711, 580, 1158, 674, 7150, 385, 1881, 848, 408, 263, 8600, 393, 756, 525, 29990, 29915, 1820, 322, 16985, 995, 13, 1678, 322, 736, 263, 8600, 411, 525, 27711, 1080, 29915, 1820, 322, 967, 18988, 995, 13, 13, 1678, 6317, 775, 29899, 1271, 1057, 3017, 13, 4706, 584, 6671, 29901, 3323, 1904, 363, 263, 10292, 29923, 1983, 6967, 8741, 13, 13, 4706, 770, 8125, 29896, 29898, 29882, 29896, 29889, 3195, 1125, 13, 9651, 822, 8500, 29898, 1311, 29892, 848, 1125, 13, 18884, 1060, 353, 848, 1839, 29990, 2033, 13, 18884, 2023, 13, 18884, 736, 11117, 27711, 1080, 2396, 500, 13, 13, 1678, 6317, 775, 29899, 1271, 1057, 3017, 13, 4706, 584, 6671, 29901, 16968, 2831, 342, 2385, 3709, 7264, 29923, 1983, 6967, 8744, 8741, 13, 13, 4706, 770, 8125, 29906, 29898, 29882, 29896, 29889, 3195, 1125, 13, 18884, 822, 8500, 29898, 1311, 29892, 848, 1125, 13, 462, 1678, 1060, 353, 848, 1839, 29990, 2033, 13, 462, 1678, 2023, 13, 462, 1678, 736, 11117, 27711, 1080, 2396, 500, 13, 13, 4706, 770, 16968, 2831, 342, 2385, 3709, 7264, 29923, 1983, 6967, 29898, 2385, 3709, 7264, 29923, 1983, 6967, 1125, 13, 9651, 822, 4770, 2344, 12035, 1311, 1125, 13, 18884, 2428, 2141, 1649, 2344, 1649, 4197, 13, 462, 1678, 8125, 29896, 2141, 1359, 877, 3259, 29918, 974, 29918, 4299, 29918, 29896, 5477, 13, 462, 1678, 8125, 29906, 2141, 1359, 877, 3259, 29918, 974, 29918, 4299, 29918, 29906, 1495, 13, 462, 2314, 13, 13, 9651, 822, 2254, 29918, 1272, 29898, 1311, 29892, 1125, 13, 18884, 2023, 13, 18884, 736, 7500, 29918, 1272, 13, 13, 9651, 822, 8273, 29898, 1311, 29892, 7500, 29918, 1272, 1125, 13, 18884, 2023, 13, 18884, 736, 13240, 29918, 1272, 13, 13, 4706, 286, 29896, 353, 8125, 29896, 580, 13, 13, 4706, 286, 29896, 29889, 1359, 29918, 15287, 29918, 14968, 29918, 14513, 580, 13, 4706, 444, 11243, 27445, 304, 13, 4706, 396, 7500, 29918, 1272, 353, 286, 29896, 29889, 1359, 29918, 1272, 580, 13, 4706, 396, 13240, 29918, 1272, 353, 286, 29896, 29889, 15287, 29898, 15638, 29918, 1272, 29897, 13, 4706, 396, 286, 29896, 29889, 14968, 29898, 15287, 1965, 29918, 1272, 29897, 13, 4706, 396, 286, 29896, 29889, 24219, 403, 29898, 15287, 1965, 29918, 1272, 29897, 13, 13, 4706, 1596, 29898, 29885, 29896, 29889, 2527, 10817, 29897, 13, 4706, 286, 29896, 29889, 6774, 391, 877, 3259, 29918, 974, 29918, 4299, 29918, 29896, 1495, 13, 13, 4706, 286, 29906, 353, 8125, 29906, 580, 13, 4706, 286, 29906, 29889, 1359, 29918, 15287, 29918, 14968, 29918, 14513, 580, 13, 4706, 1596, 29898, 29885, 29906, 29889, 2527, 10817, 29897, 13, 4706, 286, 29906, 29889, 6774, 391, 877, 3259, 29918, 974, 29918, 4299, 29918, 29906, 1495, 13, 13, 4706, 21285, 353, 16968, 2831, 342, 2385, 3709, 7264, 29923, 1983, 6967, 29898, 13, 9651, 518, 3195, 29896, 2141, 1359, 877, 3259, 29918, 974, 29918, 4299, 29918, 29896, 5477, 13, 632, 8125, 29906, 2141, 1359, 877, 3259, 29918, 974, 29918, 4299, 29918, 29906, 1495, 2314, 13, 4706, 21285, 29889, 1359, 29918, 15287, 29918, 14968, 29918, 14513, 580, 13, 4706, 1596, 29898, 24031, 29889, 2527, 10817, 29897, 13, 4706, 21285, 29889, 6774, 391, 877, 3259, 29918, 974, 29918, 4299, 29918, 24031, 1495, 13, 4706, 21285, 29889, 27711, 29077, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1014, 29918, 9794, 29901, 2391, 29961, 3195, 1402, 3579, 19290, 1125, 13, 4706, 2428, 2141, 1649, 2344, 12035, 13, 9651, 396, 14974, 6466, 2385, 3709, 29898, 17875, 2831, 342, 2385, 3709, 29898, 29876, 29918, 9057, 29879, 10457, 29896, 29892, 4236, 29918, 19488, 29922, 29946, 29892, 4036, 29918, 3859, 29922, 29946, 29906, 8243, 13, 9651, 16968, 2831, 342, 2385, 3709, 29898, 29876, 29918, 9057, 29879, 10457, 29896, 29892, 4236, 29918, 19488, 29922, 29946, 29892, 4036, 29918, 3859, 29922, 29946, 29906, 511, 13, 9651, 1014, 29918, 9794, 29892, 13, 9651, 3579, 19290, 13, 4706, 1723, 13, 2 ]
samples/sample_3D.py	bknight-i3drobotics/Stereo3D	3	65114	<reponame>bknight-i3drobotics/Stereo3D from stereo3d import Stereo3D from stereo3d.stereocalibration import StereoCalibration from stereo3d.stereocapture import StereoCapture, StereoCaptureVREP CAMERA_TYPE_PHOBOS = 0 CAMERA_TYPE_DEIMOS = 1 CAMERA_TYPE_PYLON = 2 CAMERA_TYPE_IMAGE = 3 CAMERA_TYPE_VREP = 4 camera_type = CAMERA_TYPE_IMAGE stcap = None camera_name = None if (camera_type == CAMERA_TYPE_PHOBOS): camera_name = "phobos" stcap = StereoCapture("Phobos",["22864912","22864917"]) elif (camera_type == CAMERA_TYPE_PYLON): stcap = StereoCapture("Pylon",["22864912","22864917"]) elif (camera_type == CAMERA_TYPE_DEIMOS): camera_name = "deimos" stcap = StereoCapture("Deimos",0) elif (camera_type == CAMERA_TYPE_IMAGE): #camera_name = "deimos" #stcap = StereoCapture("Image",["SampleData/deimos_left.png","SampleData/deimos_right.png"]) camera_name = "phobos" folder_i = "D:/Users/monke/OneDrive - i3d Robotics Ltd/Phobos/BenCCal/test_images" stcap = StereoCapture("Image",[folder_i+"/left_a.png",folder_i+"/right_a.png"]) elif (camera_type == CAMERA_TYPE_VREP): api_port = 20000 left_vision_sensor_name = "StereoCameraLeft" right_vision_sensor_name = "StereoCameraRight" camera_name = "vrep" stcapVREP = StereoCaptureVREP(left_vision_sensor_name,right_vision_sensor_name,api_port) stcap = StereoCapture(stcapVREP) else: print("Invalid camera type.") exit() # define inout folder #folder = "SampleData/" CAL_MODE_FROM_IMAGES = 0 CAL_MODE_FROM_YAML = 1 CAL_MODE_FROM_XML = 2 folder = "D:/Users/monke/OneDrive - i3d Robotics Ltd/Phobos/BenCCal/calibration_1/" stcal = None cal_mode = CAL_MODE_FROM_XML if (cal_mode == CAL_MODE_FROM_IMAGES): # define calibration directories left_images_folder = folder + "deimos_cal/" right_images_folder = folder + "deimos_cal/" output_folder = folder + "deimos_cal/" left_wildcard = "_l.png" right_wildcard = "_r.png" grid_size = 39.0 grid_rows = 6 grid_cols = 8 # generate calibration from images stcal = StereoCalibration() stcal.calibrate( left_images_folder,right_images_folder, output_folder,left_wildcard,right_wildcard, grid_size, grid_rows, grid_cols ) elif (cal_mode == CAL_MODE_FROM_YAML): # define calibration files for left and right image left_cal_file = folder + camera_name +"_left.yaml" right_cal_file = folder + camera_name +"_right.yaml" # get calibration from yaml files stcal = StereoCalibration() stcal.get_cal_from_yaml(left_cal_file,right_cal_file) elif (cal_mode == CAL_MODE_FROM_XML): # define calibration files for left and right image ''' left_cal_file = folder + camera_name +"_left_calibration.xml" right_cal_file = folder + camera_name +"_right_calibration.xml" stereo_cal_file = folder + camera_name +"_stereo_calibration.xml" left_rect_file = folder + camera_name +"_left_rectification.xml" right_rect_file = folder + camera_name +"_right_rectification.xml" ''' left_cal_file = folder +"left_calibration.xml" right_cal_file = folder +"right_calibration.xml" stereo_cal_file = folder +"stereo_calibration.xml" left_rect_file = folder +"left_rectification.xml" right_rect_file = folder +"right_rectification.xml" # get calibration from yaml files stcal = StereoCalibration() stcal.get_cal_from_xml(left_cal_file,right_cal_file,stereo_cal_file,left_rect_file,right_rect_file) # setup Stereo3D s3D = Stereo3D(stcap,stcal,"BM") # run Stereo3D GUI for generating 3D out_folder="D:/Users/monke/OneDrive - i3d Robotics Ltd/Phobos/BenCCal/output/" s3D.run(out_folder)	[ 1, 529, 276, 1112, 420, 29958, 29890, 3959, 523, 29899, 29875, 29941, 26419, 7451, 1199, 29914, 29903, 12358, 29877, 29941, 29928, 13, 3166, 269, 12358, 29877, 29941, 29881, 1053, 317, 12358, 29877, 29941, 29928, 13, 3166, 269, 12358, 29877, 29941, 29881, 29889, 303, 406, 18642, 26218, 1053, 317, 12358, 29877, 7856, 26218, 13, 3166, 269, 12358, 29877, 29941, 29881, 29889, 303, 406, 542, 2156, 545, 1053, 317, 12358, 29877, 21133, 545, 29892, 317, 12358, 29877, 21133, 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cloudml-template/template/inference.py	VanessaDo/cloudml-samples	3	165876	<reponame>VanessaDo/cloudml-samples<filename>cloudml-template/template/inference.py<gh_stars>1-10 from googleapiclient import discovery from oauth2client.client import GoogleCredentials GCP_PROJECT = '' # Change to GCP project where the Cloud ML Engine model is deployed CMLE_MODEL_NAME = '' # Change to the deployed Cloud ML Engine model CMLE_MODEL_VERSION = None # If None, the default version will be used def predict_cmle(instances): """ Use a deployed model to Cloud ML Engine to perform prediction Args: instances: list of json, csv, or tf.example objects, based on the serving function called Returns: response - dictionary. If no error, response will include an item with 'predictions' key """ credentials = GoogleCredentials.get_application_default() service = discovery.build('ml', 'v1', credentials=credentials) model_url = 'projects/{}/models/{}'.format(GCP_PROJECT, CMLE_MODEL_NAME) if CMLE_MODEL_VERSION is not None: model_url += '/versions/{}'.format(CMLE_MODEL_VERSION) request_data = { 'instances': instances } response = service.projects().predict( body=request_data, name=model_url ).execute() output = response return output	[ 1, 529, 276, 1112, 420, 29958, 29963, 273, 9297, 6132, 29914, 9274, 828, 29899, 27736, 29966, 9507, 29958, 9274, 828, 29899, 6886, 29914, 6886, 29914, 262, 1659, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 5386, 481, 293, 1593, 1053, 20699, 13, 3166, 288, 5150, 29906, 4645, 29889, 4645, 1053, 5087, 28037, 13, 13, 13, 29954, 6271, 29918, 8618, 17637, 353, 6629, 29871, 396, 10726, 304, 402, 6271, 2060, 988, 278, 14293, 23158, 10863, 1904, 338, 21168, 13, 24494, 1307, 29918, 20387, 29931, 29918, 5813, 353, 6629, 29871, 396, 10726, 304, 278, 21168, 14293, 23158, 10863, 1904, 13, 24494, 1307, 29918, 20387, 29931, 29918, 16358, 353, 6213, 29871, 396, 960, 6213, 29892, 278, 2322, 1873, 674, 367, 1304, 13, 13, 13, 1753, 8500, 29918, 4912, 280, 29898, 2611, 2925, 1125, 13, 1678, 9995, 4803, 263, 21168, 1904, 304, 14293, 23158, 10863, 304, 2189, 18988, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 8871, 29901, 1051, 310, 4390, 29892, 11799, 29892, 470, 15886, 29889, 4773, 3618, 29892, 2729, 373, 278, 16330, 740, 2000, 13, 1678, 16969, 29901, 13, 4706, 2933, 448, 8600, 29889, 960, 694, 1059, 29892, 2933, 674, 3160, 385, 2944, 411, 525, 27711, 1080, 29915, 1820, 13, 1678, 9995, 13, 13, 1678, 16140, 353, 5087, 28037, 29889, 657, 29918, 6214, 29918, 4381, 580, 13, 13, 1678, 2669, 353, 20699, 29889, 4282, 877, 828, 742, 525, 29894, 29896, 742, 16140, 29922, 11944, 9409, 29897, 13, 1678, 1904, 29918, 2271, 353, 525, 16418, 19248, 6822, 9794, 29914, 8875, 4286, 4830, 29898, 29954, 6271, 29918, 8618, 17637, 29892, 315, 29924, 1307, 29918, 20387, 29931, 29918, 5813, 29897, 13, 13, 1678, 565, 315, 29924, 1307, 29918, 20387, 29931, 29918, 16358, 338, 451, 6213, 29901, 13, 4706, 1904, 29918, 2271, 4619, 8207, 26100, 29914, 8875, 4286, 4830, 29898, 24494, 1307, 29918, 20387, 29931, 29918, 16358, 29897, 13, 13, 1678, 2009, 29918, 1272, 353, 426, 13, 4706, 525, 2611, 2925, 2396, 8871, 13, 1678, 500, 13, 13, 1678, 2933, 353, 2669, 29889, 16418, 2141, 27711, 29898, 13, 4706, 3573, 29922, 3827, 29918, 1272, 29892, 13, 4706, 1024, 29922, 4299, 29918, 2271, 13, 1678, 13742, 7978, 580, 13, 13, 1678, 1962, 353, 2933, 13, 1678, 736, 1962, 13, 2 ]
interstellar/sprite.py	AnythingTechPro/interstellar	1	62817	<reponame>AnythingTechPro/interstellar<filename>interstellar/sprite.py import random import pygame import time from interstellar import node, resource, audio, controller, mechanism class SpriteError(node.NodeError): """ A sprite specific runtime error """ class Sprite(node.Node): """ A controlled object within the game usually a player """ def __init__(self, parent, image, controller): super(Sprite, self).__init__() self._parent = parent self.image = image self.controller = controller(self) self._attachment = None self.attachment_sound = audio.AudioSound('assets/audio/sfx/grabbed_attachment.wav') self.attachment_deny_sound = audio.AudioSound('assets/audio/sfx/grabbed_attachment_deny.wav') self.health = 0 self.damage = 0 self.can_damage = True @property def attachment(self): return self._attachment @attachment.setter def attachment(self, attachment): if self._attachment or attachment is self._attachment: if self.attachment_deny_sound.playing: self.attachment_deny_sound.stop() self.attachment_deny_sound.play() return self._attachment = attachment self.attachment.setup() if self.attachment_sound.playing: self.attachment_sound.stop() self.attachment_sound.play() def hurt(self, attacker): if not self.can_damage: return if self.health <= 0: return self.die(attacker) self.health -= attacker.damage def die(self, args, kwargs): pass def setup(self): self.controller.setup() def update(self): self.controller.update() if self._attachment: self._attachment.update() def explicit_update(self): self.controller.explicit_update() def destroy(self): super(Sprite, self).destroy() self.image.destroy() self.image = None self.controller.destroy() self.controller = None if self._attachment: self._attachment.destroy() self.attachment_sound.destroy() self.attachment_deny_sound.destroy() class SpriteController(node.Node): """ A controller, movement management for a specific sprite object """ def __init__(self, sprite, bind_events=True): super(SpriteController, self).__init__() self.bind_events = bind_events self.sprite = sprite self.speed = 0 self._parent = sprite._parent self.image = sprite.image def update(self): self.move() def explicit_update(self): pass def move(self): if self.sprite._attachment and hasattr(self.sprite._attachment, 'image') and self.sprite._attachment.image and self.image: self.sprite._attachment.image.position = (self.image.x, self.image.y) def bind(self, args, *kwargs): if not self._parent or not self.bind_events: return None return self._parent.bind(args, *kwargs) def unbind(self, args, *kwargs): if not self._parent or not self.bind_events: return None return self._parent.unbind(args, *kwargs) def send(self, args, *kwargs): if not self._parent or not self.bind_events: return None return self._parent.send(args, *kwargs) def destroy(self): super(SpriteController, self).destroy() self.sprite = None self.speed = 0 class ShipController(SpriteController): def __init__(self, sprite): super(ShipController, self).__init__(sprite) self.music_array = resource.ResourceAudioArray(self._parent.root, [ 'assets/audio/sfx/laser_fire_0.wav', 'assets/audio/sfx/laser_fire_1.wav', 'assets/audio/sfx/laser_fire_2.wav', 'assets/audio/sfx/laser_fire_3.wav', ]) self.fire_sound = None self.speed = 15 self.health = 100 self.projectile_speed = 20 self.moving_forward = False self.moving_backward = False self.moving_right = False self.moving_left = False self.firing = False self.projectiles = [] self.maximum_projectiles = 15 self.controller = controller.GameController(self) self.current_distance = 0 def setup(self): super(ShipController, self).setup() self.bind('Up', self.move_forward) self.bind('KeyRelease-Up', lambda event: self.move_forward(event, True)) self.bind('Down', self.move_backward) self.bind('KeyRelease-Down', lambda event: self.move_backward(event, True)) self.bind('Right', self.move_right) self.bind('KeyRelease-Right', lambda event: self.move_right(event, True)) self.bind('Left', self.move_left) self.bind('KeyRelease-Left', lambda event: self.move_left(event, True)) self.bind('space', self.fire) self.bind('KeyRelease-space', lambda event: self.fire(event, True)) self.controller.setup() def move_forward(self, event, release=False): if not release: self.moving_forward = True else: self.moving_forward = False def move_backward(self, event, release=False): if not release: self.moving_backward = True else: self.moving_backward = False def move_right(self, event, release=False): if not release: self.moving_right = True else: self.moving_right = False def move_left(self, event, release=False): if not release: self.moving_left = True else: self.moving_left = False def fire(self, event, release=False): if not release: self.firing = True else: self.firing = False def explicit_update(self): self.controller.update() def move(self): super(ShipController, self).move() self.current_distance += self.speed if self.moving_forward and not self.image.y - self.image.height / 2 <= 0: self.image.y -= self.speed elif self.moving_backward and not self.image.y + self.image.height / 2 >= self._parent.root.display.height: self.image.y += self.speed / 2 self.current_distance -= self.speed / 2 if self.moving_right and not self.image.x + self.image.width / 2 >= self._parent.root.display.width: self.image.x += self.speed elif self.moving_left and not self.image.x - self.image.width / 2 <= 0: self.image.x -= self.speed if self.firing and len(self.projectiles) < self.maximum_projectiles: self.fire_projectile() self.update_projectiles() self.check_player_collisions() def update_projectiles(self): for projectile in self.projectiles: if projectile.y <= 0: self.destroy_projectile(projectile) continue if self.check_projectile_collisions(projectile): self.destroy_projectile(projectile) continue projectile.y -= random.random() self.projectile_speed * 2 def check_projectile_collisions(self, projectile): for asteroid in self._parent.asteroids: if projectile.collide_point(asteroid.image): asteroid.hurt(self.sprite) return True return False def check_player_collisions(self): for asteroid in self._parent.asteroids: if asteroid.image.collide_point(self.sprite.image): return self.sprite.hurt(asteroid) def fire_projectile(self): if self.fire_sound: self.music_array.deselect(use_pygame=True) self.fire_sound = None self.fire_sound = self.music_array.select(False, use_pygame=True) self.fire_sound.play() bullet0 = resource.ResourceImage(self._parent.root, 'assets/bullet.png') bullet0.position = (self.image.x - self.image.width / 2, self.image.y) bullet1 = resource.ResourceImage(self._parent.root, 'assets/bullet.png') bullet1.position = (self.image.x + self.image.width / 2, self.image.y) bullet0.render(self._parent.canvas) bullet1.render(self._parent.canvas) self.projectiles.append(bullet0) self.projectiles.append(bullet1) def destroy_projectile(self, projectile): projectile.destroy() self.projectiles.remove(projectile) def destroy_projectiles(self): for projectile in self.projectiles: self.destroy_projectile(projectile) def destroy(self): self.music_array.destroy() self.fire_sound = None self._parent.unbind('Up') self._parent.unbind('KeyRelease-Up') self._parent.unbind('Down') self._parent.unbind('KeyRelease-Down') self._parent.unbind('Right') self._parent.unbind('KeyRelease-Right') self._parent.unbind('Left') self._parent.unbind('KeyRelease-Left') self._parent.unbind('space') self._parent.unbind('KeyRelease-space') self.controller.destroy() self.destroy_projectiles() super(ShipController, self).destroy() class Ship(Sprite): def __init__(self, parent, controller): image = resource.ResourceImage(parent.root, 'assets/player.png') image.position = (parent.master.root.winfo_width() / 2, parent.master.root.winfo_height() - image.height) image.render(parent.canvas) super(Ship, self).__init__(parent, image, controller) self.health = 100 self.damage = 5 def die(self, killer): self._parent.explode(self.image.x, self.image.y) self._parent.end() class AsteroidController(SpriteController): def __init__(self, sprite): super(AsteroidController, self).__init__(sprite) self.speed = 25 self.speed = random.random() * self.speed def move(self): if self.image.y >= self._parent.root.display.height: self._parent.remove_asteroid(self.sprite) return self.image.y += self.speed class Asteroid(Sprite): PROBABILITY = 100 def __init__(self, parent, controller, image=None, is_mechanism=False): if not image: images = [ 'assets/asteroids/asteroid-small.png', 'assets/asteroids/asteroid-big.png' ] image = resource.ResourceImage(parent.root, random.choice(images)) image.position = (random.randrange(0, parent.root.display.width), 0) image.render(parent.canvas) super(Asteroid, self).__init__(parent, image, controller) self.is_mechanism = is_mechanism self.health = 10 self.damage = 1 def die(self, killer): self._parent.explode(self.image.x, self.image.y) self._parent.remove_asteroid(self, True) class MechanismAsteroid(Asteroid): ICON = '' ATTACHMENT = None def __init__(self, parent, controller): image = resource.ResourceImage(parent.root, self.ICON) image.position = (random.randrange(0, parent.root.display.width), 0) image.render(parent.canvas) super(MechanismAsteroid, self).__init__(parent, controller, image, True) self.health = 0 self.damage = 0 def die(self, killer): killer.attachment = self.ATTACHMENT(killer) self._parent.remove_asteroid(self, False) class ShieldMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.5 ICON = 'assets/icons/shield_icon.png' ATTACHMENT = mechanism.ShieldMechanism class InstantKillMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.1 ICON = 'assets/icons/instant_kill_icon.png' ATTACHMENT = mechanism.InstantKillMechanism class FullHealthMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.3 ICON = 'assets/icons/full_health_icon.png' ATTACHMENT = mechanism.FullHealthMechanism class DoubleHealthMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.5 ICON = 'assets/icons/double_health_icon.png' ATTACHMENT = mechanism.DoubleHealthMechanism	[ 1, 529, 276, 1112, 420, 29958, 10773, 1918, 29911, 5309, 1184, 29914, 1639, 6236, 279, 29966, 9507, 29958, 1639, 6236, 279, 29914, 15099, 568, 29889, 2272, 13, 5215, 4036, 13, 5215, 22028, 13, 5215, 931, 13, 3166, 1006, 6236, 279, 1053, 2943, 29892, 6503, 29892, 10348, 29892, 4701, 29892, 13336, 13, 13, 1990, 14314, 568, 2392, 29898, 3177, 29889, 4247, 2392, 1125, 13, 1678, 9995, 13, 1678, 319, 29227, 2702, 10073, 1059, 13, 1678, 9995, 13, 13, 1990, 14314, 568, 29898, 3177, 29889, 4247, 1125, 13, 1678, 9995, 13, 1678, 319, 20704, 1203, 2629, 278, 3748, 5491, 263, 4847, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 3847, 29892, 1967, 29892, 4701, 1125, 13, 4706, 2428, 29898, 29903, 558, 568, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 13, 4706, 1583, 3032, 3560, 353, 3847, 13, 4706, 1583, 29889, 3027, 353, 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sockmetrics.py	ChengWu-NJ/ebpf-bcc-apps	0	58044	#!/usr/bin/python3 # wucheng August 8, 2019 # sockmetrics.py --- collect sock communication info among hosts # version 0.9 # two methods to output results: # 1. to screen # 2. to TDengine, a kind of structured time series db # please run ./sockmetrics -h to get help # This program bases on https://github.com/iovisor/bcc # Licensed Apache-2.0 #from __future__ import print_function from bcc import BPF import argparse from socket import inet_ntop, AF_INET, AF_INET6 from struct import pack from time import sleep, strftime from subprocess import call from collections import namedtuple, defaultdict import os import json import taos import datetime # arguments def range_check(string): value = int(string) if value < 1 or value > 600: msg = "value must be 1 ~ 600, got %d" % (value,) raise argparse.ArgumentTypeError(msg) return value def positive_check(string): value = int(string) if value < 1: msg = "value must be stricly positive, got %d" % (value,) raise argparse.ArgumentTypeError(msg) return value examples = """examples: ./sockmetrics --conf_file='/etc/sockmetrics.conf' # input all sock trace data into tsdb configured in conf_file ./sockmetrics # trace Sock send/recv by host on screen ./sockmetrics -p 181 # only trace PID 181 on screen """ parser = argparse.ArgumentParser( description="Summarize Sock send/recv throughput by host", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=examples) parser.add_argument("--conf_file", help="this argument points the configure file, and is exclusive and discards other arguments ") parser.add_argument("-p", "--pid", help="trace this PID only") parser.add_argument("interval", nargs="?", default=1, type=range_check, help="output interval, in seconds (default 1), range 1 ~ 600") parser.add_argument("count", nargs="?", default=-1, type=positive_check, help="number of the records with the top recerived bytes to output per interval") parser.add_argument("--ebpf", action="store_true", help=argparse.SUPPRESS) args = parser.parse_args() def get_arguments_from_conf_file(conf_file): with open(conf_file, 'r') as jsonfile: _d = json.load(jsonfile) if _d.get("dbhost") and _d.get("database") \ and _d.get("user") and _d.get("password"): return _d else: raise Exception("conf_file %s is invalid."%conf_file) conf_dict = None if args.conf_file and os.stat(args.conf_file): conf_dict = get_arguments_from_conf_file(args.conf_file) args.pid = None _interval = conf_dict.get('interval') if _interval is not None and _interval > 0 and _interval <= 600: args.interval = _interval else: args.interval = 1 _count = conf_dict.get('count') if _count is not None and _count > 0: args.count = _count else: args.count = -1 debug = 0 # define BPF program bpf_text = """ #include <uapi/linux/ptrace.h> #include <net/sock.h> #include <bcc/proto.h> #include <linux/net.h> #include <uapi/linux/ip.h> #include <linux/ip.h> struct ipv4_key_t { u32 pid; u32 saddr; u32 daddr; u16 lport; u16 dport; u16 socktype; }; BPF_HASH(ipv4_send_bytes, struct ipv4_key_t); BPF_HASH(ipv4_recv_bytes, struct ipv4_key_t); struct ipv6_key_t { u32 pid; unsigned __int128 saddr; unsigned __int128 daddr; u16 lport; u16 dport; u16 socktype; }; BPF_HASH(ipv6_send_bytes, struct ipv6_key_t); BPF_HASH(ipv6_recv_bytes, struct ipv6_key_t); //static int kprobe__packet_snd(struct pt_regs ctx, struct socket sock, // struct msghdr msg, size_t len) int kprobe__inet_sendmsg(struct pt_regs ctx, struct socket sock, struct msghdr msg, size_t size) { //bpf_trace_printk("entry inet_sendmsg now. \\n"); u32 pid = bpf_get_current_pid_tgid(); FILTER struct sock sk = sock->sk; u16 dport = 0, family = sk->__sk_common.skc_family; //bpf_trace_printk("inet_sendmsg got family %d, and protocol is %d\\n", family, sk->__sk_common.skc_prot); if (family == AF_INET && sk->__sk_common.skc_rcv_saddr != sk->__sk_common.skc_daddr) { struct ipv4_key_t ipv4_key = {.pid = pid}; ipv4_key.saddr = sk->__sk_common.skc_rcv_saddr; ipv4_key.daddr = sk->__sk_common.skc_daddr; ipv4_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv4_key.dport = ntohs(dport); ipv4_key.socktype = sock->type; ipv4_send_bytes.increment(ipv4_key, size); } if (family == AF_INET6) { struct ipv6_key_t ipv6_key = {.pid = pid}; __builtin_memcpy(&ipv6_key.saddr, sk->__sk_common.skc_v6_rcv_saddr.in6_u.u6_addr32, sizeof(ipv6_key.saddr)); __builtin_memcpy(&ipv6_key.daddr, sk->__sk_common.skc_v6_daddr.in6_u.u6_addr32, sizeof(ipv6_key.daddr)); if (ipv6_key.saddr == ipv6_key.daddr) return 0; ipv6_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv6_key.dport = ntohs(dport); ipv6_key.socktype = sock->type; ipv6_send_bytes.increment(ipv6_key, size); } // else drop return 0; } int kprobe__inet_recvmsg(struct pt_regs ctx, struct socket sock, struct msghdr msg, size_t size) { u32 pid = bpf_get_current_pid_tgid(); FILTER struct sock sk = sock->sk; u16 dport = 0, family = sk->__sk_common.skc_family; u64 val, zero = 0; // bpf_trace_printk("inet_recvmsg got family %d\\n", family); if (size <= 0) return 0; if (family == AF_INET && sk->__sk_common.skc_rcv_saddr != sk->__sk_common.skc_daddr) { struct ipv4_key_t ipv4_key = {.pid = pid}; ipv4_key.saddr = sk->__sk_common.skc_rcv_saddr; ipv4_key.daddr = sk->__sk_common.skc_daddr; ipv4_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv4_key.dport = ntohs(dport); ipv4_key.socktype = sock->type; ipv4_recv_bytes.increment(ipv4_key,size); } if (family == AF_INET6) { struct ipv6_key_t ipv6_key = {.pid = pid}; __builtin_memcpy(&ipv6_key.saddr, sk->__sk_common.skc_v6_rcv_saddr.in6_u.u6_addr32, sizeof(ipv6_key.saddr)); __builtin_memcpy(&ipv6_key.daddr, sk->__sk_common.skc_v6_daddr.in6_u.u6_addr32, sizeof(ipv6_key.daddr)); if (ipv6_key.saddr == ipv6_key.daddr) return 0; ipv6_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv6_key.dport = ntohs(dport); ipv6_key.socktype = sock->type; ipv6_recv_bytes.increment(ipv6_key, size); } // else drop return 0; } """ # code substitutions if args.pid: bpf_text = bpf_text.replace('FILTER', 'if (pid != %s) { return 0; }' % args.pid) else: bpf_text = bpf_text.replace('FILTER', '') if debug or args.ebpf: print(bpf_text) if args.ebpf: exit() SockSessionKey = namedtuple('SockSession', ['pid', 'laddr', 'lport', 'daddr', 'dport', 'socktype']) def pid_to_comm(pid): try: comm = open("/proc/%d/comm" % pid, "r").read().rstrip() return comm except IOError: return str(pid) def _to_socktype(type): #https://elixir.bootlin.com/linux/latest/source/include/linux/net.h#L60 return "TCP" if type==1 else "UDP" if type==2 else "Other" def get_ipv4_session_key(k): return SockSessionKey(pid=k.pid, laddr=inet_ntop(AF_INET, pack("I", k.saddr)), lport=k.lport, daddr=inet_ntop(AF_INET, pack("I", k.daddr)), dport=k.dport, socktype=k.socktype) def get_ipv6_session_key(k): return SockSessionKey(pid=k.pid, laddr=inet_ntop(AF_INET6, k.saddr), lport=k.lport, daddr=inet_ntop(AF_INET6, k.daddr), dport=k.dport, socktype=k.socktype) def getdbconnection(cnfdict): _conn = taos.connect(host=cnfdict["dbhost"], user=cnfdict["user"], password=cnfdict["password"], database=cnfdict["database"]) print(_conn._host) return _conn def sqlexecute(cursor, sqltext): #continue when network error, etc. try: cursor.execute(sqltext) return True except: return False def createtables(cursor): _ipv4_ready = sqlexecute(cursor, """create table if not exists ipv4_metrics ( epoch timestamp, pid int, comm binary(40), type int, laddr binary(15), lport int, raddr binary(15), rport int, rx_byte bigint, tx_byte bigint, interval_sum smallint)""") _ipv6_ready = sqlexecute(cursor, """create table if not exists ipv6_metrics ( epoch timestamp, pid int, comm binary(40), type int, laddr6 binary(39), lport int, raddr6 binary(39), rport int, rx_byte bigint, tx_byte bigint, interval_sum smallint)""") if _ipv4_ready and _ipv6_ready: return True else: return False # initialize BPF b = BPF(text=bpf_text) """ if b.get_kprobe_functions(b"netif_receive_skb"): b.attach_kprobe(event="netif_receive_skb", fn_name="trace_netif_receive_skb") else: print("ERROR: netif_receive_skb() kernel function not found or traceable. " "Older kernel versions not supported.") exit() """ ipv4_send_bytes = b["ipv4_send_bytes"] ipv4_recv_bytes = b["ipv4_recv_bytes"] ipv6_send_bytes = b["ipv6_send_bytes"] ipv6_recv_bytes = b["ipv6_recv_bytes"] # tsdb if conf_file is set if conf_dict: output_db = True else: output_db =False if output_db: conn = getdbconnection(conf_dict) csr = conn.cursor() i_reconnect = 0 if not createtables(csr): raise(Exception("Something wrong when create tables.")) print('Tracing... Output every %s secs. Hit Ctrl-C to end' % args.interval) print('Collected data will input to tsdb...') # output exiting = False while not exiting: try: sleep(args.interval) except KeyboardInterrupt: exiting = True if output_db: csr.close() conn.close() # reset dbconnect. continue when network error, etc. if output_db and i_reconnect >= 100: i_reconnect = 0 try: csr.close() conn.close() conn = getdbconnection(conf_dict) csr = conn.cursor() except: pass # IPv4: build dict of all seen keys ipv4_throughput = defaultdict(lambda: [0, 0]) for k, v in ipv4_send_bytes.items(): key = get_ipv4_session_key(k) ipv4_throughput[key][0] = v.value ipv4_send_bytes.clear() for k, v in ipv4_recv_bytes.items(): key = get_ipv4_session_key(k) ipv4_throughput[key][1] = v.value ipv4_recv_bytes.clear() if not output_db and ipv4_throughput: print("%-6s %-12s %-6s %-21s %-21s %9s %9s" % ("PID", "COMM", "TYPE", "LADDR", "RADDR", "RX_Byte", "TX_Byte")) # output i = 0 for k, (send_bytes, recv_bytes) in (ipv4_throughput.items() if args.count==-1 else sorted(ipv4_throughput.items(), key=lambda kv: sum(kv[1]), reverse=True)): if args.count > 0 and i >= args.count: break if output_db: sqlexecute(csr, """insert into ipv4_metrics (epoch, pid, comm, type, laddr, lport, raddr, rport, rx_byte, tx_byte, interval_sum) values ('%s', %d, '%s', %d, '%s', %d, '%s', %d, %d, %d, %d)""" % (datetime.datetime.now(), k.pid, pid_to_comm(k.pid), k.socktype, k.laddr, k.lport, k.daddr, k.dport, recv_bytes, send_bytes, args.interval) ) else: print("%-6d %-12.12s %-6.6s %-21s %-21s %9d %9d" % (k.pid, pid_to_comm(k.pid), _to_socktype(k.socktype), k.laddr + ":" + str(k.lport), k.daddr + ":" + str(k.dport), recv_bytes, send_bytes)) i += 1 # IPv6: build dict of all seen keys ipv6_throughput = defaultdict(lambda: [0, 0]) for k, v in ipv6_send_bytes.items(): key = get_ipv6_session_key(k) ipv6_throughput[key][0] = v.value ipv6_send_bytes.clear() for k, v in ipv6_recv_bytes.items(): key = get_ipv6_session_key(k) ipv6_throughput[key][1] = v.value ipv6_recv_bytes.clear() if ipv6_throughput: # more than 80 chars, sadly. print("\n%-6s %-12s %-6s %-32s %-32s %9s %9s" % ("PID", "COMM", "TYPE", "LADDR6", "RADDR6", "RX_Byte", "TX_Byte")) # output i = 0 for k, (send_bytes, recv_bytes) in (ipv6_throughput.items() if args.count==-1 else sorted(ipv6_throughput.items(), key=lambda kv: sum(kv[1]), reverse=True)): if args.count > 0 and i >= args.count: break if output_db: sqlexecute(csr, """insert into ipv6_metrics (epoch, pid, comm, type, laddr6, lport, raddr6, rport, rx_byte, tx_byte, interval_sum) values ('%s', %d, '%s', %d, '%s', %d, '%s', %d, %d, %d, %d)""" % (datetime.datetime.now(), k.pid, pid_to_comm(k.pid), k.socktype, k.laddr, k.lport, k.daddr, k.dport, recv_bytes, send_bytes, args.interval) ) else: print("%-6d %-12.12s %-6.6s %-32s %-32s %9d %9d" % (k.pid, pid_to_comm(k.pid), _to_socktype(k.socktype), k.laddr + ":" + str(k.lport), k.daddr + ":" + str(k.dport), recv_bytes, send_bytes)) i += 1 if output_db: i_reconnect += 1	[ 1, 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train.py	gakkispy/DD-Net-Pytorch	31	183520	#! /usr/bin/env python #! coding:utf-8 from pathlib import Path import matplotlib.pyplot as plt from torch import log from tqdm import tqdm import torch import torch.nn as nn import argparse import torch.optim as optim from torch.optim.lr_scheduler import ReduceLROnPlateau from sklearn.metrics import confusion_matrix from dataloader.jhmdb_loader import load_jhmdb_data, Jdata_generator, JConfig from dataloader.shrec_loader import load_shrec_data, Sdata_generator, SConfig from models.DDNet_Original import DDNet_Original as DDNet from utils import makedir import sys import time import numpy as np import logging sys.path.insert(0, './pytorch-summary/torchsummary/') from torchsummary import summary # noqa savedir = Path('experiments') / Path(str(int(time.time()))) makedir(savedir) logging.basicConfig(filename=savedir/'train.log', level=logging.INFO) history = { "train_loss": [], "test_loss": [], "test_acc": [] } def train(args, model, device, train_loader, optimizer, epoch, criterion): model.train() train_loss = 0 for batch_idx, (data1, data2, target) in enumerate(tqdm(train_loader)): M, P, target = data1.to(device), data2.to(device), target.to(device) optimizer.zero_grad() output = model(M, P) loss = criterion(output, target) train_loss += loss.detach().item() loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: msg = ('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data1), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) print(msg) logging.info(msg) if args.dry_run: break history['train_loss'].append(train_loss) return train_loss def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 criterion = nn.CrossEntropyLoss(reduction='sum') with torch.no_grad(): for _, (data1, data2, target) in enumerate(tqdm(test_loader)): M, P, target = data1.to(device), data2.to(device), target.to(device) output = model(M, P) # sum up batch loss test_loss += criterion(output, target).item() # get the index of the max log-probability pred = output.argmax(dim=1, keepdim=True) # output shape (B,Class) # target_shape (B) # pred shape (B,1) correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) history['test_loss'].append(test_loss) history['test_acc'].append(correct / len(test_loader.dataset)) msg = ('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) print(msg) logging.info(msg) def main(): # Training settings parser = argparse.ArgumentParser() parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=199, metavar='N', help='number of epochs to train (default: 199)') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)') parser.add_argument('--gamma', type=float, default=0.5, metavar='M', help='Learning rate step gamma (default: 0.5)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--log-interval', type=int, default=2, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--save-model', action='store_true', default=False, help='For Saving the current Model') parser.add_argument('--dataset', type=int, required=True, metavar='N', help='0 for JHMDB, 1 for SHREC coarse, 2 for SHREC fine, others is undefined') parser.add_argument('--model', action='store_true', default=False, help='For Saving the current Model') parser.add_argument('--calc_time', action='store_true', default=False, help='calc calc time per sample') args = parser.parse_args() logging.info(args) use_cuda = not args.no_cuda and torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True},) # alias Config = None data_generator = None load_data = None clc_num = 0 if args.dataset == 0: Config = JConfig() data_generator = Jdata_generator load_data = load_jhmdb_data clc_num = Config.clc_num elif args.dataset == 1: Config = SConfig() load_data = load_shrec_data clc_num = Config.class_coarse_num data_generator = Sdata_generator('coarse_label') elif args.dataset == 2: Config = SConfig() clc_num = Config.class_fine_num load_data = load_shrec_data data_generator = Sdata_generator('fine_label') else: print("Unsupported dataset!") sys.exit(1) C = Config Train, Test, le = load_data() X_0, X_1, Y = data_generator(Train, C, le) X_0 = torch.from_numpy(X_0).type('torch.FloatTensor') X_1 = torch.from_numpy(X_1).type('torch.FloatTensor') Y = torch.from_numpy(Y).type('torch.LongTensor') X_0_t, X_1_t, Y_t = data_generator(Test, C, le) X_0_t = torch.from_numpy(X_0_t).type('torch.FloatTensor') X_1_t = torch.from_numpy(X_1_t).type('torch.FloatTensor') Y_t = torch.from_numpy(Y_t).type('torch.LongTensor') trainset = torch.utils.data.TensorDataset(X_0, X_1, Y) train_loader = torch.utils.data.DataLoader(trainset, **kwargs) testset = torch.utils.data.TensorDataset(X_0_t, X_1_t, Y_t) test_loader = torch.utils.data.DataLoader( testset, batch_size=args.test_batch_size) Net = DDNet(C.frame_l, C.joint_n, C.joint_d, C.feat_d, C.filters, clc_num) model = Net.to(device) summary(model, [(C.frame_l, C.feat_d), (C.frame_l, C.joint_n, C.joint_d)]) optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999)) criterion = nn.CrossEntropyLoss() scheduler = ReduceLROnPlateau( optimizer, factor=args.gamma, patience=5, cooldown=0.5, min_lr=5e-6, verbose=True) for epoch in range(1, args.epochs + 1): train_loss = train(args, model, device, train_loader, optimizer, epoch, criterion) test(model, device, test_loader) scheduler.step(train_loss) fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1) ax1.plot(history['train_loss']) ax1.plot(history['test_loss']) ax1.legend(['Train', 'Test'], loc='upper left') ax1.set_xlabel('Epoch') ax1.set_title('Loss') ax2.set_title('Model accuracy') ax2.set_ylabel('Accuracy') ax2.set_xlabel('Epoch') ax2.plot(history['test_acc']) xmax = np.argmax(history['test_acc']) ymax = np.max(history['test_acc']) text = "x={}, y={:.3f}".format(xmax, ymax) ax2.annotate(text, xy=(xmax, ymax)) ax3.set_title('Confusion matrix') model.eval() with torch.no_grad(): Y_pred = model(X_0_t.to(device), X_1_t.to( device)).cpu().numpy() Y_test = Y_t.numpy() cnf_matrix = confusion_matrix( Y_test, np.argmax(Y_pred, axis=1)) ax3.imshow(cnf_matrix) fig.tight_layout() fig.savefig(str(savedir / "perf.png")) if args.save_model: torch.save(model.state_dict(), str(savedir/"model.pt")) if args.calc_time: device = ['cpu', 'cuda'] # calc time for d in device: tmp_X_0_t = X_0_t.to(d) tmp_X_1_t = X_1_t.to(d) model = model.to(d) # warm up _ = model(tmp_X_0_t, tmp_X_1_t) tmp_X_0_t = tmp_X_0_t.unsqueeze(1) tmp_X_1_t = tmp_X_1_t.unsqueeze(1) start = time.perf_counter_ns() for i in range(tmp_X_0_t.shape[0]): _ = model(tmp_X_0_t[i, :, :, :], tmp_X_1_t[i, :, :, :]) end = time.perf_counter_ns() msg = ("total {}ns, {:.2f}ns per one on {}".format((end - start), ((end - start) / (X_0_t.shape[0])), d)) print(msg) logging.info(msg) if __name__ == '__main__': main()	[ 1, 396, 29991, 847, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 29991, 14137, 29901, 9420, 29899, 29947, 13, 3166, 2224, 1982, 1053, 10802, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 3166, 4842, 305, 1053, 1480, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 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Day7-HandyHaversacks.py	n-Holmes/AdventOfCode2020	0	43433	<reponame>n-Holmes/AdventOfCode2020 from collections import Counter from functools import lru_cache def get_contents(path : str) -> dict[str, dict[str, int]]: """Get a weighted, directed graph of bags. No protection for the case of cycles. """ with open(path, "r") as f: lines = f.readlines(); contents = {} # Could use regex here, but hardly seems necessary due to the input size. for line in lines: try: bag, contains = line.split(" bags contain "); except: continue contents[bag] = Counter() if contains.startswith("no"): continue # separate types for item in contains.split(", "): parts = item.split(); color = parts[1] + " " + parts[2] count = int(parts[0]) contents[bag][color] = count return contents def get_poss_containers( target : str, contents : dict[str, dict[str, int]] ) -> list[str]: """Returns a list of all bags which can contain a bag of a given color.""" to_check = [target] valid = set() while to_check: target_color = to_check.pop() for cont_color in contents: if cont_color not in valid and target_color in contents[cont_color]: valid.add(cont_color) to_check.append(cont_color) return list(valid) def count_contents( target : str, contents : dict[str, dict[str, int]] ) -> int: """Counts the number of bags contained within a given color of bag.""" @lru_cache() # Cache results to speed up recursion def rec_count(color : str) -> int: """Recursively count the contents of a given color.""" return sum( (1 + rec_count(child)) * count for child, count in contents[color].items() ) return rec_count(target) if __name__ == "__main__": graph = get_contents("input7.txt") print("Containers that can hold a shiny gold bag:") print(len(get_poss_containers("shiny gold", graph))) print("No of bags in a shiny gold:") print(count_contents("shiny gold", graph))	[ 1, 529, 276, 1112, 420, 29958, 29876, 29899, 19984, 4467, 29914, 3253, 794, 2776, 3399, 29906, 29900, 29906, 29900, 13, 3166, 16250, 1053, 315, 5336, 13, 3166, 2090, 312, 8789, 1053, 301, 582, 29918, 8173, 13, 13, 1753, 679, 29918, 10853, 29898, 2084, 584, 851, 29897, 1599, 9657, 29961, 710, 29892, 9657, 29961, 710, 29892, 938, 5262, 29901, 13, 1678, 9995, 2577, 263, 7688, 287, 29892, 10624, 3983, 310, 289, 810, 29889, 13, 1678, 1939, 13047, 363, 278, 1206, 310, 25785, 29889, 13, 1678, 9995, 13, 1678, 411, 1722, 29898, 2084, 29892, 376, 29878, 1159, 408, 285, 29901, 13, 4706, 3454, 353, 285, 29889, 949, 9012, 890, 13, 268, 13, 1678, 8118, 353, 6571, 13, 13, 1678, 396, 6527, 671, 6528, 1244, 29892, 541, 15155, 2444, 5181, 2861, 304, 278, 1881, 2159, 29889, 13, 1678, 363, 1196, 297, 3454, 29901, 13, 4706, 1018, 29901, 13, 9651, 19548, 29892, 3743, 353, 1196, 29889, 5451, 703, 289, 810, 1712, 14796, 13, 4706, 5174, 29901, 13, 9651, 6773, 13, 13, 4706, 8118, 29961, 23156, 29962, 353, 315, 5336, 580, 13, 4706, 565, 3743, 29889, 27382, 2541, 703, 1217, 29908, 1125, 6773, 13, 13, 4706, 396, 5004, 4072, 13, 4706, 363, 2944, 297, 3743, 29889, 5451, 28165, 376, 1125, 13, 9651, 5633, 353, 2944, 29889, 5451, 890, 13, 9651, 2927, 353, 5633, 29961, 29896, 29962, 718, 376, 376, 718, 5633, 29961, 29906, 29962, 13, 9651, 2302, 353, 938, 29898, 20895, 29961, 29900, 2314, 13, 9651, 8118, 29961, 23156, 3816, 2780, 29962, 353, 2302, 13, 268, 13, 1678, 736, 8118, 13, 13, 1753, 679, 29918, 28802, 29918, 1285, 475, 414, 29898, 13, 1678, 3646, 584, 851, 29892, 13, 1678, 8118, 584, 9657, 29961, 710, 29892, 9657, 29961, 710, 29892, 938, 5262, 13, 1678, 1723, 1599, 1051, 29961, 710, 5387, 13, 1678, 9995, 11609, 29879, 263, 1051, 310, 599, 289, 810, 607, 508, 1712, 263, 19548, 310, 263, 2183, 2927, 1213, 15945, 13, 13, 1678, 304, 29918, 3198, 353, 518, 5182, 29962, 13, 1678, 2854, 353, 731, 580, 13, 1678, 1550, 304, 29918, 3198, 29901, 13, 4706, 3646, 29918, 2780, 353, 304, 29918, 3198, 29889, 7323, 580, 13, 13, 4706, 363, 640, 29918, 2780, 297, 8118, 29901, 13, 9651, 565, 640, 29918, 2780, 451, 297, 2854, 322, 3646, 29918, 2780, 297, 8118, 29961, 1285, 29918, 2780, 5387, 13, 18884, 2854, 29889, 1202, 29898, 1285, 29918, 2780, 29897, 13, 18884, 304, 29918, 3198, 29889, 4397, 29898, 1285, 29918, 2780, 29897, 13, 268, 13, 1678, 736, 1051, 29898, 3084, 29897, 13, 462, 13, 1753, 2302, 29918, 10853, 29898, 13, 1678, 3646, 584, 851, 29892, 13, 1678, 8118, 584, 9657, 29961, 710, 29892, 9657, 29961, 710, 29892, 938, 5262, 13, 1678, 1723, 1599, 938, 29901, 13, 1678, 9995, 3981, 29879, 278, 1353, 310, 289, 810, 11122, 2629, 263, 2183, 2927, 310, 19548, 1213, 15945, 13, 13, 1678, 732, 29880, 582, 29918, 8173, 580, 396, 28540, 2582, 304, 6210, 701, 20437, 13, 1678, 822, 1162, 29918, 2798, 29898, 2780, 584, 851, 29897, 1599, 938, 29901, 13, 4706, 9995, 4789, 1295, 3598, 2302, 278, 8118, 310, 263, 2183, 2927, 1213, 15945, 13, 4706, 736, 2533, 29898, 13, 9651, 313, 29896, 718, 1162, 29918, 2798, 29898, 5145, 876, 334, 2302, 13, 9651, 363, 2278, 29892, 2302, 297, 8118, 29961, 2780, 1822, 7076, 580, 13, 4706, 1723, 13, 13, 1678, 736, 1162, 29918, 2798, 29898, 5182, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 3983, 353, 679, 29918, 10853, 703, 2080, 29955, 29889, 3945, 1159, 13, 13, 1678, 1596, 703, 1323, 475, 414, 393, 508, 4808, 263, 528, 4901, 7684, 19548, 29901, 1159, 13, 1678, 1596, 29898, 2435, 29898, 657, 29918, 28802, 29918, 1285, 475, 414, 703, 845, 4901, 7684, 613, 3983, 4961, 13, 13, 1678, 1596, 703, 3782, 310, 289, 810, 297, 263, 528, 4901, 7684, 29901, 1159, 13, 1678, 1596, 29898, 2798, 29918, 10853, 703, 845, 4901, 7684, 613, 3983, 876, 13, 2 ]
pyvs/LatentARTraining.py	snumrl/VSports	0	165149	<reponame>snumrl/VSports import math import random import time import os import sys import copy from datetime import datetime from os.path import join, exists from os import mkdir import collections from collections import namedtuple from collections import deque from itertools import count import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torchvision.transforms as T from Utils import RunningMeanStd from CAAE import CAAEDecoder, CAAEEncoder import numpy as np from pyvs import Env from IPython import embed import json from Model import * from pathlib import Path from tensorboardX import SummaryWriter summary = SummaryWriter() use_cuda = torch.cuda.is_available() FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor ByteTensor = torch.cuda.ByteTensor if use_cuda else torch.ByteTensor Tensor = FloatTensor LOW_FREQUENCY = 3 HIGH_FREQUENCY = 30 device = torch.device("cuda" if use_cuda else "cpu") nnCount = 52 baseDir = "../nn_lar_h" nndir = baseDir + "/nn"+str(nnCount) if not exists(baseDir): mkdir(baseDir) if not exists(nndir): mkdir(nndir) RNNEpisode = namedtuple('RNNEpisode', ('s','a','r','value','logprob')) class RNNEpisodeBuffer(object): def __init__(self): self.data = [] def push(self, args): self.data.append(RNNEpisode(args)) def pop(self): self.data.pop() def popleft(self): self.data.pop(0) def getData(self): return self.data def getLastData(self): return self.data[len(self.data)-1] RNNTransition = namedtuple('RNNTransition',('s','a','logprob','TD','GAE')) class RNNReplayBuffer(object): def __init__(self, buff_size = 10000): super(RNNReplayBuffer, self).__init__() self.buffer = deque(maxlen=buff_size) def push(self,args): self.buffer.append(RNNTransition(args)) def clear(self): self.buffer.clear() class Buffer(object): def __init__(self, buff_size = 10000): super(Buffer, self).__init__() self.buffer = deque(maxlen=buff_size) def push(self,replay_buffer): self.buffer.append(replay_buffer) def clear(self): self.buffer.clear() class RL(object): def __init__(self, motion_nn): np.random.seed(seed = int(time.time())) self.num_slaves = 16 self.num_agents = 1 self.env = Env(self.num_agents, motion_nn, self.num_slaves) self.num_state = self.env.getNumState() # self.num_action = self.env.getNumAction() self.num_policy = 1 self.num_epochs = 4 self.num_evaluation = 0 self.num_simulation_Hz = self.env.getSimulationHz() self.num_control_Hz = self.env.getControlHz() self.num_simulation_per_control = self.num_simulation_Hz // self.num_control_Hz self.gamma = 0.999 self.lb = 0.95 self.buffer_size = 161024 self.batch_size = 2512 self.num_action_types = 5 self.latent_size = 4 self.resetDuration = self.env.getResetDuration() self.typeFreq = self.env.getTypeFreq() #contact, finger, finger-ball self.num_action = [self.num_action_types, self.latent_size] self.num_h = len(self.num_action); self.num_tuple_so_far = [[0, 0] for _ in range(self.num_h)] self.num_tuple = [[0, 0] for _ in range(self.num_h)] self.num_tutorial_tuple = [[0, 0] for _ in range(self.num_h)] self.buffer = [[ [None] for _ in range(self.num_policy)] for _ in range(self.num_h)]; for h in range(self.num_h): for i in range(self.num_policy): self.buffer[h][i] = Buffer(100000) self.tutorial_buffer = [[ [None] for _ in range(self.num_policy)] for _ in range(self.num_h)]; for h in range(self.num_h): for i in range(self.num_policy): self.tutorial_buffer[h][i] = Buffer(100000) self.actionDecoder = CAAEDecoder().to(device) self.actionEncoder = CAAEEncoder().to(device) self.actionDecoder.load("caae_nn2/vae_action_decoder.pt") self.actionEncoder.load("caae_nn2/vae_action_encoder.pt") self.rms = RunningMeanStd(self.num_state-5) self.num_c = 4 self.stdScale = 1.0 self.target_model = [[[None] for _ in range(self.num_policy)] for _ in range(self.num_h)] acc_num_action = 0 self.num_hidden = 0 for h in range(self.num_h): for j in range(self.num_policy): if h== 0: self.target_model[h][j] = ActorCriticNN(self.num_state, self.num_action[h], log_std = 0.0, softmax = True, actionMask = True) else: self.target_model[h][j] = ActorCriticNN(self.num_state + self.num_action[0], self.num_action[h], log_std = 0.0) acc_num_action += self.num_action[h] if use_cuda: self.target_model[h][j].cuda() self.default_learning_rate = 1E-4 self.default_clip_ratio = 0.2 self.learning_rate = self.default_learning_rate self.clip_ratio = self.default_clip_ratio self.optimizer = [[[None] for _ in range(self.num_policy)] for _ in range(self.num_h)] for h in range(self.num_h): for i in range(self.num_policy): self.optimizer[h][i] = optim.Adam(self.target_model[h][i].parameters(), lr=self.learning_rate) self.max_iteration = 50000 self.w_entropy = 0.0001 self.sum_loss_actor = [[0.0 for _ in range(self.num_policy)] for _ in range(self.num_h)] self.sum_loss_critic = [[0.0 for _ in range(self.num_policy)] for _ in range(self.num_h)] self.sum_tutorial_loss_critic = [[0.0 for _ in range(self.num_policy)] for _ in range(self.num_h)] self.rewards = [] self.numSteps = [] self.num_correct_throwings = [] self.sum_return = 0.0 self.max_return = -10.0 self.max_winRate = 0.0 self.max_winRate_epoch = 0 self.max_return_epoch = 1 self.tic = time.time() self.episodes = [[[RNNEpisodeBuffer() for _ in range(self.num_agents)] for _ in range(self.num_slaves)] for _ in range(self.num_h)] self.tutorial_episodes = [[[RNNEpisodeBuffer() for _ in range(self.num_agents)] for _ in range(self.num_slaves)] for _ in range(self.num_h)] self.indexToNetDic = {0:0, 1:0} self.filecount = 0 self.num_td_reset = [0, 0] self.num_td_reset_at_goal = [0, 0] self.reset_point = [0]5 self.env.slaveResets() def loadTargetModels(self,path,index): for h in range(self.num_h): self.target_model[h][self.indexToNetDic[index]].load(nndir+'/'+path+'_'+str(self.indexToNetDic[index%self.num_agents])+'_'+str(h)+'.pt') if os.path.isfile(nndir+'/rms.ms'): self.rms.load(nndir+'/rms.ms') def saveModels(self): for i in range(self.num_policy): for h in range(self.num_h): self.target_model[h][i].save(nndir+'/'+'current_'+str(i)+'_'+str(h)+'.pt') if self.max_return_epoch == self.num_evaluation: for h in range(self.num_h): self.target_model[h][i].save(nndir+'/'+'max_'+str(i)+'_'+str(h)+'.pt') if self.num_evaluation%100 == 0: for h in range(self.num_h): self.target_model[h][i].save(nndir+'/'+str(self.num_evaluation)+'_'+str(i)+'_'+str(h)+'.pt') self.rms.save(nndir+'/rms.ms') def arrayToOneHotVector(nparr): result = np.array(list(np.copy(nparr))) for agent in range(len(nparr)): for slaves in range(len(nparr[agent])): maxIndex = 0 maxValue = -100 for i in range(len(nparr[agent][slaves])): result[agent][slaves][i] = 0.0 if nparr[agent][slaves][i] > maxValue: maxValue = nparr[agent][slaves][i] maxIndex = i result[agent][slaves][maxIndex] = 1.0 return result def generateTransitions(self): self.sum_return = 0; self.num_episode = 0; self.num_correct_throwing = 0; self.num_td_reset = [0, 0] self.num_td_reset_at_goal = [0, 0] for h in range(self.num_h): for index in range(self.num_policy): self.num_tuple[h][index] = 0 self.num_tutorial_tuple[h][index] = 0 self.reset_point = [0, 0, 0, 0, 0] self.total_episodes = [ [[] for i in range(self.num_policy)] for _ in range(self.num_h)] self.total_tutorial_episodes = [ [[] for i in range(self.num_policy)] for _ in range(self.num_h)] states = [[None for _ in range(self.num_slaves)] for _ in range(self.num_agents)] actions = [[None for _ in range(self.num_slaves)] for _ in range(self.num_agents)] rewards = [[None for _ in range(self.num_slaves)] for _ in range(self.num_agents)] accRewards = [[0 for _ in range(self.num_slaves)] for _ in range(self.num_agents)] states_h = np.array([None for _ in range(self.num_h)]) actions_h = np.array([[None for _ in range(self.num_agents)] for _ in range(self.num_h)]) logprobs_h = np.array([[None for _ in range(self.num_agents)] for _ in range(self.num_h)]) values_h = np.array([[None for _ in range(self.num_agents)] for _ in range(self.num_h)]) is_exploitation = np.array([None for _ in range(self.num_agents)]) followTutorial = [False]self.num_slaves onFoulResetProcess = [False]self.num_slaves terminated = [False]self.num_slavesself.num_agents for i in range(self.num_agents): for j in range(self.num_slaves): states[i][j] = self.env.getState(j,i).astype(np.float32) states = np.array(states) states[:,:,:-5] = self.rms.apply(states[:,:,:-5]) mask = states[:,:,-5:] learningTeam = 0 teamDic = {0: 0, 1: 0} local_step = 0 counter = 0 def arrayToOneHotVectorWithConstraint(nparr): result = np.array(list(np.copy(nparr))) for agent in range(len(nparr)): for slaves in range(len(nparr[agent])): maxIndex = 0 maxValue = -100 for i in range(len(nparr[agent][slaves])): result[agent][slaves][i] = 0.0 if nparr[agent][slaves][i] > maxValue: maxValue = nparr[agent][slaves][i] maxIndex = i maxIndex = self.env.setActionType(maxIndex, slaves, agent) result[agent][slaves][maxIndex] = 1.0 return result def arrayToScalarVectorWithConstraint(nparr, isNew): temp = np.array(list(np.copy(nparr))) tempShape = np.shape(temp) scalarVectorShape = list(tempShape) scalarVectorShape[2] = 1 result = np.zeros(scalarVectorShape); result_oneHot = np.array(list(np.copy(nparr))) oneHotVectorShape = list(tempShape) oneHotVectorShape[2] = self.num_action_types result_oneHot = np.zeros(oneHotVectorShape) for agent in range(len(nparr)): for slaves in range(len(nparr[agent])): maxIndex = 0 maxValue = -100 for i in range(self.num_action_types): result_oneHot[agent][slaves][i] = 0.0 if nparr[agent][slaves][i] > maxValue: maxValue = nparr[agent][slaves][i] maxIndex = i maxIndex = self.env.setActionType(maxIndex, slaves, agent, isNew) result[agent][slaves] = maxIndex result_oneHot[agent][slaves][maxIndex] = 1.0 return result, result_oneHot def getActionTypeFromVector(vec): maxIndex = 0 maxValue = -100 for i in range(len(vec)): if vec[i] == 1: return i print("Something wrong in vec") print(vec) return 0 def getLatentReward(nparr): weight = 1.0; tempShape = list(np.shape(nparr)) tempShape[2] = 1 # embed() # exit(0) result = np.zeros(tempShape) for i in range(len(result)): for j in range(len(result[i])): result[i][j] = weight 1.0/np.sqrt(1558.5)np.exp(-0.5np.dot(nparr[i][j], nparr[i][j])) return result h_slave = [None]self.num_agents while True: counter += 1 if counter%10 == 0: print('SIM : {}'.format(local_step),end='\r') tutorialRatio = 0.0 useEmbeding = True # generate transition of first hierachy a_dist_slave = [None]self.num_agents v_slave = [None]self.num_agents if counter%self.typeFreq == 1: states_h[0] = np.copy(states) for i in range(self.num_agents): if teamDic[i] == learningTeam: a_dist_slave_agent,v_slave_agent = \ self.target_model[0][self.indexToNetDic[i]].forward(Tensor(states_h[0][i])) a_dist_slave[i] = a_dist_slave_agent v_slave[i] = v_slave_agent actions_h[0][i] = a_dist_slave[i].sample().cpu().detach().numpy().squeeze().squeeze(); for j in range(self.num_slaves): if followTutorial[j] is True: actions_h[0][i][j][0:2] = self.env.getCorrectActionType(j,i) for i in range(self.num_agents): if teamDic[i] == learningTeam: logprobs_h[0][i] = a_dist_slave[i].log_prob(Tensor(actions_h[0][i]))\ .cpu().detach().numpy().reshape(-1); values_h[0][i] = v_slave[i].cpu().detach().numpy().reshape(-1); for i in range(self.num_agents): for j in range(self.num_slaves): if self.env.isOnFoulReset(j): temp_a_dist,_ = self.target_model[0][self.indexToNetDic[i]].forward(\ Tensor([states_h[0][i][j]]),self.stdScale) actions_h[0][i][j] = temp_a_dist.sample().cpu().detach().numpy() logprobs_h[0][i][j] = temp_a_dist.log_prob(Tensor(actions_h[0][i][j])).cpu().detach().numpy().reshape(-1)[0] actions_c = np.array(list(np.copy(actions_h[0]))) actions_c = actions_c[:,:,-self.num_c:] actions_0_scalar = None actions_0_oneHot = None useEmbeding = False if counter%self.typeFreq == 1: actions_0_scalar, actions_0_oneHot = arrayToScalarVectorWithConstraint(actions_h[0], True) else: actions_0_scalar, actions_0_oneHot = arrayToScalarVectorWithConstraint(actions_h[0], False) action_embeding_ones = np.ones(np.shape(states_h[0]),dtype=np.float32) if useEmbeding: actions_0_oneHot = actions_0_oneHot0 action_embeding_ones = 0.5 * action_embeding_onesactions_0_scalar else: action_embeding_ones = 0.0 action_embeding_onesactions_0_scalar # generate transition of second hierachy for h in range(1,self.num_h): if h == 1: embededState = states+action_embeding_ones states_h[h] = np.concatenate((embededState, actions_0_oneHot), axis=2) a_dist_slave = [None]self.num_agents v_slave = [None]self.num_agents for i in range(self.num_agents): if teamDic[i] == learningTeam: a_dist_slave_agent,v_slave_agent = self.target_model[h][self.indexToNetDic[i]].forward(\ Tensor([states_h[h][i]])) a_dist_slave[i] = a_dist_slave_agent v_slave[i] = v_slave_agent actions_h[h][i] = a_dist_slave[i].sample().cpu().detach().numpy().squeeze().squeeze(); for j in range(self.num_slaves): if followTutorial[j] is True: print("line628") exit(0) decodedActionDetail = self.env.getCorrectActionDetail(j,i) encodedActionDetail, _ = self.actionEncoder.encode(Tensor(decodedActionDetail)) encodedActionDetail= encodedActionDetail.cpu().detach().numpy() actions_h[h][i][j] = encodedActionDetail for i in range(self.num_agents): if teamDic[i] == learningTeam: logprobs_h[h][i] = a_dist_slave[i].log_prob(Tensor(actions_h[h][i]))\ .cpu().detach().numpy().reshape(-1); values_h[h][i] = v_slave[i].cpu().detach().numpy().reshape(-1); for i in range(self.num_agents): for j in range(self.num_slaves): if self.env.isOnFoulReset(j): temp_a_dist,_ = self.target_model[h][self.indexToNetDic[i]].forward(\ Tensor([states_h[h][i][j]]),self.stdScale) actions_h[h][i][j] = temp_a_dist.sample().cpu().detach().numpy() logprobs_h[h][i][j] = temp_a_dist.log_prob(Tensor(actions_h[h][i][j])).cpu().detach().numpy().reshape(-1)[0] actionsLatent = np.array(list(actions_h[1])) actionsLatentReward = getLatentReward(actionsLatent) # embed() # exit(0) actionsLatent = np.concatenate((actions_0_oneHot, actionsLatent), axis =2) decodeShape = list(np.shape(actionsLatent)) decodeShape[2] = 16 actionsDecoded =np.empty(decodeShape,dtype=np.float32) actionsDecoded = self.actionDecoder.decode(Tensor(actionsLatent)).cpu().detach().numpy() # for i in range(len(actionsLatent)): # for j in range(len(actionsLatent[i])): # curActionType = int(actions_0_scalar[i][j][0]) # actionsDecoded[i][j] = self.actionDecoders[curActionType].decode(Tensor(actionsLatent[i][j])).cpu().detach().numpy() envActions = actionsDecoded for i in range(self.num_agents): for j in range(self.num_slaves): self.env.setAction(envActions[i][j], j, i); self.env.stepsAtOnce() nan_occur = [False]self.num_slaves for j in range(self.num_slaves): if not self.env.isOnResetProcess(j): for i in range(self.num_agents): if teamDic[i] == learningTeam: # embed() # exit(0) rewards[i][j] = self.env.getReward(j, i, True) + actionsLatentReward[i][j][0] if counter%self.typeFreq == 1: accRewards[i][j] = rewards[i][j] else: accRewards[i][j] += rewards[i][j] if np.any(np.isnan(rewards[i][j])): # embed() # exit(0) nan_occur[j] = True if np.any(np.isnan(states[i][j])) or np.any(np.isnan(envActions[i][j])) or np.any(np.isnan(actions_h[0][i][j])): # embed() # exit(0) nan_occur[j] = True if followTutorial[j] is False: if not onFoulResetProcess[j]: self.sum_return += rewards[i][j] - actionsLatentReward[i][j][0] if accRewards[i][j] >= 1.0 and not self.env.isTerminalState(j): embed() exit(0) for j in range(self.num_slaves): if not self.env.isOnResetProcess(j): if nan_occur[j] is True: for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): if followTutorial[j] is False: self.total_episodes[h][self.indexToNetDic[i]].append(self.episodes[h][j][i]) self.episodes[h][j][i] = RNNEpisodeBuffer() else: self.total_episodes[h][self.indexToNetDic[i]].append(self.tutorial_episodes[h][j][i]) self.tutorial_episodes[h][j][i] = RNNEpisodeBuffer() if followTutorial[j] is False: self.num_episode += 1 print("nan", file=sys.stderr) # print("on {}".format(j)); self.env.slaveReset(j) self.env.setResetCount(self.resetDuration-counter%self.typeFreq, j); followTutorial[j] = random.random()<tutorialRatio else: # here not considered followtutorial if not onFoulResetProcess[j]: for i in range(self.num_agents): if len(self.episodes[0][j][i].data) > 0: for h in range(self.num_h): if h == 0: # if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)) or counter%self.typeFreq == 0: if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)): savedFrameDiff = self.env.getSavedFrameDiff(j) if savedFrameDiff <= 6: break; TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gammavalues_h[h][i][j]) if self.env.isTerminalState(j) : TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gammaaccRewards[i][j]) TDError = abs(TDError) TDError = 2.0pow(TDError, 1.0) TDError = min(TDError, 0.9) if random.random()<TDError : self.env.setToFoulState(j) onFoulResetProcess[j] = True; for h_ in range(self.num_h): if h_ == 0: if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ accRewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) if accRewards[i][j] >= 1.0: self.num_correct_throwing += 1 lastTuple = self.episodes[h_][j][i].data[-(1+int(savedFrameDiff/self.typeFreq))] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) else: if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ rewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) lastTuple = self.episodes[h_][j][i].data[-(1+savedFrameDiff)] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) self.num_td_reset[h] += 1 self.reset_point[int(actions_0_scalar[i][j][0])] += 1 if self.env.isTerminalState(j) : self.num_td_reset_at_goal[h] += 1 if followTutorial[j] is False: self.num_episode += 1 break else: # continue # if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)) or True: if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)): savedFrameDiff = self.env.getSavedFrameDiff(j) if savedFrameDiff <= 6: break; TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gammavalues_h[h][i][j]) if self.env.isTerminalState(j) : TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gammarewards[i][j]) TDError = abs(TDError) TDError = 2.0pow(TDError, 1.0) TDError = min(TDError, 0.9) if random.random()<TDError : self.env.setToFoulState(j) onFoulResetProcess[j] = True; for h_ in range(self.num_h): if h_ == 0: if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ accRewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) if accRewards[i][j] >= 1.0: self.num_correct_throwing += 1 lastTuple = self.episodes[h_][j][i].data[-(1+int(savedFrameDiff/self.typeFreq))] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) else : if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ rewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) lastTuple = self.episodes[h_][j][i].data[-(1+savedFrameDiff)] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) self.num_td_reset[h] += 1 self.reset_point[int(actions_0_scalar[i][j][0])] += 1 if followTutorial[j] is False: self.num_episode += 1 if self.env.isTerminalState(j) : self.num_td_reset_at_goal[h] += 1 if onFoulResetProcess[j] is True: if counter%self.typeFreq != 1: continue if self.env.isFoulState(j) is True: for i in range(self.num_agents): if teamDic[i] == learningTeam: if onFoulResetProcess[j] is True: onFoulResetProcess[j] = False accRewards[i][j] = 0.0 if followTutorial[j] is False: while len(self.episodes[0][j][i].data)>(120/self.typeFreq * 4) : self.episodes[0][j][i].popleft() while len(self.episodes[1][j][i].data)>121 : self.episodes[1][j][i].popleft() self.num_tuple[0][self.indexToNetDic[i]] += len(self.episodes[0][j][i].data) self.num_tuple[1][self.indexToNetDic[i]] += len(self.episodes[1][j][i].data) local_step += len(self.episodes[1][j][i].data) local_step += 1 actions_h[0][i][j] = self.episodes[0][j][i].getLastData().a self.env.foulReset(j) followTutorial[j] = random.random()<tutorialRatio elif self.env.isTerminalState(j) is True: for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): if h == 0: if followTutorial[j] is False: if self.env.isOnFoulReset(j): temp_s = self.episodes[h][j][i].getLastData().s temp_a = self.episodes[h][j][i].getLastData().a temp_r = accRewards[i][j] temp_value = self.episodes[h][j][i].getLastData().value temp_logprob = self.episodes[h][j][i].getLastData().logprob self.episodes[h][j][i].pop() self.episodes[h][j][i].push(temp_s, temp_a, temp_r, temp_value, temp_logprob) else: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ accRewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 if accRewards[i][j] >= 1.0: self.num_correct_throwing += 1 else : if followTutorial[j] is False: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ rewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 for h in range(self.num_h): self.total_episodes[h][self.indexToNetDic[i]].append(self.episodes[h][j][i]) self.episodes[h][j][i] = RNNEpisodeBuffer() if followTutorial[j] is False: self.num_episode += 1 local_step += 1 self.env.slaveReset(j) followTutorial[j] = random.random()<tutorialRatio self.env.setResetCount(self.resetDuration-counter%self.typeFreq, j); else: for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): if h == 0: if counter%self.typeFreq == 0: if followTutorial[j] is False: if self.env.isOnFoulReset(j): # print("111") temp_s = self.episodes[h][j][i].getLastData().s temp_a = self.episodes[h][j][i].getLastData().a temp_r = accRewards[i][j] temp_value = self.episodes[h][j][i].getLastData().value temp_logprob = self.episodes[h][j][i].getLastData().logprob self.episodes[h][j][i].pop() self.episodes[h][j][i].push(temp_s, temp_a, temp_r, temp_value, temp_logprob) else: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ accRewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 else : if followTutorial[j] is False: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ rewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 local_step += 1 if local_step >= self.buffer_size: for j in range(self.num_slaves): for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): self.total_episodes[h][self.indexToNetDic[i]].append(self.episodes[h][j][i]) self.episodes[h][j][i] = RNNEpisodeBuffer() if followTutorial[j] is False: self.num_episode += 1 self.env.slaveReset(j) followTutorial[j] = random.random()<tutorialRatio break for i in range(self.num_agents): for j in range(self.num_slaves): states[i][j] = self.env.getState(j,i).astype(np.float32) states = np.array(states) states[:,:,:-5] = self.rms.apply(states[:,:,:-5]) print('SIM : {}'.format(local_step)) def computeTDandGAE(self): for h in range(self.num_h): for index in range(self.num_policy): self.buffer[h][index].clear() for epi in self.total_episodes[h][index]: data = epi.getData() size = len(data) if size == 0: continue states, actions, rewards, values, logprobs = zip(data) values = np.concatenate((values, np.zeros(1)), axis=0) advantages = np.zeros(size) ad_t = 0 epi_return = 0.0 for i in reversed(range(len(data))): epi_return += rewards[i] delta = rewards[i] + values[i+1] self.gamma - values[i] ad_t = delta + self.gamma * self.lb * ad_t advantages[i] = ad_t if not np.isnan(epi_return): TD = values[:size] + advantages rnn_replay_buffer = RNNReplayBuffer(10000) for i in range(size): rnn_replay_buffer.push(states[i], actions[i], logprobs[i], TD[i], advantages[i]) self.buffer[h][index].push(rnn_replay_buffer) ''' counting numbers ''' for h in range(self.num_h): for index in range(self.num_policy): self.num_tuple_so_far[h][index] += self.num_tuple[h][index] def tutorial_computeTDandGAE(self): for h in range(self.num_h): for index in range(self.num_policy): self.tutorial_buffer[h][index].clear() for epi in self.total_tutorial_episodes[h][index]: data = epi.getData() size = len(data) if size == 0: continue states, actions, rewards, values, logprobs = zip(data) values = np.concatenate((values, np.zeros(1)), axis=0) advantages = np.zeros(size) ad_t = 0 epi_return = 0.0 for i in reversed(range(len(data))): epi_return += rewards[i] delta = rewards[i] + values[i+1] self.gamma - values[i] ad_t = delta + self.gamma * self.lb * ad_t advantages[i] = ad_t if not np.isnan(epi_return): TD = values[:size] + advantages rnn_replay_buffer = RNNReplayBuffer(10000) for i in range(size): rnn_replay_buffer.push(states[i], actions[i], logprobs[i], TD[i], advantages[i]) self.tutorial_buffer[h][index].push(rnn_replay_buffer) def optimizeNN_h(self, h = 0): for i in range(self.num_policy): self.sum_loss_actor[h][i] = 0.0 self.sum_loss_critic[h][i] = 0.0 for buff_index in range(self.num_policy): all_rnn_replay_buffer= np.array(self.buffer[h][buff_index].buffer) for j in range(self.num_epochs): all_segmented_transitions = [] for rnn_replay_buffer in all_rnn_replay_buffer: rnn_replay_buffer_size = len(rnn_replay_buffer.buffer) for i in range(rnn_replay_buffer_size): all_segmented_transitions.append(rnn_replay_buffer.buffer[i]) batch_size_h = self.batch_size if h == 0: batch_size_h = self.batch_size//4 np.random.shuffle(all_segmented_transitions) for i in range(len(all_segmented_transitions)//batch_size_h): batch_segmented_transitions = all_segmented_transitions[ibatch_size_h:(i+1)batch_size_h] batch = RNNTransition(zip(batch_segmented_transitions)) stack_s = np.vstack(batch.s).astype(np.float32) stack_a = np.vstack(batch.a).astype(np.float32) stack_lp = np.vstack(batch.logprob).astype(np.float32) stack_td = np.vstack(batch.TD).astype(np.float32) stack_gae = np.vstack(batch.GAE).astype(np.float32) a_dist,v = self.target_model[h][buff_index].forward(Tensor(stack_s)) loss_critic = ((v-Tensor(stack_td)).pow(2)).mean() '''Actor Loss''' ratio = torch.exp(a_dist.log_prob(Tensor(stack_a))-Tensor(stack_lp)) stack_gae = (stack_gae-stack_gae.mean())/(stack_gae.std()+1E-5) stack_gae = Tensor(stack_gae) surrogate1 = ratio * stack_gae surrogate2 = torch.clamp(ratio, min=1.0-self.clip_ratio, max=1.0+self.clip_ratio) * stack_gae loss_actor = - torch.min(surrogate1, surrogate2).mean() '''Entropy Loss''' loss_entropy = - self.w_entropy * a_dist.entropy().mean() loss = loss_actor + loss_critic + loss_entropy self.optimizer[h][buff_index].zero_grad() loss.backward() detectNan = False for param in self.target_model[h][buff_index].parameters(): if torch.isnan(param.grad).any(): detectNan = True if param.grad is not None: param.grad.data.clamp_(-0.5, 0.5) if not detectNan: self.optimizer[h][buff_index].step() self.sum_loss_actor[h][buff_index] += loss_actor.detach()batch_size_h/self.num_epochs self.sum_loss_critic[h][buff_index] += loss_critic.detach()batch_size_h/self.num_epochs loss_entropy = loss_entropy.detach() loss_actor = loss_actor.detach() loss_critic = loss_critic.detach() loss= loss.detach() print('Optimizing actor-critic nn_{} : {}/{}'.format(h, j+1,self.num_epochs),end='\r') print('') # print('') def tutorial_optimizeNN_h(self, h = 0): for i in range(self.num_policy): self.sum_tutorial_loss_critic[h][i] = 0.0 for buff_index in range(self.num_policy): all_rnn_replay_buffer= np.array(self.tutorial_buffer[h][buff_index].buffer) for j in range(self.num_epochs): all_segmented_transitions = [] for rnn_replay_buffer in all_rnn_replay_buffer: rnn_replay_buffer_size = len(rnn_replay_buffer.buffer) for i in range(rnn_replay_buffer_size): all_segmented_transitions.append(rnn_replay_buffer.buffer[i]) np.random.shuffle(all_segmented_transitions) tutorial_batchSize = self.batch_size//16 for i in range(len(all_segmented_transitions)//tutorial_batchSize): batch_segmented_transitions = all_segmented_transitions[itutorial_batchSize:(i+1)tutorial_batchSize] batch = RNNTransition(zip(batch_segmented_transitions)) stack_s = np.vstack(batch.s).astype(np.float32) stack_a = np.vstack(batch.a).astype(np.float32) stack_lp = np.vstack(batch.logprob).astype(np.float32) stack_td = np.vstack(batch.TD).astype(np.float32) stack_gae = np.vstack(batch.GAE).astype(np.float32) a_dist,v = self.target_model[h][buff_index].forward(Tensor(stack_s)) loss_critic = ((v-Tensor(stack_td)).pow(2)).mean() loss = loss_critic self.optimizer[h][buff_index].zero_grad() loss.backward() detectNan = False for param in self.target_model[h][buff_index].parameters(): if torch.isnan(param.grad).any(): detectNan = True # print("nan") if param.grad is not None: param.grad.data.clamp_(-0.5, 0.5) self.sum_tutorial_loss_critic[h][buff_index] += loss_critic.detach()tutorial_batchSize/self.num_epochs loss_critic = loss_critic.detach() loss= loss.detach() print('Optimizing actor-critic nn_{} : {}/{}'.format(h, j+1,self.num_epochs),end='\r') print('') # print('') def train(self): frac = 1.0 self.learning_rate = self.default_learning_ratefrac self.clip_ratio = self.default_clip_ratiofrac for i in range(self.num_policy): for h in range(self.num_h): for param_group in self.optimizer[h][i].param_groups: param_group['lr'] = self.learning_rate self.generateTransitions() self.computeTDandGAE() self.optimizeModel() def optimizeModel(self): for h in range(self.num_h): self.optimizeNN_h(h) def evaluate(self): self.num_evaluation = self.num_evaluation + 1 hour = int((time.time() - self.tic)//3600.0) m = int((time.time() - self.tic)//60.0) s = int((time.time() - self.tic)) m = m - hour60 s = int((time.time() - self.tic)) s = s - hour3600 - m60 if self.num_episode is 0: self.num_episode = 1 for h in range(self.num_h): for i in range(self.num_policy): if self.num_tuple[h][i] is 0: self.num_tuple[h][i] = 1 if self.num_tutorial_tuple[h][i] is 0: self.num_tutorial_tuple[h][i] = 1 if self.max_return < self.sum_return/self.num_episode: self.max_return = self.sum_return/self.num_episode self.max_return_epoch = self.num_evaluation print('# {} === {}h:{}m:{}s ==='.format(self.num_evaluation,hour,m,s)) print('\|\|--------------ActorCriticNN------------------') for i in range(self.num_policy): for h in range(self.num_h): print('\|\|Avg Loss Actor {} {} : {:.4f}'.format(h, i, self.sum_loss_actor[h][i]/self.num_tuple[h][i])) print('\|\|Avg Loss Critic {} {} : {:.4f}'.format(h, i, self.sum_loss_critic[h][i]/self.num_tuple[h][i])) print('\|\|Noise {} : {:.3f}'.format(h, self.target_model[h][i].log_std.exp().mean())) print('\|\|Num Transition So far {} {} : {}'.format(h, i, self.num_tuple_so_far[h][i])) print('\|\|Num Transition {} {} : {}'.format(h, i, self.num_tuple[h][i])) print('\|\|Num Episode : {}'.format(self.num_episode)) for i in range(self.num_policy): print('\|\|Avg Step per episode {} : {:.1f}'.format(i, self.num_tuple[1][i]/self.num_episode)) print('\|\|Avg Num Correct Throwings per episode : {:.3f}'.format(self.num_correct_throwing/self.num_episode)) print('\|\|Max Return per episode : {:.3f}'.format(self.max_return)) print('\|\|Avg Return per episode : {:.3f}'.format(self.sum_return/self.num_episode)) for h in range(self.num_h): print('\|\|Num TD Reset : {} {}'.format(h, self.num_td_reset[h])) print('\|\|Num TD Reset at goal : {} {}'.format(h, self.num_td_reset_at_goal[h])) self.rewards.append(self.sum_return/self.num_episode) self.numSteps.append(self.num_tuple[1][0]/self.num_episode) self.num_correct_throwings.append(self.num_correct_throwing/self.num_episode) self.saveModels() # embed() # exit(0) for i in range(len(self.reset_point)): if self.num_td_reset[0]+self.num_td_reset[1] > 0: self.reset_point[i] /= self.num_td_reset[0]+self.num_td_reset[1] else: self.reset_point[i] = 0 summary.add_scalars('rewards and num_correct_throwings',{'rewards' : self.sum_return/self.num_episode, 'num_ct' : self.num_correct_throwing/self.num_episode}, self.num_evaluation) summary.add_scalar('numSteps',self.num_tuple[1][0]/self.num_episode, self.num_evaluation) summary.add_scalar('ratio of resets',(self.num_td_reset[0] + self.num_td_reset[1])/self.num_episode, self.num_evaluation) summary.add_scalars('reset point',{'0':self.reset_point[0], '1':self.reset_point[1], '2':self.reset_point[2], '3':self.reset_point[3], '4':self.reset_point[4]},self.num_evaluation) for name, param in self.target_model[0][0].named_parameters(): summary.add_histogram(name+"_actionType", param.clone().cpu().data.numpy(), self.num_evaluation) for name, param in self.target_model[1][0].named_parameters(): summary.add_histogram(name+"_actionDetail", param.clone().cpu().data.numpy(), self.num_evaluation) print('=============================================') return np.array(self.rewards), np.array(self.numSteps), np.array(self.num_correct_throwings) import matplotlib import matplotlib.pyplot as plt plt.ion() def plot(y,title,num_fig=1,ylim=True,path=""): temp_y = np.zeros(y.shape) if y.shape[0]>5: temp_y[0] = y[0] temp_y[1] = 0.5(y[0] + y[1]) temp_y[2] = 0.3333(y[0] + y[1] + y[2]) temp_y[3] = 0.25(y[0] + y[1] + y[2] + y[3]) for i in range(4,y.shape[0]): temp_y[i] = np.sum(y[i-4:i+1])0.2 fig = plt.figure(num_fig) plt.clf() plt.title(title) plt.plot(y,'b') plt.plot(temp_y,'r') plt.savefig(path, format="png") # plt.show() if ylim: plt.ylim([0,1]) fig.canvas.draw() fig.canvas.flush_events() def plot_numSteps(y,title,num_fig=1,ylim=True,path=""): temp_y = np.zeros(y.shape) if y.shape[0]>5: temp_y[0] = y[0] temp_y[1] = 0.5(y[0] + y[1]) temp_y[2] = 0.3333(y[0] + y[1] + y[2]) temp_y[3] = 0.25(y[0] + y[1] + y[2] + y[3]) for i in range(4,y.shape[0]): temp_y[i] = np.sum(y[i-4:i+1])0.2 fig = plt.figure(num_fig) plt.clf() plt.title(title) plt.plot(y,'b') plt.plot(temp_y,'r') plt.savefig(path, format="png") if ylim: plt.ylim([0,1]) fig.canvas.draw() fig.canvas.flush_events() import argparse if __name__=="__main__": parser = argparse.ArgumentParser() parser.add_argument('-m','--model',help='model path') # parser.add_argument('-p','--policy',help='pretrained pollicy path') parser.add_argument('-iter','--iteration',help='num iterations') parser.add_argument('-n','--name',help='name of training setting') parser.add_argument('-motion', '--motion', help='motion nn path') args =parser.parse_args() graph_name = '' if args.motion is None: print("Please specify the motion nn path") exit(0) rl = RL(args.motion) if args.model is not None: for k in range(rl.num_agents): rl.loadTargetModels(args.model, k) if args.name is not None: graph_name = args.name # if args.policy is not None: # rl.loadTargetPolicy(args.policy) if args.iteration is not None: rl.num_evaluation = int(args.iteration) for i in range(int(args.iteration)): rl.env.endOfIteration() else: rl.saveModels() print('num states: {}, num actions: {}'.format(rl.env.getNumState(),rl.env.getNumAction()-1)) result_figure = nndir+"/"+"result.png" result_figure_numSteps = nndir+"/"+"result_numSteps.png" result_figure_numCTs = nndir+"/"+"result_numCTs.png" result_figure_num = 0 while Path(result_figure).is_file(): result_figure = nndir+"/"+"result_{}.png".format(result_figure_num) result_figure_numSteps = nndir+"/"+"result_numSteps_{}.png".format(result_figure_num) result_figure_numCTs = nndir+"/"+"result_numCTs_{}.png".format(result_figure_num) result_figure_num+=1 for i in range(5000000): rl.train() rewards, numSteps, numCTs = rl.evaluate()	[ 1, 529, 276, 1112, 420, 29958, 29879, 1949, 2096, 29914, 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setup.py	ilkka/nap	31	39635	<reponame>ilkka/nap #!/usr/bin/env python from pip.req import parse_requirements # parse_requirements() returns generator of pip.req.InstallRequirement objects install_reqs = parse_requirements('requirements.txt') # reqs is a list of requirement # e.g. ['django==1.5.1', 'mezzanine==1.4.6'] reqs = [str(ir.req) for ir in install_reqs] try: from setuptools import setup except ImportError: from distutils.core import setup readme = """Read docs from GitHub_ .. _GitHub: https://github.com/kimmobrunfeldt/nap """ setup( name='nap', version='2.0.0-dev', description='Convenient way to request HTTP APIs', long_description=readme, author='<NAME>', author_email='<EMAIL>', url='https://github.com/kimmobrunfeldt/nap', packages=[ 'nap', ], package_dir={'nap': 'nap'}, include_package_data=True, install_requires=reqs, license='MIT', zip_safe=False, keywords='nap rest requests http', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: Implementation :: PyPy', ], )	[ 1, 529, 276, 1112, 420, 29958, 309, 29895, 1335, 29914, 8971, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 3166, 8450, 29889, 7971, 1053, 6088, 29918, 12277, 1860, 13, 13, 29937, 6088, 29918, 12277, 1860, 580, 3639, 15299, 310, 8450, 29889, 7971, 29889, 23271, 1123, 1548, 358, 3618, 13, 6252, 29918, 7971, 29879, 353, 6088, 29918, 12277, 1860, 877, 12277, 1860, 29889, 3945, 1495, 13, 13, 29937, 12428, 29879, 338, 263, 1051, 310, 11809, 13, 29937, 321, 29889, 29887, 29889, 6024, 14095, 1360, 29896, 29889, 29945, 29889, 29896, 742, 525, 1004, 5617, 273, 457, 1360, 29896, 29889, 29946, 29889, 29953, 2033, 13, 7971, 29879, 353, 518, 710, 29898, 381, 29889, 7971, 29897, 363, 3805, 297, 2601, 29918, 7971, 29879, 29962, 13, 13, 13, 2202, 29901, 13, 1678, 515, 731, 21245, 8789, 1053, 6230, 13, 19499, 16032, 2392, 29901, 13, 1678, 515, 1320, 13239, 29889, 3221, 1053, 6230, 13, 13, 13, 949, 1004, 353, 9995, 6359, 10561, 515, 25492, 29918, 13, 13, 636, 903, 28712, 16046, 29901, 2045, 597, 3292, 29889, 510, 29914, 29895, 6727, 711, 3389, 10612, 29873, 29914, 8971, 13, 15945, 29908, 13, 13, 14669, 29898, 13, 1678, 1024, 2433, 8971, 742, 13, 1678, 1873, 2433, 29906, 29889, 29900, 29889, 29900, 29899, 3359, 742, 13, 1678, 6139, 2433, 1168, 854, 993, 982, 304, 2009, 7331, 23649, 742, 13, 1678, 1472, 29918, 8216, 29922, 949, 1004, 29892, 13, 1678, 4148, 2433, 29966, 5813, 29958, 742, 13, 1678, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 1678, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 29895, 6727, 711, 3389, 10612, 29873, 29914, 8971, 742, 13, 1678, 9741, 11759, 13, 4706, 525, 8971, 742, 13, 1678, 21251, 13, 1678, 3577, 29918, 3972, 3790, 29915, 8971, 2396, 525, 8971, 16675, 13, 1678, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 1678, 2601, 29918, 276, 339, 2658, 29922, 7971, 29879, 29892, 13, 1678, 19405, 2433, 26349, 742, 13, 1678, 14319, 29918, 11177, 29922, 8824, 29892, 13, 1678, 29361, 2433, 8971, 1791, 7274, 1732, 742, 13, 1678, 770, 14903, 11759, 13, 4706, 525, 21956, 358, 16034, 4761, 29871, 29946, 448, 350, 1187, 742, 13, 4706, 525, 2928, 2760, 319, 4749, 663, 4761, 10682, 414, 742, 13, 4706, 525, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 341, 1806, 19245, 742, 13, 4706, 525, 29940, 18771, 17088, 4761, 4223, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29906, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29906, 29889, 29953, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29906, 29889, 29955, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29906, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29941, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29946, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 1954, 14607, 4761, 10772, 19737, 742, 13, 1678, 21251, 13, 29897, 13, 2 ]
src-python/tests/test_trp2.py	kmascar/amazon-textract-response-parser	0	197998	from trp.t_pipeline import add_page_orientation, order_blocks_by_geo from typing import List from trp.t_pipeline import add_page_orientation, order_blocks_by_geo, pipeline_merge_tables, add_kv_ocr_confidence from trp.t_tables import MergeOptions, HeaderFooterType import trp.trp2 as t2 import trp as t1 import json import os import pytest from trp import Document from uuid import uuid4 import logging current_folder = os.path.dirname(os.path.realpath(__file__)) def return_json_for_file(filename): with open(os.path.join(current_folder, filename)) as test_json: return json.load(test_json) @pytest.fixture def json_response(): return return_json_for_file("test-response.json") def test_serialization(): """ testing that None values are removed when serializing """ bb_1 = t2.TBoundingBox(0.4, 0.3, 0.1, top=None) # type:ignore forcing some None/null values bb_2 = t2.TBoundingBox(0.4, 0.3, 0.1, top=0.2) p1 = t2.TPoint(x=0.1, y=0.1) p2 = t2.TPoint(x=0.3, y=None) # type:ignore geo = t2.TGeometry(bounding_box=bb_1, polygon=[p1, p2]) geo_s = t2.TGeometrySchema() s: str = geo_s.dumps(geo) assert not "null" in s geo = t2.TGeometry(bounding_box=bb_2, polygon=[p1, p2]) s: str = geo_s.dumps(geo) assert not "null" in s def test_tblock_order_blocks_by_geo(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) new_order = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(new_order)) assert "Value 1.1.1" == doc.pages[0].tables[0].rows[0].cells[0].text.strip() assert "Value 2.1.1" == doc.pages[0].tables[1].rows[0].cells[0].text.strip() assert "Value 3.1.1" == doc.pages[0].tables[2].rows[0].cells[0].text.strip() def test_tblock_order_block_by_geo_multi_page(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(t_document)) assert "Page 1 - Value 1.1.1" == doc.pages[0].tables[0].rows[0].cells[0].text.strip() assert "Page 1 - Value 2.1.1" == doc.pages[0].tables[1].rows[0].cells[0].text.strip() def test_tblock(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) new_order = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(new_order)) assert "Value 1.1.1" == doc.pages[0].tables[0].rows[0].cells[0].text.strip() assert "Value 2.1.1" == doc.pages[0].tables[1].rows[0].cells[0].text.strip() assert "Value 3.1.1" == doc.pages[0].tables[2].rows[0].cells[0].text.strip() def test_custom_tblock(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document.custom = {'testblock': {'here': 'is some fun stuff'}} assert 'testblock' in t2.TDocumentSchema().dumps(t_document) def test_custom_page_orientation(json_response): doc = Document(json_response) assert 1 == len(doc.pages) lines = [line for line in doc.pages[0].lines] assert 22 == len(lines) words = [word for line in lines for word in line.words] assert 53 == len(words) t_document: t2.TDocument = t2.TDocumentSchema().load(json_response) t_document.custom = {'orientation': 180} new_t_doc_json = t2.TDocumentSchema().dump(t_document) assert "Custom" in new_t_doc_json assert "orientation" in new_t_doc_json["Custom"] assert new_t_doc_json["Custom"]["orientation"] == 180 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert -1 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 2 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_10_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 5 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 15 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__15_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 10 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 20 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__25_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 17 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 30 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__180_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 170 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 190 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__270_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert -100 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < -80 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__90_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 80 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 100 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__minus_10_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert -10 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 5 doc = t1.Document(t2.TDocumentSchema().dump(t_document)) for page in doc.pages: assert page.custom['PageOrientationBasedOnWords'] def test_filter_blocks_by_type(): block_list = [t2.TBlock(id="1", block_type=t2.TextractBlockTypes.WORD.name)] assert t2.TDocument.filter_blocks_by_type(block_list=block_list, textract_block_type=[t2.TextractBlockTypes.WORD]) == block_list def test_next_token_response(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) assert j['NextToken'] t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert t_document.pages[0].custom def test_rotate_point(): assert t2.TPoint(2, 2) == t2.TPoint(2, 2) p = t2.TPoint(2, 2).rotate(degrees=180, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(-2, -2) p = t2.TPoint(3, 4).rotate(degrees=-30, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(5, 2) p = t2.TPoint(3, 4).rotate(degrees=-77, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(5, -2) p = t2.TPoint(3, 4).rotate(degrees=-90, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(4, -3) p = t2.TPoint(3, 4).rotate(degrees=-270, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(-4, 3) p = t2.TPoint(2, 2).rotate(degrees=180, origin_x=1, origin_y=1) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(0, 0) p = t2.TPoint(3, 4).rotate(degrees=-30, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(5, 2) p = t2.TPoint(3, 4).rotate(degrees=-77, origin_x=4, origin_y=4, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(4, 5) p = t2.TPoint(3, 4).rotate(degrees=-90, origin_x=4, origin_y=6, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(2, 7) p = t2.TPoint(3, 4).rotate(degrees=-270, origin_x=4, origin_y=6, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(6, 5) def test_rotate(): points = [] width = 0.05415758863091469 height = 0.011691284365952015 left = 0.13994796574115753 top = 0.8997916579246521 origin: t2.TPoint = t2.TPoint(x=0.5, y=0.5) degrees: float = 180 points.append(t2.TPoint(x=left, y=top).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) points.append(t2.TPoint(x=left + width, y=top).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) points.append(t2.TPoint(x=left, y=top + height).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) points.append( t2.TPoint(x=left + width, y=top + height).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) assert not None in points def test_adjust_bounding_boxes_and_polygons_to_orientation(): # p = os.path.dirname(os.path.realpath(__file__)) # f = open(os.path.join(p, "data/gib.json")) # j = json.load(f) # t_document: t2.TDocument = t2.TDocumentSchema().load(j) # t_document = add_page_orientation(t_document) # doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # key = "Date:" # fields = doc.pages[0].form.searchFieldsByKey(key) # for field in fields: # print(f"Field: Key: {field.key}, Value: {field.value}, Geo: {field.geometry} ") p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__180_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) new_order = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # for line in doc.pages[0].lines: # print("Line: {}".format(line.text)) # print("=========================== after rotation ========================") # doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # key = "Date:" # fields = doc.pages[0].form.searchFieldsByKey(key) # rotate_point = t2.TPoint(x=0.5, y=0.5) # for field in fields: # print(f"Field: Key: {field.key}, Value: {field.value}, Geo: {field.geometry} ") # bbox = field.geometry.boundingBox # new_point = t_pipeline.__rotate(origin=rotate_point, # point=t2.TPoint(x=bbox.left, y=bbox.top), # angle_degrees=180) # print(f"new point: {new_point}") # FIXME: remove duplicates in relationship_recursive! # [b.rotate(origin=t2.TPoint(0.5, 0.5), degrees=180) for b in t_document.relationships_recursive(block=t_document.pages[0])] # t_document.rotate(page=t_document.pages[0], degrees=180) # new_order = order_blocks_by_geo(t_document) # with open("/Users/schadem/temp/rotation/rotate_json2.jon", "w") as out_file: # out_file.write(t2.TDocumentSchema().dumps(t_document)) # doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # for line in doc.pages[0].lines: # print("Line: {}".format(line.text)) # p = t2.TPoint(x=0.75, y=0.03) # p.rotate(origin_x=0.5, origin_y=0.5, degrees=180) # print(p) # new_point = rotate(origin=t2.TPoint(x=0.5, y=0.5), point = ) # print(f"new_point: {new_point.x:.2f}, {new_point.y:.2f}") # print(rotate(origin=t2.TPoint(x=0.5, y=0.5), point = t2.TPoint(x=.75, y=0.03))) def test_scale(caplog): p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) p1.scale(doc_width=10, doc_height=10) assert (p1 == t2.TPoint(x=5, y=5)) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) b1.scale(doc_width=10, doc_height=10) assert (b1 == t2.TBoundingBox(width=1, height=1, left=5, top=5)) p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) g1: t2.TGeometry = t2.TGeometry(bounding_box=b1, polygon=[p1]) g1.scale(doc_width=10, doc_height=10) assert (g1 == t2.TGeometry(bounding_box=t2.TBoundingBox(width=1, height=1, left=5, top=5), polygon=[t2.TPoint(x=5, y=5)])) def test_ratio(caplog): p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) p2: t2.TPoint = t2.TPoint(x=5, y=5) p2.ratio(doc_width=10, doc_height=10) assert (p1 == p2) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) b2: t2.TBoundingBox = t2.TBoundingBox(width=1, height=1, left=5, top=5) b2.ratio(doc_width=10, doc_height=10) assert (b1 == b2) p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) p2: t2.TPoint = t2.TPoint(x=5, y=5) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) b2: t2.TBoundingBox = t2.TBoundingBox(width=1, height=1, left=5, top=5) g1: t2.TGeometry = t2.TGeometry(bounding_box=b1, polygon=[p1]) g2: t2.TGeometry = t2.TGeometry(bounding_box=b2, polygon=[p2]) g2.ratio(doc_width=10, doc_height=10) assert (g1 == g2) def test_get_blocks_for_relationship(caplog): caplog.set_level(logging.DEBUG) # existing relationships p = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(p, "data/gib.json")) as f: j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) page = t_document.pages[0] block = t_document.get_block_by_id("458a9301-8a9d-4eb2-9469-70302c62622e") relationships = block.get_relationships_for_type() relationships_value = block.get_relationships_for_type(relationship_type="VALUE") if relationships and relationships_value: rel = t_document.get_blocks_for_relationships(relationship=relationships) assert len(rel) == 1 rel_value = t_document.get_blocks_for_relationships(relationship=relationships_value) assert len(rel_value) == 1 child_rel: List[t2.TBlock] = list() for value_block in rel_value: child_rel.extend(t_document.get_blocks_for_relationships(value_block.get_relationships_for_type())) assert len(child_rel) == 1 else: assert False def test_add_ids_to_relationships(caplog): tdocument = t2.TDocument() page_block = t2.TBlock( id=str(uuid4()), block_type="PAGE", geometry=t2.TGeometry(bounding_box=t2.TBoundingBox(width=1, height=1, left=0, top=0), polygon=[t2.TPoint(x=0, y=0), t2.TPoint(x=1, y=1)]), ) tblock = t2.TBlock(id=str(uuid4()), block_type="WORD", text="sometest", geometry=t2.TGeometry(bounding_box=t2.TBoundingBox(width=0, height=0, left=0, top=0), polygon=[t2.TPoint(x=0, y=0), t2.TPoint(x=0, y=0)]), confidence=99, text_type="VIRTUAL") tdocument.add_block(page_block) tdocument.add_block(tblock) page_block.add_ids_to_relationships([tblock.id]) tblock.add_ids_to_relationships(["1", "2"]) assert page_block.relationships and len(page_block.relationships) > 0 assert tblock.relationships and len(tblock.relationships) > 0 def test_key_value_set_key_name(caplog): caplog.set_level(logging.DEBUG) # existing relationships p = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(p, "data/gib.json")) as f: j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) page = t_document.pages[0] keys = list(t_document.keys(page=page)) assert keys and len(keys) > 0 for key_value in keys: child_relationship = key_value.get_relationships_for_type('CHILD') if child_relationship: for id in child_relationship.ids: k_b = t_document.get_block_by_id(id=id) print(k_b.text) print(' '.join([x.text for x in t_document.value_for_key(key_value)])) def test_get_relationships_for_type(caplog): # existing relationships p = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(p, "data/gib.json")) as f: j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) page = t_document.pages[0] new_block = t2.TBlock(id=str(uuid4())) t_document.add_block(new_block) page.add_ids_to_relationships([new_block.id]) assert t_document.get_block_by_id(new_block.id) == new_block #empty relationships t_document: t2.TDocument = t2.TDocument() t_document.add_block(t2.TBlock(id=str(uuid4()), block_type="PAGE")) page = t_document.pages[0] new_block = t2.TBlock(id=str(uuid4())) t_document.add_block(new_block) page.add_ids_to_relationships([new_block.id]) assert t_document.get_block_by_id(new_block.id) == new_block def test_merge_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = 'fed02fb4-1996-4a15-98dc-29da193cc476' tbl_id2 = '47c6097f-02d5-4432-8423-13c05fbfacbd' pre_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore pre_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id2).relationships[0].ids) # type: ignore pre_merge_tbl1_lastcell = t_document.get_block_by_id(tbl_id1).relationships[0].ids[-1] # type: ignore pre_merge_tbl2_lastcell = t_document.get_block_by_id(tbl_id2).relationships[0].ids[-1] # type: ignore pre_merge_tbl1_last_row = t_document.get_block_by_id(pre_merge_tbl1_lastcell).row_index # type: ignore pre_merge_tbl2_last_row = t_document.get_block_by_id(pre_merge_tbl2_lastcell).row_index # type: ignore t_document.merge_tables([[tbl_id1, tbl_id2]]) post_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore post_merge_tbl1_lastcell = t_document.get_block_by_id(tbl_id1).relationships[0].ids[-1] # type: ignore post_merge_tbl1_last_row = t_document.get_block_by_id(post_merge_tbl1_lastcell).row_index # type: ignore assert post_merge_tbl1_cells_no == pre_merge_tbl1_cells_no + pre_merge_tbl2_cells_no assert pre_merge_tbl2_last_row assert post_merge_tbl1_last_row == pre_merge_tbl1_last_row + pre_merge_tbl2_last_row # type: ignore def test_delete_blocks(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = 'fed02fb4-1996-4a15-98dc-29da193cc476' tbl_id2 = '47c6097f-02d5-4432-8423-13c05fbfacbd' pre_delete_block_no = len(t_document.blocks) t_document.delete_blocks([tbl_id1, tbl_id2]) post_delete_block_no = len(t_document.blocks) assert post_delete_block_no == pre_delete_block_no - 2 def test_link_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = 'fed02fb4-1996-4a15-98dc-29da193cc476' tbl_id2 = '47c6097f-02d5-4432-8423-13c05fbfacbd' t_document.link_tables([[tbl_id1, tbl_id2]]) assert t_document.get_block_by_id(tbl_id1).custom['next_table'] == tbl_id2 # type: ignore assert t_document.get_block_by_id(tbl_id2).custom['previous_table'] == tbl_id1 # type: ignore def test_pipeline_merge_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_table_merge.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = '5685498d-d196-42a7-8b40-594d6d886ca9' tbl_id2 = 'a9191a66-0d32-4d36-8fd6-58e6917f4ea6' tbl_id3 = 'e0368543-c9c3-4616-bd6c-f25e66c859b2' pre_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore pre_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id2).relationships[0].ids) # type: ignore pre_merge_tbl3_cells_no = len(t_document.get_block_by_id(tbl_id3).relationships[0].ids) # type: ignore t_document = pipeline_merge_tables(t_document, MergeOptions.MERGE, None, HeaderFooterType.NONE) post_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore assert post_merge_tbl1_cells_no == pre_merge_tbl1_cells_no + pre_merge_tbl2_cells_no + pre_merge_tbl3_cells_no def test_pipeline_merge_multiple_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_tables_multi_page_sample.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = '4894d2ba-0479-4196-9cbd-c0fea4d28762' tbl_id2 = 'b5e061ec-05be-48d5-83fc-6719fdd4397a' tbl_id3 = '8bbc3f4f-0354-4999-a001-4585631bb7fe' tbl_id4 = 'cf8e09a1-c317-40c1-9c45-e830e14167d5' pre_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore pre_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id2).relationships[0].ids) # type: ignore pre_merge_tbl3_cells_no = len(t_document.get_block_by_id(tbl_id3).relationships[0].ids) # type: ignore pre_merge_tbl4_cells_no = len(t_document.get_block_by_id(tbl_id4).relationships[0].ids) # type: ignore t_document = pipeline_merge_tables(t_document, MergeOptions.MERGE, None, HeaderFooterType.NONE) post_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore post_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id3).relationships[0].ids) # type: ignore assert post_merge_tbl1_cells_no == pre_merge_tbl1_cells_no + pre_merge_tbl2_cells_no assert post_merge_tbl2_cells_no == pre_merge_tbl3_cells_no + pre_merge_tbl4_cells_no def test_kv_ocr_confidence(caplog): caplog.set_level(logging.DEBUG) p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/employment-application.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_kv_ocr_confidence(t_document) doc = t1.Document(t2.TDocumentSchema().dump(t_document)) for page in doc.pages: k1 = page.form.getFieldByKey("Home Address:") k1.key.custom['OCRConfidence'] == {'mean': 99.60698318481445} k1.value.custom['OCRConfidence'] == {'mean': 99.8596928914388} k1 = page.form.getFieldByKey("Phone Number:") k1.key.custom['OCRConfidence'] == {'mean': 99.55334854125977} k1.value.custom['OCRConfidence'] == {'mean': 99.23233032226562} # for field in page.form.fields: # print( # f"{field.key.text} - {field.key.custom['OCRConfidence']}, {field.value.text} - {field.value.custom['OCRConfidence']}" # )	[ 1, 515, 534, 29886, 29889, 29873, 29918, 13096, 5570, 1053, 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mindpong/model/serial_communication.py	PolyCortex/MindPong	22	42613	import sys from glob import glob from serial import Serial, SerialException import numpy as np BAUD_RATE = 9600 PORT = 'COM5' READ_TIMEOUT = 1 LOWER_BOUND = 0.01 UPPER_BOUND = 0.4 class SerialCommunication(): """ Manages the communication and sends the data to the Arduino """ def __init__(self): self._serial_channel = Serial() self._serial_channel.port = PORT self._serial_channel.baudrate = BAUD_RATE @property def baudrate(self): return self._serial_channel.baudrate @baudrate.setter def baudrate(self, new_baudrate): if not self._serial_channel.is_open: self._serial_channel.baudrate = new_baudrate else: raise Exception("Close connection before changing baudrate") @property def port(self): return self._serial_channel.port @port.setter def set_port(self, new_port): if not self._serial_channel.is_open: self._serial_channel.port = new_port else: raise Exception("Close connection before changing port") def get_available_serial_ports(self): """ Returns a list of all ports that can be opened """ if self._serial_channel.is_open: raise Exception("Close connection before") result = [] for port in self._list_all_possibles_ports(): try: Serial(port).close() result.append(port) except (OSError, SerialException): pass return result def establish_communication(self): """ Enables the communication with the arduino with the latest parameters Throws a SerialException is it cannot connect to port """ try: self._serial_channel.open() except SerialException as error: print("Error when connecting to serial %s port" % (self._serial_channel.port)) raise(SerialException) def send_data(self, data): """ prints feedback data from the arduino and sends the new data """ if self._is_data_available(): print(("Reading : ", self._read_bytes(len(data)))) data = [x[1] for x in data] if self._is_data_valid(data): value_to_send = self._get_clipped_signals(data) print(('Sending', value_to_send)) try: self._serial_channel.write(bytearray(value_to_send)) except SerialTimeoutException as e: print('Error when sending data to microcontroller:' + str(e)) def close_communication(self): self._serial_channel.close() def _list_all_possibles_ports(self): if sys.platform.startswith('win'): ports = ['COM%s' % (i + 1) for i in range(256)] elif sys.platform.startswith('linux') or sys.platform.startswith('cygwin'): # this excludes your current terminal "/dev/tty" ports = glob('/dev/tty[A-Za-z]') elif sys.platform.startswith('darwin'): ports = glob('/dev/tty.') else: raise EnvironmentError('Unsupported platform') return ports def _read_bytes(self, nb_bytes=1): bytes_received = [] for _ in range(nb_bytes): bytes_received.append(self._serial_channel.read(1)) return [ord(byte) for byte in bytes_received if byte] def _is_data_available(self): return self._serial_channel is not None and self._serial_channel.is_open and self._serial_channel.in_waiting def _is_data_valid(self, data): return self._serial_channel is not None and self._serial_channel.is_open and not np.any(np.isnan(data)) def _get_clipped_signals(self, signals): clipped_list = np.clip(signals, LOWER_BOUND, UPPER_BOUND) return [int(255 * (x - LOWER_BOUND)/(UPPER_BOUND - LOWER_BOUND)) for x in clipped_list]	[ 1, 1053, 10876, 13, 3166, 13149, 1053, 13149, 13, 13, 3166, 7797, 1053, 18896, 29892, 18896, 2451, 13, 5215, 12655, 408, 7442, 13, 13, 5688, 15789, 29918, 29934, 3040, 353, 29871, 29929, 29953, 29900, 29900, 13, 15082, 353, 525, 19795, 29945, 29915, 13, 16310, 29918, 15307, 12015, 353, 29871, 29896, 13, 13, 27998, 1001, 29918, 8456, 18783, 353, 29871, 29900, 29889, 29900, 29896, 13, 4897, 13171, 29918, 8456, 18783, 353, 29871, 29900, 29889, 29946, 13, 13, 13, 1990, 18896, 5261, 2523, 362, 7295, 13, 1678, 9995, 2315, 1179, 278, 12084, 322, 16003, 278, 848, 304, 278, 826, 28955, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 3032, 15550, 29918, 12719, 353, 18896, 580, 13, 4706, 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11/11.py	PaeP3nguin/advent-of-code-2017	0	171359	#!python3 from collections import Counter def main(): with open('11.txt', 'r') as f, open('11_out.txt', 'w') as f_out: line = f.readline().strip() steps = line.split(',') directions = ['n', 'ne', 'se', 's', 'sw', 'nw'] # Part 1 step_counts = Counter(steps) pairs = [('n', 's'), ('nw', 'se'), ('ne', 'sw')] for a, b in pairs: if step_counts[a] > step_counts[b]: low, high = b, a else: low, high = a, b step_counts[high] -= step_counts[low] step_counts[low] = 0 no_more = False while not no_more: no_more = True for i, direction in enumerate(directions): left = directions[i - 1] right = directions[(i + 1) % 6] if step_counts[left] > step_counts[right]: low, high = right, left else: low, high = left, right if step_counts[low] == 0: continue no_more = False step_counts[high] -= step_counts[low] step_counts[direction] += step_counts[low] step_counts[low] = 0 total_steps = sum(step_counts.values()) print(total_steps) print(total_steps, file=f_out) # Part 2 max_steps = 0 step_counts = {d: 0 for d in directions} opposite = {d: directions[(i + 3) % 6] for i, d in enumerate(directions)} for step in steps: if step_counts[opposite[step]] > 0: step_counts[opposite[step]] -= 1 else: step_counts[step] += 1 for i, direction in enumerate(directions): left = directions[i - 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data/data_processing.py	HenryL-study/Voice-Style-Conversion-via-Seq2Seq-model	2	62122	<gh_stars>1-10 import librosa import numpy as np import os import pyworld def world_encode_spectral_envelop(sp, fs, dim=36): # Get Mel-cepstral coefficients (MCEPs) #sp = sp.astype(np.float64) coded_sp = pyworld.code_spectral_envelope(sp, fs, dim) return coded_sp def world_decompose(wav, fs, frame_period = 5.0): # Decompose speech signal into f0, spectral envelope and aperiodicity using WORLD wav = wav.astype(np.float64) f0, timeaxis = pyworld.harvest(wav, fs, frame_period = frame_period, f0_floor = 71.0, f0_ceil = 800.0) sp = pyworld.cheaptrick(wav, f0, timeaxis, fs) ap = pyworld.d4c(wav, f0, timeaxis, fs) return f0, timeaxis, sp, ap def world_speech_synthesis(f0, coded_sp, ap, fs, frame_period): decoded_sp = world_decode_spectral_envelop(coded_sp, fs) # TODO min_len = min([len(f0), len(coded_sp), len(ap)]) f0 = f0[:min_len] coded_sp = coded_sp[:min_len] ap = ap[:min_len] wav = pyworld.synthesize(f0, decoded_sp, ap, fs, frame_period) # Librosa could not save wav if not doing so wav = wav.astype(np.float32) return wav def world_decode_spectral_envelop(coded_sp, fs): # Decode Mel-cepstral to sp fftlen = pyworld.get_cheaptrick_fft_size(fs) decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen) return decoded_sp path1 = "data/VCC2SF1/" path2 = "data/VCC2SM1/" files_1 = os.listdir(path1) files_2 = os.listdir(path2) #wav_file = "VCC2SF1/10001.wav" sampling_rate, num_mcep, frame_period=22050, 40, 5 length = len(files_1) for i in range(length): print("Iteration ", i) wav, _ = librosa.load(path1+files_1[i], sr=sampling_rate, mono=True) f0, timeaxis, sp, ap = world_decompose(wav=wav, fs=sampling_rate, frame_period=frame_period) coded_sp = world_encode_spectral_envelop(sp=sp, fs=sampling_rate, dim=num_mcep) # print("This is the feature we want -> coded_sp") # print("Type of coded_sp: ", type(coded_sp)) # print("shape of coded_sp: ", coded_sp.shape) np.save("data/parameters/VCC2SF1/coded_sp/"+str(i)+".npy", coded_sp) np.save("data/parameters/VCC2SF1/f0/"+str(i)+".npy", f0) np.save("data/parameters/VCC2SF1/ap/"+str(i)+".npy", ap) #print("ap: ", ap) # wav_transformed = world_speech_synthesis(f0=f0, coded_sp=coded_sp, # ap=ap, fs=sampling_rate, frame_period=frame_period) # librosa.output.write_wav("generate/"+path1+files_1[i], wav_transformed, sampling_rate) # sampling_rate, num_mcep, frame_period=22050, 40, 5 wav, _ = librosa.load(path2+files_2[i], sr=sampling_rate, mono=True) f0, timeaxis, sp, ap = world_decompose(wav=wav, fs=sampling_rate, frame_period=frame_period) coded_sp = world_encode_spectral_envelop(sp=sp, fs=sampling_rate, dim=num_mcep) np.save("data/parameters/VCC2SM1/coded_sp/"+str(i)+".npy", coded_sp) np.save("data/parameters/VCC2SM1/f0/"+str(i)+".npy", f0) np.save("data/parameters/VCC2SM1/ap/"+str(i)+".npy", ap) # wav_transformed = world_speech_synthesis(f0=f0, coded_sp=coded_sp, # ap=ap, fs=sampling_rate, 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test/unit_tests.py	dykesk/gaussian-wake	3	124710	""" Created by <NAME>, Sep. 2018. FLOW Lab Brigham Young University """ import unittest import numpy as np from _porteagel_fortran import porteagel_analyze, x0_func, theta_c_0_func, sigmay_func, sigma_spread_func from _porteagel_fortran import sigmaz_func, wake_offset_func, deltav_func, deltav_near_wake_lin_func from _porteagel_fortran import overlap_area_func, wake_combination_func, added_ti_func, k_star_func from _porteagel_fortran import ct_to_axial_ind_func, wind_shear_func, discontinuity_point_func, smooth_max from _porteagel_fortran import interpolation, hermite_spline, point_velocity_with_shear_func from openmdao.api import Problem, Group def power_func_v80(v): # power curve fit for vestas v80 from Niayifar 2016 p = 0.17819 * v ** 5 - 6.5198 * v ** 4 + 90.623 * v ** 3 - 574.62 * v ** 2 + 1727.2 * v - 1975.0 return p class test_basic_subroutines(unittest.TestCase): def setUp(self): self.tolerance = 1E-6 self.d = 126.4 self.yaw = np.pi/6. self.ct = 0.8 self.alpha = 2.32 self.beta = 0.154 self.ti = 0.1 self.ky = 0.25 self.kz = 0.2 self.wind_speed = 8.0 def test_x0_func_hand_calc(self): x0 = x0_func(self.d, self.yaw, self.ct, self.alpha, self.ti, self.beta) self.assertAlmostEqual(x0, 353.2313474, delta=self.tolerance) def test_x0_func_data_yaw0(self): rotor_diameter = 0.15 # m yaw = 0.0np.pi/180. #radians ct = 0.8214036062840235 ti = 0.074 # # 3.77839335632898, 0.6643546778702326 # 3.704230762943225, 0.7361200568897026 # 3.849186706118913, 0.7866577299700839 # 3.848583479099574, 0.8214036062840235 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 3.862413891540104, delta=1E-2) def test_x0_func_data_yaw10(self): rotor_diameter = 0.15 # m yaw = 10.0 np.pi / 180. # radians ct = 0.7866577299700839 ti = 0.074 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 3.973368012202963, delta=1E-1) def test_x0_func_data_yaw20(self): rotor_diameter = 0.15 # m yaw = 20.0 * np.pi / 180. # radians ct = 0.7361200568897026 ti = 0.074 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 4.051040798613613, delta=1E-1) def test_x0_func_data_yaw30(self): rotor_diameter = 0.15 # m yaw = 30.0 * np.pi / 180. # radians ct = 0.6643546778702326 ti = 0.074 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 4.053814723717636, delta=1E-1) def test_discontinuity_point_func(self): x0 = 353.0 xd = discontinuity_point_func(x0, self.d, self.ky, self.kz, self.yaw, self.ct) self.assertAlmostEqual(xd, 335.5180515, delta=self.tolerance) def test_sigmay_func(self): x = 500.0 x0 = 353.0 xd = sigmay_func(x, x0, self.ky, self.d, self.yaw) self.assertAlmostEqual(xd, 75.45193794, delta=self.tolerance) def test_sigmaz_func(self): x = 500.0 x0 = 353.0 xd = sigmaz_func(x, x0, self.kz, self.d) self.assertAlmostEqual(xd, 74.08914857, delta=self.tolerance) def test_theta_c_0_func(self): theta_c_0 = theta_c_0_func(self.yaw, self.ct) self.assertAlmostEqual(theta_c_0, 0.080852297, delta=self.tolerance) def test_wake_offset_func_near_wake(self): x = 200. theta_c_0 = 0.0808 sigmay = 36. sigmaz = 43. x0 = 353. delta = wake_offset_func(x, self.d, theta_c_0, x0, self.yaw, self.ky, self.kz, self.ct, sigmay, sigmaz) self.assertAlmostEqual(delta, 16.16, delta=self.tolerance) def test_wake_offset_func_far_wake(self): x = 500. x0 = 353. theta_c_0 = 0.0808 sigmay = 75.45193794 sigmaz = 74.08914857 delta = wake_offset_func(x, self.d, theta_c_0, x0, self.yaw, self.ky, self.kz, self.ct, sigmay, sigmaz) self.assertAlmostEqual(delta, 33.89352568, delta=self.tolerance) def test_deltav_func_2016(self): d = 126.4 yaw = np.pi / 6. ky = 0.25 kz = 0.2 sigmay = 75.0 sigmaz = 74.0 ct = 0.8 z = 100.0 zh = 90.0 wec_factor = 1.0 y = 50.0 delta = 33.89 x = 500. deltay = y-delta deltaz = z-zh deltav = deltav_func(deltay, deltaz, ct, yaw, sigmay, sigmaz, d, 2016, self.ky, x, wec_factor, sigmay, sigmaz) self.assertAlmostEqual(deltav, 0.1293410999394427, delta=self.tolerance) def test_deltav_func_2014(self): d = 126.4 yaw = np.pi / 6. ky = 0.25 kz = 0.2 sigmay = 75.0 sigmaz = 74.0 ct = 0.8 z = 100.0 zh = 90.0 wec_factor = 1.0 y = 50.0 delta = 33.89 x = 500. deltay = y-delta deltaz = z-zh deltav = deltav_func(deltay, deltaz, ct, yaw, sigmay, sigmaz, d, 2014, self.ky, x, wec_factor, sigmay, sigmaz) self.assertAlmostEqual(deltav, 0.03264659097, delta=self.tolerance) def test_near_deltav_func_2014_rotor_location(self): version = 2014 x0 = 353.0 xd = 335.0 yaw = 0.0 sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 0.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.00048145926305030354, delta=self.tolerance) def test_near_deltav_func_2014_midrange_location(self): version = 2014 x0 = 353.0 xd = 335.0 yaw = 0.0 sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 200.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.027941992346249663, delta=self.tolerance) def test_near_deltav_func_2014_x0_location(self): version = 2014 x0 = 353.0 xd = 335.0 yaw = 0.0 sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 353. deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.0401329549842686, delta=self.tolerance) def test_near_deltav_func_2016_rotor_location(self): version = 2016 x0 = 353.0 xd = 335.0 yaw = np.pi/6. sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 0.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.05319773340098457, delta=self.tolerance) def test_near_deltav_func_2016_midrange_location(self): version = 2016 x0 = 353.0 xd = 335.0 yaw = np.pi/6. sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 200.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.0388723745762739, delta=self.tolerance) def test_near_deltav_func_2016_x0_location(self): version = 2016 x0 = 353.0 xd = 335.0 yaw = np.pi/6. sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 353. deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.027913475075370238, delta=self.tolerance) def test_wake_combination_func_Lissaman1979(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 0 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.4, delta=self.tolerance) def test_wake_combination_func_Katic1986(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 2 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.039607805437114, delta=self.tolerance) def test_wake_combination_func_Voutsinas1990(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 3 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.0303940504246953, delta=self.tolerance) def test_wake_combination_func_Niayifar2016(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 1 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.35, delta=self.tolerance) def test_wind_shear_func(self): z = 90.0 zo = 2.0 zr = 80.0 psi = 0.15 wind_velocity_with_shear = wind_shear_func(z, self.wind_speed, zr, zo, psi) self.assertAlmostEqual(wind_velocity_with_shear, 8.14607111996, delta=self.tolerance) def test_k_star_func(self): ti_ust = 0.1 kstar = k_star_func(ti_ust) self.assertAlmostEqual(kstar, 0.042048, delta=self.tolerance) def test_ct_to_axial_ind_func_normal_ct(self): ct = 0.84 axial_induction = ct_to_axial_ind_func(ct) self.assertAlmostEqual(axial_induction, 0.3, delta=self.tolerance) def test_ct_to_axial_ind_func_high_ct(self): ct = 0.97 axial_induction = ct_to_axial_ind_func(ct) self.assertAlmostEqual(axial_induction, 0.4119957249, delta=self.tolerance) def test_smooth_max(self): x = 12. y = 13. s = 100. smax1 = smooth_max(s, x, y) self.assertAlmostEqual(smax1, 13.0, delta=self.tolerance) def test_overlap_area_func_rotor_all_in_wake(self): turbiney = 50. turbinez = 90. rotor_diameter = 100. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 200. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.pirotor_diameter2/4, delta=self.tolerance) def test_overlap_area_func_rotor_all_in_wake_perfect_overlap(self): turbiney = 0. turbinez = 90. rotor_diameter = 100. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 200. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.pi rotor_diameter ** 2 / 4, delta=self.tolerance) def test_overlap_area_func_wake_all_in_rotor(self): turbiney = 50. turbinez = 90. rotor_diameter = 200. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 100. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.piwake_diameter2/4, delta=self.tolerance) def test_overlap_area_func_wake_all_in_rotor_perfect_overlap(self): turbiney = 0. turbinez = 90. rotor_diameter = 200. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 100. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.piwake_diameter*2/4, delta=self.tolerance) def test_overlap_area_func_no_overlap(self): turbiney = 0. turbinez = 90. rotor_diameter = 100. wake_center_y = 100.0 wake_center_z = 90.0 wake_diameter = 100. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, 0.0, delta=self.tolerance) #TODO add tests for partial overlap class test_added_ti_func(unittest.TestCase): def setUp(self): self.tolerance = 1E-2 self.yaw = 0.0 self.ct = 0.8 self.alpha = 2.32 self.beta = 0.154 self.ti = 0.1 self.ky = 0.022 self.kz = 0.022 self.wind_speed = 8.0 self.TI = 0.077 self.x = 560. self.rotor_diameter = 80. self.deltay = 0. self.wake_height = 70. self.turbine_height = 70. self.sm_smoothing = 700. def test_added_ti_func_Niayifar_2016_max_2nd_turb(self): TI_calculation_method = 4 TI_area_ratio_in = 0.0 TI_dst_in = 0.0 TI_ust = 0.077 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) def test_added_ti_func_Niayifar_2016_max_3rd_turb(self): TI_calculation_method = 4 TI_area_ratio_in = 0.0 TI_dst_in = 0.0 TI_ust = 0.1476 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) def test_added_ti_func_Niayifar_2016_smoothmax_2nd_turb(self): TI_calculation_method = 5 TI_area_ratio_in = 0.0 TI_dst_in = 0.0 TI_ust = 0.077 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) def test_added_ti_func_Niayifar_2016_smoothmax_3rd_turb(self): TI_calculation_method = 5 TI_area_ratio_in = .05 TI_dst_in = 0.077 TI_ust = 0.1476 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) class test_point_velocity_with_shear(unittest.TestCase): def setUp(self): self.tolerance = 1E-2 self.turbI = -1 self.wake_combination_method = 1 self.wake_model_version = 2016 self.sorted_x_idx = np.array([0]) self.rotorDiameter = np.array([0.15]) self.pointX = self.rotorDiameter5. self.pointY = 0.24self.rotorDiameter self.pointZ = 0.125 self.tol = 1E-12 self.alpha = 2.32 self.beta = 0.154 self.expratemultiplier = 1.0 self.wec_factor = 1.0 self.wind_speed = 4.88 self.z_ref = 0.125 self.z_0 = 0.000022 self.shear_exp = 0.1 self.turbineXw = np.array([0]) self.turbineYw = np.array([0]) self.turbineZ = np.array([0.125]) self.yaw = np.array([20. np.pi / 180.0]) self.wtVelocity = np.array([self.wind_speed]) self.Ct_local = 0.7361200568897026 * np.ones_like(self.turbineXw) # np.array([0.7374481936835376]) self.TIturbs = 0.025 * np.ones_like(self.turbineXw) # np.array([0.01]) #np.array([0.001]) #TODO check point velocity tests and ti input self.ky_local = 0.022 # np.array([0.3837TIturbs[0] + 0.003678]) self.kz_local = 0.022 # np.array([0.3837TIturbs[0] + 0.003678]) def test_point_velocity_with_shear(self): point_velocity_with_shear = point_velocity_with_shear_func(self.turbI, self.wake_combination_method, self.wake_model_version, self.sorted_x_idx, self.pointX, self.pointY, self.pointZ, self.tol, self.alpha, self.beta, self.expratemultiplier, self.wec_factor, self.wind_speed, self.z_ref, self.z_0, self.shear_exp, self.turbineXw, self.turbineYw, self.turbineZ, self.rotorDiameter, self.yaw, self.wtVelocity, self.Ct_local, self.TIturbs, self.ky_local, self.kz_local) self.assertAlmostEqual(point_velocity_with_shear/self.wind_speed, 0.406, delta=self.tolerance) class test_sigma_spread(unittest.TestCase): def setUp(self): self.tolerance = 1E-6 self.d = 126.4 self.yaw = np.pi / 6. self.ct = 0.8 self.alpha = 2.32 self.beta = 0.154 self.ti = 0.1 self.ky = 0.25 self.kz = 0.2 x = np.array([500.0, 500.0, 500.0, 200.0, -10.0]) xi_d = np.array([1.0, 2.0, 1.0, 1.0, 1.0]) xi_a = np.array([0.0, 0.0, 45.0, 0.0, 0.0]) sigma_0 = 38.7 sigma_d = 34.2 x0 = 353.0 self.sigma_spread = np.zeros_like(x) for i in np.arange(0, x.size): self.sigma_spread[i] = sigma_spread_func(x[i], x0, self.ky, sigma_0, sigma_d, xi_a[i], xi_d[i]) self.correct_results = np.array([75.45, 150.9, 534.2, 36.7495751, 0.0]) def test_sigma_spread_func_case1(self): self.assertAlmostEqual(self.sigma_spread[0], self.correct_results[0], delta=self.tolerance) def test_sigma_spread_func_case2(self): self.assertAlmostEqual(self.sigma_spread[1], self.correct_results[1], delta=self.tolerance) def test_sigma_spread_func_case3(self): self.assertAlmostEqual(self.sigma_spread[2], self.correct_results[2], delta=self.tolerance) def test_sigma_spread_func_case4(self): self.assertAlmostEqual(self.sigma_spread[3], self.correct_results[3], delta=self.tolerance) def test_sigma_spread_func_case5(self): self.assertAlmostEqual(self.sigma_spread[4], self.correct_results[4], delta=self.tolerance) # class test_sigma_spread_too_high_error(unittest.TestCase): # # def setUp(self): # self.tolerance = 1E-6 # self.d = 126.4 # self.yaw = np.pi / 6. # self.ct = 0.8 # self.alpha = 2.32 # self.beta = 0.154 # self.ti = 0.1 # self.ky = 0.25 # self.kz = 0.2 # # self.x = 500.0 # self.xi_d = 1.0 # self.xi_a = 90.000 # # # self.sigma_0 = 38.7 # self.sigma_d = 34.2 # # self.x0 = 353.0 # # def test_sigma_spread_too_high(self): # self.assertRaises(sigma_spread_func(self.x, self.x0, self.ky, self.sigma_0, self.sigma_d, self.xi_a, self.xi_d)) # class test_hermite_spline(unittest.TestCase): def test_linear(self): """"Approximate y = x - 1""" x = 1. x0 = 0. x1 = 2. y0 = -1. dy0 = 1. y1 = 1. dy1 = 1. y = hermite_spline(x, x0, x1, y0, dy0, y1, dy1) self.assertEqual(y, 0.0) def test_cubic(self): """Approximate y=x3""" x = 0. x0 = -1. x1 = 1. y0 = 0. dy0 = 2. y1 = 0. dy1 = 2. y = hermite_spline(x, x0, x1, y0, dy0, y1, dy1) self.assertEqual(y, 0.0) def test_parabolic(self): """Approximate y=x2""" x = 0. x0 = -1. x1 = 1. y0 = 1. dy0 = -2. y1 = 1. dy1 = 2. y = hermite_spline(x, x0, x1, y0, dy0, y1, dy1) self.assertEqual(y, 0.0) class test_interpolation(unittest.TestCase): # def test_cubic(self): # # # define interpolation type # interp_type = 0 # # # set up points for interpolation # x = np.array([-1., -0.5, 0., 0.5, 1.]) # y = np.array([-1., -0.125, 0., 0.125, 1.]) # # # set location of interpolation # xval = 0.125 # # # get interpolated y value # yval = interpolation(interp_type, x, y, xval, 3.0, 3.0, True) # # self.assertEqual(yval, 0.0625) def test_linear(self): # define interpolation type interp_type = 1 # set up points for interpolation x = np.array([0., 1., 2.]) y = np.array([0., 1., 0.]) # set location of interpolation xval = 0.5 # get interpolated y value yval = interpolation(interp_type, x, y, xval, 0.0, 0.0, False) self.assertEqual(yval, 0.5) class test_ctcp_curve(unittest.TestCase): def setUp(self): filename = "./input_files/NREL5MWCPCT_dict.p" import cPickle as pickle data = pickle.load(open(filename, "rb")) cp_data = np.zeros([data['wind_speed'].size]) ct_data = np.zeros([data['wind_speed'].size]) wind_speed_data = np.zeros([data['wind_speed'].size]) cp_data[:] = data['CP'] ct_data[:] = data['CT'] wind_speed_data[:] = data['wind_speed'] self.ct_data = ct_data self.cp_data = cp_data self.wind_speed_data = wind_speed_data self.options = {'use_ct_curve': True, 'ct_curve_ct': self.ct_data, 'ct_curve_wind_speed': self.wind_speed_data} def test_5mw_ct_greater_than_1_warning(self): from gaussianwake.gaussianwake import GaussianWake import pytest pytest.warns(Warning, GaussianWake, nTurbines=6, options=self.options) class test_wec(unittest.TestCase): def setUp(self): filename = "./input_files/NREL5MWCPCT_dict.p" import cPickle as pickle data = pickle.load(open(filename, "rb")) cp_data = np.zeros([data['wind_speed'].size]) ct_data = np.zeros([data['wind_speed'].size]) wind_speed_data = np.zeros([data['wind_speed'].size]) cp_data[:] = data['CP'] ct_data[:] = data['CT'] wind_speed_data[:] = data['wind_speed'] self.ct_data = ct_data self.cp_data = cp_data self.wind_speed_data = wind_speed_data self.options = {'use_ct_curve': True, 'ct_curve_ct': self.ct_data, 'ct_curve_wind_speed': self.wind_speed_data} nTurbines = 2 from gaussianwake.gaussianwake import GaussianWake prob = Problem(root=Group()) prob.root.add('wakemodel', GaussianWake(nTurbines, options=self.options), promotes=['']) prob.setup() prob['wind_speed'] = 8. self.prob = prob def test_no_change_in_deficit_by_wake_spread_rate_multiplier_at_center(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 0.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_spreading_angle'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertEqual(wspeed1, wspeed0) def test_no_change_in_deficit_by_wake_diameter_multiplier_at_center(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 0.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_spreading_angle'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertEqual(wspeed1, wspeed0) def test_increase_deficit_by_wake_diameter_expansion(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 100.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_factor'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertGreater(wspeed0, wspeed1) def test_increase_deficit_by_wake_expansion_rate_multiplier(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 100.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() prob['model_params:wec_factor'] = 1.0 wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_spreading_angle'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertGreater(wspeed0, wspeed1) class test_porteagel_analyze(unittest.TestCase): #TODO improve tolerance of test - why can't we match more closely? def setUp(self): from plantenergy.utilities import sunflower_points self.tolerance = 1E-1 self.wake_combination_method = 1 self.wake_model_version = 2016 self.rotor_diameter = 80. self.hub_height = 70. self.ct = 0.6 self.alpha = 2.32 self.beta = 0.154 self.expratemultiplier = 1.0 self.wec_factor = 1.0 self.wind_speed = 8.0 self.z_ref = self.hub_height self.z_0 = 0.0002 self.shear_exp = 0.15 self.yaw = 0.0 self.wtVelocity = np.array([self.wind_speed]) self.TI = 0.077 self.ky = 0.3837self.TI + 0.003678 # np.array([0.3837TIturbs[0] + 0.003678]) self.kz = 0.3837self.TI + 0.003678 # np.array([0.3837TIturbs[0] + 0.003678]) rotorpoints = sunflower_points(100) self.RotorPointsY = rotorpoints[0] #np.array([0, .5, 1.0, 0., 0.0, -.5, -1.0, 0., 0.]) self.RotorPointsZ = rotorpoints[1] #np.array([0, 0., 0., .5, 1.0, 0., 0.0, -0.5, -1.]) # self.RotorPointsY = np.array([0]) # self.RotorPointsZ = np.array([0]) self.RotorPointsY = np.array([0, .5, 1.0, 0., 0.0, -.5, -1.0, 0., 0.]) self.RotorPointsZ = np.array([0, 0., 0., .5, 1.0, 0., 0.0, -0.5, -1.]) self.TI_calculation_method = 4 self.calc_k_star = True self.print_ti = False self.interp_type = 1 self.sm_smoothing = 700. loc_data = np.loadtxt('input_files/horns_rev_locations.txt', delimiter=',') turbineXw = loc_data[:, 0] * self.rotor_diameter turbineYw = loc_data[:, 1] * self.rotor_diameter turbineZ = np.ones_like(turbineXw) * self.hub_height sorted_x_idx = np.argsort(turbineXw, kind='heapsort') rotorDiameter = np.ones_like(turbineXw) * self.rotor_diameter Ct = np.ones_like(turbineXw) * self.ct yaw = np.ones_like(turbineXw) * self.yaw TI_turbs = np.ones_like(turbineXw) * self.TI use_ct_curve = True # ct_data = np.loadtxt('input_files/predicted_ct_vestas_v80_niayifar2016.txt', delimiter=',') ct_data = np.loadtxt('input_files/mfg_ct_vestas_v80_niayifar2016.txt', delimiter=',') ct_curve_wind_speed = ct_data[:, 0] ct_curve_ct = ct_data[:, 1] CalculateFlowField=False wtVelocity, _ = porteagel_analyze(turbineXw, sorted_x_idx, turbineYw, turbineZ, rotorDiameter, Ct, self.wind_speed, yaw, self.ky, self.kz, self.alpha, self.beta, TI_turbs, self.RotorPointsY, self.RotorPointsZ, np.array([0]), np.array([0]), np.array([0]), self.z_ref, self.z_0, self.shear_exp, self.wake_combination_method, self.TI_calculation_method, self.calc_k_star, self.wec_factor, self.print_ti, self.wake_model_version, self.interp_type, use_ct_curve, ct_curve_wind_speed, ct_curve_ct, self.sm_smoothing, self.expratemultiplier, CalculateFlowField) free_stream_power = power_func_v80(self.wind_speed) wtPower = power_func_v80(wtVelocity) self.norm_pow_ave_by_row = np.zeros(10) for i in np.arange(0, self.norm_pow_ave_by_row.size): pow_ave_row = np.average([wtPower[40 + i], wtPower[50 + i], wtPower[60 + i]]) self.norm_pow_ave_by_row[i] = pow_ave_row / free_stream_power def test_wt_velocity_1_turb(self): turbineXw = np.array([0.0]) turbineYw = np.array([0.0]) turbineZ = np.ones_like(turbineXw)self.hub_height sorted_x_idx = np.argsort(turbineXw, kind='heapsort') rotorDiameter = np.ones_like(turbineXw)self.rotor_diameter Ct = np.ones_like(turbineXw)self.ct yaw = np.ones_like(turbineXw)self.yaw TI_turbs = np.ones_like(turbineXw)*self.TI use_ct_curve = False ct_curve_wind_speed = np.array([self.wind_speed]) ct_curve_ct = np.array([self.ct]) CalculateFlowField=False wtVelocity, _ = porteagel_analyze(turbineXw, sorted_x_idx, turbineYw, turbineZ, rotorDiameter, Ct, self.wind_speed, yaw, self.ky, self.kz, self.alpha, self.beta, TI_turbs, self.RotorPointsY, self.RotorPointsZ, np.array([0]), np.array([0]), np.array([0]), self.z_ref, self.z_0, self.shear_exp, self.wake_combination_method, self.TI_calculation_method, self.calc_k_star, self.wec_factor, self.print_ti, self.wake_model_version, self.interp_type, use_ct_curve, ct_curve_wind_speed, ct_curve_ct, self.sm_smoothing, self.expratemultiplier, CalculateFlowField) self.assertAlmostEqual(wtVelocity, 8.0, delta=self.tolerance) # # 2.009085240790877, 0.4619246861924686 # 2.0091082123225856, 0.46359832635983256 # 3.003385019279678, 0.5037656903765689 # 3.997271310197718, 0.515481171548117 # 4.996498482238084, 0.516317991631799 # 5.990212486668307, 0.515481171548117 # 7.0003626220362625, 0.5121338912133888 # 7.994042169168923, 0.5087866108786608 # 8.99869062269259, 0.5046025104602508 # 10.003339076216259, 0.5004184100418408 def test_wt_velocity_row_1_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[0], 1.0, delta=self.tolerance) def test_wt_velocity_row_2_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[1], 0.4619246861924686, delta=self.tolerance) def test_wt_velocity_row_3_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[2], 0.5037656903765689, delta=self.tolerance) def test_wt_velocity_row_4_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[3], 0.515481171548117, delta=self.tolerance) def test_wt_velocity_row_5_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[4], 0.516317991631799, delta=self.tolerance) def test_wt_velocity_row_6_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[5], 0.515481171548117, delta=self.tolerance) def test_wt_velocity_row_7_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[6], 0.5121338912133888, delta=self.tolerance) def test_wt_velocity_row_8_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[7], 0.5087866108786608, delta=self.tolerance) def test_wt_velocity_row_9_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[8], 0.5046025104602508, delta=self.tolerance) def test_wt_velocity_row_10_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[9], 0.5004184100418408, delta=self.tolerance) if __name__ == "__main__": unittest.main(verbosity=2)	[ 1, 9995, 13, 20399, 491, 529, 5813, 10202, 29639, 29889, 29871, 29906, 29900, 29896, 29947, 29889, 13, 29943, 27998, 12016, 13, 12432, 1141, 314, 10443, 3014, 13, 15945, 29908, 13, 13, 5215, 443, 27958, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 903, 13732, 351, 295, 29918, 3921, 661, 1053, 14931, 351, 295, 29918, 24209, 911, 29892, 921, 29900, 29918, 9891, 29892, 278, 941, 29918, 29883, 29918, 29900, 29918, 9891, 29892, 4365, 13029, 29918, 9891, 29892, 269, 2934, 29918, 1028, 949, 29918, 9891, 13, 3166, 903, 13732, 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29906, 29946, 29947, 29953, 29953, 29953, 29947, 29941, 29900, 29955, 29892, 29871, 29900, 29889, 29945, 29896, 29945, 29946, 29947, 29896, 29896, 29955, 29896, 29945, 29946, 29947, 29896, 29896, 29955, 13, 4706, 396, 29871, 29955, 29889, 29900, 29900, 29900, 29941, 29953, 29906, 29953, 29906, 29906, 29900, 29941, 29953, 29906, 29953, 29906, 29945, 29892, 29871, 29900, 29889, 29945, 29896, 29906, 29896, 29941, 29941, 29947, 29929, 29896, 29906, 29896, 29941, 29941, 29947, 29947, 29947, 13, 4706, 396, 29871, 29955, 29889, 29929, 29929, 29946, 29900, 29946, 29906, 29896, 29953, 29929, 29896, 29953, 29947, 29929, 29906, 29941, 29892, 29871, 29900, 29889, 29945, 29900, 29947, 29955, 29947, 29953, 29953, 29896, 29900, 29947, 29955, 29947, 29953, 29953, 29900, 29947, 13, 4706, 396, 29871, 29947, 29889, 29929, 29929, 29947, 29953, 29929, 29900, 29953, 29906, 29906, 29953, 29929, 29906, 29945, 29929, 29892, 29871, 29900, 29889, 29945, 29900, 29946, 29953, 29900, 29906, 29945, 29896, 29900, 29946, 29953, 29900, 29906, 29945, 29900, 29947, 13, 4706, 396, 29871, 29896, 29900, 29889, 29900, 29900, 29941, 29941, 29941, 29929, 29900, 29955, 29953, 29906, 29896, 29953, 29906, 29945, 29929, 29892, 29871, 29900, 29889, 29945, 29900, 29900, 29946, 29896, 29947, 29946, 29896, 29900, 29900, 29946, 29896, 29947, 29946, 29900, 29947, 13, 13, 1678, 822, 1243, 29918, 14554, 29918, 955, 25245, 29918, 798, 29918, 29896, 29918, 974, 29918, 25031, 29879, 29918, 13478, 29898, 1311, 1125, 13, 13, 4706, 1583, 29889, 9294, 2499, 3242, 9843, 29898, 1311, 29889, 12324, 29918, 12248, 29918, 1351, 29918, 1609, 29918, 798, 29961, 29900, 1402, 29871, 29896, 29889, 29900, 29892, 19471, 29922, 1311, 29889, 25027, 261, 749, 29897, 13, 13, 1678, 822, 1243, 29918, 14554, 29918, 955, 25245, 29918, 798, 29918, 29906, 29918, 974, 29918, 25031, 29879, 29918, 13478, 29898, 1311, 1125, 13, 4706, 1583, 29889, 9294, 2499, 3242, 9843, 29898, 1311, 29889, 12324, 29918, 12248, 29918, 1351, 29918, 1609, 29918, 798, 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29946, 29947, 29896, 29896, 29955, 29896, 29945, 29946, 29947, 29896, 29896, 29955, 29892, 19471, 29922, 1311, 29889, 25027, 261, 749, 29897, 13, 13, 1678, 822, 1243, 29918, 14554, 29918, 955, 25245, 29918, 798, 29918, 29955, 29918, 974, 29918, 25031, 29879, 29918, 13478, 29898, 1311, 1125, 13, 4706, 1583, 29889, 9294, 2499, 3242, 9843, 29898, 1311, 29889, 12324, 29918, 12248, 29918, 1351, 29918, 1609, 29918, 798, 29961, 29953, 1402, 29871, 29900, 29889, 29945, 29896, 29906, 29896, 29941, 29941, 29947, 29929, 29896, 29906, 29896, 29941, 29941, 29947, 29947, 29947, 29892, 19471, 29922, 1311, 29889, 25027, 261, 749, 29897, 13, 13, 1678, 822, 1243, 29918, 14554, 29918, 955, 25245, 29918, 798, 29918, 29947, 29918, 974, 29918, 25031, 29879, 29918, 13478, 29898, 1311, 1125, 13, 4706, 1583, 29889, 9294, 2499, 3242, 9843, 29898, 1311, 29889, 12324, 29918, 12248, 29918, 1351, 29918, 1609, 29918, 798, 29961, 29955, 1402, 29871, 29900, 29889, 29945, 29900, 29947, 29955, 29947, 29953, 29953, 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29900, 29946, 29896, 29947, 29946, 29900, 29947, 29892, 19471, 29922, 1311, 29889, 25027, 261, 749, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 13, 1678, 443, 27958, 29889, 3396, 29898, 18248, 359, 537, 29922, 29906, 29897, 2 ]
graph-algorithms/signal_tree.py	lucaspetry/algorithms-and-data-structures	0	195748	import random from graph import Graph def create_signal_tree(integers): vertices = [integers[0]] edges = [] for i in range(1, len(integers)): v1 = integers[i - 1] v2 = integers[i] if v2 not in vertices: vertices.append(v2) edges.append((v1, v2, min(v1, v2))) graph = Graph(vertices, edges, directed = False) print(graph.get_signal_tree()) integers = [1, 5, 2, 2, 7, 4, 1, 3] create_signal_tree(integers)	[ 1, 1053, 4036, 13, 3166, 3983, 1053, 12367, 13, 13, 1753, 1653, 29918, 25436, 29918, 8336, 29898, 6693, 5743, 1125, 13, 12, 1765, 1575, 353, 518, 6693, 5743, 29961, 29900, 5262, 13, 12, 287, 2710, 353, 5159, 13, 13, 12, 1454, 474, 297, 3464, 29898, 29896, 29892, 7431, 29898, 6693, 5743, 22164, 13, 12, 12, 29894, 29896, 353, 11920, 29961, 29875, 448, 29871, 29896, 29962, 13, 12, 12, 29894, 29906, 353, 11920, 29961, 29875, 29962, 13, 13, 12, 12, 361, 325, 29906, 451, 297, 13791, 29901, 13, 12, 12, 12, 1765, 1575, 29889, 4397, 29898, 29894, 29906, 29897, 13, 13, 12, 12, 287, 2710, 29889, 4397, 3552, 29894, 29896, 29892, 325, 29906, 29892, 1375, 29898, 29894, 29896, 29892, 325, 29906, 4961, 13, 13, 12, 4262, 353, 12367, 29898, 1765, 1575, 29892, 12770, 29892, 10624, 353, 7700, 29897, 13, 12, 2158, 29898, 4262, 29889, 657, 29918, 25436, 29918, 8336, 3101, 13, 13, 6693, 5743, 353, 518, 29896, 29892, 29871, 29945, 29892, 29871, 29906, 29892, 29871, 29906, 29892, 29871, 29955, 29892, 29871, 29946, 29892, 29871, 29896, 29892, 29871, 29941, 29962, 13, 3258, 29918, 25436, 29918, 8336, 29898, 6693, 5743, 29897, 2 ]
scripts/stats-bytes-converted.py	WojciechMula/parsing-int-series	19	128321	<reponame>WojciechMula/parsing-int-series<gh_stars>10-100 from generator import Generator from table import Table if __name__ == '__main__': gen = Generator() freq = {} for bi in gen.run(): k = bi.total_skip freq[k] = freq.get(k, 0) + 1 table = Table() table.add_header(["bytes processed", "patterns", "%", "cumulative %"]) cumulative = 0 for total_skip in sorted(freq.keys()): count = freq[total_skip] cumulative += count table.add_row([ '%d' % total_skip, '%d' % count, '%0.2f%%' % (100 * count/65536.0), '%0.2f%%' % (100 * cumulative/65536.0), ]) print table	[ 1, 529, 276, 1112, 420, 29958, 29956, 3848, 5295, 305, 29924, 2497, 29914, 862, 2976, 29899, 524, 29899, 13757, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 15299, 1053, 3251, 1061, 13, 3166, 1591, 1053, 6137, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 2531, 353, 3251, 1061, 580, 13, 13, 1678, 3005, 29939, 353, 6571, 13, 1678, 363, 4768, 297, 2531, 29889, 3389, 7295, 13, 4706, 413, 353, 4768, 29889, 7827, 29918, 11014, 13, 13, 4706, 3005, 29939, 29961, 29895, 29962, 353, 3005, 29939, 29889, 657, 29898, 29895, 29892, 29871, 29900, 29897, 718, 29871, 29896, 13, 13, 1678, 1591, 353, 6137, 580, 13, 1678, 1591, 29889, 1202, 29918, 6672, 29898, 3366, 13193, 19356, 613, 376, 11037, 29879, 613, 11860, 613, 376, 29883, 398, 28524, 1273, 20068, 13, 13, 1678, 13299, 28524, 353, 29871, 29900, 13, 1678, 363, 3001, 29918, 11014, 297, 12705, 29898, 29888, 7971, 29889, 8149, 580, 1125, 13, 4706, 2302, 353, 3005, 29939, 29961, 7827, 29918, 11014, 29962, 13, 4706, 13299, 28524, 4619, 2302, 13, 13, 4706, 1591, 29889, 1202, 29918, 798, 4197, 13, 9651, 14210, 29881, 29915, 1273, 3001, 29918, 11014, 29892, 13, 9651, 14210, 29881, 29915, 1273, 2302, 29892, 13, 9651, 14210, 29900, 29889, 29906, 29888, 7686, 29915, 1273, 313, 29896, 29900, 29900, 334, 2302, 29914, 29953, 29945, 29945, 29941, 29953, 29889, 29900, 511, 13, 9651, 14210, 29900, 29889, 29906, 29888, 7686, 29915, 1273, 313, 29896, 29900, 29900, 334, 13299, 28524, 29914, 29953, 29945, 29945, 29941, 29953, 29889, 29900, 511, 13, 308, 2314, 13, 13, 1678, 1596, 1591, 13, 2 ]
class15/class15_ci/app/ci.py	WesGtoX/python-selenium	0	94718	<filename>class15/class15_ci/app/ci.py from selenium.webdriver import Remote from sys import argv browser = Remote( command_executor=f'http://{argv[-1]}:4444/wd/hub', desired_capabilities={'browserName': 'firefox'} ) browser.get('http://selenium.dunossauro.live') print(browser.page_source)	[ 1, 529, 9507, 29958, 1990, 29896, 29945, 29914, 1990, 29896, 29945, 29918, 455, 29914, 932, 29914, 455, 29889, 2272, 13, 3166, 18866, 29889, 29813, 1053, 5240, 866, 13, 3166, 10876, 1053, 1852, 29894, 13, 13, 15965, 353, 5240, 866, 29898, 13, 1678, 1899, 29918, 4258, 3406, 29922, 29888, 29915, 1124, 597, 29912, 19218, 14352, 29896, 29962, 6177, 29946, 29946, 29946, 29946, 29914, 9970, 29914, 29882, 431, 742, 13, 1678, 7429, 29918, 5030, 11614, 3790, 29915, 15965, 1170, 2396, 29871, 525, 8696, 8944, 10827, 13, 29897, 13, 13, 15965, 29889, 657, 877, 1124, 597, 27373, 29889, 29881, 348, 2209, 585, 307, 29889, 9258, 1495, 13, 13, 2158, 29898, 15965, 29889, 3488, 29918, 4993, 29897, 13, 2 ]
src/rooms/tests/test_models.py	Bounty1993/my-blog	0	83759	<reponame>Bounty1993/my-blog from datetime import datetime from django.contrib.auth import get_user_model from django.db.models import Count, Sum from django.test import TestCase from src.rooms.models import Donation, Room User = get_user_model() class RoomTransactionModelTest(TestCase): fixtures = ['src/rooms/tests/fixtures.json', ] def setUp(self): self.user1 = User.objects.get(username='testuser') self.user2 = User.objects.get(username='testuser2') self.user3 = User.objects.get(username='testuser3') room1 = Room.objects.get(gift='gift1') room2 = Room.objects.get(gift='gift2') room3 = Room.objects.get(gift='gift3') room1.donate({'user': self.user1, 'amount': 500}) room2.donate({'user': self.user1, 'amount': 300}) room2.donate({'user': self.user2, 'amount': 200}) room3.donate({'user': self.user1, 'amount': 200}) room3.donate({'user': self.user2, 'amount': 200}) room3.donate({'user': self.user3, 'amount': 200}) self.room = Room.objects.get(receiver='receiver1') self.room2 = room2 def test_creation(self): room = Room.objects.get(receiver='receiver1') self.assertEqual(room.price, 1000) self.assertEqual(room.description, 'test') self.assertEqual(room.to_collect, 500) def test_str(self): room = Room.objects.get(receiver='receiver1') self.assertEqual(str(room), 'receiver1 - gift1') """ def test_save(self): before_score = self.room.score self.room.donate({'user': self.user1, 'amount': 5}) after_score = self.room.score print(self.room.score) self.assertTrue(after_score > before_score) """ def test_can_see(self): room4 = Room.objects.create( receiver='receiver3', creator=self.user1, gift='gift3', price=800, description='test', to_collect=800, visible=False, date_expires=datetime(2019, 6, 6) ) room4.guests.add(self.user2) self.assertTrue(room4.can_see(self.user1)) self.assertTrue(room4.can_see(self.user2)) self.assertFalse(room4.can_see(self.user3)) def test_percent_left(self): all_donations = ( Donation.objects .filter(room=self.room) .aggregate(Sum('amount'))['amount__sum'] ) expected = (all_donations / self.room.price) * 100 self.assertEqual(self.room.percent_left, expected) def test_percent_got(self): expected = 100 - self.room.percent_left self.assertEqual(self.room.percent_got, expected) def test_patrons_count(self): expected = (Donation.objects .filter(room=self.room2) .aggregate(Count('user'))['user__count']) self.assertEqual(self.room2.num_patrons, expected) def test_most_patrons(self): most_patrons = Room.get_visible.most_patrons() expected = Room.objects.get(receiver='receiver3') self.assertEqual(most_patrons[0], expected) def test_most_popular(self): most_collected = Room.get_visible.most_popular() expected = Room.objects.get(receiver='receiver3') self.assertEqual(most_collected[0], expected) def test_most_to_collect(self): most_to_collect = Room.get_visible.most_to_collect() expected = Room.objects.get(receiver='receiver1') self.assertEqual(most_to_collect[0], expected) def test_add_observer(self): num_observers = self.room.observers.count() self.room.add_observer(self.user1.id) num_observers += 1 self.assertEqual(self.room.observers.count(), num_observers) self.assertTrue(self.user1 in self.room.observers.all()) def test_donate(self): room = Room.objects.get(receiver='receiver1') beginning = room.to_collect donate_amount = 200 room.donate({'user': self.user1, 'amount': donate_amount}) rest = beginning - donate_amount self.assertEqual(room.to_collect, rest) room.donate({'user': self.user1, 'amount': rest}) self.assertFalse(room.is_active) self.assertEqual(room.to_collect, 0) def test_get_visible(self): room3 = Room.objects.create( receiver='receiver3', creator=self.user1, gift='gift3', price=800, description='test', to_collect=800, visible=False, date_expires=datetime(2019, 6, 6) ) query = Room.objects.get_visible(self.user3) self.assertFalse(room3 in query) room3.guests.add(self.user2) query = Room.objects.get_visible(self.user2) self.assertTrue(room3 in query) def test_get_patrons(self): room3 = Room.objects.get(receiver='receiver2') patrons = room3.get_patrons() ordered_patrons = ['testuser', 'testuser2'] self.assertEqual(list(patrons), ordered_patrons) def test_remove_guest(self): user = User.objects.first() self.room.guests.add(user) self.assertTrue(self.room.guests.filter(id=user.id)) self.room.guest_remove(user.username) self.assertFalse(self.room.guests.filter(id=user.id)) def test_get_guests_dict(self): self.room.guests.add(self.user1, self.user2) expected = [self.user1.username, self.user2.username] guest_list = self.room.get_guests_dict() self.assertEqual(guest_list, expected) class DonationModelTest(TestCase): fixtures = ['src/rooms/tests/fixtures.json'] def setUp(self): self.user1 = User.objects.get(username='testuser') self.room1 = Room.objects.get(gift='gift1') self.room1.donate({'user': self.user1, 'amount': 500}) def test_donation_exists(self): self.assertTrue(Donation.objects.exists()) def test_donation_fields(self): donation = Donation.objects.first() self.assertEqual(donation.user, self.user1) self.assertEqual(donation.amount, 500) self.assertEqual(donation.room, self.room1) self.assertEqual(donation.date, datetime.now().date()) def test_str(self): donation = Donation.objects.first() expected = f'{donation.room} - {donation.amount}' self.assertEqual(str(donation), expected)	[ 1, 529, 276, 1112, 420, 29958, 29933, 792, 29891, 29896, 29929, 29929, 29941, 29914, 1357, 29899, 7312, 13, 3166, 12865, 1053, 12865, 13, 13, 3166, 9557, 29889, 21570, 29889, 5150, 1053, 679, 29918, 1792, 29918, 4299, 13, 3166, 9557, 29889, 2585, 29889, 9794, 1053, 3917, 29892, 6991, 13, 3166, 9557, 29889, 1688, 1053, 4321, 8259, 13, 13, 3166, 4765, 29889, 18901, 29889, 9794, 1053, 3872, 362, 29892, 25114, 13, 13, 2659, 353, 679, 29918, 1792, 29918, 4299, 580, 13, 13, 13, 1990, 25114, 12460, 3195, 3057, 29898, 3057, 8259, 1125, 13, 1678, 5713, 486, 1973, 353, 6024, 4351, 29914, 18901, 29914, 21150, 29914, 7241, 486, 1973, 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api/app/database.py	Le96/todays-mazai	0	47420	import os from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker USER = os.getenv("DB_USER") PASS = os.getenv("DB_PASSWORD") HOST = os.getenv("DB_HOST") PORT = os.getenv("DB_PORT") SCHEMA = os.getenv("DB_SCHEMA") SQLALCHEMY_DATABASE_URL = f"postgresql+psycopg2://{USER}:{PASS}@{HOST}:{PORT}/{SCHEMA}" engine = create_engine(SQLALCHEMY_DATABASE_URL) Session = sessionmaker(bind=engine, autoflush=False, autocommit=False) Base = declarative_base() def get_session(): session = Session() try: yield session finally: session.close()	[ 1, 1053, 2897, 13, 13, 3166, 4576, 284, 305, 6764, 1053, 1653, 29918, 10599, 13, 3166, 4576, 284, 305, 6764, 29889, 1062, 29889, 311, 16544, 1230, 1053, 7669, 1230, 29918, 3188, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 4867, 28107, 13, 13, 11889, 353, 2897, 29889, 657, 6272, 703, 4051, 29918, 11889, 1159, 13, 25711, 353, 2897, 29889, 657, 6272, 703, 4051, 29918, 25711, 17013, 1159, 13, 20832, 353, 2897, 29889, 657, 6272, 703, 4051, 29918, 20832, 1159, 13, 15082, 353, 2897, 29889, 657, 6272, 703, 4051, 29918, 15082, 1159, 13, 29903, 3210, 26862, 353, 2897, 29889, 657, 6272, 703, 4051, 29918, 29903, 3210, 26862, 1159, 13, 13, 4176, 1964, 3210, 12665, 29979, 29918, 25832, 27982, 29918, 4219, 353, 285, 29908, 29272, 29974, 567, 29891, 9708, 29887, 29906, 597, 29912, 11889, 6177, 29912, 25711, 29913, 28312, 20832, 6177, 29912, 15082, 6822, 29912, 29903, 3210, 26862, 5038, 13, 10599, 353, 1653, 29918, 10599, 29898, 4176, 1964, 3210, 12665, 29979, 29918, 25832, 27982, 29918, 4219, 29897, 13, 13, 7317, 353, 4867, 28107, 29898, 5355, 29922, 10599, 29892, 1120, 974, 29880, 1878, 29922, 8824, 29892, 4469, 15060, 29922, 8824, 29897, 13, 13, 5160, 353, 7669, 1230, 29918, 3188, 580, 13, 13, 13, 1753, 679, 29918, 7924, 7295, 13, 1678, 4867, 353, 16441, 580, 13, 1678, 1018, 29901, 13, 4706, 7709, 4867, 13, 1678, 7146, 29901, 13, 4706, 4867, 29889, 5358, 580, 13, 2 ]
civis/io/_databases.py	elsander/civis-python	0	74490	<filename>civis/io/_databases.py from __future__ import absolute_import import logging from civis import APIClient from civis._utils import maybe_get_random_name from civis.futures import CivisFuture from civis._deprecation import deprecate_param log = logging.getLogger(__name__) @deprecate_param('v2.0.0', 'api_key') def query_civis(sql, database, api_key=None, client=None, credential_id=None, preview_rows=10, polling_interval=None, hidden=True): """Execute a SQL statement as a Civis query. Run a query that may return no results or where only a small preview is required. To execute a query that returns a large number of rows, see :func:`~civis.io.read_civis_sql`. Parameters ---------- sql : str The SQL statement to execute. database : str or int The name or ID of the database. api_key : DEPRECATED str, optional Your Civis API key. If not given, the :envvar:`CIVIS_API_KEY` environment variable will be used. client : :class:`civis.APIClient`, optional If not provided, an :class:`civis.APIClient` object will be created from the :envvar:`CIVIS_API_KEY`. credential_id : str or int, optional The ID of the database credential. If ``None``, the default credential will be used. preview_rows : int, optional The maximum number of rows to return. No more than 100 rows can be returned at once. polling_interval : int or float, optional Number of seconds to wait between checks for query completion. hidden : bool, optional If ``True`` (the default), this job will not appear in the Civis UI. Returns ------- results : :class:`~civis.futures.CivisFuture` A `CivisFuture` object. Examples -------- >>> run = query_civis(sql="DELETE schema.table", database='database') >>> run.result() # Wait for query to complete """ if client is None: client = APIClient(api_key=api_key) database_id = client.get_database_id(database) cred_id = credential_id or client.default_credential resp = client.queries.post(database_id, sql, preview_rows, credential=cred_id, hidden=hidden) return CivisFuture(client.queries.get, (resp.id, ), polling_interval, client=client, poll_on_creation=False) @deprecate_param('v2.0.0', 'api_key') def transfer_table(source_db, dest_db, source_table, dest_table, job_name=None, api_key=None, client=None, source_credential_id=None, dest_credential_id=None, polling_interval=None, advanced_options): """Transfer a table from one location to another. Parameters ---------- source_db : str or int The name of the database where the source table is located. Optionally, could be the database ID. dest_db : str or int The name of the database where the table will be transfered. Optionally, could be the database ID. source_table : str Full name of the table to transfer, e.g., ``'schema.table'``. dest_table : str Full name of the table in the destination database, e.g., ``'schema.table'``. job_name : str, optional A name to give the job. If omitted, a random job name will be used. api_key : DEPRECATED str, optional Your Civis API key. If not given, the :envvar:`CIVIS_API_KEY` environment variable will be used. client : :class:`civis.APIClient`, optional If not provided, an :class:`civis.APIClient` object will be created from the :envvar:`CIVIS_API_KEY`. source_credential_id : str or int, optional Optional credential ID for the source database. If ``None``, the default credential will be used. dest_credential_id : str or int, optional Optional credential ID for the destination database. If ``None``, the default credential will be used. polling_interval : int or float, optional Number of seconds to wait between checks for job completion. advanced_options : kwargs Extra keyword arguments will be passed to the import sync job. See :func:`~civis.resources._resources.Imports.post_syncs`. Returns ------- results : :class:`~civis.futures.CivisFuture` A `CivisFuture` object. Examples -------- >>> transfer_table(source_db='Cluster A', dest_db='Cluster B', ... source_table='schma.tbl', dest_table='schma.tbl') """ if client is None: client = APIClient(api_key=api_key) source_cred_id = source_credential_id or client.default_credential dest_cred_id = dest_credential_id or client.default_credential job_name = maybe_get_random_name(job_name) source = { 'remote_host_id': client.get_database_id(source_db), 'credential_id': source_cred_id } destination = { 'remote_host_id': client.get_database_id(dest_db), 'credential_id': dest_cred_id } job_id = client.imports.post(job_name, "Dbsync", True, source=source, destination=destination).id client.imports.post_syncs(id=job_id, source={'path': source_table}, destination={'path': dest_table}, advanced_options=advanced_options) run_id = client.imports.post_runs(id=job_id).run_id log.debug('Started run %d of sync for import %d', run_id, job_id) fut = CivisFuture(client.imports.get_files_runs, (job_id, run_id), polling_interval=polling_interval, client=client, poll_on_creation=False) return fut	[ 1, 529, 9507, 29958, 29883, 440, 275, 29914, 601, 19891, 29503, 2129, 29889, 2272, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 13, 13, 5215, 12183, 13, 13, 3166, 14175, 275, 1053, 3450, 4032, 13, 3166, 14175, 275, 3032, 13239, 1053, 5505, 29918, 657, 29918, 8172, 29918, 978, 13, 3166, 14175, 275, 29889, 29888, 329, 1973, 1053, 315, 440, 275, 20154, 13, 3166, 14175, 275, 3032, 311, 1457, 9252, 1053, 16460, 403, 29918, 3207, 13, 13, 1188, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 29992, 311, 17990, 403, 29918, 3207, 877, 29894, 29906, 29889, 29900, 29889, 29900, 742, 525, 2754, 29918, 1989, 1495, 13, 1753, 2346, 29918, 29883, 440, 275, 29898, 2850, 29892, 2566, 29892, 7882, 29918, 1989, 29922, 8516, 29892, 3132, 29922, 8516, 29892, 6625, 2556, 29918, 333, 29922, 8516, 29892, 13, 18884, 25267, 29918, 5727, 29922, 29896, 29900, 29892, 1248, 1847, 29918, 19207, 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2556, 29922, 11944, 29918, 333, 29892, 13, 462, 1669, 7934, 29922, 10892, 29897, 13, 1678, 736, 315, 440, 275, 20154, 29898, 4645, 29889, 339, 6358, 29889, 657, 29892, 313, 13713, 29889, 333, 29892, 10353, 1248, 1847, 29918, 19207, 29892, 13, 462, 539, 3132, 29922, 4645, 29892, 21180, 29918, 265, 29918, 1037, 362, 29922, 8824, 29897, 13, 13, 13, 29992, 311, 17990, 403, 29918, 3207, 877, 29894, 29906, 29889, 29900, 29889, 29900, 742, 525, 2754, 29918, 1989, 1495, 13, 1753, 6782, 29918, 2371, 29898, 4993, 29918, 2585, 29892, 2731, 29918, 2585, 29892, 2752, 29918, 2371, 29892, 2731, 29918, 2371, 29892, 13, 462, 259, 4982, 29918, 978, 29922, 8516, 29892, 7882, 29918, 1989, 29922, 8516, 29892, 3132, 29922, 8516, 29892, 13, 462, 259, 2752, 29918, 11944, 2556, 29918, 333, 29922, 8516, 29892, 2731, 29918, 11944, 2556, 29918, 333, 29922, 8516, 29892, 13, 462, 259, 1248, 1847, 29918, 19207, 29922, 8516, 29892, 3579, 328, 16858, 29918, 6768, 1125, 13, 1678, 9995, 4300, 571, 263, 1591, 515, 697, 4423, 304, 1790, 29889, 13, 13, 1678, 12662, 2699, 13, 1678, 448, 1378, 29899, 13, 1678, 2752, 29918, 2585, 584, 851, 470, 938, 13, 4706, 450, 1024, 310, 278, 2566, 988, 278, 2752, 1591, 338, 5982, 29889, 13, 4706, 10831, 635, 29892, 1033, 367, 278, 2566, 3553, 29889, 13, 1678, 2731, 29918, 2585, 584, 851, 470, 938, 13, 4706, 450, 1024, 310, 278, 2566, 988, 278, 1591, 674, 367, 6782, 287, 29889, 13, 4706, 10831, 635, 29892, 1033, 367, 278, 2566, 3553, 29889, 13, 1678, 2752, 29918, 2371, 584, 851, 13, 4706, 14846, 1024, 310, 278, 1591, 304, 6782, 29892, 321, 29889, 29887, 1696, 4954, 29915, 11010, 29889, 2371, 11120, 1412, 13, 1678, 2731, 29918, 2371, 584, 851, 13, 4706, 14846, 1024, 310, 278, 1591, 297, 278, 12551, 2566, 29892, 321, 29889, 29887, 1696, 13, 4706, 4954, 29915, 11010, 29889, 2371, 11120, 1412, 13, 1678, 4982, 29918, 978, 584, 851, 29892, 13136, 13, 4706, 319, 1024, 304, 2367, 278, 4982, 29889, 960, 25811, 29892, 263, 4036, 4982, 1024, 674, 367, 13, 4706, 1304, 29889, 13, 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29918, 11944, 29918, 333, 13, 1678, 500, 13, 1678, 4982, 29918, 333, 353, 3132, 29889, 326, 4011, 29889, 2490, 29898, 9057, 29918, 978, 29892, 376, 29928, 5824, 2720, 613, 5852, 29892, 2752, 29922, 4993, 29892, 13, 462, 462, 12551, 29922, 23848, 467, 333, 13, 13, 1678, 3132, 29889, 326, 4011, 29889, 2490, 29918, 19274, 2395, 29898, 333, 29922, 9057, 29918, 333, 29892, 13, 462, 795, 2752, 3790, 29915, 2084, 2396, 2752, 29918, 2371, 1118, 13, 462, 795, 12551, 3790, 29915, 2084, 2396, 2731, 29918, 2371, 1118, 13, 462, 795, 12862, 29918, 6768, 29922, 328, 16858, 29918, 6768, 29897, 13, 1678, 1065, 29918, 333, 353, 3132, 29889, 326, 4011, 29889, 2490, 29918, 3389, 29879, 29898, 333, 29922, 9057, 29918, 333, 467, 3389, 29918, 333, 13, 1678, 1480, 29889, 8382, 877, 4763, 287, 1065, 1273, 29881, 310, 16523, 363, 1053, 1273, 29881, 742, 1065, 29918, 333, 29892, 4982, 29918, 333, 29897, 13, 1678, 3105, 353, 315, 440, 275, 20154, 29898, 4645, 29889, 326, 4011, 29889, 657, 29918, 5325, 29918, 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playlist_everywhere/application/__init__.py	kde713/playlist-everywhere	4	1606175	<gh_stars>1-10 from playlist_everywhere.application.cli import CliApplication	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 1708, 1761, 29918, 17991, 3062, 29889, 6214, 29889, 11303, 1053, 315, 492, 4873, 13, 2 ]
models/rnn_model.py	intelligent-control-lab/MEKF_MAME	13	111959	<reponame>intelligent-control-lab/MEKF_MAME # Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. import torch import torch.nn as nn import torch.nn.functional as F class RNNEncoder(nn.Module): """RNN encoder.""" def __init__( self, cell_type='lstm', feat_dim=3, hidden_size=128, num_layers=1, dropout_fc=0.1, dropout_rnn=0.0, bidirectional=False,*kwargs): super(RNNEncoder, self).__init__() self.cell_type = cell_type.lower() self.feat_dim = feat_dim self.num_layers = num_layers self.dropout_fc = dropout_fc self.dropout_rnn = dropout_rnn self.bidirectional = bidirectional self.hidden_size = hidden_size input_size = feat_dim if self.cell_type == 'lstm': self.rnn = LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional, dropout=self.dropout_rnn) elif self.cell_type == 'gru': self.rnn = GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional, dropout=self.dropout_rnn) else: self.rnn = RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional, dropout=self.dropout_rnn) self.output_units = hidden_size if bidirectional: self.output_units = 2 def forward(self, src_seq): x = src_seq # B x T x C -> T x B x C x = x.transpose(0, 1) encoder_out, h_t = self.rnn(x) if self.cell_type == 'lstm': final_hiddens, final_cells = h_t else: final_hiddens, final_cells = h_t, None if self.dropout_fc>0: encoder_out = F.dropout(encoder_out, p=self.dropout_fc, training=self.training) if self.bidirectional: batch_size = src_seq.size(0) def combine_bidir(outs): out = outs.view(self.num_layers, 2, batch_size, -1).transpose(1, 2).contiguous() return out.view(self.num_layers, batch_size, -1) final_hiddens = combine_bidir(final_hiddens) if self.cell_type == 'lstm': final_cells = combine_bidir(final_cells) # T x B x C -> B x T x C encoder_out = encoder_out.transpose(0, 1) final_hiddens = final_hiddens.transpose(0, 1) if self.cell_type == 'lstm': final_cells = final_cells.transpose(0, 1) return encoder_out, (final_hiddens, final_cells) class AttentionLayer(nn.Module): def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False): super(AttentionLayer, self).__init__() self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias) self.output_proj = Linear(input_embed_dim + source_embed_dim, output_embed_dim, bias=bias) def forward(self, input, source_hids, encoder_padding_mask=None): # input: bsz x input_embed_dim # source_hids: srclen x bsz x output_embed_dim # x: bsz x output_embed_dim x = self.input_proj(input) # compute attention attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2) # don't attend over padding if encoder_padding_mask is not None: attn_scores = attn_scores.float().masked_fill_( encoder_padding_mask, float('-inf') ).type_as(attn_scores) # FP16 support: cast to float and back attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz # sum weighted sources x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0) out = torch.cat((x, input), dim=1) x = F.tanh(self.output_proj(out)) return x, attn_scores class RNNDecoder(nn.Module): """RNN decoder.""" def __init__( self, cell_type='lstm', feat_dim=3, hidden_size=128, num_layers=1, dropout_fc=0.1, dropout_rnn=0.0, encoder_output_units=128, max_seq_len=10, attention=True, traj_attn_intent_dim=0,kwargs): super(RNNDecoder, self).__init__() self.cell_type = cell_type.lower() self.dropout_fc = dropout_fc self.dropout_rnn = dropout_rnn self.hidden_size = hidden_size self.encoder_output_units = encoder_output_units self.num_layers = num_layers self.max_seq_len = max_seq_len self.feat_dim = feat_dim self.traj_attn_intent_dim =traj_attn_intent_dim input_size = feat_dim if encoder_output_units != hidden_size: self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size) if self.cell_type == 'lstm': self.encoder_cell_proj = Linear(encoder_output_units, hidden_size) else: self.encoder_cell_proj = None else: self.encoder_hidden_proj = self.encoder_cell_proj = None if self.cell_type == 'lstm': self.cell = LSTM elif self.cell_type == 'gru': self.cell = GRU else: self.cell = RNN self.rnn = self.cell(input_size=input_size, hidden_size=hidden_size, bidirectional=False, dropout=self.dropout_rnn, num_layers=num_layers) if attention: self.attention = AttentionLayer(hidden_size, encoder_output_units, hidden_size, bias=False) else: self.attention = None self.output_projection = Linear(hidden_size, feat_dim) if traj_attn_intent_dim>0: self.traj_attn_fc = Linear(hidden_size, traj_attn_intent_dim) def forward(self, encoder_out_list, start_decode=None, encoder_mask=None): x = start_decode.unsqueeze(1) bsz = x.size(0) # get outputs from encoder encoder_outs, (encoder_hiddens, encoder_cells) = encoder_out_list # B x T x C -> T x B x C encoder_outs = encoder_outs.transpose(0, 1) encoder_hiddens = encoder_hiddens.transpose(0, 1) if encoder_mask is not None: encoder_mask = encoder_mask.transpose(0, 1) prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)] if self.cell_type == 'lstm': encoder_cells = encoder_cells.transpose(0, 1) prev_cells = [encoder_cells[i] for i in range(self.num_layers)] x = x.transpose(0, 1) srclen = encoder_outs.size(0) # initialize previous states if self.encoder_hidden_proj is not None: prev_hiddens = [self.encoder_hidden_proj(x) for x in prev_hiddens] prev_hiddens = torch.stack(prev_hiddens, dim=0) if self.encoder_cell_proj is not None: prev_cells = [self.encoder_cell_proj(x) for x in prev_cells] if self.cell_type == 'lstm': prev_cells = torch.stack(prev_cells, dim=0) attn_scores = x.new_zeros(srclen, self.max_seq_len, bsz) inp = x outs = [] hidden_outs=[] for j in range(self.max_seq_len): if self.cell_type == 'lstm': output, (prev_hiddens, prev_cells) = self.rnn(inp, (prev_hiddens, prev_cells)) else: output, prev_hiddens = self.rnn(inp, prev_hiddens) output = output.view(bsz, -1) # apply attention using the last layer's hidden state if self.attention is not None: out, attn_scores[:, j, :] = self.attention(output, encoder_outs, encoder_mask) else: out = output if self.dropout_fc>0: out = F.dropout(out, p=self.dropout_fc, training=self.training) hid_out = out if self.traj_attn_intent_dim > 0: hid_out= self.traj_attn_fc(hid_out) hid_out = F.selu(hid_out) hidden_outs.append(hid_out) out = self.output_projection(out) # save final output outs.append(out) inp = out.unsqueeze(0) # collect outputs across time steps x = torch.cat(outs, dim=0).view(self.max_seq_len, bsz, self.feat_dim) hidden_outs = torch.cat(hidden_outs, dim=0).view(self.max_seq_len, bsz, -1) # T x B x C -> B x T x C x = x.transpose(1, 0) hidden_outs=hidden_outs.transpose(1, 0) # srclen x tgtlen x bsz -> bsz x tgtlen x srclen attn_scores = attn_scores.transpose(0, 2) # project back to input space return x, hidden_outs def LSTM(input_size, hidden_size, kwargs): m = nn.LSTM(input_size, hidden_size, kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def GRU(input_size, hidden_size, kwargs): m = nn.GRU(input_size, hidden_size, kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def RNN(input_size, hidden_size, kwargs): m = nn.RNN(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def Linear(in_features, out_features, bias=True): """Linear layer (input: N x T x C)""" m = nn.Linear(in_features, out_features, bias=bias) m.weight.data.uniform_(-0.1, 0.1) if bias: m.bias.data.uniform_(-0.1, 0.1) return m	[ 1, 529, 276, 1112, 420, 29958, 524, 9347, 296, 29899, 6451, 29899, 8205, 29914, 2303, 29968, 29943, 29918, 1529, 2303, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29955, 29899, 6338, 29892, 13327, 29892, 9266, 29889, 13, 29937, 2178, 10462, 21676, 29889, 13, 29937, 13, 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GUI.py	jkxing/stylized-neural-painting	0	199169	import tkinter as tk from tkinter import ttk from PIL import Image, ImageTk from tkinter import filedialog as fd from tkinter import messagebox as mb import matplotlib.pyplot as plt import numpy as np import cv2 class GUI(tk.Frame): def __init__(self, parent = None): tk.Frame.__init__(self, parent) self.parent = parent self.img_path = '' self.save_path = '' self.frame0 = tk.Frame(self, bd = 10) self.frame0.pack() self.path_label = tk.Label(self.frame0, text = '') self.path_label.pack(side='left') self.browseButton = tk.Button(self.frame0, text = 'Browse', command = self.openfile) self.browseButton.pack(side = 'left') self.slider_var = tk.IntVar() self.slider = tk.Scale(self, from_=1, to=20, orient= 'horizontal', variable = self.slider_var, command = self.slider_changed) self.slider.pack(pady = 10) self.goButton = tk.Button(self, text = 'Paint', command = self.go, width = 20) self.goButton.pack(pady = 10) self.addButton = tk.Button(self, text = 'Add Area', command = self.add_area, width = 20) self.addButton.pack(pady = 10) self.saveButton = tk.Button(self, text = 'Save as...', command = self.savefile, width = 20) self.saveButton.pack(pady = 10) self.mark_val = 1 self.oval_size = 1 def paint(self, event): python_green = "#476042" x1, y1 = ( event.x - self.oval_size ), ( event.y - self.oval_size ) x2, y2 = ( event.x + self.oval_size ), ( event.y + self.oval_size ) for x in range(x1, x2+1) : for y in range(y1, y2 + 1): self.image_mask[y][x][0] = self.mark_val self.image_mask[y][x][1] = self.mark_val self.image_mask[y][x][2] = self.mark_val self.canvas.create_oval( x1, y1, x2, y2, fill = python_green ) def add_area(self): self.mark_val += 1 def slider_changed(self, event): self.oval_size = self.slider_var.get() # print(self.slider_var.get()) def go(self): if (len(self.img_path) == 0): mb.showinfo('No image selected', 'Please browse an image to be resized') return # img = plt.imread(self.img_path) img = ImageTk.PhotoImage(Image.open(self.img_path)) offspring = tk.Toplevel() offspring.title(self.img_path.split('/')[-1]) offspring.geometry('%sx%s' % (img.width()+10, img.height()+10)) self.image_mask = np.zeros((img.height(), img.width(), 3)) self.canvas = tk.Canvas(offspring, width=img.width(), height=img.height(), borderwidth=0, highlightthickness=0) self.canvas.pack(expand=True) self.canvas.img = img # Keep reference in case this code is put into a function. self.canvas.create_image(0, 0, image=img, anchor=tk.NW) self.canvas.bind( "<B1-Motion>", self.paint ) offspring.mainloop() def openfile(self): self.img_path = fd.askopenfilename() self.path_label.config(text = self.img_path) def savefile(self): self.save_path = fd.asksaveasfilename() if len(self.save_path) == 0 : mb.showinfo('Give destination', 'Please give a destination path') return cv2.imwrite(self.save_path, self.image_mask) with open(self.save_path[:-4]+'.npy', 'wb') as f: np.save(f, np.array(self.image_mask)) if __name__ == 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msticpy/data/drivers/__init__.py	kubajir/msticpy	1	151340	# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- """Data provider sub-package.""" import importlib from typing import Union from ..query_defns import DataEnvironment # flake8: noqa: F403 from .driver_base import DriverBase from ..._version import VERSION __version__ = VERSION _ENVIRONMENT_DRIVERS = { DataEnvironment.LogAnalytics: ("kql_driver", "KqlDriver"), DataEnvironment.AzureSecurityCenter: ("kql_driver", "KqlDriver"), DataEnvironment.SecurityGraph: ("security_graph_driver", "SecurityGraphDriver"), DataEnvironment.Kusto: ("kusto_driver", "KustoDriver"), DataEnvironment.MDATP: ("mdatp_driver", "MDATPDriver"), DataEnvironment.MDE: ("mdatp_driver", "MDATPDriver"), DataEnvironment.LocalData: ("local_data_driver", "LocalDataDriver"), DataEnvironment.Splunk: ("splunk_driver", "SplunkDriver"), DataEnvironment.Mordor: ("mordor_driver", "MordorDriver"), DataEnvironment.Sumologic: ("sumologic_driver", "SumologicDriver"), DataEnvironment.ResourceGraph: ("resource_graph_driver", "ResourceGraphDriver"), DataEnvironment.M365D: ("mdatp_driver", "MDATPDriver"), } def import_driver(data_environment: DataEnvironment) -> type: """Import driver class for a data environment.""" mod_name, cls_name = _ENVIRONMENT_DRIVERS.get(data_environment, (None, None)) if not (mod_name and cls_name): raise ValueError( f"No driver available for environment {data_environment.name}.", "Possible values are:", ", ".join(env.name for env in _ENVIRONMENT_DRIVERS), ) imp_module = importlib.import_module( f"msticpy.data.drivers.{mod_name}", package="msticpy" ) return getattr(imp_module, cls_name)	[ 1, 396, 448, 2683, 2683, 2683, 2683, 1378, 13, 29937, 14187, 1266, 313, 29883, 29897, 7783, 15025, 29889, 2178, 10462, 21676, 29889, 13, 29937, 10413, 21144, 1090, 278, 341, 1806, 19245, 29889, 2823, 19245, 29889, 3945, 297, 278, 2060, 3876, 363, 13, 29937, 19405, 2472, 29889, 13, 29937, 448, 2683, 2683, 2683, 2683, 1378, 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app.py	skrzypak/soaf	0	79492	<filename>app.py import glob import shutil import subprocess import os import sys import argparse # Read and save metadata from file def exiftool_metadata(path): metadata = {} exifToolPath = 'exifTool.exe' ''' use Exif tool to get the metadata ''' process = subprocess.Popen( [ exifToolPath, path ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True ) ''' get the tags in dict ''' for tag in process.stdout: tag = tag.strip() key = tag[:tag.find(':')].strip() value = tag[tag.find(':') + 1:].strip() metadata[key] = value return metadata class File: def __init__(self, path): self.metadata = exiftool_metadata(path) def _get_file_metadata(self, key, no=''): if key in self.metadata: return self.metadata[key] else: return no def copyCore(self, source, dst_dir: str, copy_duplicate=False): logs = [] # if value of metadata not exists - folder name no_metadata = 'none' date = File._get_file_metadata(self, 'Date/Time Original') if date == '': date = File._get_file_metadata(self, 'Create Date', no_metadata) mime_type = File._get_file_metadata(self, 'MIME Type', no_metadata) dst_dir += f'''/{mime_type[:mime_type.find('/')]}/{date[:4]}/{date[5:7]}''' filename = File._get_file_metadata(self, 'File Name') f_name = filename dst = dst_dir + '/' + filename # File with the same name exists in dst. If source and dst have same size then determines 'copy_exists' if os.path.isfile(dst): i = 0 f_pth = File(dst) if_same_size: bool = f_pth._get_file_metadata("File Size") == File._get_file_metadata(self, 'File Size') if (not if_same_size) or copy_duplicate: while os.path.isfile(dst): filename = f'''{f_name[:f_name.find('.')]}_D{str(i)}.{File._get_file_metadata(self, 'File Type Extension')}''' dst = f'''{dst_dir}/{filename}''' i = i + 1 if if_same_size: logs.append(f"Warning: file already exists but I must copy all files" f" [copy_duplicate={copy_duplicate}], so I try do it ...") else: logs.append(f"Warning: file already exists but have other size, so I try copy it ...") else: logs.append(f"Warning: file already duplicate [copy_exists={copy_duplicate}]." f"\nCopy aboard: {source} -> {dst}") return logs try: if not os.path.isdir(dst_dir): os.makedirs(dst_dir) logs.append(f"New directory created: {dst_dir}") shutil.copy(source, dst) logs.append(f'''Copy done: {source} -> {dst}''') except Exception as e: logs.append(f'''Copy error [{e}]: {source} -> {dst}''') return logs def main(): # Arguments from console parser = argparse.ArgumentParser() parser.add_argument('-s', help="Obligatory: source directory path") parser.add_argument('-d', help="Obligatory: destination folder path") parser.add_argument('-e', help="Obligatory: copy duplicate files (T/True/F/False)") args = parser.parse_args(sys.argv[1:]) # Setup variable source_dir = args.s dst_dir = args.d df = { "T": True, "TRUE": True, "F": False, "FALSE": False } try: copy_duplicate = df.get(args.e.upper(), False) except AttributeError: copy_duplicate = False print(f"app.py: error: unrecognized arguments. Use -h or --help to see options") exit(1) # Number of log l_lpm = 0 # source_dir = 'C:/Users' # dst_dir = 'C:/Users' # copy_duplicate = False for f_inx, source in enumerate(glob.glob(source_dir + '/*/.*', recursive=True)): try: f = File(source) print("----------") for log in f.copyCore(source, dst_dir, copy_duplicate): l_lpm = l_lpm + 1 print(f'''{str(l_lpm)}.{f_inx + 1}) {log}''') except Exception as e: print(f'Copy error [{e}]: {source}') if __name__ == '__main__': main()	[ 1, 529, 9507, 29958, 932, 29889, 2272, 13, 5215, 13149, 13, 5215, 528, 4422, 13, 5215, 1014, 5014, 13, 5215, 2897, 13, 5215, 10876, 13, 5215, 1852, 5510, 13, 13, 13, 29937, 7523, 322, 4078, 15562, 515, 934, 13, 1753, 429, 361, 10154, 29918, 19635, 29898, 2084, 1125, 13, 1678, 15562, 353, 6571, 13, 1678, 429, 361, 12229, 2605, 353, 525, 735, 361, 12229, 29889, 8097, 29915, 13, 1678, 14550, 671, 1222, 361, 5780, 304, 679, 278, 15562, 14550, 13, 1678, 1889, 353, 1014, 5014, 29889, 29925, 3150, 29898, 13, 4706, 518, 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manage-downloads.py	hackers-terabit/download-hygeine	1	87710	#!/usr/bin/python2.7 #manage-downloads - adds/removes downloads to the catalogue #TODO: # test it all \0/ import os,json,sys import hashlib,uuid from downloadhygeine import DownloadHygeine,Util,crypto color={"green":"\33[92m","blue":"\33[96m","red":"\33[31m","yellow":"\33[93m","white":"\33[98m"} def add(dh): download={} fname='' download["uuid"]=str(uuid.uuid4()) download["name"]=raw_input("Name for this download: ") download["category"]=raw_input("What category is this download under?: ") download["type"]=raw_input("What type of a download is this? (e.g.:torrent,iso,tar,img,avi,etc...): ") torrent=dh.util.yesno_choice("Is this a bittorrent download?: ") if torrent: download["torrent"]=1 else: download["torrent"]=0 while True: download["url"]="" retry=False if retry==False: download["url"]=raw_input(color["white"]+"Enter the primary URL for this download (This will be used to download and calculate the hash of the downloaded file):") fname,success=dh.fetch(download["url"]) if not os.path.exists(fname) or os.path.getsize(fname)<1: print(color["red"]+"It seems downloading this file to: "+fname+" Has failed\nLet's try this again.") if dh.util.yesno_choice(color["yellow"]+"Use the same url as before ("+download["url"]+")?")==True: retry=False else: break if len(download["url"])<0: print(color["red"]+"Unable to find a valid download URL,exiting...") return #whoa!? else: if dh.util.yesno_choice(color["white"]+"Are there other accompanying meta-files that should be downloaded? (example: .asc gpg detached signature,hash data,readme,etc...) : "): metafiles=[] download["getmeta"]=0 print(color["yellow"]+"Enter the URLs for these additonal meta-files,separated by a new line. enter 'eof' without quotes when done. Keep in mind the validity of these URLs will not be checked at this time.") while True: entry=raw_input().strip().split(" ") if entry[0].strip().lower() =='eof': break elif len(entry[0])>0: metafiles.append(entry[0]) if len(metafiles)>0: download["getmeta"]=1 download["metafiles"]=metafiles else: download["getmeta"]=0 print(color["blue"]+">") hl=set() for algo in hashlib.algorithms_available: if algo in ["sha512","sha256","whirlpool","ripemd160"]: hl.add(hashlib.new(algo)) with open(fname,"rb") as f: sofar=0 total=os.path.getsize(fname) while True: block=f.read(65536) if len(block)<1: break else: sofar+=len(block) sys.stdout.write("\rRead so far:\t"+str(sofar/(total/100.00))[:5].ljust(5," ")+"\%") for h in hl: h.update(block) print("\n") for h in hl: digest=h.hexdigest() print("\t Calculated "+h.name+" digest:\t "+digest) download[h.name]=digest mirrors=[] if dh.util.yesno_choice("Will this download use mirrors?"): suffix=raw_input(color["yellow"]+"What is the suffix to be used across all mirrors?\nExample: A suffix of '/mirrors/project/iso/someos.iso' for a mirror 'https://www.fancymirror.co.example/opensource/mirrors' will result in a download URL 'https://www.fancymirror.co.example/opensource/mirrors/mirrors/project/iso/someos.iso'\n:") print(color["yellow"]+"Enter a list of mirrors with the format 'Mirrorname <url>' (example:superfastmirror https://secureserver.com.example/)\nEnter just 'EOF' without quotes when done:") while True: entry=raw_input().strip().split(" ") if entry[0].strip().lower() =='eof': break if len(entry) <2 or len(entry[0])<1 or len(entry[len(entry)-1])<1: print(color["red"]+"Invalid entry, please be sure to specify the mirror name and the URL") continue else: mirrors.append({"mirrorname":entry[0].strip(),"url":entry[1].strip()+suffix.strip()}) if len(mirrors)>0: download["mirrors"]=mirrors if (dh.util.yesno_choice("Have you verified the authenticity of the downloaded file '"+fname+"' ? (such as with GPG validation)?") and dh.util.yesno_choice("Sign this download? ")): content=json.dumps(download,sort_keys=True) exported=dh.crypto.sign_and_export(content) exportme=json.dumps(exported,indent=4,sort_keys=True) with open(dh.conf["localclone"]+"/"+download["name"]+".json","w+") as df: df.write(exportme) def remove(dh): dh.load_catalog() download_choice=dh.pick() for e in dh.catalog: if "uuid" in e and e["uuid"]==download_choice: print(color["green"]+"Download name: "+e["name"]) print(color["green"]+"Category: "+e["category"]) print(color["yellow"]+"File type: "+e["type"]) print(color["blue"]+"Download URL: "+e["url"]) if dh.util.yesno_choice(color["yellow"]+"Remove this download catalog entry? "): os.remove(dh.conf["localclone"]+"/"+e["name"]+".json") def gitupdate(dh): success=False print(color["yellow"]+"Synchronizing download catalog...") if os.path.exists(dh.conf["localclone"]): os.chdir(dh.conf["localclone"]) if dh.util.call(["git","add",""])!=0: print(color["red"]+"Error running 'git add ' to update the local clone. Please fix this manually.") success=False else: if dh.util.call(["git","commit","-m","Sync catalog changes","*"])!=0: print(color["red"]+"Error running 'git commit -m 'Sync catalog changes' . Please fix this manually.") success=False else: if dh.util.call(["git","push","-u","origin","master"])!=0: print(color["red"]+"Error running 'git push -u origin master' . Please fix this manually.") success=False else: success=True if success==True: print(color["blue"]+"Updated your catalog changes to the preset git repository") return success def update(dh,uuid=None): dh.load_catalog() download_choice=dh.pick(uuid=uuid) for e in dh.catalog: if "uuid" in e and e["uuid"]==download_choice: dh.crypto.showkey(e["siginfo"]) print(color["green"]+"Download name: "+e["name"]) print(color["green"]+"Category: "+e["category"]) print(color["yellow"]+"File type: "+e["type"]) print(color["blue"]+"Download URL: "+e["url"]) params=[] for p in e: if not str(p).strip() in ["siginfo"]: params.append(p) params.append("Done") while True: choice=dh.util.numeric_choice(color["yellow"]+"Please select a paramter to update for "+e["name"]+".Select 'Done' when finished: ",params) if choice=="Done": break else: print(color["yellow"]+"Current value for "+choice+":\n"+str(e[choice])) e[choice]=raw_input("["+e["name"]+"] Please enter a new value for '"+choice+"' : ") if dh.util.yesno_choice("Now that you've updated '"+e["name"]+"', Would you like to re-download the file and update it's hash values? ")==True: while True: fname=dh.fetch(e["url"]) if not os.path.exists(fname) or os.path.getsize(fname)<1: print("It seems downloading this file to: "+fname+" Has failed\nLet's try this again.") else: break hl=set() for algo in hashlib.algorithms_available: if algo in ["sha512","sha256","whirlpool","ripemd160"]: hl.add(hashlib.new(algo)) with open(fname,"rb") as f: sofar=0 total=os.path.getsize(fname) while True: block=f.read(65536) if len(block)<1: break else: sofar+=len(block) sys.stdout.write("\rRead so far:\t"+str(sofar/(total/100.00))[:5].ljust(5," ")+"\%") for h in hl: h.update(block) print("\n") for h in hl: digest=h.hexdigest() print("\t Calculated "+h.name+" digest:\t "+digest) e[h.name]=digest if (dh.util.yesno_choice("Have you verified the autenticity of the hashes for the download '"+e["name"]+"' ? (such as with GPG validation)?") and dh.util.yesno_choice("Sign this download? ")): exportme=json.dumps(dh.crypto.sign_and_export(json.dumps(e,sort_keys=True)),indent=4,sort_keys=True) with open(dh.conf["localclone"]+"/"+e["name"]+".json","w+") as df: df.write(exportme) break def fork(dh): dh.load_catalog() download_choice=dh.pick() if None is download_choice or len(dh.catalog)<0 or len(download_choice)<0: print(color["yellow"]+"Current download catalog is empty,nothing to fork.") return for e in dh.catalog: if "uuid" in e and e["uuid"]==download_choice: dh.crypto.showkey(e["siginfo"]) print(color["green"]+"Download name: "+e["name"]) print(color["green"]+"Category: "+e["category"]) print(color["yellow"]+"File type: "+e["type"]) print(color["blue"]+"Download URL: "+e["url"]) e["name"]=raw_input("Forking '"+e["name"]+"' Please enter a new name: ") e["uuid"]=str(uuid.uuid4()) exportme=json.dumps(dh.crypto.sign_and_export(json.dumps(e,sort_keys=True)),indent=4,sort_keys=True) with open(dh.conf["localclone"]+"/"+e["name"]+".json","w+") as df: df.write(exportme) dh.load_catalog() update(dh,e["uuid"]) def main(): dh=DownloadHygeine() if not dh.init_env(): print("Critical error initializing. Exiting immediately.") sys.exit(1) if not dh.util.yesno_choice("Do you have commit priviledge for the currently configured git repository? "+dh.conf["localclone"]+"\n Remote repository:'"+dh.conf["gitrepo"]+"' ?"): if dh.util.yesno_choice("Things will break if we continue to change the currently configured repository.\nIn order to avoid that, should we configure a new repository? (This new repository will be managed by you!) "): dh.conf["gitrepo"]=raw_input("Enter the remote URL that will be used to synchronize this new repository: ") if dh.util.yesno_choice("Use a different local directory for this new catalog? (Warning: if you pick no,the current contents of "+dh.conf["localclone"]+" Will be deleted.): "): dh.conf["localclone"]=dh.util.numeric_choice_flexible("Where should the download catalogue be stored?",[os.getenv('HOME')+"/Downloads/downloadhygeine-catalog",os.getenv('HOME')+"/downloadhygeine-catalog"]) dh.util.call(["rm","-rfv",dh.conf["localclone"]]) #wipe out the current catalog. if dh.util.call(["git","init",dh.conf["localclone"]])!=0: print(color["red"]+"Error initializing the chosen git repository of the download catalog. Please make sure the local clone directory '"+dh.conf["localclone"]+"' can be created by git and whether your internet connection is allowing access to your chosen git repository.") print(color["red"]+"Alternatively, please adjust your configuration to relfect any system or network changes.") sys.exit(1) else: os.chdir(dh.conf["localclone"]) if dh.util.call(["git","remote","add","origin",dh.conf["gitrepo"]])!=0: print(color["red"]+"Failed to run 'git remote add origin "+dh.conf["gitrepo"]+"' . Please fix this manually.") sys.exit(1) else: if dh.util.call(["git","fetch"])!=0: print(color["red"]+"Failed to run 'git fetch' . Please fix this manually.") sys.exit(1) else: if dh.util.call(["git","checkout","master"])!=0: print(color["red"]+"Failed to run 'git checkout master' . Please fix this manually.") sys.exit(1) else: print(color["red"]+"Sorry, you cannot manage a catalog(git repository) without being able to commit to it") sys.exit(1) with open(dh.util.pickpath(dh.config_paths),"w+") as cf: cf.write(json.dumps(dh.conf,indent=4,sort_keys=True)) while True: dh.gitsync() choice=dh.util.numeric_choice("What would you like to do?",["Add","Remove","Update","Fork","List","Exit"]) if choice=="Add": add(dh) elif choice=="Remove": remove(dh) elif choice=="Update": update(dh) elif choice=="Fork": fork(dh) elif choice=="List": dh.load_catalog() dh.dump_catalog() elif choice=="Exit": break gitupdate(dh) if __name__ == "__main__": main()	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 29906, 29889, 29955, 13, 29937, 1171, 482, 29899, 10382, 29879, 448, 12778, 29914, 1745, 586, 267, 5142, 29879, 304, 278, 16653, 434, 13, 29937, 4986, 3970, 29901, 13, 29937, 12, 1688, 372, 599, 320, 29900, 29914, 13, 13, 5215, 2897, 29892, 3126, 29892, 9675, 13, 5215, 6608, 1982, 29892, 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009_special_pythagorean_triplet.py	fbcom/project-euler	0	76309	<gh_stars>0 #!/usr/bin/env python # -- coding: utf-8 -- # # A Solution to "Special Pythagorean triplet" – Project Euler Problem No. 9 # by <NAME> # # Sourcecode: https://github.com/fbcom/project-euler # Problem statement: https://projecteuler.net/problem=9 # def isPythagoreanTriple(a, b, c): if a < b < c: return a2 + b2 == c*2 return False def findTipleHavingSum(sum): # small problem size -> brute force it for a in range(1, sum): for b in range(a, sum - a + 1): for c in range(b, sum - b + 1): if a+b+c == sum: if isPythagoreanTriple(a, b, c): return (a, b, c) sum = 1000 triple = findTipleHavingSum(sum) print "Triple:", triple print "Solution:", reduce(lambda product, factor: product factor, triple)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 13, 29937, 319, 24380, 304, 376, 24780, 349, 1541, 351, 487, 273, 21954, 29873, 29908, 785, 8010, 382, 8584, 11583, 1939, 29889, 29871, 29929, 13, 29937, 491, 529, 5813, 29958, 13, 29937, 13, 29937, 7562, 401, 29901, 2045, 597, 3292, 29889, 510, 29914, 14943, 510, 29914, 4836, 29899, 29872, 8584, 13, 29937, 11583, 3229, 29901, 2045, 597, 4836, 29872, 8584, 29889, 1212, 29914, 17199, 29922, 29929, 13, 29937, 13, 13, 13, 1753, 338, 29925, 1541, 351, 487, 273, 29565, 552, 29898, 29874, 29892, 289, 29892, 274, 1125, 13, 1678, 565, 263, 529, 289, 529, 274, 29901, 13, 4706, 736, 263, 1068, 29906, 718, 289, 1068, 29906, 1275, 274, 1068, 29906, 13, 1678, 736, 7700, 13, 13, 13, 1753, 1284, 29911, 29875, 552, 29950, 5555, 11139, 29898, 2083, 1125, 13, 1678, 396, 2319, 1108, 2159, 1599, 1506, 1082, 4889, 372, 13, 1678, 363, 263, 297, 3464, 29898, 29896, 29892, 2533, 1125, 13, 4706, 363, 289, 297, 3464, 29898, 29874, 29892, 2533, 448, 263, 718, 29871, 29896, 1125, 13, 9651, 363, 274, 297, 3464, 29898, 29890, 29892, 2533, 448, 289, 718, 29871, 29896, 1125, 13, 18884, 565, 263, 29974, 29890, 29974, 29883, 1275, 2533, 29901, 13, 462, 1678, 565, 338, 29925, 1541, 351, 487, 273, 29565, 552, 29898, 29874, 29892, 289, 29892, 274, 1125, 13, 462, 4706, 736, 313, 29874, 29892, 289, 29892, 274, 29897, 13, 13, 2083, 353, 29871, 29896, 29900, 29900, 29900, 13, 3626, 552, 353, 1284, 29911, 29875, 552, 29950, 5555, 11139, 29898, 2083, 29897, 13, 2158, 376, 29565, 552, 29901, 613, 21954, 13, 2158, 376, 13296, 918, 29901, 613, 10032, 29898, 2892, 3234, 29892, 7329, 29901, 3234, 334, 7329, 29892, 21954, 29897, 13, 2 ]
dataset/ADS_Dataset_generator_2.py	pravk03/responsible_ai_hackathon	0	113225	#!/usr/bin/env python # coding: utf-8 # <b> Run below cells from the folder that contains ads16_dataset/ unzipped </b> # In[1]: import pandas as pd import glob import pathlib import re # In[2]: pd.set_option('display.max_colwidth', -1) # In[3]: # Global constants g_userPart1PathPrefix = "./ads16-dataset/ADS16_Benchmark_part1/ADS16_Benchmark_part1/Corpus/Corpus/" g_userPart2PathPrefix = "./ads16-dataset/ADS16_Benchmark_part2/ADS16_Benchmark_part2/Corpus/Corpus/" g_userIdPrefix = "U0" g_adsPart1PathPrefix = "./ads16-dataset/ADS16_Benchmark_part1/ADS16_Benchmark_part1/Ads/Ads/" g_adsPart2PathPrefix = "./ads16-dataset/ADS16_Benchmark_part2/ADS16_Benchmark_part2/Ads/Ads/" # # UDFs # ## UDFs for generating Users Dataset # In[4]: def generate_data_User( pathPrefix, userId ): completePath = pathPrefix + userId + "/" # INF infFile = userId + "-INF.csv" userInf_df = pd.read_csv(completePath + infFile, delimiter=";") # Pref prefFile = userId + "-PREF.csv" userPref_df = pd.read_csv(completePath + prefFile, delimiter=";") user_df = pd.concat([userInf_df, userPref_df], axis=1) # Pos posFile = userId + "-IM-POS.csv" userPos_df = pd.read_csv(completePath + posFile, delimiter=";") userPos_df = userPos_df.iloc[1:] userPos_df.reset_index(drop=True, inplace=True) user_df = pd.concat([user_df, userPos_df], axis=1) # Neg negFile = userId + "-IM-NEG.csv" userNeg_df = pd.read_csv(completePath + negFile, delimiter=";") userNeg_df = userNeg_df.iloc[1:] userNeg_df.reset_index(drop=True, inplace=True) user_df = pd.concat([user_df, userNeg_df], axis=1) user_df.insert(0, "UserId", userId, True) # user_df = user_df.set_index('UserId') # user_df.info() return user_df # In[5]: def generate_data_partUsers( usersPartPathPrefix, startRange, endRange ): partUsers_df = pd.DataFrame() for i in range(startRange, endRange): thisUserIdNum = str(i) thisUserId = g_userIdPrefix + thisUserIdNum.zfill(3) # print(thisUserId) thisUser_df = generate_data_User(usersPartPathPrefix, thisUserId) partUsers_df = partUsers_df.append(thisUser_df, sort=True) partUsers_df.set_index('UserId') return partUsers_df # In[6]: def generate_data_allUsers(): allUsers_df = pd.DataFrame() part1Users_df = generate_data_partUsers(g_userPart1PathPrefix, 1, 61) allUsers_df = allUsers_df.append(part1Users_df, sort=True) part2Users_df = generate_data_partUsers(g_userPart2PathPrefix, 61, 121) allUsers_df = allUsers_df.append(part2Users_df, sort=True) return allUsers_df # ## UDFs for generating Ads Dataset # In[7]: def generate_data_adCats(): adCatsLst = [['01', "Clothing & Shoes", 16], ['02', "Automotive", 15], ['03', "Baby Products", 15], ['04', "Health & Beauty", 15], ['05', "Media (BMVD)", 15], ['06', "Consumer Electronics", 15], ['07', "Console & Video Games", 15], ['08', "DIY & Tools", 15], ['09', "Garden & Outdoor living", 15], ['10', "Grocery", 15], ['11', "Kitchen & Home", 15], ['12', "Betting", 15], ['13', "Jewellery & Watches", 15], ['14', "Musical Instruments", 15], ['15', "Office Products", 15], ['16', "Pet Supplies", 15], ['17', "Computer Software", 15], ['18', "Sports & Outdoors", 15], ['19', "Toys & Games", 15], ['20', "Dating Sites", 15] ] adCats_df = pd.DataFrame(adCatsLst, columns =['AdCatId', 'AdCatName', 'AdCatNumAds']) return adCats_df # In[8]: import re def atoi(text): return int(text) if text.isdigit() else text def natural_keys(text): return [ atoi(c) for c in re.split(r'(\d+)',text.split('/')[-1].split('.')[0]) ] def generate_data_partAds( adsPartPathPrefix, startRange, endRange ): partAds_df = pd.DataFrame() partAdsRows = [] for i in range(startRange, endRange): iStr = str(i) adsFiles = pathlib.Path(adsPartPathPrefix + iStr + "/").glob(".png") adsFileStrLst = [] for adsFile in adsFiles: adsFileStr = str(adsFile) adsFileStrLst.append(adsFileStr) adsFileStrLst.sort(key=natural_keys) for adsFileStr in adsFileStrLst: adId = adsFileStr.split('/')[-1].split('.')[0] adId = "A" + iStr.zfill(2) + "_" + adId.zfill(2) # print(adId, adsFileStr) partAdsRows.append([adId, adsFileStr]) partAds_df = pd.DataFrame(partAdsRows, columns =['AdId', 'AdFilePath']) partAds_df.set_index('AdId') return partAds_df # In[9]: # DEBUG def generate_data_allAds(): allAds_df = pd.DataFrame() part1Ads_df = generate_data_partAds(g_adsPart1PathPrefix, 1, 11) allAds_df = allAds_df.append(part1Ads_df, sort=True) part2Ads_df = generate_data_partAds(g_adsPart2PathPrefix, 11, 21) allAds_df = allAds_df.append(part2Ads_df, sort=True) allAds_df = allAds_df.set_index('AdId') return allAds_df # ## UDFs for generating Ratings Dataset # In[10]: def df_crossjoin(df1, df2): df1['_tmpkey'] = 1 df2['_tmpkey'] = 1 res = pd.merge(df1, df2, on='_tmpkey').drop('_tmpkey', axis=1) res.index = pd.MultiIndex.from_product((df1.index, df2.index)) df1.drop('_tmpkey', axis=1, inplace=True) df2.drop('_tmpkey', axis=1, inplace=True) return res # In[11]: # TODO: Move this UDF to top UDFs section def generate_data_RatingsPerUser( pathPrefix, userId ): completePath = pathPrefix + userId + "/" data = "" rtFile = userId + "-RT.csv" rtNewFile = userId + "-RT-NEW.csv" with open(completePath + rtFile) as file: data = file.read().replace("\"", "") with open(completePath + rtNewFile,"w") as file: file.write(data) my_cols = [str(i) for i in range(300)] data3 = pd.read_csv(completePath + rtNewFile, sep=";\|,", names=my_cols, header=None, engine="python") data3 = data3.iloc[2:] data3.reset_index(drop = True, inplace = True) for i in range(20): index = str(i) data3[index] = data3[index].astype('float64') data3 = data3.transpose() return data3 # In[12]: def generate_data_RatingsPartUsers( usersPartPathPrefix, startRange, endRange ): partUsers_df = pd.DataFrame() for i in range(startRange, endRange): thisUserIdNum = str(i) thisUserId = g_userIdPrefix + thisUserIdNum.zfill(3) # print(thisUserId) thisUser_df = generate_data_RatingsPerUser(usersPartPathPrefix, thisUserId) partUsers_df = partUsers_df.append(thisUser_df, sort=True) # partUsers_df.set_index('UserId') return partUsers_df # In[13]: def generate_data_RatingsAllUsers(): allUsers_df = pd.DataFrame() part1Users_df = generate_data_RatingsPartUsers(g_userPart1PathPrefix, 1, 61) allUsers_df = allUsers_df.append(part1Users_df, sort=True) part2Users_df = generate_data_RatingsPartUsers(g_userPart2PathPrefix, 61, 121) allUsers_df = allUsers_df.append(part2Users_df, sort=True) return allUsers_df # # Generate datasets # ## Generate Users dataset # In[14]: allUsers_df = generate_data_allUsers() allUsers_df = allUsers_df.set_index('UserId') # In[15]: allUsers_df.head() # In[16]: allUsers_df.info() # In[17]: allUsers_df.to_csv("AllUsers.csv", index=True) # ## Generate Ads Categories Dataset # In[18]: adCats_df = generate_data_adCats() adCats_df = adCats_df.set_index('AdCatId') # In[19]: adCats_df.head() # In[20]: adCats_df.info() # In[21]: adCats_df.to_csv("AdCats.csv", index=True) # ## Generate Ads Dataset # In[22]: allAds_df = generate_data_allAds() # In[23]: allAds_df.info() # In[24]: allAds_df.head() # In[25]: allAds_df.to_csv("AllAds.csv", index=True) # ## Generate Users\Ads Dataset # In[26]: allUsers_And_Ads_df = df_crossjoin(allUsers_df, allAds_df) # In[27]: allUsers_And_Ads_df.info() # In[28]: allUsers_And_Ads_df = allUsers_And_Ads_df.reset_index() allUsers_And_Ads_df.rename(columns={'level_0':'UserId'}, inplace=True) allUsers_And_Ads_df.rename(columns={'level_1':'AdId'}, inplace=True) allUsers_And_Ads_df.head(302) # In[29]: allUsers_And_Ads_df.to_csv("AllUsers_And_Ads.csv", index=False) # ## Generate UsersRatings Dataset # In[30]: allUsersRatings_df = generate_data_RatingsAllUsers() # In[31]: allUsersRatings_df.rename(columns={0:'Rating'}, inplace=True) # In[32]: allUsersRatings_df.info() # In[33]: allUsersRatings_df.head(301) # ## Generate Final Dataset # In[34]: allUsers_And_Ads_df.head() # In[35]: allUsersRatings_df.head() # In[36]: allUsers_And_Ads_df.reset_index(drop=True, inplace=True) allUsersRatings_df.reset_index(drop=True, inplace=True) allUsers_Ads_Ratings_df = pd.concat([allUsers_And_Ads_df, allUsersRatings_df], axis=1) # In[37]: allUsers_Ads_Ratings_df.info() # In[38]: allUsers_Ads_Ratings_df.head(5) # In[39]: allUsers_Ads_Ratings_df.to_csv("AllUsers_Ads_Ratings_df.csv", index=False) # # Scratchpad # In[ ]:	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 13, 29937, 529, 29890, 29958, 7525, 2400, 9101, 515, 278, 4138, 393, 3743, 594, 29879, 29896, 29953, 29918, 24713, 29914, 443, 2526, 2986, 1533, 29890, 29958, 13, 13, 29937, 512, 29961, 29896, 5387, 13, 13, 13, 5215, 11701, 408, 10518, 13, 5215, 13149, 13, 5215, 2224, 1982, 13, 5215, 337, 13, 13, 13, 29937, 512, 29961, 29906, 5387, 13, 13, 13, 15926, 29889, 842, 29918, 3385, 877, 4990, 29889, 3317, 29918, 1054, 2103, 742, 448, 29896, 29897, 13, 13, 13, 29937, 512, 29961, 29941, 5387, 13, 13, 13, 29937, 12002, 17727, 13, 29887, 29918, 1792, 7439, 29896, 2605, 23095, 353, 376, 6904, 7925, 29896, 29953, 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Backend/issue.py	SXH7/Library-Management	2	143578	<gh_stars>1-10 import mysql.connector as i from datetime import datetime log=i.connect(host="localhost",user="root",password="<PASSWORD>",database="library") def viewissues(): c=log.cursor() c.execute("select * from issue") result= c.fetchall() for row in result: print(row) print("\n") def issuebook(): iid=input("Enter issue ID:") mmid=input("Enter member id:") bc=input("Enter the book code:") B=(bc,) c=log.cursor() sql=("select B_Avilable from books where B_ID=%s") c.execute(sql,B) res=c.fetchall() if 'Y' in res[0]: adate=input("Enter book issue date (MM-DD-YYYY):") bdate=input("Enter due date(MM-DD-YYYY):") idate=datetime.strptime(adate, '%m-%d-%Y') duedate=datetime.strptime(bdate, '%m-%d-%Y') data=(iid,mmid,bc,idate,duedate) sq=("insert into issue values(%s,%s,%s,%s,%s)") c=log.cursor() c.execute(sq,data) log.commit() print("Book issued successfully!") data=(B) sq=("update books set B_Avilable= replace(B_Avilable, 'Y','N') where B_ID=%s") c=log.cursor() c.execute(sq,data) log.commit() else: print("Either book isnt avilable or code is wrong:") issuebook() def bookupdate(): bc=input("Enter book id:") B=(bc,) data=(B) sq=("update books set B_Avilable= replace(B_Avilable, 'N','Y') where B_ID=%s") c=log.cursor() c.execute(sq,data) log.commit()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 5749, 29889, 11958, 2801, 408, 474, 13, 3166, 12865, 1053, 12865, 13, 13, 13, 1188, 29922, 29875, 29889, 6915, 29898, 3069, 543, 7640, 613, 1792, 543, 4632, 613, 5630, 543, 29966, 25711, 17013, 28341, 9803, 543, 5258, 1159, 13, 13, 13, 1753, 1776, 12175, 7295, 13, 13, 1678, 274, 29922, 1188, 29889, 18127, 580, 13, 1678, 274, 29889, 7978, 703, 2622, 334, 515, 2228, 1159, 13, 1678, 1121, 29922, 274, 29889, 9155, 497, 580, 13, 1678, 363, 1948, 297, 1121, 29901, 13, 4706, 1596, 29898, 798, 29897, 13, 4706, 1596, 14182, 29876, 1159, 13, 13, 1753, 2228, 2909, 7295, 13, 1678, 474, 333, 29922, 2080, 703, 10399, 2228, 3553, 29901, 1159, 13, 1678, 5654, 333, 29922, 2080, 703, 10399, 4509, 1178, 29901, 1159, 13, 1678, 289, 29883, 29922, 2080, 703, 10399, 278, 3143, 775, 29901, 1159, 13, 268, 13, 1678, 350, 7607, 12328, 29892, 29897, 13, 13, 1678, 274, 29922, 1188, 29889, 18127, 580, 13, 1678, 4576, 29922, 703, 2622, 350, 29918, 12810, 309, 519, 515, 8277, 988, 350, 29918, 1367, 16328, 29879, 1159, 13, 1678, 274, 29889, 7978, 29898, 2850, 29892, 29933, 29897, 13, 1678, 620, 29922, 29883, 29889, 9155, 497, 580, 13, 268, 13, 1678, 565, 525, 29979, 29915, 297, 620, 29961, 29900, 5387, 13, 4706, 594, 403, 29922, 2080, 703, 10399, 3143, 2228, 2635, 313, 7428, 29899, 7858, 29899, 14995, 14995, 1125, 1159, 13, 4706, 289, 1256, 29922, 2080, 703, 10399, 2861, 2635, 29898, 7428, 29899, 7858, 29899, 14995, 14995, 1125, 1159, 13, 4706, 1178, 403, 29922, 12673, 29889, 710, 415, 603, 29898, 328, 403, 29892, 14210, 29885, 19222, 29881, 19222, 29979, 1495, 13, 4706, 868, 287, 403, 29922, 12673, 29889, 710, 415, 603, 29898, 29890, 1256, 29892, 14210, 29885, 19222, 29881, 19222, 29979, 1495, 13, 308, 13, 4706, 848, 7607, 29875, 333, 29892, 4317, 333, 29892, 12328, 29892, 333, 403, 29892, 700, 287, 403, 29897, 13, 4706, 18074, 29922, 703, 7851, 964, 2228, 1819, 29414, 29879, 24163, 29879, 24163, 29879, 24163, 29879, 24163, 29879, 25760, 13, 308, 13, 4706, 274, 29922, 1188, 29889, 18127, 580, 13, 4706, 274, 29889, 7978, 29898, 3044, 29892, 1272, 29897, 13, 4706, 1480, 29889, 15060, 580, 13, 4706, 1596, 703, 10967, 16610, 8472, 29991, 1159, 13, 308, 13, 308, 13, 4706, 848, 7607, 29933, 29897, 13, 4706, 18074, 29922, 703, 5504, 8277, 731, 350, 29918, 12810, 309, 519, 29922, 5191, 29898, 29933, 29918, 12810, 309, 519, 29892, 525, 29979, 3788, 29940, 1495, 988, 350, 29918, 1367, 16328, 29879, 1159, 13, 4706, 274, 29922, 1188, 29889, 18127, 580, 13, 4706, 274, 29889, 7978, 29898, 3044, 29892, 1272, 29897, 13, 4706, 1480, 29889, 15060, 580, 13, 1678, 1683, 29901, 13, 4706, 1596, 703, 29923, 2121, 3143, 338, 593, 1029, 309, 519, 470, 775, 338, 2743, 29901, 1159, 13, 4706, 2228, 2909, 580, 13, 13, 1753, 3143, 5504, 7295, 13, 1678, 289, 29883, 29922, 2080, 703, 10399, 3143, 1178, 29901, 1159, 13, 1678, 350, 7607, 12328, 29892, 29897, 13, 1678, 848, 7607, 29933, 29897, 13, 1678, 18074, 29922, 703, 5504, 8277, 731, 350, 29918, 12810, 309, 519, 29922, 5191, 29898, 29933, 29918, 12810, 309, 519, 29892, 525, 29940, 3788, 29979, 1495, 988, 350, 29918, 1367, 16328, 29879, 1159, 13, 1678, 274, 29922, 1188, 29889, 18127, 580, 13, 1678, 274, 29889, 7978, 29898, 3044, 29892, 1272, 29897, 13, 1678, 1480, 29889, 15060, 580, 13, 13, 13, 2 ]
segment_classifier/keras_lstm.py	liuhongbing1220/fundanNLP_task	0	81165	<reponame>liuhongbing1220/fundanNLP_task #!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Fri May 31 09:49:27 2019 @author: liuhongbing """ import pandas as pd import numpy as np from collections import Counter import matplotlib.pyplot as plt import re import tensorflow as tf from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense ,Embedding,Activation from tensorflow.keras.layers import LSTM from tensorflow.keras.preprocessing import sequence import io root = "/Users/liuhongbing/Documents/tensorflow/data/sentiment-analysis-on-movie-reviews/" def dataCleaning(totalText): total=[] for i in totalText: temp=i.lower() temp=re.sub('[^a-zA-Z]',' ',temp) tempArr=[j for j in temp.strip().split('\t') if isinstance(j,str)] tstr=' '.join(tempArr) total.append(tstr) return total def getTest(name): data=pd.read_csv(name,delimiter='\t') totalText=data['Phrase'] totalText=dataCleaning(totalText) return totalText def loadData(name): data=pd.read_csv(name,delimiter='\t') totalText=data['Phrase'] totalText=dataCleaning(totalText) totalLabel=data['Sentiment'] return totalText,totalLabel def load_vectors(fname): fin = io.open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore') n, d = map(int, fin.readline().split()) data = {} for line in fin: tokens = line.rstrip().split(' ') data[tokens[0]] = [float(tokens[i]) for i in range(1, len(tokens))] return data def getWordVector(word2VecSum, word2index, embedding_dim): embedding_weight = np.zeros((len(word2index)+1, embedding_dim)) for word,index in word2index.items(): try: embedding_weight[index, :] = word2VecSum[word] except KeyError: pass return embedding_weight word2vecSum = load_vectors('/Users/liuhongbing/Documents/tensorflow/fudanNLP/word_to_vector/wiki-news-300d-1M.vec') # #def calc_sim_word(word1, word2): # if (word1 not in word2vecSum) \| (word2 not in word2vecSum): # return "word not in wordvec" # else: # vec1 = word2vecSum[word1] # vec2 = word2vecSum[word2] # return np.dot(vec1, vec2) totalText,totalLabel = loadData(root+"train.tsv") testText = getTest(root+"test.tsv") train_tokenizer = Tokenizer() train_tokenizer.fit_on_texts(totalText) train_sequences = train_tokenizer.texts_to_sequences(totalText) test_sequences = train_tokenizer.texts_to_sequences(testText) # 获得所有tokens的长度 num_tokens = [ len(tokens) for tokens in train_sequences ] num_tokens = np.array(num_tokens) print(len(num_tokens)) #输出 156060 # 平均tokens的长度 print('mean',np.mean(num_tokens)) # 最长的评价tokens的长度 print('max',np.max(num_tokens)) # 最长的评价tokens的长度 print('min',np.min(num_tokens)) plt.hist((num_tokens), bins = 50) plt.ylabel('number of tokens') plt.xlabel('length of tokens') plt.title('Distribution of tokens length') plt.show() ''' 3sigma原则 ''' max_tokens = np.mean(num_tokens) + 3 * np.std(num_tokens) max_tokens = int(max_tokens) max_tokens # 取tokens的长度为19时，大约93%的样本被涵盖 #np.sum( num_tokens < max_tokens ) / len(num_tokens) train_Data=pad_sequences(train_sequences,maxlen=48) test_Data = pad_sequences(test_sequences,maxlen=48) columns = ['feat_'+str(i) for i in range(48)] train_Data2 = train_Data.copy() train_Data2 = pd.DataFrame(train_Data2) train_Data2.columns = columns train_Data2['label'] = totalLabel train_Data2.to_csv(root+"data_result.csv", index=None) train_label=tf.keras.utils.to_categorical(totalLabel,5) data_train, data_test, label_train, label_test = train_test_split(train_Data,train_label, test_size = 0.2) max_features=len(train_tokenizer.index_word)+1 max_len=48 #这个是要和前面padding时的长度一致 epochs = 5 #训练次数 emb_dim = 300 #128代表embedding层的向量维度 batch_size=80 #这是指定批量的大小 model = Sequential() embedding_weight = getWordVector(word2vecSum,train_tokenizer.word_index, emb_dim) embedding_weight_copy = embedding_weight.copy() #import pickle #with open(root+"tikenizer_word_index.pkl", 'wb') as f: # pickle.dump(train_tokenizer.word_index, f) ## 15126128 ##model.add(Embedding(max_features,emb_dim, mask_zero=True)) model.add(Embedding(max_features,emb_dim, weights=[embedding_weight], trainable=False, mask_zero=True)) ## 【（128+64）64+64】4 model.add(LSTM(64,dropout=0.4, recurrent_dropout=0.4,return_sequences=True)) ## 【（64+32）32+32】4 model.add(LSTM(32,dropout=0.5, recurrent_dropout=0.5,return_sequences=False)) ## 325 + 5 model.add(Dense(5, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) model.fit(data_train,label_train,batch_size=batch_size,epochs=epochs,validation_data=(data_test,label_test)) model.evaluate(data_test, label_test) # #print("start saver test submission") #y_pred=model.predict_classes(test_Data) # #sub_file 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Python3-Learn/even_odd_mayankgoyal.py	nikhiljain-413/Hacktoberfest2021_beginner	65	163595	""" Name : Even odd Author : [<NAME>) [https://github.com/mayankgoyal-13] """ num = int(input("Enter your number: ")) if num % 2 == 0: print('The given number is even') else: print('The given number is odd')	[ 1, 9995, 13, 1170, 584, 7753, 7736, 13, 13720, 584, 518, 29966, 5813, 12948, 518, 991, 597, 3292, 29889, 510, 29914, 13029, 804, 1484, 4605, 29899, 29896, 29941, 29962, 13, 15945, 29908, 13, 1949, 353, 938, 29898, 2080, 703, 10399, 596, 1353, 29901, 376, 876, 13, 361, 954, 1273, 29871, 29906, 1275, 29871, 29900, 29901, 13, 1678, 1596, 877, 1576, 2183, 1353, 338, 1584, 1495, 13, 2870, 29901, 13, 1678, 1596, 877, 1576, 2183, 1353, 338, 7736, 1495, 13, 2 ]
main.py	RowitZou/Lexicon-nn	134	64975	<gh_stars>100-1000 # -- coding: utf-8 -- # @Author: <NAME> # @Last Modified by: <NAME>, Contact: <EMAIL> import time import sys import argparse import random import torch import gc import pickle import os import torch.autograd as autograd import torch.optim as optim import numpy as np from utils.metric import get_ner_fmeasure from model.LGN import Graph from utils.data import Data def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def lr_decay(optimizer, epoch, decay_rate, init_lr): lr = init_lr * ((1-decay_rate)*epoch) print( " Learning rate is setted as:", lr) for param_group in optimizer.param_groups: if param_group['name'] == 'aggr': param_group['lr'] = lr 2. else: param_group['lr'] = lr return optimizer def data_initialization(data, word_file, train_file, dev_file, test_file): data.build_word_file(word_file) if train_file: data.build_alphabet(train_file) data.build_word_alphabet(train_file) if dev_file: data.build_alphabet(dev_file) data.build_word_alphabet(dev_file) if test_file: data.build_alphabet(test_file) data.build_word_alphabet(test_file) return data def predict_check(pred_variable, gold_variable, mask_variable): pred = pred_variable.cpu().data.numpy() gold = gold_variable.cpu().data.numpy() mask = mask_variable.cpu().data.numpy() overlaped = (pred == gold) right_token = np.sum(overlaped * mask) total_token = mask.sum() return right_token, total_token def recover_label(pred_variable, gold_variable, mask_variable, label_alphabet): batch_size = gold_variable.size(0) seq_len = gold_variable.size(1) mask = mask_variable.cpu().data.numpy() pred_tag = pred_variable.cpu().data.numpy() gold_tag = gold_variable.cpu().data.numpy() pred_label = [] gold_label = [] for idx in range(batch_size): pred = [label_alphabet.get_instance(pred_tag[idx][idy]) for idy in range(seq_len) if mask[idx][idy] != 0] gold = [label_alphabet.get_instance(gold_tag[idx][idy]) for idy in range(seq_len) if mask[idx][idy] != 0] assert(len(pred)==len(gold)) pred_label.append(pred) gold_label.append(gold) return pred_label, gold_label def print_args(args): print("CONFIG SUMMARY:") print(" Batch size: %s" % (args.batch_size)) print(" If use GPU: %s" % (args.use_gpu)) print(" If use CRF: %s" % (args.use_crf)) print(" Epoch number: %s" % (args.num_epoch)) print(" Learning rate: %s" % (args.lr)) print(" L2 normalization rate: %s" % (args.weight_decay)) print(" If use edge embedding: %s" % (args.use_edge)) print(" If use global node: %s" % (args.use_global)) print(" Bidirectional digraph: %s" % (args.bidirectional)) print(" Update step number: %s" % (args.iters)) print(" Attention dropout rate: %s" % (args.tf_drop_rate)) print(" Embedding dropout rate: %s" % (args.emb_drop_rate)) print(" Hidden state dimension: %s" % (args.hidden_dim)) print(" Learning rate decay ratio: %s" % (args.lr_decay)) print(" Aggregation module dropout rate: %s" % (args.cell_drop_rate)) print(" Head number of attention: %s" % (args.num_head)) print(" Head dimension of attention: %s" % (args.head_dim)) print("CONFIG SUMMARY END.") sys.stdout.flush() def evaluate(data, args, model, name): if name == "train": instances = data.train_Ids elif name == "dev": instances = data.dev_Ids elif name == 'test': instances = data.test_Ids elif name == 'raw': instances = data.raw_Ids else: print("Error: wrong evaluate name,", name) exit(0) pred_results = [] gold_results = [] # set model in eval model model.eval() batch_size = args.batch_size start_time = time.time() train_num = len(instances) total_batch = train_num // batch_size + 1 for batch_id in range(total_batch): start = batch_idbatch_size end = (batch_id+1)batch_size if end > train_num: end = train_num instance = instances[start:end] if not instance: continue word_list, batch_char, batch_label, mask = batchify_with_label(instance, args.use_gpu) _, tag_seq = model(word_list, batch_char, mask) pred_label, gold_label = recover_label(tag_seq, batch_label, mask, data.label_alphabet) pred_results += pred_label gold_results += gold_label decode_time = time.time() - start_time speed = len(instances) / decode_time acc, p, r, f = get_ner_fmeasure(gold_results, pred_results) return speed, acc, p, r, f, pred_results def batchify_with_label(input_batch_list, gpu): batch_size = len(input_batch_list) chars = [sent[0] for sent in input_batch_list] words = [sent[1] for sent in input_batch_list] labels = [sent[2] for sent in input_batch_list] sent_lengths = torch.LongTensor(list(map(len, chars))) max_sent_len = sent_lengths.max() char_seq_tensor = autograd.Variable(torch.zeros((batch_size, max_sent_len))).long() label_seq_tensor = autograd.Variable(torch.zeros((batch_size, max_sent_len))).long() mask = autograd.Variable(torch.zeros((batch_size, max_sent_len))).byte() for idx, (seq, label, seq_len) in enumerate(zip(chars, labels, sent_lengths)): char_seq_tensor[idx, :seq_len] = torch.LongTensor(seq) label_seq_tensor[idx, :seq_len] = torch.LongTensor(label) mask[idx, :seq_len] = torch.Tensor([1] * int(seq_len)) if gpu: char_seq_tensor = char_seq_tensor.cuda() label_seq_tensor = label_seq_tensor.cuda() mask = mask.cuda() return words, char_seq_tensor, label_seq_tensor, mask def train(data, args, saved_model_path): print( "Training model...") model = Graph(data, args) if args.use_gpu: model = model.cuda() print('# generated parameters:', sum(param.numel() for param in model.parameters())) print( "Finished built model.") best_dev_epoch = 0 best_dev_f = -1 best_dev_p = -1 best_dev_r = -1 best_test_f = -1 best_test_p = -1 best_test_r = -1 # Initialize the optimizer aggr_module_params = [] other_module_params = [] for m_name in model._modules: m = model._modules[m_name] if isinstance(m, torch.nn.ModuleList): for p in m.parameters(): if p.requires_grad: aggr_module_params.append(p) else: for p in m.parameters(): if p.requires_grad: other_module_params.append(p) optimizer = optim.Adam([ {"params": (aggr_module_params), "name": "aggr"}, {"params": (other_module_params), "name": "other"} ], lr=args.lr, weight_decay=args.weight_decay ) for idx in range(args.num_epoch): epoch_start = time.time() temp_start = epoch_start print(("Epoch: %s/%s" %(idx, args.num_epoch))) optimizer = lr_decay(optimizer, idx, args.lr_decay, args.lr) sample_loss = 0 batch_loss = 0 total_loss = 0 right_token = 0 whole_token = 0 random.shuffle(data.train_Ids) # set model in train model model.train() model.zero_grad() batch_size = args.batch_size train_num = len(data.train_Ids) total_batch = train_num // batch_size + 1 for batch_id in range(total_batch): # Get one batch-sized instance start = batch_id * batch_size end = (batch_id + 1) * batch_size if end > train_num: end = train_num instance = data.train_Ids[start:end] if not instance: continue word_list, batch_char, batch_label, mask = batchify_with_label(instance, args.use_gpu) loss, tag_seq = model(word_list, batch_char, mask, batch_label) right, whole = predict_check(tag_seq, batch_label, mask) right_token += right whole_token += whole sample_loss += loss.data total_loss += loss.data batch_loss += loss if end % 500 == 0: temp_time = time.time() temp_cost = temp_time - temp_start temp_start = temp_time print((" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f" % (end, temp_cost, sample_loss, right_token, whole_token, (right_token+0.)/whole_token))) sys.stdout.flush() sample_loss = 0 if end % args.batch_size == 0: batch_loss.backward() optimizer.step() model.zero_grad() batch_loss = 0 temp_time = time.time() temp_cost = temp_time - temp_start print((" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f" % (end, temp_cost, sample_loss, right_token, whole_token, (right_token+0.)/whole_token))) epoch_finish = time.time() epoch_cost = epoch_finish - epoch_start print(("Epoch: %s training finished. Time: %.2fs, speed: %.2fst/s, total loss: %s" % (idx, epoch_cost, train_num/epoch_cost, total_loss))) # dev speed, acc, dev_p, dev_r, dev_f, _ = evaluate(data, args, model, "dev") dev_finish = time.time() dev_cost = dev_finish - epoch_finish print(("Dev: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (dev_cost, speed, acc, dev_p, dev_r, dev_f))) # test speed, acc, test_p, test_r, test_f, _ = evaluate(data, args, model, "test") test_finish = time.time() test_cost = test_finish - dev_finish print(("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (test_cost, speed, acc, test_p, test_r, test_f))) if dev_f > best_dev_f: print("Exceed previous best f score: %.4f" % best_dev_f) torch.save(model.state_dict(), saved_model_path + "_best") best_dev_p = dev_p best_dev_r = dev_r best_dev_f = dev_f best_dev_epoch = idx + 1 best_test_p = test_p best_test_r = test_r best_test_f = test_f model_idx_path = saved_model_path + "_" + str(idx) torch.save(model.state_dict(), model_idx_path) with open(saved_model_path + "_result.txt", "a") as file: file.write(model_idx_path + '\n') file.write("Dev score: %.4f, r: %.4f, f: %.4f\n" % (dev_p, dev_r, dev_f)) file.write("Test score: %.4f, r: %.4f, f: %.4f\n\n" % (test_p, test_r, test_f)) file.close() print("Best dev epoch: %d" % best_dev_epoch) print("Best dev score: p: %.4f, r: %.4f, f: %.4f" % (best_dev_p, best_dev_r, best_dev_f)) print("Best test score: p: %.4f, r: %.4f, f: %.4f" % (best_test_p, best_test_r, best_test_f)) gc.collect() with open(saved_model_path + "_result.txt", "a") as file: file.write("Best epoch: %d" % best_dev_epoch + '\n') file.write("Best Dev score: %.4f, r: %.4f, f: %.4f\n" % (best_dev_p, best_dev_r, best_dev_f)) file.write("Test score: %.4f, r: %.4f, f: %.4f\n\n" % (best_test_p, best_test_r, best_test_f)) file.close() with open(saved_model_path + "_best_HP.config", "wb") as file: pickle.dump(args, file) def load_model_decode(model_dir, data, args, name): model_dir = model_dir + "_best" print("Load Model from file: ", model_dir) model = Graph(data, args) model.load_state_dict(torch.load(model_dir)) # load model need consider if the model trained in GPU and load in CPU, or vice versa if args.use_gpu: model = model.cuda() print(("Decode %s data ..." % name)) start_time = time.time() speed, acc, p, r, f, pred_results = evaluate(data, args, model, name) end_time = time.time() time_cost = end_time - start_time print(("%s: time:%.2fs, speed:%.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (name, time_cost, speed, acc, p, r, f))) return pred_results if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--status', choices=['train', 'test', 'decode'], help='Function status.', default='train') parser.add_argument('--use_gpu', type=str2bool, default=True) parser.add_argument('--train', help='Training set.', default='data/onto4ner.cn/train.char.bmes') parser.add_argument('--dev', help='Developing set.', default='data/onto4ner.cn/dev.char.bmes') parser.add_argument('--test', help='Testing set.', default='data/onto4ner.cn/test.char.bmes') parser.add_argument('--raw', help='Raw file for decoding.') parser.add_argument('--output', help='Output results for decoding.') parser.add_argument('--saved_set', help='Path of saved data set.', default='data/onto4ner.cn/saved.dset') parser.add_argument('--saved_model', help='Path of saved model.', default="saved_model/model_onto4ner") parser.add_argument('--char_emb', help='Path of character embedding file.', default="data/gigaword_chn.all.a2b.uni.ite50.vec") parser.add_argument('--word_emb', help='Path of word embedding file.', default="data/ctb.50d.vec") parser.add_argument('--use_crf', type=str2bool, default=True) parser.add_argument('--use_edge', type=str2bool, default=True, help='If use lexicon embeddings (edge embeddings).') parser.add_argument('--use_global', type=str2bool, default=True, help='If use the global node.') parser.add_argument('--bidirectional', type=str2bool, default=True, help='If use bidirectional digraph.') parser.add_argument('--seed', help='Random seed', default=1023, type=int) parser.add_argument('--batch_size', help='Batch size.', default=1, type=int) parser.add_argument('--num_epoch',default=100, type=int, help="Epoch number.") parser.add_argument('--iters', default=4, type=int, help='The number of Graph iterations.') parser.add_argument('--hidden_dim', default=50, type=int, help='Hidden state size.') parser.add_argument('--num_head', default=10, type=int, help='Number of transformer head.') parser.add_argument('--head_dim', default=20, type=int, help='Head dimension of transformer.') parser.add_argument('--tf_drop_rate', default=0.1, type=float, help='Transformer dropout rate.') parser.add_argument('--emb_drop_rate', default=0.5, type=float, help='Embedding dropout rate.') parser.add_argument('--cell_drop_rate', default=0.2, type=float, help='Aggregation module dropout rate.') parser.add_argument('--word_alphabet_size', type=int, help='Word alphabet size.') parser.add_argument('--char_alphabet_size', type=int, help='Char alphabet size.') parser.add_argument('--label_alphabet_size', type=int, help='Label alphabet size.') parser.add_argument('--char_dim', type=int, help='Char embedding size.') parser.add_argument('--word_dim', type=int, help='Word embedding size.') parser.add_argument('--lr', type=float, default=2e-05) parser.add_argument('--lr_decay', type=float, default=0) parser.add_argument('--weight_decay', type=float, default=0) args = parser.parse_args() status = args.status.lower() seed_num = args.seed random.seed(seed_num) torch.manual_seed(seed_num) np.random.seed(seed_num) train_file = args.train dev_file = args.dev test_file = args.test raw_file = args.raw output_file = args.output saved_set_path = args.saved_set saved_model_path = args.saved_model char_file = args.char_emb word_file = args.word_emb if status == 'train': if os.path.exists(saved_set_path): print('Loading saved data set...') with open(saved_set_path, 'rb') as f: data = pickle.load(f) else: data = Data() data_initialization(data, word_file, train_file, dev_file, test_file) data.generate_instance_with_words(train_file, 'train') data.generate_instance_with_words(dev_file, 'dev') data.generate_instance_with_words(test_file, 'test') data.build_char_pretrain_emb(char_file) data.build_word_pretrain_emb(word_file) if saved_set_path is not None: print('Dumping data...') with open(saved_set_path, 'wb') as f: pickle.dump(data, f) data.show_data_summary() args.word_alphabet_size = data.word_alphabet.size() args.char_alphabet_size = data.char_alphabet.size() args.label_alphabet_size = data.label_alphabet.size() args.char_dim = data.char_emb_dim args.word_dim = data.word_emb_dim print_args(args) train(data, args, saved_model_path) elif status == 'test': assert not (test_file is None) if os.path.exists(saved_set_path): print('Loading saved data set...') with open(saved_set_path, 'rb') as f: data = pickle.load(f) else: print("Cannot find saved data set: ", saved_set_path) exit(0) data.generate_instance_with_words(test_file, 'test') with open(saved_model_path + "_best_HP.config", "rb") as f: args = pickle.load(f) data.show_data_summary() print_args(args) load_model_decode(saved_model_path, data, args, "test") elif status == 'decode': assert not (raw_file is None or output_file is None) if os.path.exists(saved_set_path): print('Loading saved data set...') with open(saved_set_path, 'rb') as f: data = pickle.load(f) else: print("Cannot find saved data set: ", saved_set_path) exit(0) data.generate_instance_with_words(raw_file, 'raw') with open(saved_model_path + "_best_HP.config", "rb") as f: args = pickle.load(f) data.show_data_summary() print_args(args) decode_results = load_model_decode(saved_model_path, data, args, "raw") data.write_decoded_results(output_file, decode_results, 'raw') else: print("Invalid argument! 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generate_character_pair_counts.py	Anima108/malwareAnalysis-midtermproject	0	133762	<gh_stars>0 #!/usr/bin/python ############################################### # This script computes char co-occurence within # alexa's top 1-million web domains. # The co-occurence counts, probability and # log(probability) and stored in a json doc. ############################################### import json import math file = 'alexa-top-1m.csv' def bigrams(input_list): return zip(input_list, input_list[1:]) pairs = {} line_count = 0 with open(file, 'r') as fi: for line in fi: # keep track of progress line_count += 1 # extract ranking arr = line.split(',') ranking = arr[0] # extract basedomain basedomain = arr[1].split('.')[0] # sometimes basedomain is made up of 2 or more words with hyphens between, we split them on the hyphen to obtain 'words' # e.g. my-malicious-domain.com, x-site.com etc. words = basedomain.split('-') # for each domain 'word' greater than 1 character # obtain character pairs for word in words: if len(word) > 1: b = bigrams(word) #combine bigram char pairs and maintain count for char_tuple in b: pair = char_tuple[0] + char_tuple[1] if pair in pairs: pairs[pair] += 1 else: pairs[pair] = 1 # now that we have pairs, compute sum of all pair combinations # for example: #if pairs['go'] = 4, pairs['im'] = 5, pairs['to'] = 9 then total_pairs = 18. total_pairs = sum(pairs.values()) print (total_pairs) # calculate probability for each pair of letters update the dict value with a dict of following format # we use logarithm base 10 because later we would need to multiple pairwise to obtain an overall probability # however, instead of multiplying small floats we add their logarithms as # log(xy) = log(x) + log(y) # # key = { count: 2000, # probability: 0.00008580898447093862, # log: -4.605170185988091 # } for k,v in pairs.items(): # probability = val / total_pairs probability = float(v) / total_pairs pairs[k] = { 'count': v, 'probability': probability, 'log': 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nasbench301/surrogate_models/bananas/bananas_src/darts/arch.py	Basvanstein/nasbench301	55	198419	import numpy as np import sys import os sys.path.append(os.path.expanduser('~/darts/cnn')) #from train_class import Train OPS = ['max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NUM_VERTICES = 4 INPUT_1 = 'c_k-2' INPUT_2 = 'c_k-1' class Arch: def __init__(self, arch): self.arch = arch def serialize(self): return self.arch def query(self, epochs=50): trainer = Train() val_losses, test_losses = trainer.main(self.arch, epochs=epochs) val_loss = 100 - np.mean(val_losses) test_loss = 100 - test_losses[-1] return val_loss, test_loss @classmethod def random_arch(cls): # output a uniformly random architecture spec # from the DARTS repository # https://github.com/quark0/darts normal = [] reduction = [] for i in range(NUM_VERTICES): ops = np.random.choice(range(len(OPS)), NUM_VERTICES) #input nodes for conv nodes_in_normal = np.random.choice(range(i+2), 2, replace=False) #input nodes for reduce nodes_in_reduce = np.random.choice(range(i+2), 2, replace=False) normal.extend([(nodes_in_normal[0], ops[0]), (nodes_in_normal[1], ops[1])]) reduction.extend([(nodes_in_reduce[0], ops[2]), (nodes_in_reduce[1], ops[3])]) return (normal, reduction) def mutate(self, edits): """ mutate a single arch """ # first convert tuple to array so that it is mutable mutation = [] for cell in self.arch: mutation.append([]) for pair in cell: mutation[-1].append([]) for num in pair: mutation[-1][-1].append(num) #make mutations for _ in range(edits): cell = np.random.choice(2) pair = np.random.choice(len(OPS)) num = np.random.choice(2) if num == 1: mutation[cell][pair][num] = np.random.choice(len(OPS)) else: inputs = pair // 2 + 2 choice = np.random.choice(inputs) if pair % 2 == 0 and mutation[cell][pair+1][num] != choice: mutation[cell][pair][num] = choice elif pair % 2 != 0 and mutation[cell][pair-1][num] != choice: mutation[cell][pair][num] = choice return mutation def get_paths(self): """ return all paths from input to output """ path_builder = [[[], [], [], []], [[], [], [], [], ]] paths = [[], []] for i, cell in enumerate(self.arch): for j in range(len(OPS)): if cell[j][0] == 0: path = [INPUT_1, OPS[cell[j][1]]] path_builder[i][j//2].append(path) paths[i].append(path) elif cell[j][0] == 1: path = [INPUT_2, OPS[cell[j][1]]] path_builder[i][j//2].append(path) paths[i].append(path) else: for path in path_builder[i][cell[j][0] - 2]: path = [path, OPS[cell[j][1]]] path_builder[i][j//2].append(path) paths[i].append(path) # check if there are paths of length >=5 contains_long_path = [False, False] if max([len(path) for path in paths[0]]) >= 5: contains_long_path[0] = True if max([len(path) for path in paths[1]]) >= 5: contains_long_path[1] = True return paths, contains_long_path def get_path_indices(self, long_paths=True): """ compute the index of each path There are 4 (8^0 + ... + 8^4) paths total If long_paths = False, we give a single boolean to all paths of size 4, so there are only 4 * (1 + 8^0 + ... + 8^3) paths """ paths, contains_long_path = self.get_paths() normal_paths, reduce_paths = paths num_ops = len(OPS) """ Compute the max number of paths per input per cell. 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locale/pot/api/core/_autosummary/pyvista-Light-exponent-1.py	tkoyama010/pyvista-doc-translations	4	143373	<reponame>tkoyama010/pyvista-doc-translations # Plot three planes lit by three spotlights with exponents of 1, # 2 and 5. The one with the lowest exponent has the broadest # beam. # import pyvista as pv plotter = pv.Plotter(lighting='none') for offset, exponent in zip([0, 1.5, 3], [1, 2, 5]): _ = plotter.add_mesh(pv.Plane((offset, 0, 0)), color='white') light = pv.Light(position=(offset, 0, 0.1), focal_point=(offset, 0, 0)) light.exponent = exponent light.positional = True light.cone_angle = 80 plotter.add_light(light) plotter.view_xy() plotter.show()	[ 1, 529, 276, 1112, 420, 29958, 29873, 2901, 29891, 3304, 29900, 29896, 29900, 29914, 2272, 29894, 2079, 29899, 1514, 29899, 3286, 29880, 800, 13, 29937, 18399, 2211, 3814, 267, 11872, 491, 2211, 9758, 4366, 29879, 411, 429, 9340, 310, 29871, 29896, 29892, 13, 29937, 29871, 29906, 322, 29871, 29945, 29889, 29871, 450, 697, 411, 278, 19604, 28869, 756, 278, 7300, 342, 13, 29937, 22913, 29889, 13, 29937, 13, 5215, 11451, 29894, 2079, 408, 282, 29894, 13, 5317, 357, 353, 282, 29894, 29889, 20867, 357, 29898, 4366, 292, 2433, 9290, 1495, 13, 1454, 9210, 29892, 28869, 297, 14319, 4197, 29900, 29892, 29871, 29896, 29889, 29945, 29892, 29871, 29941, 1402, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29945, 29962, 1125, 13, 1678, 903, 353, 6492, 357, 29889, 1202, 29918, 4467, 29882, 29898, 29886, 29894, 29889, 3247, 1662, 3552, 10289, 29892, 29871, 29900, 29892, 29871, 29900, 8243, 2927, 2433, 10921, 1495, 13, 1678, 3578, 353, 282, 29894, 29889, 20769, 29898, 3283, 7607, 10289, 29892, 29871, 29900, 29892, 29871, 29900, 29889, 29896, 511, 12789, 284, 29918, 3149, 7607, 10289, 29892, 29871, 29900, 29892, 29871, 29900, 876, 13, 1678, 3578, 29889, 735, 3296, 353, 28869, 13, 1678, 3578, 29889, 1066, 3245, 353, 5852, 13, 1678, 3578, 29889, 535, 29872, 29918, 2521, 353, 29871, 29947, 29900, 13, 1678, 6492, 357, 29889, 1202, 29918, 4366, 29898, 4366, 29897, 13, 5317, 357, 29889, 1493, 29918, 3594, 580, 13, 5317, 357, 29889, 4294, 580, 13, 2 ]
code/cleantools.py	MarkCHarris/ames_housing_regression	1	118199	<reponame>MarkCHarris/ames_housing_regression ############# DEPENDENCIES ############## import numpy as np import pandas as pd ############# TOOLS FOR EXAMINING DATA FRAMES ############## def info_frame(df): """ Given a DataFrame, gets the name, type, and number of NaN values for each column and stores this information in a new DataFrame. """ info = pd.DataFrame([[col, df[col].dtype, df[col].isna().sum()] for col in df], columns=['name', 'type', 'num_nans']) return info def count_nan(df): """ Given a DataFrame, gets the name of each column and the number of NaN values in it. Returns this information in a new DataFrame, sorted by number of NaN values. Leaves out any columns that have no NaN values. If no NaN values found, returns an empty DataFrame. """ null_counts = df.isna().sum().sort_values(ascending=False) i = 0 if null_counts[i] == 0: return pd.DataFrame(None) while i < len(null_counts): if null_counts[i] != 0: i += 1 else: return pd.DataFrame(zip(null_counts.index[:i], null_counts[:i])) return pd.DataFrame(zip(null_counts.index[:i], null_counts[:i])) def find_non_numeric(in_col): """ Finds each unique entry in an iterable that cannot be typecast as float and returns a list of those entries. """ non_nums = [] for item in in_col: try: float(item) except: if item not in non_nums: non_nums.append(item) return non_nums ############# TOOLS FOR MODIFYING DATA FRAMES ############## def df_cols_to_snake_case(df): """ Given a DataFrame, converts column titles from separate words to snake_case. Will not work on CamelCase. """ return df.rename(mapper={col : col.lower().replace(' ', '_') for col in df.columns}, axis=1) def convert_to_float(in_item, non_numeric_fill=np.nan): """ Converts any input that can be typecast as numeric input to a float. non_numeric_fill specifies what to return if the input cannot be cast as float. Default np.nan. 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utils/models/densevoxel/parts.py	bhklab/ptl-oar-segmentation	3	77215	import torch import torch.nn as nn # from torchsummary import summary # from lib.medzoo.BaseModelClass import BaseModel """ Implementation od DenseVoxelNet based on https://arxiv.org/abs/1708.00573 Hyperparameters used: batch size = 3 weight decay = 0.0005 momentum = 0.9 lr = 0.05 """ def init_weights(m): """ The weights were randomly initialized with a Gaussian distribution (µ = 0, σ = 0.01) """ torch.seed(777) # for reproducibility classname = m.__class__.__name__ if classname.find('Conv') != -1 or classname.find('BatchNorm') != -1: m.weight.data.normal_(0.00, 0.01) class DenseLayer(nn.Sequential): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate=0.2): super(DenseLayer, self).__init__() self.add_module('norm1', nn.BatchNorm3d(num_input_features)), self.add_module('relu1', nn.LeakyReLU(0.1, inplace=True)), self.add_module('conv1', nn.Conv3d(num_input_features, bn_size * growth_rate, kernel_size=3, stride=1, padding=1, bias=False)), self.drop_rate = drop_rate if self.drop_rate > 0: self.drop_layer = nn.Dropout(p=self.drop_rate) def forward(self, x): new_features = super(DenseLayer, self).forward(x) if self.drop_rate > 0: new_features = self.drop_layer(new_features) return torch.cat([x, new_features], 1) class DenseBlock(nn.Sequential): """ to keep the spatial dims o=i, this formula is applied o = [i + 2p - k - (k-1)(d-1)]/s + 1 """ def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate=0.2): super(DenseBlock, self).__init__() for i in range(num_layers): layer = DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate) self.add_module('denselayer%d' % (i + 1), layer) class Transition(nn.Module): def __init__(self, num_input_features, num_output_features): super(Transition, self).__init__() norm = nn.BatchNorm3d(num_input_features) relu = nn.LeakyReLU(0.1, inplace=True) conv3d = nn.Conv3d(num_input_features, num_output_features, kernel_size=1, padding=0, stride=1) self.conv = nn.Sequential(norm, relu, conv3d) self.max_pool = nn.MaxPool3d(kernel_size=2, stride=2) def forward(self, x): k = self.conv(x) y = self.max_pool(k) return y, k class Upsampling(nn.Sequential): """ For transpose conv o = output, p = padding, k = kernel_size, s = stride, d = dilation o = (i -1)s - 2p + k + output_padding = (i-1)2 +2 = 2i """ def __init__(self, input_features, out_features): super(Upsampling, self).__init__() self.tr_conv1_features = 128 # defined in the paper self.tr_conv2_features = out_features self.add_module('norm', nn.BatchNorm3d(input_features)) self.add_module('relu', nn.LeakyReLU(0.1, inplace=True)) self.add_module('conv', nn.Conv3d(input_features, input_features, kernel_size=1, stride=1, padding=0, bias=False)) # Transposed convolutions must be un-padded? self.add_module('transp_conv_1', nn.ConvTranspose3d(input_features, self.tr_conv1_features, kernel_size=2, padding=0, output_padding=0, stride=2)) self.add_module('transp_conv_2', nn.ConvTranspose3d(self.tr_conv1_features, self.tr_conv2_features, kernel_size=2, padding=0, output_padding=0, stride=2))	[ 1, 1053, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 29937, 515, 4842, 305, 7727, 1053, 15837, 13, 29937, 515, 4303, 29889, 2168, 2502, 29877, 29889, 5160, 3195, 2385, 1053, 7399, 3195, 13, 13, 15945, 29908, 13, 1888, 14607, 2413, 360, 1947, 29963, 2251, 295, 6779, 2729, 373, 2045, 597, 279, 26560, 29889, 990, 29914, 6897, 29914, 29896, 29955, 29900, 29947, 29889, 29900, 29900, 29945, 29955, 29941, 13, 26322, 546, 16744, 1304, 29901, 13, 16175, 2159, 353, 29871, 29941, 13, 7915, 20228, 353, 29871, 29900, 29889, 29900, 29900, 29900, 29945, 13, 29885, 2932, 398, 353, 29871, 29900, 29889, 29929, 13, 29212, 353, 29871, 29900, 29889, 29900, 29945, 13, 15945, 29908, 13, 13, 13, 1753, 2069, 29918, 705, 5861, 29898, 29885, 1125, 13, 1678, 9995, 13, 1678, 450, 18177, 892, 20459, 16601, 411, 263, 22477, 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werckercli/tests/test_metrics.py	wercker/DEPRECATED-wercker-cli	1	92829	import mock from mock import ANY import requests from werckercli.metrics import track_application_startup, default_command_name from werckercli.tests import TestCase track_command_usage_path = "werckercli.metrics.track_command_usage" class MetricsTests(TestCase): def test_track_application_startup_fails_silently_on_ConnectionError(self): err = requests.ConnectionError() the_method = mock.Mock(side_effect=err) with mock.patch(track_command_usage_path, the_method) as puts: try: track_application_startup() except requests.ConnectionError: self.fail("track_application_startup didn't fail silently") def test_track_application_startup_calls_track_command_usage(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: the_method.assert_called_once() @mock.patch("sys.argv", ['main.py']) def test_track_application_startup_uses_default_command_on_empty_arg(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() the_method.assert_called_with(default_command_name, ANY) @mock.patch("sys.argv", ['main.py', 'validate']) def test_track_application_startup_passes_command_from_sys_args(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() the_method.assert_called_with('validate', ANY) @mock.patch("sys.argv", [None, 'validate']) def test_track_application_startup_handles_missing_script_arg(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() @mock.patch("sys.argv", [None, 'validate']) def test_track_application_startup_handles_missing_script_arg(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() @mock.patch("sys.argv", [None, 'validate', 'foo', 'bar']) def test_track_application_startup_passes_args(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() expected_args = ['foo', 'bar'] the_method.assert_called_with(ANY, expected_args) @mock.patch("sys.argv", [None, 'validate']) def test_track_application_startup_passes_None_for_missing_arguments(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() expected_args = None the_method.assert_called_with(ANY, expected_args)	[ 1, 1053, 11187, 13, 3166, 11187, 1053, 13764, 29979, 13, 5215, 7274, 13, 3166, 2949, 4937, 11303, 29889, 2527, 10817, 1053, 5702, 29918, 6214, 29918, 2962, 786, 29892, 2322, 29918, 6519, 29918, 978, 13, 13, 3166, 2949, 4937, 11303, 29889, 21150, 1053, 4321, 8259, 13, 13, 11294, 29918, 6519, 29918, 21125, 29918, 2084, 353, 376, 556, 4937, 11303, 29889, 2527, 10817, 29889, 11294, 29918, 6519, 29918, 21125, 29908, 13, 13, 13, 1990, 4737, 10817, 24376, 29898, 3057, 8259, 1125, 13, 13, 1678, 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selfdrive/can/can_define.py	matthewklinko/openpilot	4	141582	<gh_stars>1-10 from collections import defaultdict from selfdrive.can.libdbc_py import libdbc, ffi class CANDefine(object): def __init__(self, dbc_name): self.dv = defaultdict(dict) self.dbc_name = dbc_name self.dbc = libdbc.dbc_lookup(dbc_name) num_vals = self.dbc[0].num_vals self.address_to_msg_name = {} num_msgs = self.dbc[0].num_msgs for i in range(num_msgs): msg = self.dbc[0].msgs[i] name = ffi.string(msg.name) address = msg.address self.address_to_msg_name[address] = name for i in range(num_vals): val = self.dbc[0].vals[i] sgname = ffi.string(val.name) address = val.address def_val = ffi.string(val.def_val) #separate definition/value pairs def_val = def_val.split() values = [int(v) for v in def_val[::2]] defs = def_val[1::2] if address not in self.dv: self.dv[address] = {} msgname = self.address_to_msg_name[address] self.dv[msgname] = {} # two ways to lookup: address or msg name self.dv[address][sgname] = {v: d for v, d in zip(values, defs)} #build dict self.dv[msgname][sgname] = self.dv[address][sgname]	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 16250, 1053, 2322, 8977, 13, 3166, 1583, 21594, 29889, 3068, 29889, 1982, 11140, 29918, 2272, 1053, 4303, 11140, 29892, 285, 7241, 13, 13, 1990, 315, 2190, 3206, 457, 29898, 3318, 1125, 13, 29871, 822, 4770, 2344, 12035, 1311, 29892, 4833, 29883, 29918, 978, 1125, 13, 1678, 1583, 29889, 29881, 29894, 353, 2322, 8977, 29898, 8977, 29897, 13, 1678, 1583, 29889, 11140, 29918, 978, 353, 4833, 29883, 29918, 978, 13, 1678, 1583, 29889, 11140, 353, 4303, 11140, 29889, 11140, 29918, 20401, 29898, 11140, 29918, 978, 29897, 13, 13, 1678, 954, 29918, 791, 29879, 353, 1583, 29889, 11140, 29961, 29900, 1822, 1949, 29918, 791, 29879, 13, 13, 1678, 1583, 29889, 7328, 29918, 517, 29918, 7645, 29918, 978, 353, 6571, 13, 1678, 954, 29918, 1516, 3174, 353, 1583, 29889, 11140, 29961, 29900, 1822, 1949, 29918, 1516, 3174, 13, 1678, 363, 474, 297, 3464, 29898, 1949, 29918, 1516, 3174, 1125, 13, 418, 10191, 353, 1583, 29889, 11140, 29961, 29900, 1822, 1516, 3174, 29961, 29875, 29962, 13, 418, 1024, 353, 285, 7241, 29889, 1807, 29898, 7645, 29889, 978, 29897, 13, 418, 3211, 353, 10191, 29889, 7328, 13, 418, 1583, 29889, 7328, 29918, 517, 29918, 7645, 29918, 978, 29961, 7328, 29962, 353, 1024, 13, 13, 1678, 363, 474, 297, 3464, 29898, 1949, 29918, 791, 29879, 1125, 13, 418, 659, 353, 1583, 29889, 11140, 29961, 29900, 1822, 791, 29879, 29961, 29875, 29962, 13, 13, 418, 269, 29887, 978, 353, 285, 7241, 29889, 1807, 29898, 791, 29889, 978, 29897, 13, 418, 3211, 353, 659, 29889, 7328, 13, 418, 822, 29918, 791, 353, 285, 7241, 29889, 1807, 29898, 791, 29889, 1753, 29918, 791, 29897, 13, 13, 418, 396, 25048, 403, 5023, 29914, 1767, 11000, 13, 418, 822, 29918, 791, 353, 822, 29918, 791, 29889, 5451, 580, 13, 418, 1819, 353, 518, 524, 29898, 29894, 29897, 363, 325, 297, 822, 29918, 791, 29961, 1057, 29906, 5262, 13, 418, 822, 29879, 353, 822, 29918, 791, 29961, 29896, 1057, 29906, 29962, 13, 13, 418, 565, 3211, 451, 297, 1583, 29889, 29881, 29894, 29901, 13, 4706, 1583, 29889, 29881, 29894, 29961, 7328, 29962, 353, 6571, 13, 4706, 10191, 978, 353, 1583, 29889, 7328, 29918, 517, 29918, 7645, 29918, 978, 29961, 7328, 29962, 13, 4706, 1583, 29889, 29881, 29894, 29961, 7645, 978, 29962, 353, 6571, 13, 13, 418, 396, 1023, 5837, 304, 16280, 29901, 3211, 470, 10191, 1024, 13, 418, 1583, 29889, 29881, 29894, 29961, 7328, 3816, 5311, 978, 29962, 353, 426, 29894, 29901, 270, 363, 325, 29892, 270, 297, 14319, 29898, 5975, 29892, 822, 29879, 2915, 396, 4282, 9657, 13, 418, 1583, 29889, 29881, 29894, 29961, 7645, 978, 3816, 5311, 978, 29962, 353, 1583, 29889, 29881, 29894, 29961, 7328, 3816, 5311, 978, 29962, 13, 2 ]
graphics/ground.py	bdemin/M113_Visualization	0	68962	<reponame>bdemin/M113_Visualization import numpy as np from vtk import vtkLookupTable, vtkUnsignedCharArray, vtkNamedColors, \ vtkCellLocator, vtkPoints, mutable, vtkParametricSpline, vtkParametricFunctionSource, \ vtkPolyDataMapper, vtkActor def visualize_elevation(PolyData): # Create colormap bounds = 6[0.0] PolyData.GetBounds(bounds) # Find min and max z minz = bounds[4] maxz = bounds[5] colorLookupTable = vtkLookupTable() colorLookupTable.SetTableRange(minz, maxz) colorLookupTable.Build() # Generate the colors for each point based on elevation colors = vtkUnsignedCharArray() colors.SetNumberOfComponents(3) colors.SetName("Colors") for i in range(0, PolyData.GetNumberOfPoints()): p= 3[0.0] PolyData.GetPoint(i,p) dcolor = 3[0.0] colorLookupTable.GetColor(p[2], dcolor) color=3[0.0] for j in range(0,3): color[j] = int(255.0 * dcolor[j]) colors.InsertNextTypedTuple(color) # Assign colors to the vtkPolyData PolyData.GetPointData().SetScalars(colors) def get_soil_color(soil_type_key, colors_dict): # Function that returns the defined color for a specific soil type if soil_type_key in colors_dict: return colors_dict[soil_type_key] return (255,255,255) # If undefined, return white color def visualize_soil(PolyData, soil_type_array): # Assign colors to the ground based on input soil type array # Create a color dictionary colors = vtkNamedColors() colors_dict = { 0: colors.HTMLColorToRGB('DarkKhaki'), 1: colors.HTMLColorToRGB('Wheat'), 2: colors.HTMLColorToRGB('BurlyWood'), 3: colors.HTMLColorToRGB('SandyBrown'), 4: colors.HTMLColorToRGB('Khaki'), 5: colors.HTMLColorToRGB('DarkOliveGreen'), 6: colors.HTMLColorToRGB('DarkGreen')} m, n = soil_type_array.shape soil_color_map = vtkUnsignedCharArray() soil_color_map.SetNumberOfComponents(3) soil_color_map.SetName("Colors") for i in range(m-1): for j in range(n-1): for _ in range(6): soil_color_map.InsertNextTypedTuple(get_soil_color(soil_type_array[i,j], colors_dict)) # Assign colors to the vtkPolyData PolyData.GetPointData().SetScalars(soil_color_map) def generate_perlin_noise_2d(size, res): # Return Perlin Noise def factors(n): from functools import reduce return list(reduce(list.__add__, ([i, n//i] for i in range(1, int(n*0.5) + 1) if n % i == 0))) def f(t): return 6t*5 - 15t*4 + 10t*3 m_factors = sorted(factors(size[0])) n_factors = sorted(factors(size[1])) m_mid_index = (len(m_factors) - 1) // 2 n_mid_index = (len(n_factors) - 1) // 2 res = (m_factors[m_mid_index], n_factors[n_mid_index]) delta = (res[0] / size[0], res[1] / size[1]) d = (size[0] // res[0], size[1] // res[1]) grid = np.mgrid[0:res[0]:delta[0],0:res[1]:delta[1]].transpose(1, 2, 0) % 1 # Gradients angles = 2np.pinp.random.rand(res[0]+1, res[1]+1) gradients = np.dstack((np.cos(angles), np.sin(angles))) g00 = gradients[0:-1,0:-1].repeat(d[0], 0).repeat(d[1], 1) g10 = gradients[1: ,0:-1].repeat(d[0], 0).repeat(d[1], 1) g01 = gradients[0:-1,1: ].repeat(d[0], 0).repeat(d[1], 1) g11 = gradients[1: ,1: ].repeat(d[0], 0).repeat(d[1], 1) # Ramps n00 = np.sum(np.dstack((grid[:,:,0] , grid[:,:,1] )) g00, 2) n10 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1] )) * g10, 2) n01 = np.sum(np.dstack((grid[:,:,0] , grid[:,:,1]-1)) * g01, 2) n11 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]-1)) * g11, 2) # Interpolation t = f(grid) n0 = n00(1-t[:,:,0]) + t[:,:,0]n10 n1 = n01(1-t[:,:,0]) + t[:,:,0]n11 return np.sqrt(2)((1-t[:,:,1])n0 + t[:,:,1]n1) def perlin2D(m): from scipy.interpolate import interp2d s = np.zeros((m,m)) # Prepare output image (size: m x m) w = m i = 0 while w > 3: i = i + 1 # d = interp2(randn([m,m]), i-1, 'spline') x = i y = i z = np.random.randn(m,m) d = interp2d(x,y,z, kind='spline') s = s + i d[1:m, 1:m] w = w - np.ceil(w/2 - 1) s = (s - min(min(s[:,:]))) / (max(max(s[:,:])) - min(min(s[:,:]))) return s def create_soil_type_arr(size): # Create an array of different soil types scatterd randomly using Perlin Noise np.random.seed(1) noise = generate_perlin_noise_2d((size), (10, 10)) # Get perlin noise soil_type_arr = np.zeros(size) num_soil_types = 3 # Define how many soil types (colors) should be set arr_min = noise.min() soil_type_step = (noise.max() - arr_min)/num_soil_types # Fill array with different soil types for k in range(num_soil_types): for i in range(size[0]): for j in range(size[1]): if (noise[i,j] <= arr_min + (k+1)soil_type_step) and (arr_min + ksoil_type_step <= noise[i,j]): soil_type_arr[i,j] = k return soil_type_arr def get_spline_actor(surface_data, chassis_cg_path, surface_bounds): # Iterate over chassis CG points and create a spline which marks the driving path. # Return the spline as a vtkActor for being added later to the renderer. # Update the pipeline so that vtkCellLocator finds cells surface_data.Update() # Define a cellLocator to be able to compute intersections between lines # and the surface locator = vtkCellLocator() locator.SetDataSet(surface_data.GetOutput()) locator.BuildLocator() tolerance = 0.01 # Set intersection searching tolerance # Make a list of points. Each point is the intersection of a vertical line # defined by p1 and p2 and the surface. points = vtkPoints() for chassis_cg in chassis_cg_path: p1 = [chassis_cg[0], chassis_cg[1], surface_bounds[4]] p2 = [chassis_cg[0], chassis_cg[1], surface_bounds[5]] t = mutable(0) pos = [0.0, 0.0, 0.0] pcoords = [0.0, 0.0, 0.0] subId = mutable(0) locator.IntersectWithLine(p1, p2, tolerance, t, pos, pcoords, subId) # Add a slight offset in z pos[2] += 0.05 # Add the x, y, z position of the intersection points.InsertNextPoint(pos) # Create a spline and add the pointsoi spline = vtkParametricSpline() spline.SetPoints(points) spline_function = vtkParametricFunctionSource() spline_function.SetUResolution(len(chassis_cg_path)) spline_function.SetParametricFunction(spline) # Map the spline spline_mapper = vtkPolyDataMapper() spline_mapper.SetInputConnection(spline_function.GetOutputPort()) # Define the line actor spline_actor = vtkActor() spline_actor.SetMapper(spline_mapper) 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what_can_i_cook/urls.py	s-maibuecher/what_can_i_cook	0	1318	from django.urls import path from what_can_i_cook.views import WCICFilterView, WCICResultView app_name = "wcic" urlpatterns = [ path("", WCICFilterView.as_view(), name="wcic-start"), path("results/", WCICResultView.as_view(), name="wcic-results"), ]	[ 1, 515, 9557, 29889, 26045, 1053, 2224, 13, 13, 3166, 825, 29918, 3068, 29918, 29875, 29918, 15108, 29889, 7406, 1053, 399, 29907, 2965, 5072, 1043, 29892, 399, 29907, 2965, 3591, 1043, 13, 13, 932, 29918, 978, 353, 376, 29893, 29883, 293, 29908, 13, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 2224, 703, 613, 399, 29907, 2965, 5072, 1043, 29889, 294, 29918, 1493, 3285, 1024, 543, 29893, 29883, 293, 29899, 2962, 4968, 13, 1678, 2224, 703, 9902, 29914, 613, 399, 29907, 2965, 3591, 1043, 29889, 294, 29918, 1493, 3285, 1024, 543, 29893, 29883, 293, 29899, 9902, 4968, 13, 29962, 13, 13, 2 ]
tests/functional/CrudTests.py	idle-code/ContextShell	0	141475	<gh_stars>0 import unittest from tests.functional.ShellTestsBase import NodeTreeTestsBase from tests.functional.TestExecutor import script_test class CrudTestsBase(NodeTreeTestsBase): pass class CreateTests(CrudTestsBase): @script_test def test_create(self): """ $ .: create foo $ .: contains foo True """ @script_test def test_create_many_parts(self): """ $ .: create foo.bar $ .: contains foo.bar True """ @unittest.skip("Check when type system will be working") @script_test def test_create_int_type(self): """ $ .: create.int i 3 $ .i: get 3 """ class ContainsTests(CrudTestsBase): @script_test def test_nonexistent(self): """ $ .: contains unknown False """ class GetTests(CrudTestsBase): @script_test def test_get_existing(self): """ $ .: create foo 1 $ .foo: get 1 """ @script_test def test_get_nonexistent(self): """ $ .foo: get NameError: '.foo' doesn't exists """ class SetTests(CrudTestsBase): @script_test def test_set_existing(self): """ $ .: create foo 1 $ .foo: set 2 $ .foo: get 2 """ @script_test def test_set_nonexistent(self): """ $ .foo: set 1 NameError: '.foo' doesn't exists """ @script_test def test_set_no_new_value(self): """ $ .: create foo 1 $ .foo: set TypeError: set_action() missing 1 required positional argument: 'new_value' """ @script_test def test_set_different_type(self): """ $ .: create foo 2 $ .foo: set "rabarbar" TypeError: Cannot assign value with type 'str' to 'int' node """ class ListTests(CrudTestsBase): @script_test def test_list_empty(self): """ $ .: create foo $ .foo: list """ @script_test def test_list_in_creation_order(self): """ $ .: create foo.Z_first $ .: create foo.A_second $ .foo: list Z_first A_second """ @script_test def test_list_only_attributes(self): """ $ .: create .test.@attr $ .: create .test.key $ .test: list.attributes @attr """ @script_test def test_list_all(self): """ $ .: create .test.@attr $ .: create .test.key $ .test: list.all @attr key """ @script_test def test_list_only_normal(self): """ $ .: create .test.@attr $ .: create .test.key $ .test: list key """ @script_test def test_list_actions(self): """ $ .: list.actions create contains get set list remove find """ class RemoveTests(CrudTestsBase): @script_test def test_remove_existing(self): """ $ .: create foo $ .foo: remove $ .: contains foo False """ @script_test def test_remove_nonexistent(self): """ $ .foo: remove NameError: '.foo' doesn't exists """	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 443, 27958, 13, 13, 3166, 6987, 29889, 2220, 284, 29889, 16037, 24376, 5160, 1053, 9071, 9643, 24376, 5160, 13, 3166, 6987, 29889, 2220, 284, 29889, 3057, 13366, 1053, 2471, 29918, 1688, 13, 13, 13, 1990, 6781, 566, 24376, 5160, 29898, 4247, 9643, 24376, 5160, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 6204, 24376, 29898, 20647, 566, 24376, 5160, 1125, 13, 1678, 732, 2154, 29918, 1688, 13, 1678, 822, 1243, 29918, 3258, 29898, 1311, 1125, 13, 4706, 9995, 13, 4706, 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app/web/book.py	Marticles/BookCrossing	2	102368	from flask import jsonify,request ,current_app, url_for, render_template, flash from app.view_models.trade import TradeInfo from app.forms.book import SearchForm from app.libs.helper import is_isbn_or_key from app.models.shupiao_book import ShuPiaoBook from app.view_models.book import BookViewModel,BookCollection from . import web import json from flask_login import current_user from app.models.gift import Gift from app.models.wish import Wish @web.route('/book/search/') def search(): """ q:普通关键字orISBN号 page """ form = SearchForm(request.args) books = BookCollection() #参数校验 if form.validate(): q = form.q.data.strip() page = form.page.data isbn_or_key = is_isbn_or_key(q) shupiao_book = ShuPiaoBook() if isbn_or_key == 'isbn': shupiao_book.search_by_isbn(q) else: shupiao_book.search_by_keyword(q,page) #dict转str books.fill(shupiao_book,q) #return json.dumps(books, default = lambda o: o.__dict__) #return json.dumps(books),200,{'content-type':'application/json'} else : flash('搜索关键字错误') #return jsonify(form.errors) return render_template('search_result.html',books=books,form=form) @web.route('/book/<isbn>/detail') def book_detail(isbn): has_in_gifts = False has_in_wishes = False # 取书籍详情数据 shupiao_book = ShuPiaoBook() shupiao_book.search_by_isbn(isbn) book = BookViewModel(shupiao_book.first) if current_user.is_authenticated: if Gift.query.filter_by(uid=current_user.id, isbn=isbn, launched=False).first(): has_in_gifts = True if Wish.query.filter_by(uid=current_user.id, isbn=isbn, launched=False).first(): has_in_wishes = True trade_gifts = Gift.query.filter_by(isbn=isbn, launched=False).all() trade_wishes = Wish.query.filter_by(isbn=isbn, launched=False).all() trade_gifts_model = TradeInfo(trade_gifts) trade_wishes_model = TradeInfo(trade_wishes) return render_template('book_detail.html', book=book, wishes=trade_wishes_model, gifts=trade_gifts_model, has_in_wishes=has_in_wishes, has_in_gifts=has_in_gifts) ''' @web.route('/hello') def hello(): return 'hello' ''' ''' @web.route('/test') def test(): r = { 'name':'XXXX', 'age':'18' } flash('[q,a,c]',category = 'q') flash('aaaaaa',category = 'a') return render_template('test.html',data =r) '''	[ 1, 515, 29784, 1053, 4390, 1598, 29892, 3827, 1919, 3784, 29918, 932, 29892, 3142, 29918, 1454, 29892, 4050, 29918, 6886, 29892, 11013, 13, 3166, 623, 29889, 1493, 29918, 9794, 29889, 3018, 311, 1053, 27226, 3401, 13, 3166, 623, 29889, 9514, 29889, 2909, 1053, 11856, 2500, 13, 3166, 623, 29889, 10254, 29889, 20907, 1053, 338, 29918, 275, 11197, 29918, 272, 29918, 1989, 13, 3166, 623, 29889, 9794, 29889, 845, 786, 29653, 29918, 2909, 1053, 1383, 29884, 29925, 29653, 10967, 13, 3166, 623, 29889, 1493, 29918, 9794, 29889, 2909, 1053, 6726, 15166, 29892, 10967, 7196, 13, 3166, 869, 1053, 1856, 29871, 13, 5215, 4390, 13, 13, 3166, 29784, 29918, 7507, 1053, 1857, 29918, 1792, 13, 3166, 623, 29889, 9794, 29889, 29887, 2027, 1053, 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sort-array-by-parity-ii/sort-array-by-parity-ii.py	Atri10/Leet-code---Atri_Patel	1	1608496	<gh_stars>1-10 class Solution: def sortArrayByParityII(self, nums: List[int]) -> List[int]: ans = [] even = 0 odd = 1 for i in nums: if i%2==0:ans.insert(even,i);even +=2 else:ans.insert(odd,i);odd +=2 return ans	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1990, 24380, 29901, 13, 1678, 822, 2656, 2588, 2059, 2177, 537, 2687, 29898, 1311, 29892, 954, 29879, 29901, 2391, 29961, 524, 2314, 1599, 2391, 29961, 524, 5387, 13, 4706, 6063, 353, 5159, 13, 4706, 1584, 353, 29871, 29900, 13, 4706, 7736, 353, 29871, 29896, 13, 4706, 363, 474, 297, 954, 29879, 29901, 13, 9651, 565, 474, 29995, 29906, 1360, 29900, 29901, 550, 29889, 7851, 29898, 11884, 29892, 29875, 416, 11884, 4619, 29906, 13, 9651, 1683, 29901, 550, 29889, 7851, 29898, 22861, 29892, 29875, 416, 22861, 4619, 29906, 13, 4706, 736, 6063, 2 ]
aoc/2015/p18-2.py	neizod/problems	1	1609200	<gh_stars>1-10 #!/usr/bin/env python3 def fix_corner(grid): for y in [0, -1]: for x in [0, -1]: grid[y][x] = True def surround(grid, x, y): for dy in [-1, 0, 1]: for dx in [-1, 0, 1]: if (dx, dy) == (0, 0): continue if 0 <= x+dx < len(grid[0]) and 0 <= y+dy < len(grid): yield grid[y+dy][x+dx] def increment(grid): new_grid = [[None for _ in range(len(grid[0]))] for _ in range(len(grid))] for y, line in enumerate(grid): for x, current in enumerate(line): if current: new_grid[y][x] = sum(surround(grid, x, y)) in {2, 3} else: new_grid[y][x] = sum(surround(grid, x, y)) == 3 fix_corner(new_grid) return new_grid def main(): steps = 100 grid = [[c == '#' for c in input().strip()] for _ in range(100)] fix_corner(grid) for i in range(steps): grid = increment(grid) print(sum(i for line in grid for i in line)) if __name__ == '__main__': main()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 13, 1753, 2329, 29918, 2616, 1089, 29898, 7720, 1125, 13, 1678, 363, 343, 297, 518, 29900, 29892, 448, 29896, 5387, 13, 4706, 363, 921, 297, 518, 29900, 29892, 448, 29896, 5387, 13, 9651, 6856, 29961, 29891, 3816, 29916, 29962, 353, 5852, 13, 13, 13, 1753, 8388, 618, 29898, 7720, 29892, 921, 29892, 343, 1125, 13, 1678, 363, 13475, 297, 21069, 29896, 29892, 29871, 29900, 29892, 29871, 29896, 5387, 13, 4706, 363, 15414, 297, 21069, 29896, 29892, 29871, 29900, 29892, 29871, 29896, 5387, 13, 9651, 565, 313, 8235, 29892, 13475, 29897, 1275, 313, 29900, 29892, 29871, 29900, 1125, 13, 18884, 6773, 13, 9651, 565, 29871, 29900, 5277, 921, 29974, 8235, 529, 7431, 29898, 7720, 29961, 29900, 2314, 322, 29871, 29900, 5277, 343, 29974, 4518, 529, 7431, 29898, 7720, 1125, 13, 18884, 7709, 6856, 29961, 29891, 29974, 4518, 3816, 29916, 29974, 8235, 29962, 13, 13, 13, 1753, 11924, 29898, 7720, 1125, 13, 1678, 716, 29918, 7720, 353, 5519, 8516, 363, 903, 297, 3464, 29898, 2435, 29898, 7720, 29961, 29900, 12622, 29962, 363, 903, 297, 3464, 29898, 2435, 29898, 7720, 28166, 13, 1678, 363, 343, 29892, 1196, 297, 26985, 29898, 7720, 1125, 13, 4706, 363, 921, 29892, 1857, 297, 26985, 29898, 1220, 1125, 13, 9651, 565, 1857, 29901, 13, 18884, 716, 29918, 7720, 29961, 29891, 3816, 29916, 29962, 353, 2533, 29898, 29879, 1038, 618, 29898, 7720, 29892, 921, 29892, 343, 876, 297, 426, 29906, 29892, 29871, 29941, 29913, 13, 9651, 1683, 29901, 13, 18884, 716, 29918, 7720, 29961, 29891, 3816, 29916, 29962, 353, 2533, 29898, 29879, 1038, 618, 29898, 7720, 29892, 921, 29892, 343, 876, 1275, 29871, 29941, 13, 1678, 2329, 29918, 2616, 1089, 29898, 1482, 29918, 7720, 29897, 13, 1678, 736, 716, 29918, 7720, 13, 13, 13, 1753, 1667, 7295, 13, 1678, 6576, 353, 29871, 29896, 29900, 29900, 13, 1678, 6856, 353, 5519, 29883, 1275, 16321, 29915, 363, 274, 297, 1881, 2141, 17010, 580, 29962, 363, 903, 297, 3464, 29898, 29896, 29900, 29900, 4638, 13, 1678, 2329, 29918, 2616, 1089, 29898, 7720, 29897, 13, 1678, 363, 474, 297, 3464, 29898, 24530, 1125, 13, 4706, 6856, 353, 11924, 29898, 7720, 29897, 13, 1678, 1596, 29898, 2083, 29898, 29875, 363, 1196, 297, 6856, 363, 474, 297, 1196, 876, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 13, 2 ]
edb/edgeql/compiler/inference/cardinality.py	ambv/edgedb	4	179926	# # This source file is part of the EdgeDB open source project. # # Copyright 2008-present MagicStack Inc. and the EdgeDB authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import annotations from typing import * import enum import functools from edb import errors from edb.edgeql import qltypes from edb.schema import objtypes as s_objtypes from edb.schema import pointers as s_pointers from edb.ir import ast as irast from edb.ir import utils as irutils from .. import context if TYPE_CHECKING: from edb.schema import constraints as s_constr AT_MOST_ONE = qltypes.Cardinality.AT_MOST_ONE ONE = qltypes.Cardinality.ONE MANY = qltypes.Cardinality.MANY AT_LEAST_ONE = qltypes.Cardinality.AT_LEAST_ONE class CardinalityBound(int, enum.Enum): '''This enum is used to perform some of the cardinality operations.''' ZERO = 0 ONE = 1 MANY = 2 def as_required(self) -> bool: return self is CB_ONE def as_schema_cardinality(self) -> qltypes.SchemaCardinality: if self is CB_MANY: return qltypes.SchemaCardinality.MANY else: return qltypes.SchemaCardinality.ONE @classmethod def from_required(cls, required: bool) -> CardinalityBound: return CB_ONE if required else CB_ZERO @classmethod def from_schema_value( cls, card: qltypes.SchemaCardinality ) -> CardinalityBound: if card is qltypes.SchemaCardinality.MANY: return CB_MANY else: return CB_ONE CB_ZERO = CardinalityBound.ZERO CB_ONE = CardinalityBound.ONE CB_MANY = CardinalityBound.MANY def _card_to_bounds( card: qltypes.Cardinality ) -> Tuple[CardinalityBound, CardinalityBound]: lower, upper = card.to_schema_value() return ( CardinalityBound.from_required(lower), CardinalityBound.from_schema_value(upper), ) def _bounds_to_card( lower: CardinalityBound, upper: CardinalityBound, ) -> qltypes.Cardinality: return qltypes.Cardinality.from_schema_value( lower.as_required(), upper.as_schema_cardinality(), ) def _get_set_scope( ir_set: irast.Set, scope_tree: irast.ScopeTreeNode) -> irast.ScopeTreeNode: if ir_set.path_scope_id: new_scope = scope_tree.root.find_by_unique_id(ir_set.path_scope_id) if new_scope is None: raise errors.InternalServerError( f'dangling scope pointer to node with uid' f':{ir_set.path_scope_id} in {ir_set!r}' ) else: new_scope = scope_tree return new_scope def cartesian_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Cardinality of Cartesian product of multiple args.''' card = list(zip((_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card(min(lower), max(upper)) else: # no args is indicative of a empty set return AT_MOST_ONE def max_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Maximum lower and upper bound of specified cardinalities.''' card = list(zip((_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card(max(lower), max(upper)) else: # no args is indicative of a empty set return AT_MOST_ONE def _union_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Cardinality of UNION of multiple args.''' card = list(zip((_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card( max(lower), CB_MANY if len(upper) > 1 else upper[0], ) else: # no args is indicative of a empty set return AT_MOST_ONE def _coalesce_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Cardinality of ?? of multiple args.''' card = list(zip((_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card(max(lower), max(upper)) else: # no args is indicative of a empty set return AT_MOST_ONE def _common_cardinality( args: Iterable[irast.Base], scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return cartesian_cardinality( infer_cardinality( a, scope_tree=scope_tree, singletons=singletons, env=env ) for a in args ) @functools.singledispatch def _infer_cardinality( ir: irast.Expr, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: raise ValueError(f'infer_cardinality: cannot handle {ir!r}') @_infer_cardinality.register def __infer_none( ir: None, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # Here for debugging purposes. raise ValueError('invalid infer_cardinality(None, schema) call') @_infer_cardinality.register def __infer_statement( ir: irast.Statement, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.expr, scope_tree=scope_tree, singletons=singletons, env=env) @_infer_cardinality.register def __infer_config_insert( ir: irast.ConfigInsert, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.expr, scope_tree=scope_tree, singletons=singletons, env=env) @_infer_cardinality.register def __infer_emptyset( ir: irast.EmptySet, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return AT_MOST_ONE @_infer_cardinality.register def __infer_typeref( ir: irast.TypeRef, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return AT_MOST_ONE @_infer_cardinality.register def __infer_type_introspection( ir: irast.TypeIntrospection, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE def _find_visible( ir: irast.Set, scope_tree: irast.ScopeTreeNode, env: context.Environment, ) -> Optional[irast.ScopeTreeNode]: parent_fence = scope_tree.parent_fence if parent_fence is not None: if scope_tree.namespaces: path_id = ir.path_id.strip_namespace(scope_tree.namespaces) else: path_id = ir.path_id return parent_fence.find_visible(path_id) else: return None @_infer_cardinality.register def __infer_set( ir: irast.Set, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.path_id in singletons: return ONE if (node := _find_visible(ir, scope_tree, env)) is not None: return AT_MOST_ONE if node.optional else ONE rptr = ir.rptr if rptr is not None: rptrref = rptr.ptrref if isinstance(rptrref, irast.TypeIntersectionPointerRef): ind_prefix, ind_ptrs = irutils.collapse_type_intersection(ir) new_scope = _get_set_scope(ir, scope_tree) if ind_prefix.rptr is None: prefix_card = infer_cardinality( ind_prefix, scope_tree=new_scope, singletons=singletons, env=env, ) return cartesian_cardinality([prefix_card, AT_MOST_ONE]) else: # Expression before type intersection is a path, # i.e Foo.<bar[IS Type]. In this case we must # take possible intersection specialization of the # link union into account. # We're basically restating the body of this function # in this block, but with extra conditions. if _find_visible(ind_prefix, new_scope, env) is not None: return AT_MOST_ONE else: rptr_spec: Set[irast.PointerRef] = set() for ind_ptr in ind_ptrs: rptr_spec.update(ind_ptr.ptrref.rptr_specialization) rptr_spec_card = _union_cardinality( s.dir_cardinality for s in rptr_spec) base_card = infer_cardinality( rptr.source, scope_tree=new_scope, singletons=singletons, env=env, ) # The resulting cardinality is the cartesian # product of the base to which the type # intersection is applied and the cardinality due # to type intersection itself. return cartesian_cardinality([base_card, rptr_spec_card]) else: if rptrref.union_components: # We use cartesian cardinality instead of union cardinality # because the union of pointers in this context is disjoint # in a sense that for any specific source only a given union # component is used. rptrref_card = cartesian_cardinality( c.dir_cardinality for c in rptrref.union_components ) else: rptrref_card = rptrref.dir_cardinality if rptrref_card.is_single(): new_scope = _get_set_scope(ir, scope_tree) source_card = infer_cardinality( rptr.source, scope_tree=new_scope, singletons=singletons, env=env, ) return cartesian_cardinality((source_card, rptrref_card)) else: return MANY elif ir.expr is not None: new_scope = _get_set_scope(ir, scope_tree) return infer_cardinality( ir.expr, scope_tree=new_scope, singletons=singletons, env=env, ) else: return MANY @_infer_cardinality.register def __infer_func_call( ir: irast.FunctionCall, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # the cardinality of the function call depends on the cardinality # of non-SET_OF arguments AND the cardinality of the function # return value SET_OF = qltypes.TypeModifier.SET_OF if ir.typemod is SET_OF: return MANY else: args = [] # process positional args for arg, typemod in zip(ir.args, ir.params_typemods): if typemod is not SET_OF: args.append(arg.expr) if args: return _common_cardinality( args, scope_tree=scope_tree, singletons=singletons, env=env, ) else: if ir.typemod is qltypes.TypeModifier.OPTIONAL: return AT_MOST_ONE else: return ONE @_infer_cardinality.register def __infer_oper_call( ir: irast.OperatorCall, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.func_shortname == 'std::UNION': # UNION needs to "add up" cardinalities. return _union_cardinality( infer_cardinality( a.expr, scope_tree=scope_tree, singletons=singletons, env=env ) for a in ir.args ) elif ir.func_shortname == 'std::??': # Coalescing takes the maximum of both lower and upper bounds. return _coalesce_cardinality( infer_cardinality( a.expr, scope_tree=scope_tree, singletons=singletons, env=env ) for a in ir.args ) else: args: List[irast.Base] = [] all_optional = False if ir.typemod is qltypes.TypeModifier.SET_OF: # this is DISTINCT and IF..ELSE args = [a.expr for a in ir.args] else: all_optional = True for arg, typemod in zip(ir.args, ir.params_typemods): if typemod is not qltypes.TypeModifier.SET_OF: all_optional &= typemod is qltypes.TypeModifier.OPTIONAL args.append(arg.expr) if args: card = _common_cardinality( args, scope_tree=scope_tree, singletons=singletons, env=env, ) if all_optional: # An operator that has all optional arguments and # doesn't return a SET OF returns at least ONE result # (we currently don't have operators that return # OPTIONAL). So we upgrade the lower bound. _, upper = _card_to_bounds(card) card = _bounds_to_card(CB_ONE, upper) return card else: return AT_MOST_ONE @_infer_cardinality.register def __infer_const( ir: irast.BaseConstant, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE @_infer_cardinality.register def __infer_param( ir: irast.Parameter, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE if ir.required else AT_MOST_ONE @_infer_cardinality.register def __infer_const_set( ir: irast.ConstantSet, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE if len(ir.elements) == 1 else AT_LEAST_ONE @_infer_cardinality.register def __infer_typecheckop( ir: irast.TypeCheckOp, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.left, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_typecast( ir: irast.TypeCast, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.expr, scope_tree=scope_tree, singletons=singletons, env=env, ) def _is_ptr_or_self_ref( ir_expr: irast.Base, result_expr: irast.Set, env: context.Environment, ) -> bool: if not isinstance(ir_expr, irast.Set): return False else: ir_set = ir_expr srccls = env.set_types[result_expr] return ( isinstance(srccls, s_objtypes.ObjectType) and ir_set.expr is None and (env.set_types[ir_set] == srccls or ( ir_set.rptr is not None and srccls.getptr( env.schema, ir_set.rptr.ptrref.shortname.name) is not None )) ) def extract_filters( result_set: irast.Set, filter_set: irast.Set, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, , # When strict=False, ignore any clauses in the filter_set we don't # care about. If True, return None if any exist. strict: bool = False, ) -> Optional[Sequence[Tuple[s_pointers.Pointer, irast.Set]]]: schema = env.schema scope_tree = _get_set_scope(filter_set, scope_tree) ptr: s_pointers.Pointer expr = filter_set.expr if isinstance(expr, irast.OperatorCall): if expr.func_shortname == 'std::=': left, right = (a.expr for a in expr.args) op_card = _common_cardinality( [left, right], scope_tree=scope_tree, singletons=singletons, env=env, ) result_stype = env.set_types[result_set] if op_card.is_multi(): pass elif _is_ptr_or_self_ref(left, result_set, env): if infer_cardinality( right, scope_tree=scope_tree, singletons=singletons, env=env, ).is_single(): left_stype = env.set_types[left] if left_stype == result_stype: assert isinstance(left_stype, s_objtypes.ObjectType) _ptr = left_stype.getptr(schema, 'id') else: _ptr = env.schema.get(left.rptr.ptrref.name) assert isinstance(_ptr, s_pointers.Pointer) ptr = _ptr return [(ptr, right)] elif _is_ptr_or_self_ref(right, result_set, env): if infer_cardinality( left, scope_tree=scope_tree, singletons=singletons, env=env, ).is_single(): right_stype = env.set_types[right] if right_stype == result_stype: assert isinstance(right_stype, s_objtypes.ObjectType) _ptr = right_stype.getptr(schema, 'id') else: _ptr = env.schema.get(right.rptr.ptrref.name) assert isinstance(_ptr, s_pointers.Pointer) ptr = _ptr return [(ptr, right)] elif expr.func_shortname == 'std::AND': left, right = (a.expr for a in expr.args) left_filters = extract_filters( result_set, left, scope_tree, singletons, env ) right_filters = extract_filters( result_set, right, scope_tree, singletons, env ) ptr_filters: List[Tuple[s_pointers.Pointer, irast.Set]] = [] if left_filters is not None: ptr_filters.extend(left_filters) elif strict: return None if right_filters is not None: ptr_filters.extend(right_filters) elif strict: return None return ptr_filters return None def _analyse_filter_clause( result_set: irast.Set, result_card: qltypes.Cardinality, filter_clause: irast.Set, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: schema = env.schema filtered_ptrs = extract_filters( result_set, filter_clause, scope_tree, singletons, env) if filtered_ptrs: exclusive_constr: s_constr.Constraint = schema.get('std::exclusive') for ptr, _ in filtered_ptrs: ptr = ptr.get_nearest_non_derived_parent(env.schema) is_unique = ( ptr.is_id_pointer(schema) or any(c.issubclass(schema, exclusive_constr) for c in ptr.get_constraints(schema).objects(schema)) ) if is_unique: # Bingo, got an equality filter on a link with a # unique constraint return AT_MOST_ONE return result_card def _infer_stmt_cardinality( result_set: irast.Set, filter_clause: Optional[irast.Set], scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: result_card = infer_cardinality( result_set, scope_tree=scope_tree, singletons=singletons, env=env, ) if result_card.is_single() or filter_clause is None: return result_card return _analyse_filter_clause( result_set, result_card, filter_clause, scope_tree, singletons, env) @_infer_cardinality.register def __infer_select_stmt( ir: irast.SelectStmt, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: if (ir.limit is not None and isinstance(ir.limit.expr, irast.IntegerConstant) and ir.limit.expr.value == '1'): # Explicit LIMIT 1 clause. stmt_card = AT_MOST_ONE else: stmt_card = _infer_stmt_cardinality( ir.result, ir.where, scope_tree=scope_tree, singletons=singletons, env=env, ) if ir.iterator_stmt: iter_card = infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) stmt_card = cartesian_cardinality((stmt_card, iter_card)) return stmt_card @_infer_cardinality.register def __infer_insert_stmt( ir: irast.InsertStmt, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: if ir.iterator_stmt: # XXX: is this branch ever invoked? return infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) else: # INSERT without a FOR is always a singleton. return ONE @_infer_cardinality.register def __infer_update_stmt( ir: irast.UpdateStmt, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: stmt_card = _infer_stmt_cardinality( ir.subject, ir.where, scope_tree=scope_tree, singletons=singletons, env=env, ) if ir.iterator_stmt: iter_card = infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) stmt_card = cartesian_cardinality((stmt_card, iter_card)) return stmt_card @_infer_cardinality.register def __infer_delete_stmt( ir: irast.DeleteStmt, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: stmt_card = _infer_stmt_cardinality( ir.subject, None, singletons=singletons, scope_tree=scope_tree, env=env, ) if ir.iterator_stmt: iter_card = infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) stmt_card = cartesian_cardinality((stmt_card, iter_card)) return stmt_card @_infer_cardinality.register def __infer_stmt( ir: irast.Stmt, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: return infer_cardinality( ir.result, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_slice( ir: irast.SliceIndirection, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # slice indirection cardinality depends on the cardinality of # the base expression and the slice index expressions args = [ir.expr] if ir.start is not None: args.append(ir.start) if ir.stop is not None: args.append(ir.stop) return _common_cardinality( args, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_index( ir: irast.IndexIndirection, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # index indirection cardinality depends on both the cardinality of # the base expression and the index expression return _common_cardinality( [ir.expr, ir.index], scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_array( ir: irast.Array, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return _common_cardinality( ir.elements, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_tuple( ir: irast.Tuple, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return _common_cardinality( [el.val for el in ir.elements], scope_tree=scope_tree, singletons=singletons, env=env, ) def infer_cardinality( ir: irast.Base, , scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId] = (), env: context.Environment, ) -> qltypes.Cardinality: result = env.inferred_cardinality.get((ir, scope_tree)) if result is not None: return result result = _infer_cardinality( ir, scope_tree=scope_tree, singletons=singletons, env=env, ) if result not in {AT_MOST_ONE, ONE, MANY, AT_LEAST_ONE}: raise errors.QueryError( 'could not determine the cardinality of ' 'set produced by expression', context=ir.context) 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mafia_tally/config.py	auscompgeek/mafia-tally	0	142949	<filename>mafia_tally/config.py import json import os from typing import Dict, Set import werkzeug.security config_dir = os.path.join(os.path.dirname(__file__), '..', 'config') get_path = lambda filename: os.path.join(config_dir, filename) day_config_file = get_path('day.json') access_token_file = get_path('access_token.txt') players_file = get_path('players.txt') admin_passhash_file = get_path('passhash.txt') group_id: int = 328346913872436 # lol let's hardcode this post_id: int day_id: int cutoff: str players: Set[str] pics: Dict[str, str] def get_access_token() -> str: with open(access_token_file) as f: return f.read().strip() def get_passhash() -> str: with open(admin_passhash_file) as f: return f.read().strip() def check_admin_password(password: str) -> bool: return werkzeug.security.check_password_hash(get_passhash(), password) def load_day_config(): global post_id global day_id global cutoff with open(day_config_file) as f: day_info = json.load(f) post_id = day_info['post_id'] day_id = day_info['day_id'] cutoff = day_info['cutoff'] def get_players() -> Set[str]: with open(players_file) as f: return set(map(str.strip, f)) def load_players(): global players players = get_players() def load_pics(): global pics with open(get_path('pics.json')) as f: pics = json.load(f) def load(): load_day_config() load_players() load_pics() def save_day_config(): with open(day_config_file, 'w') as f: json.dump( {'post_id': post_id, 'day_id': day_id, 'cutoff': cutoff}, f, indent='\t' ) def save_players(): with open(players_file, 'w') as f: print(*players, sep='\n', file=f) def save(): save_day_config() save_players() load()	[ 1, 529, 9507, 29958, 655, 29888, 423, 29918, 29873, 635, 29914, 2917, 29889, 2272, 13, 5215, 4390, 13, 5215, 2897, 13, 3166, 19229, 1053, 360, 919, 29892, 3789, 13, 13, 5215, 23085, 13289, 29889, 8926, 13, 13, 2917, 29918, 3972, 353, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 25721, 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Algorithms/Opt2_Algorithm/main.py	varshneypratyaksh2017/TSP_Using_AI	1	113215	import time from Algorithms.Opt2_Algorithm.algorithm import Opt2 # 2Opt Algorithm requires no configurations def opt2Algorithm(): opt2 = Opt2() startTime = time.time() route, _ = opt2.findSolution() endTime = time.time() time_taken = endTime - startTime solution = route distance = opt2.getDistance() return solution, distance, time_taken	[ 1, 1053, 931, 13, 3166, 11545, 12404, 29889, 20624, 29906, 29918, 22461, 4540, 29889, 20567, 1053, 20693, 29906, 13, 13, 13, 29937, 29871, 29906, 20624, 29068, 6858, 694, 22920, 13, 1753, 3523, 29906, 22461, 4540, 7295, 13, 13, 1678, 3523, 29906, 353, 20693, 29906, 580, 13, 13, 1678, 1369, 2481, 353, 931, 29889, 2230, 580, 13, 1678, 5782, 29892, 903, 353, 3523, 29906, 29889, 2886, 13296, 918, 580, 13, 1678, 1095, 2481, 353, 931, 29889, 2230, 580, 13, 13, 1678, 931, 29918, 29873, 9424, 353, 1095, 2481, 448, 1369, 2481, 13, 1678, 1650, 353, 5782, 13, 1678, 5418, 353, 3523, 29906, 29889, 657, 27469, 580, 13, 13, 1678, 736, 1650, 29892, 5418, 29892, 931, 29918, 29873, 9424, 13, 2 ]
wsgi.py	ObjectionTheory/rover-voice-integration	0	1601249	import flask import json import requests import time from rover import Rover application = flask.Flask(__name__) print("Started") rovers = {1: Rover(1)} currentRover = 1 @application.route('/') def getStatus(): print("Hello? Can anyone hear me?") return "Hello, I am running...:" @application.route('/control', methods=['GET']) def controller(): global rovers req = flask.request.args roverid = int(req.get("roverid")) if roverid not in rovers.keys(): rovers[roverid] = Rover(roverid) rovers[roverid].updateData( req.get("left"), req.get("right"), req.get("duration"), req.get("claw") ) res = "Moved!" return res @application.route('/getcommands', methods=['GET']) def returnCommand(): global rovers req = flask.request.args print(req) roverid = int(req.get("roverid")) print(rovers[roverid].postData(reset=False)) if roverid not in rovers.keys(): rovers[roverid] = Rover(roverid) res = rovers[roverid].postData() if req.get("kill"): del rovers[roverid] return return flask.jsonify(res) @application.route('/webhooks', methods=['POST']) def webhook(): req = flask.request.get_json(silent=True, force=True) res = processRequest(req) return flask.jsonify(res) def processRequest(req): global rovers, currentRover print("Request:") print(json.dumps(req, indent=4)) action = req["result"]["action"] if action == "sallyForth": moveRover(req, 255, 255) res = speech("Sallying forth!") elif action == "turn": direction = req["result"]["parameters"]["direction"] if direction == "left": moveRover(req, -255, 255) res = speech("Turning left!") elif direction == "right": moveRover(req, 255, -255) res = speech("Turning right!") elif action == "retreat": moveRover(req, -255, -255) res = speech("Going back!") elif action == "halt": moveRover(req, 0, 0) res = speech("Stopping!") elif action == "openClaw": rovers[currentRover].claw = 0 res = speech("Opening the claw!") elif action == "closeClaw": rovers[currentRover].claw = 1 res = speech("Closing the claw!") elif action == "lightPreset": lights = req.get("result").get("parameters").get("lightSel") rovers[currentRover].lightPreset = int(lights) + 1 res = speech("Lights set to preset "+ lights + ".") elif action == "clearLights": rovers[currentRover].lightPreset = 1 res = speech("Lights cleared.") elif action == "setLights": hue = req.get("results").get("parameters").get("hue") brightness = req.get("results").get("parameters").get("brightness") rovers[currentRover].setLights(hue, brightness) res = speech("Lights configured!") elif action == "setRover": currentRover = req.get("result").get("parameters").get("roverid") if currentRover not in rovers: rovers[currentRover] = Rover(currentRover) print(rovers) res = speech("Set rover " + str(currentRover) + " to be active!") elif action == "numberOfRovers": if len(rovers) == 1: res = speech("There is 1 rover connected to the server.") else: res = speech("There are " + str(len(rovers)) + " connected to the server.") elif action == "activeRovers": activeRovers = 0 for rover in rovers.values(): if rover.isMoving(): activeRovers += 1 if activeRovers == 1: activeSpeech = "There is 1 rover moving at the moment." else: activeSpeech = "There are " + str(activeRovers) + " rovers moving at the moment." if len(rovers)-activeRovers == 1: stoppedSpeech = "There is 1 inactive rover." else: stoppedSpeech = "There are " + str(len(rovers)-activeRovers) + " inactive rovers." res = speech(activeSpeech + "\n" + stoppedSpeech) else: res = speech("Oops, I didn't get that...") return res def moveRover(req, left, right): global rovers, currentRover travelTime = 2 roverid = currentRover if req["result"]["parameters"].get("duration"): duration = req["result"]["parameters"]["duration"] if duration["unit"] == "s": travelTime = duration["amount"] elif duration["unit"] == "min": travelTime = duration["amount"] * 60 print(roverid, rovers.keys()) if roverid in rovers.keys(): print(roverid) rovers[roverid].updateData(left, right, travelTime) def speech(speech): return { "speech": speech, "displayText": speech, "source": "webhookdata" } if __name__ == '__main__': application.run()	[ 1, 1053, 29784, 13, 5215, 4390, 13, 5215, 7274, 13, 5215, 931, 13, 3166, 696, 369, 1053, 390, 957, 13, 13, 6214, 353, 29784, 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manhattan/backend/rollups.py	cartlogic/manhattan	1	70026	<reponame>cartlogic/manhattan from __future__ import absolute_import, division, print_function """ This module contains some example ``Rollup`` objects, each implementing the interface expected by the Manhattan backend for rollup aggregations. """ import time from datetime import datetime, timedelta import pytz class LocalRollup(object): def __init__(self, tzname): self.tz = pytz.timezone(tzname) def start_date_for(self, timestamp): dt = datetime.utcfromtimestamp(timestamp).replace(tzinfo=pytz.utc) dt_local = dt.astimezone(self.tz).replace(tzinfo=None) return dt_local.date() class LocalDayRollup(LocalRollup): def get_bucket(self, timestamp, history): return time.mktime(self.start_date_for(timestamp).timetuple()) class LocalWeekRollup(LocalRollup): def get_bucket(self, timestamp, history): day = self.start_date_for(timestamp) days_from_sunday = day.isoweekday() % 7 day -= timedelta(days=days_from_sunday) return time.mktime(day.timetuple()) class AllRollup(object): def get_bucket(self, timestamp, history): return 0 class BrowserRollup(object): def browser_from_user_agent(self, user_agent): # FIXME This is a pretty naive and less useful implementation. if 'Chrome' in user_agent: return u'Chrome' elif 'Safari' in user_agent: return u'Safari' elif 'Firefox' in user_agent: return u'Firefox' elif 'MSIE' in user_agent: return u'IE' else: return u'Unknown' def get_bucket(self, timestamp, history): return (history.user_agents and self.browser_from_user_agent(list(history.user_agents)[0]) or u'')	[ 1, 529, 276, 1112, 420, 29958, 13823, 19227, 29914, 1171, 29882, 23586, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 29892, 8542, 29892, 1596, 29918, 2220, 13, 15945, 29908, 13, 4013, 3883, 3743, 777, 1342, 4954, 29934, 3028, 786, 16159, 3618, 29892, 1269, 16049, 278, 13, 13248, 3806, 491, 278, 29093, 23586, 14998, 363, 9679, 786, 11404, 800, 29889, 13, 15945, 29908, 13, 13, 5215, 931, 13, 3166, 12865, 1053, 12865, 29892, 5335, 287, 2554, 13, 13, 5215, 282, 3637, 29920, 13, 13, 13, 1990, 9959, 29934, 3028, 786, 29898, 3318, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 260, 29920, 978, 1125, 13, 4706, 1583, 29889, 17559, 353, 282, 3637, 29920, 29889, 2230, 8028, 29898, 17559, 978, 29897, 13, 13, 1678, 822, 1369, 29918, 1256, 29918, 1454, 29898, 1311, 29892, 14334, 1125, 13, 4706, 11636, 353, 12865, 29889, 329, 29883, 3166, 16394, 29898, 16394, 467, 6506, 29898, 17559, 3888, 29922, 2272, 17559, 29889, 329, 29883, 29897, 13, 4706, 11636, 29918, 2997, 353, 11636, 29889, 579, 603, 8028, 29898, 1311, 29889, 17559, 467, 6506, 29898, 17559, 3888, 29922, 8516, 29897, 13, 4706, 736, 11636, 29918, 2997, 29889, 1256, 580, 13, 13, 13, 1990, 9959, 12742, 29934, 3028, 786, 29898, 7717, 29934, 3028, 786, 1125, 13, 13, 1678, 822, 679, 29918, 21454, 29898, 1311, 29892, 14334, 29892, 4955, 1125, 13, 4706, 736, 931, 29889, 29885, 1193, 603, 29898, 1311, 29889, 2962, 29918, 1256, 29918, 1454, 29898, 16394, 467, 9346, 24120, 552, 3101, 13, 13, 13, 1990, 9959, 22606, 29934, 3028, 786, 29898, 7717, 29934, 3028, 786, 1125, 13, 13, 1678, 822, 679, 29918, 21454, 29898, 1311, 29892, 14334, 29892, 4955, 1125, 13, 4706, 2462, 353, 1583, 29889, 2962, 29918, 1256, 29918, 1454, 29898, 16394, 29897, 13, 4706, 3841, 29918, 3166, 29918, 29879, 870, 388, 353, 2462, 29889, 275, 4657, 1416, 3250, 580, 1273, 29871, 29955, 13, 4706, 2462, 22361, 5335, 287, 2554, 29898, 16700, 29922, 16700, 29918, 3166, 29918, 29879, 870, 388, 29897, 13, 4706, 736, 931, 29889, 29885, 1193, 603, 29898, 3250, 29889, 9346, 24120, 552, 3101, 13, 13, 13, 1990, 2178, 29934, 3028, 786, 29898, 3318, 1125, 13, 13, 1678, 822, 679, 29918, 21454, 29898, 1311, 29892, 14334, 29892, 4955, 1125, 13, 4706, 736, 29871, 29900, 13, 13, 13, 1990, 27088, 29934, 3028, 786, 29898, 3318, 1125, 13, 13, 1678, 822, 4714, 29918, 3166, 29918, 1792, 29918, 14748, 29898, 1311, 29892, 1404, 29918, 14748, 1125, 13, 4706, 396, 383, 6415, 2303, 910, 338, 263, 5051, 1055, 573, 322, 3109, 5407, 5314, 29889, 13, 4706, 565, 525, 1451, 4871, 29915, 297, 1404, 29918, 14748, 29901, 13, 9651, 736, 318, 29915, 1451, 4871, 29915, 13, 4706, 25342, 525, 29903, 2142, 1306, 29915, 297, 1404, 29918, 14748, 29901, 13, 9651, 736, 318, 29915, 29903, 2142, 1306, 29915, 13, 4706, 25342, 525, 18654, 8944, 29915, 297, 1404, 29918, 14748, 29901, 13, 9651, 736, 318, 29915, 18654, 8944, 29915, 13, 4706, 25342, 525, 4345, 8673, 29915, 297, 1404, 29918, 14748, 29901, 13, 9651, 736, 318, 29915, 8673, 29915, 13, 4706, 1683, 29901, 13, 9651, 736, 318, 29915, 14148, 29915, 13, 13, 1678, 822, 679, 29918, 21454, 29898, 1311, 29892, 14334, 29892, 4955, 1125, 13, 4706, 736, 313, 18434, 29889, 1792, 29918, 351, 1237, 322, 13, 18884, 1583, 29889, 15965, 29918, 3166, 29918, 1792, 29918, 14748, 29898, 1761, 29898, 18434, 29889, 1792, 29918, 351, 1237, 9601, 29900, 2314, 470, 13, 18884, 318, 29915, 1495, 13, 2 ]
bluebottle/donations/migrations/0009_auto_20190130_1140.py	jayvdb/bluebottle	0	17281	<reponame>jayvdb/bluebottle<filename>bluebottle/donations/migrations/0009_auto_20190130_1140.py # -- coding: utf-8 -- # Generated by Django 1.10.8 on 2019-01-30 10:40 from __future__ import unicode_literals import bluebottle.utils.fields from decimal import Decimal from django.db import migrations, models import django.db.models.deletion import djmoney.models.fields class Migration(migrations.Migration): dependencies = [ ('donations', '0008_auto_20170927_1021'), ] operations = [ migrations.AddField( model_name='donation', name='payout_amount', field=bluebottle.utils.fields.MoneyField(currency_choices="[('EUR', u'Euro')]", decimal_places=2, default=Decimal('0.0'), max_digits=12, verbose_name='Payout amount'), ), migrations.AddField( model_name='donation', name='payout_amount_currency', field=djmoney.models.fields.CurrencyField(choices=[(b'EUR', 'Euro')], default='EUR', editable=False, max_length=3), ), ]	[ 1, 529, 276, 1112, 420, 29958, 29926, 388, 29894, 2585, 29914, 9539, 29890, 1501, 280, 29966, 9507, 29958, 9539, 29890, 1501, 280, 29914, 9176, 800, 29914, 26983, 800, 29914, 29900, 29900, 29900, 29929, 29918, 6921, 29918, 29906, 29900, 29896, 29929, 29900, 29896, 29941, 29900, 29918, 29896, 29896, 29946, 29900, 29889, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3251, 630, 491, 15337, 29871, 29896, 29889, 29896, 29900, 29889, 29947, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29896, 29899, 29941, 29900, 29871, 29896, 29900, 29901, 29946, 29900, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 5215, 7254, 29890, 1501, 280, 29889, 13239, 29889, 9621, 13, 3166, 13677, 1053, 3826, 3039, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 13, 5215, 270, 21231, 4992, 29889, 9794, 29889, 9621, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 9176, 800, 742, 525, 29900, 29900, 29900, 29947, 29918, 6921, 29918, 29906, 29900, 29896, 29955, 29900, 29929, 29906, 29955, 29918, 29896, 29900, 29906, 29896, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2528, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 9176, 362, 742, 13, 9651, 1024, 2433, 29886, 1534, 29918, 14506, 742, 13, 9651, 1746, 29922, 9539, 29890, 1501, 280, 29889, 13239, 29889, 9621, 29889, 29924, 4992, 3073, 29898, 26095, 29918, 1859, 1575, 543, 29961, 877, 29923, 4574, 742, 318, 29915, 29923, 2192, 1495, 29962, 613, 13677, 29918, 29886, 6048, 29922, 29906, 29892, 2322, 29922, 23307, 877, 29900, 29889, 29900, 5477, 4236, 29918, 7501, 1169, 29922, 29896, 29906, 29892, 26952, 29918, 978, 2433, 29925, 1534, 5253, 5477, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2528, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 9176, 362, 742, 13, 9651, 1024, 2433, 29886, 1534, 29918, 14506, 29918, 26095, 742, 13, 9651, 1746, 29922, 19776, 29885, 4992, 29889, 9794, 29889, 9621, 29889, 29907, 10880, 3073, 29898, 1859, 1575, 11759, 29898, 29890, 29915, 29923, 4574, 742, 525, 29923, 2192, 1495, 1402, 2322, 2433, 29923, 4574, 742, 3863, 519, 29922, 8824, 29892, 4236, 29918, 2848, 29922, 29941, 511, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
openid.py	DaniilSezonov/AuthServerClient	0	40561	import jwt import requests from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicNumbers from oauth2 import Oauth2, ResponseObject, RemoteUserProfile, OAuthDiscoveryError class OpenID(Oauth2): state_token: str or None = None well_know_path_part: str = ".well-known" # it is path in url to .well-know https://tools.ietf.org/html/rfc5785 openid_conf_path_part = "openid-configuration" # it is path in url to openid configuration auth_server_domain_name: str or None = None _configuration: dict or None = None # https://openid.net/specs/openid-connect-discovery-1_0.html#ProviderConfig was_load_openid_conf: bool = False audience: str or None = None jwk_cert: dict or None = None def __init__(self, args, kwargs): super().__init__(args, kwargs) self.openid_conf_path_part = kwargs.get("openid_conf_path_part") or self.openid_conf_path_part self.state_token = kwargs.get('state_token') self.jwk_cert = kwargs.get('jwk_cert') self.audience = kwargs.get('audience') @staticmethod def get_jwt_header(token): """ :param token: the token is a jwt (that has name id_token in google notation) :return: dict with key alg (algorithm), kid (key id), typ (token type(JWT always)) """ return jwt.get_unverified_header(token) @staticmethod def jwk_to_rsa(jwk): """ Decoding jwk format to public rsa key carried out by next steps: (jwk['e'] (RSA exponent) and jwk['n'] (RSA modulus) -> decode from base64url to binary -> encode binary to DEC (десятичная)) :param jwk: JSON Web Key https://tools.ietf.org/html/rfc7517 :return: RSA public key as instance of cryptography.hazmat.backends.openssl.rsa._RSAPublicKey """ e = jwk.get('e') # exponent assert e, "jwk must contains the exponent value by key 'e'" n = jwk.get('n') # modulus assert n, "jwk must contains the modulus value by key 'n'" base64_url_exp = jwt.api_jws.base64url_decode(e) base64_url_modulus = jwt.api_jws.base64url_decode(n) dec64_url_exp = int(base64_url_exp.hex(), base=16) dec64_url_modulus = int(base64_url_modulus.hex(), base=16) return RSAPublicNumbers(e=dec64_url_exp, n=dec64_url_modulus).public_key(default_backend()) def get_user_info_by_jwt(self, token, with_verify=True, options)-> dict: """ :param token: jwt :param with_verify: verify that response is not compromised :param options: if verify is True we need to pass JWK value for verification :return: """ user_info = {} jwk = options.get("jwk") jwt_decode_options = {} audience = options.get('audience') if audience: jwt_decode_options['audience'] = audience elif self.audience: jwt_decode_options['audience'] = self.audience rsa_key = self.jwk_to_rsa(jwk) if with_verify: user_info = jwt.decode(token, key=rsa_key, jwt_decode_options) else: user_info = jwt.decode(token, verify=False) return user_info def is_state_token_valid(self, state): # todo return self.state_token == state def get_user_profile(self, exchange_code_response: ResponseObject) -> RemoteUserProfile: id_token = exchange_code_response.get('id_token') assert id_token, "Wrong openid response from auth server. " \ "response must contains id_token (with JWT data) field" options = { 'jwk': self.get_jwk_for_jwt(id_token) } return self.get_user_info_by_jwt(id_token, options) def load_configuration(self, configuration: dict = None): if configuration is None: configuration = self.get_oauth_conf_by_discovery_doc() self._configuration = configuration self.was_load_openid_conf = True def get_oauth_conf_by_discovery_doc(self): discovery_path = f'https://{self.auth_server_domain_name}/{self.well_know_path_part}/{self.openid_conf_path_part}' response = requests.get(url=discovery_path) if response.status_code != 200: raise OAuthDiscoveryError() configuration = response.json() return configuration def get_jwk_for_jwt(self, token): if self.jwk_cert is None: jwt_header = self.get_jwt_header(token) self.load_jwk_by_remote(jwt_header.get('kid')) return self.jwk_cert def load_jwk_by_remote(self, kid: str): """ :param kid: remote key id :return: jwk """ assert kid, "You cant load jwk if key id (kid) is not set" 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port_scanner.py	Adel-Charef/scripts	0	112754	from queue import Queue import socket import threading target = "127.0.0.1" queue = Queue() open_ports = [] """ port 80 (HTTP) or port 443 (HTTPS) 21 (FTP), 22 (SSH), 25 (SMTP) """ # connect to a target on a specific port def portscan(port): try: sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((target, port)) return True except: return False def get_ports(mode): # for standardized ports if mode == 1: for port in range(1, 1024): queue.put(port) # for reserved ports elif mode == 2: for port in range(1, 49152): queue.put(port) # for the most important ports only elif mode == 3: ports = [20, 21, 22, 23, 25, 53, 80, 110, 443] for port in ports: queue.put(port) # to choose our ports manually elif mode == 4: ports = input("Enter your ports (separate by blank): ") ports = ports.split() ports = list(map(int, ports)) for port in ports: queue.put(port) # get the port numbers from the queue, scan them and print the results def worker(): while not queue.empty(): port = queue.get() if portscan(port): print(f"Port {port} is open!") open_ports.append(port) else: print(f"Port {port} is closed!") # let's create start and manage our threads def main(threads, mode): get_ports(mode) thread_list = [] for t in range(threads): thread = threading.Thread(target=worker) thread_list.append(thread) for thread in thread_list: thread.start() for thread in thread_list: thread.join() print("The open ports are:", open_ports) if __name__ == "__main__": main(100, 4)	[ 1, 515, 9521, 1053, 5462, 434, 13, 5215, 9909, 13, 5215, 3244, 292, 13, 13, 5182, 353, 376, 29896, 29906, 29955, 29889, 29900, 29889, 29900, 29889, 29896, 29908, 13, 9990, 353, 5462, 434, 580, 13, 3150, 29918, 4011, 353, 5159, 13, 15945, 29908, 13, 2011, 29871, 29947, 29900, 313, 10493, 29897, 470, 2011, 29871, 29946, 29946, 29941, 313, 10493, 29903, 29897, 29871, 29906, 29896, 313, 29943, 3557, 511, 29871, 29906, 29906, 313, 1799, 29950, 511, 29871, 29906, 29945, 313, 17061, 3557, 29897, 13, 15945, 29908, 13, 29937, 4511, 304, 263, 3646, 373, 263, 2702, 2011, 13, 13, 13, 1753, 2011, 16192, 29898, 637, 1125, 13, 1678, 1018, 29901, 13, 4706, 577, 384, 353, 9909, 29889, 11514, 29898, 11514, 29889, 5098, 29918, 1177, 2544, 29892, 9909, 29889, 6156, 7077, 29918, 1254, 1525, 5194, 29897, 13, 4706, 577, 384, 29889, 6915, 3552, 5182, 29892, 2011, 876, 13, 4706, 736, 5852, 13, 1678, 5174, 29901, 13, 4706, 736, 7700, 13, 13, 13, 1753, 679, 29918, 4011, 29898, 8513, 1125, 13, 1678, 396, 363, 3918, 1891, 16169, 13, 1678, 565, 4464, 1275, 29871, 29896, 29901, 13, 4706, 363, 2011, 297, 3464, 29898, 29896, 29892, 29871, 29896, 29900, 29906, 29946, 1125, 13, 9651, 9521, 29889, 649, 29898, 637, 29897, 13, 1678, 396, 363, 21676, 16169, 13, 1678, 25342, 4464, 1275, 29871, 29906, 29901, 13, 4706, 363, 2011, 297, 3464, 29898, 29896, 29892, 29871, 29946, 29929, 29896, 29945, 29906, 1125, 13, 9651, 9521, 29889, 649, 29898, 637, 29897, 13, 1678, 396, 363, 278, 1556, 4100, 16169, 871, 13, 1678, 25342, 4464, 1275, 29871, 29941, 29901, 13, 4706, 16169, 353, 518, 29906, 29900, 29892, 29871, 29906, 29896, 29892, 29871, 29906, 29906, 29892, 29871, 29906, 29941, 29892, 29871, 29906, 29945, 29892, 29871, 29945, 29941, 29892, 29871, 29947, 29900, 29892, 29871, 29896, 29896, 29900, 29892, 29871, 29946, 29946, 29941, 29962, 13, 4706, 363, 2011, 297, 16169, 29901, 13, 9651, 9521, 29889, 649, 29898, 637, 29897, 13, 1678, 396, 304, 6755, 1749, 16169, 7522, 13, 1678, 25342, 4464, 1275, 29871, 29946, 29901, 13, 4706, 16169, 353, 1881, 703, 10399, 596, 16169, 313, 25048, 403, 491, 9654, 1125, 16521, 13, 4706, 16169, 353, 16169, 29889, 5451, 580, 13, 4706, 16169, 353, 1051, 29898, 1958, 29898, 524, 29892, 16169, 876, 13, 4706, 363, 2011, 297, 16169, 29901, 13, 9651, 9521, 29889, 649, 29898, 637, 29897, 13, 13, 13, 29937, 679, 278, 2011, 3694, 515, 278, 9521, 29892, 12812, 963, 322, 1596, 278, 2582, 13, 1753, 15645, 7295, 13, 1678, 1550, 451, 9521, 29889, 6310, 7295, 13, 4706, 2011, 353, 9521, 29889, 657, 580, 13, 4706, 565, 2011, 16192, 29898, 637, 1125, 13, 9651, 1596, 29898, 29888, 29908, 2290, 426, 637, 29913, 338, 1722, 29991, 1159, 13, 9651, 1722, 29918, 4011, 29889, 4397, 29898, 637, 29897, 13, 4706, 1683, 29901, 13, 9651, 1596, 29898, 29888, 29908, 2290, 426, 637, 29913, 338, 5764, 29991, 1159, 13, 13, 13, 29937, 1235, 29915, 29879, 1653, 1369, 322, 10933, 1749, 9717, 13, 1753, 1667, 29898, 28993, 29892, 4464, 1125, 13, 1678, 679, 29918, 4011, 29898, 8513, 29897, 13, 1678, 3244, 29918, 1761, 353, 5159, 13, 1678, 363, 260, 297, 3464, 29898, 28993, 1125, 13, 4706, 3244, 353, 3244, 292, 29889, 4899, 29898, 5182, 29922, 24602, 29897, 13, 4706, 3244, 29918, 1761, 29889, 4397, 29898, 7097, 29897, 13, 1678, 363, 3244, 297, 3244, 29918, 1761, 29901, 13, 4706, 3244, 29889, 2962, 580, 13, 1678, 363, 3244, 297, 3244, 29918, 1761, 29901, 13, 4706, 3244, 29889, 7122, 580, 13, 13, 1678, 1596, 703, 1576, 1722, 16169, 526, 29901, 613, 1722, 29918, 4011, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 29898, 29896, 29900, 29900, 29892, 29871, 29946, 29897, 13, 2 ]
h/streamer/__init__.py	kevinjalbert/h	1	125717	<filename>h/streamer/__init__.py from h.streamer.tweens import close_db_session_tween_factory __all__ = ["close_db_session_tween_factory"] def includeme(config): config.include("h.streamer.views") config.add_subscriber( "h.streamer.streamer.start", "pyramid.events.ApplicationCreated" )	[ 1, 529, 9507, 29958, 29882, 29914, 5461, 261, 29914, 1649, 2344, 26914, 2272, 13, 3166, 298, 29889, 5461, 261, 29889, 29873, 705, 575, 1053, 3802, 29918, 2585, 29918, 7924, 29918, 29873, 1452, 29918, 14399, 13, 13, 1649, 497, 1649, 353, 6796, 5358, 29918, 2585, 29918, 7924, 29918, 29873, 1452, 29918, 14399, 3108, 13, 13, 13, 1753, 3160, 1004, 29898, 2917, 1125, 13, 1678, 2295, 29889, 2856, 703, 29882, 29889, 5461, 261, 29889, 7406, 1159, 13, 13, 1678, 2295, 29889, 1202, 29918, 1491, 7588, 495, 29898, 13, 4706, 376, 29882, 29889, 5461, 261, 29889, 5461, 261, 29889, 2962, 613, 376, 2272, 2572, 333, 29889, 13604, 29889, 4873, 20399, 29908, 13, 1678, 1723, 13, 2 ]
src/app_contexts/app_factory.py	marcoprado17/flask-bone	0	156656	<filename>src/app_contexts/app_factory.py # !/usr/bin/env python # -- coding: utf-8 -- # ====================================================================================================================== # The MIT License (MIT) # ====================================================================================================================== # Copyright (c) 2016 [<NAME> - <EMAIL>] # ====================================================================================================================== import sys if sys.version_info.major < 3: reload(sys) sys.setdefaultencoding('utf8') from flask import Flask, redirect, request, url_for from configs import default_app_config from configs.instance import instance_app_config from configs.instance import unit_test_app_config from extensions import db from flask_bombril.log import log_request def __create_app(configs): static_folder = None for config in configs: if hasattr(config, 'STATIC_FOLDER'): static_folder = config.STATIC_FOLDER app = Flask(__name__, instance_relative_config=True, static_folder=static_folder) for config in configs: app.config.from_object(config) # Initializing extensions from extensions import bcrypt bcrypt.init_app(app) from extensions import db db.init_app(app) from extensions import mail mail.init_app(app) return app def create_app(): app = __create_app([default_app_config, instance_app_config]) @app.route("/") def home_redirect(): return redirect("home") # ================================================================================================================== # # # # # Registering blueprints # ================================================================================================================== # # Components # from blueprints.components.lightly_route_dependent.navbar import navbar_blueprint app.register_blueprint(navbar_blueprint, url_prefix="/navbar") # # Routers # from blueprints.routers.home import home_blueprint app.register_blueprint(home_blueprint, url_prefix="/home") from blueprints.routers.register import register_blueprint app.register_blueprint(register_blueprint, url_prefix="/cadastrar") if app.config["DEBUG"]: from blueprints.routers.debug import debug_blueprint app.register_blueprint(debug_blueprint, url_prefix="/debug") # # Wrappers # from blueprints.wrappers.base import base_blueprint app.register_blueprint(base_blueprint, url_prefix="/base") # # Macros # from macros import macros_blueprint app.register_blueprint(macros_blueprint) # # Email # from email_blueprint import email_blueprint app.register_blueprint(email_blueprint) # ================================================================================================================== # # # # # Registering jinja filters # ================================================================================================================== from flask_bombril.jinja_filters import assert_defined, assert_callable, call, if_filter, is_static, is_toast, get_level app.jinja_env.filters['assert_defined'] = assert_defined app.jinja_env.filters['assert_callable'] = assert_callable app.jinja_env.filters['call'] = call app.jinja_env.filters['if'] = if_filter app.jinja_env.filters['is_static'] = is_static app.jinja_env.filters['is_toast'] = is_toast app.jinja_env.filters['get_level'] = get_level # ================================================================================================================== # # # # # Registering lightly route dependent components context_processors # ================================================================================================================== from blueprints.components.lightly_route_dependent.navbar.navbar_data_provider import navbar_data_provider from r import R @app.context_processor def _(): return dict( get_navbar_data=lambda: navbar_data_provider.get_data(), R=R, ) # ================================================================================================================== # # # # # Configuring Logging # ================================================================================================================== import logging from logging.handlers import TimedRotatingFileHandler handler = TimedRotatingFileHandler( filename=app.config['LOGGING_FILENAME'], when=app.config['LOGGING_WHEN'], interval=app.config['LOGGING_INTERVAL'], backupCount=app.config['LOGGING_BACKUP_COUNT'] ) formatter = logging.Formatter(app.config['LOGGING_FORMAT']) handler.setFormatter(formatter) app.logger.addHandler(handler) # ================================================================================================================== # # # # # Registering 500 error handler # ================================================================================================================== @app.errorhandler(500) def handle_error(error): db.session.rollback() 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VPLVisitor.py	lamhacker/VPL-Compiler	2	163511	# Generated from VPL.g4 by ANTLR 4.7 from antlr4 import * if __name__ is not None and "." in __name__: from .VPLParser import VPLParser else: from VPLParser import VPLParser # This class defines a complete generic visitor for a parse tree produced by VPLParser. class VPLVisitor(ParseTreeVisitor): # Visit a parse tree produced by VPLParser#program. def visitProgram(self, ctx:VPLParser.ProgramContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#function_declaration. def visitFunction_declaration(self, ctx:VPLParser.Function_declarationContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#parameter. def visitParameter(self, ctx:VPLParser.ParameterContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#parameterName. def visitParameterName(self, ctx:VPLParser.ParameterNameContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#multParameterName. def visitMultParameterName(self, ctx:VPLParser.MultParameterNameContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#variable_declaration. def visitVariable_declaration(self, ctx:VPLParser.Variable_declarationContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#condition. def visitCondition(self, ctx:VPLParser.ConditionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#whileloop. def visitWhileloop(self, ctx:VPLParser.WhileloopContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#assign. def visitAssign(self, ctx:VPLParser.AssignContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#noneStatement. def visitNoneStatement(self, ctx:VPLParser.NoneStatementContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#nest_statement. def visitNest_statement(self, ctx:VPLParser.Nest_statementContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#addExpression. def visitAddExpression(self, ctx:VPLParser.AddExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#minusExpression. def visitMinusExpression(self, ctx:VPLParser.MinusExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#multExpression. def visitMultExpression(self, ctx:VPLParser.MultExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#divExpression. def visitDivExpression(self, ctx:VPLParser.DivExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#minExpression. def visitMinExpression(self, ctx:VPLParser.MinExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#parenthesisExpression. def visitParenthesisExpression(self, ctx:VPLParser.ParenthesisExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#idenetExpression. def visitIdenetExpression(self, ctx:VPLParser.IdenetExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#numExpression. def visitNumExpression(self, ctx:VPLParser.NumExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#lessThan. def visitLessThan(self, ctx:VPLParser.LessThanContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#largeThan. def visitLargeThan(self, ctx:VPLParser.LargeThanContext): return self.visitChildren(ctx) del VPLParser	[ 1, 396, 3251, 630, 515, 478, 7390, 29889, 29887, 29946, 491, 13764, 14632, 29934, 29871, 29946, 29889, 29955, 13, 3166, 3677, 29212, 29946, 1053, 334, 13, 361, 4770, 978, 1649, 338, 451, 6213, 322, 376, 1213, 297, 4770, 978, 1649, 29901, 13, 1678, 515, 869, 29963, 7390, 11726, 1053, 478, 7390, 11726, 13, 2870, 29901, 13, 1678, 515, 478, 7390, 11726, 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moodle/mod/workshop/__init__.py	Hardikris/moodlepy	0	1600350	from .base import BaseWorkshop __all__ = [ "BaseWorkshop", ]	[ 1, 515, 869, 3188, 1053, 7399, 5531, 19032, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 376, 5160, 5531, 19032, 613, 13, 29962, 13, 2 ]
rob599_hw3/scripts/noiser.py	4-legends/Software-for-robotics-rob599	0	65983	#!/usr/bin/env python import rospy import math import numpy as np from sensor_msgs.msg import LaserScan ####################################### # Laser Scan: # Header: Seq, Stamp, frame_id # Angle_min, Angle_max, Angle_Increment, Time_Increment # Scan time, range_min, range_max, ranges, intensities ####################################### class Noise_class: def __init__(self): #rospy.on_shutdown(self.save_csv) self.laser_sub = rospy.Subscriber('/base_scan', LaserScan, self.laser_callback) self.scan_pub = rospy.Publisher('/gaus_err_laser_scan', LaserScan, queue_size= 1) def laser_callback(self, msg): filtered_values = LaserScan() distance = np.array(msg.ranges) filtered_values.header = msg.header filtered_values.angle_increment = msg.angle_increment filtered_values.time_increment = msg.time_increment filtered_values.scan_time = msg.scan_time filtered_values.range_min = msg.range_min filtered_values.range_max = msg.range_max filtered_values.intensities = msg.intensities angle = filtered_values.angle_increment min_angle = msg.angle_min max_angle = msg.angle_max laser_noise_variance = rospy.get_param('laser_noise_variance') if laser_noise_variance <= 0: laser_noise_variance = 0.1 filtered_values_ranges = np.zeros(len(distance)) noise_values_ranges = np.random.normal(loc = 0, scale=laser_noise_variance, size=len(distance)) for i in range(len(distance)): filtered_values_ranges[i] = noise_values_ranges[i]+distance[i] filtered_values.ranges = filtered_values_ranges filtered_values.angle_min = min_angle filtered_values.angle_max = max_angle self.scan_pub.publish(filtered_values) if __name__ == '__main__': rospy.init_node('noiser', anonymous=True) noisy = Noise_class() try: rospy.spin() except KeyboardInterrupt: print("Shutting down")	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 5215, 696, 1028, 29891, 13, 5215, 5844, 13, 5215, 12655, 408, 7442, 13, 3166, 23530, 29918, 1516, 3174, 29889, 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openGaussBase/testcase/AI/AI_MODEL/Opengauss_Function_Snapshot_Case0007.py	opengauss-mirror/Yat	0	1610319	<reponame>opengauss-mirror/Yat """ Copyright (c) 2022 Huawei Technologies Co.,Ltd. openGauss is licensed under Mulan PSL v2. You can use this software according to the terms and conditions of the Mulan PSL v2. You may obtain a copy of Mulan PSL v2 at: http://license.coscl.org.cn/MulanPSL2 THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. See the Mulan PSL v2 for more details. """ """ Case Type : 智能运维snapshot模块 Case Name : 创建快照后使用linear_regression算子创建model Description : 1.建表并插入数据 2.创建快照 3.创建训练集与测试集 4.查看当前数据表快照 5.查询创建的训练集数据 6.使用linear_regression算子创建model 7.查找系统表 8.预测表数据 9.清理环境 Expect : 1.建表并插入数据成功 2.创建快照成功 3.创建训练集与测试集成功 4.返回当前数据表快照 5.返回创建的训练集数据 6.使用linear_regression算子创建model成功 7.返回创建model的modelname 8.返回预测的表数据 9.清理环境成功 History : """ import os import unittest from testcase.utils.Constant import Constant from testcase.utils.CommonSH import CommonSH from testcase.utils.Logger import Logger class AI(unittest.TestCase): def setUp(self): self.logger = Logger() self.primary_sh = CommonSH('PrimaryDbUser') self.Con = Constant() self.table = 't_snapshot_tab_0007' self.snapshot = '[email protected]' self.snapshot1 = '[email protected]' self.snapshot2 = '[email protected]' self.model = 'm_model_snapshot_0007' def test_ai_snapshot(self): self.logger.info(f'-----{os.path.basename(__file__)} start-----') step = 'step1:建表并插入数据 expect:建表并插入数据成功' self.logger.info(step) create_table = self.primary_sh.execut_db_sql( f'''drop table if exists {self.table}; create table {self.table}(id int,tax int,bedroom int , bath float,price int,size int,lot int ); insert into {self.table} values(1,590,2,1,50000,770,22100), (2,1050,3,2 ,85000,1410,2000),(3,20 ,3,1,22500,1060,3500), (4,870,2,2,90000,1300,17500),(5,1320,3,2,133000,1500,30000), (6,1350,2,1,90500,820,25700),(7,2790,3,2.5,260000,2130,25000), (8,680,2,1,142500,1170,22000),(9,1840,3,2,160000,1500,19000), (10,3680,4,2,240000,2790,20000),(111,660,3,1,87000,1030,17500);''') self.logger.info(create_table) self.assertTrue(self.Con.TABLE_CREATE_SUCCESS in create_table and self.Con.INSERT_SUCCESS_MSG in create_table, "建表并插入数据失败" + step) step = 'step2:创建快照 expect:创建快照成功' self.logger.info(step) create_snapshot = self.primary_sh.execut_db_sql( f'''create snapshot {self.snapshot} as select * from {self.table}; ''') self.logger.info(create_snapshot) self.assertIn(self.snapshot, create_snapshot, "执行失败" + step) step = 'step3:创建训练集与测试集 expect:创建训练集与测试集成功' self.logger.info(step) create_snapshot = self.primary_sh.execut_db_sql( f'''sample snapshot {self.snapshot} stratify by price as _test at ratio .2, as _train at ratio .8 comment is 'training'; ''') self.logger.info(create_snapshot) self.assertIn(self.snapshot1, create_snapshot, "执行失败" + step) self.assertIn(self.snapshot2, create_snapshot, "执行失败" + step) step = 'step4:查看当前数据表快照 expect:返回当前数据表快照' self.logger.info(step) select_snapshot = self.primary_sh.execut_db_sql( f'''select * from db4ai.snapshot;''') self.logger.info(select_snapshot) self.assertIn(self.snapshot, select_snapshot, "执行失败" + step) self.assertIn(self.snapshot1, select_snapshot, "执行失败" + step) self.assertIn(self.snapshot2, select_snapshot, "执行失败" + step) step = 'step5:查询创建的训练集数据 expect:返回创建的训练集数据' self.logger.info(step) select_snapshot = self.primary_sh.execut_db_sql( f'''select * from {self.snapshot1};''') self.logger.info(select_snapshot) self.assertNotIn('(0 rows)', select_snapshot, "执行失败" + step) step = 'step6:使用linear_regression算子创建model;' \ 'expect:使用linear_regression算子创建model成功' self.logger.info(step) create_model = self.primary_sh.execut_db_sql( f'''create model {self.model} using linear_regression features 1,tax,bath,size target price from {self.snapshot1};''') self.logger.info(create_model) self.assertIn('MODEL CREATED. PROCESSED 1', create_model, "执行失败" + step) step = 'step7:查找系统表;expect:返回创建model的modelname' self.logger.info(step) select_model = self.primary_sh.execut_db_sql( f'''select modelname from gs_model_warehouse;''') self.logger.info(select_model) self.assertIn(self.model, select_model, "执行失败" + step) step = 'step8:预测表数据;expect:返回预测的表数据' self.logger.info(step) predict_data = self.primary_sh.execut_db_sql( f'''select predict by {self.model} (features 1,tax,bath,size) from {self.snapshot1};''') self.logger.info(predict_data) self.assertNotIn('(0 rows)', predict_data, "执行失败" + step) def tearDown(self): step = 'step9:清理环境 expect:清理环境成功' self.logger.info(step) clean_environment = self.primary_sh.execut_db_sql(f''' purge snapshot {self.snapshot1}; purge snapshot {self.snapshot2}; purge snapshot {self.snapshot}; drop table {self.table}; drop model {self.model}''') self.logger.info(clean_environment) self.assertIn(self.Con.TABLE_DROP_SUCCESS, clean_environment, "执行失败" + step) 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yearn/scripts/yearn.py	jonesnc/yearn-client	0	126773	import click from yearn.scripts.load import load as script_load @click.group() def cli(): pass @cli.command() @click.argument('path', default='.') @click.option('--scgi', default=None) @click.option('--mount', default=None) # TODO: support multiple mounts def load(path, scgi, mount): click.echo('Loading torrent files...') click.echo(f'Using SCGI: {scgi}') click.echo(f'Using path: {path}') click.echo(f'Using mount: {mount}') script_load(path, scgi, mount)	[ 1, 1053, 2828, 13, 13, 3166, 1629, 29876, 29889, 16713, 29889, 1359, 1053, 2254, 408, 2471, 29918, 1359, 13, 13, 13, 29992, 3808, 29889, 2972, 580, 13, 1753, 9335, 7295, 13, 1678, 1209, 13, 13, 13, 29992, 11303, 29889, 6519, 580, 13, 29992, 3808, 29889, 23516, 877, 2084, 742, 2322, 2433, 29889, 1495, 13, 29992, 3808, 29889, 3385, 877, 489, 1557, 3146, 742, 2322, 29922, 8516, 29897, 13, 29992, 3808, 29889, 3385, 877, 489, 16476, 742, 2322, 29922, 8516, 29897, 29871, 396, 14402, 29901, 2304, 2999, 5766, 29879, 13, 1753, 2254, 29898, 2084, 29892, 885, 3146, 29892, 5766, 1125, 13, 1678, 2828, 29889, 8057, 877, 23456, 4842, 7771, 2066, 856, 1495, 13, 1678, 2828, 29889, 8057, 29898, 29888, 29915, 15156, 317, 11135, 29902, 29901, 426, 1557, 3146, 29913, 1495, 13, 1678, 2828, 29889, 8057, 29898, 29888, 29915, 15156, 2224, 29901, 426, 2084, 29913, 1495, 13, 1678, 2828, 29889, 8057, 29898, 29888, 29915, 15156, 5766, 29901, 426, 16476, 29913, 1495, 13, 1678, 2471, 29918, 1359, 29898, 2084, 29892, 885, 3146, 29892, 5766, 29897, 13, 2 ]
bot coontrol/CollisionControl.py	aritrog/sarathi	3	55412	import BotDecidesPos import numpy as np class Collision_check: def __init__(self): self.m=0.0 self.n=0.0 def load_data(self,bot): tgx,tgy=bot.getTarget() mpx,mpy=bot.getPos() spd=bot.getSpeed() return spd,mpx,mpy,tgx,tgy def checkCollision(self,bot1,bot2): eg = Engine() sp1,x,y,x1,y1=self.load_data(bot1) sp2,a,b,a1,b1=self.load_data(bot2) p=eg.findDist(x,y,x1,y1) q=eg.findDist(a,b,a1,b1) #v1=[sp1(x-x1)/p,sp1(y-y1)/p] #v2 = [sp2 * (a - a1) / q, sp2 * (b - b1) / q] #Ax=C, which is the matrix from of the equation on the path of the vehicle s=[[x-x1,y-y1],[a-a1,b-b1]] t=[yx1-xy1,ba1-ab1] self.m,self.n=eg.eq_StraightLine(s,t) p1=eg.findDist(x,y,self.m,self.n) q1=eg.findDist(a,b,self.m,self.n) eta1=p1/sp1; eta2=q1/sp2; if np.absolute(eta1-eta2)<1 : return True else: return False def getCollisionIndex(self): return self.m,self.n def setCollisionIndex(self,a,b): self.m=a self.n=b	[ 1, 1053, 11273, 6185, 2247, 9135, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 30004, 13, 1990, 13435, 2459, 29918, 3198, 29901, 30004, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 30004, 13, 4706, 1583, 29889, 29885, 29922, 29900, 29889, 29900, 30004, 13, 4706, 1583, 29889, 29876, 29922, 29900, 29889, 29900, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 1678, 822, 2254, 29918, 1272, 29898, 1311, 29892, 7451, 1125, 30004, 13, 4706, 260, 29887, 29916, 29892, 29873, 1927, 29922, 7451, 29889, 657, 8667, 26471, 13, 4706, 286, 1756, 29892, 1526, 29891, 29922, 7451, 29889, 657, 9135, 26471, 13, 4706, 805, 29881, 29922, 7451, 29889, 657, 26539, 26471, 13, 4706, 736, 805, 29881, 29892, 1526, 29916, 29892, 1526, 29891, 29892, 29873, 29887, 29916, 29892, 29873, 1927, 30004, 13, 30004, 13, 1678, 822, 29871, 1423, 28377, 2459, 29898, 1311, 29892, 7451, 29896, 29892, 7451, 29906, 1125, 30004, 13, 4706, 8087, 353, 10863, 26471, 13, 4706, 805, 29896, 29892, 29916, 29892, 29891, 29892, 29916, 29896, 29892, 29891, 29896, 29922, 1311, 29889, 1359, 29918, 1272, 29898, 7451, 29896, 8443, 13, 4706, 805, 29906, 29892, 29874, 29892, 29890, 29892, 29874, 29896, 29892, 29890, 29896, 29922, 1311, 29889, 1359, 29918, 1272, 29898, 7451, 29906, 8443, 13, 4706, 282, 29922, 387, 29889, 2886, 13398, 29898, 29916, 29892, 29891, 29892, 29916, 29896, 29892, 29891, 29896, 8443, 13, 4706, 3855, 29922, 387, 29889, 2886, 13398, 29898, 29874, 29892, 29890, 29892, 29874, 29896, 29892, 29890, 29896, 8443, 13, 4706, 396, 29894, 29896, 11759, 1028, 29896, 16395, 29916, 29899, 29916, 29896, 6802, 29886, 29892, 1028, 29896, 16395, 29891, 29899, 29891, 29896, 6802, 29886, 29962, 30004, 13, 4706, 396, 29894, 29906, 353, 518, 1028, 29906, 334, 313, 29874, 448, 263, 29896, 29897, 847, 3855, 29892, 805, 29906, 334, 313, 29890, 448, 289, 29896, 29897, 847, 3855, 29962, 30004, 13, 4706, 396, 29909, 29916, 29922, 29907, 29892, 607, 338, 278, 4636, 515, 310, 278, 6306, 373, 278, 2224, 310, 278, 19716, 30004, 13, 4706, 269, 29922, 8999, 29916, 29899, 29916, 29896, 29892, 29891, 29899, 29891, 29896, 16272, 29874, 29899, 29874, 29896, 29892, 29890, 29899, 29890, 29896, 5262, 30004, 13, 4706, 260, 11759, 29891, 29930, 29916, 29896, 29899, 29916, 29930, 29891, 29896, 29892, 29890, 29930, 29874, 29896, 29899, 29874, 29930, 29890, 29896, 29962, 30004, 13, 4706, 1583, 29889, 29885, 29892, 1311, 29889, 29876, 29922, 387, 29889, 1837, 29918, 855, 336, 523, 3542, 29898, 29879, 29892, 29873, 8443, 13, 4706, 282, 29896, 29922, 387, 29889, 2886, 13398, 29898, 29916, 29892, 29891, 29892, 1311, 29889, 29885, 29892, 1311, 29889, 29876, 8443, 13, 4706, 3855, 29896, 29922, 387, 29889, 2886, 13398, 29898, 29874, 29892, 29890, 29892, 1311, 29889, 29885, 29892, 1311, 29889, 29876, 8443, 13, 4706, 634, 29874, 29896, 29922, 29886, 29896, 29914, 1028, 29896, 2104, 13, 4706, 634, 29874, 29906, 29922, 29939, 29896, 29914, 1028, 29906, 2104, 13, 4706, 565, 7442, 29889, 23552, 29898, 1187, 29896, 29899, 1187, 29906, 29897, 29966, 29896, 584, 30004, 13, 9651, 736, 5852, 30004, 13, 4706, 1683, 29901, 30004, 13, 9651, 736, 7700, 30004, 13, 30004, 13, 1678, 822, 679, 28377, 2459, 3220, 29898, 1311, 1125, 30004, 13, 4706, 736, 1583, 29889, 29885, 29892, 1311, 29889, 29876, 30004, 13, 30004, 13, 1678, 822, 731, 28377, 2459, 3220, 29898, 1311, 29892, 29874, 29892, 29890, 1125, 30004, 13, 4706, 1583, 29889, 29885, 29922, 29874, 30004, 13, 4706, 1583, 29889, 29876, 29922, 29890, 30004, 13, 2 ]
lib/gstreamer/vaapi/vpp.py	ChipsnMedia/vaapi-fits	0	162450	<filename>lib/gstreamer/vaapi/vpp.py ### ### Copyright (C) 2018-2021 Intel Corporation ### ### SPDX-License-Identifier: BSD-3-Clause ### import os import slash from ....lib.gstreamer.vppbase import BaseVppTest from ....lib.gstreamer.util import have_gst_element from ....lib.gstreamer.vaapi.util import map_best_hw_format, mapformat, mapformatu from ....lib.common import get_media, mapRange, mapRangeWithDefault @slash.requires(have_gst_element("vaapi")) @slash.requires(have_gst_element("vaapipostproc")) class VppTest(BaseVppTest): def before(self): super().before() os.environ["GST_VAAPI_DRM_DEVICE"] = get_media().render_device vars(self).update(gstvpp = "vaapipostproc") def map_best_hw_format(self, format, hwformats): return map_best_hw_format(format, hwformats) def map_format(self, format): return mapformat(format) def map_formatu(self, format): return mapformatu(format) def gen_vpp_opts(self): opts = "" procamp = dict( brightness = [ -1.0, 0.0, 1.0], contrast = [ 0.0, 1.0, 2.0], hue = [-180.0, 0.0, 180.0], saturation = [ 0.0, 1.0, 2.0], ) if self.vpp_op in procamp: self.mlevel = mapRangeWithDefault( self.level, [0.0, 50.0, 100.0], procamp[self.vpp_op] ) opts += " {vpp_op}={mlevel}" elif self.vpp_op in ["denoise"]: self.mlevel = mapRange(self.level, [0, 100], [0.0, 1.0]) opts += " denoise={mlevel}" elif self.vpp_op in ["sharpen"]: self.mlevel = mapRange(self.level, [0, 100], [-1.0, 1.0]) opts += " sharpen={mlevel}" elif self.vpp_op in ["deinterlace"]: opts += " deinterlace-mode=1 deinterlace-method={mmethod}" elif self.vpp_op in ["transpose"]: opts += " video-direction={direction}" elif self.vpp_op in ["crop"]: opts += " crop-left={left} crop-right={right} crop-top={top} crop-bottom={bottom}" return opts	[ 1, 529, 9507, 29958, 1982, 29914, 29887, 5461, 261, 29914, 1564, 2754, 29914, 29894, 407, 29889, 2272, 13, 2277, 29937, 13, 2277, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29896, 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src/start_horovod.py	jakob-ed/pytorch-dist-pos-tagger	0	84265	<reponame>jakob-ed/pytorch-dist-pos-tagger<gh_stars>0 #!/usr/bin/env python import os import random import torch from horovod_adasum.horovod_adasum import HorovodAdaSum MIN_FREQ = int(os.environ['SUSML_MIN_FREQ']) RAND_SEED = int(os.environ['SUSML_RAND_SEED']) NUM_EPOCHS = int(os.environ['SUSML_NUM_EPOCHS']) BATCH_SIZE = int(os.environ['SUSML_BATCH_SIZE']) LR = float(os.environ['SUSML_LR']) PARALLELISM_LEVEL = int(os.environ['SUSML_PARALLELISM_LEVEL']) RAY_PARAMETER_SERVER_STRATEGY = os.environ['RAY_PARAMETER_SERVER_STRATEGY'] random.seed(RAND_SEED) torch.manual_seed(RAND_SEED) torch.backends.cudnn.deterministic = True if __name__ == "__main__": HorovodAdaSum().run()	[ 1, 529, 276, 1112, 420, 29958, 24744, 711, 29899, 287, 29914, 2272, 7345, 305, 29899, 5721, 29899, 1066, 29899, 4039, 914, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 2897, 13, 5215, 4036, 13, 13, 5215, 4842, 305, 13, 13, 3166, 4029, 586, 397, 29918, 3922, 398, 29889, 2015, 586, 397, 29918, 3922, 398, 1053, 6912, 586, 397, 29909, 1388, 11139, 13, 13, 16173, 29918, 29943, 1525, 29984, 353, 938, 29898, 359, 29889, 21813, 1839, 29903, 3308, 1988, 29918, 16173, 29918, 29943, 1525, 29984, 11287, 13, 29934, 9468, 29918, 1660, 3352, 353, 938, 29898, 359, 29889, 21813, 1839, 29903, 3308, 1988, 29918, 29934, 9468, 29918, 1660, 3352, 11287, 13, 13967, 29918, 29923, 13152, 3210, 29903, 353, 938, 29898, 359, 29889, 21813, 1839, 29903, 3308, 1988, 29918, 13967, 29918, 29923, 13152, 3210, 29903, 11287, 13, 29933, 14789, 29918, 14226, 353, 938, 29898, 359, 29889, 21813, 1839, 29903, 3308, 1988, 29918, 29933, 14789, 29918, 14226, 11287, 13, 29519, 353, 5785, 29898, 359, 29889, 21813, 1839, 29903, 3308, 1988, 29918, 29519, 11287, 13, 16320, 1964, 1307, 29931, 3235, 29924, 29918, 1307, 29963, 6670, 353, 938, 29898, 359, 29889, 21813, 1839, 29903, 3308, 1988, 29918, 16320, 1964, 1307, 29931, 3235, 29924, 29918, 1307, 29963, 6670, 11287, 13, 22800, 29918, 16320, 25797, 4945, 29918, 18603, 29918, 10810, 3040, 29954, 29979, 353, 2897, 29889, 21813, 1839, 22800, 29918, 16320, 25797, 4945, 29918, 18603, 29918, 10810, 3040, 29954, 29979, 2033, 13, 13, 8172, 29889, 26776, 29898, 29934, 9468, 29918, 1660, 3352, 29897, 13, 7345, 305, 29889, 11288, 29918, 26776, 29898, 29934, 9468, 29918, 1660, 3352, 29897, 13, 7345, 305, 29889, 1627, 1975, 29889, 29883, 566, 15755, 29889, 4801, 837, 262, 4695, 353, 5852, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 6912, 586, 397, 29909, 1388, 11139, 2141, 3389, 580, 13, 2 ]
myproject/app/serializer.py	xiaoxiansheng19/data_analysis	0	1606430	#! -- coding:utf-8 -- from rest_framework import serializers from .models import Job,File from djcelery.models import TaskMeta class JobSerializer(serializers.ModelSerializer): tasks = serializers.SerializerMethodField() def get_tasks(self, row): # role_obj_list = row.taskmeta.to_dict() # ret = [] # # 获取角色的id和名字 # # 以字典的键值对方式显示 # for item in role_obj_list: # ret.append({"id": item.id, "result": item.result}) return role_obj_list class Meta: model = Job fields = ('id','task_id', 'name', 'runtime', 'taskmeta', 'file','create_date','tasks') # depth = 1 # class TaskMetaSerializer(serializers.ModelSerializer): # # job_set = JobSerializer(many=True) # class Meta: # model = TaskMeta # fields = ('id','task_id', 'status', 'result', 'date_done', 'traceback','hidden','meta') # depth =1	[ 1, 396, 29991, 448, 29930, 29899, 14137, 29901, 9420, 29899, 29947, 448, 29930, 29899, 13, 3166, 1791, 29918, 4468, 1053, 7797, 19427, 13, 3166, 869, 9794, 1053, 17163, 29892, 2283, 13, 3166, 270, 29926, 2242, 708, 29889, 9794, 1053, 9330, 19346, 13, 13, 13, 13, 1990, 17163, 17679, 29898, 15550, 19427, 29889, 3195, 17679, 1125, 13, 1678, 9595, 353, 7797, 19427, 29889, 17679, 4062, 3073, 580, 13, 13, 1678, 822, 679, 29918, 20673, 29898, 1311, 29892, 1948, 1125, 13, 4706, 396, 13, 4706, 6297, 29918, 5415, 29918, 1761, 353, 1948, 29889, 7662, 7299, 29889, 517, 29918, 8977, 580, 13, 4706, 396, 3240, 353, 5159, 13, 4706, 396, 396, 29871, 31024, 30683, 31432, 31085, 30210, 333, 30503, 30548, 30578, 13, 4706, 396, 396, 29871, 30651, 30578, 31259, 30210, 236, 151, 177, 30959, 30783, 30525, 30607, 31542, 30858, 13, 4706, 396, 363, 2944, 297, 6297, 29918, 5415, 29918, 1761, 29901, 13, 4706, 396, 268, 3240, 29889, 4397, 3319, 29908, 333, 1115, 2944, 29889, 333, 29892, 376, 2914, 1115, 2944, 29889, 2914, 1800, 13, 4706, 736, 6297, 29918, 5415, 29918, 1761, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 1904, 353, 17163, 13, 4706, 4235, 353, 6702, 333, 3788, 7662, 29918, 333, 742, 525, 978, 742, 525, 15634, 742, 525, 7662, 7299, 742, 525, 1445, 3788, 3258, 29918, 1256, 3788, 20673, 1495, 13, 4706, 396, 10809, 353, 29871, 29896, 13, 13, 29937, 770, 9330, 19346, 17679, 29898, 15550, 19427, 29889, 3195, 17679, 1125, 13, 29937, 268, 396, 4982, 29918, 842, 353, 17163, 17679, 29898, 13011, 29922, 5574, 29897, 13, 29937, 268, 770, 20553, 29901, 13, 29937, 308, 1904, 353, 9330, 19346, 13, 29937, 308, 4235, 353, 6702, 333, 3788, 7662, 29918, 333, 742, 525, 4882, 742, 525, 2914, 742, 525, 1256, 29918, 15091, 742, 525, 15003, 1627, 3788, 10892, 3788, 7299, 1495, 13, 29937, 308, 10809, 353, 29896, 2 ]
search/serialize/__init__.py	ID2797370/arxiv-search	35	53460	<filename>search/serialize/__init__.py """Provides serialization functions for API responses.""" __all__ = ["JSONSerializer", "as_json", "AtomXMLSerializer", "as_atom"] from search.serialize.json import JSONSerializer, as_json from search.serialize.atom import AtomXMLSerializer, as_atom	[ 1, 529, 9507, 29958, 4478, 29914, 643, 6646, 29914, 1649, 2344, 26914, 2272, 13, 15945, 29908, 1184, 29894, 2247, 7797, 2133, 3168, 363, 3450, 20890, 1213, 15945, 13, 1649, 497, 1649, 353, 6796, 7249, 17679, 613, 376, 294, 29918, 3126, 613, 376, 4178, 290, 9165, 17679, 613, 376, 294, 29918, 8678, 3108, 13, 13, 3166, 2740, 29889, 643, 6646, 29889, 3126, 1053, 4663, 17679, 29892, 408, 29918, 3126, 13, 3166, 2740, 29889, 643, 6646, 29889, 8678, 1053, 2180, 290, 9165, 17679, 29892, 408, 29918, 8678, 13, 2 ]
basicThings/frozenLakeTime.py	ASH1998/RLnew	0	112485	# import gym # env = gym.make('FrozenLake8x8-v0') # env.reset() # for _ in range(10): # env.render() # env.step(env.action_space.sample()) # take a random action # env.close() # from gym import envs # import gym # frozen = gym.make('FrozenLake8x8-v0') # numEpisodes = 10 # for episode in range(numEpisodes): # observation = frozen.reset() # for epochs in range(100): # # frozen.render() # # print(observation) # action = frozen.action_space.sample() # # print(action) # obs, reward, done, info = frozen.step(action) # if done: #this is needed to be changed # print("Episode finished after {} timesteps".format(epochs+1)) # break # frozen.close() import gym import numpy as np import matplotlib.pyplot as plt from tqdm import trange ngames = 1000 percentage = [] scores = [] env = gym.make('FrozenLake8x8-v0') for i in trange(ngames): done = False obs = env.reset() score = 0 while not done: action = env.action_space.sample() obs, reward, done, info = env.step(action=action) score+=reward scores.append(score) if i%10==0: average = np.mean(scores[-10:]) percentage.append(average) plt.plot(percentage) plt.show()	[ 1, 396, 1053, 330, 962, 13, 29937, 8829, 353, 330, 962, 29889, 5675, 877, 29943, 307, 2256, 29931, 1296, 29947, 29916, 29947, 29899, 29894, 29900, 1495, 13, 29937, 8829, 29889, 12071, 580, 13, 29937, 363, 903, 297, 3464, 29898, 29896, 29900, 1125, 13, 29937, 268, 8829, 29889, 9482, 580, 13, 29937, 268, 8829, 29889, 10568, 29898, 6272, 29889, 2467, 29918, 3493, 29889, 11249, 3101, 396, 2125, 263, 4036, 3158, 13, 29937, 8829, 29889, 5358, 580, 13, 13, 13, 13, 13, 29937, 515, 330, 962, 1053, 427, 4270, 13, 29937, 1053, 330, 962, 13, 13, 29937, 14671, 2256, 353, 330, 962, 29889, 5675, 877, 29943, 307, 2256, 29931, 1296, 29947, 29916, 29947, 29899, 29894, 29900, 1495, 13, 13, 29937, 954, 29923, 3334, 2631, 353, 29871, 29896, 29900, 13, 13, 29937, 363, 12720, 297, 3464, 29898, 1949, 29923, 3334, 2631, 1125, 13, 29937, 268, 15500, 353, 14671, 2256, 29889, 12071, 580, 13, 13, 29937, 268, 363, 21502, 12168, 297, 3464, 29898, 29896, 29900, 29900, 1125, 13, 29937, 308, 396, 14671, 2256, 29889, 9482, 580, 13, 29937, 308, 396, 1596, 29898, 26739, 362, 29897, 13, 29937, 308, 3158, 353, 14671, 2256, 29889, 2467, 29918, 3493, 29889, 11249, 580, 13, 29937, 308, 396, 1596, 29898, 2467, 29897, 13, 13, 29937, 308, 20881, 29892, 20751, 29892, 2309, 29892, 5235, 353, 14671, 2256, 29889, 10568, 29898, 2467, 29897, 13, 308, 13, 29937, 308, 565, 2309, 29901, 396, 1366, 338, 4312, 304, 367, 3939, 13, 29937, 632, 1596, 703, 29923, 12907, 7743, 1156, 6571, 5335, 4196, 567, 1642, 4830, 29898, 1022, 2878, 29879, 29974, 29896, 876, 13, 29937, 632, 2867, 13, 13, 29937, 14671, 2256, 29889, 5358, 580, 13, 308, 13, 5215, 330, 962, 13, 5215, 12655, 408, 7442, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 3166, 260, 29939, 18933, 1053, 534, 927, 13, 13, 865, 1280, 353, 29871, 29896, 29900, 29900, 29900, 13, 25376, 482, 353, 5159, 13, 1557, 2361, 353, 5159, 13, 13, 6272, 353, 330, 962, 29889, 5675, 877, 29943, 307, 2256, 29931, 1296, 29947, 29916, 29947, 29899, 29894, 29900, 1495, 13, 13, 1454, 474, 297, 534, 927, 29898, 865, 1280, 1125, 13, 1678, 2309, 353, 7700, 13, 1678, 20881, 353, 8829, 29889, 12071, 580, 13, 13, 1678, 8158, 353, 29871, 29900, 13, 13, 1678, 1550, 451, 2309, 29901, 13, 4706, 3158, 353, 8829, 29889, 2467, 29918, 3493, 29889, 11249, 580, 13, 4706, 20881, 29892, 20751, 29892, 2309, 29892, 5235, 353, 8829, 29889, 10568, 29898, 2467, 29922, 2467, 29897, 13, 4706, 8158, 23661, 276, 1328, 13, 1678, 19435, 29889, 4397, 29898, 13628, 29897, 13, 13, 1678, 565, 474, 29995, 29896, 29900, 1360, 29900, 29901, 13, 4706, 6588, 353, 7442, 29889, 12676, 29898, 1557, 2361, 14352, 29896, 29900, 29901, 2314, 13, 4706, 19649, 29889, 4397, 29898, 12483, 482, 29897, 13, 13, 572, 29873, 29889, 5317, 29898, 25376, 482, 29897, 13, 572, 29873, 29889, 4294, 580, 13, 2 ]
platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/sol/calculators/calc_radio.py	PascalGuenther/gecko_sdk	82	187755	<reponame>PascalGuenther/gecko_sdk from pyradioconfig.parts.ocelot.calculators.calc_radio import CALC_Radio_ocelot class Calc_Radio_Sol(CALC_Radio_ocelot): def calc_txofdm_reg(self, model): #Read in model vars softmodem_modulator_select = model.vars.softmodem_modulator_select.value if softmodem_modulator_select == model.vars.softmodem_modulator_select.var_enum.IQ_MOD: #This method enables OFDM TX txenbbreg = 1 txenmix = 1 else: txenbbreg = 0 txenmix = 0 #Write the registers self._reg_write(model.vars.RAC_TXOFDM_TXENBBREG, txenbbreg) self._reg_write(model.vars.RAC_TXOFDM_TXENMIX, txenmix) def calc_rx_fefiltoutputsel_reg(self, model): #This method calulates the FEFILTOUTPUTSEL field # Read in model vars demod_select = model.vars.demod_select.value # Calculate the field if demod_select == model.vars.demod_select.var_enum.SOFT_DEMOD: fefiltoutputsel = 1 else: fefiltoutputsel = 0 # Write the register self._reg_write(model.vars.RAC_RX_FEFILTOUTPUTSEL, fefiltoutputsel)	[ 1, 529, 276, 1112, 420, 29958, 29925, 294, 1052, 29954, 8122, 2276, 29914, 479, 27604, 29918, 15348, 13, 3166, 11451, 13399, 2917, 29889, 20895, 29889, 542, 295, 327, 29889, 15807, 4097, 29889, 28667, 29918, 13399, 1053, 315, 1964, 29907, 29918, 21818, 29918, 542, 295, 327, 13, 13, 1990, 3037, 29883, 29918, 21818, 29918, 13296, 29898, 29907, 1964, 29907, 29918, 21818, 29918, 542, 295, 327, 1125, 13, 13, 1678, 822, 22235, 29918, 7508, 974, 18933, 29918, 1727, 29898, 1311, 29892, 1904, 1125, 13, 13, 4706, 396, 6359, 297, 1904, 24987, 13, 4706, 4964, 8513, 29885, 29918, 1545, 9183, 29918, 2622, 353, 1904, 29889, 16908, 29889, 2695, 8513, 29885, 29918, 1545, 9183, 29918, 2622, 29889, 1767, 13, 13, 4706, 565, 4964, 8513, 29885, 29918, 1545, 9183, 29918, 2622, 1275, 1904, 29889, 16908, 29889, 2695, 8513, 29885, 29918, 1545, 9183, 29918, 2622, 29889, 1707, 29918, 18605, 29889, 29902, 29984, 29918, 6720, 29928, 29901, 13, 9651, 396, 4013, 1158, 28936, 8079, 23560, 323, 29990, 13, 9651, 25568, 264, 29890, 1030, 29887, 353, 29871, 29896, 13, 9651, 25568, 264, 28084, 353, 29871, 29896, 13, 4706, 1683, 29901, 13, 9651, 25568, 264, 29890, 1030, 29887, 353, 29871, 29900, 13, 9651, 25568, 264, 28084, 353, 29871, 29900, 13, 13, 4706, 396, 6113, 278, 28975, 13, 4706, 1583, 3032, 1727, 29918, 3539, 29898, 4299, 29889, 16908, 29889, 29934, 2477, 29918, 28627, 9800, 23560, 29918, 28627, 1430, 14388, 18166, 29892, 25568, 264, 29890, 1030, 29887, 29897, 13, 4706, 1583, 3032, 1727, 29918, 3539, 29898, 4299, 29889, 16908, 29889, 29934, 2477, 29918, 28627, 9800, 23560, 29918, 28627, 1430, 29924, 6415, 29892, 25568, 264, 28084, 29897, 13, 13, 1678, 822, 22235, 29918, 17697, 29918, 29888, 1389, 2782, 4905, 2838, 29918, 1727, 29898, 1311, 29892, 1904, 1125, 13, 4706, 396, 4013, 1158, 1208, 352, 1078, 278, 383, 29923, 3738, 29931, 4986, 2692, 12336, 1660, 29931, 1746, 13, 13, 4706, 396, 7523, 297, 1904, 24987, 13, 4706, 1261, 397, 29918, 2622, 353, 1904, 29889, 16908, 29889, 311, 1545, 29918, 2622, 29889, 1767, 13, 13, 4706, 396, 20535, 403, 278, 1746, 13, 4706, 565, 1261, 397, 29918, 2622, 1275, 1904, 29889, 16908, 29889, 311, 1545, 29918, 2622, 29889, 1707, 29918, 18605, 29889, 6156, 7818, 29918, 2287, 6720, 29928, 29901, 13, 9651, 1238, 1777, 29873, 4905, 2838, 353, 29871, 29896, 13, 4706, 1683, 29901, 13, 9651, 1238, 1777, 29873, 4905, 2838, 353, 29871, 29900, 13, 13, 4706, 396, 14350, 278, 6036, 13, 4706, 1583, 3032, 1727, 29918, 3539, 29898, 4299, 29889, 16908, 29889, 29934, 2477, 29918, 29934, 29990, 29918, 16359, 3738, 29931, 4986, 2692, 12336, 1660, 29931, 29892, 1238, 1777, 29873, 4905, 2838, 29897, 13, 2 ]
synergy/scheduler/abstract_state_machine.py	mushkevych/scheduler	15	1615931	<reponame>mushkevych/scheduler<filename>synergy/scheduler/abstract_state_machine.py __author__ = '<NAME>' from datetime import datetime from logging import INFO, WARNING, ERROR from synergy.db.model import job from synergy.db.model.job import Job from synergy.db.error import DuplicateKeyError from synergy.db.dao.unit_of_work_dao import UnitOfWorkDao from synergy.db.dao.job_dao import JobDao from synergy.db.model import unit_of_work from synergy.db.model.unit_of_work import UnitOfWork from synergy.system.mq_transmitter import MqTransmitter from synergy.conf import context from synergy.system.decorator import with_reconnect from synergy.system import time_helper from synergy.scheduler.tree_node import DependentOnSummary class AbstractStateMachine(object): """ Abstract state machine used to host common logic for the rest of registered State Machines """ def __init__(self, logger, timetable, name): self.name = name self.logger = logger self.mq_transmitter = MqTransmitter(self.logger) self.timetable = timetable self.uow_dao = UnitOfWorkDao(self.logger) self.job_dao = JobDao(self.logger) @property def run_on_active_timeperiod(self): """ :return: True if given State Machine allows execution on live timeperiod, as opposed to the finished one """ raise NotImplementedError(f'property run_on_active_timeperiod must be implemented by {self.__class__.__name__}') def _log_message(self, level:int, process_name:str, timeperiod:str, msg:str): """ method performs logging into log file and Timetable's tree node""" self.timetable.add_log_entry(process_name, timeperiod, msg) self.logger.log(level, msg) @with_reconnect def _insert_uow(self, process_name, timeperiod, start_timeperiod, end_timeperiod, start_id, end_id): """creates unit_of_work and inserts it into the DB :raise DuplicateKeyError: if unit_of_work with given parameters already exists """ uow = UnitOfWork() uow.process_name = process_name uow.timeperiod = timeperiod uow.start_id = str(start_id) uow.end_id = str(end_id) uow.start_timeperiod = start_timeperiod uow.end_timeperiod = end_timeperiod uow.created_at = datetime.utcnow() uow.submitted_at = datetime.utcnow() uow.source = context.process_context[process_name].source uow.sink = context.process_context[process_name].sink uow.state = unit_of_work.STATE_REQUESTED uow.unit_of_work_type = unit_of_work.TYPE_MANAGED uow.number_of_retries = 0 uow.arguments = context.process_context[process_name].arguments uow.db_id = self.uow_dao.insert(uow) msg = 'Created: UOW {0} for {1}@{2} over [{3}:{4}).'\ .format(uow.db_id, process_name, timeperiod, start_timeperiod, end_timeperiod) self._log_message(INFO, process_name, timeperiod, msg) return uow def _publish_uow(self, uow): self.mq_transmitter.publish_managed_uow(uow) msg = 'Published: UOW {0} for {1}@{2} over [{3}:{4}).'\ .format(uow.db_id, uow.process_name, uow.timeperiod, uow.start_timeperiod, uow.end_timeperiod) self._log_message(INFO, uow.process_name, uow.timeperiod, msg) def insert_and_publish_uow(self, job_record, start_id, end_id): """ method creates and publishes a unit_of_work. it also handles DuplicateKeyError and attempts recovery :return: tuple (uow, is_duplicate) :raise UserWarning: if the recovery from DuplicateKeyError was unsuccessful """ process_name = job_record.process_name timeperiod = job_record.timeperiod start_timeperiod = self.compute_start_timeperiod(job_record.process_name, job_record.timeperiod) end_timeperiod = self.compute_end_timeperiod(job_record.process_name, job_record.timeperiod) try: is_duplicate = False uow = self._insert_uow(process_name, timeperiod, start_timeperiod, end_timeperiod, start_id, end_id) except DuplicateKeyError as e: is_duplicate = True msg = 'Catching up with latest UOW {0}@{1} over [{2}:{3}), because of: {4}' \ .format(process_name, timeperiod, start_timeperiod, end_timeperiod, e) self._log_message(WARNING, process_name, timeperiod, msg) uow = self.uow_dao.recover_from_duplicatekeyerror(e) if not uow: msg = 'PERSISTENT TIER ERROR! Unable to locate UOW for {0}@{1} over [{2}:{3})' \ .format(process_name, timeperiod, start_timeperiod, end_timeperiod) self._log_message(WARNING, process_name, timeperiod, msg) raise UserWarning(msg) if uow.is_canceled: # this UOW was marked for re-processing. recycle it uow.created_at = datetime.utcnow() # reset created_at to bypass GC cancellation logic uow.submitted_at = datetime.utcnow() # reset submitted_at to allow 1 hour free of GC resubmitting del uow.started_at del uow.finished_at del uow.number_of_aggregated_documents del uow.number_of_processed_documents uow.state = unit_of_work.STATE_REQUESTED self.uow_dao.update(uow) # publish the created/recovered/recycled unit_of_work self._publish_uow(uow) return uow, is_duplicate def notify(self, uow): """ method is used by StateMachine's users to provide notifications about change in UOW if applicable, method will update job_record state and Timetable tree node state :assumptions: uow is in [STATE_NOOP, STATE_CANCELED, STATE_PROCESSED] """ pass def compute_start_timeperiod(self, process_name, timeperiod): """ computes lowest inclusive timeperiod boundary for job to process for process with time_grouping == 1, it returns given timeperiod with no change for process with time_grouping != 1, it computes first timeperiod, not processed by the previous job run For instance: with time_grouping = 3, QUALIFIER_HOURLY, and timeperiod = 2016042018, the start_timeperiod will be = 2016042016 (computed as 2016042018 - 3 + 1) """ time_grouping = context.process_context[process_name].time_grouping if time_grouping == 1: return timeperiod # step1: translate given timeperiod to the time grouped one process_hierarchy = self.timetable.get_tree(process_name).process_hierarchy timeperiod_dict = process_hierarchy[process_name].timeperiod_dict translated_timeperiod = timeperiod_dict._translate_timeperiod(timeperiod) # step 2: compute previous grouped period # NOTICE: simple `time_helper.increment_timeperiod(time_qualifier, timeperiod)` is insufficient # as it does not address edge cases, such as the last day of the month or the last hour of the day # For instance: with time_grouping=3, QUALIFIER_DAILY, and 2016123100 # the `increment_timeperiod` will yield 2016122800 instead of 2016123100 time_qualifier = context.process_context[process_name].time_qualifier for i in range(1, time_grouping + 1): prev_timeperiod = time_helper.increment_timeperiod(time_qualifier, translated_timeperiod, delta=-i) if prev_timeperiod == timeperiod_dict._translate_timeperiod(prev_timeperiod): # prev_timeperiod is currently at the last grouped timeperiod break # step 3: compute first exclusive timeperiod after the prev_timeperiod, # which becomes first inclusive timeperiod for this job run over_the_edge_timeperiod = time_helper.increment_timeperiod(time_qualifier, prev_timeperiod, delta=-1) if prev_timeperiod != timeperiod_dict._translate_timeperiod(over_the_edge_timeperiod): # over_the_edge_timeperiod fell into previous day or month or year # prev_timeperiod points to the first month, first day of the month or 00 hour start_timeperiod = prev_timeperiod else: start_timeperiod = self.compute_end_timeperiod(process_name, prev_timeperiod) return start_timeperiod def compute_end_timeperiod(self, process_name, timeperiod): """ computes first exclusive timeperiod for job to process """ time_qualifier = context.process_context[process_name].time_qualifier end_timeperiod = time_helper.increment_timeperiod(time_qualifier, timeperiod) return end_timeperiod def _is_noop_timeperiod(self, process_name, timeperiod): """ method verifies if the given timeperiod for given process is valid or falls in-between grouping checkpoints :param process_name: name of the process :param timeperiod: timeperiod to verify :return: False, if given process has no time_grouping set or it is equal to 1. False, if time_grouping is custom but the given timeperiod matches the grouped timeperiod. True, if the timeperiod falls in-between grouping cracks """ time_grouping = context.process_context[process_name].time_grouping if time_grouping == 1: return False process_hierarchy = self.timetable.get_tree(process_name).process_hierarchy timeperiod_dict = process_hierarchy[process_name].timeperiod_dict return timeperiod_dict._translate_timeperiod(timeperiod) != timeperiod def _process_noop_timeperiod(self, job_record): """ method is valid for processes having time_grouping != 1. should a job record fall in-between grouped time milestones, its state should be set to STATE_NOOP without any processing """ job_record.state = job.STATE_NOOP self.job_dao.update(job_record) time_grouping = context.process_context[job_record.process_name].time_grouping msg = 'Job {0}@{1} with time_grouping {2} was transferred to STATE_NOOP' \ .format(job_record.process_name, job_record.timeperiod, time_grouping) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) def _process_state_embryo(self, job_record): """ method that takes care of processing job records in STATE_EMBRYO state""" pass def _process_state_in_progress(self, job_record): """ method that takes care of processing job records in STATE_IN_PROGRESS state""" pass def _process_state_final_run(self, job_record): """method takes care of processing job records in STATE_FINAL_RUN state""" uow = self.uow_dao.get_one(job_record.related_unit_of_work) if uow.is_processed: self.update_job(job_record, uow, job.STATE_PROCESSED) elif uow.is_noop: self.update_job(job_record, uow, job.STATE_NOOP) elif uow.is_canceled: self.update_job(job_record, uow, job.STATE_SKIPPED) elif uow.is_invalid: msg = 'Job {0}: UOW for {1}@{2} is in {3}; ' \ 'relying on the Garbage Collector to either recycle or cancel the UOW.' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, uow.state) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) else: msg = 'Suppressed creating UOW for {0}@{1}; Job is in {2}; uow is in {3}' \ .format(job_record.process_name, job_record.timeperiod, job_record.state, uow.state) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) timetable_tree = self.timetable.get_tree(job_record.process_name) timetable_tree.build_tree() def _process_terminal_state(self, job_record): """ method logs a warning message notifying that the job is no longer govern by this state machine """ msg = 'Job {0} for {1}@{2} is in the terminal state {3}, ' \ 'and is no further govern by the State Machine {4}' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, job_record.state, self.name) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) def manage_job_with_blocking_children(self, job_record): """ method will trigger job processing only if: - all children are finished (STATE_PROCESSED, STATE_SKIPPED, STATE_NOOP) - all external dependencies are finalized (STATE_PROCESSED, STATE_SKIPPED, STATE_NOOP) """ is_job_finalizable = self.timetable.is_job_record_finalizable(job_record) if is_job_finalizable: self.manage_job(job_record) else: msg = 'Job {0}@{1} is blocked by unprocessed children/dependencies. Waiting another tick' \ .format(job_record.process_name, job_record.timeperiod) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) def manage_job_with_blocking_dependencies(self, job_record): """ method will trigger job processing only if: - all dependencies are in [STATE_PROCESSED, STATE_NOOP] NOTICE: method will transfer current job into STATE_SKIPPED if any dependency is in STATE_SKIPPED """ depon_summary = self.timetable.dependent_on_summary(job_record) assert isinstance(depon_summary, DependentOnSummary) if depon_summary.all_processed: self.manage_job(job_record) elif depon_summary.skipped_present: # As soon as among <dependent on> periods are in STATE_SKIPPED # there is very little sense in waiting for them to become STATE_PROCESSED # Skip this timeperiod itself job_record.state = job.STATE_SKIPPED self.job_dao.update(job_record) self.mq_transmitter.publish_job_status(job_record) depon_summary.log_skipped(WARNING) msg = 'Job {0}@{1} is blocked by STATE_SKIPPED dependencies. ' \ 'Transferred the job to STATE_SKIPPED'.format(job_record.process_name, job_record.timeperiod) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) else: depon_summary.log_unprocessed(INFO) msg = 'Job {0}@{1} is blocked by unprocessed dependencies. Waiting another tick' \ .format(job_record.process_name, job_record.timeperiod) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) def manage_job(self, job_record): """ method main duty - is to _avoid_ publishing another unit_of_work, if previous was not yet processed In case the State Machine sees that the unit_of_work already exist it could either: - update boundaries of the processing; republish - wait another tick; idle """ assert isinstance(job_record, Job) try: if self._is_noop_timeperiod(job_record.process_name, job_record.timeperiod): self._process_noop_timeperiod(job_record) return if job_record.is_embryo: self._process_state_embryo(job_record) elif job_record.is_in_progress: self._process_state_in_progress(job_record) elif job_record.is_final_run: self._process_state_final_run(job_record) elif job_record.is_skipped: self._process_terminal_state(job_record) elif job_record.is_processed: self._process_terminal_state(job_record) elif job_record.is_noop: self._process_terminal_state(job_record) else: msg = 'Unknown state {0} of the job {1}'.format(job_record.state, job_record.db_id) self._log_message(ERROR, job_record.process_name, job_record.timeperiod, msg) except LookupError as e: job_record.number_of_failures += 1 self.job_dao.update(job_record) self.timetable.skip_if_needed(job_record) msg = 'Increasing fail counter for Job {0}@{1}, because of: {2}' \ .format(job_record.process_name, job_record.timeperiod, e) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) finally: self.mq_transmitter.publish_job_status(job_record) def reprocess_job(self, job_record): """ method marks given job for reprocessing: - if no UOW was bind with the job record - it is transferred to STATE_EMBRYO only - if related UOW was not finished - we mark the UOW as STATE_CANCELED and pass the job record thru the _process_in_progress routine - otherwise job record is passed thru the _process_in_progress routine """ original_job_state = job_record.state job_record.number_of_failures = 0 if not job_record.related_unit_of_work: job_record.state = job.STATE_EMBRYO self.job_dao.update(job_record) else: uow = self.uow_dao.get_one(job_record.related_unit_of_work) if not uow.is_finished: uow.state = unit_of_work.STATE_CANCELED uow.submitted_at = datetime.utcnow() self.uow_dao.update(uow) self._process_state_in_progress(job_record) msg = 'Reprocessed Job {0} for {1}@{2}: state transfer {3} -> {4};' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, original_job_state, job_record.state) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) def skip_job(self, job_record): """ method transfers: - given job into STATE_SKIPPED if it is not not in finished state - UOW into STATE_CANCELED if it is not in finished state """ original_job_state = job_record.state if not job_record.is_finished: job_record.state = job.STATE_SKIPPED self.job_dao.update(job_record) if job_record.related_unit_of_work: uow = self.uow_dao.get_one(job_record.related_unit_of_work) if not uow.is_finished: uow.state = unit_of_work.STATE_CANCELED uow.submitted_at = datetime.utcnow() self.uow_dao.update(uow) msg = 'Skipped Job {0} for {1}@{2}: state transfer {3} -> {4};' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, original_job_state, job_record.state) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) def create_job(self, process_name, timeperiod): """ method creates a job record in STATE_EMBRYO for given process_name and timeperiod :returns: created job record of type <Job>""" job_record = Job() job_record.state = job.STATE_EMBRYO job_record.timeperiod = timeperiod job_record.process_name = process_name self.job_dao.update(job_record) self.logger.info('Created Job {0} for {1}@{2}' .format(job_record.db_id, job_record.process_name, job_record.timeperiod)) return job_record def update_job(self, job_record, uow, 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reconstruction_depth_to_3d/open3d/create_point_cloud.py	iwatake2222/opencv_sample	35	160485	import open3d as o3d image_rgb = o3d.io.read_image("image.png") image_depth = o3d.io.read_image("image_depth.png") width = int(image_rgb.get_max_bound()[0]) height = int(image_rgb.get_max_bound()[1]) camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(width=width, height=height, fx=500, fy=500,cx=width/2, cy=height/2) image_rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(image_rgb, image_depth, convert_rgb_to_intensity=False) pcd = o3d.geometry.PointCloud.create_from_rgbd_image(image_rgbd, camera_intrinsic) pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)) # Flip it, otherwise the pointcloud will be upside down pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) o3d.visualization.draw_geometries([pcd], width=640, height=480) o3d.io.write_point_cloud("output.ply", pcd)	[ 1, 1053, 1722, 29941, 29881, 408, 288, 29941, 29881, 13, 13, 3027, 29918, 23973, 353, 288, 29941, 29881, 29889, 601, 29889, 949, 29918, 3027, 703, 3027, 29889, 2732, 1159, 13, 3027, 29918, 19488, 353, 288, 29941, 29881, 29889, 601, 29889, 949, 29918, 3027, 703, 3027, 29918, 19488, 29889, 2732, 1159, 13, 2103, 353, 938, 29898, 3027, 29918, 23973, 29889, 657, 29918, 3317, 29918, 9917, 580, 29961, 29900, 2314, 13, 3545, 353, 938, 29898, 3027, 29918, 23973, 29889, 657, 29918, 3317, 29918, 9917, 580, 29961, 29896, 2314, 13, 26065, 29918, 262, 509, 28594, 353, 288, 29941, 29881, 29889, 26065, 29889, 29925, 21307, 280, 20717, 797, 509, 28594, 29898, 2103, 29922, 2103, 29892, 3171, 29922, 3545, 29892, 285, 29916, 29922, 29945, 29900, 29900, 29892, 285, 29891, 29922, 29945, 29900, 29900, 29892, 18904, 29922, 2103, 29914, 29906, 29892, 5094, 29922, 3545, 29914, 29906, 29897, 13, 3027, 29918, 11007, 6448, 353, 288, 29941, 29881, 29889, 19156, 29889, 28212, 29928, 2940, 29889, 3258, 29918, 3166, 29918, 2780, 29918, 392, 29918, 19488, 29898, 3027, 29918, 23973, 29892, 1967, 29918, 19488, 29892, 3588, 29918, 23973, 29918, 517, 29918, 524, 575, 537, 29922, 8824, 29897, 13, 29886, 2252, 353, 288, 29941, 29881, 29889, 19156, 29889, 5228, 20442, 29889, 3258, 29918, 3166, 29918, 11007, 6448, 29918, 3027, 29898, 3027, 29918, 11007, 6448, 29892, 10656, 29918, 262, 509, 28594, 29897, 13, 29886, 2252, 29889, 342, 6490, 29918, 12324, 1338, 29898, 4478, 29918, 3207, 29922, 29877, 29941, 29881, 29889, 19156, 29889, 29968, 29928, 9643, 7974, 4736, 26322, 19515, 29898, 13471, 29922, 29900, 29889, 29896, 29892, 4236, 29918, 15755, 29922, 29941, 29900, 876, 13, 29937, 383, 3466, 372, 29892, 6467, 278, 1298, 9274, 674, 367, 24081, 680, 1623, 13, 29886, 2252, 29889, 9067, 4197, 29961, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 1402, 518, 29900, 29892, 448, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 1402, 518, 29900, 29892, 29871, 29900, 29892, 448, 29896, 29892, 29871, 29900, 1402, 518, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29896, 24960, 13, 29877, 29941, 29881, 29889, 20119, 2133, 29889, 4012, 29918, 479, 3297, 2722, 4197, 29886, 2252, 1402, 2920, 29922, 29953, 29946, 29900, 29892, 3171, 29922, 29946, 29947, 29900, 29897, 13, 29877, 29941, 29881, 29889, 601, 29889, 3539, 29918, 3149, 29918, 9274, 703, 4905, 29889, 17632, 613, 282, 2252, 29897, 13, 2 ]
geolocator.py	Kugeleis/TeslaInventoryChecker	7	18850	<reponame>Kugeleis/TeslaInventoryChecker import http.client import json from types import SimpleNamespace def get_token(): conn = http.client.HTTPSConnection("www.tesla.com") payload = { "resource": "geocodesvc", "csrf_name": "", "csrf_value": "" } headers = { 'Content-Type': 'application/json' } conn.request("POST", "/inventory/api/v1/refresh_token", json.dumps(payload), headers) res = conn.getresponse() data = res.read() auth = json.loads(data, object_hook=lambda d: SimpleNamespace(d)) return auth.token def decode_zip(token, zip_code, country_code): conn = http.client.HTTPSConnection("www.tesla.com") payload = { "token": token, "postal_code": zip_code, "country_code": country_code, "csrf_name": "", "csrf_value": "" } headers = { 'Content-Type': 'application/json' } conn.request("POST", "/inventory/api/v1/address", json.dumps(payload), headers) res = conn.getresponse() data = res.read() geo_result = json.loads(data, object_hook=lambda d: SimpleNamespace(d)) # Example Data: # { # "city": "Montreal", # "stateProvince": "Quebec", # "postalCode": "H1K 3T2", # "countryCode": "CA", # "countryName": "Canada", # "longitude": -73.5614205, # "latitude": 45.60802700000001, # "county": "Montreal", # "stateCode": "QC" # } return geo_result.data	[ 1, 529, 276, 1112, 420, 29958, 29968, 5710, 280, 275, 29914, 29911, 267, 433, 797, 23886, 5596, 261, 13, 5215, 1732, 29889, 4645, 13, 5215, 4390, 13, 3166, 4072, 1053, 12545, 23335, 13, 13, 1753, 679, 29918, 6979, 7295, 13, 1678, 11009, 353, 1732, 29889, 4645, 29889, 10493, 29903, 5350, 703, 1636, 29889, 2167, 433, 29889, 510, 1159, 13, 1678, 20092, 353, 426, 13, 4706, 376, 10314, 1115, 376, 479, 542, 2631, 7071, 613, 13, 4706, 376, 2395, 9600, 29918, 978, 1115, 12633, 13, 4706, 376, 2395, 9600, 29918, 1767, 1115, 5124, 13, 1678, 500, 13, 1678, 9066, 353, 426, 13, 4706, 525, 3916, 29899, 1542, 2396, 525, 6214, 29914, 3126, 29915, 13, 1678, 500, 13, 1678, 11009, 29889, 3827, 703, 5438, 613, 5591, 262, 23886, 29914, 2754, 29914, 29894, 29896, 29914, 22379, 29918, 6979, 613, 4390, 29889, 29881, 17204, 29898, 23813, 511, 9066, 29897, 13, 1678, 620, 353, 11009, 29889, 657, 5327, 580, 13, 1678, 848, 353, 620, 29889, 949, 580, 13, 1678, 4817, 353, 4390, 29889, 18132, 29898, 1272, 29892, 1203, 29918, 20849, 29922, 2892, 270, 29901, 12545, 23335, 29898, 1068, 29881, 876, 13, 1678, 736, 4817, 29889, 6979, 13, 13, 1753, 21822, 29918, 7554, 29898, 6979, 29892, 14319, 29918, 401, 29892, 4234, 29918, 401, 1125, 13, 1678, 11009, 353, 1732, 29889, 4645, 29889, 10493, 29903, 5350, 703, 1636, 29889, 2167, 433, 29889, 510, 1159, 13, 1678, 20092, 353, 426, 13, 4706, 376, 6979, 1115, 5993, 29892, 13, 4706, 376, 2490, 284, 29918, 401, 1115, 14319, 29918, 401, 29892, 13, 4706, 376, 13509, 29918, 401, 1115, 4234, 29918, 401, 29892, 13, 4706, 376, 2395, 9600, 29918, 978, 1115, 12633, 13, 4706, 376, 2395, 9600, 29918, 1767, 1115, 5124, 13, 1678, 500, 13, 1678, 9066, 353, 426, 13, 4706, 525, 3916, 29899, 1542, 2396, 525, 6214, 29914, 3126, 29915, 13, 1678, 500, 13, 1678, 11009, 29889, 3827, 703, 5438, 613, 5591, 262, 23886, 29914, 2754, 29914, 29894, 29896, 29914, 7328, 613, 4390, 29889, 29881, 17204, 29898, 23813, 511, 9066, 29897, 13, 1678, 620, 353, 11009, 29889, 657, 5327, 580, 13, 1678, 848, 353, 620, 29889, 949, 580, 13, 1678, 1737, 29877, 29918, 2914, 353, 4390, 29889, 18132, 29898, 1272, 29892, 1203, 29918, 20849, 29922, 2892, 270, 29901, 12545, 23335, 29898, 1068, 29881, 876, 13, 13, 1678, 396, 8741, 3630, 29901, 13, 1678, 396, 426, 13, 1678, 396, 268, 376, 12690, 1115, 376, 24665, 6370, 613, 13, 1678, 396, 268, 376, 3859, 1184, 29894, 1239, 1115, 376, 8654, 19385, 613, 13, 1678, 396, 268, 376, 2490, 284, 3399, 1115, 376, 29950, 29896, 29968, 29871, 29941, 29911, 29906, 613, 13, 1678, 396, 268, 376, 13509, 3399, 1115, 376, 5454, 613, 13, 1678, 396, 268, 376, 13509, 1170, 1115, 376, 6028, 1114, 613, 13, 1678, 396, 268, 376, 5426, 4279, 1115, 448, 29955, 29941, 29889, 29945, 29953, 29896, 29946, 29906, 29900, 29945, 29892, 13, 1678, 396, 268, 376, 5066, 4279, 1115, 29871, 29946, 29945, 29889, 29953, 29900, 29947, 29900, 29906, 29955, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29896, 29892, 13, 1678, 396, 268, 376, 2798, 29891, 1115, 376, 24665, 6370, 613, 13, 1678, 396, 268, 376, 3859, 3399, 1115, 376, 29984, 29907, 29908, 13, 1678, 396, 500, 13, 13, 1678, 736, 1737, 29877, 29918, 2914, 29889, 1272, 2 ]
tests/test_create_account.py	moctarjallo/ebank	0	152881	<filename>tests/test_create_account.py import unittest import pythonapi as api from banking import CreateAccount class TestCreateAccount(unittest.TestCase): def setUp(self): self.create_account = CreateAccount() def test_normal(self): request = api.Request({ 'firstname': 'moctar', 'lastname': 'diallo', 'address': 'medina', 'balance': 400 }) account = self.create_account.execute(request.data) response = api.Response(account) self.assertLess(response.data['code'], 10000) self.assertGreaterEqual(response.data['code'], 1) del response.data['code'] self.assertEqual(response.data, { 'client':{ 'firstname': 'moctar', 'lastname': 'diallo', 'address': 'medina', }, 'balance': 400, }) if __name__ == '__main__': unittest.main()	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 3258, 29918, 10149, 29889, 2272, 13, 5215, 443, 27958, 13, 13, 5215, 3017, 2754, 408, 7882, 13, 13, 3166, 9124, 292, 1053, 6204, 10601, 13, 13, 1990, 4321, 4391, 10601, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1583, 29889, 3258, 29918, 10149, 353, 6204, 10601, 580, 13, 13, 1678, 822, 1243, 29918, 8945, 29898, 1311, 1125, 13, 4706, 2009, 353, 7882, 29889, 3089, 3319, 13, 9651, 525, 4102, 978, 2396, 525, 4346, 312, 279, 742, 13, 9651, 525, 4230, 978, 2396, 525, 29881, 616, 417, 742, 13, 9651, 525, 7328, 2396, 525, 2168, 1099, 742, 13, 9651, 525, 5521, 749, 2396, 29871, 29946, 29900, 29900, 13, 4706, 5615, 13, 13, 4706, 3633, 353, 1583, 29889, 3258, 29918, 10149, 29889, 7978, 29898, 3827, 29889, 1272, 29897, 13, 4706, 2933, 353, 7882, 29889, 5103, 29898, 10149, 29897, 13, 13, 4706, 1583, 29889, 9294, 29931, 404, 29898, 5327, 29889, 1272, 1839, 401, 7464, 29871, 29896, 29900, 29900, 29900, 29900, 29897, 13, 4706, 1583, 29889, 9294, 25120, 1008, 9843, 29898, 5327, 29889, 1272, 1839, 401, 7464, 29871, 29896, 29897, 13, 13, 4706, 628, 2933, 29889, 1272, 1839, 401, 2033, 13, 4706, 1583, 29889, 9294, 9843, 29898, 5327, 29889, 1272, 29892, 426, 13, 9651, 525, 4645, 2396, 29912, 13, 18884, 525, 4102, 978, 2396, 525, 4346, 312, 279, 742, 13, 18884, 525, 4230, 978, 2396, 525, 29881, 616, 417, 742, 13, 18884, 525, 7328, 2396, 525, 2168, 1099, 742, 13, 9651, 2981, 13, 9651, 525, 5521, 749, 2396, 29871, 29946, 29900, 29900, 29892, 13, 4706, 5615, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 2 ]
future_annotations.py	asottile/future-annotations	21	114123	import argparse import ast import codecs import encodings import io import sys import tokenize import warnings from typing import Match from typing import Optional from typing import Sequence from typing import Set from typing import Tuple import tokenize_rt def _ast_parse(contents_text: str) -> ast.Module: # intentionally ignore warnings, we might be fixing warning-ridden syntax with warnings.catch_warnings(): warnings.simplefilter('ignore') return ast.parse(contents_text.encode()) def _ast_to_offset(node: ast.expr) -> tokenize_rt.Offset: return tokenize_rt.Offset(node.lineno, node.col_offset) class Visitor(ast.NodeVisitor): def __init__(self) -> None: self.offsets: Set[tokenize_rt.Offset] = set() def visit_AnnAssign(self, node: ast.AnnAssign) -> None: self.offsets.add(_ast_to_offset(node.annotation)) self.generic_visit(node) def visit_FunctionDef(self, node: ast.FunctionDef) -> None: args = [] if hasattr(node.args, 'posonlyargs'): # pragma: no cover (py38+) args.extend(node.args.posonlyargs) args.extend(node.args.args) if node.args.vararg is not None: args.append(node.args.vararg) args.extend(node.args.kwonlyargs) if node.args.kwarg is not None: args.append(node.args.kwarg) for arg in args: if arg.annotation is not None: self.offsets.add(_ast_to_offset(arg.annotation)) if node.returns is not None: self.offsets.add(_ast_to_offset(node.returns)) self.generic_visit(node) utf_8 = encodings.search_function('utf8') def _new_coding_cookie(match: Match[str]) -> str: s = match[0] i = 0 while s[i].isspace(): i += 1 ret = f'{s[:i]}# {"" (len(s) - 2 - i)}' assert len(ret) == len(s), (len(ret), len(s)) return ret def decode(b: bytes, errors: str = 'strict') -> Tuple[str, int]: u, length = utf_8.decode(b, errors) # replace encoding cookie so there isn't a recursion problem lines = u.splitlines(True) for idx in (0, 1): if idx >= len(lines): break lines[idx] = tokenize.cookie_re.sub(_new_coding_cookie, lines[idx]) u = ''.join(lines) visitor = Visitor() visitor.visit(_ast_parse(u)) tokens = tokenize_rt.src_to_tokens(u) for i, token in tokenize_rt.reversed_enumerate(tokens): if token.offset in visitor.offsets: # look forward for a `:`, `,`, `=`, ')' depth = 0 j = i + 1 while depth or tokens[j].src not in {':', ',', '=', ')', '\n'}: if tokens[j].src in {'(', '{', '['}: depth += 1 elif tokens[j].src in {')', '}', ']'}: depth -= 1 j += 1 j -= 1 # look backward to delete whitespace / comments / etc. while tokens[j].name in tokenize_rt.NON_CODING_TOKENS: j -= 1 quoted = repr(tokenize_rt.tokens_to_src(tokens[i:j + 1])) tokens[i:j + 1] = [tokenize_rt.Token('STRING', quoted)] return tokenize_rt.tokens_to_src(tokens), length class IncrementalDecoder(codecs.BufferedIncrementalDecoder): def _buffer_decode(self, input, errors, final): # pragma: no cover if final: return decode(input, errors) else: return '', 0 class StreamReader(utf_8.streamreader): """decode is deferred to support better error messages""" _stream = None _decoded = False @property def stream(self): if not self._decoded: text, _ = decode(self._stream.read()) self._stream = io.BytesIO(text.encode('UTF-8')) self._decoded = True return self._stream @stream.setter def stream(self, stream): self._stream = stream self._decoded = False # codec api codec_map = { name: codecs.CodecInfo( name=name, encode=utf_8.encode, decode=decode, incrementalencoder=utf_8.incrementalencoder, incrementaldecoder=IncrementalDecoder, streamreader=StreamReader, streamwriter=utf_8.streamwriter, ) for name in ('future-annotations', 'future_annotations') } def register() -> None: # pragma: no cover codecs.register(codec_map.get) def main(argv: Optional[Sequence[str]] = None) -> int: parser = argparse.ArgumentParser(description='Prints transformed source.') parser.add_argument('filename') args = parser.parse_args(argv) with open(args.filename, 'rb') as f: text, _ = decode(f.read()) getattr(sys.stdout, 'buffer', 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Python Files/preprocessing.py	errohankumar/thesis	0	45163	import os import pandas as pd from datetime import datetime from preprocessing_service import PreProcessingService class PreProcessing(PreProcessingService): """ A class used to automatically scrape CSV files from ENTSOE Transparecny Platform ... Attributes ---------- _preProcessing : str A PreProcessingService instance for handling CSV files _preProcessing2: str A PreProcessingService instance for handling CSV files Methods ------- find_time_column(df) A function to find the timestamp column for a given CSV files start_preprocess() Initializes pre-processing """ def __init__(self): super().__init__() self._preProcessing = PreProcessingService() self._preProcessing2 = PreProcessingService() @staticmethod def find_time_column(df): """Finds the timestamp column for a given dataset. Parameters ---------- df : DataFrame The dataset containing historical-observations Raises ------ ValueError If no timestamp column is found. """ for column in df.get_columns(): try: found = datetime.strptime(df.df[column][0][:10], "%d.%m.%Y") return column except: continue raise ValueError("Timestamp column not found!") @staticmethod def last_date_for_column(df, column_name): """ Return last date of a specific column """ return df[column_name].dropna().index[-1:][0] def start_preprocess(self): """Initiates a sequence of pre-processing tasks """ # Get all the CSV files in the directory file_directory = os.getcwd() + "/download/" file_names = os.listdir(file_directory) actual_columns = ['Solar - Actual Aggregated [MW]', 'Wind Offshore - Actual Aggregated [MW]', 'Wind Onshore - Actual Aggregated [MW]', 'Actual Total Load [MW] - BZN\|DE-LU'] forecast_columns = ['Generation - Solar [MW] Day Ahead/ BZN\|DE-LU', 'Generation - Wind Offshore [MW] Day Ahead/ BZN\|DE-LU', 'Generation - Wind Onshore [MW] Day Ahead/ BZN\|DE-LU', 'Day-ahead Total Load Forecast [MW] - BZN\|DE-LU'] filter_columns = ['Day-ahead Price [EUR/MWh]', 'Generation - Solar [MW] Day Ahead/ BZN\|DE-LU', 'Generation - Wind Offshore [MW] Day Ahead/ BZN\|DE-LU', 'Generation - Wind Onshore [MW] Day Ahead/ BZN\|DE-LU', 'Solar - Actual Aggregated [MW]', 'Wind Offshore - Actual Aggregated [MW]', 'Wind Onshore - Actual Aggregated [MW]', 'Day-ahead Total Load Forecast [MW] - BZN\|DE-LU', 'Actual Total Load [MW] - BZN\|DE-LU'] for i in range(0, len(file_names)): # First CSV is used as the main file to combine the rest of the files if i == 0: self._preProcessing.open_csv(file_directory + file_names[i]) # Find time column and set it as index time_column = self.find_time_column(self._preProcessing) self._preProcessing.reformat_time(colname=time_column) self._preProcessing.rename_colname(time_column, "cet_timestamp") self._preProcessing.set_index(colname="cet_timestamp") # Convert arguments (non numeric values) to invalid NAN for j in self._preProcessing.get_columns(): self._preProcessing.df[j] = pd.to_numeric(self._preProcessing.df[j], errors='coerce') # Drop first duplicate self._preProcessing.drop_duplicates(keep="first", inplace=True) # Resample the quarterly values in the 'Total Load' and 'Actual Generation' to hourly if ("Actual" or "Load" or "Forecasts") in file_names[i]: self._preProcessing = self._preProcessing.resample_rows() else: self._preProcessing2.open_csv(file_directory + file_names[i]) # Find time column and set it as index time_column = self.find_time_column(self._preProcessing2) self._preProcessing2.reformat_time(colname=time_column) self._preProcessing2.rename_colname(time_column, "cet_timestamp") self._preProcessing2.set_index(colname="cet_timestamp") # Drop all the columns that are not numeric for column_index in self._preProcessing2.get_columns(): try: float(self._preProcessing2.df[column_index][0]) except: self._preProcessing2.df.drop(columns=column_index, inplace=True) continue # Convert the non-numeric items to Nans # If ‘coerce’, then invalid parsing will be set as NaN. for column_name in self._preProcessing2.get_columns(): self._preProcessing2.df[column_name] = pd.to_numeric(self._preProcessing2.df[column_name], errors='coerce') # To resample quarter hour to hour if present self._preProcessing2 = self._preProcessing2.resample_rows() # concatenate all the columns in one single dataset print(self._preProcessing2.df) self._preProcessing.df = pd.concat([self._preProcessing.df, self._preProcessing2.df], axis=1, sort=False) # Filter and drop all rows with four NAN values (to remove NAN values beyond latest time of available # day-ahead price) self._preProcessing.df = self._preProcessing.df[filter_columns].dropna(thresh=4, axis=0) print(self._preProcessing.df.columns) # Get three month of past observations if len(self._preProcessing.df) >= 2160: self._preProcessing.df = self._preProcessing.df[-2160:] # the missing values in the Actuals can be replaced by the generation forecast # the last date of the actuals column just before the missing values can be replaced with the values from the # last date of the forecast columns for column_name, forecast_column_name in zip(actual_columns, forecast_columns): last_date = self.last_date_for_column(self._preProcessing.df, column_name) self._preProcessing.df[column_name][last_date:] = self._preProcessing.df[forecast_column_name][ last_date:].values # Generate and add 'WeekDays' feature week_day_col = self._preProcessing.df.index.weekday self._preProcessing.df["WeekDays"] = week_day_col # Imputation through a interpolation method called 'cubic' to fulfill forecasting method criteria self._preProcessing = self._preProcessing.interpolate_ts() # Remove forecast columns, since they are not needed anymore self._preProcessing.remove_columns(cols = ['Generation - Solar [MW] Day Ahead/ BZN\|DE-LU', 'Generation - Wind Offshore [MW] Day Ahead/ BZN\|DE-LU', 'Generation - Wind Onshore [MW] Day Ahead/ BZN\|DE-LU', 'Day-ahead Total Load Forecast [MW] - BZN\|DE-LU']) self._preProcessing.df.to_csv(os.getcwd() + 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ai2business/kpi_collector/test/test_finance_collector.py	Anselmoo/ai2business	0	147504	"""Test-Enviroment for finance_collector.py""" import pandas as pd from ai2business.kpi_collector import finance_collector as fnc ticker = fnc.FinanceCollector() builder = fnc.DesignerFinanceCollector(["SPY", "AAPL", "MSFT"]) ticker.builder = builder def test_find_chart_history(): ticker.find_chart_histogram() assert type(builder.return_dataframe) == type(pd.DataFrame()) def test_find_isin_code(): ticker.find_isin_code() assert type(builder.return_dict) == type(dict()) def test_find_major_holders_dict(): ticker.find_major_holders() assert type(builder.return_dict) == type(dict()) def test_find_institutional_holders(): ticker.find_institutional_holders() assert type(builder.return_dict) == type(dict()) def test_find_mutualfund_holders(): ticker.find_mutualfund_holders() assert type(builder.return_dict) == type(dict()) def test_find_dividends(): ticker.find_dividends() assert type(builder.return_dict) == type(dict()) def test_find_splits(): ticker.find_splits() assert type(builder.return_dict) == type(dict()) def test_find_actions(): ticker.find_actions() assert type(builder.return_dict) == type(dict()) def test_find_info(): ticker.find_info() assert type(builder.return_dict) == type(dict()) def test_find_calendar(): ticker.find_calendar() assert type(builder.return_dict) == type(dict()) def test_find_recommendation(): ticker.find_recommendations() assert type(builder.return_dict) == type(dict()) def test_find_earnings(): ticker.find_earnings() assert type(builder.return_dict) == type(dict()) def test_find_quarterly_earnings(): ticker.find_quarterly_earnings() assert type(builder.return_dict) == type(dict()) def test_find_financials(): ticker.find_financials() assert type(builder.return_dict) == type(dict()) def test_find_quarterly_financials(): ticker.find_quarterly_financials() assert type(builder.return_dict) == type(dict()) def test_find_balancesheet(): 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venv/Lib/site-packages/pandas/core/array_algos/transforms.py	arnoyu-hub/COMP0016miemie	0	4945	""" transforms.py is for shape-preserving functions. """ import numpy as np def shift(values: np.ndarray, periods: int, axis: int, fill_value) -> np.ndarray: new_values = values if periods == 0 or values.size == 0: return new_values.copy() # make sure array sent to np.roll is c_contiguous f_ordered = values.flags.f_contiguous if f_ordered: new_values = new_values.T axis = new_values.ndim - axis - 1 if new_values.size: new_values = np.roll( new_values, np.intp(periods), axis=axis, ) axis_indexer = [slice(None)] * values.ndim if periods > 0: axis_indexer[axis] = slice(None, periods) else: axis_indexer[axis] = slice(periods, None) new_values[tuple(axis_indexer)] = fill_value # restore original order if f_ordered: new_values = new_values.T return new_values	[ 1, 9995, 30004, 13, 9067, 29879, 29889, 2272, 338, 363, 8267, 29899, 4569, 29530, 3168, 22993, 13, 15945, 19451, 13, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 30004, 13, 30004, 13, 1753, 9500, 29898, 5975, 29901, 7442, 29889, 299, 2378, 29892, 23704, 29901, 938, 29892, 9685, 29901, 938, 29892, 5445, 29918, 1767, 29897, 1599, 7442, 29889, 299, 2378, 29901, 30004, 13, 1678, 716, 29918, 5975, 353, 1819, 30004, 13, 30004, 13, 1678, 565, 23704, 1275, 29871, 29900, 470, 1819, 29889, 2311, 1275, 29871, 29900, 29901, 30004, 13, 4706, 736, 716, 29918, 5975, 29889, 8552, 26471, 13, 30004, 13, 1678, 396, 1207, 1854, 1409, 2665, 304, 7442, 29889, 1245, 338, 274, 29918, 1285, 5526, 681, 30004, 13, 1678, 285, 29918, 21693, 353, 1819, 29889, 15764, 29889, 29888, 29918, 1285, 5526, 681, 30004, 13, 1678, 565, 285, 29918, 21693, 29901, 30004, 13, 4706, 716, 29918, 5975, 353, 716, 29918, 5975, 29889, 29911, 30004, 13, 4706, 9685, 353, 716, 29918, 5975, 29889, 299, 326, 448, 9685, 448, 29871, 29896, 30004, 13, 30004, 13, 1678, 565, 716, 29918, 5975, 29889, 2311, 29901, 30004, 13, 4706, 716, 29918, 5975, 353, 7442, 29889, 1245, 29898, 30004, 13, 9651, 716, 29918, 5975, 11167, 13, 9651, 7442, 29889, 524, 29886, 29898, 19145, 29879, 511, 30004, 13, 9651, 9685, 29922, 8990, 11167, 13, 4706, 1723, 30004, 13, 30004, 13, 1678, 9685, 29918, 2248, 261, 353, 518, 18337, 29898, 8516, 4638, 334, 1819, 29889, 299, 326, 30004, 13, 1678, 565, 23704, 1405, 29871, 29900, 29901, 30004, 13, 4706, 9685, 29918, 2248, 261, 29961, 8990, 29962, 353, 22780, 29898, 8516, 29892, 23704, 8443, 13, 1678, 1683, 29901, 30004, 13, 4706, 9685, 29918, 2248, 261, 29961, 8990, 29962, 353, 22780, 29898, 19145, 29879, 29892, 6213, 8443, 13, 1678, 716, 29918, 5975, 29961, 23583, 29898, 8990, 29918, 2248, 261, 4638, 353, 5445, 29918, 1767, 30004, 13, 30004, 13, 1678, 396, 17749, 2441, 1797, 30004, 13, 1678, 565, 285, 29918, 21693, 29901, 30004, 13, 4706, 716, 29918, 5975, 353, 716, 29918, 5975, 29889, 29911, 30004, 13, 30004, 13, 1678, 736, 716, 29918, 5975, 30004, 13, 2 ]
fixture/soap.py	AlekseyVR/Python_mantis	0	44631	from suds.client import Client from suds import WebFault from model.project import Project class SoapHelper: def __init__(self, app): self.app = app def can_login(self, username, password): client = Client(self.app.base_url + "api/soap/mantisconnect.php?wsdl") try: client.service.mc_login(username, password) return True except WebFault: return False def get_project_list_user(self, username, password): self.can_login(username, password) client = Client(self.app.base_url + "api/soap/mantisconnect.php?wsdl") try: response = client.service.mc_projects_get_user_accessible(username, password) project_list = [] for element in response: project = Project(id=element.id, name_project=element.name, description_project=element.description) project_list.append(project) return project_list except WebFault: return False	[ 1, 515, 5053, 29879, 29889, 4645, 1053, 12477, 13, 3166, 5053, 29879, 1053, 2563, 29943, 1292, 13, 3166, 1904, 29889, 4836, 1053, 8010, 13, 13, 1990, 1105, 481, 10739, 29901, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 623, 1125, 13, 4706, 1583, 29889, 932, 353, 623, 13, 13, 1678, 822, 508, 29918, 7507, 29898, 1311, 29892, 8952, 29892, 4800, 1125, 13, 4706, 3132, 353, 12477, 29898, 1311, 29889, 932, 29889, 3188, 29918, 2271, 718, 376, 2754, 29914, 21993, 29914, 29885, 424, 275, 6915, 29889, 1961, 29973, 29893, 29222, 1159, 13, 4706, 1018, 29901, 13, 9651, 3132, 29889, 5509, 29889, 14047, 29918, 7507, 29898, 6786, 29892, 4800, 29897, 13, 9651, 736, 5852, 13, 4706, 5174, 2563, 29943, 1292, 29901, 13, 9651, 736, 7700, 13, 13, 1678, 822, 679, 29918, 4836, 29918, 1761, 29918, 1792, 29898, 1311, 29892, 8952, 29892, 4800, 1125, 13, 4706, 1583, 29889, 3068, 29918, 7507, 29898, 6786, 29892, 4800, 29897, 13, 4706, 3132, 353, 12477, 29898, 1311, 29889, 932, 29889, 3188, 29918, 2271, 718, 376, 2754, 29914, 21993, 29914, 29885, 424, 275, 6915, 29889, 1961, 29973, 29893, 29222, 1159, 13, 13, 4706, 1018, 29901, 13, 9651, 2933, 353, 3132, 29889, 5509, 29889, 14047, 29918, 16418, 29918, 657, 29918, 1792, 29918, 5943, 1821, 29898, 6786, 29892, 4800, 29897, 13, 9651, 2060, 29918, 1761, 353, 5159, 13, 9651, 363, 1543, 297, 2933, 29901, 13, 18884, 2060, 353, 8010, 29898, 333, 29922, 5029, 29889, 333, 29892, 1024, 29918, 4836, 29922, 5029, 29889, 978, 29892, 6139, 29918, 4836, 29922, 5029, 29889, 8216, 29897, 13, 18884, 2060, 29918, 1761, 29889, 4397, 29898, 4836, 29897, 13, 9651, 736, 2060, 29918, 1761, 13, 4706, 5174, 2563, 29943, 1292, 29901, 13, 9651, 736, 7700, 13, 13, 2 ]
my_weather_api/admin.py	swingthrough/weather-api-task-django	0	155557	from django.contrib import admin from .models import WeatherForecastDay # Register your models here. admin.site.register(WeatherForecastDay)	[ 1, 515, 9557, 29889, 21570, 1053, 4113, 13, 3166, 869, 9794, 1053, 1334, 1624, 29943, 487, 4384, 12742, 13, 13, 29937, 12577, 596, 4733, 1244, 29889, 13, 6406, 29889, 2746, 29889, 9573, 29898, 4806, 1624, 29943, 487, 4384, 12742, 29897, 2 ]
d_dataprocess.py	adamyalei/Property-Graphs-Lab-Assignment	0	164617	import re regex_str = [ r'<[^>]+>', # HTML tags r'(?:@[\w_]+)', # @-mentions r"(?:\#+[\w_]+[\w\'_\-][\w_]+)", # hash-tags r'http[s]?://(?:[a-z]\|[0-9]\|[$-_@.&+]\|[!\(\),]\|(?:%[0-9a-f][0-9a-f]))+', # URLs r'(?:(?:\d+,?)+(?:\.?\d+)?)', # numbers r"(?:[a-z][a-z'\-_]+[a-z])", # words with - and ' r'(?:[\w_]+)', # other words r'(?:\S)' # anything else ] tokens_re = re.compile(r'('+'\|'.join(regex_str)+')', re.VERBOSE \| re.IGNORECASE) def tokenize(s): return tokens_re.findall(s) def preprocess(s, lowercase=False): tokens = tokenize(s) if lowercase: tokens = [token.lower() for token in tokens] return tokens def extract_keyword_from_title(title): keywords = [] title = str(title) for token in preprocess(title): keywords.append(token) ALL_KEYWORDS.extend(keywords) return keywords ALL_KEYWORDS = [] articles = pd.read_csv('dblp_dump/output_article.csv', delimiter=';', usecols=['key','author','title','year','ee', 'cite', 'mdate','pages','journal','volume'], dtype=str) # Extract Keyword articles['keyword'] = articles['title'].apply(extract_keyword_from_title).str.join('\|') # Store keyword ALL_KEYWORDS = pd.DataFrame(ALL_KEYWORDS, columns=["keyword"]).drop_duplicates() articles.to_csv('out/papers.csv', index=None, header=True) ALL_KEYWORDS.to_csv('out/keywords.csv', index=None, header=True)	[ 1, 1053, 337, 13, 13, 13087, 29918, 710, 353, 518, 13, 1678, 364, 29915, 29966, 22896, 29958, 10062, 29958, 742, 396, 4544, 8282, 13, 1678, 364, 29915, 10780, 26984, 7110, 29893, 29918, 10062, 29897, 742, 396, 732, 29899, 358, 1080, 13, 1678, 364, 29908, 10780, 3583, 29937, 29974, 7110, 29893, 29918, 10062, 7110, 29893, 20333, 3187, 29899, 14178, 7110, 29893, 29918, 10062, 19123, 396, 6608, 29899, 11338, 13, 1678, 364, 29915, 1124, 29961, 29879, 29962, 29973, 597, 10780, 10834, 29874, 29899, 29920, 29962, 29989, 29961, 29900, 29899, 29929, 29962, 29989, 29961, 4388, 29918, 29992, 29889, 29987, 29974, 29962, 29989, 29961, 29991, 17710, 1194, 511, 29962, 29989, 10780, 16664, 29961, 29900, 29899, 29929, 29874, 29899, 29888, 3816, 29900, 29899, 29929, 29874, 29899, 29888, 12622, 29974, 742, 396, 24295, 13, 1678, 364, 29915, 10780, 5919, 29973, 3583, 29881, 29974, 29892, 29973, 7240, 10780, 3583, 29889, 29973, 29905, 29881, 29974, 6877, 29897, 742, 396, 3694, 13, 1678, 364, 29908, 10780, 10834, 29874, 29899, 29920, 3816, 29874, 29899, 29920, 12764, 29899, 29918, 10062, 29961, 29874, 29899, 29920, 2314, 613, 396, 3838, 411, 448, 322, 525, 13, 1678, 364, 29915, 10780, 29901, 7110, 29893, 29918, 10062, 29897, 742, 396, 916, 3838, 13, 1678, 364, 29915, 10780, 3583, 29903, 16029, 396, 3099, 1683, 13, 29962, 13, 13, 517, 12360, 29918, 276, 353, 337, 29889, 12198, 29898, 29878, 29915, 877, 23097, 29989, 4286, 7122, 29898, 13087, 29918, 710, 7240, 1495, 742, 337, 29889, 5348, 8456, 1660, 891, 337, 29889, 6259, 6632, 1525, 23487, 29897, 13, 13, 1753, 5993, 675, 29898, 29879, 1125, 13, 1678, 736, 18897, 29918, 276, 29889, 2886, 497, 29898, 29879, 29897, 13, 13, 1753, 758, 5014, 29898, 29879, 29892, 5224, 4878, 29922, 8824, 1125, 13, 1678, 18897, 353, 5993, 675, 29898, 29879, 29897, 13, 1678, 565, 5224, 4878, 29901, 13, 4706, 18897, 353, 518, 6979, 29889, 13609, 580, 363, 5993, 297, 18897, 29962, 13, 1678, 736, 18897, 13, 13, 1753, 6597, 29918, 26766, 29918, 3166, 29918, 3257, 29898, 3257, 1125, 13, 1678, 29361, 353, 5159, 13, 1678, 3611, 353, 851, 29898, 3257, 29897, 13, 1678, 363, 5993, 297, 758, 5014, 29898, 3257, 1125, 13, 4706, 29361, 29889, 4397, 29898, 6979, 29897, 13, 1678, 15149, 29918, 10818, 11686, 8452, 29889, 21843, 29898, 1989, 9303, 29897, 13, 1678, 736, 29361, 13, 13, 9818, 29918, 10818, 11686, 8452, 353, 5159, 13, 13, 18569, 353, 10518, 29889, 949, 29918, 7638, 877, 29881, 2204, 29886, 29918, 15070, 29914, 4905, 29918, 7914, 29889, 7638, 742, 13, 462, 1669, 28552, 2433, 29936, 742, 13, 462, 1669, 671, 22724, 29922, 1839, 1989, 3788, 8921, 3788, 3257, 3788, 6360, 3788, 3905, 742, 525, 2036, 742, 13, 462, 462, 4706, 525, 29885, 1256, 3788, 12292, 3788, 29926, 4659, 3788, 24623, 7464, 26688, 29922, 710, 29897, 13, 13, 29937, 7338, 1461, 7670, 1742, 13, 18569, 1839, 26766, 2033, 353, 7456, 1839, 3257, 13359, 7302, 29898, 21111, 29918, 26766, 29918, 3166, 29918, 3257, 467, 710, 29889, 7122, 877, 29989, 1495, 13, 13, 29937, 14491, 13553, 13, 9818, 29918, 10818, 11686, 8452, 353, 10518, 29889, 17271, 29898, 9818, 29918, 10818, 11686, 8452, 29892, 4341, 29922, 3366, 26766, 3108, 467, 8865, 29918, 20908, 15815, 580, 13, 13, 18569, 29889, 517, 29918, 7638, 877, 449, 29914, 29886, 21321, 29889, 7638, 742, 2380, 29922, 8516, 29892, 4839, 29922, 5574, 29897, 13, 9818, 29918, 10818, 11686, 8452, 29889, 517, 29918, 7638, 877, 449, 29914, 1989, 9303, 29889, 7638, 742, 2380, 29922, 8516, 29892, 4839, 29922, 5574, 29897, 13, 2 ]
examples/rsa_client.py	datalogics/flask-oauthprovider	1	170757	from os.path import join, dirname from client import app def fread(fn): with open(join(dirname(__file__), fn), 'r') as f: return f.read().decode("utf-8") app.config["OAUTH_CREDENTIALS"] = { u"rsa_key": fread("mykey.pem"), u"signature_method": u"RSA-SHA1", "signature_type": "body" } app.config["CLIENT_KEY"] = u"<KEY>" app.run(debug=True, port=5001)	[ 1, 515, 2897, 29889, 2084, 1053, 5988, 29892, 4516, 978, 13, 3166, 3132, 1053, 623, 13, 13, 13, 1753, 285, 949, 29898, 9144, 1125, 13, 1678, 411, 1722, 29898, 7122, 29898, 25721, 22168, 1445, 1649, 511, 7876, 511, 525, 29878, 1495, 408, 285, 29901, 13, 4706, 736, 285, 29889, 949, 2141, 13808, 703, 9420, 29899, 29947, 1159, 13, 13, 932, 29889, 2917, 3366, 29949, 20656, 29950, 29918, 29907, 19386, 3919, 25758, 29903, 3108, 353, 426, 13, 1678, 318, 29908, 2288, 29874, 29918, 1989, 1115, 285, 949, 703, 1357, 1989, 29889, 29886, 331, 4968, 13, 1678, 318, 29908, 4530, 1535, 29918, 5696, 1115, 318, 29908, 29934, 8132, 29899, 23498, 29896, 613, 13, 1678, 376, 4530, 1535, 29918, 1853, 1115, 376, 2587, 29908, 13, 29913, 13, 932, 29889, 2917, 3366, 27205, 3919, 29918, 10818, 3108, 353, 318, 29908, 29966, 10818, 11903, 13, 932, 29889, 3389, 29898, 8382, 29922, 5574, 29892, 2011, 29922, 29945, 29900, 29900, 29896, 29897, 13, 2 ]
library/cogs/rss_feed.py	og2701/C-3PO	3	183898	<reponame>og2701/C-3PO<filename>library/cogs/rss_feed.py<gh_stars>1-10 from discord.ext.commands import Cog, command from discord.ext import tasks from discord import Embed, Client from feedparser import parse client = Client() class RSS(Cog): def __init__(self, bot): self.bot = bot self.SWnews.start() @tasks.loop(seconds=3600) async def SWnews(self): nf = parse("https://www.starwars.com/news/feed").entries[0] with open("./data/rss_feed.txt","r") as f: old_feed = f.read() if old_feed != str(nf): with open("./data/rss_feed.txt","w") as f: f.write(str(nf)) Mbed = Embed(colour=0xE5B233, description=nf.summary+f"\n[Link to article]({nf.link})") Mbed.set_author(name=nf.title) Mbed.set_thumbnail(url=nf.media_thumbnail[0]["url"]) Mbed.set_footer(text='Author: '+nf.author+' \| Published: '+nf.published) try: await self.stdout.send(embed=Mbed) except AttributeError as exc: print(f"[!] {exc}") @Cog.listener() async def on_ready(self): if not self.bot.ready: self.stdout = self.bot.get_channel(797266342822412338) self.bot.cogs_ready.ready_up("RSS") self.SWnews.start() def setup(bot): bot.add_cog(RSS(bot))	[ 1, 529, 276, 1112, 420, 29958, 468, 29906, 29955, 29900, 29896, 29914, 29907, 29899, 29941, 13152, 29966, 9507, 29958, 5258, 29914, 29883, 12099, 29914, 29878, 893, 29918, 18798, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 2313, 536, 29889, 1062, 29889, 26381, 1053, 315, 468, 29892, 1899, 13, 3166, 2313, 536, 29889, 1062, 1053, 9595, 13, 3166, 2313, 536, 1053, 2812, 2580, 29892, 12477, 13, 13, 3166, 8343, 16680, 1053, 6088, 13, 13, 13, 4645, 353, 12477, 580, 13, 13, 1990, 390, 1799, 29898, 29907, 468, 1125, 13, 12, 1753, 4770, 2344, 12035, 1311, 29892, 9225, 1125, 13, 12, 12, 1311, 29889, 7451, 353, 9225, 13, 12, 12, 1311, 29889, 23066, 15753, 29889, 2962, 580, 13, 13, 13, 12, 29992, 20673, 29889, 7888, 29898, 23128, 29922, 29941, 29953, 29900, 29900, 29897, 13, 12, 12674, 822, 25289, 15753, 29898, 1311, 1125, 13, 13, 12, 12, 29876, 29888, 353, 6088, 703, 991, 597, 1636, 29889, 8508, 29893, 1503, 29889, 510, 29914, 15753, 29914, 18798, 2564, 26586, 29961, 29900, 29962, 13, 13, 12, 12, 2541, 1722, 703, 6904, 1272, 29914, 29878, 893, 29918, 18798, 29889, 3945, 3284, 29878, 1159, 408, 285, 29901, 13, 12, 12, 12, 1025, 29918, 18798, 353, 285, 29889, 949, 580, 13, 12, 12, 13, 12, 12, 361, 2030, 29918, 18798, 2804, 851, 29898, 29876, 29888, 1125, 13, 12, 12, 12, 2541, 1722, 703, 6904, 1272, 29914, 29878, 893, 29918, 18798, 29889, 3945, 3284, 29893, 1159, 408, 285, 29901, 13, 12, 12, 12, 12, 29888, 29889, 3539, 29898, 710, 29898, 29876, 29888, 876, 13, 13, 12, 12, 12, 29924, 2580, 353, 2812, 2580, 29898, 1054, 473, 29922, 29900, 29916, 29923, 29945, 29933, 29906, 29941, 29941, 29892, 6139, 29922, 29876, 29888, 29889, 7727, 29974, 29888, 26732, 29876, 29961, 6595, 304, 4274, 850, 29912, 29876, 29888, 29889, 2324, 1800, 1159, 13, 13, 12, 12, 12, 29924, 2580, 29889, 842, 29918, 8921, 29898, 978, 29922, 29876, 29888, 29889, 3257, 29897, 13, 12, 12, 12, 29924, 2580, 29889, 842, 29918, 386, 21145, 29898, 2271, 29922, 29876, 29888, 29889, 9799, 29918, 386, 21145, 29961, 29900, 29962, 3366, 2271, 20068, 13, 12, 12, 12, 29924, 2580, 29889, 842, 29918, 21720, 29898, 726, 2433, 13720, 29901, 525, 29974, 29876, 29888, 29889, 8921, 23097, 891, 8042, 3726, 29901, 525, 29974, 29876, 29888, 29889, 5467, 3726, 29897, 13, 13, 12, 12, 12, 2202, 29901, 13, 12, 12, 12, 12, 20675, 1583, 29889, 25393, 29889, 6717, 29898, 17987, 29922, 29924, 2580, 29897, 13, 12, 12, 12, 19499, 23833, 2392, 408, 5566, 29901, 13, 12, 12, 12, 12, 2158, 29898, 29888, 29908, 29961, 29991, 29962, 426, 735, 29883, 27195, 13, 13, 12, 29992, 29907, 468, 29889, 25894, 580, 13, 12, 12674, 822, 373, 29918, 2040, 29898, 1311, 1125, 13, 12, 12, 361, 451, 1583, 29889, 7451, 29889, 2040, 29901, 13, 12, 12, 12, 1311, 29889, 25393, 353, 1583, 29889, 7451, 29889, 657, 29918, 12719, 29898, 29955, 29929, 29955, 29906, 29953, 29953, 29941, 29946, 29906, 29947, 29906, 29906, 29946, 29896, 29906, 29941, 29941, 29947, 29897, 13, 12, 12, 12, 1311, 29889, 7451, 29889, 29883, 12099, 29918, 2040, 29889, 2040, 29918, 786, 703, 29934, 1799, 1159, 13, 12, 12, 12, 1311, 29889, 23066, 15753, 29889, 2962, 580, 13, 13, 1753, 6230, 29898, 7451, 1125, 13, 12, 7451, 29889, 1202, 29918, 29883, 468, 29898, 29934, 1799, 29898, 7451, 876, 13, 2 ]
cplcom/moa/device/mcdaq.py	matham/cplcom	0	23791	'''Barst Measurement Computing DAQ Wrapper ========================================== ''' from functools import partial from pybarst.mcdaq import MCDAQChannel from kivy.properties import NumericProperty, ObjectProperty from moa.threads import ScheduledEventLoop from moa.device.digital import ButtonViewPort from cplcom.moa.device import DeviceExceptionBehavior __all__ = ('MCDAQDevice', ) class MCDAQDevice(DeviceExceptionBehavior, ButtonViewPort, ScheduledEventLoop): '''A :class:`moa.device.digital.ButtonViewPort` wrapper around a :class:`pybarst.mcdaq.MCDAQChannel` instance which controls a Switch and Sense 8/8. For this class, :class:`moa.device.digital.ButtonViewPort.dev_map` must be provided upon creation and it's a dict whose keys are the property names and whose values are the Switch and Sense 8/8 channel numbers that the property controls. E.g. for a light switch connected to channel 3 on the Switch and Sense 8/8 output port define the class:: class MyMCDAQDevice(MCDAQDevice): light = BooleanProperty(False) And then create the instance with:: dev = MyMCDAQDevice(dev_map={'light': 3}) And then we can set it high by calling e.g.:: dev.set_state(high=['light']) For an input devices it can defined similarly and the state of the property reflects the value of the port. A switch and sense which has both a input and output device still needs to create two device for each. ''' __settings_attrs__ = ('SAS_chan', ) _read_event = None def _write_callback(self, value, mask, result): self.timestamp = result for idx, name in self.chan_dev_map.iteritems(): if mask & (1 << idx): setattr(self, name, bool(value & (1 << idx))) self.dispatch('on_data_update', self) def _read_callback(self, result, kwargs): t, val = result self.timestamp = t for idx, name in self.chan_dev_map.iteritems(): setattr(self, name, bool(val & (1 << idx))) self.dispatch('on_data_update', self) def set_state(self, high=[], low=[], kwargs): if self.activation != 'active': raise TypeError('Can only set state of an active device. Device ' 'is currently "{}"'.format(self.activation)) if 'o' not in self.direction: raise TypeError('Cannot write state for a input device') dev_map = self.dev_map mask = 0 val = 0 for name in high: idx = dev_map[name] val \|= (1 << idx) mask \|= (1 << idx) for name in low: mask \|= (1 << dev_map[name]) self.request_callback( self.chan.write, callback=partial(self._write_callback, val, mask), mask=mask, value=val) def get_state(self): if self.activation != 'active': raise TypeError('Can only read state of an active device. Device ' 'is currently "{}"'.format(self.activation)) if 'i' in self.direction: # happens anyway return self._read_event = self.request_callback( self.chan.read, callback=self._read_callback) def activate(self, largs, kwargs): kwargs['state'] = 'activating' if not super(MCDAQDevice, self).activate(largs, *kwargs): return False self.start_thread() self.chan = MCDAQChannel(chan=self.SAS_chan, server=self.server.server) def finish_activate(largs): self.activation = 'active' if 'i' in self.direction: self._read_event = self.request_callback( self.chan.read, repeat=True, callback=self._read_callback) self.request_callback(self._start_channel, finish_activate) return True def _start_channel(self): chan = self.chan chan.open_channel() if 'o' in self.direction: chan.write(mask=0xFF, value=0) def deactivate(self, largs, kwargs): kwargs['state'] = 'deactivating' if not super(MCDAQDevice, self).deactivate(largs, *kwargs): return False self.remove_request(self.chan.read, self._read_event) self._read_event = None def finish_deactivate(largs): self.activation = 'inactive' self.stop_thread() self.request_callback(self._stop_channel, finish_deactivate) return True def _stop_channel(self): if 'o' in self.direction: self.chan.write(mask=0xFF, value=0) if 'i' in self.direction and self.chan.continuous: self.chan.cancel_read(flush=True) chan = ObjectProperty(None) '''The internal :class:`pybarst.mcdaq.MCDAQChannel` instance. It is read only and is automatically created. ''' server = ObjectProperty(None, allownone=True) '''The internal barst :class:`pybarst.core.server.BarstServer`. It must be provided to the instance. ''' SAS_chan = NumericProperty(0) '''The channel number of the Switch & Sense 8/8 as configured in InstaCal. Defaults to zero. 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Lib/hTools2/modules/unicode.py	gferreira/hTools2	11	67418	<gh_stars>10-100 # [h] hTools2.modules.unicode from collections import OrderedDict '''Tools to work with Unicode, convert glyph names to hex/unicode etc.''' def clear_unicodes(font): '''Remove unicodes from all glyphs in the font.''' for g in font: g.unicodes = [] font.update() def auto_unicodes(font, custom_unicodes={}): '''Automatically set unicode values for all glyphs in the font.''' clear_unicodes(font) for g in font: if g is not None: auto_unicode(g, custom_unicodes) font.update() def auto_unicode(g, custom_unicodes={}): ''' Automatically set unicode value(s) for the specified glyph. The method uses RoboFab's ``glyph.autoUnicodes()`` function for common glyphs, and complements it with additional values from ``unicodes_extra``. ''' if g.name is not None: # handle 'uni' names if g.name[:3] == "uni" and len(g.name) in [7, 8]: c = g.name g.unicode = int(c.split('uni')[1], 16) # handle extra cases elif g.name in unicodes_extra.keys(): uString = 'uni%s' % unicodes_extra[g.name] g.unicode = unicode_hexstr_to_int(uString) # use auto unicode for everything else elif custom_unicodes.get(g.name) is not None: uString = 'uni%s' % custom_unicodes[g.name] g.unicode = unicode_hexstr_to_int(uString) else: g.autoUnicodes() g.update() #------------------------------ # unicode-to-string conversion #------------------------------ def unicode_int_to_hexstr(intUnicode, _0x=False, uni=False): ''' Converts unicode integers to hexadecimal. See also the reverse function ``unicode_hexstr_to_int``. Note that ``glyph.unicodes`` is a list (a glyph can have many unicodes), so we need to pass the first value only. The optional parameters ``uni`` and ``_0x`` add the respective prefixes. ''' hexUnicode = "%X".lstrip("0x") % intUnicode hexUnicode = "0" * (4 - len(hexUnicode)) + hexUnicode if _0x: return "0x%s" % hexUnicode elif uni: return "uni%s" % hexUnicode return hexUnicode def unicode_hexstr_to_int(hexUnicode, replaceUni=True): ''' Converts a unicode hexadecimal value into an integer. It does exactly the reverse of ``unicode_int_to_hexstr``. ''' if replaceUni: return int(hexUnicode.replace("uni",""), 16) return int(hexUnicode.lstrip("x"), 16) #----------------------------- # additional unicode mappings #----------------------------- #: A dict containing additional `glyphName` to `unicode` mappings. unicodes_extra = { # extended latin lc 'aemacron' : '01E3', 'dotlessj' : '0237', 'schwa' : '0259', 'ymacron' : '0233', 'eszett' : '00DF', # extended latin uc 'AEmacron' : '01E2', 'Schwa' : '018F', 'Uppercaseeszett' : '1E9E', # ligatures 'fi' : 'FB01', 'fl' : 'FB02', 'f_f' : 'FB00', 'f_f_i' : 'FB03', 'f_f_l' : 'FB04', # greek exceptions 'Delta' : '2206', # 0394 'Omega' : '2126', # 03A9 'mu' : '00B5', # 03BC # superiors 'zerosuperior' : '2070', 'onesuperior' : '00B9', 'twosuperior' : '00B2', 'threesuperior' : '00B3', 'foursuperior' : '2074', 'fivesuperior' : '2075', 'sixsuperior' : '2076', 'sevensuperior' : '2077', 'eightsuperior' : '2078', 'ninesuperior' : '2079', # inferiors 'zeroinferior' : '2080', 'oneinferior' : '2081', 'twoinferior' : '2082', 'threeinferior' : '2083', 'fourinferior' : '2084', 'fiveinferior' : '2085', 'sixinferior' : '2086', 'seveninferior' : '2087', 'eightinferior' : '2088', 'nineinferior' : '2089', # spaces 'enspace' : '2002', 'emspace' : '2003', 'nbspace' : '00A0', 'hairspace' : '200A', 'thinspace' : '2009', 'thickspace' : '2004', 'figurespace' : '2007', 'zerowidthspace' : '200B', # combining accents 'gravecomb' : '0300', 'acutecomb' : '0301', 'circumflexcomb' : '0302', 'tildecomb' : '0303', 'dieresiscomb' : '0308', 'dotbelowcomb' : '0323', 'cedillacomb' : '0327', # arrows 'arrowleft' : '2190', 'arrowup' : '2191', 'arrowright' : '2192', 'arrowdown' : '2193', 'arrowleftright' : '2194', 'arrowupdown' : '2195', 'arrowupleft' : '2196', 'arrowupright' : '2197', 'arrowdownright' : '2198', 'arrowdownleft' : '2199', # latin accented lc 'adotbelow' : '1EA1', 'aringacute' : '01FB', 'edotbelow' : '1EB9', 'etilde' : '1EBD', 'gcaron' : '01E7', 'idotbelow' : '1ECB', 'ndotbelow' : '1E47', 'nhookleft' : '0272', 'odotbelow' : '1ECD', 'oogonek' : '01EB', 'sdotbelow' : '1E63', 'udotbelow' : '1EE5', 'ymacron' : '0233', 'ytilde' : '1EF9', # latin accented uc 'Adotbelow' : '1EA0', 'Aringacute' : '01FA', 'Edotbelow' : '1EB8', 'Etilde' : '1EBC', 'Gcaron' : '01E6', 'Idotbelow' : '1ECA', 'Ndotbelow' : '1E46', 'Nhookleft' : '019D', 'Odotbelow' : '1ECC', 'Oogonek' : '01EA', 'Sdotbelow' : '1E62', 'Udotbelow' : '1EE4', 'Ymacron' : '0232', 'Ytilde' : '1EF8', # symbols etc 'bulletoperator' : '2219', 'florin' : '0192', } #----------------------------- # unicode to psNames mappings #----------------------------- #: A dictionary mapping unicode values to psNames. unicode2psnames = { None : '.notdef', 32 : 'space', 33 : 'exclam', 34 : 'quotedbl', 35 : 'numbersign', 36 : 'dollar', 37 : 'percent', 38 : 'ampersand', 39 : 'quotesingle', 40 : 'parenleft', 41 : 'parenright', 42 : 'asterisk', 43 : 'plus', 44 : 'comma', 45 : 'hyphen', 46 : 'period', 47 : 'slash', 48 : 'zero', 49 : 'one', 50 : 'two', 51 : 'three', 52 : 'four', 53 : 'five', 54 : 'six', 55 : 'seven', 56 : 'eight', 57 : 'nine', 58 : 'colon', 59 : 'semicolon', 60 : 'less', 61 : 'equal', 62 : 'greater', 63 : 'question', 64 : 'at', 65 : 'A', 66 : 'B', 67 : 'C', 68 : 'D', 69 : 'E', 70 : 'F', 71 : 'G', 72 : 'H', 73 : 'I', 74 : 'J', 75 : 'K', 76 : 'L', 77 : 'M', 78 : 'N', 79 : 'O', 80 : 'P', 81 : 'Q', 82 : 'R', 83 : 'S', 84 : 'T', 85 : 'U', 86 : 'V', 87 : 'W', 88 : 'X', 89 : 'Y', 90 : 'Z', 91 : 'bracketleft', 92 : 'backslash', 93 : 'bracketright', 94 : 'asciicircum', 95 : 'underscore', 96 : 'grave', 97 : 'a', 98 : 'b', 99 : 'c', 100 : 'd', 101 : 'e', 102 : 'f', 103 : 'g', 104 : 'h', 105 : 'i', 106 : 'j', 107 : 'k', 108 : 'l', 109 : 'm', 110 : 'n', 111 : 'o', 112 : 'p', 113 : 'q', 114 : 'r', 115 : 's', 116 : 't', 117 : 'u', 118 : 'v', 119 : 'w', 120 : 'x', 121 : 'y', 122 : 'z', 123 : 'braceleft', 124 : 'bar', 125 : 'braceright', 126 : 'asciitilde', 161 : 'exclamdown', 162 : 'cent', 163 : 'sterling', 164 : 'currency', 165 : 'yen', 166 : 'brokenbar', 167 : 'section', 168 : 'dieresis', 169 : 'copyright', 170 : 'ordfeminine', 171 : 'guillemotleft', 172 : 'logicalnot', 174 : 'registered', 175 : 'macron', 176 : 'degree', 177 : 'plusminus', 178 : 'twosuperior', 179 : 'threesuperior', 180 : 'acute', 181 : 'mu', 182 : 'paragraph', 183 : 'periodcentered', 184 : 'cedilla', 185 : 'onesuperior', 186 : 'ordmasculine', 187 : 'guillemotright', 188 : 'onequarter', 189 : 'onehalf', 190 : 'threequarters', 191 : 'questiondown', 192 : 'Agrave', 193 : 'Aacute', 194 : 'Acircumflex', 195 : 'Atilde', 196 : 'Adieresis', 197 : 'Aring', 198 : 'AE', 199 : 'Ccedilla', 200 : 'Egrave', 201 : 'Eacute', 202 : 'Ecircumflex', 203 : 'Edieresis', 204 : 'Igrave', 205 : 'Iacute', 206 : 'Icircumflex', 207 : 'Idieresis', 208 : 'Eth', 209 : 'Ntilde', 210 : 'Ograve', 211 : 'Oacute', 212 : 'Ocircumflex', 213 : 'Otilde', 214 : 'Odieresis', 215 : 'multiply', 216 : 'Oslash', 217 : 'Ugrave', 218 : 'Uacute', 219 : 'Ucircumflex', 220 : 'Udieresis', 221 : 'Yacute', 222 : 'Thorn', 223 : 'germandbls', 224 : 'agrave', 225 : 'aacute', 226 : 'acircumflex', 227 : 'atilde', 228 : 'adieresis', 229 : 'aring', 230 : 'ae', 231 : 'ccedilla', 232 : 'egrave', 233 : 'eacute', 234 : 'ecircumflex', 235 : 'edieresis', 236 : 'igrave', 237 : 'iacute', 238 : 'icircumflex', 239 : 'idieresis', 240 : 'eth', 241 : 'ntilde', 242 : 'ograve', 243 : 'oacute', 244 : 'ocircumflex', 245 : 'otilde', 246 : 'odieresis', 247 : 'divide', 248 : 'oslash', 249 : 'ugrave', 250 : 'uacute', 251 : 'ucircumflex', 252 : 'udieresis', 253 : 'yacute', 254 : 'thorn', 255 : 'ydieresis', 256 : 'Amacron', 257 : 'amacron', 258 : 'Abreve', 259 : 'abreve', 260 : 'Aogonek', 261 : 'aogonek', 262 : 'Cacute', 263 : 'cacute', 264 : 'Ccircumflex', 265 : 'ccircumflex', 266 : 'Cdotaccent', 267 : 'cdotaccent', 268 : 'Ccaron', 269 : 'ccaron', 270 : 'Dcaron', 271 : 'dcaron', 272 : 'Dcroat', 273 : 'dcroat', 274 : 'Emacron', 275 : 'emacron', 276 : 'Ebreve', 277 : 'ebreve', 278 : 'Edotaccent', 279 : 'edotaccent', 280 : 'Eogonek', 281 : 'eogonek', 282 : 'Ecaron', 283 : 'ecaron', 284 : 'Gcircumflex', 285 : 'gcircumflex', 286 : 'Gbreve', 287 : 'gbreve', 288 : 'Gdotaccent', 289 : 'gdotaccent', 290 : 'Gcommaaccent', 291 : 'gcommaaccent', 292 : 'Hcircumflex', 293 : 'hcircumflex', 294 : 'Hbar', 295 : 'hbar', 296 : 'Itilde', 297 : 'itilde', 298 : 'Imacron', 299 : 'imacron', 300 : 'Ibreve', 301 : 'ibreve', 302 : 'Iogonek', 303 : 'iogonek', 304 : 'Idotaccent', 305 : 'dotlessi', 306 : 'IJ', 307 : 'ij', 308 : 'Jcircumflex', 309 : 'jcircumflex', 310 : 'Kcommaaccent', 311 : 'kcommaaccent', 312 : 'kgreenlandic', 313 : 'Lacute', 314 : 'lacute', 315 : 'Lcommaaccent', 316 : 'lcommaaccent', 317 : 'Lcaron', 318 : 'lcaron', 319 : 'Ldot', 320 : 'ldot', 321 : 'Lslash', 322 : 'lslash', 323 : 'Nacute', 324 : 'nacute', 325 : 'Ncommaaccent', 326 : 'ncommaaccent', 327 : 'Ncaron', 328 : 'ncaron', 329 : 'napostrophe', 330 : 'Eng', 331 : 'eng', 332 : 'Omacron', 333 : 'omacron', 334 : 'Obreve', 335 : 'obreve', 336 : 'Ohungarumlaut', 337 : 'ohungarumlaut', 338 : 'OE', 339 : 'oe', 340 : 'Racute', 341 : 'racute', 342 : 'Rcommaaccent', 343 : 'rcommaaccent', 344 : 'Rcaron', 345 : 'rcaron', 346 : 'Sacute', 347 : 'sacute', 348 : 'Scircumflex', 349 : 'scircumflex', 350 : 'Scedilla', 351 : 'scedilla', 352 : 'Scaron', 353 : 'scaron', 354 : 'Tcommaaccent', 355 : 'tcommaaccent', 356 : 'Tcaron', 357 : 'tcaron', 358 : 'Tbar', 359 : 'tbar', 360 : 'Utilde', 361 : 'utilde', 362 : 'Umacron', 363 : 'umacron', 364 : 'Ubreve', 365 : 'ubreve', 366 : 'Uring', 367 : 'uring', 368 : 'Uhungarumlaut', 369 : 'uhungarumlaut', 370 : 'Uogonek', 371 : 'uogonek', 372 : 'Wcircumflex', 373 : 'wcircumflex', 374 : 'Ycircumflex', 375 : 'ycircumflex', 376 : 'Ydieresis', 377 : 'Zacute', 378 : 'zacute', 379 : 'Zdotaccent', 380 : 'zdotaccent', 381 : 'Zcaron', 382 : 'zcaron', 402 : 'florin', 508 : 'AEacute', 509 : 'aeacute', 510 : 'Oslashacute', 511 : 'oslashacute', 536 : 'Scommaaccent', 537 : 'scommaaccent', 710 : 'circumflex', 711 : 'caron', 728 : 'breve', 729 : 'dotaccent', 730 : 'ring', 731 : 'ogonek', 732 : 'tilde', 733 : 'hungarumlaut', 894 : 'uni037E', 900 : 'tonos', 901 : 'dieresistonos', 902 : 'Alphatonos', 903 : 'anoteleia', 904 : 'Epsilontonos', 905 : 'Etatonos', 906 : 'Iotatonos', 908 : 'Omicrontonos', 910 : 'Upsilontonos', 911 : 'Omegatonos', 912 : 'iotadieresistonos', 913 : 'Alpha', 914 : 'Beta', 915 : 'Gamma', # 916 : 'Delta', 917 : 'Epsilon', 918 : 'Zeta', 919 : 'Eta', 920 : 'Theta', 921 : 'Iota', 922 : 'Kappa', 923 : 'Lambda', 924 : 'Mu', 925 : 'Nu', 926 : 'Xi', 927 : 'Omicron', 928 : 'Pi', 929 : 'Rho', 931 : 'Sigma', 932 : 'Tau', 933 : 'Upsilon', 934 : 'Phi', 935 : 'Chi', 936 : 'Psi', 937 : 'Omega', 938 : 'Iotadieresis', 939 : 'Upsilondieresis', 940 : 'alphatonos', 941 : 'epsilontonos', 942 : 'etatonos', 943 : 'iotatonos', 944 : 'upsilondieresistonos', 945 : 'alpha', 946 : 'beta', 947 : 'gamma', 948 : 'delta', 949 : 'epsilon', 950 : 'zeta', 951 : 'eta', 952 : 'theta', 953 : 'iota', 954 : 'kappa', 955 : 'lambda', 956 : 'uni03BC', 957 : 'nu', 958 : 'xi', 959 : 'omicron', 960 : 'pi', 961 : 'rho', 962 : 'uni03C2', 963 : 'sigma', 964 : 'tau', 965 : 'upsilon', 966 : 'phi', 967 : 'chi', 968 : 'psi', 969 : 'omega', 970 : 'iotadieresis', 971 : 'upsilondieresis', 972 : 'omicrontonos', 973 : 'upsilontonos', 974 : 'omegatonos', 1025 : 'afii10023', 1026 : 'afii10051', 1027 : 'afii10052', 1028 : 'afii10053', 1029 : 'afii10054', 1030 : 'afii10055', 1031 : 'afii10056', 1032 : 'afii10057', 1033 : 'afii10058', 1034 : 'afii10059', 1035 : 'afii10060', 1036 : 'afii10061', 1038 : 'afii10062', 1039 : 'afii10145', 1040 : 'afii10017', 1041 : 'afii10018', 1042 : 'afii10019', 1043 : 'afii10020', 1044 : 'afii10021', 1045 : 'afii10022', 1046 : 'afii10024', 1047 : 'afii10025', 1048 : 'afii10026', 1049 : 'afii10027', 1050 : 'afii10028', 1051 : 'afii10029', 1052 : 'afii10030', 1053 : 'afii10031', 1054 : 'afii10032', 1055 : 'afii10033', 1056 : 'afii10034', 1057 : 'afii10035', 1058 : 'afii10036', 1059 : 'afii10037', 1060 : 'afii10038', 1061 : 'afii10039', 1062 : 'afii10040', 1063 : 'afii10041', 1064 : 'afii10042', 1065 : 'afii10043', 1066 : 'afii10044', 1067 : 'afii10045', 1068 : 'afii10046', 1069 : 'afii10047', 1070 : 'afii10048', 1071 : 'afii10049', 1072 : 'afii10065', 1073 : 'afii10066', 1074 : 'afii10067', 1075 : 'afii10068', 1076 : 'afii10069', 1077 : 'afii10070', 1078 : 'afii10072', 1079 : 'afii10073', 1080 : 'afii10074', 1081 : 'afii10075', 1082 : 'afii10076', 1083 : 'afii10077', 1084 : 'afii10078', 1085 : 'afii10079', 1086 : 'afii10080', 1087 : 'afii10081', 1088 : 'afii10082', 1089 : 'afii10083', 1090 : 'afii10084', 1091 : 'afii10085', 1092 : 'afii10086', 1093 : 'afii10087', 1094 : 'afii10088', 1095 : 'afii10089', 1096 : 'afii10090', 1097 : 'afii10091', 1098 : 'afii10092', 1099 : 'afii10093', 1100 : 'afii10094', 1101 : 'afii10095', 1102 : 'afii10096', 1103 : 'afii10097', 1105 : 'afii10071', 1106 : 'afii10099', 1107 : 'afii10100', 1108 : 'afii10101', 1109 : 'afii10102', 1110 : 'afii10103', 1111 : 'afii10104', 1112 : 'afii10105', 1113 : 'afii10106', 1114 : 'afii10107', 1115 : 'afii10108', 1116 : 'afii10109', 1118 : 'afii10110', 1119 : 'afii10193', 1168 : 'afii10050', 1169 : 'afii10098', 1241 : 'afii10846', 7808 : 'Wgrave', 7809 : 'wgrave', 7810 : 'Wacute', 7811 : 'wacute', 7812 : 'Wdieresis', 7813 : 'wdieresis', 7922 : 'Ygrave', 7923 : 'ygrave', 8211 : 'endash', 8212 : 'emdash', 8216 : 'quoteleft', 8217 : 'quoteright', 8218 : 'quotesinglbase', 8220 : 'quotedblleft', 8221 : 'quotedblright', 8222 : 'quotedblbase', 8224 : 'dagger', 8225 : 'daggerdbl', 8226 : 'bullet', 8230 : 'ellipsis', 8240 : 'perthousand', 8249 : 'guilsinglleft', 8250 : 'guilsinglright', 8260 : 'fraction', 8304 : 'zerosuperior', 8308 : 'foursuperior', 8309 : 'fivesuperior', 8310 : 'sixsuperior', 8311 : 'sevensuperior', 8312 : 'eightsuperior', 8313 : 'ninesuperior', 8317 : 'parenleftsuperior', 8318 : 'parenrightsuperior', 8320 : 'zeroinferior', 8321 : 'oneinferior', 8322 : 'twoinferior', 8323 : 'threeinferior', 8324 : 'fourinferior', 8325 : 'fiveinferior', 8326 : 'sixinferior', 8327 : 'seveninferior', 8328 : 'eightinferior', 8329 : 'nineinferior', 8333 : 'parenleftinferior', 8334 : 'parenrightinferior', 8364 : 'Euro', 8467 : 'afii61289', 8470 : 'afii61352', 8482 : 'trademark', 8494 : 'estimated', 8706 : 'partialdiff', 8710 : 'uni2206', 8719 : 'product', 8721 : 'summation', 8722 : 'minus', 8730 : 'radical', 8734 : 'infinity', 8747 : 'integral', 8776 : 'approxequal', 8800 : 'notequal', 8804 : 'lessequal', 8805 : 'greaterequal', 9674 : 'lozenge', 57345 : 'f_f_j', 57346 : 'f_j', 61421 : 'uniEFED', 61422 : 'uniEFEE', 61425 : 'uniEFF1', 61426 : 'uniEFF2', 61427 : 'uniEFF3', 61429 : 'uniEFF5', 61431 : 'uniEFF7', 63017 : 'uniF629', 63018 : 'uniF62A', 63019 : 'uniF62B', 63020 : 'uniF62C', 63021 : 'uniF62D', 63022 : 'uniF62E', 63023 : 'uniF62F', 63024 : 'uniF630', 63025 : 'uniF631', 63026 : 'uniF632', 63027 : 'uniF633', 63028 : 'uniF634', 63032 : 'uniF638', 63033 : 'uniF639', 63034 : 'uniF63A', 63035 : 'uniF63B', 63036 : 'uniF63C', 63037 : 'uniF63D', 63038 : 'uniF63E', 63039 : 'uniF63F', 63040 : 'uniF640', 63041 : 'uniF641', 63042 : 'uniF642', 63043 : 'uniF643', 63044 : 'uniF644', 63045 : 'uniF645', 63046 : 'uniF646', 63047 : 'uniF647', 63048 : 'uniF648', 63049 : 'uniF649', 63050 : 'uniF64A', 63051 : 'uniF64B', 63052 : 'uniF64C', 63054 : 'uniF64E', 63055 : 'uniF64F', 63056 : 'uniF650', 63057 : 'uniF651', 63058 : 'uniF652', 63059 : 'uniF653', 63060 : 'uniF654', 63061 : 'uniF655', 63062 : 'uniF656', 63063 : 'uniF657', 63064 : 'uniF658', 63065 : 'uniF659', 63066 : 'uniF65A', 63067 : 'uniF65B', 63068 : 'uniF65C', 63069 : 'uniF65D', 63070 : 'uniF65E', 63071 : 'uniF65F', 63072 : 'uniF660', 63073 : 'uniF661', 63074 : 'uniF662', 63075 : 'uniF663', 63076 : 'uniF664', 63077 : 'uniF665', 63078 : 'uniF666', 63079 : 'uniF667', 63080 : 'uniF668', 63081 : 'uniF669', 63082 : 'uniF66A', 63083 : 'uniF66B', 63084 : 'uniF66C', 63150 : 'uniF6AE', 63151 : 'uniF6AF', 63152 : 'uniF6B0', 63153 : 'uniF6B1', 63154 : 'uniF6B2', 63155 : 'uniF6B3', 63156 : 'uniF6B4', 63157 : 'uniF6B5', 63158 : 'uniF6B6', 63159 : 'uniF6B7', 63160 : 'uniF6B8', 63161 : 'uniF6B9', 63162 : 'uniF6BA', 63163 : 'uniF6BB', 63164 : 'uniF6BC', 63165 : 'uniF6BD', 63171 : 'commaaccent', 63177 : 'Acute', 63178 : 'Caron', 63179 : 'Dieresis', 63180 : 'DieresisAcute', 63181 : 'DieresisGrave', 63182 : 'Grave', 63183 : 'Hungarumlaut', 63184 : 'Macron', 63185 : 'cyrBreve', 63186 : 'cyrFlex', 63187 : 'dblGrave', 63188 : 'cyrbreve', 63189 : 'cyrflex', 63190 : 'dblgrave', 63191 : 'dieresisacute', 63192 : 'dieresisgrave', 63196 : 'uniF6DC', 63199 : 'centinferior', 63200 : 'centsuperior', 63201 : 'commainferior', 63202 : 'commasuperior', 63203 : 'dollarinferior', 63204 : 'dollarsuperior', 63205 : 'hypheninferior', 63206 : 'hyphensuperior', 63207 : 'periodinferior', 63208 : 'periodsuperior', 63280 : 'zerooldstyle', 63281 : 'oneoldstyle', 63282 : 'twooldstyle', 63283 : 'threeoldstyle', 63284 : 'fouroldstyle', 63285 : 'fiveoldstyle', 63286 : 'sixoldstyle', 63287 : 'sevenoldstyle', 63288 : 'eightoldstyle', 63289 : 'nineoldstyle', 64256 : 'ff', 64257 : 'fi', 64258 : 'fl', 64259 : 'ffi', 64260 : 'ffl', } #: A dictionary mapping psNames to unicode values. psnames2unicodes = dict([[v, k] for k, v in unicode2psnames.items()]) #---------------- # unicode ranges #---------------- # https://www.microsoft.com/typography/otspec/os2.htm OS2_unicode_ranges_src = '''\ 0; Basic Latin; 0000; 007F; 1; Latin-1 Supplement; 0080; 00FF; 2; Latin Extended-A; 0100; 017F; 3; Latin Extended-B; 0180; 024F; 4; IPA Extensions; 0250; 02AF; 4; Phonetic Extensions; 1D00; 1D7F; 4; Phonetic Extensions Supplement; 1D80; 1DBF; 5; Spacing Modifier Letters; 02B0; 02FF; 5; Modifier Tone Letters; A700; A71F; 6; Combining Diacritical Marks; 0300; 036F; 6; Combining Diacritical Marks Supplement; 1DC0; 1DFF; 7; Greek and Coptic; 0370; 03FF; 8; Coptic; 2C80; 2CFF; 9; Cyrillic; 0400; 04FF; 9; Cyrillic Supplement; 0500; 052F; 9; Cyrillic Extended-A; 2DE0; 2DFF; 9; Cyrillic Extended-B; A640; A69F; 10; Armenian; 0530; 058F; 11; Hebrew; 0590; 05FF; 12; Vai; A500; A63F; 13; Arabic; 0600; 06FF; 13; Arabic Supplement; 0750; 077F; 14; NKo; 07C0; 07FF; 15; Devanagari; 0900; 097F; 16; Bengali; 0980; 09FF; 17; Gurmukhi; 0A00; 0A7F; 18; Gujarati; 0A80; 0AFF; 19; Oriya; 0B00; 0B7F; 20; Tamil; 0B80; 0BFF; 21; Telugu; 0C00; 0C7F; 22; Kannada; 0C80; 0CFF; 23; Malayalam; 0D00; 0D7F; 24; Thai; 0E00; 0E7F; 25; Lao; 0E80; 0EFF; 26; Georgian; 10A0; 10FF; 26; Georgian Supplement; 2D00; 2D2F; 27; Balinese; 1B00; 1B7F; 28; Hangul Jamo; 1100; 11FF; 29; Latin Extended Additional; 1E00; 1EFF; 29; Latin Extended-C; 2C60; 2C7F; 29; Latin Extended-D; A720; A7FF; 30; Greek Extended; 1F00; 1FFF; 31; General Punctuation; 2000; 206F; 31; Supplemental Punctuation; 2E00; 2E7F; 32; Superscripts And Subscripts; 2070; 209F; 33; Currency Symbols; 20A0; 20CF; 34; Combining Diacritical Marks For Symbols; 20D0; 20FF; 35; Letterlike Symbols; 2100; 214F; 36; Number Forms; 2150; 218F; 37; Arrows; 2190; 21FF; 37; Supplemental Arrows-A; 27F0; 27FF; 37; Supplemental Arrows-B; 2900; 297F; 37; Miscellaneous Symbols and Arrows; 2B00; 2BFF; 38; Mathematical Operators; 2200; 22FF; 38; Supplemental Mathematical Operators; 2A00; 2AFF; 38; Miscellaneous Mathematical Symbols-A; 27C0; 27EF; 38; Miscellaneous Mathematical Symbols-B; 2980; 29FF; 39; Miscellaneous Technical; 2300; 23FF; 40; Control Pictures; 2400; 243F; 41; Optical Character Recognition; 2440; 245F; 42; Enclosed Alphanumerics; 2460; 24FF; 43; Box Drawing; 2500; 257F; 44; Block Elements; 2580; 259F; 45; Geometric Shapes; 25A0; 25FF; 46; Miscellaneous Symbols; 2600; 26FF; 47; Dingbats; 2700; 27BF; 48; CJK Symbols And Punctuation; 3000; 303F; 49; Hiragana; 3040; 309F; 50; Katakana; 30A0; 30FF; 50; Katakana Phonetic Extensions; 31F0; 31FF; 51; Bopomofo; 3100; 312F; 51; Bopomofo Extended; 31A0; 31BF; 52; Hangul Compatibility Jamo; 3130; 318F; 53; Phags-pa; A840; A87F; 54; Enclosed CJK Letters And Months; 3200; 32FF; 55; CJK Compatibility; 3300; 33FF; 56; Hangul Syllables; AC00; D7AF; 57; Non-Plane 0 *; D800; DFFF; 58; Phoenician; 10900; 1091F; 59; CJK Unified Ideographs; 4E00; 9FFF; 59; CJK Radicals Supplement; 2E80; 2EFF; 59; Kangxi Radicals; 2F00; 2FDF; 59; Ideographic Description Characters; 2FF0; 2FFF; 59; CJK Unified Ideographs Extension A; 3400; 4DBF; 59; CJK Unified Ideographs Extension B; 20000; 2A6DF; 59; Kanbun; 3190; 319F; 60; Private Use Area; E000; F8FF; 61; CJK Strokes; 31C0; 31EF; 61; CJK Compatibility Ideographs; F900; FAFF; 61; CJK Compatibility Ideographs Supplement; 2F800; 2FA1F; 62; Alphabetic Presentation Forms; FB00; FB4F; 63; Arabic Presentation Forms-A; FB50; FDFF; 64; Combining Half Marks; FE20; FE2F; 65; Vertical Forms; FE10; FE1F; 65; CJK Compatibility Forms; FE30; FE4F; 66; Small Form Variants; FE50; FE6F; 67; Arabic Presentation Forms-B; FE70; FEFF; 68; Halfwidth And Fullwidth Forms; FF00; FFEF; 69; Specials; FFF0; FFFF; 70; Tibetan; 0F00; 0FFF; 71; Syriac; 0700; 074F; 72; Thaana; 0780; 07BF; 73; Sinhala; 0D80; 0DFF; 74; Myanmar; 1000; 109F; 75; Ethiopic; 1200; 137F; 75; Ethiopic Supplement; 1380; 139F; 75; Ethiopic Extended; 2D80; 2DDF; 76; Cherokee; 13A0; 13FF; 77; Unified Canadian Aboriginal Syllabics; 1400; 167F; 78; Ogham; 1680; 169F; 79; Runic; 16A0; 16FF; 80; Khmer; 1780; 17FF; 80; Khmer Symbols; 19E0; 19FF; 81; Mongolian; 1800; 18AF; 82; Braille Patterns; 2800; 28FF; 83; Yi Syllables; A000; A48F; 83; Yi Radicals; A490; A4CF; 84; Tagalog; 1700; 171F; 84; Hanunoo; 1720; 173F; 84; Buhid; 1740; 175F; 84; Tagbanwa; 1760; 177F; 85; Old Italic; 10300; 1032F; 86; Gothic; 10330; 1034F; 87; Deseret; 10400; 1044F; 88; Byzantine Musical Symbols; 1D000; 1D0FF; 88; Musical Symbols; 1D100; 1D1FF; 88; Ancient Greek Musical Notation; 1D200; 1D24F; 89; Mathematical Alphanumeric Symbols; 1D400; 1D7FF; 90; Private Use (plane 15); FF000; FFFFD; 90; Private Use (plane 16); 100000; 10FFFD; 91; Variation Selectors; FE00; FE0F; 91; Variation Selectors Supplement; E0100; E01EF; 92; Tags; E0000; E007F; 93; Limbu; 1900; 194F; 94; Tai Le; 1950; 197F; 95; New Tai Lue; 1980; 19DF; 96; Buginese; 1A00; 1A1F; 97; Glagolitic; 2C00; 2C5F; 98; Tifinagh; 2D30; 2D7F; 99; Yijing Hexagram Symbols; 4DC0; 4DFF; 100; Syloti Nagri; A800; A82F; 101; Linear B Syllabary; 10000; 1007F; 101; Linear B Ideograms; 10080; 100FF; 101; Aegean Numbers; 10100; 1013F; 102; Ancient Greek Numbers; 10140; 1018F; 103; Ugaritic; 10380; 1039F; 104; Old Persian; 103A0; 103DF; 105; Shavian; 10450; 1047F; 106; Osmanya; 10480; 104AF; 107; Cypriot Syllabary; 10800; 1083F; 108; Kharoshthi; 10A00; 10A5F; 109; Tai Xuan Jing Symbols; 1D300; 1D35F; 110; Cuneiform; 12000; 123FF; 110; Cuneiform Numbers and Punctuation; 12400; 1247F; 111; Counting Rod Numerals; 1D360; 1D37F; 112; Sundanese; 1B80; 1BBF; 113; Lepcha; 1C00; 1C4F; 114; <NAME>; 1C50; 1C7F; 115; Saurashtra; A880; A8DF; 116; <NAME>; A900; A92F; 117; Rejang; A930; A95F; 118; Cham; AA00; AA5F; 119; Ancient Symbols; 10190; 101CF; 120; Phaistos Disc; 101D0; 101FF; 121; Carian; 102A0; 102DF; 121; Lycian; 10280; 1029F; 121; Lydian; 10920; 1093F; 122; <NAME>; 1F030; 1F09F; 122; <NAME>; 1F000; 1F02F; ''' OS2_unicode_ranges = OrderedDict() for line in OS2_unicode_ranges_src.split('\n'): if len(line.split(';')) == 5: bit, unicode_range, block_start, block_end = line.split(';')[:4] OS2_unicode_ranges[unicode_range.strip()] = [int(bit), (block_start.strip(), block_end.strip())]	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 518, 29882, 29962, 298, 24183, 29906, 29889, 7576, 29889, 2523, 356, 13, 13, 3166, 16250, 1053, 8170, 287, 21533, 13, 13, 12008, 24183, 304, 664, 411, 23862, 29892, 3588, 330, 27026, 2983, 304, 15090, 29914, 2523, 356, 2992, 29889, 12008, 13, 13, 1753, 2821, 29918, 2523, 2631, 29898, 5657, 1125, 13, 1678, 14550, 15941, 443, 293, 2631, 515, 599, 330, 27026, 29879, 297, 278, 4079, 29889, 12008, 13, 1678, 363, 330, 297, 4079, 29901, 13, 4706, 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darling_ansible/python_venv/lib/python3.7/site-packages/oci/os_management/models/installable_package_summary.py	revnav/sandbox	0	75731	<gh_stars>0 # coding: utf-8 # Copyright (c) 2016, 2020, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class InstallablePackageSummary(object): """ A software package available for install on a managed instance """ def __init__(self, kwargs): """ Initializes a new InstallablePackageSummary object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param display_name: The value to assign to the display_name property of this InstallablePackageSummary. :type display_name: str :param name: The value to assign to the name property of this InstallablePackageSummary. :type name: str :param type: The value to assign to the type property of this InstallablePackageSummary. :type type: str :param version: The value to assign to the version property of this InstallablePackageSummary. :type version: str :param architecture: The value to assign to the architecture property of this InstallablePackageSummary. :type architecture: str :param software_sources: The value to assign to the software_sources property of this InstallablePackageSummary. :type software_sources: list[SoftwareSourceId] """ self.swagger_types = { 'display_name': 'str', 'name': 'str', 'type': 'str', 'version': 'str', 'architecture': 'str', 'software_sources': 'list[SoftwareSourceId]' } self.attribute_map = { 'display_name': 'displayName', 'name': 'name', 'type': 'type', 'version': 'version', 'architecture': 'architecture', 'software_sources': 'softwareSources' } self._display_name = None self._name = None self._type = None self._version = None self._architecture = None self._software_sources = None @property def display_name(self): """ [Required] Gets the display_name of this InstallablePackageSummary. Package name :return: The display_name of this InstallablePackageSummary. :rtype: str """ return self._display_name @display_name.setter def display_name(self, display_name): """ Sets the display_name of this InstallablePackageSummary. Package name :param display_name: The display_name of this InstallablePackageSummary. :type: str """ self._display_name = display_name @property def name(self): """ [Required] Gets the name of this InstallablePackageSummary. Unique identifier for the package. NOTE - This is not an OCID :return: The name of this InstallablePackageSummary. :rtype: str """ return self._name @name.setter def name(self, name): """ Sets the name of this InstallablePackageSummary. Unique identifier for the package. NOTE - This is not an OCID :param name: The name of this InstallablePackageSummary. :type: str """ self._name = name @property def type(self): """ [Required] Gets the type of this InstallablePackageSummary. Type of the package :return: The type of this InstallablePackageSummary. :rtype: str """ return self._type @type.setter def type(self, type): """ Sets the type of this InstallablePackageSummary. Type of the package :param type: The type of this InstallablePackageSummary. :type: str """ self._type = type @property def version(self): """ [Required]** Gets the version of this InstallablePackageSummary. Version of the package :return: The version of this InstallablePackageSummary. :rtype: str """ return self._version @version.setter def version(self, version): """ Sets the version of this InstallablePackageSummary. Version of the package :param version: The version of this InstallablePackageSummary. :type: str """ self._version = version @property def architecture(self): """ Gets the architecture of this InstallablePackageSummary. The architecture for which this package was built :return: The architecture of this InstallablePackageSummary. :rtype: str """ return self._architecture @architecture.setter def architecture(self, architecture): """ Sets the architecture of this InstallablePackageSummary. The architecture for which this package was built :param architecture: The architecture of this InstallablePackageSummary. :type: str """ self._architecture = architecture @property def software_sources(self): """ Gets the software_sources of this InstallablePackageSummary. list of software sources that provide the software package :return: The software_sources of this InstallablePackageSummary. :rtype: list[SoftwareSourceId] """ return self._software_sources @software_sources.setter def software_sources(self, software_sources): """ Sets the software_sources of this InstallablePackageSummary. list of software sources that provide the software package :param software_sources: The software_sources of this InstallablePackageSummary. :type: list[SoftwareSourceId] """ self._software_sources = software_sources def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not 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ngspy/Promotor.py	manuSrep/ngspy	0	42304	#!/usr/bin/python # -- coding: utf8 -- """ Functions and classes for handling next generation sequencing data. :author: <NAME> :license: FreeBSD License ---------- Copyright (c) 2016, <NAME> All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ from __future__ import division, absolute_import, unicode_literals, print_function import datetime from .Formats import GFF, GFF3Entry def extractProm(inGFF, name, path, upstram=499, downstream=100, filter={}): """ Extract promotor regions from GFF file as provided by GENCODE: http://www.gencodegenes.org Genes can be filtered by GFF fields and attributes. Results are stored in a new GFF file. Parameters --------- inGFF : GFF The GFF object with a filled dataset. The entries describe the gene regions. From these regions the promoter is extracted. name : basestring The filename of the resulting GFF file. path : basestring The path where to save the results upstream : int Determines how many genes upstream of the TSS shall regarded as promoter. (in front of the gene) downstream : int Determines how many genes downstream of the TSS shall regarded as promoter. (inside the gene) filter : dict Regions in the GFF are only considerd if they match the filter entries. Keys can be any attribute in the GFFEntry. For most tags it is checked if the attribute value is in the filter. For "start" it is checked if the value is higher than the filter. The same is for score. For "end" it is checked if the value is lower than the filter. """ def check_attr(filter_attr, item_attr): for key in filter_attr.keys(): if not item_attr[key] in filter_attr[key]: return False return True if len(inGFF) == 0: raise ValueError("No data in input GFF") outGFF = GFF() outGFF._header_general.append("## Promotors extracted at: " + str(datetime.date.today()) + "\n") for gff in inGFF: # filter if item shall be extracted # shall we filer for seqid if "seqid" in filter: if not gff.seqid in filter["seqid"]: continue if "source" in filter: if not gff.source in filter["source"]: continue if "type" in filter: if not gff.type in filter["type"]: continue if "start" in filter: if not gff.start > filter["start"]: continue if "end" in filter: if not gff.end < filter["end"]: continue if "score" in filter: if gff.score < filter["score"]: continue if "strand" in filter: if not gff.strand in filter["strand"]: continue if "phase" in filter: if not gff.phase in filter["phase"]: continue if "attributes" in filter: if not check_attr(filter["attributes"], gff.attributes): continue seqid = gff.seqid source = "ROI" type = "promotor" strand = gff.strand attributes = {} attributes["ID"] = gff.attributes["ID"] # TSS varies from + and - strands if gff.strand == '-': start = gff.end - downstream end = gff.end + upstram else: # = is assumed for '.' start = gff.start - upstram end = gff.start + downstream entry = GFF3Entry(seqid, source, type, start, end, strand=strand, attributes=attributes) outGFF.append(entry) outGFF.sort() outGFF.write(name, path)	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29947, 448, 29930, 29899, 13, 13, 15945, 29908, 13, 6678, 29879, 322, 4413, 363, 11415, 2446, 12623, 8617, 16750, 848, 29889, 13, 29901, 8921, 29901, 529, 5813, 29958, 13, 29901, 506, 1947, 29901, 12362, 29933, 7230, 13, 13, 29931, 293, 1947, 13, 28400, 13, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29953, 29892, 529, 5813, 29958, 13, 3596, 10462, 21676, 29889, 13, 9039, 391, 3224, 322, 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ircclient.py	and3rson/notify	0	90703	import socket from select import select from time import sleep class IRCClient(object): def __init__(self, server, port, username, password): # super(Client, self).__init__() self.server = server self.port = port self.username = username self.password = password self.alive = False def send(self, message): self.conn.send(message + '\r\n') def start(self): self.alive = True while self.alive: while not self.connect(): pass self.process() def process(self): while self.alive: i, o, e = select([self.conn], [], [], 1) if self.conn in i: data = filter(None, self.conn.recv(1024).split('\r\n')) if not len(data): return for line in data: self.on_recv(line) def connect(self): try: self.conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.conn.connect((self.server, self.port)) self.send('NICK {}'.format(self.username)) self.send('PASS {}'.format(self.password)) self.send('USER {u} {u} {u} NotifyClient {u}'.format(u=self.username)) self.alive = True except: sleep(1) return False return True def on_recv(self, line): parts = line.split(' ') if parts[0].lower() == 'ping': self.send('PONG {}'.format(parts[1])) elif parts[1] == '001': self.on_connect() else: self.on_message(line) def on_message(self, line): raise NotImplementedError() def join(self, channel): self.send('JOIN #{}'.format(channel)) def privmsg(self, channel, message): self.send('PRIVMSG #{} {}'.format(channel, message)) def stop(self): self.alive = False print 'See ya!' self.send('QUIT')	[ 1, 1053, 9909, 13, 3166, 1831, 1053, 1831, 13, 3166, 931, 1053, 8709, 13, 13, 13, 1990, 306, 10363, 4032, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1923, 29892, 2011, 29892, 8952, 29892, 4800, 1125, 13, 4706, 396, 2428, 29898, 4032, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 13, 4706, 1583, 29889, 2974, 353, 1923, 13, 4706, 1583, 29889, 637, 353, 2011, 13, 4706, 1583, 29889, 6786, 353, 8952, 13, 4706, 1583, 29889, 5630, 353, 4800, 13, 13, 4706, 1583, 29889, 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test/testDecorators.py	redhog/ferenda	18	87187	# -- coding: utf-8 -- from __future__ import (absolute_import, division, print_function, unicode_literals) from builtins import * import sys import os import datetime import shutil import tempfile from ferenda.compat import unittest, Mock, MagicMock, patch from ferenda import util from ferenda import DocumentRepository, DocumentStore, Document, DocumentEntry from ferenda.errors import DocumentRemovedError, ParseError # SUT from ferenda.decorators import (timed, parseifneeded, ifneeded, render, handleerror, makedocument, recordlastdownload, downloadmax) class Decorators(unittest.TestCase): def test_timed(self): # Test that a wrapped method... @timed def testfunc(repo,doc): pass # ...passed a particular docrepo and doc mockrepo = Mock() mockdoc = Mock() mockdoc.basefile = "1234" # ...has it's instances logger called... testfunc(mockrepo,mockdoc) call_args = mockrepo.log.info.call_args # ...with the correct method and arguments self.assertEqual(len(call_args[0]), 2) self.assertEqual(call_args[0][0], 'parse OK (%.3f sec)') def test_parseifneeded(self): @parseifneeded def testfunc(repo, basefile): repo.called = True # mockdoc = Mock() # mockdoc.basefile="1234" mockbasefile = "1234" mockrepo = Mock() mockrepo.store.needed = DocumentStore(datadir='fake').needed mockrepo.called = False mockrepo.config.force = False # test 1: Outfile is newer - the parseifneeded decorator # should make sure the actual testfunc code is never reached with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older with patch('ferenda.util.outfile_is_newer', return_value=False): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False # test 3: Outfile is newer, but the global force option was set mockrepo.config.force = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False # test 4: Outfile is newer, but the module parseforce option was set mockrepo.config.parseforce = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False def test_ifneeded_parse(self): @ifneeded("parse") def testfunc(repo, basefile): repo.called = True # mockdoc = Mock() # mockdoc.basefile="1234" mockbasefile = "1234" mockrepo = Mock() mockrepo.store.needed = DocumentStore(datadir='fake').needed mockrepo.called = False mockrepo.config.force = False # test 1: Outfile is newer - the ifneeded decorator should # make sure the actual testfunc code is never reached with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older with patch('ferenda.util.outfile_is_newer', return_value=False): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False # test 3: Outfile is newer, but the global force option was set mockrepo.config.force = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False def test_ifneeded_relate(self): @ifneeded("relate") def testfunc(repo, basefile, needed): repo.called = True repo.needed = needed try: datadir = tempfile.mkdtemp() mockbasefile = "1234" mockrepo = Mock() mockrepo.store = DocumentStore(datadir=datadir) mockrepo.called = False mockrepo.config.force = False # create some docentry file in a good place de = DocumentEntry(mockrepo.store.documententry_path("1234")) now = datetime.datetime.now() if hasattr(now, 'timestamp'): # py3 timestamp = now.timestamp() else: import time # this isn't as good as the python3 timestamp() method # because this loses fractions of a second, but it'll # do. timestamp = time.mktime(now.timetuple()) de.indexed_ts = now + datetime.timedelta(seconds=3600) de.indexed_ft = now + datetime.timedelta(seconds=-3600) de.indexed_dep = now + datetime.timedelta(seconds=-3600) de.save() # test 1: Outfile is newer - the ifneeded decorator should # make sure the actual testfunc code is never reached # NOTE: the "relate" branch of DocumentStore.needed # doesn't use outfile_is_newer, so we can't patch that, we # have to create actual files parsedpath = mockrepo.store.parsed_path("1234") util.writefile(parsedpath, "dummy") os.utime(parsedpath, (timestamp, timestamp - 7200)) testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older than the information in the documententry file os.utime(parsedpath, (timestamp, timestamp)) testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) self.assertTrue(mockrepo.needed) self.assertFalse(mockrepo.needed.triples) self.assertFalse(mockrepo.needed.dependencies) self.assertTrue(mockrepo.needed.fulltext) mockrepo.called = False # test 3: Outfile is newer, but the global force option was set os.utime(parsedpath, (timestamp, timestamp - 7200)) mockrepo.config.force = True testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False finally: if os.path.exists(datadir): shutil.rmtree(datadir) def test_ifneeded_generate(self): @ifneeded("generate") def testfunc(repo, basefile): repo.called = True mockbasefile = "1234" mockrepo = Mock() mockrepo.store.needed = DocumentStore(datadir='fake').needed mockrepo.called = False mockrepo.config.force = False # test 1: Outfile is newer - the ifneeded decorator should # make sure the actual testfunc code is never reached with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older than source file with patch('ferenda.util.outfile_is_newer', return_value=False): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False # FIXME: we could add more tests, eg create a dependency file # and make sure an arbitrary file named in that depfile is # newer then outfile. but the tests in testDocStore.Needed # should cover that pretty well. # test 3: Outfile is newer, but the global force option was set mockrepo.config.force = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False @patch('ferenda.documentrepository.Graph') def test_render(self,mock_graph): @render def testfunc(repo,doc): pass mockdoc = Mock() mockrepo = MagicMock() mockrepo.store.parsed_path.return_value = "parsed_path.xhtml" with open("parsed_path.xhtml", "w") as fp: fp.write("""<?xml version="1.0" encoding="utf-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML+RDFa 1.0//EN" "http://www.w3.org/MarkUp/DTD/xhtml-rdfa-1.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" xmlns:dcterms="http://purl.org/dc/terms/"> <head about="http://example.org/doc"> <title property="dcterms:title">Document title</title> </head> <body> <h1>Hello!</h1> </body> </html>""") with open("entry_path.json", "w") as fp: fp.write("""{ "id": "https://lagen.nu/concept/St\\u00e5ende_anbud", "title": "St\\u00e5ende anbud", }""") mockrepo.store.distilled_path.return_value = "distilled_path.xhtml" mockrepo.get_globals.return_value = {'symbol table':'fake'} mockrepo.required_predicates = [] mockrepo.config = MagicMock() mockdoc.meta = MagicMock() # need Magicmock which supports magic funcs like __iter__ bodypart = MagicMock() bodypart.meta = MagicMock() mockdoc.body = [bodypart] mockdoc.meta.__iter__.return_value = [] mockdoc.uri = "http://example.org/doc" mockrepo.store.documententry_path.return_value = None with patch('ferenda.util.ensure_dir', return_value=True): testfunc(mockrepo, mockdoc) # 1 ensure that DocumentRepository.render_xhtml is called with # four arguments mockrepo.render_xhtml.assert_called_with(mockdoc, "parsed_path.xhtml") # 2 ensure that DocumentRepository.create_external_resources # is called with 1 argument mockrepo.create_external_resources.assert_called_with(mockdoc) # 3 ensure that a Graph object is created, its parse and # serialize methods called # FIXME: Why doesn't the patching work?! # self.assertTrue(mock_graph().parse.called) # self.assertTrue(mock_graph().serialize.called) # (4. ensure that a warning gets printed if doc.meta and # distilled_graph do not agree) mock_graph().__iter__.return_value = ['a','b'] mockdoc.meta.__iter__.return_value = ['a','b','c'] mockdoc.meta.serialize.return_value = b"<c>" with patch('ferenda.util.ensure_dir', return_value=True): testfunc(mockrepo, mockdoc) self.assertTrue(mockrepo.log.warning.called) os.remove("parsed_path.xhtml") os.remove("distilled_path.xhtml") os.remove("entry_path.json") def test_handleerror(self): @handleerror def testfunc(repo, doc): if doc.exception: raise doc.exception else: return True mockrepo = Mock() mockrepo.config = MagicMock() # must support __in__() mockdoc = Mock() # 1. should not raise an exception (but should call log.info # and util.robust_remove, and return false) with patch('ferenda.util.robust_remove') as robust_remove: mockdoc.exception = DocumentRemovedError self.assertFalse(testfunc(mockrepo, mockdoc)) self.assertTrue(mockrepo.log.info.called) self.assertTrue(robust_remove.called) # 2. should raise the same exception mockdoc.exception = KeyboardInterrupt with self.assertRaises(KeyboardInterrupt): testfunc(mockrepo, mockdoc) # 3.1 Should raise the same exeption mockdoc.exception = ParseError mockrepo.config.fatalexceptions = True with self.assertRaises(ParseError): testfunc(mockrepo, mockdoc) mockrepo.config.fatalexceptions = None # 3.2 Should not raise an exception (but should call log.error and return false) mockdoc.exception = ParseError self.assertFalse(testfunc(mockrepo, mockdoc)) self.assertTrue(mockrepo.log.error.called) # 4.1 Should raise the same exception mockdoc.exception = Exception mockrepo.config.fatalexceptions = True with self.assertRaises(Exception): testfunc(mockrepo, mockdoc) mockrepo.config.fatalexceptions = None # 4.2 Should not raise an exception mockdoc.exception = Exception self.assertFalse(testfunc(mockrepo, mockdoc)) self.assertTrue(mockrepo.log.error.called) # 5. No exceptions - everything should go fine mockdoc.exception = None self.assertTrue(testfunc(mockrepo, mockdoc)) def test_makedocument(self): @makedocument def testfunc(repo, doc): return doc doc = testfunc(DocumentRepository(), "base/file") self.assertIsInstance(doc, Document) self.assertEqual(doc.basefile, "base/file") def test_recordlastdownload(self): @recordlastdownload def testfunc(repo): pass mockrepo = Mock() with patch('ferenda.decorators.LayeredConfig.write') as mockconf: testfunc(mockrepo) # check that config.lastdownload has been set to a datetime self.assertIsInstance(mockrepo.config.lastdownload, datetime.datetime) # and that LayeredConfig.write has been called self.assertTrue(mockconf.called) def test_downloadmax(self): @downloadmax def testfunc(repo, source): for x in range(100): yield x mockrepo = MagicMock() mockrepo.config.downloadmax = None self.assertEqual(100, len(list(testfunc(mockrepo, None)))) mockrepo.config.downloadmax = 10 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076_Minimum_Window_Substring.py	joshlyman/Josh-LeetCode	0	6090	# Other solution # V2 def minWindow(s, t): need = collections.Counter(t) #hash table to store char frequency missing = len(t) #total number of chars we care start, end = 0, 0 i = 0 for j, char in enumerate(s, 1): #index j from 1 if need[char] > 0: missing -= 1 need[char] -= 1 if missing == 0: #match all chars while i < j and need[s[i]] < 0: #remove chars to find the real start need[s[i]] += 1 i += 1 need[s[i]] += 1 #make sure the first appearing char satisfies need[char]>0 missing += 1 #we missed this first char, so add missing by 1 if end == 0 or j-i < end-start: #update window start, end = i, j i += 1 #update i to start+1 for next window return s[start:end] # Time: O(\|S\|+\|T\|) # Space:O(\|S\|+\|T\|) # Refer from: # https://leetcode.com/problems/minimum-window-substring/solution/ # Sliding Window # We start with two pointers, leftleft and rightright initially pointing to the first element of the string S. # We use the rightright pointer to expand the window until we get a desirable window i.e. a window that contains all of the characters of T. # Once we have a window with all the characters, we can move the left pointer ahead one by one. If the window is still a desirable one we keep on updating the minimum window size. # If the window is not desirable any more, we repeat step 2 onwards. # The current window is s[i:j] and the result window is s[I:J]. In need[c] I store how many times I # need character c (can be negative) and missing tells how many characters are still missing. # In the loop, first add the new character to the window. Then, if nothing is missing, # remove as much as possible from the window start and then update the result. class Solution: def minWindow(self, s: str, t: str) -> str: m = len(s) n = len(t) if m < n: return '' lt = {} # put t into dict (lt) and count how many # for each char for i in t: if i not in lt: lt[i] = 1 else: lt[i] += 1 # missing is to count how many remaining char needed from substring # finally get candidate substring which satisfy need of t missing = n i = I = J = 0 for j, c in enumerate(s, 1): if c in lt and lt[c] > 0: missing -= 1 if c in lt: # lt can be negative lt[c] -= 1 # i is index of candidate substring, remove as many as char from candidate while i < j and not missing: if not J or j-i < J-I: I, J = i, j if s[i] not in lt: i += 1 continue else: # if lt contains s[i], then # of s[i] +1, might reach to 0 lt[s[i]] += 1 # if > 0, means we need more, then missing +1 if lt[s[i]] > 0: missing += 1 i += 1 return s[I:J] # Time: O(\|S\|+\|T\|) # Space:O(\|S\|+\|T\|) # Optimized Sliding Window # A small improvement to the above approach can reduce the time complexity of the algorithm to O(2*∣filtered_S∣+∣S∣+∣T∣), # where filtered(S) is the string formed from S by removing all the elements not present in T	[ 1, 29871, 13, 29937, 5901, 1650, 13, 29937, 478, 29906, 13, 13, 1753, 1375, 5907, 29898, 29879, 29892, 260, 1125, 13, 1678, 817, 353, 16250, 29889, 17779, 29898, 29873, 29897, 9651, 396, 8568, 1591, 304, 3787, 1373, 10868, 13, 1678, 4567, 353, 7431, 29898, 29873, 29897, 462, 308, 396, 7827, 1353, 310, 22524, 591, 2562, 13, 1678, 1369, 29892, 1095, 353, 29871, 29900, 29892, 29871, 29900, 13, 1678, 474, 353, 29871, 29900, 13, 1678, 363, 432, 29892, 1373, 297, 26985, 29898, 29879, 29892, 29871, 29896, 1125, 3986, 396, 2248, 432, 515, 29871, 29896, 13, 4706, 565, 817, 29961, 3090, 29962, 1405, 29871, 29900, 29901, 13, 9651, 4567, 22361, 29871, 29896, 13, 4706, 817, 29961, 3090, 29962, 22361, 29871, 29896, 13, 4706, 565, 4567, 1275, 29871, 29900, 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coreapi/models/company.py	recentfahim/smartbusinessbd	0	191965	<gh_stars>0 from generic.models import BaseModel from django.db import models from .city import City from .country import Country class Company(BaseModel): name = models.CharField(max_length=55) website = models.CharField(max_length=50, null=True, blank=True) email = models.EmailField(max_length=30, null=True, blank=True) address = models.CharField(max_length=100, blank=True, null=True) city = models.ForeignKey(City, on_delete=models.CASCADE, blank=True, null=True) region = models.CharField(max_length=20, blank=True, null=True) postcode = models.IntegerField(blank=True, null=True) country = models.ForeignKey(Country, on_delete=models.CASCADE, blank=True, null=True) phone = models.CharField(max_length=15, null=True, blank=True) fax = models.CharField(max_length=15, null=True, blank=True) image = models.CharField(max_length=50, blank=True, null=True) logo = models.CharField(max_length=50, blank=True, null=True) class Meta: db_table = 'company' verbose_name_plural = 'companies' def __str__(self): return self.name	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 10035, 29889, 9794, 1053, 7399, 3195, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 3166, 869, 12690, 1053, 4412, 13, 3166, 869, 13509, 1053, 15456, 13, 13, 13, 1990, 6938, 29898, 5160, 3195, 1125, 13, 1678, 1024, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29945, 29945, 29897, 13, 1678, 4700, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29945, 29900, 29892, 1870, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 4876, 353, 4733, 29889, 9823, 3073, 29898, 3317, 29918, 2848, 29922, 29941, 29900, 29892, 1870, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 3211, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29900, 29900, 29892, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 4272, 353, 4733, 29889, 27755, 2558, 29898, 16885, 29892, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 29892, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 5120, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29900, 29892, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 1400, 401, 353, 4733, 29889, 7798, 3073, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 4234, 353, 4733, 29889, 27755, 2558, 29898, 20779, 29892, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 29892, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 9008, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29945, 29892, 1870, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 285, 1165, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29945, 29892, 1870, 29922, 5574, 29892, 9654, 29922, 5574, 29897, 13, 1678, 1967, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29945, 29900, 29892, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 20194, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29945, 29900, 29892, 9654, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 4833, 29918, 2371, 353, 525, 14518, 29915, 13, 4706, 26952, 29918, 978, 29918, 572, 3631, 353, 525, 2388, 273, 583, 29915, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 978, 13, 13, 2 ]
circuit_maintenance_parser/data.py	pke11y/circuit-maintenance-parser	0	78155	<gh_stars>0 """Definition of Data classes.""" import logging from typing import List, NamedTuple, Optional, Type, Set import email from pydantic import BaseModel, Extra from circuit_maintenance_parser.constants import EMAIL_HEADER_SUBJECT, EMAIL_HEADER_DATE logger = logging.getLogger(__name__) class DataPart(NamedTuple): """Simplest data unit to be parsed.""" # type is an arbitrary string that is used to match the DataPart to the Parser class, that contains _data_types type: str content: bytes class NotificationData(BaseModel, extra=Extra.forbid): """Base class for Notification Data types.""" data_parts: List[DataPart] = [] def add_data_part(self, data_type: str, data_content: bytes): """Add a DataPart element into the instance data_parts.""" self.data_parts.append(DataPart(data_type, data_content)) @classmethod def init_from_raw( cls: Type["NotificationData"], data_type: str, data_content: bytes ) -> Optional["NotificationData"]: """Initialize the data_parts with only one DataPart object.""" try: return cls(data_parts=[DataPart(data_type, data_content)]) except Exception: # pylint: disable=broad-except logger.exception("Error found initializing data raw: %s, %s", data_type, data_content) return None @classmethod def init_from_email_bytes(cls: Type["NotificationData"], raw_email_bytes: bytes) -> Optional["NotificationData"]: """Initialize the data_parts from an email defined as raw bytes..""" try: raw_email_string = raw_email_bytes.decode("utf-8") email_message = email.message_from_string(raw_email_string) return cls.init_from_emailmessage(email_message) except Exception: # pylint: disable=broad-except logger.exception("Error found initializing data from email raw bytes: %s", raw_email_bytes) return None @classmethod def walk_email(cls, email_message, data_parts): """Recursive walk_email using Set to not duplicate data entries.""" for part in email_message.walk(): if "image" in part.get_content_type(): # Not interested in parsing images/QRs yet continue if "multipart" in part.get_content_type(): for inner_part in part.get_payload(): if isinstance(inner_part, email.message.Message): cls.walk_email(inner_part, data_parts) elif "message/rfc822" in part.get_content_type(): if isinstance(part.get_payload(), email.message.Message): cls.walk_email(part.get_payload(), data_parts) else: data_parts.add(DataPart(part.get_content_type(), part.get_payload(decode=True))) @classmethod def init_from_emailmessage(cls: Type["NotificationData"], email_message) -> Optional["NotificationData"]: """Initialize the data_parts from an email.message.Email object.""" try: data_parts: Set[DataPart] = set() cls.walk_email(email_message, data_parts) # Adding extra headers that are interesting to be parsed data_parts.add( DataPart( EMAIL_HEADER_SUBJECT, # decode_header() handles conversion from RFC2047 ASCII representation of non-ASCII content to # a list of (string, charset) tuples. # 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doc/epsilon_pretty_printing.py	Jojendersie/Epsilon-Intersection	14	119848	<filename>doc/epsilon_pretty_printing.py import gdb.printing import gdb class EiMatrixPrinter: "Prints ei::Matrix<> type" def __init__(self, val): self.val = val # Does not work: https://stackoverflow.com/questions/26472066/gdb-pretty-printing-returning-string-from-a-childrens-iterator-but-displaye/29752860 def children(self): rows = self.val.type.template_argument(1) cols = self.val.type.template_argument(2) data = self.val["m_data"] for y in range(rows): line = '' for x in range(cols): idx = x+ycols yield (str(idx), data[idx]) def to_string(self): return 'Matrix' #rows = self.val.type.template_argument(1) #cols = self.val.type.template_argument(2) #ps = '[' #data = self.val["m_data"] #if rows == 1 or cols == 1: # for i in range(rows cols): # ps += str(data[i]) # if (i+1) != rowscols: ps += ', ' # if rows > 1: ps += "]'" # else: ps += ']' #else: # for y in range(rows): # for x in range(cols): # ps += "%12.6g" % self.val["m_data"][x+ycols] # Multiversion line, looks good on hover, but fails in oneline views # if x != cols-1: ps += ' ' # if y != rows-1: ps +=']\n[' # else: ps += ']' #return ps def build_pretty_printer(): pp = gdb.printing.RegexpCollectionPrettyPrinter( "Epsilon Intersection") pp.add_printer('ei::Matrix', '^ei::Matrix.*$', EiMatrixPrinter) return pp	[ 1, 529, 9507, 29958, 1514, 29914, 5463, 29918, 1457, 4349, 29918, 2158, 292, 29889, 2272, 13, 5215, 330, 2585, 29889, 2158, 292, 13, 5215, 330, 2585, 13, 13, 1990, 382, 29875, 14609, 4040, 1639, 29901, 13, 1678, 376, 4040, 9466, 321, 29875, 1057, 14609, 25299, 1134, 29908, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 659, 1125, 13, 4706, 1583, 29889, 791, 353, 659, 13, 13, 1678, 396, 5538, 451, 664, 29901, 2045, 597, 2417, 29889, 510, 29914, 2619, 29914, 29906, 29953, 29946, 29955, 29906, 29900, 29953, 29953, 29914, 29887, 2585, 29899, 1457, 4349, 29899, 2158, 292, 29899, 2457, 292, 29899, 1807, 29899, 3166, 29899, 29874, 29899, 5145, 29878, 575, 29899, 17609, 29899, 4187, 29899, 4990, 29872, 29914, 29906, 29929, 29955, 29945, 29906, 29947, 29953, 29900, 13, 1678, 822, 4344, 29898, 1311, 1125, 13, 4706, 4206, 353, 1583, 29889, 791, 29889, 1853, 29889, 6886, 29918, 23516, 29898, 29896, 29897, 13, 4706, 28730, 353, 1583, 29889, 791, 29889, 1853, 29889, 6886, 29918, 23516, 29898, 29906, 29897, 13, 4706, 848, 353, 1583, 29889, 791, 3366, 29885, 29918, 1272, 3108, 13, 4706, 363, 343, 297, 3464, 29898, 5727, 1125, 13, 9651, 1196, 353, 6629, 13, 9651, 363, 921, 297, 3464, 29898, 22724, 1125, 13, 18884, 22645, 353, 921, 29974, 29891, 29930, 22724, 13, 18884, 7709, 313, 710, 29898, 13140, 511, 848, 29961, 13140, 2314, 13, 13, 1678, 822, 304, 29918, 1807, 29898, 1311, 1125, 13, 4706, 736, 525, 14609, 29915, 13, 4706, 396, 5727, 353, 1583, 29889, 791, 29889, 1853, 29889, 6886, 29918, 23516, 29898, 29896, 29897, 13, 4706, 396, 22724, 353, 1583, 29889, 791, 29889, 1853, 29889, 6886, 29918, 23516, 29898, 29906, 29897, 13, 4706, 396, 567, 353, 525, 1839, 13, 4706, 396, 1272, 353, 1583, 29889, 791, 3366, 29885, 29918, 1272, 3108, 13, 4706, 396, 361, 4206, 1275, 29871, 29896, 470, 28730, 1275, 29871, 29896, 29901, 13, 4706, 396, 1678, 363, 474, 297, 3464, 29898, 5727, 334, 28730, 1125, 13, 4706, 396, 4706, 6529, 4619, 851, 29898, 1272, 29961, 29875, 2314, 13, 4706, 396, 4706, 565, 313, 29875, 29974, 29896, 29897, 2804, 4206, 29930, 22724, 29901, 6529, 4619, 13420, 525, 13, 4706, 396, 1678, 565, 4206, 1405, 29871, 29896, 29901, 6529, 4619, 376, 29962, 11838, 13, 4706, 396, 1678, 1683, 29901, 6529, 4619, 525, 29962, 29915, 13, 4706, 396, 2870, 29901, 13, 4706, 396, 1678, 363, 343, 297, 3464, 29898, 5727, 1125, 13, 4706, 396, 4706, 363, 921, 297, 3464, 29898, 22724, 1125, 13, 4706, 396, 9651, 6529, 4619, 11860, 29896, 29906, 29889, 29953, 29887, 29908, 1273, 1583, 29889, 791, 3366, 29885, 29918, 1272, 3108, 29961, 29916, 29974, 29891, 29930, 22724, 29962, 396, 9683, 1536, 291, 1196, 29892, 3430, 1781, 373, 16758, 29892, 541, 8465, 297, 373, 5570, 8386, 13, 4706, 396, 9651, 565, 921, 2804, 28730, 29899, 29896, 29901, 6529, 4619, 525, 525, 13, 4706, 396, 4706, 565, 343, 2804, 4206, 29899, 29896, 29901, 6529, 4619, 2033, 29905, 29876, 1839, 13, 4706, 396, 4706, 1683, 29901, 6529, 4619, 525, 29962, 29915, 13, 4706, 396, 2457, 6529, 13, 13, 13, 1753, 2048, 29918, 1457, 4349, 29918, 558, 1639, 7295, 13, 1678, 6499, 353, 330, 2585, 29889, 2158, 292, 29889, 4597, 4548, 7196, 6572, 4349, 4040, 1639, 29898, 13, 4706, 376, 29923, 3232, 4124, 2042, 1159, 13, 1678, 6499, 29889, 1202, 29918, 558, 1639, 877, 10096, 1057, 14609, 742, 525, 29985, 10096, 1057, 14609, 5575, 29938, 742, 382, 29875, 14609, 4040, 1639, 29897, 13, 1678, 736, 6499, 2 ]
tests/unit/props/types/test_number.py	manoadamro/jason	0	193348	import pytest from jason import props def test_validate(): props.Number().load(5) def test_loads_int_from_string(): assert props.Number().load("5") == 5 def test_loads_float_from_string(): assert props.Number().load("5.5") == 5.5 def test_not_allow_strings(): with pytest.raises(props.PropertyValidationError): assert props.Number(allow_strings=False).load("5") == 5 def test_accepts_int(): assert props.Number().load(5) == 5 def test_accepts_float(): assert props.Number().load(5.5) == 5.5 def test_too_low(): with pytest.raises(props.PropertyValidationError): props.Number(min_value=10).load(5) def test_too_high(): with pytest.raises(props.PropertyValidationError): props.Number(max_value=10).load(20) def test_nullable(): props.Number(nullable=True).load(None) def test_not_nullable(): with pytest.raises(props.PropertyValidationError): props.Number().load(None) def test_wrong_type(): with pytest.raises(props.PropertyValidationError): props.Number().load("nope") def test_default(): assert props.Number(default=123).load(None) == 123	[ 1, 1053, 11451, 1688, 13, 13, 3166, 432, 1658, 1053, 17761, 13, 13, 13, 1753, 1243, 29918, 15480, 7295, 13, 1678, 17761, 29889, 4557, 2141, 1359, 29898, 29945, 29897, 13, 13, 13, 1753, 1243, 29918, 18132, 29918, 524, 29918, 3166, 29918, 1807, 7295, 13, 1678, 4974, 17761, 29889, 4557, 2141, 1359, 703, 29945, 1159, 1275, 29871, 29945, 13, 13, 13, 1753, 1243, 29918, 18132, 29918, 7411, 29918, 3166, 29918, 1807, 7295, 13, 1678, 4974, 17761, 29889, 4557, 2141, 1359, 703, 29945, 29889, 29945, 1159, 1275, 29871, 29945, 29889, 29945, 13, 13, 13, 1753, 1243, 29918, 1333, 29918, 9536, 29918, 19651, 7295, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 11030, 29889, 4854, 19448, 2392, 1125, 13, 4706, 4974, 17761, 29889, 4557, 29898, 9536, 29918, 19651, 29922, 8824, 467, 1359, 703, 29945, 1159, 1275, 29871, 29945, 13, 13, 13, 1753, 1243, 29918, 16044, 29879, 29918, 524, 7295, 13, 1678, 4974, 17761, 29889, 4557, 2141, 1359, 29898, 29945, 29897, 1275, 29871, 29945, 13, 13, 13, 1753, 1243, 29918, 16044, 29879, 29918, 7411, 7295, 13, 1678, 4974, 17761, 29889, 4557, 2141, 1359, 29898, 29945, 29889, 29945, 29897, 1275, 29871, 29945, 29889, 29945, 13, 13, 13, 1753, 1243, 29918, 517, 29877, 29918, 677, 7295, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 11030, 29889, 4854, 19448, 2392, 1125, 13, 4706, 17761, 29889, 4557, 29898, 1195, 29918, 1767, 29922, 29896, 29900, 467, 1359, 29898, 29945, 29897, 13, 13, 13, 1753, 1243, 29918, 517, 29877, 29918, 9812, 7295, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 11030, 29889, 4854, 19448, 2392, 1125, 13, 4706, 17761, 29889, 4557, 29898, 3317, 29918, 1767, 29922, 29896, 29900, 467, 1359, 29898, 29906, 29900, 29897, 13, 13, 13, 1753, 1243, 29918, 4304, 519, 7295, 13, 1678, 17761, 29889, 4557, 29898, 4304, 519, 29922, 5574, 467, 1359, 29898, 8516, 29897, 13, 13, 13, 1753, 1243, 29918, 1333, 29918, 4304, 519, 7295, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 11030, 29889, 4854, 19448, 2392, 1125, 13, 4706, 17761, 29889, 4557, 2141, 1359, 29898, 8516, 29897, 13, 13, 13, 1753, 1243, 29918, 15866, 549, 29918, 1853, 7295, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 11030, 29889, 4854, 19448, 2392, 1125, 13, 4706, 17761, 29889, 4557, 2141, 1359, 703, 1217, 412, 1159, 13, 13, 13, 1753, 1243, 29918, 4381, 7295, 13, 1678, 4974, 17761, 29889, 4557, 29898, 4381, 29922, 29896, 29906, 29941, 467, 1359, 29898, 8516, 29897, 1275, 29871, 29896, 29906, 29941, 13, 2 ]
outerJoin.py	eyalsus/python-fun	0	43738	import pandas as pd def leftOuterJoin(left_df, right_df, key): right_df['tmp'] = '@' join_df = left_df.merge(right_df[['tmp', key]], how='left', on=key) join_df = join_df[pd.isnull(join_df['tmp'])] join_df.drop('tmp', axis=1, inplace=True) right_df.drop('tmp', axis=1, inplace=True) return join_df def rightOuterJoin(left_df, right_df, key): return leftOuterJoin(right_df, left_df, key) existing_df = pd.DataFrame.from_dict([{'a': 1, 'b':2}, {'a': 11, 'b':22}, {'a': 111, 'b':222}]) new_df = pd.DataFrame.from_dict([{'a': 11, 'c':33}, {'a': 111, 'c':333}, {'a': 1111, 'c':3333}]) key = 'a' print leftOuterJoin(existing_df, new_df, key) print rightOuterJoin(existing_df, new_df, key)	[ 1, 1053, 11701, 408, 10518, 13, 13, 1753, 2175, 3744, 261, 17242, 29898, 1563, 29918, 2176, 29892, 1492, 29918, 2176, 29892, 1820, 1125, 13, 1678, 1492, 29918, 2176, 1839, 7050, 2033, 353, 18803, 29915, 13, 1678, 5988, 29918, 2176, 353, 2175, 29918, 2176, 29889, 14634, 29898, 1266, 29918, 2176, 29961, 1839, 7050, 742, 1820, 20526, 920, 2433, 1563, 742, 373, 29922, 1989, 29897, 13, 1678, 5988, 29918, 2176, 353, 5988, 29918, 2176, 29961, 15926, 29889, 275, 4304, 29898, 7122, 29918, 2176, 1839, 7050, 2033, 4638, 13, 1678, 5988, 29918, 2176, 29889, 8865, 877, 7050, 742, 9685, 29922, 29896, 29892, 297, 6689, 29922, 5574, 29897, 13, 1678, 1492, 29918, 2176, 29889, 8865, 877, 7050, 742, 9685, 29922, 29896, 29892, 297, 6689, 29922, 5574, 29897, 13, 1678, 736, 5988, 29918, 2176, 13, 13, 1753, 1492, 3744, 261, 17242, 29898, 1563, 29918, 2176, 29892, 1492, 29918, 2176, 29892, 1820, 1125, 13, 1678, 736, 2175, 3744, 261, 17242, 29898, 1266, 29918, 2176, 29892, 2175, 29918, 2176, 29892, 1820, 29897, 13, 13, 13, 735, 15423, 29918, 2176, 353, 10518, 29889, 17271, 29889, 3166, 29918, 8977, 4197, 10998, 29874, 2396, 29871, 29896, 29892, 525, 29890, 2396, 29906, 1118, 11117, 29874, 2396, 29871, 29896, 29896, 29892, 525, 29890, 2396, 29906, 29906, 1118, 11117, 29874, 2396, 29871, 29896, 29896, 29896, 29892, 525, 29890, 2396, 29906, 29906, 29906, 29913, 2314, 13, 1482, 29918, 2176, 353, 10518, 29889, 17271, 29889, 3166, 29918, 8977, 4197, 10998, 29874, 2396, 29871, 29896, 29896, 29892, 525, 29883, 2396, 29941, 29941, 1118, 11117, 29874, 2396, 29871, 29896, 29896, 29896, 29892, 525, 29883, 2396, 29941, 29941, 29941, 1118, 11117, 29874, 2396, 29871, 29896, 29896, 29896, 29896, 29892, 525, 29883, 2396, 29941, 29941, 29941, 29941, 29913, 2314, 13, 1989, 353, 525, 29874, 29915, 13, 13, 2158, 2175, 3744, 261, 17242, 29898, 735, 15423, 29918, 2176, 29892, 716, 29918, 2176, 29892, 1820, 29897, 13, 2158, 1492, 3744, 261, 17242, 29898, 735, 15423, 29918, 2176, 29892, 716, 29918, 2176, 29892, 1820, 29897, 13, 2 ]
prml/nn/math/product.py	brunomaga/PRML	11,017	48233	import numpy as np from prml.nn.function import Function class Product(Function): def __init__(self, axis=None, keepdims=False): if isinstance(axis, int): axis = (axis,) elif isinstance(axis, tuple): axis = tuple(sorted(axis)) self.axis = axis self.keepdims = keepdims def _forward(self, x): self.output = np.prod(x, axis=self.axis, keepdims=True) if not self.keepdims: return np.squeeze(self.output) else: return self.output def backward(self, delta, x): if not self.keepdims and self.axis is not None: for ax in self.axis: delta = np.expand_dims(delta, ax) dx = delta * self.output / x return dx def prod(x, axis=None, keepdims=False): """ product of all element in the array Parameters ---------- x : tensor_like input array axis : int, tuple of ints axis or axes along which a product is performed keepdims : bool keep dimensionality or not Returns ------- product : tensor_like product of all element """ return Product(axis=axis, keepdims=keepdims).forward(x)	[ 1, 1053, 12655, 408, 7442, 13, 3166, 544, 828, 29889, 15755, 29889, 2220, 1053, 6680, 13, 13, 13, 1990, 10969, 29898, 6678, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 9685, 29922, 8516, 29892, 3013, 6229, 29879, 29922, 8824, 1125, 13, 4706, 565, 338, 8758, 29898, 8990, 29892, 938, 1125, 13, 9651, 9685, 353, 313, 8990, 29892, 29897, 13, 4706, 25342, 338, 8758, 29898, 8990, 29892, 18761, 1125, 13, 9651, 9685, 353, 18761, 29898, 24582, 29898, 8990, 876, 13, 4706, 1583, 29889, 8990, 353, 9685, 13, 4706, 1583, 29889, 17462, 6229, 29879, 353, 3013, 6229, 29879, 13, 13, 1678, 822, 903, 11333, 29898, 1311, 29892, 921, 1125, 13, 4706, 1583, 29889, 4905, 353, 7442, 29889, 10633, 29898, 29916, 29892, 9685, 29922, 1311, 29889, 8990, 29892, 3013, 6229, 29879, 29922, 5574, 29897, 13, 4706, 565, 451, 1583, 29889, 17462, 6229, 29879, 29901, 13, 9651, 736, 7442, 29889, 29879, 802, 29872, 911, 29898, 1311, 29889, 4905, 29897, 13, 4706, 1683, 29901, 13, 9651, 736, 1583, 29889, 4905, 13, 13, 1678, 822, 1250, 1328, 29898, 1311, 29892, 19471, 29892, 921, 1125, 13, 4706, 565, 451, 1583, 29889, 17462, 6229, 29879, 322, 1583, 29889, 8990, 338, 451, 6213, 29901, 13, 9651, 363, 4853, 297, 1583, 29889, 8990, 29901, 13, 18884, 19471, 353, 7442, 29889, 18837, 29918, 6229, 29879, 29898, 4181, 29892, 4853, 29897, 13, 4706, 15414, 353, 19471, 334, 1583, 29889, 4905, 847, 921, 13, 4706, 736, 15414, 13, 13, 13, 1753, 11859, 29898, 29916, 29892, 9685, 29922, 8516, 29892, 3013, 6229, 29879, 29922, 8824, 1125, 13, 1678, 9995, 13, 1678, 3234, 310, 599, 1543, 297, 278, 1409, 13, 1678, 12662, 2699, 13, 1678, 448, 1378, 29899, 13, 1678, 921, 584, 12489, 29918, 4561, 13, 4706, 1881, 1409, 13, 1678, 9685, 584, 938, 29892, 18761, 310, 938, 29879, 13, 4706, 9685, 470, 27815, 3412, 607, 263, 3234, 338, 8560, 13, 1678, 3013, 6229, 29879, 584, 6120, 13, 4706, 3013, 22112, 537, 470, 451, 13, 1678, 16969, 13, 1678, 448, 22158, 13, 1678, 3234, 584, 12489, 29918, 4561, 13, 4706, 3234, 310, 599, 1543, 13, 1678, 9995, 13, 1678, 736, 10969, 29898, 8990, 29922, 8990, 29892, 3013, 6229, 29879, 29922, 17462, 6229, 29879, 467, 11333, 29898, 29916, 29897, 13, 2 ]
.history/app/scrape_20201229173546.py	Super-Web112/python-scraping-for-hosting	0	47370	from django.contrib.auth.decorators import login_required from django.shortcuts import render, get_object_or_404, redirect from django.http import HttpResponse, JsonResponse from selenium import webdriver from webdriver_manager.chrome import ChromeDriverManager from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By from selenium.webdriver.remote.webelement import WebElement from selenium.webdriver.common.action_chains import ActionChains from app.models import ItalianName, City import csv import os @login_required(login_url="/login/") def scrape_data(request): scrape_name = request.GET.get('scrape_name', None) start_id = request.GET.get('start_id', None) end_id = request.GET.get('end_id', None) option = webdriver.ChromeOptions() option.add_argument("window-size=1280,800") # option.add_argument("--headless") # Setup wait for later # wait = WebDriverWait(driver, 10) driver = '' url = '' id = int(start_id) while(id <= int(end_id)): csv_file = "csv_files/"+scrape_name+"_"+str(id)+".csv" if os.path.exists(csv_file): os.remove(csv_file) try: CLICK_NEXT_BUTTON = True CLICK_NEXT_BUTTON_NUM = 0; aaaaaaaaaa = 0 while (CLICK_NEXT_BUTTON): driver = webdriver.Chrome(ChromeDriverManager().install(), options=option) driver.set_window_position(0, 0) # driver.set_window_size(0, 0) url = 'https://sfera.sferabit.com/servizi/alboonlineBoot/index.php?id='+str(id) driver.get(url) original_window = driver.current_window_handle # Check we don't have other windows open already assert len(driver.window_handles) == 1 driver.switch_to.window(driver.window_handles[0]) driver.implicitly_wait(10) name_input = driver.find_element(By.ID, 'filtroRagioneSociale') name_input.send_keys(scrape_name) # ActionChains(driver).move_to_element(name_input).key_up('Francesco', name_input).perform() # ActionChains(driver).move_to_element(name_input).send_keys(name_input, 'Francesco').perform() # ActionChains(driver).move_to_element(name_input).key_down(Keys.CONTROL).send_keys('Francesco').key_up(Keys.CONTROL).perform() send_button = driver.find_element(By.CSS_SELECTOR, 'button.btn-primary') ActionChains(driver).move_to_element(send_button).click(send_button).perform() driver.implicitly_wait(10) if (CLICK_NEXT_BUTTON): for i in range(CLICK_NEXT_BUTTON_NUM): next_button = driver.find_elements(By.CSS_SELECTOR, '#risultatoRicerca a')[2] ActionChains(driver).move_to_element(next_button).click(next_button).perform() driver.implicitly_wait(10) pros_num_table = driver.find_element(By.CSS_SELECTOR, '#risultatoRicerca>table') pros_num_td = pros_num_table.find_elements(By.TAG_NAME, 'td')[1].get_attribute('innerHTML').strip().split(' ') pros_now_num = pros_num_td[0].strip() pros_total_num = pros_num_td[2].strip() print(pros_now_num) print(pros_total_num) if(pros_total_num in pros_now_num): CLICK_NEXT_BUTTON = False else: CLICK_NEXT_BUTTON = True modal_buttons = driver.find_elements(By.CSS_SELECTOR, 'button.buttonAnagrafica') for modal_button in modal_buttons: ActionChains(driver).move_to_element(modal_button).click(modal_button).perform() driver.implicitly_wait(30) fonts = driver.find_elements(By.CSS_SELECTOR, '#modalPersona td') address = name = birth = email = pec = tel = phone = '' if(len(fonts) > 5): use_nums = [2, 3, 5] first_pros = [] result_pros = [] for use_num in use_nums: first_pros.extend(fonts[use_num].get_attribute("innerHTML").strip().split("<br>")) for first_pro in first_pros: temp = first_pro.replace(" ", " ").strip() temp1 = temp.lower() temp_flag = ('foro di appartenenza' in temp1) or (('data' in temp1) and ('nascita' in temp1)) or ('email' in temp1) or ('pec' in temp1) or ('tel' in temp1) or ('cell' in temp1) if(temp_flag): result_pros.append(temp) for result_pro in result_pros: if(":" in result_pro): rows = result_pro.split(":") temp = rows[0].lower() if("foro di appartenenza" in temp): address = rows[1].strip() if("data" in temp): birth = rows[1].strip() if("email" in temp): email = result_pro.split(">")[1].strip()[:-3] if("pec" in temp): pec = result_pro.split(">")[1].strip()[:-3] if("tel" in temp): tel = rows[1].strip() if("cell" in temp): phone = rows[1].strip() name = fonts[1].get_attribute("innerHTML").split("<b>")[1].strip().split("</b>")[0].strip() print(address) print(name) print(birth) print(email) print(pec) print(tel) print(phone) with open(csv_file, mode='a') as employee_file: employee_writer = csv.writer(employee_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) employee_writer.writerow([address, name, birth, email, pec, tel, phone]) aaaaaaaaaa = aaaaaaaaaa + 1 print(aaaaaaaaaa) close_buttons = driver.find_element(By.CSS_SELECTOR, "button.btn-secondary") driver.implicitly_wait(10) ActionChains(driver).move_to_element(close_buttons).click(close_buttons).perform() driver.implicitly_wait(10) CLICK_NEXT_BUTTON_NUM = CLICK_NEXT_BUTTON_NUM + 1 print(CLICK_NEXT_BUTTON) print(CLICK_NEXT_BUTTON_NUM) driver.implicitly_wait(10) driver.quit() except: driver.quit() print("id="+str(id)+" is not support.") continue finally: # driver.implicitly_wait(10) # driver.quit() id = id + 1 print(str(id)) data = { 'is_taken': 'sdfsdfsdfsdfsdf' } return JsonResponse(data) @login_required(login_url="/login/") def scrape_aaa(request): ItalianName.objects.all().delete() # username = request.GET.get('username', None) option = webdriver.ChromeOptions() option.add_argument("window-size=1280,800") # option.add_argument("--headless") driver = webdriver.Chrome(ChromeDriverManager().install(), options=option) driver.set_window_position(0, 0) driver.get('https://www.nomix.it/nomi-italiani-maschili-e-femminili.php') urls = {'https://www.nomix.it/nomi-italiani-maschili-e-femminili.php', 'https://www.nomix.it/nomi-italiani-lettera-B.php', 'https://www.nomix.it/nomi-italiani-lettera-C.php', 'https://www.nomix.it/nomi-italiani-lettera-D.php', 'https://www.nomix.it/nomi-italiani-lettera-E.php', 'https://www.nomix.it/nomi-italiani-lettera-F.php', 'https://www.nomix.it/nomi-italiani-lettera-G.php', 'https://www.nomix.it/nomi-italiani-lettera-I.php', 'https://www.nomix.it/nomi-italiani-lettera-L.php', 'https://www.nomix.it/nomi-italiani-lettera-M.php', 'https://www.nomix.it/nomi-italiani-lettera-NO.php', 'https://www.nomix.it/nomi-italiani-lettera-PQ.php', 'https://www.nomix.it/nomi-italiani-lettera-R.php', 'https://www.nomix.it/nomi-italiani-lettera-S.php', 'https://www.nomix.it/nomi-italiani-lettera-TUV.php', 'https://www.nomix.it/nomi-italiani-lettera-WZ.php'} for url in urls: driver.get(url) driver.implicitly_wait(10) original_window = driver.current_window_handle # Check we don't have other windows open already assert len(driver.window_handles) == 1 driver.switch_to.window(driver.window_handles[0]) tables = driver.find_elements(By.TAG_NAME, 'table') male_tds = tables[2].find_elements(By.TAG_NAME, 'td') for male_td in male_tds: td_content = male_td.get_attribute('innerHTML') if('<div' not in td_content): if('strong' in td_content): td_content = td_content.replace(' ', '') td_content = td_content.replace('<strong>', '') td_content = td_content.replace('</strong>', '').strip() else: td_content = td_content.replace(' ', '') td_content = td_content.strip() print(td_content) italy_name = ItalianName(names_col=td_content, gender_col='male') italy_name.save() # italy_name.names_col = td_content # italy_name.gender_col = 'male' # italy_name.save() male_tds = tables[3].find_elements(By.TAG_NAME, 'td') for male_td in male_tds: td_content = male_td.get_attribute('innerHTML') if('<div' not in td_content): if('strong' in td_content): td_content = td_content.replace(' ', '') td_content = td_content.replace('<strong>', '') td_content = td_content.replace('</strong>', '').strip() else: td_content = td_content.replace(' ', '') td_content = td_content.strip() print(td_content) italy_name = ItalianName(names_col=td_content, gender_col='male') italy_name.save() driver.implicitly_wait(10) driver.quit() data = { 'is_taken': 'sdfsdfsdfsdfsdf' } return JsonResponse(data) @login_required(login_url="/login/") def scrape_name(request): # username = request.GET.get('username', None) i = 1080 driver = '' while(i<=1086): try: option = webdriver.ChromeOptions() option.add_argument("window-size=1280,800") # option.add_argument("--headless") driver = webdriver.Chrome(ChromeDriverManager().install(), options=option) driver.get('https://sfera.sferabit.com/servizi/alboonlineBoot/index.php?id='+str(i)) original_window = driver.current_window_handle # Check we don't have other windows open already assert len(driver.window_handles) == 1 driver.switch_to.window(driver.window_handles[0]) name_input = driver.find_element(By.ID, 'filtroRagioneSociale') name_input.send_keys('an') send_button = driver.find_element(By.CSS_SELECTOR, 'button.btn-primary') 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normalizer/__init__.py	TCC-ADS-2020/Normalizador	7	110900	from .normalizer import *	[ 1, 515, 869, 8945, 3950, 1053, 334, 13, 2 ]
nels-test-integration/api/job.py	elixir-no-nels/nels-core	3	69426	# -- coding: utf-8 -- ''' Created on Sep 2, 2016 @author: Kidane ''' import requests import sys import config from utils import feed_utils STORAGE_COPY = 100 STORAGE_MOVE = 101 SBI_PUSH = 106 #norstore: 102 SBI_PULL = 107 #norstore: 103 TSD_PUSH = 104 TSD_PULL = 105 def add_job(job_type_id, nels_id, src_items, dest_folder): try: url = config.api_url("jobs/add") if job_type_id == STORAGE_COPY or job_type_id == STORAGE_MOVE: params = {"nels_id": nels_id, "job_type_id": job_type_id, "parameters": {"source": src_items, "destination": dest_folder}} response = requests.post(url, json=params, auth=(config.API_KEY, config.API_SECRET)) if (response.status_code == requests.codes.created): return response.json()['job_id'] except: feed_utils.error(sys.exc_info()[0]) return -1 def add_sbi_job(job_type_id, nels_id, files_list, folders_list, remote_host, user_name, ssh_key, dataset_id, dataset, subtype, subtype_id, parent_path_of_source, destination_path): try: url = config.api_url("jobs/add") if job_type_id == SBI_PUSH or job_type_id == SBI_PULL: params = {"nels_id": nels_id, "job_type_id": job_type_id, "parameters": {"remote_host": remote_host, "user_name": user_name, "ssh_key": ssh_key, "dataset_id": dataset_id, "dataset": dataset, "subtype": subtype, "subtype_id": subtype_id, "parent_path_of_source": parent_path_of_source, "destination_path": destination_path, "files": files_list, "folders": folders_list }} feed_utils.info("Job type id: %s" % params["job_type_id"]) response = requests.post(url, json=params, auth=(config.API_KEY, config.API_SECRET)) if (response.status_code == requests.codes.created): feed_utils.info(response.text) return response.json()['job_id'] else: feed_utils.info(response.text) except: feed_utils.error(sys.exc_info()[0]) return -1 def get_job_info(job_id): try: url = config.api_url("jobs/%s" % job_id) params = {"jobId": job_id} response = requests.get(url, json=params, auth=(config.API_KEY, config.API_SECRET)) if (response.status_code == requests.codes.ok): return response.json(); except: feed_utils.error(sys.exc_info()[0]) return None def delete_job(job_id): try: url = config.api_url("jobs/%s" % job_id) response = requests.delete(url, auth=(config.API_KEY, config.API_SECRET)) return (response.status_code == requests.codes.no_content) except: feed_utils.error(sys.exc_info()[0]) return False def wait_for_job(job_id): completion = -1 job_info = get_job_info(job_id) while (job_info["completion"] != 100): if (job_info["completion"] != completion): completion = job_info["completion"] feed_utils.info("waiting for job to finish: completion %s" % completion) job_info = get_job_info(job_id) feed_utils.info("job completed")	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 12008, 13, 20399, 373, 29639, 29871, 29906, 29892, 29871, 29906, 29900, 29896, 29953, 13, 13, 29992, 8921, 29901, 27912, 1662, 13, 12008, 13, 5215, 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urlunescape.py	chys87/scripts	0	172960	<reponame>chys87/scripts #! /usr/bin/env python3 # -- coding: utf-8 -- # vim:ts=8 sts=4 sw=4 expandtab ft=python # # Copyright (c) 2014, chys <<EMAIL>> # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # Neither the name of chys <<EMAIL>> nor the names of other # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # import sys from urllib.parse import unquote DEFAULT_CHARSETS = ['utf-8', 'gb18030'] def unescape(url, attempt_charsets=DEFAULT_CHARSETS): for enc in attempt_charsets: try: return unquote(url, encoding=enc, errors='strict') except UnicodeDecodeError: pass return url def main(): if len(sys.argv) > 1: for url in sys.argv[1:]: print(unescape(url)) else: while True: try: url = input() except EOFError: break print(unescape(url)) if __name__ == '__main__': main()	[ 1, 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Project3/Demo_use_q2_point.py

yunchen-yang/CV_classes

0

163994

<filename>Project3/Demo_use_q2_point.py import cv2 import numpy as np import copy import sys path=sys.path[0]+'/' img_input=cv2.imread(path+"original_imgs/point.jpg",0) filter_kernel=np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]) size_img_input=img_input.shape kernel_size=filter_kernel.shape decenter=int((kernel_size[1]-1)/2) new_img=np.zeros(img_input.shape) for i in range(0,size_img_input[0]): for j in range(0,size_img_input[1]): H=np.zeros((kernel_size[0],kernel_size[1])).astype('int') G=np.zeros((kernel_size[0],kernel_size[1])).astype('int') for n in range(-decenter,decenter+1): for m in range(-decenter,decenter+1): if i+n in range(0,size_img_input[0]) and j+m in range(0,size_img_input[1]): H[decenter+n,decenter+m]=img_input[i+n,j+m] else: H[decenter+n,decenter+m]=0 for x in range(-decenter,decenter+1): for y in range(-decenter,decenter+1): G[decenter+x,decenter+y]=H[decenter+x,decenter+y]*filter_kernel[decenter+x,decenter+y] new_img[i,j]=sum(sum(G)) #img_new_adjusted=new_img.clip(0, 255) #img_new=np.rint(img_new_adjusted).astype('uint8') #cv2.imshow('img_mask',img_new)

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slippy/core/tests/test_materials.py

KDriesen/slippy

12

17278

import numpy as np import numpy.testing as npt import slippy import slippy.core as core """ If you add a material you need to add the properties that it will be tested with to the material_parameters dict, the key should be the name of the class (what ever it is declared as after the class key word). The value should be a tuple of dicts: The first dict in the tuple will be unpacked to instantiate the class, The second will be used with the displacement from loads method The third will be used with the loads from displacement method to ensure that the methods are inverses of each other If there is a limit the applicability of the displacements from loads method (such as for a perfectly plastic material the _max_load key word should be set in the second dict. For more complex behaviour please also implement your own tests """ material_parameters = { 'Elastic': ({'name': 'steel_5', 'properties': {'E': 200e9, 'v': 0.3}}, {'grid_spacing': 0.01, 'simple': True}, {'grid_spacing': 0.01, 'simple': True, 'tol': 1e-9}), 'Rigid': ({}, {}, {}) } exceptions = [core.Rigid] def test_materials_basic(): # check that one of influence matrix or displacement from loading is given for material in core.materials._IMMaterial._subclass_registry: if material in exceptions: continue try: mat_params = material_parameters[material.material_type] except KeyError: raise AssertionError(f"Material test parameters are not specified, for material {material.material_type}") mat_instance = material(**mat_params[0]) max_load = mat_params[1].pop('_max_load', 1) np.random.seed(0) loads = np.random.rand(16, 16) * max_load # check that the loads and displacement functions are inverse of each other for direction in {'x', 'y', 'z'}: load_in_direction = {direction: loads} displacement = mat_instance.displacement_from_surface_loads(load_in_direction, **mat_params[1]) set_disp = displacement[direction] loads_calc = mat_instance.loads_from_surface_displacement(displacements={direction: set_disp}, **mat_params[2]) npt.assert_allclose(loads, slippy.asnumpy(loads_calc[direction]), atol=max_load * 0.02) def test_elastic_coupled(): mat = core.Elastic('steel_6', {'E': 200e9, 'v': 0.3}) np.random.seed(0) loads1 = np.random.rand(16, 16) loads2 = np.random.rand(16, 16) directions = 'xyzx' for i in range(3): dir_1 = directions[i] dir_2 = directions[i+1] loads_in_direction = {dir_1: loads1, dir_2: loads2} displacement = mat.displacement_from_surface_loads(loads_in_direction, grid_spacing=0.01, simple=True) loads_calc = mat.loads_from_surface_displacement(displacements=displacement, grid_spacing=0.01, simple=True) for direction in [dir_1, dir_2]: npt.assert_allclose(loads_in_direction[direction], slippy.asnumpy(loads_calc[direction]), atol=0.02) displacement = mat.displacement_from_surface_loads(loads_in_direction, grid_spacing=0.01, simple=False) loads_calc = mat.loads_from_surface_displacement(displacements=displacement, grid_spacing=0.01, simple=False) for direction in [dir_1, dir_2]: npt.assert_allclose(loads_in_direction[direction], slippy.asnumpy(loads_calc[direction]), atol=0.02)

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pandaserver/workflow/pcwl_utils.py

virthead/panda-server

0

156696

<reponame>virthead/panda-server<filename>pandaserver/workflow/pcwl_utils.py import copy import re import six import os.path from pathlib import Path from ruamel import yaml from urllib.parse import urlparse from .workflow_utils import Node, ConditionItem WORKFLOW_NAMES = ['prun', 'phpo', 'junction', 'reana'] # extract id def extract_id(id_str): if not id_str: return id_str if not isinstance(id_str, list): id_str = [id_str] not_list = True else: not_list = False items = [re.search(r'[^/]+#.+$', s).group(0) for s in id_str] if not_list: return items[0] return items # topological sorting def top_sort(list_data, visited): if not list_data: return [] new_list = [] new_visited = [] for node in list_data: isOK = True for p in node.parents: if p not in visited: isOK = False break if isOK: new_visited.append(node) visited.add(node.id) else: new_list.append(node) return new_visited + top_sort(new_list, visited) # parse CWL file def parse_workflow_file(workflow_file, log_stream, in_loop=False): # read the file from yaml cwl_file = Path(os.path.abspath(workflow_file)) with open(cwl_file, "r") as cwl_h: yaml_obj = yaml.main.round_trip_load(cwl_h, preserve_quotes=True) # Check CWLVersion if 'cwlVersion' not in list(yaml_obj.keys()): log_stream.error("could not get the cwlVersion in {}".format(cwl_file.as_uri())) return False, None # Import parser based on CWL Version if yaml_obj['cwlVersion'] == 'v1.0': from cwl_utils import parser_v1_0 as parser elif yaml_obj['cwlVersion'] == 'v1.1': from cwl_utils import parser_v1_1 as parser elif yaml_obj['cwlVersion'] == 'v1.2': from cwl_utils import parser_v1_2 as parser else: log_stream.error("Version error. Did not recognise {} as a CWL version".format(yaml_obj["CWLVersion"])) return False, None # Import CWL Object root_obj = parser.load_document_by_yaml(yaml_obj, cwl_file.as_uri()) # root inputs root_inputs = {extract_id(s.id): s.default for s in root_obj.inputs} # root outputs root_outputs = set([extract_id(s.id.split('#')[0] + '#' + re.sub(s.id+'/', '', s.outputSource)) for s in root_obj.outputs]) # loop over steps node_list = [] output_map = {} serial_id = 0 for step in root_obj.steps: cwl_name = os.path.basename(step.run) # check cwl command if not cwl_name.endswith('.cwl') and cwl_name not in WORKFLOW_NAMES: log_stream.error("Unknown workflow {}".format(step.run)) return False, None serial_id += 1 workflow_name = step.id.split('#')[-1] # leaf workflow and sub-workflow if cwl_name == 'prun.cwl': node = Node(serial_id, 'prun', None, True, workflow_name) elif cwl_name == 'phpo.cwl': node = Node(serial_id, 'phpo', None, True, workflow_name) elif cwl_name in WORKFLOW_NAMES: node = Node(serial_id, cwl_name, None, True, workflow_name) else: node = Node(serial_id, 'workflow', None, False, workflow_name) node.inputs = {extract_id(s.id): {'default': s.default, 'source': extract_id(s.source)} for s in step.in_} node.outputs = {extract_id(s): {} for s in step.out} # add outDS if no output is defined if not node.outputs: node.outputs = {extract_id(step.id + '/outDS'): {}} output_map.update({name: serial_id for name in node.outputs}) if step.scatter: node.scatter = [extract_id(s) for s in step.scatter] if hasattr(step, 'when') and step.when: # parse condition node.condition = parse_condition_string(step.when) # suppress inputs based on condition suppress_inputs_based_on_condition(node.condition, node.inputs) if step.hints and 'loop' in step.hints: node.loop = True if node.loop or in_loop: node.in_loop = True # expand sub-workflow if not node.is_leaf: p = urlparse(step.run) tmp_path = os.path.abspath(os.path.join(p.netloc, p.path)) node.sub_nodes, node.root_inputs = parse_workflow_file(tmp_path, log_stream, node.in_loop) # check if tail if root_outputs & set(node.outputs): node.is_tail = True node_list.append(node) # look for parents for node in node_list: for tmp_name, tmp_data in six.iteritems(node.inputs): if not tmp_data['source']: continue if isinstance(tmp_data['source'], list): sources = tmp_data['source'] is_str = False else: sources = [tmp_data['source']] is_str = True parent_ids = [] for tmp_source in sources: if tmp_source in output_map: parent_id = output_map[tmp_source] node.add_parent(parent_id) parent_ids.append(parent_id) if parent_ids: if is_str: parent_ids = parent_ids[0] tmp_data['parent_id'] = parent_ids # sort node_list = top_sort(node_list, set()) return node_list, root_inputs # resolve nodes def resolve_nodes(node_list, root_inputs, data, serial_id, parent_ids, out_ds_name, log_stream): for k in root_inputs: kk = k.split('#')[-1] if kk in data: root_inputs[k] = data[kk] tmp_to_real_id_map = {} resolved_map = {} all_nodes = [] for node in node_list: # resolve input for tmp_name, tmp_data in six.iteritems(node.inputs): if not tmp_data['source']: continue if isinstance(tmp_data['source'], list): tmp_sources = tmp_data['source'] if 'parent_id' in tmp_data: tmp_parent_ids = tmp_data['parent_id'] else: tmp_parent_ids = [None] * len(tmp_sources) else: tmp_sources = [tmp_data['source']] if 'parent_id' in tmp_data: tmp_parent_ids = [tmp_data['parent_id']] else: tmp_parent_ids = [None] * len(tmp_sources) for tmp_source, tmp_parent_id in zip(tmp_sources, tmp_parent_ids): isOK = False # check root input if tmp_source in root_inputs: node.is_head = True node.set_input_value(tmp_name, tmp_source, root_inputs[tmp_source]) continue # check parent output for i in node.parents: for r_node in resolved_map[i]: if tmp_source in r_node.outputs: node.set_input_value(tmp_name, tmp_source, r_node.outputs[tmp_source]['value']) isOK = True break if isOK: break if isOK: continue # check resolved parent outputs if tmp_parent_id is not None: values = [list(r_node.outputs.values())[0]['value'] for r_node in resolved_map[tmp_parent_id]] if len(values) == 1: values = values[0] node.set_input_value(tmp_name, tmp_source, values) continue # scatter if node.scatter: # resolve scattered parameters scatters = None sc_nodes = [] for item in node.scatter: if scatters is None: scatters = [{item: v} for v in node.inputs[item]['value']] else: [i.update({item: v}) for i, v in zip(scatters, node.inputs[item]['value'])] for idx, item in enumerate(scatters): sc_node = copy.deepcopy(node) for k, v in six.iteritems(item): sc_node.inputs[k]['value'] = v for tmp_node in sc_node.sub_nodes: tmp_node.scatter_index = idx tmp_node.upper_root_inputs = sc_node.root_inputs sc_nodes.append(sc_node) else: sc_nodes = [node] # loop over scattered nodes for sc_node in sc_nodes: all_nodes.append(sc_node) # set real node ID resolved_map.setdefault(sc_node.id, []) tmp_to_real_id_map.setdefault(sc_node.id, set()) # resolve parents real_parens = set() for i in sc_node.parents: real_parens |= tmp_to_real_id_map[i] sc_node.parents = real_parens if sc_node.is_head: sc_node.parents |= parent_ids if sc_node.is_leaf: resolved_map[sc_node.id].append(sc_node) tmp_to_real_id_map[sc_node.id].add(serial_id) sc_node.id = serial_id serial_id += 1 else: serial_id, sub_tail_nodes, sc_node.sub_nodes = \ resolve_nodes(sc_node.sub_nodes, sc_node.root_inputs, sc_node.convert_dict_inputs(), serial_id, sc_node.parents, out_ds_name, log_stream) resolved_map[sc_node.id] += sub_tail_nodes tmp_to_real_id_map[sc_node.id] |= set([n.id for n in sub_tail_nodes]) sc_node.id = serial_id serial_id += 1 # convert parameters to parent IDs in conditions if sc_node.condition: convert_params_in_condition_to_parent_ids(sc_node.condition, sc_node.inputs, tmp_to_real_id_map) # resolve outputs if sc_node.is_leaf: for tmp_name, tmp_data in six.iteritems(sc_node.outputs): tmp_data['value'] = "{}_{:03d}_{}".format(out_ds_name, sc_node.id, sc_node.name) # add loop count for nodes in a loop if sc_node.in_loop: tmp_data['value'] += '.___idds___num_run___' # return tails tail_nodes = [] for node in all_nodes: if node.is_tail: if node.is_tail: tail_nodes.append(node) else: tail_nodes += resolved_map[node.id] return serial_id, tail_nodes, all_nodes # parse condition string def parse_condition_string(cond_string): # remove $() cond_string = re.sub(r'\$$(?P<aaa>.+)$', r'\g<aaa>', cond_string) cond_map = {} id = 0 while True: # look for the most inner parentheses item_list = re.findall(r'$([^\($]+)\)', cond_string) if not item_list: return convert_plain_condition_string(cond_string, cond_map) else: for item in item_list: cond = convert_plain_condition_string(item, cond_map) key = '___{}___'.format(id) id += 1 cond_map[key] = cond cond_string = cond_string.replace('('+item+')', key) # extract parameter from token def extract_parameter(token): m = re.search(r'self\.([^!=]+)', token) return m.group(1) # convert plain condition string def convert_plain_condition_string(cond_string, cond_map): cond_string = re.sub(r' *! *', r'!', cond_string) cond_string = re.sub(r'\|\|', r' || ', cond_string) cond_string = re.sub(r'&&', r' && ', cond_string) tokens = cond_string.split() left = None operator = None for token in tokens: token = token.strip() if token == '||': operator = 'or' continue elif token == '&&': operator = 'and' continue elif token.startswith('self.'): param = extract_parameter(token) right = ConditionItem(param) if not left: left = right continue elif token.startswith('!self.'): param = extract_parameter(token) right = ConditionItem(param, operator='not') if not left: left = right continue elif re.search(r'^___\d+___$', token) and token in cond_map: right = cond_map[token] if not left: left = right continue elif re.search(r'^!___\d+___$', token) and token[1:] in cond_map: right = ConditionItem(cond_map[token[1:]], operator='not') if not left: left = right continue else: raise TypeError('unknown token "{}"'.format(token)) left = ConditionItem(left, right, operator) return left # convert parameter names to parent IDs def convert_params_in_condition_to_parent_ids(condition_item, input_data, id_map): for item in ['left', 'right']: param = getattr(condition_item, item) if isinstance(param, str): m = re.search(r'^[^\[]+\[(\d+)\]', param) if m: param = param.split('[')[0] idx = int(m.group(1)) else: idx = None isOK = False for tmp_name, tmp_data in six.iteritems(input_data): if param == tmp_name.split('/')[-1]: isOK = True if isinstance(tmp_data['parent_id'], list): if idx is not None: setattr(condition_item, item, id_map[tmp_data['parent_id'][idx]]) else: setattr(condition_item, item, id_map[tmp_data['parent_id']]) else: setattr(condition_item, item, id_map[tmp_data['parent_id']]) break if not isOK: raise ReferenceError('unresolved paramter {} in the condition string'.format(param)) elif isinstance(param, ConditionItem): convert_params_in_condition_to_parent_ids(param, input_data, id_map) # suppress inputs based on condition def suppress_inputs_based_on_condition(condition_item, input_data): if condition_item.right is None and condition_item.operator == 'not' and isinstance(condition_item.left, str): for tmp_name, tmp_data in six.iteritems(input_data): if condition_item.left == tmp_name.split('/')[-1]: tmp_data['suppressed'] = True else: for item in ['left', 'right']: param = getattr(condition_item, item) if isinstance(param, ConditionItem): suppress_inputs_based_on_condition(param, input_data)

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h1st/model/ensemble/randomforest_classifier_stack_ensemble.py

Adatao/H1st

796

135115

<reponame>Adatao/H1st from typing import List from sklearn.ensemble import RandomForestClassifier from h1st.model.ensemble.classifier_stack_ensemble import ClassifierStackEnsemble from h1st.model.model import Model class RandomForestClassifierStackEnsemble(ClassifierStackEnsemble): """ A ready to use StackEnsemble for classifier with ensembler is a sklearn's MultiOutputClassifier using RandomForestClassifier Each sub model must be a subclass of h1.Model and its .predict() method will receive an input data as a dictionary that has 'X' key and numeric value and return a dictionary with 'predictions' key and its prediction value .. code-block:: python :caption: Sub model for a StackEnsemble Example class Model1(h1.Model): def predict(self, data): X = data['X'] ... return {'predictions': } .. code-block:: python :caption: RandomForestClassifierStackEnsemble usage Example class Model2(h1.Model): def predict(self, data): X = data['X'] ... return {'predictions': } class RandomForestClassifierStackEnsemble(ClassifierStackEnsemble): def __init__(self): super().__init__([ Model1().load('version_of_model_1'), Model2().load('version_of_model_2') ]) def load_data(self,): ... return loaded_data def prep(self, loaded_data): ... return prepared_data m1 = Model1() m1.load_prep_train_eval() ## Equivalent to # loaded_data = m1.load_data() # prepared_data = m1.prep(loaded_data) # m1.train(prepared_data) # m1.evaluate(prepared_data) print(m1.metrics) m1.persist('version_of_model_1') m2 = Model2() m2.load_prep_train_eval() print(m2.metrics) m2.persist('version_of_model_2') ensemble = RandomForestClassifierStackEnsemble( [Model1().load('version_of_model_1'), Model2().load('version_of_model_2')]) ensemble.load_prep_train_eval() print(ensemble.metrics) ensemble.persist('version_of_model_ensemble') ensemble.predict(...) """ def __init__(self, sub_models: List[Model], **kwargs): super().__init__( # MultiOutputClassifier(RandomForestClassifier(n_jobs=-1, max_depth=4, random_state=42)), RandomForestClassifier(n_jobs=-1, max_depth=4, random_state=42), sub_models, **kwargs )

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samples/sample_3D.py

bknight-i3drobotics/Stereo3D

3

65114

<reponame>bknight-i3drobotics/Stereo3D from stereo3d import Stereo3D from stereo3d.stereocalibration import StereoCalibration from stereo3d.stereocapture import StereoCapture, StereoCaptureVREP CAMERA_TYPE_PHOBOS = 0 CAMERA_TYPE_DEIMOS = 1 CAMERA_TYPE_PYLON = 2 CAMERA_TYPE_IMAGE = 3 CAMERA_TYPE_VREP = 4 camera_type = CAMERA_TYPE_IMAGE stcap = None camera_name = None if (camera_type == CAMERA_TYPE_PHOBOS): camera_name = "phobos" stcap = StereoCapture("Phobos",["22864912","22864917"]) elif (camera_type == CAMERA_TYPE_PYLON): stcap = StereoCapture("Pylon",["22864912","22864917"]) elif (camera_type == CAMERA_TYPE_DEIMOS): camera_name = "deimos" stcap = StereoCapture("Deimos",0) elif (camera_type == CAMERA_TYPE_IMAGE): #camera_name = "deimos" #stcap = StereoCapture("Image",["SampleData/deimos_left.png","SampleData/deimos_right.png"]) camera_name = "phobos" folder_i = "D:/Users/monke/OneDrive - i3d Robotics Ltd/Phobos/BenCCal/test_images" stcap = StereoCapture("Image",[folder_i+"/left_a.png",folder_i+"/right_a.png"]) elif (camera_type == CAMERA_TYPE_VREP): api_port = 20000 left_vision_sensor_name = "StereoCameraLeft" right_vision_sensor_name = "StereoCameraRight" camera_name = "vrep" stcapVREP = StereoCaptureVREP(left_vision_sensor_name,right_vision_sensor_name,api_port) stcap = StereoCapture(stcapVREP) else: print("Invalid camera type.") exit() # define inout folder #folder = "SampleData/" CAL_MODE_FROM_IMAGES = 0 CAL_MODE_FROM_YAML = 1 CAL_MODE_FROM_XML = 2 folder = "D:/Users/monke/OneDrive - i3d Robotics Ltd/Phobos/BenCCal/calibration_1/" stcal = None cal_mode = CAL_MODE_FROM_XML if (cal_mode == CAL_MODE_FROM_IMAGES): # define calibration directories left_images_folder = folder + "deimos_cal/" right_images_folder = folder + "deimos_cal/" output_folder = folder + "deimos_cal/" left_wildcard = "*_l.png" right_wildcard = "*_r.png" grid_size = 39.0 grid_rows = 6 grid_cols = 8 # generate calibration from images stcal = StereoCalibration() stcal.calibrate( left_images_folder,right_images_folder, output_folder,left_wildcard,right_wildcard, grid_size, grid_rows, grid_cols ) elif (cal_mode == CAL_MODE_FROM_YAML): # define calibration files for left and right image left_cal_file = folder + camera_name +"_left.yaml" right_cal_file = folder + camera_name +"_right.yaml" # get calibration from yaml files stcal = StereoCalibration() stcal.get_cal_from_yaml(left_cal_file,right_cal_file) elif (cal_mode == CAL_MODE_FROM_XML): # define calibration files for left and right image ''' left_cal_file = folder + camera_name +"_left_calibration.xml" right_cal_file = folder + camera_name +"_right_calibration.xml" stereo_cal_file = folder + camera_name +"_stereo_calibration.xml" left_rect_file = folder + camera_name +"_left_rectification.xml" right_rect_file = folder + camera_name +"_right_rectification.xml" ''' left_cal_file = folder +"left_calibration.xml" right_cal_file = folder +"right_calibration.xml" stereo_cal_file = folder +"stereo_calibration.xml" left_rect_file = folder +"left_rectification.xml" right_rect_file = folder +"right_rectification.xml" # get calibration from yaml files stcal = StereoCalibration() stcal.get_cal_from_xml(left_cal_file,right_cal_file,stereo_cal_file,left_rect_file,right_rect_file) # setup Stereo3D s3D = Stereo3D(stcap,stcal,"BM") # run Stereo3D GUI for generating 3D out_folder="D:/Users/monke/OneDrive - i3d Robotics Ltd/Phobos/BenCCal/output/" s3D.run(out_folder)

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cloudml-template/template/inference.py

VanessaDo/cloudml-samples

3

165876

<reponame>VanessaDo/cloudml-samples<filename>cloudml-template/template/inference.py<gh_stars>1-10 from googleapiclient import discovery from oauth2client.client import GoogleCredentials GCP_PROJECT = '' # Change to GCP project where the Cloud ML Engine model is deployed CMLE_MODEL_NAME = '' # Change to the deployed Cloud ML Engine model CMLE_MODEL_VERSION = None # If None, the default version will be used def predict_cmle(instances): """ Use a deployed model to Cloud ML Engine to perform prediction Args: instances: list of json, csv, or tf.example objects, based on the serving function called Returns: response - dictionary. If no error, response will include an item with 'predictions' key """ credentials = GoogleCredentials.get_application_default() service = discovery.build('ml', 'v1', credentials=credentials) model_url = 'projects/{}/models/{}'.format(GCP_PROJECT, CMLE_MODEL_NAME) if CMLE_MODEL_VERSION is not None: model_url += '/versions/{}'.format(CMLE_MODEL_VERSION) request_data = { 'instances': instances } response = service.projects().predict( body=request_data, name=model_url ).execute() output = response return output

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interstellar/sprite.py

AnythingTechPro/interstellar

1

62817

<reponame>AnythingTechPro/interstellar<filename>interstellar/sprite.py import random import pygame import time from interstellar import node, resource, audio, controller, mechanism class SpriteError(node.NodeError): """ A sprite specific runtime error """ class Sprite(node.Node): """ A controlled object within the game usually a player """ def __init__(self, parent, image, controller): super(Sprite, self).__init__() self._parent = parent self.image = image self.controller = controller(self) self._attachment = None self.attachment_sound = audio.AudioSound('assets/audio/sfx/grabbed_attachment.wav') self.attachment_deny_sound = audio.AudioSound('assets/audio/sfx/grabbed_attachment_deny.wav') self.health = 0 self.damage = 0 self.can_damage = True @property def attachment(self): return self._attachment @attachment.setter def attachment(self, attachment): if self._attachment or attachment is self._attachment: if self.attachment_deny_sound.playing: self.attachment_deny_sound.stop() self.attachment_deny_sound.play() return self._attachment = attachment self.attachment.setup() if self.attachment_sound.playing: self.attachment_sound.stop() self.attachment_sound.play() def hurt(self, attacker): if not self.can_damage: return if self.health <= 0: return self.die(attacker) self.health -= attacker.damage def die(self, *args, **kwargs): pass def setup(self): self.controller.setup() def update(self): self.controller.update() if self._attachment: self._attachment.update() def explicit_update(self): self.controller.explicit_update() def destroy(self): super(Sprite, self).destroy() self.image.destroy() self.image = None self.controller.destroy() self.controller = None if self._attachment: self._attachment.destroy() self.attachment_sound.destroy() self.attachment_deny_sound.destroy() class SpriteController(node.Node): """ A controller, movement management for a specific sprite object """ def __init__(self, sprite, bind_events=True): super(SpriteController, self).__init__() self.bind_events = bind_events self.sprite = sprite self.speed = 0 self._parent = sprite._parent self.image = sprite.image def update(self): self.move() def explicit_update(self): pass def move(self): if self.sprite._attachment and hasattr(self.sprite._attachment, 'image') and self.sprite._attachment.image and self.image: self.sprite._attachment.image.position = (self.image.x, self.image.y) def bind(self, *args, **kwargs): if not self._parent or not self.bind_events: return None return self._parent.bind(*args, **kwargs) def unbind(self, *args, **kwargs): if not self._parent or not self.bind_events: return None return self._parent.unbind(*args, **kwargs) def send(self, *args, **kwargs): if not self._parent or not self.bind_events: return None return self._parent.send(*args, **kwargs) def destroy(self): super(SpriteController, self).destroy() self.sprite = None self.speed = 0 class ShipController(SpriteController): def __init__(self, sprite): super(ShipController, self).__init__(sprite) self.music_array = resource.ResourceAudioArray(self._parent.root, [ 'assets/audio/sfx/laser_fire_0.wav', 'assets/audio/sfx/laser_fire_1.wav', 'assets/audio/sfx/laser_fire_2.wav', 'assets/audio/sfx/laser_fire_3.wav', ]) self.fire_sound = None self.speed = 15 self.health = 100 self.projectile_speed = 20 self.moving_forward = False self.moving_backward = False self.moving_right = False self.moving_left = False self.firing = False self.projectiles = [] self.maximum_projectiles = 15 self.controller = controller.GameController(self) self.current_distance = 0 def setup(self): super(ShipController, self).setup() self.bind('Up', self.move_forward) self.bind('KeyRelease-Up', lambda event: self.move_forward(event, True)) self.bind('Down', self.move_backward) self.bind('KeyRelease-Down', lambda event: self.move_backward(event, True)) self.bind('Right', self.move_right) self.bind('KeyRelease-Right', lambda event: self.move_right(event, True)) self.bind('Left', self.move_left) self.bind('KeyRelease-Left', lambda event: self.move_left(event, True)) self.bind('space', self.fire) self.bind('KeyRelease-space', lambda event: self.fire(event, True)) self.controller.setup() def move_forward(self, event, release=False): if not release: self.moving_forward = True else: self.moving_forward = False def move_backward(self, event, release=False): if not release: self.moving_backward = True else: self.moving_backward = False def move_right(self, event, release=False): if not release: self.moving_right = True else: self.moving_right = False def move_left(self, event, release=False): if not release: self.moving_left = True else: self.moving_left = False def fire(self, event, release=False): if not release: self.firing = True else: self.firing = False def explicit_update(self): self.controller.update() def move(self): super(ShipController, self).move() self.current_distance += self.speed if self.moving_forward and not self.image.y - self.image.height / 2 <= 0: self.image.y -= self.speed elif self.moving_backward and not self.image.y + self.image.height / 2 >= self._parent.root.display.height: self.image.y += self.speed / 2 self.current_distance -= self.speed / 2 if self.moving_right and not self.image.x + self.image.width / 2 >= self._parent.root.display.width: self.image.x += self.speed elif self.moving_left and not self.image.x - self.image.width / 2 <= 0: self.image.x -= self.speed if self.firing and len(self.projectiles) < self.maximum_projectiles: self.fire_projectile() self.update_projectiles() self.check_player_collisions() def update_projectiles(self): for projectile in self.projectiles: if projectile.y <= 0: self.destroy_projectile(projectile) continue if self.check_projectile_collisions(projectile): self.destroy_projectile(projectile) continue projectile.y -= random.random() * self.projectile_speed * 2 def check_projectile_collisions(self, projectile): for asteroid in self._parent.asteroids: if projectile.collide_point(asteroid.image): asteroid.hurt(self.sprite) return True return False def check_player_collisions(self): for asteroid in self._parent.asteroids: if asteroid.image.collide_point(self.sprite.image): return self.sprite.hurt(asteroid) def fire_projectile(self): if self.fire_sound: self.music_array.deselect(use_pygame=True) self.fire_sound = None self.fire_sound = self.music_array.select(False, use_pygame=True) self.fire_sound.play() bullet0 = resource.ResourceImage(self._parent.root, 'assets/bullet.png') bullet0.position = (self.image.x - self.image.width / 2, self.image.y) bullet1 = resource.ResourceImage(self._parent.root, 'assets/bullet.png') bullet1.position = (self.image.x + self.image.width / 2, self.image.y) bullet0.render(self._parent.canvas) bullet1.render(self._parent.canvas) self.projectiles.append(bullet0) self.projectiles.append(bullet1) def destroy_projectile(self, projectile): projectile.destroy() self.projectiles.remove(projectile) def destroy_projectiles(self): for projectile in self.projectiles: self.destroy_projectile(projectile) def destroy(self): self.music_array.destroy() self.fire_sound = None self._parent.unbind('Up') self._parent.unbind('KeyRelease-Up') self._parent.unbind('Down') self._parent.unbind('KeyRelease-Down') self._parent.unbind('Right') self._parent.unbind('KeyRelease-Right') self._parent.unbind('Left') self._parent.unbind('KeyRelease-Left') self._parent.unbind('space') self._parent.unbind('KeyRelease-space') self.controller.destroy() self.destroy_projectiles() super(ShipController, self).destroy() class Ship(Sprite): def __init__(self, parent, controller): image = resource.ResourceImage(parent.root, 'assets/player.png') image.position = (parent.master.root.winfo_width() / 2, parent.master.root.winfo_height() - image.height) image.render(parent.canvas) super(Ship, self).__init__(parent, image, controller) self.health = 100 self.damage = 5 def die(self, killer): self._parent.explode(self.image.x, self.image.y) self._parent.end() class AsteroidController(SpriteController): def __init__(self, sprite): super(AsteroidController, self).__init__(sprite) self.speed = 25 self.speed = random.random() * self.speed def move(self): if self.image.y >= self._parent.root.display.height: self._parent.remove_asteroid(self.sprite) return self.image.y += self.speed class Asteroid(Sprite): PROBABILITY = 100 def __init__(self, parent, controller, image=None, is_mechanism=False): if not image: images = [ 'assets/asteroids/asteroid-small.png', 'assets/asteroids/asteroid-big.png' ] image = resource.ResourceImage(parent.root, random.choice(images)) image.position = (random.randrange(0, parent.root.display.width), 0) image.render(parent.canvas) super(Asteroid, self).__init__(parent, image, controller) self.is_mechanism = is_mechanism self.health = 10 self.damage = 1 def die(self, killer): self._parent.explode(self.image.x, self.image.y) self._parent.remove_asteroid(self, True) class MechanismAsteroid(Asteroid): ICON = '' ATTACHMENT = None def __init__(self, parent, controller): image = resource.ResourceImage(parent.root, self.ICON) image.position = (random.randrange(0, parent.root.display.width), 0) image.render(parent.canvas) super(MechanismAsteroid, self).__init__(parent, controller, image, True) self.health = 0 self.damage = 0 def die(self, killer): killer.attachment = self.ATTACHMENT(killer) self._parent.remove_asteroid(self, False) class ShieldMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.5 ICON = 'assets/icons/shield_icon.png' ATTACHMENT = mechanism.ShieldMechanism class InstantKillMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.1 ICON = 'assets/icons/instant_kill_icon.png' ATTACHMENT = mechanism.InstantKillMechanism class FullHealthMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.3 ICON = 'assets/icons/full_health_icon.png' ATTACHMENT = mechanism.FullHealthMechanism class DoubleHealthMechanismAsteroid(MechanismAsteroid): PROBABILITY = 0.5 ICON = 'assets/icons/double_health_icon.png' ATTACHMENT = mechanism.DoubleHealthMechanism

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sockmetrics.py

ChengWu-NJ/ebpf-bcc-apps

0

58044

#!/usr/bin/python3 # wucheng August 8, 2019 # sockmetrics.py --- collect sock communication info among hosts # version 0.9 # two methods to output results: # 1. to screen # 2. to TDengine, a kind of structured time series db # please run ./sockmetrics -h to get help # This program bases on https://github.com/iovisor/bcc # Licensed Apache-2.0 #from __future__ import print_function from bcc import BPF import argparse from socket import inet_ntop, AF_INET, AF_INET6 from struct import pack from time import sleep, strftime from subprocess import call from collections import namedtuple, defaultdict import os import json import taos import datetime # arguments def range_check(string): value = int(string) if value < 1 or value > 600: msg = "value must be 1 ~ 600, got %d" % (value,) raise argparse.ArgumentTypeError(msg) return value def positive_check(string): value = int(string) if value < 1: msg = "value must be stricly positive, got %d" % (value,) raise argparse.ArgumentTypeError(msg) return value examples = """examples: ./sockmetrics --conf_file='/etc/sockmetrics.conf' # input all sock trace data into tsdb configured in conf_file ./sockmetrics # trace Sock send/recv by host on screen ./sockmetrics -p 181 # only trace PID 181 on screen """ parser = argparse.ArgumentParser( description="Summarize Sock send/recv throughput by host", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=examples) parser.add_argument("--conf_file", help="this argument points the configure file, and is exclusive and discards other arguments ") parser.add_argument("-p", "--pid", help="trace this PID only") parser.add_argument("interval", nargs="?", default=1, type=range_check, help="output interval, in seconds (default 1), range 1 ~ 600") parser.add_argument("count", nargs="?", default=-1, type=positive_check, help="number of the records with the top recerived bytes to output per interval") parser.add_argument("--ebpf", action="store_true", help=argparse.SUPPRESS) args = parser.parse_args() def get_arguments_from_conf_file(conf_file): with open(conf_file, 'r') as jsonfile: _d = json.load(jsonfile) if _d.get("dbhost") and _d.get("database") \ and _d.get("user") and _d.get("password"): return _d else: raise Exception("conf_file %s is invalid."%conf_file) conf_dict = None if args.conf_file and os.stat(args.conf_file): conf_dict = get_arguments_from_conf_file(args.conf_file) args.pid = None _interval = conf_dict.get('interval') if _interval is not None and _interval > 0 and _interval <= 600: args.interval = _interval else: args.interval = 1 _count = conf_dict.get('count') if _count is not None and _count > 0: args.count = _count else: args.count = -1 debug = 0 # define BPF program bpf_text = """ #include <uapi/linux/ptrace.h> #include <net/sock.h> #include <bcc/proto.h> #include <linux/net.h> #include <uapi/linux/ip.h> #include <linux/ip.h> struct ipv4_key_t { u32 pid; u32 saddr; u32 daddr; u16 lport; u16 dport; u16 socktype; }; BPF_HASH(ipv4_send_bytes, struct ipv4_key_t); BPF_HASH(ipv4_recv_bytes, struct ipv4_key_t); struct ipv6_key_t { u32 pid; unsigned __int128 saddr; unsigned __int128 daddr; u16 lport; u16 dport; u16 socktype; }; BPF_HASH(ipv6_send_bytes, struct ipv6_key_t); BPF_HASH(ipv6_recv_bytes, struct ipv6_key_t); //static int kprobe__packet_snd(struct pt_regs *ctx, struct socket *sock, // struct msghdr *msg, size_t len) int kprobe__inet_sendmsg(struct pt_regs *ctx, struct socket *sock, struct msghdr *msg, size_t size) { //bpf_trace_printk("entry inet_sendmsg now. \\n"); u32 pid = bpf_get_current_pid_tgid(); FILTER struct sock *sk = sock->sk; u16 dport = 0, family = sk->__sk_common.skc_family; //bpf_trace_printk("inet_sendmsg got family %d, and protocol is %d\\n", family, sk->__sk_common.skc_prot); if (family == AF_INET && sk->__sk_common.skc_rcv_saddr != sk->__sk_common.skc_daddr) { struct ipv4_key_t ipv4_key = {.pid = pid}; ipv4_key.saddr = sk->__sk_common.skc_rcv_saddr; ipv4_key.daddr = sk->__sk_common.skc_daddr; ipv4_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv4_key.dport = ntohs(dport); ipv4_key.socktype = sock->type; ipv4_send_bytes.increment(ipv4_key, size); } if (family == AF_INET6) { struct ipv6_key_t ipv6_key = {.pid = pid}; __builtin_memcpy(&ipv6_key.saddr, sk->__sk_common.skc_v6_rcv_saddr.in6_u.u6_addr32, sizeof(ipv6_key.saddr)); __builtin_memcpy(&ipv6_key.daddr, sk->__sk_common.skc_v6_daddr.in6_u.u6_addr32, sizeof(ipv6_key.daddr)); if (ipv6_key.saddr == ipv6_key.daddr) return 0; ipv6_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv6_key.dport = ntohs(dport); ipv6_key.socktype = sock->type; ipv6_send_bytes.increment(ipv6_key, size); } // else drop return 0; } int kprobe__inet_recvmsg(struct pt_regs *ctx, struct socket *sock, struct msghdr *msg, size_t size) { u32 pid = bpf_get_current_pid_tgid(); FILTER struct sock *sk = sock->sk; u16 dport = 0, family = sk->__sk_common.skc_family; u64 *val, zero = 0; // bpf_trace_printk("inet_recvmsg got family %d\\n", family); if (size <= 0) return 0; if (family == AF_INET && sk->__sk_common.skc_rcv_saddr != sk->__sk_common.skc_daddr) { struct ipv4_key_t ipv4_key = {.pid = pid}; ipv4_key.saddr = sk->__sk_common.skc_rcv_saddr; ipv4_key.daddr = sk->__sk_common.skc_daddr; ipv4_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv4_key.dport = ntohs(dport); ipv4_key.socktype = sock->type; ipv4_recv_bytes.increment(ipv4_key,size); } if (family == AF_INET6) { struct ipv6_key_t ipv6_key = {.pid = pid}; __builtin_memcpy(&ipv6_key.saddr, sk->__sk_common.skc_v6_rcv_saddr.in6_u.u6_addr32, sizeof(ipv6_key.saddr)); __builtin_memcpy(&ipv6_key.daddr, sk->__sk_common.skc_v6_daddr.in6_u.u6_addr32, sizeof(ipv6_key.daddr)); if (ipv6_key.saddr == ipv6_key.daddr) return 0; ipv6_key.lport = sk->__sk_common.skc_num; dport = sk->__sk_common.skc_dport; ipv6_key.dport = ntohs(dport); ipv6_key.socktype = sock->type; ipv6_recv_bytes.increment(ipv6_key, size); } // else drop return 0; } """ # code substitutions if args.pid: bpf_text = bpf_text.replace('FILTER', 'if (pid != %s) { return 0; }' % args.pid) else: bpf_text = bpf_text.replace('FILTER', '') if debug or args.ebpf: print(bpf_text) if args.ebpf: exit() SockSessionKey = namedtuple('SockSession', ['pid', 'laddr', 'lport', 'daddr', 'dport', 'socktype']) def pid_to_comm(pid): try: comm = open("/proc/%d/comm" % pid, "r").read().rstrip() return comm except IOError: return str(pid) def _to_socktype(type): #https://elixir.bootlin.com/linux/latest/source/include/linux/net.h#L60 return "TCP" if type==1 else "UDP" if type==2 else "Other" def get_ipv4_session_key(k): return SockSessionKey(pid=k.pid, laddr=inet_ntop(AF_INET, pack("I", k.saddr)), lport=k.lport, daddr=inet_ntop(AF_INET, pack("I", k.daddr)), dport=k.dport, socktype=k.socktype) def get_ipv6_session_key(k): return SockSessionKey(pid=k.pid, laddr=inet_ntop(AF_INET6, k.saddr), lport=k.lport, daddr=inet_ntop(AF_INET6, k.daddr), dport=k.dport, socktype=k.socktype) def getdbconnection(cnfdict): _conn = taos.connect(host=cnfdict["dbhost"], user=cnfdict["user"], password=cnfdict["password"], database=cnfdict["database"]) print(_conn._host) return _conn def sqlexecute(cursor, sqltext): #continue when network error, etc. try: cursor.execute(sqltext) return True except: return False def createtables(cursor): _ipv4_ready = sqlexecute(cursor, """create table if not exists ipv4_metrics ( epoch timestamp, pid int, comm binary(40), type int, laddr binary(15), lport int, raddr binary(15), rport int, rx_byte bigint, tx_byte bigint, interval_sum smallint)""") _ipv6_ready = sqlexecute(cursor, """create table if not exists ipv6_metrics ( epoch timestamp, pid int, comm binary(40), type int, laddr6 binary(39), lport int, raddr6 binary(39), rport int, rx_byte bigint, tx_byte bigint, interval_sum smallint)""") if _ipv4_ready and _ipv6_ready: return True else: return False # initialize BPF b = BPF(text=bpf_text) """ if b.get_kprobe_functions(b"netif_receive_skb"): b.attach_kprobe(event="netif_receive_skb", fn_name="trace_netif_receive_skb") else: print("ERROR: netif_receive_skb() kernel function not found or traceable. " "Older kernel versions not supported.") exit() """ ipv4_send_bytes = b["ipv4_send_bytes"] ipv4_recv_bytes = b["ipv4_recv_bytes"] ipv6_send_bytes = b["ipv6_send_bytes"] ipv6_recv_bytes = b["ipv6_recv_bytes"] # tsdb if conf_file is set if conf_dict: output_db = True else: output_db =False if output_db: conn = getdbconnection(conf_dict) csr = conn.cursor() i_reconnect = 0 if not createtables(csr): raise(Exception("Something wrong when create tables.")) print('Tracing... Output every %s secs. Hit Ctrl-C to end' % args.interval) print('Collected data will input to tsdb...') # output exiting = False while not exiting: try: sleep(args.interval) except KeyboardInterrupt: exiting = True if output_db: csr.close() conn.close() # reset dbconnect. continue when network error, etc. if output_db and i_reconnect >= 100: i_reconnect = 0 try: csr.close() conn.close() conn = getdbconnection(conf_dict) csr = conn.cursor() except: pass # IPv4: build dict of all seen keys ipv4_throughput = defaultdict(lambda: [0, 0]) for k, v in ipv4_send_bytes.items(): key = get_ipv4_session_key(k) ipv4_throughput[key][0] = v.value ipv4_send_bytes.clear() for k, v in ipv4_recv_bytes.items(): key = get_ipv4_session_key(k) ipv4_throughput[key][1] = v.value ipv4_recv_bytes.clear() if not output_db and ipv4_throughput: print("%-6s %-12s %-6s %-21s %-21s %9s %9s" % ("PID", "COMM", "TYPE", "LADDR", "RADDR", "RX_Byte", "TX_Byte")) # output i = 0 for k, (send_bytes, recv_bytes) in (ipv4_throughput.items() if args.count==-1 else sorted(ipv4_throughput.items(), key=lambda kv: sum(kv[1]), reverse=True)): if args.count > 0 and i >= args.count: break if output_db: sqlexecute(csr, """insert into ipv4_metrics (epoch, pid, comm, type, laddr, lport, raddr, rport, rx_byte, tx_byte, interval_sum) values ('%s', %d, '%s', %d, '%s', %d, '%s', %d, %d, %d, %d)""" % (datetime.datetime.now(), k.pid, pid_to_comm(k.pid), k.socktype, k.laddr, k.lport, k.daddr, k.dport, recv_bytes, send_bytes, args.interval) ) else: print("%-6d %-12.12s %-6.6s %-21s %-21s %9d %9d" % (k.pid, pid_to_comm(k.pid), _to_socktype(k.socktype), k.laddr + ":" + str(k.lport), k.daddr + ":" + str(k.dport), recv_bytes, send_bytes)) i += 1 # IPv6: build dict of all seen keys ipv6_throughput = defaultdict(lambda: [0, 0]) for k, v in ipv6_send_bytes.items(): key = get_ipv6_session_key(k) ipv6_throughput[key][0] = v.value ipv6_send_bytes.clear() for k, v in ipv6_recv_bytes.items(): key = get_ipv6_session_key(k) ipv6_throughput[key][1] = v.value ipv6_recv_bytes.clear() if ipv6_throughput: # more than 80 chars, sadly. print("\n%-6s %-12s %-6s %-32s %-32s %9s %9s" % ("PID", "COMM", "TYPE", "LADDR6", "RADDR6", "RX_Byte", "TX_Byte")) # output i = 0 for k, (send_bytes, recv_bytes) in (ipv6_throughput.items() if args.count==-1 else sorted(ipv6_throughput.items(), key=lambda kv: sum(kv[1]), reverse=True)): if args.count > 0 and i >= args.count: break if output_db: sqlexecute(csr, """insert into ipv6_metrics (epoch, pid, comm, type, laddr6, lport, raddr6, rport, rx_byte, tx_byte, interval_sum) values ('%s', %d, '%s', %d, '%s', %d, '%s', %d, %d, %d, %d)""" % (datetime.datetime.now(), k.pid, pid_to_comm(k.pid), k.socktype, k.laddr, k.lport, k.daddr, k.dport, recv_bytes, send_bytes, args.interval) ) else: print("%-6d %-12.12s %-6.6s %-32s %-32s %9d %9d" % (k.pid, pid_to_comm(k.pid), _to_socktype(k.socktype), k.laddr + ":" + str(k.lport), k.daddr + ":" + str(k.dport), recv_bytes, send_bytes)) i += 1 if output_db: i_reconnect += 1

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train.py

gakkispy/DD-Net-Pytorch

31

183520

#! /usr/bin/env python #! coding:utf-8 from pathlib import Path import matplotlib.pyplot as plt from torch import log from tqdm import tqdm import torch import torch.nn as nn import argparse import torch.optim as optim from torch.optim.lr_scheduler import ReduceLROnPlateau from sklearn.metrics import confusion_matrix from dataloader.jhmdb_loader import load_jhmdb_data, Jdata_generator, JConfig from dataloader.shrec_loader import load_shrec_data, Sdata_generator, SConfig from models.DDNet_Original import DDNet_Original as DDNet from utils import makedir import sys import time import numpy as np import logging sys.path.insert(0, './pytorch-summary/torchsummary/') from torchsummary import summary # noqa savedir = Path('experiments') / Path(str(int(time.time()))) makedir(savedir) logging.basicConfig(filename=savedir/'train.log', level=logging.INFO) history = { "train_loss": [], "test_loss": [], "test_acc": [] } def train(args, model, device, train_loader, optimizer, epoch, criterion): model.train() train_loss = 0 for batch_idx, (data1, data2, target) in enumerate(tqdm(train_loader)): M, P, target = data1.to(device), data2.to(device), target.to(device) optimizer.zero_grad() output = model(M, P) loss = criterion(output, target) train_loss += loss.detach().item() loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: msg = ('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data1), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) print(msg) logging.info(msg) if args.dry_run: break history['train_loss'].append(train_loss) return train_loss def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 criterion = nn.CrossEntropyLoss(reduction='sum') with torch.no_grad(): for _, (data1, data2, target) in enumerate(tqdm(test_loader)): M, P, target = data1.to(device), data2.to(device), target.to(device) output = model(M, P) # sum up batch loss test_loss += criterion(output, target).item() # get the index of the max log-probability pred = output.argmax(dim=1, keepdim=True) # output shape (B,Class) # target_shape (B) # pred shape (B,1) correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) history['test_loss'].append(test_loss) history['test_acc'].append(correct / len(test_loader.dataset)) msg = ('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) print(msg) logging.info(msg) def main(): # Training settings parser = argparse.ArgumentParser() parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=199, metavar='N', help='number of epochs to train (default: 199)') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)') parser.add_argument('--gamma', type=float, default=0.5, metavar='M', help='Learning rate step gamma (default: 0.5)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--log-interval', type=int, default=2, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--save-model', action='store_true', default=False, help='For Saving the current Model') parser.add_argument('--dataset', type=int, required=True, metavar='N', help='0 for JHMDB, 1 for SHREC coarse, 2 for SHREC fine, others is undefined') parser.add_argument('--model', action='store_true', default=False, help='For Saving the current Model') parser.add_argument('--calc_time', action='store_true', default=False, help='calc calc time per sample') args = parser.parse_args() logging.info(args) use_cuda = not args.no_cuda and torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'batch_size': args.batch_size} if use_cuda: kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True},) # alias Config = None data_generator = None load_data = None clc_num = 0 if args.dataset == 0: Config = JConfig() data_generator = Jdata_generator load_data = load_jhmdb_data clc_num = Config.clc_num elif args.dataset == 1: Config = SConfig() load_data = load_shrec_data clc_num = Config.class_coarse_num data_generator = Sdata_generator('coarse_label') elif args.dataset == 2: Config = SConfig() clc_num = Config.class_fine_num load_data = load_shrec_data data_generator = Sdata_generator('fine_label') else: print("Unsupported dataset!") sys.exit(1) C = Config Train, Test, le = load_data() X_0, X_1, Y = data_generator(Train, C, le) X_0 = torch.from_numpy(X_0).type('torch.FloatTensor') X_1 = torch.from_numpy(X_1).type('torch.FloatTensor') Y = torch.from_numpy(Y).type('torch.LongTensor') X_0_t, X_1_t, Y_t = data_generator(Test, C, le) X_0_t = torch.from_numpy(X_0_t).type('torch.FloatTensor') X_1_t = torch.from_numpy(X_1_t).type('torch.FloatTensor') Y_t = torch.from_numpy(Y_t).type('torch.LongTensor') trainset = torch.utils.data.TensorDataset(X_0, X_1, Y) train_loader = torch.utils.data.DataLoader(trainset, **kwargs) testset = torch.utils.data.TensorDataset(X_0_t, X_1_t, Y_t) test_loader = torch.utils.data.DataLoader( testset, batch_size=args.test_batch_size) Net = DDNet(C.frame_l, C.joint_n, C.joint_d, C.feat_d, C.filters, clc_num) model = Net.to(device) summary(model, [(C.frame_l, C.feat_d), (C.frame_l, C.joint_n, C.joint_d)]) optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999)) criterion = nn.CrossEntropyLoss() scheduler = ReduceLROnPlateau( optimizer, factor=args.gamma, patience=5, cooldown=0.5, min_lr=5e-6, verbose=True) for epoch in range(1, args.epochs + 1): train_loss = train(args, model, device, train_loader, optimizer, epoch, criterion) test(model, device, test_loader) scheduler.step(train_loss) fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1) ax1.plot(history['train_loss']) ax1.plot(history['test_loss']) ax1.legend(['Train', 'Test'], loc='upper left') ax1.set_xlabel('Epoch') ax1.set_title('Loss') ax2.set_title('Model accuracy') ax2.set_ylabel('Accuracy') ax2.set_xlabel('Epoch') ax2.plot(history['test_acc']) xmax = np.argmax(history['test_acc']) ymax = np.max(history['test_acc']) text = "x={}, y={:.3f}".format(xmax, ymax) ax2.annotate(text, xy=(xmax, ymax)) ax3.set_title('Confusion matrix') model.eval() with torch.no_grad(): Y_pred = model(X_0_t.to(device), X_1_t.to( device)).cpu().numpy() Y_test = Y_t.numpy() cnf_matrix = confusion_matrix( Y_test, np.argmax(Y_pred, axis=1)) ax3.imshow(cnf_matrix) fig.tight_layout() fig.savefig(str(savedir / "perf.png")) if args.save_model: torch.save(model.state_dict(), str(savedir/"model.pt")) if args.calc_time: device = ['cpu', 'cuda'] # calc time for d in device: tmp_X_0_t = X_0_t.to(d) tmp_X_1_t = X_1_t.to(d) model = model.to(d) # warm up _ = model(tmp_X_0_t, tmp_X_1_t) tmp_X_0_t = tmp_X_0_t.unsqueeze(1) tmp_X_1_t = tmp_X_1_t.unsqueeze(1) start = time.perf_counter_ns() for i in range(tmp_X_0_t.shape[0]): _ = model(tmp_X_0_t[i, :, :, :], tmp_X_1_t[i, :, :, :]) end = time.perf_counter_ns() msg = ("total {}ns, {:.2f}ns per one on {}".format((end - start), ((end - start) / (X_0_t.shape[0])), d)) print(msg) logging.info(msg) if __name__ == '__main__': main()

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Day7-HandyHaversacks.py

n-Holmes/AdventOfCode2020

0

43433

<reponame>n-Holmes/AdventOfCode2020 from collections import Counter from functools import lru_cache def get_contents(path : str) -> dict[str, dict[str, int]]: """Get a weighted, directed graph of bags. No protection for the case of cycles. """ with open(path, "r") as f: lines = f.readlines(); contents = {} # Could use regex here, but hardly seems necessary due to the input size. for line in lines: try: bag, contains = line.split(" bags contain "); except: continue contents[bag] = Counter() if contains.startswith("no"): continue # separate types for item in contains.split(", "): parts = item.split(); color = parts[1] + " " + parts[2] count = int(parts[0]) contents[bag][color] = count return contents def get_poss_containers( target : str, contents : dict[str, dict[str, int]] ) -> list[str]: """Returns a list of all bags which can contain a bag of a given color.""" to_check = [target] valid = set() while to_check: target_color = to_check.pop() for cont_color in contents: if cont_color not in valid and target_color in contents[cont_color]: valid.add(cont_color) to_check.append(cont_color) return list(valid) def count_contents( target : str, contents : dict[str, dict[str, int]] ) -> int: """Counts the number of bags contained within a given color of bag.""" @lru_cache() # Cache results to speed up recursion def rec_count(color : str) -> int: """Recursively count the contents of a given color.""" return sum( (1 + rec_count(child)) * count for child, count in contents[color].items() ) return rec_count(target) if __name__ == "__main__": graph = get_contents("input7.txt") print("Containers that can hold a shiny gold bag:") print(len(get_poss_containers("shiny gold", graph))) print("No of bags in a shiny gold:") print(count_contents("shiny gold", graph))

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pyvs/LatentARTraining.py

snumrl/VSports

0

165149

<reponame>snumrl/VSports import math import random import time import os import sys import copy from datetime import datetime from os.path import join, exists from os import mkdir import collections from collections import namedtuple from collections import deque from itertools import count import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torchvision.transforms as T from Utils import RunningMeanStd from CAAE import CAAEDecoder, CAAEEncoder import numpy as np from pyvs import Env from IPython import embed import json from Model import * from pathlib import Path from tensorboardX import SummaryWriter summary = SummaryWriter() use_cuda = torch.cuda.is_available() FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor ByteTensor = torch.cuda.ByteTensor if use_cuda else torch.ByteTensor Tensor = FloatTensor LOW_FREQUENCY = 3 HIGH_FREQUENCY = 30 device = torch.device("cuda" if use_cuda else "cpu") nnCount = 52 baseDir = "../nn_lar_h" nndir = baseDir + "/nn"+str(nnCount) if not exists(baseDir): mkdir(baseDir) if not exists(nndir): mkdir(nndir) RNNEpisode = namedtuple('RNNEpisode', ('s','a','r','value','logprob')) class RNNEpisodeBuffer(object): def __init__(self): self.data = [] def push(self, *args): self.data.append(RNNEpisode(*args)) def pop(self): self.data.pop() def popleft(self): self.data.pop(0) def getData(self): return self.data def getLastData(self): return self.data[len(self.data)-1] RNNTransition = namedtuple('RNNTransition',('s','a','logprob','TD','GAE')) class RNNReplayBuffer(object): def __init__(self, buff_size = 10000): super(RNNReplayBuffer, self).__init__() self.buffer = deque(maxlen=buff_size) def push(self,*args): self.buffer.append(RNNTransition(*args)) def clear(self): self.buffer.clear() class Buffer(object): def __init__(self, buff_size = 10000): super(Buffer, self).__init__() self.buffer = deque(maxlen=buff_size) def push(self,replay_buffer): self.buffer.append(replay_buffer) def clear(self): self.buffer.clear() class RL(object): def __init__(self, motion_nn): np.random.seed(seed = int(time.time())) self.num_slaves = 16 self.num_agents = 1 self.env = Env(self.num_agents, motion_nn, self.num_slaves) self.num_state = self.env.getNumState() # self.num_action = self.env.getNumAction() self.num_policy = 1 self.num_epochs = 4 self.num_evaluation = 0 self.num_simulation_Hz = self.env.getSimulationHz() self.num_control_Hz = self.env.getControlHz() self.num_simulation_per_control = self.num_simulation_Hz // self.num_control_Hz self.gamma = 0.999 self.lb = 0.95 self.buffer_size = 16*1024 self.batch_size = 2*512 self.num_action_types = 5 self.latent_size = 4 self.resetDuration = self.env.getResetDuration() self.typeFreq = self.env.getTypeFreq() #contact, finger, finger-ball self.num_action = [self.num_action_types, self.latent_size] self.num_h = len(self.num_action); self.num_tuple_so_far = [[0, 0] for _ in range(self.num_h)] self.num_tuple = [[0, 0] for _ in range(self.num_h)] self.num_tutorial_tuple = [[0, 0] for _ in range(self.num_h)] self.buffer = [[ [None] for _ in range(self.num_policy)] for _ in range(self.num_h)]; for h in range(self.num_h): for i in range(self.num_policy): self.buffer[h][i] = Buffer(100000) self.tutorial_buffer = [[ [None] for _ in range(self.num_policy)] for _ in range(self.num_h)]; for h in range(self.num_h): for i in range(self.num_policy): self.tutorial_buffer[h][i] = Buffer(100000) self.actionDecoder = CAAEDecoder().to(device) self.actionEncoder = CAAEEncoder().to(device) self.actionDecoder.load("caae_nn2/vae_action_decoder.pt") self.actionEncoder.load("caae_nn2/vae_action_encoder.pt") self.rms = RunningMeanStd(self.num_state-5) self.num_c = 4 self.stdScale = 1.0 self.target_model = [[[None] for _ in range(self.num_policy)] for _ in range(self.num_h)] acc_num_action = 0 self.num_hidden = 0 for h in range(self.num_h): for j in range(self.num_policy): if h== 0: self.target_model[h][j] = ActorCriticNN(self.num_state, self.num_action[h], log_std = 0.0, softmax = True, actionMask = True) else: self.target_model[h][j] = ActorCriticNN(self.num_state + self.num_action[0], self.num_action[h], log_std = 0.0) acc_num_action += self.num_action[h] if use_cuda: self.target_model[h][j].cuda() self.default_learning_rate = 1E-4 self.default_clip_ratio = 0.2 self.learning_rate = self.default_learning_rate self.clip_ratio = self.default_clip_ratio self.optimizer = [[[None] for _ in range(self.num_policy)] for _ in range(self.num_h)] for h in range(self.num_h): for i in range(self.num_policy): self.optimizer[h][i] = optim.Adam(self.target_model[h][i].parameters(), lr=self.learning_rate) self.max_iteration = 50000 self.w_entropy = 0.0001 self.sum_loss_actor = [[0.0 for _ in range(self.num_policy)] for _ in range(self.num_h)] self.sum_loss_critic = [[0.0 for _ in range(self.num_policy)] for _ in range(self.num_h)] self.sum_tutorial_loss_critic = [[0.0 for _ in range(self.num_policy)] for _ in range(self.num_h)] self.rewards = [] self.numSteps = [] self.num_correct_throwings = [] self.sum_return = 0.0 self.max_return = -10.0 self.max_winRate = 0.0 self.max_winRate_epoch = 0 self.max_return_epoch = 1 self.tic = time.time() self.episodes = [[[RNNEpisodeBuffer() for _ in range(self.num_agents)] for _ in range(self.num_slaves)] for _ in range(self.num_h)] self.tutorial_episodes = [[[RNNEpisodeBuffer() for _ in range(self.num_agents)] for _ in range(self.num_slaves)] for _ in range(self.num_h)] self.indexToNetDic = {0:0, 1:0} self.filecount = 0 self.num_td_reset = [0, 0] self.num_td_reset_at_goal = [0, 0] self.reset_point = [0]*5 self.env.slaveResets() def loadTargetModels(self,path,index): for h in range(self.num_h): self.target_model[h][self.indexToNetDic[index]].load(nndir+'/'+path+'_'+str(self.indexToNetDic[index%self.num_agents])+'_'+str(h)+'.pt') if os.path.isfile(nndir+'/rms.ms'): self.rms.load(nndir+'/rms.ms') def saveModels(self): for i in range(self.num_policy): for h in range(self.num_h): self.target_model[h][i].save(nndir+'/'+'current_'+str(i)+'_'+str(h)+'.pt') if self.max_return_epoch == self.num_evaluation: for h in range(self.num_h): self.target_model[h][i].save(nndir+'/'+'max_'+str(i)+'_'+str(h)+'.pt') if self.num_evaluation%100 == 0: for h in range(self.num_h): self.target_model[h][i].save(nndir+'/'+str(self.num_evaluation)+'_'+str(i)+'_'+str(h)+'.pt') self.rms.save(nndir+'/rms.ms') def arrayToOneHotVector(nparr): result = np.array(list(np.copy(nparr))) for agent in range(len(nparr)): for slaves in range(len(nparr[agent])): maxIndex = 0 maxValue = -100 for i in range(len(nparr[agent][slaves])): result[agent][slaves][i] = 0.0 if nparr[agent][slaves][i] > maxValue: maxValue = nparr[agent][slaves][i] maxIndex = i result[agent][slaves][maxIndex] = 1.0 return result def generateTransitions(self): self.sum_return = 0; self.num_episode = 0; self.num_correct_throwing = 0; self.num_td_reset = [0, 0] self.num_td_reset_at_goal = [0, 0] for h in range(self.num_h): for index in range(self.num_policy): self.num_tuple[h][index] = 0 self.num_tutorial_tuple[h][index] = 0 self.reset_point = [0, 0, 0, 0, 0] self.total_episodes = [ [[] for i in range(self.num_policy)] for _ in range(self.num_h)] self.total_tutorial_episodes = [ [[] for i in range(self.num_policy)] for _ in range(self.num_h)] states = [[None for _ in range(self.num_slaves)] for _ in range(self.num_agents)] actions = [[None for _ in range(self.num_slaves)] for _ in range(self.num_agents)] rewards = [[None for _ in range(self.num_slaves)] for _ in range(self.num_agents)] accRewards = [[0 for _ in range(self.num_slaves)] for _ in range(self.num_agents)] states_h = np.array([None for _ in range(self.num_h)]) actions_h = np.array([[None for _ in range(self.num_agents)] for _ in range(self.num_h)]) logprobs_h = np.array([[None for _ in range(self.num_agents)] for _ in range(self.num_h)]) values_h = np.array([[None for _ in range(self.num_agents)] for _ in range(self.num_h)]) is_exploitation = np.array([None for _ in range(self.num_agents)]) followTutorial = [False]*self.num_slaves onFoulResetProcess = [False]*self.num_slaves terminated = [False]*self.num_slaves*self.num_agents for i in range(self.num_agents): for j in range(self.num_slaves): states[i][j] = self.env.getState(j,i).astype(np.float32) states = np.array(states) states[:,:,:-5] = self.rms.apply(states[:,:,:-5]) mask = states[:,:,-5:] learningTeam = 0 teamDic = {0: 0, 1: 0} local_step = 0 counter = 0 def arrayToOneHotVectorWithConstraint(nparr): result = np.array(list(np.copy(nparr))) for agent in range(len(nparr)): for slaves in range(len(nparr[agent])): maxIndex = 0 maxValue = -100 for i in range(len(nparr[agent][slaves])): result[agent][slaves][i] = 0.0 if nparr[agent][slaves][i] > maxValue: maxValue = nparr[agent][slaves][i] maxIndex = i maxIndex = self.env.setActionType(maxIndex, slaves, agent) result[agent][slaves][maxIndex] = 1.0 return result def arrayToScalarVectorWithConstraint(nparr, isNew): temp = np.array(list(np.copy(nparr))) tempShape = np.shape(temp) scalarVectorShape = list(tempShape) scalarVectorShape[2] = 1 result = np.zeros(scalarVectorShape); result_oneHot = np.array(list(np.copy(nparr))) oneHotVectorShape = list(tempShape) oneHotVectorShape[2] = self.num_action_types result_oneHot = np.zeros(oneHotVectorShape) for agent in range(len(nparr)): for slaves in range(len(nparr[agent])): maxIndex = 0 maxValue = -100 for i in range(self.num_action_types): result_oneHot[agent][slaves][i] = 0.0 if nparr[agent][slaves][i] > maxValue: maxValue = nparr[agent][slaves][i] maxIndex = i maxIndex = self.env.setActionType(maxIndex, slaves, agent, isNew) result[agent][slaves] = maxIndex result_oneHot[agent][slaves][maxIndex] = 1.0 return result, result_oneHot def getActionTypeFromVector(vec): maxIndex = 0 maxValue = -100 for i in range(len(vec)): if vec[i] == 1: return i print("Something wrong in vec") print(vec) return 0 def getLatentReward(nparr): weight = 1.0; tempShape = list(np.shape(nparr)) tempShape[2] = 1 # embed() # exit(0) result = np.zeros(tempShape) for i in range(len(result)): for j in range(len(result[i])): result[i][j] = weight * 1.0/np.sqrt(1558.5)*np.exp(-0.5*np.dot(nparr[i][j], nparr[i][j])) return result h_slave = [None]*self.num_agents while True: counter += 1 if counter%10 == 0: print('SIM : {}'.format(local_step),end='\r') tutorialRatio = 0.0 useEmbeding = True # generate transition of first hierachy a_dist_slave = [None]*self.num_agents v_slave = [None]*self.num_agents if counter%self.typeFreq == 1: states_h[0] = np.copy(states) for i in range(self.num_agents): if teamDic[i] == learningTeam: a_dist_slave_agent,v_slave_agent = \ self.target_model[0][self.indexToNetDic[i]].forward(Tensor(states_h[0][i])) a_dist_slave[i] = a_dist_slave_agent v_slave[i] = v_slave_agent actions_h[0][i] = a_dist_slave[i].sample().cpu().detach().numpy().squeeze().squeeze(); for j in range(self.num_slaves): if followTutorial[j] is True: actions_h[0][i][j][0:2] = self.env.getCorrectActionType(j,i) for i in range(self.num_agents): if teamDic[i] == learningTeam: logprobs_h[0][i] = a_dist_slave[i].log_prob(Tensor(actions_h[0][i]))\ .cpu().detach().numpy().reshape(-1); values_h[0][i] = v_slave[i].cpu().detach().numpy().reshape(-1); for i in range(self.num_agents): for j in range(self.num_slaves): if self.env.isOnFoulReset(j): temp_a_dist,_ = self.target_model[0][self.indexToNetDic[i]].forward(\ Tensor([states_h[0][i][j]]),self.stdScale) actions_h[0][i][j] = temp_a_dist.sample().cpu().detach().numpy() logprobs_h[0][i][j] = temp_a_dist.log_prob(Tensor(actions_h[0][i][j])).cpu().detach().numpy().reshape(-1)[0] actions_c = np.array(list(np.copy(actions_h[0]))) actions_c = actions_c[:,:,-self.num_c:] actions_0_scalar = None actions_0_oneHot = None useEmbeding = False if counter%self.typeFreq == 1: actions_0_scalar, actions_0_oneHot = arrayToScalarVectorWithConstraint(actions_h[0], True) else: actions_0_scalar, actions_0_oneHot = arrayToScalarVectorWithConstraint(actions_h[0], False) action_embeding_ones = np.ones(np.shape(states_h[0]),dtype=np.float32) if useEmbeding: actions_0_oneHot = actions_0_oneHot*0 action_embeding_ones = 0.5 * action_embeding_ones*actions_0_scalar else: action_embeding_ones = 0.0 * action_embeding_ones*actions_0_scalar # generate transition of second hierachy for h in range(1,self.num_h): if h == 1: embededState = states+action_embeding_ones states_h[h] = np.concatenate((embededState, actions_0_oneHot), axis=2) a_dist_slave = [None]*self.num_agents v_slave = [None]*self.num_agents for i in range(self.num_agents): if teamDic[i] == learningTeam: a_dist_slave_agent,v_slave_agent = self.target_model[h][self.indexToNetDic[i]].forward(\ Tensor([states_h[h][i]])) a_dist_slave[i] = a_dist_slave_agent v_slave[i] = v_slave_agent actions_h[h][i] = a_dist_slave[i].sample().cpu().detach().numpy().squeeze().squeeze(); for j in range(self.num_slaves): if followTutorial[j] is True: print("line628") exit(0) decodedActionDetail = self.env.getCorrectActionDetail(j,i) encodedActionDetail, _ = self.actionEncoder.encode(Tensor(decodedActionDetail)) encodedActionDetail= encodedActionDetail.cpu().detach().numpy() actions_h[h][i][j] = encodedActionDetail for i in range(self.num_agents): if teamDic[i] == learningTeam: logprobs_h[h][i] = a_dist_slave[i].log_prob(Tensor(actions_h[h][i]))\ .cpu().detach().numpy().reshape(-1); values_h[h][i] = v_slave[i].cpu().detach().numpy().reshape(-1); for i in range(self.num_agents): for j in range(self.num_slaves): if self.env.isOnFoulReset(j): temp_a_dist,_ = self.target_model[h][self.indexToNetDic[i]].forward(\ Tensor([states_h[h][i][j]]),self.stdScale) actions_h[h][i][j] = temp_a_dist.sample().cpu().detach().numpy() logprobs_h[h][i][j] = temp_a_dist.log_prob(Tensor(actions_h[h][i][j])).cpu().detach().numpy().reshape(-1)[0] actionsLatent = np.array(list(actions_h[1])) actionsLatentReward = getLatentReward(actionsLatent) # embed() # exit(0) actionsLatent = np.concatenate((actions_0_oneHot, actionsLatent), axis =2) decodeShape = list(np.shape(actionsLatent)) decodeShape[2] = 16 actionsDecoded =np.empty(decodeShape,dtype=np.float32) actionsDecoded = self.actionDecoder.decode(Tensor(actionsLatent)).cpu().detach().numpy() # for i in range(len(actionsLatent)): # for j in range(len(actionsLatent[i])): # curActionType = int(actions_0_scalar[i][j][0]) # actionsDecoded[i][j] = self.actionDecoders[curActionType].decode(Tensor(actionsLatent[i][j])).cpu().detach().numpy() envActions = actionsDecoded for i in range(self.num_agents): for j in range(self.num_slaves): self.env.setAction(envActions[i][j], j, i); self.env.stepsAtOnce() nan_occur = [False]*self.num_slaves for j in range(self.num_slaves): if not self.env.isOnResetProcess(j): for i in range(self.num_agents): if teamDic[i] == learningTeam: # embed() # exit(0) rewards[i][j] = self.env.getReward(j, i, True) + actionsLatentReward[i][j][0] if counter%self.typeFreq == 1: accRewards[i][j] = rewards[i][j] else: accRewards[i][j] += rewards[i][j] if np.any(np.isnan(rewards[i][j])): # embed() # exit(0) nan_occur[j] = True if np.any(np.isnan(states[i][j])) or np.any(np.isnan(envActions[i][j])) or np.any(np.isnan(actions_h[0][i][j])): # embed() # exit(0) nan_occur[j] = True if followTutorial[j] is False: if not onFoulResetProcess[j]: self.sum_return += rewards[i][j] - actionsLatentReward[i][j][0] if accRewards[i][j] >= 1.0 and not self.env.isTerminalState(j): embed() exit(0) for j in range(self.num_slaves): if not self.env.isOnResetProcess(j): if nan_occur[j] is True: for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): if followTutorial[j] is False: self.total_episodes[h][self.indexToNetDic[i]].append(self.episodes[h][j][i]) self.episodes[h][j][i] = RNNEpisodeBuffer() else: self.total_episodes[h][self.indexToNetDic[i]].append(self.tutorial_episodes[h][j][i]) self.tutorial_episodes[h][j][i] = RNNEpisodeBuffer() if followTutorial[j] is False: self.num_episode += 1 print("nan", file=sys.stderr) # print("on {}".format(j)); self.env.slaveReset(j) self.env.setResetCount(self.resetDuration-counter%self.typeFreq, j); followTutorial[j] = random.random()<tutorialRatio else: # here not considered followtutorial if not onFoulResetProcess[j]: for i in range(self.num_agents): if len(self.episodes[0][j][i].data) > 0: for h in range(self.num_h): if h == 0: # if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)) or counter%self.typeFreq == 0: if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)): savedFrameDiff = self.env.getSavedFrameDiff(j) if savedFrameDiff <= 6: break; TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gamma*values_h[h][i][j]) if self.env.isTerminalState(j) : TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gamma*accRewards[i][j]) TDError = abs(TDError) TDError = 2.0*pow(TDError, 1.0) TDError = min(TDError, 0.9) if random.random()<TDError : self.env.setToFoulState(j) onFoulResetProcess[j] = True; for h_ in range(self.num_h): if h_ == 0: if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ accRewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) if accRewards[i][j] >= 1.0: self.num_correct_throwing += 1 lastTuple = self.episodes[h_][j][i].data[-(1+int(savedFrameDiff/self.typeFreq))] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) else: if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ rewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) lastTuple = self.episodes[h_][j][i].data[-(1+savedFrameDiff)] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) self.num_td_reset[h] += 1 self.reset_point[int(actions_0_scalar[i][j][0])] += 1 if self.env.isTerminalState(j) : self.num_td_reset_at_goal[h] += 1 if followTutorial[j] is False: self.num_episode += 1 break else: # continue # if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)) or True: if (self.env.isTerminalState(j) and not self.env.isTimeOut(j)): savedFrameDiff = self.env.getSavedFrameDiff(j) if savedFrameDiff <= 6: break; TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gamma*values_h[h][i][j]) if self.env.isTerminalState(j) : TDError = self.episodes[h][j][i].getLastData().value -\ (self.episodes[h][j][i].getLastData().r + self.gamma*rewards[i][j]) TDError = abs(TDError) TDError = 2.0*pow(TDError, 1.0) TDError = min(TDError, 0.9) if random.random()<TDError : self.env.setToFoulState(j) onFoulResetProcess[j] = True; for h_ in range(self.num_h): if h_ == 0: if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ accRewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) if accRewards[i][j] >= 1.0: self.num_correct_throwing += 1 lastTuple = self.episodes[h_][j][i].data[-(1+int(savedFrameDiff/self.typeFreq))] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) else : if followTutorial[j] is False: self.episodes[h_][j][i].push(states_h[h_][i][j], actions_h[h_][i][j],\ rewards[i][j], values_h[h_][i][j], logprobs_h[h_][i][j]) self.num_tuple[h_][self.indexToNetDic[i]] += 1 self.total_episodes[h_][self.indexToNetDic[i]].append(self.episodes[h_][j][i]) lastTuple = self.episodes[h_][j][i].data[-(1+savedFrameDiff)] self.episodes[h_][j][i] = RNNEpisodeBuffer() self.episodes[h_][j][i].data.append(lastTuple) self.num_td_reset[h] += 1 self.reset_point[int(actions_0_scalar[i][j][0])] += 1 if followTutorial[j] is False: self.num_episode += 1 if self.env.isTerminalState(j) : self.num_td_reset_at_goal[h] += 1 if onFoulResetProcess[j] is True: if counter%self.typeFreq != 1: continue if self.env.isFoulState(j) is True: for i in range(self.num_agents): if teamDic[i] == learningTeam: if onFoulResetProcess[j] is True: onFoulResetProcess[j] = False accRewards[i][j] = 0.0 if followTutorial[j] is False: while len(self.episodes[0][j][i].data)>(120/self.typeFreq * 4) : self.episodes[0][j][i].popleft() while len(self.episodes[1][j][i].data)>121 : self.episodes[1][j][i].popleft() self.num_tuple[0][self.indexToNetDic[i]] += len(self.episodes[0][j][i].data) self.num_tuple[1][self.indexToNetDic[i]] += len(self.episodes[1][j][i].data) local_step += len(self.episodes[1][j][i].data) local_step += 1 actions_h[0][i][j] = self.episodes[0][j][i].getLastData().a self.env.foulReset(j) followTutorial[j] = random.random()<tutorialRatio elif self.env.isTerminalState(j) is True: for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): if h == 0: if followTutorial[j] is False: if self.env.isOnFoulReset(j): temp_s = self.episodes[h][j][i].getLastData().s temp_a = self.episodes[h][j][i].getLastData().a temp_r = accRewards[i][j] temp_value = self.episodes[h][j][i].getLastData().value temp_logprob = self.episodes[h][j][i].getLastData().logprob self.episodes[h][j][i].pop() self.episodes[h][j][i].push(temp_s, temp_a, temp_r, temp_value, temp_logprob) else: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ accRewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 if accRewards[i][j] >= 1.0: self.num_correct_throwing += 1 else : if followTutorial[j] is False: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ rewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 for h in range(self.num_h): self.total_episodes[h][self.indexToNetDic[i]].append(self.episodes[h][j][i]) self.episodes[h][j][i] = RNNEpisodeBuffer() if followTutorial[j] is False: self.num_episode += 1 local_step += 1 self.env.slaveReset(j) followTutorial[j] = random.random()<tutorialRatio self.env.setResetCount(self.resetDuration-counter%self.typeFreq, j); else: for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): if h == 0: if counter%self.typeFreq == 0: if followTutorial[j] is False: if self.env.isOnFoulReset(j): # print("111") temp_s = self.episodes[h][j][i].getLastData().s temp_a = self.episodes[h][j][i].getLastData().a temp_r = accRewards[i][j] temp_value = self.episodes[h][j][i].getLastData().value temp_logprob = self.episodes[h][j][i].getLastData().logprob self.episodes[h][j][i].pop() self.episodes[h][j][i].push(temp_s, temp_a, temp_r, temp_value, temp_logprob) else: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ accRewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 else : if followTutorial[j] is False: self.episodes[h][j][i].push(states_h[h][i][j], actions_h[h][i][j],\ rewards[i][j], values_h[h][i][j], logprobs_h[h][i][j]) self.num_tuple[h][self.indexToNetDic[i]] += 1 local_step += 1 if local_step >= self.buffer_size: for j in range(self.num_slaves): for i in range(self.num_agents): if teamDic[i] == learningTeam: for h in range(self.num_h): self.total_episodes[h][self.indexToNetDic[i]].append(self.episodes[h][j][i]) self.episodes[h][j][i] = RNNEpisodeBuffer() if followTutorial[j] is False: self.num_episode += 1 self.env.slaveReset(j) followTutorial[j] = random.random()<tutorialRatio break for i in range(self.num_agents): for j in range(self.num_slaves): states[i][j] = self.env.getState(j,i).astype(np.float32) states = np.array(states) states[:,:,:-5] = self.rms.apply(states[:,:,:-5]) print('SIM : {}'.format(local_step)) def computeTDandGAE(self): for h in range(self.num_h): for index in range(self.num_policy): self.buffer[h][index].clear() for epi in self.total_episodes[h][index]: data = epi.getData() size = len(data) if size == 0: continue states, actions, rewards, values, logprobs = zip(*data) values = np.concatenate((values, np.zeros(1)), axis=0) advantages = np.zeros(size) ad_t = 0 epi_return = 0.0 for i in reversed(range(len(data))): epi_return += rewards[i] delta = rewards[i] + values[i+1] * self.gamma - values[i] ad_t = delta + self.gamma * self.lb * ad_t advantages[i] = ad_t if not np.isnan(epi_return): TD = values[:size] + advantages rnn_replay_buffer = RNNReplayBuffer(10000) for i in range(size): rnn_replay_buffer.push(states[i], actions[i], logprobs[i], TD[i], advantages[i]) self.buffer[h][index].push(rnn_replay_buffer) ''' counting numbers ''' for h in range(self.num_h): for index in range(self.num_policy): self.num_tuple_so_far[h][index] += self.num_tuple[h][index] def tutorial_computeTDandGAE(self): for h in range(self.num_h): for index in range(self.num_policy): self.tutorial_buffer[h][index].clear() for epi in self.total_tutorial_episodes[h][index]: data = epi.getData() size = len(data) if size == 0: continue states, actions, rewards, values, logprobs = zip(*data) values = np.concatenate((values, np.zeros(1)), axis=0) advantages = np.zeros(size) ad_t = 0 epi_return = 0.0 for i in reversed(range(len(data))): epi_return += rewards[i] delta = rewards[i] + values[i+1] * self.gamma - values[i] ad_t = delta + self.gamma * self.lb * ad_t advantages[i] = ad_t if not np.isnan(epi_return): TD = values[:size] + advantages rnn_replay_buffer = RNNReplayBuffer(10000) for i in range(size): rnn_replay_buffer.push(states[i], actions[i], logprobs[i], TD[i], advantages[i]) self.tutorial_buffer[h][index].push(rnn_replay_buffer) def optimizeNN_h(self, h = 0): for i in range(self.num_policy): self.sum_loss_actor[h][i] = 0.0 self.sum_loss_critic[h][i] = 0.0 for buff_index in range(self.num_policy): all_rnn_replay_buffer= np.array(self.buffer[h][buff_index].buffer) for j in range(self.num_epochs): all_segmented_transitions = [] for rnn_replay_buffer in all_rnn_replay_buffer: rnn_replay_buffer_size = len(rnn_replay_buffer.buffer) for i in range(rnn_replay_buffer_size): all_segmented_transitions.append(rnn_replay_buffer.buffer[i]) batch_size_h = self.batch_size if h == 0: batch_size_h = self.batch_size//4 np.random.shuffle(all_segmented_transitions) for i in range(len(all_segmented_transitions)//batch_size_h): batch_segmented_transitions = all_segmented_transitions[i*batch_size_h:(i+1)*batch_size_h] batch = RNNTransition(*zip(*batch_segmented_transitions)) stack_s = np.vstack(batch.s).astype(np.float32) stack_a = np.vstack(batch.a).astype(np.float32) stack_lp = np.vstack(batch.logprob).astype(np.float32) stack_td = np.vstack(batch.TD).astype(np.float32) stack_gae = np.vstack(batch.GAE).astype(np.float32) a_dist,v = self.target_model[h][buff_index].forward(Tensor(stack_s)) loss_critic = ((v-Tensor(stack_td)).pow(2)).mean() '''Actor Loss''' ratio = torch.exp(a_dist.log_prob(Tensor(stack_a))-Tensor(stack_lp)) stack_gae = (stack_gae-stack_gae.mean())/(stack_gae.std()+1E-5) stack_gae = Tensor(stack_gae) surrogate1 = ratio * stack_gae surrogate2 = torch.clamp(ratio, min=1.0-self.clip_ratio, max=1.0+self.clip_ratio) * stack_gae loss_actor = - torch.min(surrogate1, surrogate2).mean() '''Entropy Loss''' loss_entropy = - self.w_entropy * a_dist.entropy().mean() loss = loss_actor + loss_critic + loss_entropy self.optimizer[h][buff_index].zero_grad() loss.backward() detectNan = False for param in self.target_model[h][buff_index].parameters(): if torch.isnan(param.grad).any(): detectNan = True if param.grad is not None: param.grad.data.clamp_(-0.5, 0.5) if not detectNan: self.optimizer[h][buff_index].step() self.sum_loss_actor[h][buff_index] += loss_actor.detach()*batch_size_h/self.num_epochs self.sum_loss_critic[h][buff_index] += loss_critic.detach()*batch_size_h/self.num_epochs loss_entropy = loss_entropy.detach() loss_actor = loss_actor.detach() loss_critic = loss_critic.detach() loss= loss.detach() print('Optimizing actor-critic nn_{} : {}/{}'.format(h, j+1,self.num_epochs),end='\r') print('') # print('') def tutorial_optimizeNN_h(self, h = 0): for i in range(self.num_policy): self.sum_tutorial_loss_critic[h][i] = 0.0 for buff_index in range(self.num_policy): all_rnn_replay_buffer= np.array(self.tutorial_buffer[h][buff_index].buffer) for j in range(self.num_epochs): all_segmented_transitions = [] for rnn_replay_buffer in all_rnn_replay_buffer: rnn_replay_buffer_size = len(rnn_replay_buffer.buffer) for i in range(rnn_replay_buffer_size): all_segmented_transitions.append(rnn_replay_buffer.buffer[i]) np.random.shuffle(all_segmented_transitions) tutorial_batchSize = self.batch_size//16 for i in range(len(all_segmented_transitions)//tutorial_batchSize): batch_segmented_transitions = all_segmented_transitions[i*tutorial_batchSize:(i+1)*tutorial_batchSize] batch = RNNTransition(*zip(*batch_segmented_transitions)) stack_s = np.vstack(batch.s).astype(np.float32) stack_a = np.vstack(batch.a).astype(np.float32) stack_lp = np.vstack(batch.logprob).astype(np.float32) stack_td = np.vstack(batch.TD).astype(np.float32) stack_gae = np.vstack(batch.GAE).astype(np.float32) a_dist,v = self.target_model[h][buff_index].forward(Tensor(stack_s)) loss_critic = ((v-Tensor(stack_td)).pow(2)).mean() loss = loss_critic self.optimizer[h][buff_index].zero_grad() loss.backward() detectNan = False for param in self.target_model[h][buff_index].parameters(): if torch.isnan(param.grad).any(): detectNan = True # print("nan") if param.grad is not None: param.grad.data.clamp_(-0.5, 0.5) self.sum_tutorial_loss_critic[h][buff_index] += loss_critic.detach()*tutorial_batchSize/self.num_epochs loss_critic = loss_critic.detach() loss= loss.detach() print('Optimizing actor-critic nn_{} : {}/{}'.format(h, j+1,self.num_epochs),end='\r') print('') # print('') def train(self): frac = 1.0 self.learning_rate = self.default_learning_rate*frac self.clip_ratio = self.default_clip_ratio*frac for i in range(self.num_policy): for h in range(self.num_h): for param_group in self.optimizer[h][i].param_groups: param_group['lr'] = self.learning_rate self.generateTransitions() self.computeTDandGAE() self.optimizeModel() def optimizeModel(self): for h in range(self.num_h): self.optimizeNN_h(h) def evaluate(self): self.num_evaluation = self.num_evaluation + 1 hour = int((time.time() - self.tic)//3600.0) m = int((time.time() - self.tic)//60.0) s = int((time.time() - self.tic)) m = m - hour*60 s = int((time.time() - self.tic)) s = s - hour*3600 - m*60 if self.num_episode is 0: self.num_episode = 1 for h in range(self.num_h): for i in range(self.num_policy): if self.num_tuple[h][i] is 0: self.num_tuple[h][i] = 1 if self.num_tutorial_tuple[h][i] is 0: self.num_tutorial_tuple[h][i] = 1 if self.max_return < self.sum_return/self.num_episode: self.max_return = self.sum_return/self.num_episode self.max_return_epoch = self.num_evaluation print('# {} === {}h:{}m:{}s ==='.format(self.num_evaluation,hour,m,s)) print('||--------------ActorCriticNN------------------') for i in range(self.num_policy): for h in range(self.num_h): print('||Avg Loss Actor {} {} : {:.4f}'.format(h, i, self.sum_loss_actor[h][i]/self.num_tuple[h][i])) print('||Avg Loss Critic {} {} : {:.4f}'.format(h, i, self.sum_loss_critic[h][i]/self.num_tuple[h][i])) print('||Noise {} : {:.3f}'.format(h, self.target_model[h][i].log_std.exp().mean())) print('||Num Transition So far {} {} : {}'.format(h, i, self.num_tuple_so_far[h][i])) print('||Num Transition {} {} : {}'.format(h, i, self.num_tuple[h][i])) print('||Num Episode : {}'.format(self.num_episode)) for i in range(self.num_policy): print('||Avg Step per episode {} : {:.1f}'.format(i, self.num_tuple[1][i]/self.num_episode)) print('||Avg Num Correct Throwings per episode : {:.3f}'.format(self.num_correct_throwing/self.num_episode)) print('||Max Return per episode : {:.3f}'.format(self.max_return)) print('||Avg Return per episode : {:.3f}'.format(self.sum_return/self.num_episode)) for h in range(self.num_h): print('||Num TD Reset : {} {}'.format(h, self.num_td_reset[h])) print('||Num TD Reset at goal : {} {}'.format(h, self.num_td_reset_at_goal[h])) self.rewards.append(self.sum_return/self.num_episode) self.numSteps.append(self.num_tuple[1][0]/self.num_episode) self.num_correct_throwings.append(self.num_correct_throwing/self.num_episode) self.saveModels() # embed() # exit(0) for i in range(len(self.reset_point)): if self.num_td_reset[0]+self.num_td_reset[1] > 0: self.reset_point[i] /= self.num_td_reset[0]+self.num_td_reset[1] else: self.reset_point[i] = 0 summary.add_scalars('rewards and num_correct_throwings',{'rewards' : self.sum_return/self.num_episode, 'num_ct' : self.num_correct_throwing/self.num_episode}, self.num_evaluation) summary.add_scalar('numSteps',self.num_tuple[1][0]/self.num_episode, self.num_evaluation) summary.add_scalar('ratio of resets',(self.num_td_reset[0] + self.num_td_reset[1])/self.num_episode, self.num_evaluation) summary.add_scalars('reset point',{'0':self.reset_point[0], '1':self.reset_point[1], '2':self.reset_point[2], '3':self.reset_point[3], '4':self.reset_point[4]},self.num_evaluation) for name, param in self.target_model[0][0].named_parameters(): summary.add_histogram(name+"_actionType", param.clone().cpu().data.numpy(), self.num_evaluation) for name, param in self.target_model[1][0].named_parameters(): summary.add_histogram(name+"_actionDetail", param.clone().cpu().data.numpy(), self.num_evaluation) print('=============================================') return np.array(self.rewards), np.array(self.numSteps), np.array(self.num_correct_throwings) import matplotlib import matplotlib.pyplot as plt plt.ion() def plot(y,title,num_fig=1,ylim=True,path=""): temp_y = np.zeros(y.shape) if y.shape[0]>5: temp_y[0] = y[0] temp_y[1] = 0.5*(y[0] + y[1]) temp_y[2] = 0.3333*(y[0] + y[1] + y[2]) temp_y[3] = 0.25*(y[0] + y[1] + y[2] + y[3]) for i in range(4,y.shape[0]): temp_y[i] = np.sum(y[i-4:i+1])*0.2 fig = plt.figure(num_fig) plt.clf() plt.title(title) plt.plot(y,'b') plt.plot(temp_y,'r') plt.savefig(path, format="png") # plt.show() if ylim: plt.ylim([0,1]) fig.canvas.draw() fig.canvas.flush_events() def plot_numSteps(y,title,num_fig=1,ylim=True,path=""): temp_y = np.zeros(y.shape) if y.shape[0]>5: temp_y[0] = y[0] temp_y[1] = 0.5*(y[0] + y[1]) temp_y[2] = 0.3333*(y[0] + y[1] + y[2]) temp_y[3] = 0.25*(y[0] + y[1] + y[2] + y[3]) for i in range(4,y.shape[0]): temp_y[i] = np.sum(y[i-4:i+1])*0.2 fig = plt.figure(num_fig) plt.clf() plt.title(title) plt.plot(y,'b') plt.plot(temp_y,'r') plt.savefig(path, format="png") if ylim: plt.ylim([0,1]) fig.canvas.draw() fig.canvas.flush_events() import argparse if __name__=="__main__": parser = argparse.ArgumentParser() parser.add_argument('-m','--model',help='model path') # parser.add_argument('-p','--policy',help='pretrained pollicy path') parser.add_argument('-iter','--iteration',help='num iterations') parser.add_argument('-n','--name',help='name of training setting') parser.add_argument('-motion', '--motion', help='motion nn path') args =parser.parse_args() graph_name = '' if args.motion is None: print("Please specify the motion nn path") exit(0) rl = RL(args.motion) if args.model is not None: for k in range(rl.num_agents): rl.loadTargetModels(args.model, k) if args.name is not None: graph_name = args.name # if args.policy is not None: # rl.loadTargetPolicy(args.policy) if args.iteration is not None: rl.num_evaluation = int(args.iteration) for i in range(int(args.iteration)): rl.env.endOfIteration() else: rl.saveModels() print('num states: {}, num actions: {}'.format(rl.env.getNumState(),rl.env.getNumAction()-1)) result_figure = nndir+"/"+"result.png" result_figure_numSteps = nndir+"/"+"result_numSteps.png" result_figure_numCTs = nndir+"/"+"result_numCTs.png" result_figure_num = 0 while Path(result_figure).is_file(): result_figure = nndir+"/"+"result_{}.png".format(result_figure_num) result_figure_numSteps = nndir+"/"+"result_numSteps_{}.png".format(result_figure_num) result_figure_numCTs = nndir+"/"+"result_numCTs_{}.png".format(result_figure_num) result_figure_num+=1 for i in range(5000000): rl.train() rewards, numSteps, numCTs = rl.evaluate()

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setup.py

ilkka/nap

31

39635

<reponame>ilkka/nap #!/usr/bin/env python from pip.req import parse_requirements # parse_requirements() returns generator of pip.req.InstallRequirement objects install_reqs = parse_requirements('requirements.txt') # reqs is a list of requirement # e.g. ['django==1.5.1', 'mezzanine==1.4.6'] reqs = [str(ir.req) for ir in install_reqs] try: from setuptools import setup except ImportError: from distutils.core import setup readme = """Read docs from GitHub_ .. _GitHub: https://github.com/kimmobrunfeldt/nap """ setup( name='nap', version='2.0.0-dev', description='Convenient way to request HTTP APIs', long_description=readme, author='<NAME>', author_email='<EMAIL>', url='https://github.com/kimmobrunfeldt/nap', packages=[ 'nap', ], package_dir={'nap': 'nap'}, include_package_data=True, install_requires=reqs, license='MIT', zip_safe=False, keywords='nap rest requests http', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: Implementation :: PyPy', ], )

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src-python/tests/test_trp2.py

kmascar/amazon-textract-response-parser

0

197998

from trp.t_pipeline import add_page_orientation, order_blocks_by_geo from typing import List from trp.t_pipeline import add_page_orientation, order_blocks_by_geo, pipeline_merge_tables, add_kv_ocr_confidence from trp.t_tables import MergeOptions, HeaderFooterType import trp.trp2 as t2 import trp as t1 import json import os import pytest from trp import Document from uuid import uuid4 import logging current_folder = os.path.dirname(os.path.realpath(__file__)) def return_json_for_file(filename): with open(os.path.join(current_folder, filename)) as test_json: return json.load(test_json) @pytest.fixture def json_response(): return return_json_for_file("test-response.json") def test_serialization(): """ testing that None values are removed when serializing """ bb_1 = t2.TBoundingBox(0.4, 0.3, 0.1, top=None) # type:ignore forcing some None/null values bb_2 = t2.TBoundingBox(0.4, 0.3, 0.1, top=0.2) p1 = t2.TPoint(x=0.1, y=0.1) p2 = t2.TPoint(x=0.3, y=None) # type:ignore geo = t2.TGeometry(bounding_box=bb_1, polygon=[p1, p2]) geo_s = t2.TGeometrySchema() s: str = geo_s.dumps(geo) assert not "null" in s geo = t2.TGeometry(bounding_box=bb_2, polygon=[p1, p2]) s: str = geo_s.dumps(geo) assert not "null" in s def test_tblock_order_blocks_by_geo(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) new_order = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(new_order)) assert "Value 1.1.1" == doc.pages[0].tables[0].rows[0].cells[0].text.strip() assert "Value 2.1.1" == doc.pages[0].tables[1].rows[0].cells[0].text.strip() assert "Value 3.1.1" == doc.pages[0].tables[2].rows[0].cells[0].text.strip() def test_tblock_order_block_by_geo_multi_page(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(t_document)) assert "Page 1 - Value 1.1.1" == doc.pages[0].tables[0].rows[0].cells[0].text.strip() assert "Page 1 - Value 2.1.1" == doc.pages[0].tables[1].rows[0].cells[0].text.strip() def test_tblock(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) new_order = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(new_order)) assert "Value 1.1.1" == doc.pages[0].tables[0].rows[0].cells[0].text.strip() assert "Value 2.1.1" == doc.pages[0].tables[1].rows[0].cells[0].text.strip() assert "Value 3.1.1" == doc.pages[0].tables[2].rows[0].cells[0].text.strip() def test_custom_tblock(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document.custom = {'testblock': {'here': 'is some fun stuff'}} assert 'testblock' in t2.TDocumentSchema().dumps(t_document) def test_custom_page_orientation(json_response): doc = Document(json_response) assert 1 == len(doc.pages) lines = [line for line in doc.pages[0].lines] assert 22 == len(lines) words = [word for line in lines for word in line.words] assert 53 == len(words) t_document: t2.TDocument = t2.TDocumentSchema().load(json_response) t_document.custom = {'orientation': 180} new_t_doc_json = t2.TDocumentSchema().dump(t_document) assert "Custom" in new_t_doc_json assert "orientation" in new_t_doc_json["Custom"] assert new_t_doc_json["Custom"]["orientation"] == 180 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert -1 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 2 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_10_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 5 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 15 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__15_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 10 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 20 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__25_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 17 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 30 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__180_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 170 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 190 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__270_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert -100 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < -80 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__90_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert 80 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 100 p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__minus_10_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert -10 < t_document.pages[0].custom['PageOrientationBasedOnWords'] < 5 doc = t1.Document(t2.TDocumentSchema().dump(t_document)) for page in doc.pages: assert page.custom['PageOrientationBasedOnWords'] def test_filter_blocks_by_type(): block_list = [t2.TBlock(id="1", block_type=t2.TextractBlockTypes.WORD.name)] assert t2.TDocument.filter_blocks_by_type(block_list=block_list, textract_block_type=[t2.TextractBlockTypes.WORD]) == block_list def test_next_token_response(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib.json")) j = json.load(f) assert j['NextToken'] t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) assert t_document.pages[0].custom def test_rotate_point(): assert t2.TPoint(2, 2) == t2.TPoint(2, 2) p = t2.TPoint(2, 2).rotate(degrees=180, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(-2, -2) p = t2.TPoint(3, 4).rotate(degrees=-30, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(5, 2) p = t2.TPoint(3, 4).rotate(degrees=-77, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(5, -2) p = t2.TPoint(3, 4).rotate(degrees=-90, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(4, -3) p = t2.TPoint(3, 4).rotate(degrees=-270, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(-4, 3) p = t2.TPoint(2, 2).rotate(degrees=180, origin_x=1, origin_y=1) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(0, 0) p = t2.TPoint(3, 4).rotate(degrees=-30, origin_y=0, origin_x=0, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(5, 2) p = t2.TPoint(3, 4).rotate(degrees=-77, origin_x=4, origin_y=4, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(4, 5) p = t2.TPoint(3, 4).rotate(degrees=-90, origin_x=4, origin_y=6, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(2, 7) p = t2.TPoint(3, 4).rotate(degrees=-270, origin_x=4, origin_y=6, force_limits=False) assert t2.TPoint(x=round(p.x), y=round(p.y)) == t2.TPoint(6, 5) def test_rotate(): points = [] width = 0.05415758863091469 height = 0.011691284365952015 left = 0.13994796574115753 top = 0.8997916579246521 origin: t2.TPoint = t2.TPoint(x=0.5, y=0.5) degrees: float = 180 points.append(t2.TPoint(x=left, y=top).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) points.append(t2.TPoint(x=left + width, y=top).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) points.append(t2.TPoint(x=left, y=top + height).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) points.append( t2.TPoint(x=left + width, y=top + height).rotate(origin_x=origin.x, origin_y=origin.y, degrees=degrees)) assert not None in points def test_adjust_bounding_boxes_and_polygons_to_orientation(): # p = os.path.dirname(os.path.realpath(__file__)) # f = open(os.path.join(p, "data/gib.json")) # j = json.load(f) # t_document: t2.TDocument = t2.TDocumentSchema().load(j) # t_document = add_page_orientation(t_document) # doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # key = "Date:" # fields = doc.pages[0].form.searchFieldsByKey(key) # for field in fields: # print(f"Field: Key: {field.key}, Value: {field.value}, Geo: {field.geometry} ") p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib__180_degrees.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_page_orientation(t_document) new_order = order_blocks_by_geo(t_document) doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # for line in doc.pages[0].lines: # print("Line: {}".format(line.text)) # print("=========================== after rotation ========================") # doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # key = "Date:" # fields = doc.pages[0].form.searchFieldsByKey(key) # rotate_point = t2.TPoint(x=0.5, y=0.5) # for field in fields: # print(f"Field: Key: {field.key}, Value: {field.value}, Geo: {field.geometry} ") # bbox = field.geometry.boundingBox # new_point = t_pipeline.__rotate(origin=rotate_point, # point=t2.TPoint(x=bbox.left, y=bbox.top), # angle_degrees=180) # print(f"new point: {new_point}") # FIXME: remove duplicates in relationship_recursive! # [b.rotate(origin=t2.TPoint(0.5, 0.5), degrees=180) for b in t_document.relationships_recursive(block=t_document.pages[0])] # t_document.rotate(page=t_document.pages[0], degrees=180) # new_order = order_blocks_by_geo(t_document) # with open("/Users/schadem/temp/rotation/rotate_json2.jon", "w") as out_file: # out_file.write(t2.TDocumentSchema().dumps(t_document)) # doc = t1.Document(t2.TDocumentSchema().dump(t_document)) # for line in doc.pages[0].lines: # print("Line: {}".format(line.text)) # p = t2.TPoint(x=0.75, y=0.03) # p.rotate(origin_x=0.5, origin_y=0.5, degrees=180) # print(p) # new_point = rotate(origin=t2.TPoint(x=0.5, y=0.5), point = ) # print(f"new_point: {new_point.x:.2f}, {new_point.y:.2f}") # print(rotate(origin=t2.TPoint(x=0.5, y=0.5), point = t2.TPoint(x=.75, y=0.03))) def test_scale(caplog): p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) p1.scale(doc_width=10, doc_height=10) assert (p1 == t2.TPoint(x=5, y=5)) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) b1.scale(doc_width=10, doc_height=10) assert (b1 == t2.TBoundingBox(width=1, height=1, left=5, top=5)) p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) g1: t2.TGeometry = t2.TGeometry(bounding_box=b1, polygon=[p1]) g1.scale(doc_width=10, doc_height=10) assert (g1 == t2.TGeometry(bounding_box=t2.TBoundingBox(width=1, height=1, left=5, top=5), polygon=[t2.TPoint(x=5, y=5)])) def test_ratio(caplog): p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) p2: t2.TPoint = t2.TPoint(x=5, y=5) p2.ratio(doc_width=10, doc_height=10) assert (p1 == p2) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) b2: t2.TBoundingBox = t2.TBoundingBox(width=1, height=1, left=5, top=5) b2.ratio(doc_width=10, doc_height=10) assert (b1 == b2) p1: t2.TPoint = t2.TPoint(x=0.5, y=0.5) p2: t2.TPoint = t2.TPoint(x=5, y=5) b1: t2.TBoundingBox = t2.TBoundingBox(width=0.1, height=0.1, left=0.5, top=0.5) b2: t2.TBoundingBox = t2.TBoundingBox(width=1, height=1, left=5, top=5) g1: t2.TGeometry = t2.TGeometry(bounding_box=b1, polygon=[p1]) g2: t2.TGeometry = t2.TGeometry(bounding_box=b2, polygon=[p2]) g2.ratio(doc_width=10, doc_height=10) assert (g1 == g2) def test_get_blocks_for_relationship(caplog): caplog.set_level(logging.DEBUG) # existing relationships p = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(p, "data/gib.json")) as f: j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) page = t_document.pages[0] block = t_document.get_block_by_id("458a9301-8a9d-4eb2-9469-70302c62622e") relationships = block.get_relationships_for_type() relationships_value = block.get_relationships_for_type(relationship_type="VALUE") if relationships and relationships_value: rel = t_document.get_blocks_for_relationships(relationship=relationships) assert len(rel) == 1 rel_value = t_document.get_blocks_for_relationships(relationship=relationships_value) assert len(rel_value) == 1 child_rel: List[t2.TBlock] = list() for value_block in rel_value: child_rel.extend(t_document.get_blocks_for_relationships(value_block.get_relationships_for_type())) assert len(child_rel) == 1 else: assert False def test_add_ids_to_relationships(caplog): tdocument = t2.TDocument() page_block = t2.TBlock( id=str(uuid4()), block_type="PAGE", geometry=t2.TGeometry(bounding_box=t2.TBoundingBox(width=1, height=1, left=0, top=0), polygon=[t2.TPoint(x=0, y=0), t2.TPoint(x=1, y=1)]), ) tblock = t2.TBlock(id=str(uuid4()), block_type="WORD", text="sometest", geometry=t2.TGeometry(bounding_box=t2.TBoundingBox(width=0, height=0, left=0, top=0), polygon=[t2.TPoint(x=0, y=0), t2.TPoint(x=0, y=0)]), confidence=99, text_type="VIRTUAL") tdocument.add_block(page_block) tdocument.add_block(tblock) page_block.add_ids_to_relationships([tblock.id]) tblock.add_ids_to_relationships(["1", "2"]) assert page_block.relationships and len(page_block.relationships) > 0 assert tblock.relationships and len(tblock.relationships) > 0 def test_key_value_set_key_name(caplog): caplog.set_level(logging.DEBUG) # existing relationships p = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(p, "data/gib.json")) as f: j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) page = t_document.pages[0] keys = list(t_document.keys(page=page)) assert keys and len(keys) > 0 for key_value in keys: child_relationship = key_value.get_relationships_for_type('CHILD') if child_relationship: for id in child_relationship.ids: k_b = t_document.get_block_by_id(id=id) print(k_b.text) print(' '.join([x.text for x in t_document.value_for_key(key_value)])) def test_get_relationships_for_type(caplog): # existing relationships p = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(p, "data/gib.json")) as f: j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) page = t_document.pages[0] new_block = t2.TBlock(id=str(uuid4())) t_document.add_block(new_block) page.add_ids_to_relationships([new_block.id]) assert t_document.get_block_by_id(new_block.id) == new_block #empty relationships t_document: t2.TDocument = t2.TDocument() t_document.add_block(t2.TBlock(id=str(uuid4()), block_type="PAGE")) page = t_document.pages[0] new_block = t2.TBlock(id=str(uuid4())) t_document.add_block(new_block) page.add_ids_to_relationships([new_block.id]) assert t_document.get_block_by_id(new_block.id) == new_block def test_merge_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = 'fed02fb4-1996-4a15-98dc-29da193cc476' tbl_id2 = '47c6097f-02d5-4432-8423-13c05fbfacbd' pre_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore pre_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id2).relationships[0].ids) # type: ignore pre_merge_tbl1_lastcell = t_document.get_block_by_id(tbl_id1).relationships[0].ids[-1] # type: ignore pre_merge_tbl2_lastcell = t_document.get_block_by_id(tbl_id2).relationships[0].ids[-1] # type: ignore pre_merge_tbl1_last_row = t_document.get_block_by_id(pre_merge_tbl1_lastcell).row_index # type: ignore pre_merge_tbl2_last_row = t_document.get_block_by_id(pre_merge_tbl2_lastcell).row_index # type: ignore t_document.merge_tables([[tbl_id1, tbl_id2]]) post_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore post_merge_tbl1_lastcell = t_document.get_block_by_id(tbl_id1).relationships[0].ids[-1] # type: ignore post_merge_tbl1_last_row = t_document.get_block_by_id(post_merge_tbl1_lastcell).row_index # type: ignore assert post_merge_tbl1_cells_no == pre_merge_tbl1_cells_no + pre_merge_tbl2_cells_no assert pre_merge_tbl2_last_row assert post_merge_tbl1_last_row == pre_merge_tbl1_last_row + pre_merge_tbl2_last_row # type: ignore def test_delete_blocks(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = 'fed02fb4-1996-4a15-98dc-29da193cc476' tbl_id2 = '47c6097f-02d5-4432-8423-13c05fbfacbd' pre_delete_block_no = len(t_document.blocks) t_document.delete_blocks([tbl_id1, tbl_id2]) post_delete_block_no = len(t_document.blocks) assert post_delete_block_no == pre_delete_block_no - 2 def test_link_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_tables.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = 'fed02fb4-1996-4a15-98dc-29da193cc476' tbl_id2 = '47c6097f-02d5-4432-8423-13c05fbfacbd' t_document.link_tables([[tbl_id1, tbl_id2]]) assert t_document.get_block_by_id(tbl_id1).custom['next_table'] == tbl_id2 # type: ignore assert t_document.get_block_by_id(tbl_id2).custom['previous_table'] == tbl_id1 # type: ignore def test_pipeline_merge_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_page_table_merge.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = '5685498d-d196-42a7-8b40-594d6d886ca9' tbl_id2 = 'a9191a66-0d32-4d36-8fd6-58e6917f4ea6' tbl_id3 = 'e0368543-c9c3-4616-bd6c-f25e66c859b2' pre_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore pre_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id2).relationships[0].ids) # type: ignore pre_merge_tbl3_cells_no = len(t_document.get_block_by_id(tbl_id3).relationships[0].ids) # type: ignore t_document = pipeline_merge_tables(t_document, MergeOptions.MERGE, None, HeaderFooterType.NONE) post_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore assert post_merge_tbl1_cells_no == pre_merge_tbl1_cells_no + pre_merge_tbl2_cells_no + pre_merge_tbl3_cells_no def test_pipeline_merge_multiple_tables(): p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/gib_multi_tables_multi_page_sample.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) tbl_id1 = '4894d2ba-0479-4196-9cbd-c0fea4d28762' tbl_id2 = 'b5e061ec-05be-48d5-83fc-6719fdd4397a' tbl_id3 = '8bbc3f4f-0354-4999-a001-4585631bb7fe' tbl_id4 = 'cf8e09a1-c317-40c1-9c45-e830e14167d5' pre_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore pre_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id2).relationships[0].ids) # type: ignore pre_merge_tbl3_cells_no = len(t_document.get_block_by_id(tbl_id3).relationships[0].ids) # type: ignore pre_merge_tbl4_cells_no = len(t_document.get_block_by_id(tbl_id4).relationships[0].ids) # type: ignore t_document = pipeline_merge_tables(t_document, MergeOptions.MERGE, None, HeaderFooterType.NONE) post_merge_tbl1_cells_no = len(t_document.get_block_by_id(tbl_id1).relationships[0].ids) # type: ignore post_merge_tbl2_cells_no = len(t_document.get_block_by_id(tbl_id3).relationships[0].ids) # type: ignore assert post_merge_tbl1_cells_no == pre_merge_tbl1_cells_no + pre_merge_tbl2_cells_no assert post_merge_tbl2_cells_no == pre_merge_tbl3_cells_no + pre_merge_tbl4_cells_no def test_kv_ocr_confidence(caplog): caplog.set_level(logging.DEBUG) p = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(p, "data/employment-application.json")) j = json.load(f) t_document: t2.TDocument = t2.TDocumentSchema().load(j) t_document = add_kv_ocr_confidence(t_document) doc = t1.Document(t2.TDocumentSchema().dump(t_document)) for page in doc.pages: k1 = page.form.getFieldByKey("Home Address:") k1.key.custom['OCRConfidence'] == {'mean': 99.60698318481445} k1.value.custom['OCRConfidence'] == {'mean': 99.8596928914388} k1 = page.form.getFieldByKey("Phone Number:") k1.key.custom['OCRConfidence'] == {'mean': 99.55334854125977} k1.value.custom['OCRConfidence'] == {'mean': 99.23233032226562} # for field in page.form.fields: # print( # f"{field.key.text} - {field.key.custom['OCRConfidence']}, {field.value.text} - {field.value.custom['OCRConfidence']}" # )

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mindpong/model/serial_communication.py

PolyCortex/MindPong

22

42613

import sys from glob import glob from serial import Serial, SerialException import numpy as np BAUD_RATE = 9600 PORT = 'COM5' READ_TIMEOUT = 1 LOWER_BOUND = 0.01 UPPER_BOUND = 0.4 class SerialCommunication(): """ Manages the communication and sends the data to the Arduino """ def __init__(self): self._serial_channel = Serial() self._serial_channel.port = PORT self._serial_channel.baudrate = BAUD_RATE @property def baudrate(self): return self._serial_channel.baudrate @baudrate.setter def baudrate(self, new_baudrate): if not self._serial_channel.is_open: self._serial_channel.baudrate = new_baudrate else: raise Exception("Close connection before changing baudrate") @property def port(self): return self._serial_channel.port @port.setter def set_port(self, new_port): if not self._serial_channel.is_open: self._serial_channel.port = new_port else: raise Exception("Close connection before changing port") def get_available_serial_ports(self): """ Returns a list of all ports that can be opened """ if self._serial_channel.is_open: raise Exception("Close connection before") result = [] for port in self._list_all_possibles_ports(): try: Serial(port).close() result.append(port) except (OSError, SerialException): pass return result def establish_communication(self): """ Enables the communication with the arduino with the latest parameters Throws a SerialException is it cannot connect to port """ try: self._serial_channel.open() except SerialException as error: print("Error when connecting to serial %s port" % (self._serial_channel.port)) raise(SerialException) def send_data(self, data): """ prints feedback data from the arduino and sends the new data """ if self._is_data_available(): print(("Reading : ", self._read_bytes(len(data)))) data = [x[1] for x in data] if self._is_data_valid(data): value_to_send = self._get_clipped_signals(data) print(('Sending', value_to_send)) try: self._serial_channel.write(bytearray(value_to_send)) except SerialTimeoutException as e: print('Error when sending data to microcontroller:' + str(e)) def close_communication(self): self._serial_channel.close() def _list_all_possibles_ports(self): if sys.platform.startswith('win'): ports = ['COM%s' % (i + 1) for i in range(256)] elif sys.platform.startswith('linux') or sys.platform.startswith('cygwin'): # this excludes your current terminal "/dev/tty" ports = glob('/dev/tty[A-Za-z]*') elif sys.platform.startswith('darwin'): ports = glob('/dev/tty.*') else: raise EnvironmentError('Unsupported platform') return ports def _read_bytes(self, nb_bytes=1): bytes_received = [] for _ in range(nb_bytes): bytes_received.append(self._serial_channel.read(1)) return [ord(byte) for byte in bytes_received if byte] def _is_data_available(self): return self._serial_channel is not None and self._serial_channel.is_open and self._serial_channel.in_waiting def _is_data_valid(self, data): return self._serial_channel is not None and self._serial_channel.is_open and not np.any(np.isnan(data)) def _get_clipped_signals(self, signals): clipped_list = np.clip(signals, LOWER_BOUND, UPPER_BOUND) return [int(255 * (x - LOWER_BOUND)/(UPPER_BOUND - LOWER_BOUND)) for x in clipped_list]

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11/11.py

PaeP3nguin/advent-of-code-2017

0

171359

#!python3 from collections import Counter def main(): with open('11.txt', 'r') as f, open('11_out.txt', 'w') as f_out: line = f.readline().strip() steps = line.split(',') directions = ['n', 'ne', 'se', 's', 'sw', 'nw'] # Part 1 step_counts = Counter(steps) pairs = [('n', 's'), ('nw', 'se'), ('ne', 'sw')] for a, b in pairs: if step_counts[a] > step_counts[b]: low, high = b, a else: low, high = a, b step_counts[high] -= step_counts[low] step_counts[low] = 0 no_more = False while not no_more: no_more = True for i, direction in enumerate(directions): left = directions[i - 1] right = directions[(i + 1) % 6] if step_counts[left] > step_counts[right]: low, high = right, left else: low, high = left, right if step_counts[low] == 0: continue no_more = False step_counts[high] -= step_counts[low] step_counts[direction] += step_counts[low] step_counts[low] = 0 total_steps = sum(step_counts.values()) print(total_steps) print(total_steps, file=f_out) # Part 2 max_steps = 0 step_counts = {d: 0 for d in directions} opposite = {d: directions[(i + 3) % 6] for i, d in enumerate(directions)} for step in steps: if step_counts[opposite[step]] > 0: step_counts[opposite[step]] -= 1 else: step_counts[step] += 1 for i, direction in enumerate(directions): left = directions[i - 1] right = directions[(i + 1) % 6] if step_counts[left] > step_counts[right]: low, high = right, left else: low, high = left, right if step_counts[low] == 0: continue step_counts[high] -= step_counts[low] step_counts[direction] += step_counts[low] step_counts[low] = 0 max_steps = max(max_steps, sum(step_counts.values())) print(max_steps) print(max_steps, file=f_out) if __name__ == '__main__': main()

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data/data_processing.py

HenryL-study/Voice-Style-Conversion-via-Seq2Seq-model

2

62122

<gh_stars>1-10 import librosa import numpy as np import os import pyworld def world_encode_spectral_envelop(sp, fs, dim=36): # Get Mel-cepstral coefficients (MCEPs) #sp = sp.astype(np.float64) coded_sp = pyworld.code_spectral_envelope(sp, fs, dim) return coded_sp def world_decompose(wav, fs, frame_period = 5.0): # Decompose speech signal into f0, spectral envelope and aperiodicity using WORLD wav = wav.astype(np.float64) f0, timeaxis = pyworld.harvest(wav, fs, frame_period = frame_period, f0_floor = 71.0, f0_ceil = 800.0) sp = pyworld.cheaptrick(wav, f0, timeaxis, fs) ap = pyworld.d4c(wav, f0, timeaxis, fs) return f0, timeaxis, sp, ap def world_speech_synthesis(f0, coded_sp, ap, fs, frame_period): decoded_sp = world_decode_spectral_envelop(coded_sp, fs) # TODO min_len = min([len(f0), len(coded_sp), len(ap)]) f0 = f0[:min_len] coded_sp = coded_sp[:min_len] ap = ap[:min_len] wav = pyworld.synthesize(f0, decoded_sp, ap, fs, frame_period) # Librosa could not save wav if not doing so wav = wav.astype(np.float32) return wav def world_decode_spectral_envelop(coded_sp, fs): # Decode Mel-cepstral to sp fftlen = pyworld.get_cheaptrick_fft_size(fs) decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen) return decoded_sp path1 = "data/VCC2SF1/" path2 = "data/VCC2SM1/" files_1 = os.listdir(path1) files_2 = os.listdir(path2) #wav_file = "VCC2SF1/10001.wav" sampling_rate, num_mcep, frame_period=22050, 40, 5 length = len(files_1) for i in range(length): print("Iteration ", i) wav, _ = librosa.load(path1+files_1[i], sr=sampling_rate, mono=True) f0, timeaxis, sp, ap = world_decompose(wav=wav, fs=sampling_rate, frame_period=frame_period) coded_sp = world_encode_spectral_envelop(sp=sp, fs=sampling_rate, dim=num_mcep) # print("This is the feature we want -> coded_sp") # print("Type of coded_sp: ", type(coded_sp)) # print("shape of coded_sp: ", coded_sp.shape) np.save("data/parameters/VCC2SF1/coded_sp/"+str(i)+".npy", coded_sp) np.save("data/parameters/VCC2SF1/f0/"+str(i)+".npy", f0) np.save("data/parameters/VCC2SF1/ap/"+str(i)+".npy", ap) #print("ap: ", ap) # wav_transformed = world_speech_synthesis(f0=f0, coded_sp=coded_sp, # ap=ap, fs=sampling_rate, frame_period=frame_period) # librosa.output.write_wav("generate/"+path1+files_1[i], wav_transformed, sampling_rate) # sampling_rate, num_mcep, frame_period=22050, 40, 5 wav, _ = librosa.load(path2+files_2[i], sr=sampling_rate, mono=True) f0, timeaxis, sp, ap = world_decompose(wav=wav, fs=sampling_rate, frame_period=frame_period) coded_sp = world_encode_spectral_envelop(sp=sp, fs=sampling_rate, dim=num_mcep) np.save("data/parameters/VCC2SM1/coded_sp/"+str(i)+".npy", coded_sp) np.save("data/parameters/VCC2SM1/f0/"+str(i)+".npy", f0) np.save("data/parameters/VCC2SM1/ap/"+str(i)+".npy", ap) # wav_transformed = world_speech_synthesis(f0=f0, coded_sp=coded_sp, # ap=ap, fs=sampling_rate, frame_period=frame_period) # librosa.output.write_wav("data/generate/"+path2+files_2[i], wav_transformed, sampling_rate)

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test/unit_tests.py

dykesk/gaussian-wake

3

124710

""" Created by <NAME>, Sep. 2018. FLOW Lab Brigham Young University """ import unittest import numpy as np from _porteagel_fortran import porteagel_analyze, x0_func, theta_c_0_func, sigmay_func, sigma_spread_func from _porteagel_fortran import sigmaz_func, wake_offset_func, deltav_func, deltav_near_wake_lin_func from _porteagel_fortran import overlap_area_func, wake_combination_func, added_ti_func, k_star_func from _porteagel_fortran import ct_to_axial_ind_func, wind_shear_func, discontinuity_point_func, smooth_max from _porteagel_fortran import interpolation, hermite_spline, point_velocity_with_shear_func from openmdao.api import Problem, Group def power_func_v80(v): # power curve fit for vestas v80 from Niayifar 2016 p = 0.17819 * v ** 5 - 6.5198 * v ** 4 + 90.623 * v ** 3 - 574.62 * v ** 2 + 1727.2 * v - 1975.0 return p class test_basic_subroutines(unittest.TestCase): def setUp(self): self.tolerance = 1E-6 self.d = 126.4 self.yaw = np.pi/6. self.ct = 0.8 self.alpha = 2.32 self.beta = 0.154 self.ti = 0.1 self.ky = 0.25 self.kz = 0.2 self.wind_speed = 8.0 def test_x0_func_hand_calc(self): x0 = x0_func(self.d, self.yaw, self.ct, self.alpha, self.ti, self.beta) self.assertAlmostEqual(x0, 353.2313474, delta=self.tolerance) def test_x0_func_data_yaw0(self): rotor_diameter = 0.15 # m yaw = 0.0*np.pi/180. #radians ct = 0.8214036062840235 ti = 0.074 # # 3.77839335632898, 0.6643546778702326 # 3.704230762943225, 0.7361200568897026 # 3.849186706118913, 0.7866577299700839 # 3.848583479099574, 0.8214036062840235 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 3.862413891540104, delta=1E-2) def test_x0_func_data_yaw10(self): rotor_diameter = 0.15 # m yaw = 10.0 * np.pi / 180. # radians ct = 0.7866577299700839 ti = 0.074 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 3.973368012202963, delta=1E-1) def test_x0_func_data_yaw20(self): rotor_diameter = 0.15 # m yaw = 20.0 * np.pi / 180. # radians ct = 0.7361200568897026 ti = 0.074 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 4.051040798613613, delta=1E-1) def test_x0_func_data_yaw30(self): rotor_diameter = 0.15 # m yaw = 30.0 * np.pi / 180. # radians ct = 0.6643546778702326 ti = 0.074 x0 = x0_func(rotor_diameter, yaw, ct, self.alpha, ti, self.beta) self.assertAlmostEqual(x0/rotor_diameter, 4.053814723717636, delta=1E-1) def test_discontinuity_point_func(self): x0 = 353.0 xd = discontinuity_point_func(x0, self.d, self.ky, self.kz, self.yaw, self.ct) self.assertAlmostEqual(xd, 335.5180515, delta=self.tolerance) def test_sigmay_func(self): x = 500.0 x0 = 353.0 xd = sigmay_func(x, x0, self.ky, self.d, self.yaw) self.assertAlmostEqual(xd, 75.45193794, delta=self.tolerance) def test_sigmaz_func(self): x = 500.0 x0 = 353.0 xd = sigmaz_func(x, x0, self.kz, self.d) self.assertAlmostEqual(xd, 74.08914857, delta=self.tolerance) def test_theta_c_0_func(self): theta_c_0 = theta_c_0_func(self.yaw, self.ct) self.assertAlmostEqual(theta_c_0, 0.080852297, delta=self.tolerance) def test_wake_offset_func_near_wake(self): x = 200. theta_c_0 = 0.0808 sigmay = 36. sigmaz = 43. x0 = 353. delta = wake_offset_func(x, self.d, theta_c_0, x0, self.yaw, self.ky, self.kz, self.ct, sigmay, sigmaz) self.assertAlmostEqual(delta, 16.16, delta=self.tolerance) def test_wake_offset_func_far_wake(self): x = 500. x0 = 353. theta_c_0 = 0.0808 sigmay = 75.45193794 sigmaz = 74.08914857 delta = wake_offset_func(x, self.d, theta_c_0, x0, self.yaw, self.ky, self.kz, self.ct, sigmay, sigmaz) self.assertAlmostEqual(delta, 33.89352568, delta=self.tolerance) def test_deltav_func_2016(self): d = 126.4 yaw = np.pi / 6. ky = 0.25 kz = 0.2 sigmay = 75.0 sigmaz = 74.0 ct = 0.8 z = 100.0 zh = 90.0 wec_factor = 1.0 y = 50.0 delta = 33.89 x = 500. deltay = y-delta deltaz = z-zh deltav = deltav_func(deltay, deltaz, ct, yaw, sigmay, sigmaz, d, 2016, self.ky, x, wec_factor, sigmay, sigmaz) self.assertAlmostEqual(deltav, 0.1293410999394427, delta=self.tolerance) def test_deltav_func_2014(self): d = 126.4 yaw = np.pi / 6. ky = 0.25 kz = 0.2 sigmay = 75.0 sigmaz = 74.0 ct = 0.8 z = 100.0 zh = 90.0 wec_factor = 1.0 y = 50.0 delta = 33.89 x = 500. deltay = y-delta deltaz = z-zh deltav = deltav_func(deltay, deltaz, ct, yaw, sigmay, sigmaz, d, 2014, self.ky, x, wec_factor, sigmay, sigmaz) self.assertAlmostEqual(deltav, 0.03264659097, delta=self.tolerance) def test_near_deltav_func_2014_rotor_location(self): version = 2014 x0 = 353.0 xd = 335.0 yaw = 0.0 sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 0.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.00048145926305030354, delta=self.tolerance) def test_near_deltav_func_2014_midrange_location(self): version = 2014 x0 = 353.0 xd = 335.0 yaw = 0.0 sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 200.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.027941992346249663, delta=self.tolerance) def test_near_deltav_func_2014_x0_location(self): version = 2014 x0 = 353.0 xd = 335.0 yaw = 0.0 sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 353. deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.0401329549842686, delta=self.tolerance) def test_near_deltav_func_2016_rotor_location(self): version = 2016 x0 = 353.0 xd = 335.0 yaw = np.pi/6. sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 0.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.05319773340098457, delta=self.tolerance) def test_near_deltav_func_2016_midrange_location(self): version = 2016 x0 = 353.0 xd = 335.0 yaw = np.pi/6. sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 200.0 deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.0388723745762739, delta=self.tolerance) def test_near_deltav_func_2016_x0_location(self): version = 2016 x0 = 353.0 xd = 335.0 yaw = np.pi/6. sigmay_spread = 45. sigmaz_spread = 45. sigmay_0 = 50.0 sigmaz_0 = 50.0 sigmay_d = 40.0 sigmaz_d = 40.0 wec_factor = 1.0 deltay = 100.0 deltaz = 5.0 x = 353. deltav = deltav_near_wake_lin_func(deltay, deltaz, self.ct, yaw, sigmay_0, sigmaz_0, x0, self.d, x, xd, sigmay_d, sigmaz_d, version, self.ky, x, sigmay_spread, sigmaz_spread, wec_factor) self.assertAlmostEqual(deltav, 0.027913475075370238, delta=self.tolerance) def test_wake_combination_func_Lissaman1979(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 0 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.4, delta=self.tolerance) def test_wake_combination_func_Katic1986(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 2 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.039607805437114, delta=self.tolerance) def test_wake_combination_func_Voutsinas1990(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 3 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.0303940504246953, delta=self.tolerance) def test_wake_combination_func_Niayifar2016(self): Uk = 7.0 deltav = 0.05 wake_combination_method = 1 deficit_sum = 2.0 new_sum = wake_combination_func(self.wind_speed, Uk, deltav, wake_combination_method, deficit_sum) self.assertAlmostEqual(new_sum, 2.35, delta=self.tolerance) def test_wind_shear_func(self): z = 90.0 zo = 2.0 zr = 80.0 psi = 0.15 wind_velocity_with_shear = wind_shear_func(z, self.wind_speed, zr, zo, psi) self.assertAlmostEqual(wind_velocity_with_shear, 8.14607111996, delta=self.tolerance) def test_k_star_func(self): ti_ust = 0.1 kstar = k_star_func(ti_ust) self.assertAlmostEqual(kstar, 0.042048, delta=self.tolerance) def test_ct_to_axial_ind_func_normal_ct(self): ct = 0.84 axial_induction = ct_to_axial_ind_func(ct) self.assertAlmostEqual(axial_induction, 0.3, delta=self.tolerance) def test_ct_to_axial_ind_func_high_ct(self): ct = 0.97 axial_induction = ct_to_axial_ind_func(ct) self.assertAlmostEqual(axial_induction, 0.4119957249, delta=self.tolerance) def test_smooth_max(self): x = 12. y = 13. s = 100. smax1 = smooth_max(s, x, y) self.assertAlmostEqual(smax1, 13.0, delta=self.tolerance) def test_overlap_area_func_rotor_all_in_wake(self): turbiney = 50. turbinez = 90. rotor_diameter = 100. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 200. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.pi*rotor_diameter**2/4, delta=self.tolerance) def test_overlap_area_func_rotor_all_in_wake_perfect_overlap(self): turbiney = 0. turbinez = 90. rotor_diameter = 100. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 200. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.pi * rotor_diameter ** 2 / 4, delta=self.tolerance) def test_overlap_area_func_wake_all_in_rotor(self): turbiney = 50. turbinez = 90. rotor_diameter = 200. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 100. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.pi*wake_diameter**2/4, delta=self.tolerance) def test_overlap_area_func_wake_all_in_rotor_perfect_overlap(self): turbiney = 0. turbinez = 90. rotor_diameter = 200. wake_center_y = 0.0 wake_center_z = 90.0 wake_diameter = 100. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, np.pi*wake_diameter**2/4, delta=self.tolerance) def test_overlap_area_func_no_overlap(self): turbiney = 0. turbinez = 90. rotor_diameter = 100. wake_center_y = 100.0 wake_center_z = 90.0 wake_diameter = 100. wake_overlap = overlap_area_func(turbiney, turbinez, rotor_diameter, wake_center_y, wake_center_z, wake_diameter) self.assertAlmostEqual(wake_overlap, 0.0, delta=self.tolerance) #TODO add tests for partial overlap class test_added_ti_func(unittest.TestCase): def setUp(self): self.tolerance = 1E-2 self.yaw = 0.0 self.ct = 0.8 self.alpha = 2.32 self.beta = 0.154 self.ti = 0.1 self.ky = 0.022 self.kz = 0.022 self.wind_speed = 8.0 self.TI = 0.077 self.x = 560. self.rotor_diameter = 80. self.deltay = 0. self.wake_height = 70. self.turbine_height = 70. self.sm_smoothing = 700. def test_added_ti_func_Niayifar_2016_max_2nd_turb(self): TI_calculation_method = 4 TI_area_ratio_in = 0.0 TI_dst_in = 0.0 TI_ust = 0.077 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) def test_added_ti_func_Niayifar_2016_max_3rd_turb(self): TI_calculation_method = 4 TI_area_ratio_in = 0.0 TI_dst_in = 0.0 TI_ust = 0.1476 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) def test_added_ti_func_Niayifar_2016_smoothmax_2nd_turb(self): TI_calculation_method = 5 TI_area_ratio_in = 0.0 TI_dst_in = 0.0 TI_ust = 0.077 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) def test_added_ti_func_Niayifar_2016_smoothmax_3rd_turb(self): TI_calculation_method = 5 TI_area_ratio_in = .05 TI_dst_in = 0.077 TI_ust = 0.1476 ti_area_ratio, ti_dst = added_ti_func(self.TI, self.ct, self.x, self.ky, self.rotor_diameter, self.rotor_diameter, self.deltay, self.wake_height, self.turbine_height, self.sm_smoothing, TI_ust, TI_calculation_method, TI_area_ratio_in, TI_dst_in) self.assertAlmostEqual(ti_dst, 0.1476, delta=self.tolerance) class test_point_velocity_with_shear(unittest.TestCase): def setUp(self): self.tolerance = 1E-2 self.turbI = -1 self.wake_combination_method = 1 self.wake_model_version = 2016 self.sorted_x_idx = np.array([0]) self.rotorDiameter = np.array([0.15]) self.pointX = self.rotorDiameter*5. self.pointY = 0.24*self.rotorDiameter self.pointZ = 0.125 self.tol = 1E-12 self.alpha = 2.32 self.beta = 0.154 self.expratemultiplier = 1.0 self.wec_factor = 1.0 self.wind_speed = 4.88 self.z_ref = 0.125 self.z_0 = 0.000022 self.shear_exp = 0.1 self.turbineXw = np.array([0]) self.turbineYw = np.array([0]) self.turbineZ = np.array([0.125]) self.yaw = np.array([20. * np.pi / 180.0]) self.wtVelocity = np.array([self.wind_speed]) self.Ct_local = 0.7361200568897026 * np.ones_like(self.turbineXw) # np.array([0.7374481936835376]) self.TIturbs = 0.025 * np.ones_like(self.turbineXw) # *np.array([0.01]) #np.array([0.001]) #TODO check point velocity tests and ti input self.ky_local = 0.022 # np.array([0.3837*TIturbs[0] + 0.003678]) self.kz_local = 0.022 # np.array([0.3837*TIturbs[0] + 0.003678]) def test_point_velocity_with_shear(self): point_velocity_with_shear = point_velocity_with_shear_func(self.turbI, self.wake_combination_method, self.wake_model_version, self.sorted_x_idx, self.pointX, self.pointY, self.pointZ, self.tol, self.alpha, self.beta, self.expratemultiplier, self.wec_factor, self.wind_speed, self.z_ref, self.z_0, self.shear_exp, self.turbineXw, self.turbineYw, self.turbineZ, self.rotorDiameter, self.yaw, self.wtVelocity, self.Ct_local, self.TIturbs, self.ky_local, self.kz_local) self.assertAlmostEqual(point_velocity_with_shear/self.wind_speed, 0.406, delta=self.tolerance) class test_sigma_spread(unittest.TestCase): def setUp(self): self.tolerance = 1E-6 self.d = 126.4 self.yaw = np.pi / 6. self.ct = 0.8 self.alpha = 2.32 self.beta = 0.154 self.ti = 0.1 self.ky = 0.25 self.kz = 0.2 x = np.array([500.0, 500.0, 500.0, 200.0, -10.0]) xi_d = np.array([1.0, 2.0, 1.0, 1.0, 1.0]) xi_a = np.array([0.0, 0.0, 45.0, 0.0, 0.0]) sigma_0 = 38.7 sigma_d = 34.2 x0 = 353.0 self.sigma_spread = np.zeros_like(x) for i in np.arange(0, x.size): self.sigma_spread[i] = sigma_spread_func(x[i], x0, self.ky, sigma_0, sigma_d, xi_a[i], xi_d[i]) self.correct_results = np.array([75.45, 150.9, 534.2, 36.7495751, 0.0]) def test_sigma_spread_func_case1(self): self.assertAlmostEqual(self.sigma_spread[0], self.correct_results[0], delta=self.tolerance) def test_sigma_spread_func_case2(self): self.assertAlmostEqual(self.sigma_spread[1], self.correct_results[1], delta=self.tolerance) def test_sigma_spread_func_case3(self): self.assertAlmostEqual(self.sigma_spread[2], self.correct_results[2], delta=self.tolerance) def test_sigma_spread_func_case4(self): self.assertAlmostEqual(self.sigma_spread[3], self.correct_results[3], delta=self.tolerance) def test_sigma_spread_func_case5(self): self.assertAlmostEqual(self.sigma_spread[4], self.correct_results[4], delta=self.tolerance) # class test_sigma_spread_too_high_error(unittest.TestCase): # # def setUp(self): # self.tolerance = 1E-6 # self.d = 126.4 # self.yaw = np.pi / 6. # self.ct = 0.8 # self.alpha = 2.32 # self.beta = 0.154 # self.ti = 0.1 # self.ky = 0.25 # self.kz = 0.2 # # self.x = 500.0 # self.xi_d = 1.0 # self.xi_a = 90.000 # # # self.sigma_0 = 38.7 # self.sigma_d = 34.2 # # self.x0 = 353.0 # # def test_sigma_spread_too_high(self): # self.assertRaises(sigma_spread_func(self.x, self.x0, self.ky, self.sigma_0, self.sigma_d, self.xi_a, self.xi_d)) # class test_hermite_spline(unittest.TestCase): def test_linear(self): """"Approximate y = x - 1""" x = 1. x0 = 0. x1 = 2. y0 = -1. dy0 = 1. y1 = 1. dy1 = 1. y = hermite_spline(x, x0, x1, y0, dy0, y1, dy1) self.assertEqual(y, 0.0) def test_cubic(self): """Approximate y=x**3""" x = 0. x0 = -1. x1 = 1. y0 = 0. dy0 = 2. y1 = 0. dy1 = 2. y = hermite_spline(x, x0, x1, y0, dy0, y1, dy1) self.assertEqual(y, 0.0) def test_parabolic(self): """Approximate y=x**2""" x = 0. x0 = -1. x1 = 1. y0 = 1. dy0 = -2. y1 = 1. dy1 = 2. y = hermite_spline(x, x0, x1, y0, dy0, y1, dy1) self.assertEqual(y, 0.0) class test_interpolation(unittest.TestCase): # def test_cubic(self): # # # define interpolation type # interp_type = 0 # # # set up points for interpolation # x = np.array([-1., -0.5, 0., 0.5, 1.]) # y = np.array([-1., -0.125, 0., 0.125, 1.]) # # # set location of interpolation # xval = 0.125 # # # get interpolated y value # yval = interpolation(interp_type, x, y, xval, 3.0, 3.0, True) # # self.assertEqual(yval, 0.0625) def test_linear(self): # define interpolation type interp_type = 1 # set up points for interpolation x = np.array([0., 1., 2.]) y = np.array([0., 1., 0.]) # set location of interpolation xval = 0.5 # get interpolated y value yval = interpolation(interp_type, x, y, xval, 0.0, 0.0, False) self.assertEqual(yval, 0.5) class test_ctcp_curve(unittest.TestCase): def setUp(self): filename = "./input_files/NREL5MWCPCT_dict.p" import cPickle as pickle data = pickle.load(open(filename, "rb")) cp_data = np.zeros([data['wind_speed'].size]) ct_data = np.zeros([data['wind_speed'].size]) wind_speed_data = np.zeros([data['wind_speed'].size]) cp_data[:] = data['CP'] ct_data[:] = data['CT'] wind_speed_data[:] = data['wind_speed'] self.ct_data = ct_data self.cp_data = cp_data self.wind_speed_data = wind_speed_data self.options = {'use_ct_curve': True, 'ct_curve_ct': self.ct_data, 'ct_curve_wind_speed': self.wind_speed_data} def test_5mw_ct_greater_than_1_warning(self): from gaussianwake.gaussianwake import GaussianWake import pytest pytest.warns(Warning, GaussianWake, nTurbines=6, options=self.options) class test_wec(unittest.TestCase): def setUp(self): filename = "./input_files/NREL5MWCPCT_dict.p" import cPickle as pickle data = pickle.load(open(filename, "rb")) cp_data = np.zeros([data['wind_speed'].size]) ct_data = np.zeros([data['wind_speed'].size]) wind_speed_data = np.zeros([data['wind_speed'].size]) cp_data[:] = data['CP'] ct_data[:] = data['CT'] wind_speed_data[:] = data['wind_speed'] self.ct_data = ct_data self.cp_data = cp_data self.wind_speed_data = wind_speed_data self.options = {'use_ct_curve': True, 'ct_curve_ct': self.ct_data, 'ct_curve_wind_speed': self.wind_speed_data} nTurbines = 2 from gaussianwake.gaussianwake import GaussianWake prob = Problem(root=Group()) prob.root.add('wakemodel', GaussianWake(nTurbines, options=self.options), promotes=['*']) prob.setup() prob['wind_speed'] = 8. self.prob = prob def test_no_change_in_deficit_by_wake_spread_rate_multiplier_at_center(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 0.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_spreading_angle'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertEqual(wspeed1, wspeed0) def test_no_change_in_deficit_by_wake_diameter_multiplier_at_center(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 0.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_spreading_angle'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertEqual(wspeed1, wspeed0) def test_increase_deficit_by_wake_diameter_expansion(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 100.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_factor'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertGreater(wspeed0, wspeed1) def test_increase_deficit_by_wake_expansion_rate_multiplier(self): prob = self.prob turbineX = np.array([0., 400.]) turbineY = np.array([0., 100.]) rotor_diameter = 50. prob['turbineXw'] = turbineX prob['turbineYw'] = turbineY prob['rotorDiameter'] = np.array([rotor_diameter, rotor_diameter]) prob['model_params:wec_spreading_angle'] = 0.0 prob['model_params:wec_factor'] = 1.0 prob.run_once() prob['model_params:wec_factor'] = 1.0 wspeed0 = prob['wtVelocity0'][1] prob['model_params:wec_spreading_angle'] = 2.0 prob.run_once() wspeed1 = prob['wtVelocity0'][1] self.assertGreater(wspeed0, wspeed1) class test_porteagel_analyze(unittest.TestCase): #TODO improve tolerance of test - why can't we match more closely? def setUp(self): from plantenergy.utilities import sunflower_points self.tolerance = 1E-1 self.wake_combination_method = 1 self.wake_model_version = 2016 self.rotor_diameter = 80. self.hub_height = 70. self.ct = 0.6 self.alpha = 2.32 self.beta = 0.154 self.expratemultiplier = 1.0 self.wec_factor = 1.0 self.wind_speed = 8.0 self.z_ref = self.hub_height self.z_0 = 0.0002 self.shear_exp = 0.15 self.yaw = 0.0 self.wtVelocity = np.array([self.wind_speed]) self.TI = 0.077 self.ky = 0.3837*self.TI + 0.003678 # np.array([0.3837*TIturbs[0] + 0.003678]) self.kz = 0.3837*self.TI + 0.003678 # np.array([0.3837*TIturbs[0] + 0.003678]) rotorpoints = sunflower_points(100) self.RotorPointsY = rotorpoints[0] #np.array([0, .5, 1.0, 0., 0.0, -.5, -1.0, 0., 0.]) self.RotorPointsZ = rotorpoints[1] #np.array([0, 0., 0., .5, 1.0, 0., 0.0, -0.5, -1.]) # self.RotorPointsY = np.array([0]) # self.RotorPointsZ = np.array([0]) self.RotorPointsY = np.array([0, .5, 1.0, 0., 0.0, -.5, -1.0, 0., 0.]) self.RotorPointsZ = np.array([0, 0., 0., .5, 1.0, 0., 0.0, -0.5, -1.]) self.TI_calculation_method = 4 self.calc_k_star = True self.print_ti = False self.interp_type = 1 self.sm_smoothing = 700. loc_data = np.loadtxt('input_files/horns_rev_locations.txt', delimiter=',') turbineXw = loc_data[:, 0] * self.rotor_diameter turbineYw = loc_data[:, 1] * self.rotor_diameter turbineZ = np.ones_like(turbineXw) * self.hub_height sorted_x_idx = np.argsort(turbineXw, kind='heapsort') rotorDiameter = np.ones_like(turbineXw) * self.rotor_diameter Ct = np.ones_like(turbineXw) * self.ct yaw = np.ones_like(turbineXw) * self.yaw TI_turbs = np.ones_like(turbineXw) * self.TI use_ct_curve = True # ct_data = np.loadtxt('input_files/predicted_ct_vestas_v80_niayifar2016.txt', delimiter=',') ct_data = np.loadtxt('input_files/mfg_ct_vestas_v80_niayifar2016.txt', delimiter=',') ct_curve_wind_speed = ct_data[:, 0] ct_curve_ct = ct_data[:, 1] CalculateFlowField=False wtVelocity, _ = porteagel_analyze(turbineXw, sorted_x_idx, turbineYw, turbineZ, rotorDiameter, Ct, self.wind_speed, yaw, self.ky, self.kz, self.alpha, self.beta, TI_turbs, self.RotorPointsY, self.RotorPointsZ, np.array([0]), np.array([0]), np.array([0]), self.z_ref, self.z_0, self.shear_exp, self.wake_combination_method, self.TI_calculation_method, self.calc_k_star, self.wec_factor, self.print_ti, self.wake_model_version, self.interp_type, use_ct_curve, ct_curve_wind_speed, ct_curve_ct, self.sm_smoothing, self.expratemultiplier, CalculateFlowField) free_stream_power = power_func_v80(self.wind_speed) wtPower = power_func_v80(wtVelocity) self.norm_pow_ave_by_row = np.zeros(10) for i in np.arange(0, self.norm_pow_ave_by_row.size): pow_ave_row = np.average([wtPower[40 + i], wtPower[50 + i], wtPower[60 + i]]) self.norm_pow_ave_by_row[i] = pow_ave_row / free_stream_power def test_wt_velocity_1_turb(self): turbineXw = np.array([0.0]) turbineYw = np.array([0.0]) turbineZ = np.ones_like(turbineXw)*self.hub_height sorted_x_idx = np.argsort(turbineXw, kind='heapsort') rotorDiameter = np.ones_like(turbineXw)*self.rotor_diameter Ct = np.ones_like(turbineXw)*self.ct yaw = np.ones_like(turbineXw)*self.yaw TI_turbs = np.ones_like(turbineXw)*self.TI use_ct_curve = False ct_curve_wind_speed = np.array([self.wind_speed]) ct_curve_ct = np.array([self.ct]) CalculateFlowField=False wtVelocity, _ = porteagel_analyze(turbineXw, sorted_x_idx, turbineYw, turbineZ, rotorDiameter, Ct, self.wind_speed, yaw, self.ky, self.kz, self.alpha, self.beta, TI_turbs, self.RotorPointsY, self.RotorPointsZ, np.array([0]), np.array([0]), np.array([0]), self.z_ref, self.z_0, self.shear_exp, self.wake_combination_method, self.TI_calculation_method, self.calc_k_star, self.wec_factor, self.print_ti, self.wake_model_version, self.interp_type, use_ct_curve, ct_curve_wind_speed, ct_curve_ct, self.sm_smoothing, self.expratemultiplier, CalculateFlowField) self.assertAlmostEqual(wtVelocity, 8.0, delta=self.tolerance) # # 2.009085240790877, 0.4619246861924686 # 2.0091082123225856, 0.46359832635983256 # 3.003385019279678, 0.5037656903765689 # 3.997271310197718, 0.515481171548117 # 4.996498482238084, 0.516317991631799 # 5.990212486668307, 0.515481171548117 # 7.0003626220362625, 0.5121338912133888 # 7.994042169168923, 0.5087866108786608 # 8.99869062269259, 0.5046025104602508 # 10.003339076216259, 0.5004184100418408 def test_wt_velocity_row_1_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[0], 1.0, delta=self.tolerance) def test_wt_velocity_row_2_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[1], 0.4619246861924686, delta=self.tolerance) def test_wt_velocity_row_3_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[2], 0.5037656903765689, delta=self.tolerance) def test_wt_velocity_row_4_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[3], 0.515481171548117, delta=self.tolerance) def test_wt_velocity_row_5_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[4], 0.516317991631799, delta=self.tolerance) def test_wt_velocity_row_6_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[5], 0.515481171548117, delta=self.tolerance) def test_wt_velocity_row_7_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[6], 0.5121338912133888, delta=self.tolerance) def test_wt_velocity_row_8_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[7], 0.5087866108786608, delta=self.tolerance) def test_wt_velocity_row_9_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[8], 0.5046025104602508, delta=self.tolerance) def test_wt_velocity_row_10_of_horns_rev(self): self.assertAlmostEqual(self.norm_pow_ave_by_row[9], 0.5004184100418408, delta=self.tolerance) if __name__ == "__main__": unittest.main(verbosity=2)

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graph-algorithms/signal_tree.py

lucaspetry/algorithms-and-data-structures

0

195748

import random from graph import Graph def create_signal_tree(integers): vertices = [integers[0]] edges = [] for i in range(1, len(integers)): v1 = integers[i - 1] v2 = integers[i] if v2 not in vertices: vertices.append(v2) edges.append((v1, v2, min(v1, v2))) graph = Graph(vertices, edges, directed = False) print(graph.get_signal_tree()) integers = [1, 5, 2, 2, 7, 4, 1, 3] create_signal_tree(integers)

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scripts/stats-bytes-converted.py

WojciechMula/parsing-int-series

19

128321

<reponame>WojciechMula/parsing-int-series<gh_stars>10-100 from generator import Generator from table import Table if __name__ == '__main__': gen = Generator() freq = {} for bi in gen.run(): k = bi.total_skip freq[k] = freq.get(k, 0) + 1 table = Table() table.add_header(["bytes processed", "patterns", "%", "cumulative %"]) cumulative = 0 for total_skip in sorted(freq.keys()): count = freq[total_skip] cumulative += count table.add_row([ '%d' % total_skip, '%d' % count, '%0.2f%%' % (100 * count/65536.0), '%0.2f%%' % (100 * cumulative/65536.0), ]) print table

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class15/class15_ci/app/ci.py

WesGtoX/python-selenium

0

94718

<filename>class15/class15_ci/app/ci.py from selenium.webdriver import Remote from sys import argv browser = Remote( command_executor=f'http://{argv[-1]}:4444/wd/hub', desired_capabilities={'browserName': 'firefox'} ) browser.get('http://selenium.dunossauro.live') print(browser.page_source)

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src/rooms/tests/test_models.py

Bounty1993/my-blog

0

83759

<reponame>Bounty1993/my-blog from datetime import datetime from django.contrib.auth import get_user_model from django.db.models import Count, Sum from django.test import TestCase from src.rooms.models import Donation, Room User = get_user_model() class RoomTransactionModelTest(TestCase): fixtures = ['src/rooms/tests/fixtures.json', ] def setUp(self): self.user1 = User.objects.get(username='testuser') self.user2 = User.objects.get(username='testuser2') self.user3 = User.objects.get(username='testuser3') room1 = Room.objects.get(gift='gift1') room2 = Room.objects.get(gift='gift2') room3 = Room.objects.get(gift='gift3') room1.donate({'user': self.user1, 'amount': 500}) room2.donate({'user': self.user1, 'amount': 300}) room2.donate({'user': self.user2, 'amount': 200}) room3.donate({'user': self.user1, 'amount': 200}) room3.donate({'user': self.user2, 'amount': 200}) room3.donate({'user': self.user3, 'amount': 200}) self.room = Room.objects.get(receiver='receiver1') self.room2 = room2 def test_creation(self): room = Room.objects.get(receiver='receiver1') self.assertEqual(room.price, 1000) self.assertEqual(room.description, 'test') self.assertEqual(room.to_collect, 500) def test_str(self): room = Room.objects.get(receiver='receiver1') self.assertEqual(str(room), 'receiver1 - gift1') """ def test_save(self): before_score = self.room.score self.room.donate({'user': self.user1, 'amount': 5}) after_score = self.room.score print(self.room.score) self.assertTrue(after_score > before_score) """ def test_can_see(self): room4 = Room.objects.create( receiver='receiver3', creator=self.user1, gift='gift3', price=800, description='test', to_collect=800, visible=False, date_expires=datetime(2019, 6, 6) ) room4.guests.add(self.user2) self.assertTrue(room4.can_see(self.user1)) self.assertTrue(room4.can_see(self.user2)) self.assertFalse(room4.can_see(self.user3)) def test_percent_left(self): all_donations = ( Donation.objects .filter(room=self.room) .aggregate(Sum('amount'))['amount__sum'] ) expected = (all_donations / self.room.price) * 100 self.assertEqual(self.room.percent_left, expected) def test_percent_got(self): expected = 100 - self.room.percent_left self.assertEqual(self.room.percent_got, expected) def test_patrons_count(self): expected = (Donation.objects .filter(room=self.room2) .aggregate(Count('user'))['user__count']) self.assertEqual(self.room2.num_patrons, expected) def test_most_patrons(self): most_patrons = Room.get_visible.most_patrons() expected = Room.objects.get(receiver='receiver3') self.assertEqual(most_patrons[0], expected) def test_most_popular(self): most_collected = Room.get_visible.most_popular() expected = Room.objects.get(receiver='receiver3') self.assertEqual(most_collected[0], expected) def test_most_to_collect(self): most_to_collect = Room.get_visible.most_to_collect() expected = Room.objects.get(receiver='receiver1') self.assertEqual(most_to_collect[0], expected) def test_add_observer(self): num_observers = self.room.observers.count() self.room.add_observer(self.user1.id) num_observers += 1 self.assertEqual(self.room.observers.count(), num_observers) self.assertTrue(self.user1 in self.room.observers.all()) def test_donate(self): room = Room.objects.get(receiver='receiver1') beginning = room.to_collect donate_amount = 200 room.donate({'user': self.user1, 'amount': donate_amount}) rest = beginning - donate_amount self.assertEqual(room.to_collect, rest) room.donate({'user': self.user1, 'amount': rest}) self.assertFalse(room.is_active) self.assertEqual(room.to_collect, 0) def test_get_visible(self): room3 = Room.objects.create( receiver='receiver3', creator=self.user1, gift='gift3', price=800, description='test', to_collect=800, visible=False, date_expires=datetime(2019, 6, 6) ) query = Room.objects.get_visible(self.user3) self.assertFalse(room3 in query) room3.guests.add(self.user2) query = Room.objects.get_visible(self.user2) self.assertTrue(room3 in query) def test_get_patrons(self): room3 = Room.objects.get(receiver='receiver2') patrons = room3.get_patrons() ordered_patrons = ['testuser', 'testuser2'] self.assertEqual(list(patrons), ordered_patrons) def test_remove_guest(self): user = User.objects.first() self.room.guests.add(user) self.assertTrue(self.room.guests.filter(id=user.id)) self.room.guest_remove(user.username) self.assertFalse(self.room.guests.filter(id=user.id)) def test_get_guests_dict(self): self.room.guests.add(self.user1, self.user2) expected = [self.user1.username, self.user2.username] guest_list = self.room.get_guests_dict() self.assertEqual(guest_list, expected) class DonationModelTest(TestCase): fixtures = ['src/rooms/tests/fixtures.json'] def setUp(self): self.user1 = User.objects.get(username='testuser') self.room1 = Room.objects.get(gift='gift1') self.room1.donate({'user': self.user1, 'amount': 500}) def test_donation_exists(self): self.assertTrue(Donation.objects.exists()) def test_donation_fields(self): donation = Donation.objects.first() self.assertEqual(donation.user, self.user1) self.assertEqual(donation.amount, 500) self.assertEqual(donation.room, self.room1) self.assertEqual(donation.date, datetime.now().date()) def test_str(self): donation = Donation.objects.first() expected = f'{donation.room} - {donation.amount}' self.assertEqual(str(donation), expected)

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api/app/database.py

Le96/todays-mazai

0

47420

import os from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker USER = os.getenv("DB_USER") PASS = os.getenv("DB_PASSWORD") HOST = os.getenv("DB_HOST") PORT = os.getenv("DB_PORT") SCHEMA = os.getenv("DB_SCHEMA") SQLALCHEMY_DATABASE_URL = f"postgresql+psycopg2://{USER}:{PASS}@{HOST}:{PORT}/{SCHEMA}" engine = create_engine(SQLALCHEMY_DATABASE_URL) Session = sessionmaker(bind=engine, autoflush=False, autocommit=False) Base = declarative_base() def get_session(): session = Session() try: yield session finally: session.close()

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civis/io/_databases.py

elsander/civis-python

0

74490

<filename>civis/io/_databases.py from __future__ import absolute_import import logging from civis import APIClient from civis._utils import maybe_get_random_name from civis.futures import CivisFuture from civis._deprecation import deprecate_param log = logging.getLogger(__name__) @deprecate_param('v2.0.0', 'api_key') def query_civis(sql, database, api_key=None, client=None, credential_id=None, preview_rows=10, polling_interval=None, hidden=True): """Execute a SQL statement as a Civis query. Run a query that may return no results or where only a small preview is required. To execute a query that returns a large number of rows, see :func:`~civis.io.read_civis_sql`. Parameters ---------- sql : str The SQL statement to execute. database : str or int The name or ID of the database. api_key : DEPRECATED str, optional Your Civis API key. If not given, the :envvar:`CIVIS_API_KEY` environment variable will be used. client : :class:`civis.APIClient`, optional If not provided, an :class:`civis.APIClient` object will be created from the :envvar:`CIVIS_API_KEY`. credential_id : str or int, optional The ID of the database credential. If ``None``, the default credential will be used. preview_rows : int, optional The maximum number of rows to return. No more than 100 rows can be returned at once. polling_interval : int or float, optional Number of seconds to wait between checks for query completion. hidden : bool, optional If ``True`` (the default), this job will not appear in the Civis UI. Returns ------- results : :class:`~civis.futures.CivisFuture` A `CivisFuture` object. Examples -------- >>> run = query_civis(sql="DELETE schema.table", database='database') >>> run.result() # Wait for query to complete """ if client is None: client = APIClient(api_key=api_key) database_id = client.get_database_id(database) cred_id = credential_id or client.default_credential resp = client.queries.post(database_id, sql, preview_rows, credential=cred_id, hidden=hidden) return CivisFuture(client.queries.get, (resp.id, ), polling_interval, client=client, poll_on_creation=False) @deprecate_param('v2.0.0', 'api_key') def transfer_table(source_db, dest_db, source_table, dest_table, job_name=None, api_key=None, client=None, source_credential_id=None, dest_credential_id=None, polling_interval=None, **advanced_options): """Transfer a table from one location to another. Parameters ---------- source_db : str or int The name of the database where the source table is located. Optionally, could be the database ID. dest_db : str or int The name of the database where the table will be transfered. Optionally, could be the database ID. source_table : str Full name of the table to transfer, e.g., ``'schema.table'``. dest_table : str Full name of the table in the destination database, e.g., ``'schema.table'``. job_name : str, optional A name to give the job. If omitted, a random job name will be used. api_key : DEPRECATED str, optional Your Civis API key. If not given, the :envvar:`CIVIS_API_KEY` environment variable will be used. client : :class:`civis.APIClient`, optional If not provided, an :class:`civis.APIClient` object will be created from the :envvar:`CIVIS_API_KEY`. source_credential_id : str or int, optional Optional credential ID for the source database. If ``None``, the default credential will be used. dest_credential_id : str or int, optional Optional credential ID for the destination database. If ``None``, the default credential will be used. polling_interval : int or float, optional Number of seconds to wait between checks for job completion. **advanced_options : kwargs Extra keyword arguments will be passed to the import sync job. See :func:`~civis.resources._resources.Imports.post_syncs`. Returns ------- results : :class:`~civis.futures.CivisFuture` A `CivisFuture` object. Examples -------- >>> transfer_table(source_db='Cluster A', dest_db='Cluster B', ... source_table='schma.tbl', dest_table='schma.tbl') """ if client is None: client = APIClient(api_key=api_key) source_cred_id = source_credential_id or client.default_credential dest_cred_id = dest_credential_id or client.default_credential job_name = maybe_get_random_name(job_name) source = { 'remote_host_id': client.get_database_id(source_db), 'credential_id': source_cred_id } destination = { 'remote_host_id': client.get_database_id(dest_db), 'credential_id': dest_cred_id } job_id = client.imports.post(job_name, "Dbsync", True, source=source, destination=destination).id client.imports.post_syncs(id=job_id, source={'path': source_table}, destination={'path': dest_table}, advanced_options=advanced_options) run_id = client.imports.post_runs(id=job_id).run_id log.debug('Started run %d of sync for import %d', run_id, job_id) fut = CivisFuture(client.imports.get_files_runs, (job_id, run_id), polling_interval=polling_interval, client=client, poll_on_creation=False) return fut

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playlist_everywhere/application/__init__.py

kde713/playlist-everywhere

4

1606175

<gh_stars>1-10 from playlist_everywhere.application.cli import CliApplication

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models/rnn_model.py

intelligent-control-lab/MEKF_MAME

13

111959

<reponame>intelligent-control-lab/MEKF_MAME # Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. import torch import torch.nn as nn import torch.nn.functional as F class RNNEncoder(nn.Module): """RNN encoder.""" def __init__( self, cell_type='lstm', feat_dim=3, hidden_size=128, num_layers=1, dropout_fc=0.1, dropout_rnn=0.0, bidirectional=False,**kwargs): super(RNNEncoder, self).__init__() self.cell_type = cell_type.lower() self.feat_dim = feat_dim self.num_layers = num_layers self.dropout_fc = dropout_fc self.dropout_rnn = dropout_rnn self.bidirectional = bidirectional self.hidden_size = hidden_size input_size = feat_dim if self.cell_type == 'lstm': self.rnn = LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional, dropout=self.dropout_rnn) elif self.cell_type == 'gru': self.rnn = GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional, dropout=self.dropout_rnn) else: self.rnn = RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional, dropout=self.dropout_rnn) self.output_units = hidden_size if bidirectional: self.output_units *= 2 def forward(self, src_seq): x = src_seq # B x T x C -> T x B x C x = x.transpose(0, 1) encoder_out, h_t = self.rnn(x) if self.cell_type == 'lstm': final_hiddens, final_cells = h_t else: final_hiddens, final_cells = h_t, None if self.dropout_fc>0: encoder_out = F.dropout(encoder_out, p=self.dropout_fc, training=self.training) if self.bidirectional: batch_size = src_seq.size(0) def combine_bidir(outs): out = outs.view(self.num_layers, 2, batch_size, -1).transpose(1, 2).contiguous() return out.view(self.num_layers, batch_size, -1) final_hiddens = combine_bidir(final_hiddens) if self.cell_type == 'lstm': final_cells = combine_bidir(final_cells) # T x B x C -> B x T x C encoder_out = encoder_out.transpose(0, 1) final_hiddens = final_hiddens.transpose(0, 1) if self.cell_type == 'lstm': final_cells = final_cells.transpose(0, 1) return encoder_out, (final_hiddens, final_cells) class AttentionLayer(nn.Module): def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False): super(AttentionLayer, self).__init__() self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias) self.output_proj = Linear(input_embed_dim + source_embed_dim, output_embed_dim, bias=bias) def forward(self, input, source_hids, encoder_padding_mask=None): # input: bsz x input_embed_dim # source_hids: srclen x bsz x output_embed_dim # x: bsz x output_embed_dim x = self.input_proj(input) # compute attention attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2) # don't attend over padding if encoder_padding_mask is not None: attn_scores = attn_scores.float().masked_fill_( encoder_padding_mask, float('-inf') ).type_as(attn_scores) # FP16 support: cast to float and back attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz # sum weighted sources x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0) out = torch.cat((x, input), dim=1) x = F.tanh(self.output_proj(out)) return x, attn_scores class RNNDecoder(nn.Module): """RNN decoder.""" def __init__( self, cell_type='lstm', feat_dim=3, hidden_size=128, num_layers=1, dropout_fc=0.1, dropout_rnn=0.0, encoder_output_units=128, max_seq_len=10, attention=True, traj_attn_intent_dim=0,**kwargs): super(RNNDecoder, self).__init__() self.cell_type = cell_type.lower() self.dropout_fc = dropout_fc self.dropout_rnn = dropout_rnn self.hidden_size = hidden_size self.encoder_output_units = encoder_output_units self.num_layers = num_layers self.max_seq_len = max_seq_len self.feat_dim = feat_dim self.traj_attn_intent_dim =traj_attn_intent_dim input_size = feat_dim if encoder_output_units != hidden_size: self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size) if self.cell_type == 'lstm': self.encoder_cell_proj = Linear(encoder_output_units, hidden_size) else: self.encoder_cell_proj = None else: self.encoder_hidden_proj = self.encoder_cell_proj = None if self.cell_type == 'lstm': self.cell = LSTM elif self.cell_type == 'gru': self.cell = GRU else: self.cell = RNN self.rnn = self.cell(input_size=input_size, hidden_size=hidden_size, bidirectional=False, dropout=self.dropout_rnn, num_layers=num_layers) if attention: self.attention = AttentionLayer(hidden_size, encoder_output_units, hidden_size, bias=False) else: self.attention = None self.output_projection = Linear(hidden_size, feat_dim) if traj_attn_intent_dim>0: self.traj_attn_fc = Linear(hidden_size, traj_attn_intent_dim) def forward(self, encoder_out_list, start_decode=None, encoder_mask=None): x = start_decode.unsqueeze(1) bsz = x.size(0) # get outputs from encoder encoder_outs, (encoder_hiddens, encoder_cells) = encoder_out_list # B x T x C -> T x B x C encoder_outs = encoder_outs.transpose(0, 1) encoder_hiddens = encoder_hiddens.transpose(0, 1) if encoder_mask is not None: encoder_mask = encoder_mask.transpose(0, 1) prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)] if self.cell_type == 'lstm': encoder_cells = encoder_cells.transpose(0, 1) prev_cells = [encoder_cells[i] for i in range(self.num_layers)] x = x.transpose(0, 1) srclen = encoder_outs.size(0) # initialize previous states if self.encoder_hidden_proj is not None: prev_hiddens = [self.encoder_hidden_proj(x) for x in prev_hiddens] prev_hiddens = torch.stack(prev_hiddens, dim=0) if self.encoder_cell_proj is not None: prev_cells = [self.encoder_cell_proj(x) for x in prev_cells] if self.cell_type == 'lstm': prev_cells = torch.stack(prev_cells, dim=0) attn_scores = x.new_zeros(srclen, self.max_seq_len, bsz) inp = x outs = [] hidden_outs=[] for j in range(self.max_seq_len): if self.cell_type == 'lstm': output, (prev_hiddens, prev_cells) = self.rnn(inp, (prev_hiddens, prev_cells)) else: output, prev_hiddens = self.rnn(inp, prev_hiddens) output = output.view(bsz, -1) # apply attention using the last layer's hidden state if self.attention is not None: out, attn_scores[:, j, :] = self.attention(output, encoder_outs, encoder_mask) else: out = output if self.dropout_fc>0: out = F.dropout(out, p=self.dropout_fc, training=self.training) hid_out = out if self.traj_attn_intent_dim > 0: hid_out= self.traj_attn_fc(hid_out) hid_out = F.selu(hid_out) hidden_outs.append(hid_out) out = self.output_projection(out) # save final output outs.append(out) inp = out.unsqueeze(0) # collect outputs across time steps x = torch.cat(outs, dim=0).view(self.max_seq_len, bsz, self.feat_dim) hidden_outs = torch.cat(hidden_outs, dim=0).view(self.max_seq_len, bsz, -1) # T x B x C -> B x T x C x = x.transpose(1, 0) hidden_outs=hidden_outs.transpose(1, 0) # srclen x tgtlen x bsz -> bsz x tgtlen x srclen attn_scores = attn_scores.transpose(0, 2) # project back to input space return x, hidden_outs def LSTM(input_size, hidden_size, **kwargs): m = nn.LSTM(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def GRU(input_size, hidden_size, **kwargs): m = nn.GRU(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def RNN(input_size, hidden_size, **kwargs): m = nn.RNN(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): if 'weight' in name or 'bias' in name: param.data.uniform_(-0.1, 0.1) return m def Linear(in_features, out_features, bias=True): """Linear layer (input: N x T x C)""" m = nn.Linear(in_features, out_features, bias=bias) m.weight.data.uniform_(-0.1, 0.1) if bias: m.bias.data.uniform_(-0.1, 0.1) return m

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GUI.py

jkxing/stylized-neural-painting

0

199169

import tkinter as tk from tkinter import ttk from PIL import Image, ImageTk from tkinter import filedialog as fd from tkinter import messagebox as mb import matplotlib.pyplot as plt import numpy as np import cv2 class GUI(tk.Frame): def __init__(self, parent = None): tk.Frame.__init__(self, parent) self.parent = parent self.img_path = '' self.save_path = '' self.frame0 = tk.Frame(self, bd = 10) self.frame0.pack() self.path_label = tk.Label(self.frame0, text = '') self.path_label.pack(side='left') self.browseButton = tk.Button(self.frame0, text = 'Browse', command = self.openfile) self.browseButton.pack(side = 'left') self.slider_var = tk.IntVar() self.slider = tk.Scale(self, from_=1, to=20, orient= 'horizontal', variable = self.slider_var, command = self.slider_changed) self.slider.pack(pady = 10) self.goButton = tk.Button(self, text = 'Paint', command = self.go, width = 20) self.goButton.pack(pady = 10) self.addButton = tk.Button(self, text = 'Add Area', command = self.add_area, width = 20) self.addButton.pack(pady = 10) self.saveButton = tk.Button(self, text = 'Save as...', command = self.savefile, width = 20) self.saveButton.pack(pady = 10) self.mark_val = 1 self.oval_size = 1 def paint(self, event): python_green = "#476042" x1, y1 = ( event.x - self.oval_size ), ( event.y - self.oval_size ) x2, y2 = ( event.x + self.oval_size ), ( event.y + self.oval_size ) for x in range(x1, x2+1) : for y in range(y1, y2 + 1): self.image_mask[y][x][0] = self.mark_val self.image_mask[y][x][1] = self.mark_val self.image_mask[y][x][2] = self.mark_val self.canvas.create_oval( x1, y1, x2, y2, fill = python_green ) def add_area(self): self.mark_val += 1 def slider_changed(self, event): self.oval_size = self.slider_var.get() # print(self.slider_var.get()) def go(self): if (len(self.img_path) == 0): mb.showinfo('No image selected', 'Please browse an image to be resized') return # img = plt.imread(self.img_path) img = ImageTk.PhotoImage(Image.open(self.img_path)) offspring = tk.Toplevel() offspring.title(self.img_path.split('/')[-1]) offspring.geometry('%sx%s' % (img.width()+10, img.height()+10)) self.image_mask = np.zeros((img.height(), img.width(), 3)) self.canvas = tk.Canvas(offspring, width=img.width(), height=img.height(), borderwidth=0, highlightthickness=0) self.canvas.pack(expand=True) self.canvas.img = img # Keep reference in case this code is put into a function. self.canvas.create_image(0, 0, image=img, anchor=tk.NW) self.canvas.bind( "<B1-Motion>", self.paint ) offspring.mainloop() def openfile(self): self.img_path = fd.askopenfilename() self.path_label.config(text = self.img_path) def savefile(self): self.save_path = fd.asksaveasfilename() if len(self.save_path) == 0 : mb.showinfo('Give destination', 'Please give a destination path') return cv2.imwrite(self.save_path, self.image_mask) with open(self.save_path[:-4]+'.npy', 'wb') as f: np.save(f, np.array(self.image_mask)) if __name__ == '__main__': root = tk.Tk() root.geometry('%sx%s' % (400, 300)) gui = GUI(root) gui.pack() root.mainloop()

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msticpy/data/drivers/__init__.py

kubajir/msticpy

1

151340

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- """Data provider sub-package.""" import importlib from typing import Union from ..query_defns import DataEnvironment # flake8: noqa: F403 from .driver_base import DriverBase from ..._version import VERSION __version__ = VERSION _ENVIRONMENT_DRIVERS = { DataEnvironment.LogAnalytics: ("kql_driver", "KqlDriver"), DataEnvironment.AzureSecurityCenter: ("kql_driver", "KqlDriver"), DataEnvironment.SecurityGraph: ("security_graph_driver", "SecurityGraphDriver"), DataEnvironment.Kusto: ("kusto_driver", "KustoDriver"), DataEnvironment.MDATP: ("mdatp_driver", "MDATPDriver"), DataEnvironment.MDE: ("mdatp_driver", "MDATPDriver"), DataEnvironment.LocalData: ("local_data_driver", "LocalDataDriver"), DataEnvironment.Splunk: ("splunk_driver", "SplunkDriver"), DataEnvironment.Mordor: ("mordor_driver", "MordorDriver"), DataEnvironment.Sumologic: ("sumologic_driver", "SumologicDriver"), DataEnvironment.ResourceGraph: ("resource_graph_driver", "ResourceGraphDriver"), DataEnvironment.M365D: ("mdatp_driver", "MDATPDriver"), } def import_driver(data_environment: DataEnvironment) -> type: """Import driver class for a data environment.""" mod_name, cls_name = _ENVIRONMENT_DRIVERS.get(data_environment, (None, None)) if not (mod_name and cls_name): raise ValueError( f"No driver available for environment {data_environment.name}.", "Possible values are:", ", ".join(env.name for env in _ENVIRONMENT_DRIVERS), ) imp_module = importlib.import_module( f"msticpy.data.drivers.{mod_name}", package="msticpy" ) return getattr(imp_module, cls_name)

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app.py

skrzypak/soaf

0

79492

<filename>app.py import glob import shutil import subprocess import os import sys import argparse # Read and save metadata from file def exiftool_metadata(path): metadata = {} exifToolPath = 'exifTool.exe' ''' use Exif tool to get the metadata ''' process = subprocess.Popen( [ exifToolPath, path ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True ) ''' get the tags in dict ''' for tag in process.stdout: tag = tag.strip() key = tag[:tag.find(':')].strip() value = tag[tag.find(':') + 1:].strip() metadata[key] = value return metadata class File: def __init__(self, path): self.metadata = exiftool_metadata(path) def _get_file_metadata(self, key, no=''): if key in self.metadata: return self.metadata[key] else: return no def copyCore(self, source, dst_dir: str, copy_duplicate=False): logs = [] # if value of metadata not exists - folder name no_metadata = 'none' date = File._get_file_metadata(self, 'Date/Time Original') if date == '': date = File._get_file_metadata(self, 'Create Date', no_metadata) mime_type = File._get_file_metadata(self, 'MIME Type', no_metadata) dst_dir += f'''/{mime_type[:mime_type.find('/')]}/{date[:4]}/{date[5:7]}''' filename = File._get_file_metadata(self, 'File Name') f_name = filename dst = dst_dir + '/' + filename # File with the same name exists in dst. If source and dst have same size then determines 'copy_exists' if os.path.isfile(dst): i = 0 f_pth = File(dst) if_same_size: bool = f_pth._get_file_metadata("File Size") == File._get_file_metadata(self, 'File Size') if (not if_same_size) or copy_duplicate: while os.path.isfile(dst): filename = f'''{f_name[:f_name.find('.')]}_D{str(i)}.{File._get_file_metadata(self, 'File Type Extension')}''' dst = f'''{dst_dir}/{filename}''' i = i + 1 if if_same_size: logs.append(f"Warning: file already exists but I must copy all files" f" [copy_duplicate={copy_duplicate}], so I try do it ...") else: logs.append(f"Warning: file already exists but have other size, so I try copy it ...") else: logs.append(f"Warning: file already duplicate [copy_exists={copy_duplicate}]." f"\nCopy aboard: {source} -> {dst}") return logs try: if not os.path.isdir(dst_dir): os.makedirs(dst_dir) logs.append(f"New directory created: {dst_dir}") shutil.copy(source, dst) logs.append(f'''Copy done: {source} -> {dst}''') except Exception as e: logs.append(f'''Copy error [{e}]: {source} -> {dst}''') return logs def main(): # Arguments from console parser = argparse.ArgumentParser() parser.add_argument('-s', help="Obligatory: source directory path") parser.add_argument('-d', help="Obligatory: destination folder path") parser.add_argument('-e', help="Obligatory: copy duplicate files (T/True/F/False)") args = parser.parse_args(sys.argv[1:]) # Setup variable source_dir = args.s dst_dir = args.d df = { "T": True, "TRUE": True, "F": False, "FALSE": False } try: copy_duplicate = df.get(args.e.upper(), False) except AttributeError: copy_duplicate = False print(f"app.py: error: unrecognized arguments. Use -h or --help to see options") exit(1) # Number of log l_lpm = 0 # source_dir = 'C:/Users' # dst_dir = 'C:/Users' # copy_duplicate = False for f_inx, source in enumerate(glob.glob(source_dir + '/**/*.*', recursive=True)): try: f = File(source) print("----------") for log in f.copyCore(source, dst_dir, copy_duplicate): l_lpm = l_lpm + 1 print(f'''{str(l_lpm)}.{f_inx + 1}) {log}''') except Exception as e: print(f'Copy error [{e}]: {source}') if __name__ == '__main__': main()

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manage-downloads.py

hackers-terabit/download-hygeine

1

87710

#!/usr/bin/python2.7 #manage-downloads - adds/removes downloads to the catalogue #TODO: # test it all \0/ import os,json,sys import hashlib,uuid from downloadhygeine import DownloadHygeine,Util,crypto color={"green":"\33[92m","blue":"\33[96m","red":"\33[31m","yellow":"\33[93m","white":"\33[98m"} def add(dh): download={} fname='' download["uuid"]=str(uuid.uuid4()) download["name"]=raw_input("Name for this download: ") download["category"]=raw_input("What category is this download under?: ") download["type"]=raw_input("What type of a download is this? (e.g.:torrent,iso,tar,img,avi,etc...): ") torrent=dh.util.yesno_choice("Is this a bittorrent download?: ") if torrent: download["torrent"]=1 else: download["torrent"]=0 while True: download["url"]="" retry=False if retry==False: download["url"]=raw_input(color["white"]+"Enter the primary URL for this download (This will be used to download and calculate the hash of the downloaded file):") fname,success=dh.fetch(download["url"]) if not os.path.exists(fname) or os.path.getsize(fname)<1: print(color["red"]+"It seems downloading this file to: "+fname+" Has failed\nLet's try this again.") if dh.util.yesno_choice(color["yellow"]+"Use the same url as before ("+download["url"]+")?")==True: retry=False else: break if len(download["url"])<0: print(color["red"]+"Unable to find a valid download URL,exiting...") return #whoa!? else: if dh.util.yesno_choice(color["white"]+"Are there other accompanying meta-files that should be downloaded? (example: .asc gpg detached signature,hash data,readme,etc...) : "): metafiles=[] download["getmeta"]=0 print(color["yellow"]+"Enter the URLs for these additonal meta-files,separated by a new line. enter 'eof' without quotes when done. Keep in mind the validity of these URLs will not be checked at this time.") while True: entry=raw_input().strip().split(" ") if entry[0].strip().lower() =='eof': break elif len(entry[0])>0: metafiles.append(entry[0]) if len(metafiles)>0: download["getmeta"]=1 download["metafiles"]=metafiles else: download["getmeta"]=0 print(color["blue"]+">") hl=set() for algo in hashlib.algorithms_available: if algo in ["sha512","sha256","whirlpool","ripemd160"]: hl.add(hashlib.new(algo)) with open(fname,"rb") as f: sofar=0 total=os.path.getsize(fname) while True: block=f.read(65536) if len(block)<1: break else: sofar+=len(block) sys.stdout.write("\rRead so far:\t"+str(sofar/(total/100.00))[:5].ljust(5," ")+"\%") for h in hl: h.update(block) print("\n") for h in hl: digest=h.hexdigest() print("\t Calculated "+h.name+" digest:\t "+digest) download[h.name]=digest mirrors=[] if dh.util.yesno_choice("Will this download use mirrors?"): suffix=raw_input(color["yellow"]+"What is the suffix to be used across all mirrors?\nExample: A suffix of '/mirrors/project/iso/someos.iso' for a mirror 'https://www.fancymirror.co.example/opensource/mirrors' will result in a download URL 'https://www.fancymirror.co.example/opensource/mirrors/mirrors/project/iso/someos.iso'\n:") print(color["yellow"]+"Enter a list of mirrors with the format 'Mirrorname <url>' (example:superfastmirror https://secureserver.com.example/)\nEnter just 'EOF' without quotes when done:") while True: entry=raw_input().strip().split(" ") if entry[0].strip().lower() =='eof': break if len(entry) <2 or len(entry[0])<1 or len(entry[len(entry)-1])<1: print(color["red"]+"Invalid entry, please be sure to specify the mirror name and the URL") continue else: mirrors.append({"mirrorname":entry[0].strip(),"url":entry[1].strip()+suffix.strip()}) if len(mirrors)>0: download["mirrors"]=mirrors if (dh.util.yesno_choice("Have you verified the authenticity of the downloaded file '"+fname+"' ? (such as with GPG validation)?") and dh.util.yesno_choice("Sign this download? ")): content=json.dumps(download,sort_keys=True) exported=dh.crypto.sign_and_export(content) exportme=json.dumps(exported,indent=4,sort_keys=True) with open(dh.conf["localclone"]+"/"+download["name"]+".json","w+") as df: df.write(exportme) def remove(dh): dh.load_catalog() download_choice=dh.pick() for e in dh.catalog: if "uuid" in e and e["uuid"]==download_choice: print(color["green"]+"Download name: "+e["name"]) print(color["green"]+"Category: "+e["category"]) print(color["yellow"]+"File type: "+e["type"]) print(color["blue"]+"Download URL: "+e["url"]) if dh.util.yesno_choice(color["yellow"]+"Remove this download catalog entry? "): os.remove(dh.conf["localclone"]+"/"+e["name"]+".json") def gitupdate(dh): success=False print(color["yellow"]+"Synchronizing download catalog...") if os.path.exists(dh.conf["localclone"]): os.chdir(dh.conf["localclone"]) if dh.util.call(["git","add","*"])!=0: print(color["red"]+"Error running 'git add *' to update the local clone. Please fix this manually.") success=False else: if dh.util.call(["git","commit","-m","Sync catalog changes","*"])!=0: print(color["red"]+"Error running 'git commit -m 'Sync catalog changes' . Please fix this manually.") success=False else: if dh.util.call(["git","push","-u","origin","master"])!=0: print(color["red"]+"Error running 'git push -u origin master' . Please fix this manually.") success=False else: success=True if success==True: print(color["blue"]+"Updated your catalog changes to the preset git repository") return success def update(dh,uuid=None): dh.load_catalog() download_choice=dh.pick(uuid=uuid) for e in dh.catalog: if "uuid" in e and e["uuid"]==download_choice: dh.crypto.showkey(e["siginfo"]) print(color["green"]+"Download name: "+e["name"]) print(color["green"]+"Category: "+e["category"]) print(color["yellow"]+"File type: "+e["type"]) print(color["blue"]+"Download URL: "+e["url"]) params=[] for p in e: if not str(p).strip() in ["siginfo"]: params.append(p) params.append("Done") while True: choice=dh.util.numeric_choice(color["yellow"]+"Please select a paramter to update for "+e["name"]+".Select 'Done' when finished: ",params) if choice=="Done": break else: print(color["yellow"]+"Current value for "+choice+":\n"+str(e[choice])) e[choice]=raw_input("["+e["name"]+"] Please enter a new value for '"+choice+"' : ") if dh.util.yesno_choice("Now that you've updated '"+e["name"]+"', Would you like to re-download the file and update it's hash values? ")==True: while True: fname=dh.fetch(e["url"]) if not os.path.exists(fname) or os.path.getsize(fname)<1: print("It seems downloading this file to: "+fname+" Has failed\nLet's try this again.") else: break hl=set() for algo in hashlib.algorithms_available: if algo in ["sha512","sha256","whirlpool","ripemd160"]: hl.add(hashlib.new(algo)) with open(fname,"rb") as f: sofar=0 total=os.path.getsize(fname) while True: block=f.read(65536) if len(block)<1: break else: sofar+=len(block) sys.stdout.write("\rRead so far:\t"+str(sofar/(total/100.00))[:5].ljust(5," ")+"\%") for h in hl: h.update(block) print("\n") for h in hl: digest=h.hexdigest() print("\t Calculated "+h.name+" digest:\t "+digest) e[h.name]=digest if (dh.util.yesno_choice("Have you verified the autenticity of the hashes for the download '"+e["name"]+"' ? (such as with GPG validation)?") and dh.util.yesno_choice("Sign this download? ")): exportme=json.dumps(dh.crypto.sign_and_export(json.dumps(e,sort_keys=True)),indent=4,sort_keys=True) with open(dh.conf["localclone"]+"/"+e["name"]+".json","w+") as df: df.write(exportme) break def fork(dh): dh.load_catalog() download_choice=dh.pick() if None is download_choice or len(dh.catalog)<0 or len(download_choice)<0: print(color["yellow"]+"Current download catalog is empty,nothing to fork.") return for e in dh.catalog: if "uuid" in e and e["uuid"]==download_choice: dh.crypto.showkey(e["siginfo"]) print(color["green"]+"Download name: "+e["name"]) print(color["green"]+"Category: "+e["category"]) print(color["yellow"]+"File type: "+e["type"]) print(color["blue"]+"Download URL: "+e["url"]) e["name"]=raw_input("Forking '"+e["name"]+"' Please enter a new name: ") e["uuid"]=str(uuid.uuid4()) exportme=json.dumps(dh.crypto.sign_and_export(json.dumps(e,sort_keys=True)),indent=4,sort_keys=True) with open(dh.conf["localclone"]+"/"+e["name"]+".json","w+") as df: df.write(exportme) dh.load_catalog() update(dh,e["uuid"]) def main(): dh=DownloadHygeine() if not dh.init_env(): print("Critical error initializing. Exiting immediately.") sys.exit(1) if not dh.util.yesno_choice("Do you have commit priviledge for the currently configured git repository? "+dh.conf["localclone"]+"\n Remote repository:'"+dh.conf["gitrepo"]+"' ?"): if dh.util.yesno_choice("Things will break if we continue to change the currently configured repository.\nIn order to avoid that, should we configure a new repository? (This new repository will be managed by you!) "): dh.conf["gitrepo"]=raw_input("Enter the remote URL that will be used to synchronize this new repository: ") if dh.util.yesno_choice("Use a different local directory for this new catalog? (Warning: if you pick no,the current contents of "+dh.conf["localclone"]+" Will be deleted.): "): dh.conf["localclone"]=dh.util.numeric_choice_flexible("Where should the download catalogue be stored?",[os.getenv('HOME')+"/Downloads/downloadhygeine-catalog",os.getenv('HOME')+"/downloadhygeine-catalog"]) dh.util.call(["rm","-rfv",dh.conf["localclone"]]) #wipe out the current catalog. if dh.util.call(["git","init",dh.conf["localclone"]])!=0: print(color["red"]+"Error initializing the chosen git repository of the download catalog. Please make sure the local clone directory '"+dh.conf["localclone"]+"' can be created by git and whether your internet connection is allowing access to your chosen git repository.") print(color["red"]+"Alternatively, please adjust your configuration to relfect any system or network changes.") sys.exit(1) else: os.chdir(dh.conf["localclone"]) if dh.util.call(["git","remote","add","origin",dh.conf["gitrepo"]])!=0: print(color["red"]+"Failed to run 'git remote add origin "+dh.conf["gitrepo"]+"' . Please fix this manually.") sys.exit(1) else: if dh.util.call(["git","fetch"])!=0: print(color["red"]+"Failed to run 'git fetch' . Please fix this manually.") sys.exit(1) else: if dh.util.call(["git","checkout","master"])!=0: print(color["red"]+"Failed to run 'git checkout master' . Please fix this manually.") sys.exit(1) else: print(color["red"]+"Sorry, you cannot manage a catalog(git repository) without being able to commit to it") sys.exit(1) with open(dh.util.pickpath(dh.config_paths),"w+") as cf: cf.write(json.dumps(dh.conf,indent=4,sort_keys=True)) while True: dh.gitsync() choice=dh.util.numeric_choice("What would you like to do?",["Add","Remove","Update","Fork","List","Exit"]) if choice=="Add": add(dh) elif choice=="Remove": remove(dh) elif choice=="Update": update(dh) elif choice=="Fork": fork(dh) elif choice=="List": dh.load_catalog() dh.dump_catalog() elif choice=="Exit": break gitupdate(dh) if __name__ == "__main__": main()

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009_special_pythagorean_triplet.py

fbcom/project-euler

0

76309

<gh_stars>0 #!/usr/bin/env python # -*- coding: utf-8 -*- # # A Solution to "Special Pythagorean triplet" – Project Euler Problem No. 9 # by <NAME> # # Sourcecode: https://github.com/fbcom/project-euler # Problem statement: https://projecteuler.net/problem=9 # def isPythagoreanTriple(a, b, c): if a < b < c: return a**2 + b**2 == c**2 return False def findTipleHavingSum(sum): # small problem size -> brute force it for a in range(1, sum): for b in range(a, sum - a + 1): for c in range(b, sum - b + 1): if a+b+c == sum: if isPythagoreanTriple(a, b, c): return (a, b, c) sum = 1000 triple = findTipleHavingSum(sum) print "Triple:", triple print "Solution:", reduce(lambda product, factor: product * factor, triple)

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dataset/ADS_Dataset_generator_2.py

pravk03/responsible_ai_hackathon

0

113225

#!/usr/bin/env python # coding: utf-8 # <b> Run below cells from the folder that contains ads16_dataset/ unzipped </b> # In[1]: import pandas as pd import glob import pathlib import re # In[2]: pd.set_option('display.max_colwidth', -1) # In[3]: # Global constants g_userPart1PathPrefix = "./ads16-dataset/ADS16_Benchmark_part1/ADS16_Benchmark_part1/Corpus/Corpus/" g_userPart2PathPrefix = "./ads16-dataset/ADS16_Benchmark_part2/ADS16_Benchmark_part2/Corpus/Corpus/" g_userIdPrefix = "U0" g_adsPart1PathPrefix = "./ads16-dataset/ADS16_Benchmark_part1/ADS16_Benchmark_part1/Ads/Ads/" g_adsPart2PathPrefix = "./ads16-dataset/ADS16_Benchmark_part2/ADS16_Benchmark_part2/Ads/Ads/" # # UDFs # ## UDFs for generating Users Dataset # In[4]: def generate_data_User( pathPrefix, userId ): completePath = pathPrefix + userId + "/" # INF infFile = userId + "-INF.csv" userInf_df = pd.read_csv(completePath + infFile, delimiter=";") # Pref prefFile = userId + "-PREF.csv" userPref_df = pd.read_csv(completePath + prefFile, delimiter=";") user_df = pd.concat([userInf_df, userPref_df], axis=1) # Pos posFile = userId + "-IM-POS.csv" userPos_df = pd.read_csv(completePath + posFile, delimiter=";") userPos_df = userPos_df.iloc[1:] userPos_df.reset_index(drop=True, inplace=True) user_df = pd.concat([user_df, userPos_df], axis=1) # Neg negFile = userId + "-IM-NEG.csv" userNeg_df = pd.read_csv(completePath + negFile, delimiter=";") userNeg_df = userNeg_df.iloc[1:] userNeg_df.reset_index(drop=True, inplace=True) user_df = pd.concat([user_df, userNeg_df], axis=1) user_df.insert(0, "UserId", userId, True) # user_df = user_df.set_index('UserId') # user_df.info() return user_df # In[5]: def generate_data_partUsers( usersPartPathPrefix, startRange, endRange ): partUsers_df = pd.DataFrame() for i in range(startRange, endRange): thisUserIdNum = str(i) thisUserId = g_userIdPrefix + thisUserIdNum.zfill(3) # print(thisUserId) thisUser_df = generate_data_User(usersPartPathPrefix, thisUserId) partUsers_df = partUsers_df.append(thisUser_df, sort=True) partUsers_df.set_index('UserId') return partUsers_df # In[6]: def generate_data_allUsers(): allUsers_df = pd.DataFrame() part1Users_df = generate_data_partUsers(g_userPart1PathPrefix, 1, 61) allUsers_df = allUsers_df.append(part1Users_df, sort=True) part2Users_df = generate_data_partUsers(g_userPart2PathPrefix, 61, 121) allUsers_df = allUsers_df.append(part2Users_df, sort=True) return allUsers_df # ## UDFs for generating Ads Dataset # In[7]: def generate_data_adCats(): adCatsLst = [['01', "Clothing & Shoes", 16], ['02', "Automotive", 15], ['03', "Baby Products", 15], ['04', "Health & Beauty", 15], ['05', "Media (BMVD)", 15], ['06', "Consumer Electronics", 15], ['07', "Console & Video Games", 15], ['08', "DIY & Tools", 15], ['09', "Garden & Outdoor living", 15], ['10', "Grocery", 15], ['11', "Kitchen & Home", 15], ['12', "Betting", 15], ['13', "Jewellery & Watches", 15], ['14', "Musical Instruments", 15], ['15', "Office Products", 15], ['16', "Pet Supplies", 15], ['17', "Computer Software", 15], ['18', "Sports & Outdoors", 15], ['19', "Toys & Games", 15], ['20', "Dating Sites", 15] ] adCats_df = pd.DataFrame(adCatsLst, columns =['AdCatId', 'AdCatName', 'AdCatNumAds']) return adCats_df # In[8]: import re def atoi(text): return int(text) if text.isdigit() else text def natural_keys(text): return [ atoi(c) for c in re.split(r'(\d+)',text.split('/')[-1].split('.')[0]) ] def generate_data_partAds( adsPartPathPrefix, startRange, endRange ): partAds_df = pd.DataFrame() partAdsRows = [] for i in range(startRange, endRange): iStr = str(i) adsFiles = pathlib.Path(adsPartPathPrefix + iStr + "/").glob("*.png") adsFileStrLst = [] for adsFile in adsFiles: adsFileStr = str(adsFile) adsFileStrLst.append(adsFileStr) adsFileStrLst.sort(key=natural_keys) for adsFileStr in adsFileStrLst: adId = adsFileStr.split('/')[-1].split('.')[0] adId = "A" + iStr.zfill(2) + "_" + adId.zfill(2) # print(adId, adsFileStr) partAdsRows.append([adId, adsFileStr]) partAds_df = pd.DataFrame(partAdsRows, columns =['AdId', 'AdFilePath']) partAds_df.set_index('AdId') return partAds_df # In[9]: # DEBUG def generate_data_allAds(): allAds_df = pd.DataFrame() part1Ads_df = generate_data_partAds(g_adsPart1PathPrefix, 1, 11) allAds_df = allAds_df.append(part1Ads_df, sort=True) part2Ads_df = generate_data_partAds(g_adsPart2PathPrefix, 11, 21) allAds_df = allAds_df.append(part2Ads_df, sort=True) allAds_df = allAds_df.set_index('AdId') return allAds_df # ## UDFs for generating Ratings Dataset # In[10]: def df_crossjoin(df1, df2): df1['_tmpkey'] = 1 df2['_tmpkey'] = 1 res = pd.merge(df1, df2, on='_tmpkey').drop('_tmpkey', axis=1) res.index = pd.MultiIndex.from_product((df1.index, df2.index)) df1.drop('_tmpkey', axis=1, inplace=True) df2.drop('_tmpkey', axis=1, inplace=True) return res # In[11]: # TODO: Move this UDF to top UDFs section def generate_data_RatingsPerUser( pathPrefix, userId ): completePath = pathPrefix + userId + "/" data = "" rtFile = userId + "-RT.csv" rtNewFile = userId + "-RT-NEW.csv" with open(completePath + rtFile) as file: data = file.read().replace("\"", "") with open(completePath + rtNewFile,"w") as file: file.write(data) my_cols = [str(i) for i in range(300)] data3 = pd.read_csv(completePath + rtNewFile, sep=";|,", names=my_cols, header=None, engine="python") data3 = data3.iloc[2:] data3.reset_index(drop = True, inplace = True) for i in range(20): index = str(i) data3[index] = data3[index].astype('float64') data3 = data3.transpose() return data3 # In[12]: def generate_data_RatingsPartUsers( usersPartPathPrefix, startRange, endRange ): partUsers_df = pd.DataFrame() for i in range(startRange, endRange): thisUserIdNum = str(i) thisUserId = g_userIdPrefix + thisUserIdNum.zfill(3) # print(thisUserId) thisUser_df = generate_data_RatingsPerUser(usersPartPathPrefix, thisUserId) partUsers_df = partUsers_df.append(thisUser_df, sort=True) # partUsers_df.set_index('UserId') return partUsers_df # In[13]: def generate_data_RatingsAllUsers(): allUsers_df = pd.DataFrame() part1Users_df = generate_data_RatingsPartUsers(g_userPart1PathPrefix, 1, 61) allUsers_df = allUsers_df.append(part1Users_df, sort=True) part2Users_df = generate_data_RatingsPartUsers(g_userPart2PathPrefix, 61, 121) allUsers_df = allUsers_df.append(part2Users_df, sort=True) return allUsers_df # # Generate datasets # ## Generate Users dataset # In[14]: allUsers_df = generate_data_allUsers() allUsers_df = allUsers_df.set_index('UserId') # In[15]: allUsers_df.head() # In[16]: allUsers_df.info() # In[17]: allUsers_df.to_csv("AllUsers.csv", index=True) # ## Generate Ads Categories Dataset # In[18]: adCats_df = generate_data_adCats() adCats_df = adCats_df.set_index('AdCatId') # In[19]: adCats_df.head() # In[20]: adCats_df.info() # In[21]: adCats_df.to_csv("AdCats.csv", index=True) # ## Generate Ads Dataset # In[22]: allAds_df = generate_data_allAds() # In[23]: allAds_df.info() # In[24]: allAds_df.head() # In[25]: allAds_df.to_csv("AllAds.csv", index=True) # ## Generate Users\*Ads Dataset # In[26]: allUsers_And_Ads_df = df_crossjoin(allUsers_df, allAds_df) # In[27]: allUsers_And_Ads_df.info() # In[28]: allUsers_And_Ads_df = allUsers_And_Ads_df.reset_index() allUsers_And_Ads_df.rename(columns={'level_0':'UserId'}, inplace=True) allUsers_And_Ads_df.rename(columns={'level_1':'AdId'}, inplace=True) allUsers_And_Ads_df.head(302) # In[29]: allUsers_And_Ads_df.to_csv("AllUsers_And_Ads.csv", index=False) # ## Generate UsersRatings Dataset # In[30]: allUsersRatings_df = generate_data_RatingsAllUsers() # In[31]: allUsersRatings_df.rename(columns={0:'Rating'}, inplace=True) # In[32]: allUsersRatings_df.info() # In[33]: allUsersRatings_df.head(301) # ## Generate Final Dataset # In[34]: allUsers_And_Ads_df.head() # In[35]: allUsersRatings_df.head() # In[36]: allUsers_And_Ads_df.reset_index(drop=True, inplace=True) allUsersRatings_df.reset_index(drop=True, inplace=True) allUsers_Ads_Ratings_df = pd.concat([allUsers_And_Ads_df, allUsersRatings_df], axis=1) # In[37]: allUsers_Ads_Ratings_df.info() # In[38]: allUsers_Ads_Ratings_df.head(5) # In[39]: allUsers_Ads_Ratings_df.to_csv("AllUsers_Ads_Ratings_df.csv", index=False) # # Scratchpad # In[ ]:

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Backend/issue.py

SXH7/Library-Management

2

143578

<gh_stars>1-10 import mysql.connector as i from datetime import datetime log=i.connect(host="localhost",user="root",password="<PASSWORD>",database="library") def viewissues(): c=log.cursor() c.execute("select * from issue") result= c.fetchall() for row in result: print(row) print("\n") def issuebook(): iid=input("Enter issue ID:") mmid=input("Enter member id:") bc=input("Enter the book code:") B=(bc,) c=log.cursor() sql=("select B_Avilable from books where B_ID=%s") c.execute(sql,B) res=c.fetchall() if 'Y' in res[0]: adate=input("Enter book issue date (MM-DD-YYYY):") bdate=input("Enter due date(MM-DD-YYYY):") idate=datetime.strptime(adate, '%m-%d-%Y') duedate=datetime.strptime(bdate, '%m-%d-%Y') data=(iid,mmid,bc,idate,duedate) sq=("insert into issue values(%s,%s,%s,%s,%s)") c=log.cursor() c.execute(sq,data) log.commit() print("Book issued successfully!") data=(B) sq=("update books set B_Avilable= replace(B_Avilable, 'Y','N') where B_ID=%s") c=log.cursor() c.execute(sq,data) log.commit() else: print("Either book isnt avilable or code is wrong:") issuebook() def bookupdate(): bc=input("Enter book id:") B=(bc,) data=(B) sq=("update books set B_Avilable= replace(B_Avilable, 'N','Y') where B_ID=%s") c=log.cursor() c.execute(sq,data) log.commit()

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segment_classifier/keras_lstm.py

liuhongbing1220/fundanNLP_task

0

81165

<reponame>liuhongbing1220/fundanNLP_task #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri May 31 09:49:27 2019 @author: liuhongbing """ import pandas as pd import numpy as np from collections import Counter import matplotlib.pyplot as plt import re import tensorflow as tf from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense ,Embedding,Activation from tensorflow.keras.layers import LSTM from tensorflow.keras.preprocessing import sequence import io root = "/Users/liuhongbing/Documents/tensorflow/data/sentiment-analysis-on-movie-reviews/" def dataCleaning(totalText): total=[] for i in totalText: temp=i.lower() temp=re.sub('[^a-zA-Z]',' ',temp) tempArr=[j for j in temp.strip().split('\t') if isinstance(j,str)] tstr=' '.join(tempArr) total.append(tstr) return total def getTest(name): data=pd.read_csv(name,delimiter='\t') totalText=data['Phrase'] totalText=dataCleaning(totalText) return totalText def loadData(name): data=pd.read_csv(name,delimiter='\t') totalText=data['Phrase'] totalText=dataCleaning(totalText) totalLabel=data['Sentiment'] return totalText,totalLabel def load_vectors(fname): fin = io.open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore') n, d = map(int, fin.readline().split()) data = {} for line in fin: tokens = line.rstrip().split(' ') data[tokens[0]] = [float(tokens[i]) for i in range(1, len(tokens))] return data def getWordVector(word2VecSum, word2index, embedding_dim): embedding_weight = np.zeros((len(word2index)+1, embedding_dim)) for word,index in word2index.items(): try: embedding_weight[index, :] = word2VecSum[word] except KeyError: pass return embedding_weight word2vecSum = load_vectors('/Users/liuhongbing/Documents/tensorflow/fudanNLP/word_to_vector/wiki-news-300d-1M.vec') # #def calc_sim_word(word1, word2): # if (word1 not in word2vecSum) | (word2 not in word2vecSum): # return "word not in wordvec" # else: # vec1 = word2vecSum[word1] # vec2 = word2vecSum[word2] # return np.dot(vec1, vec2) totalText,totalLabel = loadData(root+"train.tsv") testText = getTest(root+"test.tsv") train_tokenizer = Tokenizer() train_tokenizer.fit_on_texts(totalText) train_sequences = train_tokenizer.texts_to_sequences(totalText) test_sequences = train_tokenizer.texts_to_sequences(testText) # 获得所有tokens的长度 num_tokens = [ len(tokens) for tokens in train_sequences ] num_tokens = np.array(num_tokens) print(len(num_tokens)) #输出 156060 # 平均tokens的长度 print('mean',np.mean(num_tokens)) # 最长的评价tokens的长度 print('max',np.max(num_tokens)) # 最长的评价tokens的长度 print('min',np.min(num_tokens)) plt.hist((num_tokens), bins = 50) plt.ylabel('number of tokens') plt.xlabel('length of tokens') plt.title('Distribution of tokens length') plt.show() ''' 3sigma原则 ''' max_tokens = np.mean(num_tokens) + 3 * np.std(num_tokens) max_tokens = int(max_tokens) max_tokens # 取tokens的长度为19时，大约93%的样本被涵盖 #np.sum( num_tokens < max_tokens ) / len(num_tokens) train_Data=pad_sequences(train_sequences,maxlen=48) test_Data = pad_sequences(test_sequences,maxlen=48) columns = ['feat_'+str(i) for i in range(48)] train_Data2 = train_Data.copy() train_Data2 = pd.DataFrame(train_Data2) train_Data2.columns = columns train_Data2['label'] = totalLabel train_Data2.to_csv(root+"data_result.csv", index=None) train_label=tf.keras.utils.to_categorical(totalLabel,5) data_train, data_test, label_train, label_test = train_test_split(train_Data,train_label, test_size = 0.2) max_features=len(train_tokenizer.index_word)+1 max_len=48 #这个是要和前面padding时的长度一致 epochs = 5 #训练次数 emb_dim = 300 #128代表embedding层的向量维度 batch_size=80 #这是指定批量的大小 model = Sequential() embedding_weight = getWordVector(word2vecSum,train_tokenizer.word_index, emb_dim) embedding_weight_copy = embedding_weight.copy() #import pickle #with open(root+"tikenizer_word_index.pkl", 'wb') as f: # pickle.dump(train_tokenizer.word_index, f) ## 15126*128 ##model.add(Embedding(max_features,emb_dim, mask_zero=True)) model.add(Embedding(max_features,emb_dim, weights=[embedding_weight], trainable=False, mask_zero=True)) ## 【（128+64）*64+64】*4 model.add(LSTM(64,dropout=0.4, recurrent_dropout=0.4,return_sequences=True)) ## 【（64+32）*32+32】*4 model.add(LSTM(32,dropout=0.5, recurrent_dropout=0.5,return_sequences=False)) ## 32*5 + 5 model.add(Dense(5, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) model.fit(data_train,label_train,batch_size=batch_size,epochs=epochs,validation_data=(data_test,label_test)) model.evaluate(data_test, label_test) # #print("start saver test submission") #y_pred=model.predict_classes(test_Data) # #sub_file = pd.read_csv(root + 'sampleSubmission.csv', sep=',') #sub_file.Sentiment=y_pred #sub_file.to_csv('Submission_2.csv',index=False) model.save('/Users/liuhongbing/Documents/tensorflow/fudanNLP/model_2.h5')

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3317, 29918, 22100, 29922, 2435, 29898, 14968, 29918, 6979, 3950, 29889, 2248, 29918, 1742, 7240, 29896, 13, 3317, 29918, 2435, 29922, 29946, 29947, 29871, 396, 30810, 30502, 30392, 30698, 30503, 30658, 30806, 12791, 30594, 30210, 31143, 30898, 30287, 235, 138, 183, 13, 1022, 2878, 29879, 353, 29871, 29945, 29871, 396, 235, 177, 176, 234, 190, 134, 30936, 30354, 13, 1590, 29918, 6229, 353, 29871, 29941, 29900, 29900, 396, 29896, 29906, 29947, 30690, 30746, 17987, 8497, 232, 180, 133, 30210, 31331, 31180, 234, 190, 183, 30898, 13, 16175, 29918, 2311, 29922, 29947, 29900, 259, 396, 30810, 30392, 31084, 30495, 233, 140, 188, 31180, 30210, 30257, 30446, 13, 13, 13, 4299, 353, 922, 339, 2556, 580, 13, 13, 17987, 8497, 29918, 7915, 353, 679, 14463, 12877, 29898, 1742, 29906, 2003, 11139, 29892, 14968, 29918, 6979, 3950, 29889, 1742, 29918, 2248, 29892, 7232, 29918, 6229, 29897, 13, 17987, 8497, 29918, 7915, 29918, 8552, 353, 23655, 29918, 7915, 29889, 8552, 580, 13, 13, 13, 29937, 5215, 5839, 280, 13, 29937, 2541, 1722, 29898, 4632, 13578, 29873, 14004, 3950, 29918, 1742, 29918, 2248, 29889, 29886, 6321, 613, 525, 29893, 29890, 1495, 408, 285, 29901, 13, 29937, 1678, 5839, 280, 29889, 15070, 29898, 14968, 29918, 6979, 3950, 29889, 1742, 29918, 2248, 29892, 285, 29897, 13, 2277, 259, 29896, 29945, 29896, 29906, 29953, 29930, 29896, 29906, 29947, 13, 13, 13, 2277, 4299, 29889, 1202, 29898, 6026, 2580, 8497, 29898, 3317, 29918, 22100, 29892, 1590, 29918, 6229, 29892, 11105, 29918, 9171, 29922, 5574, 876, 13, 4299, 29889, 1202, 29898, 6026, 2580, 8497, 29898, 3317, 29918, 22100, 29892, 1590, 29918, 6229, 29892, 18177, 11759, 17987, 8497, 29918, 7915, 1402, 7945, 519, 29922, 8824, 29892, 11105, 29918, 9171, 29922, 5574, 876, 13, 2277, 29871, 31478, 30419, 29896, 29906, 29947, 29974, 29953, 29946, 30409, 29930, 29953, 29946, 29974, 29953, 29946, 31472, 29930, 29946, 13, 4299, 29889, 1202, 29898, 29931, 1254, 29924, 29898, 29953, 29946, 29892, 8865, 449, 29922, 29900, 29889, 29946, 29892, 1162, 1264, 29918, 8865, 449, 29922, 29900, 29889, 29946, 29892, 2457, 29918, 6831, 2063, 29922, 5574, 876, 13, 2277, 29871, 31478, 30419, 29953, 29946, 29974, 29941, 29906, 30409, 29930, 29941, 29906, 29974, 29941, 29906, 31472, 29930, 29946, 13, 4299, 29889, 1202, 29898, 29931, 1254, 29924, 29898, 29941, 29906, 29892, 8865, 449, 29922, 29900, 29889, 29945, 29892, 1162, 1264, 29918, 8865, 449, 29922, 29900, 29889, 29945, 29892, 2457, 29918, 6831, 2063, 29922, 8824, 876, 13, 13, 2277, 29871, 29941, 29906, 29930, 29945, 718, 29871, 29945, 13, 4299, 29889, 1202, 29898, 29928, 1947, 29898, 29945, 29892, 26229, 2433, 2695, 3317, 8785, 13, 4299, 29889, 12198, 29898, 6758, 2433, 29883, 20440, 936, 29918, 19128, 296, 14441, 742, 5994, 3950, 2433, 328, 314, 742, 21556, 29922, 1839, 562, 2764, 4135, 11287, 13, 13, 2158, 29898, 4299, 29889, 7727, 3101, 13, 4299, 29889, 9202, 29898, 1272, 29918, 14968, 29892, 1643, 29918, 14968, 29892, 16175, 29918, 2311, 29922, 16175, 29918, 2311, 29892, 1022, 2878, 29879, 29922, 1022, 2878, 29879, 29892, 18157, 29918, 1272, 7607, 1272, 29918, 1688, 29892, 1643, 29918, 1688, 876, 13, 13, 4299, 29889, 24219, 403, 29898, 1272, 29918, 1688, 29892, 3858, 29918, 1688, 29897, 13, 29937, 13, 29937, 2158, 703, 2962, 872, 369, 1243, 29240, 1159, 13, 29937, 29891, 29918, 11965, 29922, 4299, 29889, 27711, 29918, 13203, 29898, 1688, 29918, 1469, 29897, 13, 29937, 13, 29937, 1491, 29918, 1445, 353, 10518, 29889, 949, 29918, 7638, 29898, 4632, 718, 525, 11249, 4035, 6737, 29889, 7638, 742, 16345, 29922, 742, 1495, 13, 29937, 1491, 29918, 1445, 29889, 29903, 296, 2073, 29922, 29891, 29918, 11965, 13, 29937, 1491, 29918, 1445, 29889, 517, 29918, 7638, 877, 4035, 6737, 29918, 29906, 29889, 7638, 742, 2248, 29922, 8824, 29897, 13, 13, 4299, 29889, 7620, 11219, 5959, 29914, 492, 16099, 549, 10549, 29914, 20128, 29914, 29056, 29914, 29888, 566, 273, 29940, 13208, 29914, 4299, 29918, 29906, 29889, 29882, 29945, 1495, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 2 ]

Python3-Learn/even_odd_mayankgoyal.py

nikhiljain-413/Hacktoberfest2021_beginner

65

163595

""" Name : Even odd Author : [<NAME>) [https://github.com/mayankgoyal-13] """ num = int(input("Enter your number: ")) if num % 2 == 0: print('The given number is even') else: print('The given number is odd')

[ 1, 9995, 13, 1170, 584, 7753, 7736, 13, 13720, 584, 518, 29966, 5813, 12948, 518, 991, 597, 3292, 29889, 510, 29914, 13029, 804, 1484, 4605, 29899, 29896, 29941, 29962, 13, 15945, 29908, 13, 1949, 353, 938, 29898, 2080, 703, 10399, 596, 1353, 29901, 376, 876, 13, 361, 954, 1273, 29871, 29906, 1275, 29871, 29900, 29901, 13, 1678, 1596, 877, 1576, 2183, 1353, 338, 1584, 1495, 13, 2870, 29901, 13, 1678, 1596, 877, 1576, 2183, 1353, 338, 7736, 1495, 13, 2 ]

main.py

RowitZou/Lexicon-nn

134

64975

<gh_stars>100-1000 # -*- coding: utf-8 -*- # @Author: <NAME> # @Last Modified by: <NAME>, Contact: <EMAIL> import time import sys import argparse import random import torch import gc import pickle import os import torch.autograd as autograd import torch.optim as optim import numpy as np from utils.metric import get_ner_fmeasure from model.LGN import Graph from utils.data import Data def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def lr_decay(optimizer, epoch, decay_rate, init_lr): lr = init_lr * ((1-decay_rate)**epoch) print( " Learning rate is setted as:", lr) for param_group in optimizer.param_groups: if param_group['name'] == 'aggr': param_group['lr'] = lr * 2. else: param_group['lr'] = lr return optimizer def data_initialization(data, word_file, train_file, dev_file, test_file): data.build_word_file(word_file) if train_file: data.build_alphabet(train_file) data.build_word_alphabet(train_file) if dev_file: data.build_alphabet(dev_file) data.build_word_alphabet(dev_file) if test_file: data.build_alphabet(test_file) data.build_word_alphabet(test_file) return data def predict_check(pred_variable, gold_variable, mask_variable): pred = pred_variable.cpu().data.numpy() gold = gold_variable.cpu().data.numpy() mask = mask_variable.cpu().data.numpy() overlaped = (pred == gold) right_token = np.sum(overlaped * mask) total_token = mask.sum() return right_token, total_token def recover_label(pred_variable, gold_variable, mask_variable, label_alphabet): batch_size = gold_variable.size(0) seq_len = gold_variable.size(1) mask = mask_variable.cpu().data.numpy() pred_tag = pred_variable.cpu().data.numpy() gold_tag = gold_variable.cpu().data.numpy() pred_label = [] gold_label = [] for idx in range(batch_size): pred = [label_alphabet.get_instance(pred_tag[idx][idy]) for idy in range(seq_len) if mask[idx][idy] != 0] gold = [label_alphabet.get_instance(gold_tag[idx][idy]) for idy in range(seq_len) if mask[idx][idy] != 0] assert(len(pred)==len(gold)) pred_label.append(pred) gold_label.append(gold) return pred_label, gold_label def print_args(args): print("CONFIG SUMMARY:") print(" Batch size: %s" % (args.batch_size)) print(" If use GPU: %s" % (args.use_gpu)) print(" If use CRF: %s" % (args.use_crf)) print(" Epoch number: %s" % (args.num_epoch)) print(" Learning rate: %s" % (args.lr)) print(" L2 normalization rate: %s" % (args.weight_decay)) print(" If use edge embedding: %s" % (args.use_edge)) print(" If use global node: %s" % (args.use_global)) print(" Bidirectional digraph: %s" % (args.bidirectional)) print(" Update step number: %s" % (args.iters)) print(" Attention dropout rate: %s" % (args.tf_drop_rate)) print(" Embedding dropout rate: %s" % (args.emb_drop_rate)) print(" Hidden state dimension: %s" % (args.hidden_dim)) print(" Learning rate decay ratio: %s" % (args.lr_decay)) print(" Aggregation module dropout rate: %s" % (args.cell_drop_rate)) print(" Head number of attention: %s" % (args.num_head)) print(" Head dimension of attention: %s" % (args.head_dim)) print("CONFIG SUMMARY END.") sys.stdout.flush() def evaluate(data, args, model, name): if name == "train": instances = data.train_Ids elif name == "dev": instances = data.dev_Ids elif name == 'test': instances = data.test_Ids elif name == 'raw': instances = data.raw_Ids else: print("Error: wrong evaluate name,", name) exit(0) pred_results = [] gold_results = [] # set model in eval model model.eval() batch_size = args.batch_size start_time = time.time() train_num = len(instances) total_batch = train_num // batch_size + 1 for batch_id in range(total_batch): start = batch_id*batch_size end = (batch_id+1)*batch_size if end > train_num: end = train_num instance = instances[start:end] if not instance: continue word_list, batch_char, batch_label, mask = batchify_with_label(instance, args.use_gpu) _, tag_seq = model(word_list, batch_char, mask) pred_label, gold_label = recover_label(tag_seq, batch_label, mask, data.label_alphabet) pred_results += pred_label gold_results += gold_label decode_time = time.time() - start_time speed = len(instances) / decode_time acc, p, r, f = get_ner_fmeasure(gold_results, pred_results) return speed, acc, p, r, f, pred_results def batchify_with_label(input_batch_list, gpu): batch_size = len(input_batch_list) chars = [sent[0] for sent in input_batch_list] words = [sent[1] for sent in input_batch_list] labels = [sent[2] for sent in input_batch_list] sent_lengths = torch.LongTensor(list(map(len, chars))) max_sent_len = sent_lengths.max() char_seq_tensor = autograd.Variable(torch.zeros((batch_size, max_sent_len))).long() label_seq_tensor = autograd.Variable(torch.zeros((batch_size, max_sent_len))).long() mask = autograd.Variable(torch.zeros((batch_size, max_sent_len))).byte() for idx, (seq, label, seq_len) in enumerate(zip(chars, labels, sent_lengths)): char_seq_tensor[idx, :seq_len] = torch.LongTensor(seq) label_seq_tensor[idx, :seq_len] = torch.LongTensor(label) mask[idx, :seq_len] = torch.Tensor([1] * int(seq_len)) if gpu: char_seq_tensor = char_seq_tensor.cuda() label_seq_tensor = label_seq_tensor.cuda() mask = mask.cuda() return words, char_seq_tensor, label_seq_tensor, mask def train(data, args, saved_model_path): print( "Training model...") model = Graph(data, args) if args.use_gpu: model = model.cuda() print('# generated parameters:', sum(param.numel() for param in model.parameters())) print( "Finished built model.") best_dev_epoch = 0 best_dev_f = -1 best_dev_p = -1 best_dev_r = -1 best_test_f = -1 best_test_p = -1 best_test_r = -1 # Initialize the optimizer aggr_module_params = [] other_module_params = [] for m_name in model._modules: m = model._modules[m_name] if isinstance(m, torch.nn.ModuleList): for p in m.parameters(): if p.requires_grad: aggr_module_params.append(p) else: for p in m.parameters(): if p.requires_grad: other_module_params.append(p) optimizer = optim.Adam([ {"params": (aggr_module_params), "name": "aggr"}, {"params": (other_module_params), "name": "other"} ], lr=args.lr, weight_decay=args.weight_decay ) for idx in range(args.num_epoch): epoch_start = time.time() temp_start = epoch_start print(("Epoch: %s/%s" %(idx, args.num_epoch))) optimizer = lr_decay(optimizer, idx, args.lr_decay, args.lr) sample_loss = 0 batch_loss = 0 total_loss = 0 right_token = 0 whole_token = 0 random.shuffle(data.train_Ids) # set model in train model model.train() model.zero_grad() batch_size = args.batch_size train_num = len(data.train_Ids) total_batch = train_num // batch_size + 1 for batch_id in range(total_batch): # Get one batch-sized instance start = batch_id * batch_size end = (batch_id + 1) * batch_size if end > train_num: end = train_num instance = data.train_Ids[start:end] if not instance: continue word_list, batch_char, batch_label, mask = batchify_with_label(instance, args.use_gpu) loss, tag_seq = model(word_list, batch_char, mask, batch_label) right, whole = predict_check(tag_seq, batch_label, mask) right_token += right whole_token += whole sample_loss += loss.data total_loss += loss.data batch_loss += loss if end % 500 == 0: temp_time = time.time() temp_cost = temp_time - temp_start temp_start = temp_time print((" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f" % (end, temp_cost, sample_loss, right_token, whole_token, (right_token+0.)/whole_token))) sys.stdout.flush() sample_loss = 0 if end % args.batch_size == 0: batch_loss.backward() optimizer.step() model.zero_grad() batch_loss = 0 temp_time = time.time() temp_cost = temp_time - temp_start print((" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f" % (end, temp_cost, sample_loss, right_token, whole_token, (right_token+0.)/whole_token))) epoch_finish = time.time() epoch_cost = epoch_finish - epoch_start print(("Epoch: %s training finished. Time: %.2fs, speed: %.2fst/s, total loss: %s" % (idx, epoch_cost, train_num/epoch_cost, total_loss))) # dev speed, acc, dev_p, dev_r, dev_f, _ = evaluate(data, args, model, "dev") dev_finish = time.time() dev_cost = dev_finish - epoch_finish print(("Dev: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (dev_cost, speed, acc, dev_p, dev_r, dev_f))) # test speed, acc, test_p, test_r, test_f, _ = evaluate(data, args, model, "test") test_finish = time.time() test_cost = test_finish - dev_finish print(("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (test_cost, speed, acc, test_p, test_r, test_f))) if dev_f > best_dev_f: print("Exceed previous best f score: %.4f" % best_dev_f) torch.save(model.state_dict(), saved_model_path + "_best") best_dev_p = dev_p best_dev_r = dev_r best_dev_f = dev_f best_dev_epoch = idx + 1 best_test_p = test_p best_test_r = test_r best_test_f = test_f model_idx_path = saved_model_path + "_" + str(idx) torch.save(model.state_dict(), model_idx_path) with open(saved_model_path + "_result.txt", "a") as file: file.write(model_idx_path + '\n') file.write("Dev score: %.4f, r: %.4f, f: %.4f\n" % (dev_p, dev_r, dev_f)) file.write("Test score: %.4f, r: %.4f, f: %.4f\n\n" % (test_p, test_r, test_f)) file.close() print("Best dev epoch: %d" % best_dev_epoch) print("Best dev score: p: %.4f, r: %.4f, f: %.4f" % (best_dev_p, best_dev_r, best_dev_f)) print("Best test score: p: %.4f, r: %.4f, f: %.4f" % (best_test_p, best_test_r, best_test_f)) gc.collect() with open(saved_model_path + "_result.txt", "a") as file: file.write("Best epoch: %d" % best_dev_epoch + '\n') file.write("Best Dev score: %.4f, r: %.4f, f: %.4f\n" % (best_dev_p, best_dev_r, best_dev_f)) file.write("Test score: %.4f, r: %.4f, f: %.4f\n\n" % (best_test_p, best_test_r, best_test_f)) file.close() with open(saved_model_path + "_best_HP.config", "wb") as file: pickle.dump(args, file) def load_model_decode(model_dir, data, args, name): model_dir = model_dir + "_best" print("Load Model from file: ", model_dir) model = Graph(data, args) model.load_state_dict(torch.load(model_dir)) # load model need consider if the model trained in GPU and load in CPU, or vice versa if args.use_gpu: model = model.cuda() print(("Decode %s data ..." % name)) start_time = time.time() speed, acc, p, r, f, pred_results = evaluate(data, args, model, name) end_time = time.time() time_cost = end_time - start_time print(("%s: time:%.2fs, speed:%.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (name, time_cost, speed, acc, p, r, f))) return pred_results if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--status', choices=['train', 'test', 'decode'], help='Function status.', default='train') parser.add_argument('--use_gpu', type=str2bool, default=True) parser.add_argument('--train', help='Training set.', default='data/onto4ner.cn/train.char.bmes') parser.add_argument('--dev', help='Developing set.', default='data/onto4ner.cn/dev.char.bmes') parser.add_argument('--test', help='Testing set.', default='data/onto4ner.cn/test.char.bmes') parser.add_argument('--raw', help='Raw file for decoding.') parser.add_argument('--output', help='Output results for decoding.') parser.add_argument('--saved_set', help='Path of saved data set.', default='data/onto4ner.cn/saved.dset') parser.add_argument('--saved_model', help='Path of saved model.', default="saved_model/model_onto4ner") parser.add_argument('--char_emb', help='Path of character embedding file.', default="data/gigaword_chn.all.a2b.uni.ite50.vec") parser.add_argument('--word_emb', help='Path of word embedding file.', default="data/ctb.50d.vec") parser.add_argument('--use_crf', type=str2bool, default=True) parser.add_argument('--use_edge', type=str2bool, default=True, help='If use lexicon embeddings (edge embeddings).') parser.add_argument('--use_global', type=str2bool, default=True, help='If use the global node.') parser.add_argument('--bidirectional', type=str2bool, default=True, help='If use bidirectional digraph.') parser.add_argument('--seed', help='Random seed', default=1023, type=int) parser.add_argument('--batch_size', help='Batch size.', default=1, type=int) parser.add_argument('--num_epoch',default=100, type=int, help="Epoch number.") parser.add_argument('--iters', default=4, type=int, help='The number of Graph iterations.') parser.add_argument('--hidden_dim', default=50, type=int, help='Hidden state size.') parser.add_argument('--num_head', default=10, type=int, help='Number of transformer head.') parser.add_argument('--head_dim', default=20, type=int, help='Head dimension of transformer.') parser.add_argument('--tf_drop_rate', default=0.1, type=float, help='Transformer dropout rate.') parser.add_argument('--emb_drop_rate', default=0.5, type=float, help='Embedding dropout rate.') parser.add_argument('--cell_drop_rate', default=0.2, type=float, help='Aggregation module dropout rate.') parser.add_argument('--word_alphabet_size', type=int, help='Word alphabet size.') parser.add_argument('--char_alphabet_size', type=int, help='Char alphabet size.') parser.add_argument('--label_alphabet_size', type=int, help='Label alphabet size.') parser.add_argument('--char_dim', type=int, help='Char embedding size.') parser.add_argument('--word_dim', type=int, help='Word embedding size.') parser.add_argument('--lr', type=float, default=2e-05) parser.add_argument('--lr_decay', type=float, default=0) parser.add_argument('--weight_decay', type=float, default=0) args = parser.parse_args() status = args.status.lower() seed_num = args.seed random.seed(seed_num) torch.manual_seed(seed_num) np.random.seed(seed_num) train_file = args.train dev_file = args.dev test_file = args.test raw_file = args.raw output_file = args.output saved_set_path = args.saved_set saved_model_path = args.saved_model char_file = args.char_emb word_file = args.word_emb if status == 'train': if os.path.exists(saved_set_path): print('Loading saved data set...') with open(saved_set_path, 'rb') as f: data = pickle.load(f) else: data = Data() data_initialization(data, word_file, train_file, dev_file, test_file) data.generate_instance_with_words(train_file, 'train') data.generate_instance_with_words(dev_file, 'dev') data.generate_instance_with_words(test_file, 'test') data.build_char_pretrain_emb(char_file) data.build_word_pretrain_emb(word_file) if saved_set_path is not None: print('Dumping data...') with open(saved_set_path, 'wb') as f: pickle.dump(data, f) data.show_data_summary() args.word_alphabet_size = data.word_alphabet.size() args.char_alphabet_size = data.char_alphabet.size() args.label_alphabet_size = data.label_alphabet.size() args.char_dim = data.char_emb_dim args.word_dim = data.word_emb_dim print_args(args) train(data, args, saved_model_path) elif status == 'test': assert not (test_file is None) if os.path.exists(saved_set_path): print('Loading saved data set...') with open(saved_set_path, 'rb') as f: data = pickle.load(f) else: print("Cannot find saved data set: ", saved_set_path) exit(0) data.generate_instance_with_words(test_file, 'test') with open(saved_model_path + "_best_HP.config", "rb") as f: args = pickle.load(f) data.show_data_summary() print_args(args) load_model_decode(saved_model_path, data, args, "test") elif status == 'decode': assert not (raw_file is None or output_file is None) if os.path.exists(saved_set_path): print('Loading saved data set...') with open(saved_set_path, 'rb') as f: data = pickle.load(f) else: print("Cannot find saved data set: ", saved_set_path) exit(0) data.generate_instance_with_words(raw_file, 'raw') with open(saved_model_path + "_best_HP.config", "rb") as f: args = pickle.load(f) data.show_data_summary() print_args(args) decode_results = load_model_decode(saved_model_path, data, args, "raw") data.write_decoded_results(output_file, decode_results, 'raw') else: print("Invalid argument! Please use valid arguments! (train/test/decode)")

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29991, 313, 14968, 29914, 1688, 29914, 13808, 25760, 13, 2 ]

generate_character_pair_counts.py

Anima108/malwareAnalysis-midtermproject

0

133762

<gh_stars>0 #!/usr/bin/python ############################################### # This script computes char co-occurence within # alexa's top 1-million web domains. # The co-occurence counts, probability and # log(probability) and stored in a json doc. ############################################### import json import math file = 'alexa-top-1m.csv' def bigrams(input_list): return zip(input_list, input_list[1:]) pairs = {} line_count = 0 with open(file, 'r') as fi: for line in fi: # keep track of progress line_count += 1 # extract ranking arr = line.split(',') ranking = arr[0] # extract basedomain basedomain = arr[1].split('.')[0] # sometimes basedomain is made up of 2 or more words with hyphens between, we split them on the hyphen to obtain 'words' # e.g. my-malicious-domain.com, x-site.com etc. words = basedomain.split('-') # for each domain 'word' greater than 1 character # obtain character pairs for word in words: if len(word) > 1: b = bigrams(word) #combine bigram char pairs and maintain count for char_tuple in b: pair = char_tuple[0] + char_tuple[1] if pair in pairs: pairs[pair] += 1 else: pairs[pair] = 1 # now that we have pairs, compute sum of all pair combinations # for example: #if pairs['go'] = 4, pairs['im'] = 5, pairs['to'] = 9 then total_pairs = 18. total_pairs = sum(pairs.values()) print (total_pairs) # calculate probability for each pair of letters update the dict value with a dict of following format # we use logarithm base 10 because later we would need to multiple pairwise to obtain an overall probability # however, instead of multiplying small floats we add their logarithms as # log(xy) = log(x) + log(y) # # key = { count: 2000, # probability: 0.00008580898447093862, # log: -4.605170185988091 # } for k,v in pairs.items(): # probability = val / total_pairs probability = float(v) / total_pairs pairs[k] = { 'count': v, 'probability': probability, 'log': round(math.log10(probability), 5) } # finally add total_pairs to dict. useful to calculate pair counts later pairs['total_pairs'] = total_pairs j = json.dumps(pairs) with open('character_pair_probabilities.json', 'w') as fo: fo.write(j) exit()

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13, 12, 1181, 29889, 3539, 29898, 29926, 29897, 13, 13, 13322, 580, 13, 13, 2 ]

nasbench301/surrogate_models/bananas/bananas_src/darts/arch.py

Basvanstein/nasbench301

55

198419

import numpy as np import sys import os sys.path.append(os.path.expanduser('~/darts/cnn')) #from train_class import Train OPS = ['max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NUM_VERTICES = 4 INPUT_1 = 'c_k-2' INPUT_2 = 'c_k-1' class Arch: def __init__(self, arch): self.arch = arch def serialize(self): return self.arch def query(self, epochs=50): trainer = Train() val_losses, test_losses = trainer.main(self.arch, epochs=epochs) val_loss = 100 - np.mean(val_losses) test_loss = 100 - test_losses[-1] return val_loss, test_loss @classmethod def random_arch(cls): # output a uniformly random architecture spec # from the DARTS repository # https://github.com/quark0/darts normal = [] reduction = [] for i in range(NUM_VERTICES): ops = np.random.choice(range(len(OPS)), NUM_VERTICES) #input nodes for conv nodes_in_normal = np.random.choice(range(i+2), 2, replace=False) #input nodes for reduce nodes_in_reduce = np.random.choice(range(i+2), 2, replace=False) normal.extend([(nodes_in_normal[0], ops[0]), (nodes_in_normal[1], ops[1])]) reduction.extend([(nodes_in_reduce[0], ops[2]), (nodes_in_reduce[1], ops[3])]) return (normal, reduction) def mutate(self, edits): """ mutate a single arch """ # first convert tuple to array so that it is mutable mutation = [] for cell in self.arch: mutation.append([]) for pair in cell: mutation[-1].append([]) for num in pair: mutation[-1][-1].append(num) #make mutations for _ in range(edits): cell = np.random.choice(2) pair = np.random.choice(len(OPS)) num = np.random.choice(2) if num == 1: mutation[cell][pair][num] = np.random.choice(len(OPS)) else: inputs = pair // 2 + 2 choice = np.random.choice(inputs) if pair % 2 == 0 and mutation[cell][pair+1][num] != choice: mutation[cell][pair][num] = choice elif pair % 2 != 0 and mutation[cell][pair-1][num] != choice: mutation[cell][pair][num] = choice return mutation def get_paths(self): """ return all paths from input to output """ path_builder = [[[], [], [], []], [[], [], [], [], ]] paths = [[], []] for i, cell in enumerate(self.arch): for j in range(len(OPS)): if cell[j][0] == 0: path = [INPUT_1, OPS[cell[j][1]]] path_builder[i][j//2].append(path) paths[i].append(path) elif cell[j][0] == 1: path = [INPUT_2, OPS[cell[j][1]]] path_builder[i][j//2].append(path) paths[i].append(path) else: for path in path_builder[i][cell[j][0] - 2]: path = [*path, OPS[cell[j][1]]] path_builder[i][j//2].append(path) paths[i].append(path) # check if there are paths of length >=5 contains_long_path = [False, False] if max([len(path) for path in paths[0]]) >= 5: contains_long_path[0] = True if max([len(path) for path in paths[1]]) >= 5: contains_long_path[1] = True return paths, contains_long_path def get_path_indices(self, long_paths=True): """ compute the index of each path There are 4 * (8^0 + ... + 8^4) paths total If long_paths = False, we give a single boolean to all paths of size 4, so there are only 4 * (1 + 8^0 + ... + 8^3) paths """ paths, contains_long_path = self.get_paths() normal_paths, reduce_paths = paths num_ops = len(OPS) """ Compute the max number of paths per input per cell. Since there are two cells and two inputs per cell, total paths = 4 * max_paths """ if not long_paths: max_paths = 1 + sum([num_ops ** i for i in range(NUM_VERTICES)]) else: max_paths = sum([num_ops ** i for i in range(NUM_VERTICES + 1)]) path_indices = [] # set the base index based on the cell and the input for i, paths in enumerate((normal_paths, reduce_paths)): for path in paths: index = i * 2 * max_paths if path[0] == INPUT_2: index += max_paths # recursively compute the index of the path for j in range(NUM_VERTICES + 1): if j == len(path) - 1: path_indices.append(index) break elif j == (NUM_VERTICES - 1) and not long_paths: path_indices.append(2 * (i + 1) * max_paths - 1) break else: index += num_ops ** j * (OPS.index(path[j + 1]) + 1) return (tuple(path_indices), contains_long_path) def encode_paths(self, long_paths=True): # output one-hot encoding of paths path_indices, _ = self.get_path_indices(long_paths=long_paths) num_ops = len(OPS) if not long_paths: max_paths = 1 + sum([num_ops ** i for i in range(NUM_VERTICES)]) else: max_paths = sum([num_ops ** i for i in range(NUM_VERTICES + 1)]) path_encoding = np.zeros(4 * max_paths) for index in path_indices: path_encoding[index] = 1 return path_encoding def path_distance(self, other): # compute the distance between two architectures # by comparing their path encodings return np.sum(np.array(self.encode_paths() != np.array(other.encode_paths())))

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locale/pot/api/core/_autosummary/pyvista-Light-exponent-1.py

tkoyama010/pyvista-doc-translations

4

143373

<reponame>tkoyama010/pyvista-doc-translations # Plot three planes lit by three spotlights with exponents of 1, # 2 and 5. The one with the lowest exponent has the broadest # beam. # import pyvista as pv plotter = pv.Plotter(lighting='none') for offset, exponent in zip([0, 1.5, 3], [1, 2, 5]): _ = plotter.add_mesh(pv.Plane((offset, 0, 0)), color='white') light = pv.Light(position=(offset, 0, 0.1), focal_point=(offset, 0, 0)) light.exponent = exponent light.positional = True light.cone_angle = 80 plotter.add_light(light) plotter.view_xy() plotter.show()

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code/cleantools.py

MarkCHarris/ames_housing_regression

1

118199

<reponame>MarkCHarris/ames_housing_regression ############# DEPENDENCIES ############## import numpy as np import pandas as pd ############# TOOLS FOR EXAMINING DATA FRAMES ############## def info_frame(df): """ Given a DataFrame, gets the name, type, and number of NaN values for each column and stores this information in a new DataFrame. """ info = pd.DataFrame([[col, df[col].dtype, df[col].isna().sum()] for col in df], columns=['name', 'type', 'num_nans']) return info def count_nan(df): """ Given a DataFrame, gets the name of each column and the number of NaN values in it. Returns this information in a new DataFrame, sorted by number of NaN values. Leaves out any columns that have no NaN values. If no NaN values found, returns an empty DataFrame. """ null_counts = df.isna().sum().sort_values(ascending=False) i = 0 if null_counts[i] == 0: return pd.DataFrame(None) while i < len(null_counts): if null_counts[i] != 0: i += 1 else: return pd.DataFrame(zip(null_counts.index[:i], null_counts[:i])) return pd.DataFrame(zip(null_counts.index[:i], null_counts[:i])) def find_non_numeric(in_col): """ Finds each unique entry in an iterable that cannot be typecast as float and returns a list of those entries. """ non_nums = [] for item in in_col: try: float(item) except: if item not in non_nums: non_nums.append(item) return non_nums ############# TOOLS FOR MODIFYING DATA FRAMES ############## def df_cols_to_snake_case(df): """ Given a DataFrame, converts column titles from separate words to snake_case. Will not work on CamelCase. """ return df.rename(mapper={col : col.lower().replace(' ', '_') for col in df.columns}, axis=1) def convert_to_float(in_item, non_numeric_fill=np.nan): """ Converts any input that can be typecast as numeric input to a float. non_numeric_fill specifies what to return if the input cannot be cast as float. Default np.nan. """ try: return float(in_item) except: return non_numeric_fill

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utils/models/densevoxel/parts.py

bhklab/ptl-oar-segmentation

3

77215

import torch import torch.nn as nn # from torchsummary import summary # from lib.medzoo.BaseModelClass import BaseModel """ Implementation od DenseVoxelNet based on https://arxiv.org/abs/1708.00573 Hyperparameters used: batch size = 3 weight decay = 0.0005 momentum = 0.9 lr = 0.05 """ def init_weights(m): """ The weights were randomly initialized with a Gaussian distribution (µ = 0, σ = 0.01) """ torch.seed(777) # for reproducibility classname = m.__class__.__name__ if classname.find('Conv') != -1 or classname.find('BatchNorm') != -1: m.weight.data.normal_(0.00, 0.01) class DenseLayer(nn.Sequential): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate=0.2): super(DenseLayer, self).__init__() self.add_module('norm1', nn.BatchNorm3d(num_input_features)), self.add_module('relu1', nn.LeakyReLU(0.1, inplace=True)), self.add_module('conv1', nn.Conv3d(num_input_features, bn_size * growth_rate, kernel_size=3, stride=1, padding=1, bias=False)), self.drop_rate = drop_rate if self.drop_rate > 0: self.drop_layer = nn.Dropout(p=self.drop_rate) def forward(self, x): new_features = super(DenseLayer, self).forward(x) if self.drop_rate > 0: new_features = self.drop_layer(new_features) return torch.cat([x, new_features], 1) class DenseBlock(nn.Sequential): """ to keep the spatial dims o=i, this formula is applied o = [i + 2*p - k - (k-1)*(d-1)]/s + 1 """ def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate=0.2): super(DenseBlock, self).__init__() for i in range(num_layers): layer = DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate) self.add_module('denselayer%d' % (i + 1), layer) class Transition(nn.Module): def __init__(self, num_input_features, num_output_features): super(Transition, self).__init__() norm = nn.BatchNorm3d(num_input_features) relu = nn.LeakyReLU(0.1, inplace=True) conv3d = nn.Conv3d(num_input_features, num_output_features, kernel_size=1, padding=0, stride=1) self.conv = nn.Sequential(norm, relu, conv3d) self.max_pool = nn.MaxPool3d(kernel_size=2, stride=2) def forward(self, x): k = self.conv(x) y = self.max_pool(k) return y, k class Upsampling(nn.Sequential): """ For transpose conv o = output, p = padding, k = kernel_size, s = stride, d = dilation o = (i -1)*s - 2*p + k + output_padding = (i-1)*2 +2 = 2*i """ def __init__(self, input_features, out_features): super(Upsampling, self).__init__() self.tr_conv1_features = 128 # defined in the paper self.tr_conv2_features = out_features self.add_module('norm', nn.BatchNorm3d(input_features)) self.add_module('relu', nn.LeakyReLU(0.1, inplace=True)) self.add_module('conv', nn.Conv3d(input_features, input_features, kernel_size=1, stride=1, padding=0, bias=False)) # Transposed convolutions must be un-padded? self.add_module('transp_conv_1', nn.ConvTranspose3d(input_features, self.tr_conv1_features, kernel_size=2, padding=0, output_padding=0, stride=2)) self.add_module('transp_conv_2', nn.ConvTranspose3d(self.tr_conv1_features, self.tr_conv2_features, kernel_size=2, padding=0, output_padding=0, stride=2))

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werckercli/tests/test_metrics.py

wercker/DEPRECATED-wercker-cli

1

92829

import mock from mock import ANY import requests from werckercli.metrics import track_application_startup, default_command_name from werckercli.tests import TestCase track_command_usage_path = "werckercli.metrics.track_command_usage" class MetricsTests(TestCase): def test_track_application_startup_fails_silently_on_ConnectionError(self): err = requests.ConnectionError() the_method = mock.Mock(side_effect=err) with mock.patch(track_command_usage_path, the_method) as puts: try: track_application_startup() except requests.ConnectionError: self.fail("track_application_startup didn't fail silently") def test_track_application_startup_calls_track_command_usage(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: the_method.assert_called_once() @mock.patch("sys.argv", ['main.py']) def test_track_application_startup_uses_default_command_on_empty_arg(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() the_method.assert_called_with(default_command_name, ANY) @mock.patch("sys.argv", ['main.py', 'validate']) def test_track_application_startup_passes_command_from_sys_args(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() the_method.assert_called_with('validate', ANY) @mock.patch("sys.argv", [None, 'validate']) def test_track_application_startup_handles_missing_script_arg(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() @mock.patch("sys.argv", [None, 'validate']) def test_track_application_startup_handles_missing_script_arg(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() @mock.patch("sys.argv", [None, 'validate', 'foo', 'bar']) def test_track_application_startup_passes_args(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() expected_args = ['foo', 'bar'] the_method.assert_called_with(ANY, expected_args) @mock.patch("sys.argv", [None, 'validate']) def test_track_application_startup_passes_None_for_missing_arguments(self): the_method = mock.Mock() with mock.patch(track_command_usage_path, the_method) as puts: track_application_startup() expected_args = None the_method.assert_called_with(ANY, expected_args)

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selfdrive/can/can_define.py

matthewklinko/openpilot

4

141582

<gh_stars>1-10 from collections import defaultdict from selfdrive.can.libdbc_py import libdbc, ffi class CANDefine(object): def __init__(self, dbc_name): self.dv = defaultdict(dict) self.dbc_name = dbc_name self.dbc = libdbc.dbc_lookup(dbc_name) num_vals = self.dbc[0].num_vals self.address_to_msg_name = {} num_msgs = self.dbc[0].num_msgs for i in range(num_msgs): msg = self.dbc[0].msgs[i] name = ffi.string(msg.name) address = msg.address self.address_to_msg_name[address] = name for i in range(num_vals): val = self.dbc[0].vals[i] sgname = ffi.string(val.name) address = val.address def_val = ffi.string(val.def_val) #separate definition/value pairs def_val = def_val.split() values = [int(v) for v in def_val[::2]] defs = def_val[1::2] if address not in self.dv: self.dv[address] = {} msgname = self.address_to_msg_name[address] self.dv[msgname] = {} # two ways to lookup: address or msg name self.dv[address][sgname] = {v: d for v, d in zip(values, defs)} #build dict self.dv[msgname][sgname] = self.dv[address][sgname]

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graphics/ground.py

bdemin/M113_Visualization

0

68962

<reponame>bdemin/M113_Visualization import numpy as np from vtk import vtkLookupTable, vtkUnsignedCharArray, vtkNamedColors, \ vtkCellLocator, vtkPoints, mutable, vtkParametricSpline, vtkParametricFunctionSource, \ vtkPolyDataMapper, vtkActor def visualize_elevation(PolyData): # Create colormap bounds = 6*[0.0] PolyData.GetBounds(bounds) # Find min and max z minz = bounds[4] maxz = bounds[5] colorLookupTable = vtkLookupTable() colorLookupTable.SetTableRange(minz, maxz) colorLookupTable.Build() # Generate the colors for each point based on elevation colors = vtkUnsignedCharArray() colors.SetNumberOfComponents(3) colors.SetName("Colors") for i in range(0, PolyData.GetNumberOfPoints()): p= 3*[0.0] PolyData.GetPoint(i,p) dcolor = 3*[0.0] colorLookupTable.GetColor(p[2], dcolor) color=3*[0.0] for j in range(0,3): color[j] = int(255.0 * dcolor[j]) colors.InsertNextTypedTuple(color) # Assign colors to the vtkPolyData PolyData.GetPointData().SetScalars(colors) def get_soil_color(soil_type_key, colors_dict): # Function that returns the defined color for a specific soil type if soil_type_key in colors_dict: return colors_dict[soil_type_key] return (255,255,255) # If undefined, return white color def visualize_soil(PolyData, soil_type_array): # Assign colors to the ground based on input soil type array # Create a color dictionary colors = vtkNamedColors() colors_dict = { 0: colors.HTMLColorToRGB('DarkKhaki'), 1: colors.HTMLColorToRGB('Wheat'), 2: colors.HTMLColorToRGB('BurlyWood'), 3: colors.HTMLColorToRGB('SandyBrown'), 4: colors.HTMLColorToRGB('Khaki'), 5: colors.HTMLColorToRGB('DarkOliveGreen'), 6: colors.HTMLColorToRGB('DarkGreen')} m, n = soil_type_array.shape soil_color_map = vtkUnsignedCharArray() soil_color_map.SetNumberOfComponents(3) soil_color_map.SetName("Colors") for i in range(m-1): for j in range(n-1): for _ in range(6): soil_color_map.InsertNextTypedTuple(get_soil_color(soil_type_array[i,j], colors_dict)) # Assign colors to the vtkPolyData PolyData.GetPointData().SetScalars(soil_color_map) def generate_perlin_noise_2d(size, res): # Return Perlin Noise def factors(n): from functools import reduce return list(reduce(list.__add__, ([i, n//i] for i in range(1, int(n**0.5) + 1) if n % i == 0))) def f(t): return 6*t**5 - 15*t**4 + 10*t**3 m_factors = sorted(factors(size[0])) n_factors = sorted(factors(size[1])) m_mid_index = (len(m_factors) - 1) // 2 n_mid_index = (len(n_factors) - 1) // 2 res = (m_factors[m_mid_index], n_factors[n_mid_index]) delta = (res[0] / size[0], res[1] / size[1]) d = (size[0] // res[0], size[1] // res[1]) grid = np.mgrid[0:res[0]:delta[0],0:res[1]:delta[1]].transpose(1, 2, 0) % 1 # Gradients angles = 2*np.pi*np.random.rand(res[0]+1, res[1]+1) gradients = np.dstack((np.cos(angles), np.sin(angles))) g00 = gradients[0:-1,0:-1].repeat(d[0], 0).repeat(d[1], 1) g10 = gradients[1: ,0:-1].repeat(d[0], 0).repeat(d[1], 1) g01 = gradients[0:-1,1: ].repeat(d[0], 0).repeat(d[1], 1) g11 = gradients[1: ,1: ].repeat(d[0], 0).repeat(d[1], 1) # Ramps n00 = np.sum(np.dstack((grid[:,:,0] , grid[:,:,1] )) * g00, 2) n10 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1] )) * g10, 2) n01 = np.sum(np.dstack((grid[:,:,0] , grid[:,:,1]-1)) * g01, 2) n11 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]-1)) * g11, 2) # Interpolation t = f(grid) n0 = n00*(1-t[:,:,0]) + t[:,:,0]*n10 n1 = n01*(1-t[:,:,0]) + t[:,:,0]*n11 return np.sqrt(2)*((1-t[:,:,1])*n0 + t[:,:,1]*n1) def perlin2D(m): from scipy.interpolate import interp2d s = np.zeros((m,m)) # Prepare output image (size: m x m) w = m i = 0 while w > 3: i = i + 1 # d = interp2(randn([m,m]), i-1, 'spline') x = i y = i z = np.random.randn(m,m) d = interp2d(x,y,z, kind='spline') s = s + i * d[1:m, 1:m] w = w - np.ceil(w/2 - 1) s = (s - min(min(s[:,:]))) / (max(max(s[:,:])) - min(min(s[:,:]))) return s def create_soil_type_arr(size): # Create an array of different soil types scatterd randomly using Perlin Noise np.random.seed(1) noise = generate_perlin_noise_2d((size), (10, 10)) # Get perlin noise soil_type_arr = np.zeros(size) num_soil_types = 3 # Define how many soil types (colors) should be set arr_min = noise.min() soil_type_step = (noise.max() - arr_min)/num_soil_types # Fill array with different soil types for k in range(num_soil_types): for i in range(size[0]): for j in range(size[1]): if (noise[i,j] <= arr_min + (k+1)*soil_type_step) and (arr_min + k*soil_type_step <= noise[i,j]): soil_type_arr[i,j] = k return soil_type_arr def get_spline_actor(surface_data, chassis_cg_path, surface_bounds): # Iterate over chassis CG points and create a spline which marks the driving path. # Return the spline as a vtkActor for being added later to the renderer. # Update the pipeline so that vtkCellLocator finds cells surface_data.Update() # Define a cellLocator to be able to compute intersections between lines # and the surface locator = vtkCellLocator() locator.SetDataSet(surface_data.GetOutput()) locator.BuildLocator() tolerance = 0.01 # Set intersection searching tolerance # Make a list of points. Each point is the intersection of a vertical line # defined by p1 and p2 and the surface. points = vtkPoints() for chassis_cg in chassis_cg_path: p1 = [chassis_cg[0], chassis_cg[1], surface_bounds[4]] p2 = [chassis_cg[0], chassis_cg[1], surface_bounds[5]] t = mutable(0) pos = [0.0, 0.0, 0.0] pcoords = [0.0, 0.0, 0.0] subId = mutable(0) locator.IntersectWithLine(p1, p2, tolerance, t, pos, pcoords, subId) # Add a slight offset in z pos[2] += 0.05 # Add the x, y, z position of the intersection points.InsertNextPoint(pos) # Create a spline and add the pointsoi spline = vtkParametricSpline() spline.SetPoints(points) spline_function = vtkParametricFunctionSource() spline_function.SetUResolution(len(chassis_cg_path)) spline_function.SetParametricFunction(spline) # Map the spline spline_mapper = vtkPolyDataMapper() spline_mapper.SetInputConnection(spline_function.GetOutputPort()) # Define the line actor spline_actor = vtkActor() spline_actor.SetMapper(spline_mapper) spline_actor.GetProperty().SetColor([0,0.7,0]) spline_actor.GetProperty().SetLineWidth(10) return spline_actor

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what_can_i_cook/urls.py

s-maibuecher/what_can_i_cook

0

1318

from django.urls import path from what_can_i_cook.views import WCICFilterView, WCICResultView app_name = "wcic" urlpatterns = [ path("", WCICFilterView.as_view(), name="wcic-start"), path("results/", WCICResultView.as_view(), name="wcic-results"), ]

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tests/functional/CrudTests.py

idle-code/ContextShell

0

141475

<gh_stars>0 import unittest from tests.functional.ShellTestsBase import NodeTreeTestsBase from tests.functional.TestExecutor import script_test class CrudTestsBase(NodeTreeTestsBase): pass class CreateTests(CrudTestsBase): @script_test def test_create(self): """ $ .: create foo $ .: contains foo True """ @script_test def test_create_many_parts(self): """ $ .: create foo.bar $ .: contains foo.bar True """ @unittest.skip("Check when type system will be working") @script_test def test_create_int_type(self): """ $ .: create.int i 3 $ .i: get 3 """ class ContainsTests(CrudTestsBase): @script_test def test_nonexistent(self): """ $ .: contains unknown False """ class GetTests(CrudTestsBase): @script_test def test_get_existing(self): """ $ .: create foo 1 $ .foo: get 1 """ @script_test def test_get_nonexistent(self): """ $ .foo: get NameError: '.foo' doesn't exists """ class SetTests(CrudTestsBase): @script_test def test_set_existing(self): """ $ .: create foo 1 $ .foo: set 2 $ .foo: get 2 """ @script_test def test_set_nonexistent(self): """ $ .foo: set 1 NameError: '.foo' doesn't exists """ @script_test def test_set_no_new_value(self): """ $ .: create foo 1 $ .foo: set TypeError: set_action() missing 1 required positional argument: 'new_value' """ @script_test def test_set_different_type(self): """ $ .: create foo 2 $ .foo: set "rabarbar" TypeError: Cannot assign value with type 'str' to 'int' node """ class ListTests(CrudTestsBase): @script_test def test_list_empty(self): """ $ .: create foo $ .foo: list """ @script_test def test_list_in_creation_order(self): """ $ .: create foo.Z_first $ .: create foo.A_second $ .foo: list Z_first A_second """ @script_test def test_list_only_attributes(self): """ $ .: create .test.@attr $ .: create .test.key $ .test: list.attributes @attr """ @script_test def test_list_all(self): """ $ .: create .test.@attr $ .: create .test.key $ .test: list.all @attr key """ @script_test def test_list_only_normal(self): """ $ .: create .test.@attr $ .: create .test.key $ .test: list key """ @script_test def test_list_actions(self): """ $ .: list.actions create contains get set list remove find """ class RemoveTests(CrudTestsBase): @script_test def test_remove_existing(self): """ $ .: create foo $ .foo: remove $ .: contains foo False """ @script_test def test_remove_nonexistent(self): """ $ .foo: remove NameError: '.foo' doesn't exists """

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app/web/book.py

Marticles/BookCrossing

2

102368

from flask import jsonify,request ,current_app, url_for, render_template, flash from app.view_models.trade import TradeInfo from app.forms.book import SearchForm from app.libs.helper import is_isbn_or_key from app.models.shupiao_book import ShuPiaoBook from app.view_models.book import BookViewModel,BookCollection from . import web import json from flask_login import current_user from app.models.gift import Gift from app.models.wish import Wish @web.route('/book/search/') def search(): """ q:普通关键字orISBN号 page """ form = SearchForm(request.args) books = BookCollection() #参数校验 if form.validate(): q = form.q.data.strip() page = form.page.data isbn_or_key = is_isbn_or_key(q) shupiao_book = ShuPiaoBook() if isbn_or_key == 'isbn': shupiao_book.search_by_isbn(q) else: shupiao_book.search_by_keyword(q,page) #dict转str books.fill(shupiao_book,q) #return json.dumps(books, default = lambda o: o.__dict__) #return json.dumps(books),200,{'content-type':'application/json'} else : flash('搜索关键字错误') #return jsonify(form.errors) return render_template('search_result.html',books=books,form=form) @web.route('/book/<isbn>/detail') def book_detail(isbn): has_in_gifts = False has_in_wishes = False # 取书籍详情数据 shupiao_book = ShuPiaoBook() shupiao_book.search_by_isbn(isbn) book = BookViewModel(shupiao_book.first) if current_user.is_authenticated: if Gift.query.filter_by(uid=current_user.id, isbn=isbn, launched=False).first(): has_in_gifts = True if Wish.query.filter_by(uid=current_user.id, isbn=isbn, launched=False).first(): has_in_wishes = True trade_gifts = Gift.query.filter_by(isbn=isbn, launched=False).all() trade_wishes = Wish.query.filter_by(isbn=isbn, launched=False).all() trade_gifts_model = TradeInfo(trade_gifts) trade_wishes_model = TradeInfo(trade_wishes) return render_template('book_detail.html', book=book, wishes=trade_wishes_model, gifts=trade_gifts_model, has_in_wishes=has_in_wishes, has_in_gifts=has_in_gifts) ''' @web.route('/hello') def hello(): return 'hello' ''' ''' @web.route('/test') def test(): r = { 'name':'XXXX', 'age':'18' } flash('[q,a,c]',category = 'q') flash('aaaaaa',category = 'a') return render_template('test.html',data =r) '''

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sort-array-by-parity-ii/sort-array-by-parity-ii.py

Atri10/Leet-code---Atri_Patel

1

1608496

<gh_stars>1-10 class Solution: def sortArrayByParityII(self, nums: List[int]) -> List[int]: ans = [] even = 0 odd = 1 for i in nums: if i%2==0:ans.insert(even,i);even +=2 else:ans.insert(odd,i);odd +=2 return ans

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aoc/2015/p18-2.py

neizod/problems

1

1609200

<gh_stars>1-10 #!/usr/bin/env python3 def fix_corner(grid): for y in [0, -1]: for x in [0, -1]: grid[y][x] = True def surround(grid, x, y): for dy in [-1, 0, 1]: for dx in [-1, 0, 1]: if (dx, dy) == (0, 0): continue if 0 <= x+dx < len(grid[0]) and 0 <= y+dy < len(grid): yield grid[y+dy][x+dx] def increment(grid): new_grid = [[None for _ in range(len(grid[0]))] for _ in range(len(grid))] for y, line in enumerate(grid): for x, current in enumerate(line): if current: new_grid[y][x] = sum(surround(grid, x, y)) in {2, 3} else: new_grid[y][x] = sum(surround(grid, x, y)) == 3 fix_corner(new_grid) return new_grid def main(): steps = 100 grid = [[c == '#' for c in input().strip()] for _ in range(100)] fix_corner(grid) for i in range(steps): grid = increment(grid) print(sum(i for line in grid for i in line)) if __name__ == '__main__': main()

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edb/edgeql/compiler/inference/cardinality.py

ambv/edgedb

4

179926

# # This source file is part of the EdgeDB open source project. # # Copyright 2008-present MagicStack Inc. and the EdgeDB authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import annotations from typing import * import enum import functools from edb import errors from edb.edgeql import qltypes from edb.schema import objtypes as s_objtypes from edb.schema import pointers as s_pointers from edb.ir import ast as irast from edb.ir import utils as irutils from .. import context if TYPE_CHECKING: from edb.schema import constraints as s_constr AT_MOST_ONE = qltypes.Cardinality.AT_MOST_ONE ONE = qltypes.Cardinality.ONE MANY = qltypes.Cardinality.MANY AT_LEAST_ONE = qltypes.Cardinality.AT_LEAST_ONE class CardinalityBound(int, enum.Enum): '''This enum is used to perform some of the cardinality operations.''' ZERO = 0 ONE = 1 MANY = 2 def as_required(self) -> bool: return self is CB_ONE def as_schema_cardinality(self) -> qltypes.SchemaCardinality: if self is CB_MANY: return qltypes.SchemaCardinality.MANY else: return qltypes.SchemaCardinality.ONE @classmethod def from_required(cls, required: bool) -> CardinalityBound: return CB_ONE if required else CB_ZERO @classmethod def from_schema_value( cls, card: qltypes.SchemaCardinality ) -> CardinalityBound: if card is qltypes.SchemaCardinality.MANY: return CB_MANY else: return CB_ONE CB_ZERO = CardinalityBound.ZERO CB_ONE = CardinalityBound.ONE CB_MANY = CardinalityBound.MANY def _card_to_bounds( card: qltypes.Cardinality ) -> Tuple[CardinalityBound, CardinalityBound]: lower, upper = card.to_schema_value() return ( CardinalityBound.from_required(lower), CardinalityBound.from_schema_value(upper), ) def _bounds_to_card( lower: CardinalityBound, upper: CardinalityBound, ) -> qltypes.Cardinality: return qltypes.Cardinality.from_schema_value( lower.as_required(), upper.as_schema_cardinality(), ) def _get_set_scope( ir_set: irast.Set, scope_tree: irast.ScopeTreeNode) -> irast.ScopeTreeNode: if ir_set.path_scope_id: new_scope = scope_tree.root.find_by_unique_id(ir_set.path_scope_id) if new_scope is None: raise errors.InternalServerError( f'dangling scope pointer to node with uid' f':{ir_set.path_scope_id} in {ir_set!r}' ) else: new_scope = scope_tree return new_scope def cartesian_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Cardinality of Cartesian product of multiple args.''' card = list(zip(*(_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card(min(lower), max(upper)) else: # no args is indicative of a empty set return AT_MOST_ONE def max_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Maximum lower and upper bound of specified cardinalities.''' card = list(zip(*(_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card(max(lower), max(upper)) else: # no args is indicative of a empty set return AT_MOST_ONE def _union_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Cardinality of UNION of multiple args.''' card = list(zip(*(_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card( max(lower), CB_MANY if len(upper) > 1 else upper[0], ) else: # no args is indicative of a empty set return AT_MOST_ONE def _coalesce_cardinality( args: Iterable[qltypes.Cardinality], ) -> qltypes.Cardinality: '''Cardinality of ?? of multiple args.''' card = list(zip(*(_card_to_bounds(a) for a in args))) if card: lower, upper = card return _bounds_to_card(max(lower), max(upper)) else: # no args is indicative of a empty set return AT_MOST_ONE def _common_cardinality( args: Iterable[irast.Base], scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return cartesian_cardinality( infer_cardinality( a, scope_tree=scope_tree, singletons=singletons, env=env ) for a in args ) @functools.singledispatch def _infer_cardinality( ir: irast.Expr, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: raise ValueError(f'infer_cardinality: cannot handle {ir!r}') @_infer_cardinality.register def __infer_none( ir: None, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # Here for debugging purposes. raise ValueError('invalid infer_cardinality(None, schema) call') @_infer_cardinality.register def __infer_statement( ir: irast.Statement, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.expr, scope_tree=scope_tree, singletons=singletons, env=env) @_infer_cardinality.register def __infer_config_insert( ir: irast.ConfigInsert, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.expr, scope_tree=scope_tree, singletons=singletons, env=env) @_infer_cardinality.register def __infer_emptyset( ir: irast.EmptySet, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return AT_MOST_ONE @_infer_cardinality.register def __infer_typeref( ir: irast.TypeRef, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return AT_MOST_ONE @_infer_cardinality.register def __infer_type_introspection( ir: irast.TypeIntrospection, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE def _find_visible( ir: irast.Set, scope_tree: irast.ScopeTreeNode, env: context.Environment, ) -> Optional[irast.ScopeTreeNode]: parent_fence = scope_tree.parent_fence if parent_fence is not None: if scope_tree.namespaces: path_id = ir.path_id.strip_namespace(scope_tree.namespaces) else: path_id = ir.path_id return parent_fence.find_visible(path_id) else: return None @_infer_cardinality.register def __infer_set( ir: irast.Set, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.path_id in singletons: return ONE if (node := _find_visible(ir, scope_tree, env)) is not None: return AT_MOST_ONE if node.optional else ONE rptr = ir.rptr if rptr is not None: rptrref = rptr.ptrref if isinstance(rptrref, irast.TypeIntersectionPointerRef): ind_prefix, ind_ptrs = irutils.collapse_type_intersection(ir) new_scope = _get_set_scope(ir, scope_tree) if ind_prefix.rptr is None: prefix_card = infer_cardinality( ind_prefix, scope_tree=new_scope, singletons=singletons, env=env, ) return cartesian_cardinality([prefix_card, AT_MOST_ONE]) else: # Expression before type intersection is a path, # i.e Foo.<bar[IS Type]. In this case we must # take possible intersection specialization of the # link union into account. # We're basically restating the body of this function # in this block, but with extra conditions. if _find_visible(ind_prefix, new_scope, env) is not None: return AT_MOST_ONE else: rptr_spec: Set[irast.PointerRef] = set() for ind_ptr in ind_ptrs: rptr_spec.update(ind_ptr.ptrref.rptr_specialization) rptr_spec_card = _union_cardinality( s.dir_cardinality for s in rptr_spec) base_card = infer_cardinality( rptr.source, scope_tree=new_scope, singletons=singletons, env=env, ) # The resulting cardinality is the cartesian # product of the base to which the type # intersection is applied and the cardinality due # to type intersection itself. return cartesian_cardinality([base_card, rptr_spec_card]) else: if rptrref.union_components: # We use cartesian cardinality instead of union cardinality # because the union of pointers in this context is disjoint # in a sense that for any specific source only a given union # component is used. rptrref_card = cartesian_cardinality( c.dir_cardinality for c in rptrref.union_components ) else: rptrref_card = rptrref.dir_cardinality if rptrref_card.is_single(): new_scope = _get_set_scope(ir, scope_tree) source_card = infer_cardinality( rptr.source, scope_tree=new_scope, singletons=singletons, env=env, ) return cartesian_cardinality((source_card, rptrref_card)) else: return MANY elif ir.expr is not None: new_scope = _get_set_scope(ir, scope_tree) return infer_cardinality( ir.expr, scope_tree=new_scope, singletons=singletons, env=env, ) else: return MANY @_infer_cardinality.register def __infer_func_call( ir: irast.FunctionCall, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # the cardinality of the function call depends on the cardinality # of non-SET_OF arguments AND the cardinality of the function # return value SET_OF = qltypes.TypeModifier.SET_OF if ir.typemod is SET_OF: return MANY else: args = [] # process positional args for arg, typemod in zip(ir.args, ir.params_typemods): if typemod is not SET_OF: args.append(arg.expr) if args: return _common_cardinality( args, scope_tree=scope_tree, singletons=singletons, env=env, ) else: if ir.typemod is qltypes.TypeModifier.OPTIONAL: return AT_MOST_ONE else: return ONE @_infer_cardinality.register def __infer_oper_call( ir: irast.OperatorCall, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.func_shortname == 'std::UNION': # UNION needs to "add up" cardinalities. return _union_cardinality( infer_cardinality( a.expr, scope_tree=scope_tree, singletons=singletons, env=env ) for a in ir.args ) elif ir.func_shortname == 'std::??': # Coalescing takes the maximum of both lower and upper bounds. return _coalesce_cardinality( infer_cardinality( a.expr, scope_tree=scope_tree, singletons=singletons, env=env ) for a in ir.args ) else: args: List[irast.Base] = [] all_optional = False if ir.typemod is qltypes.TypeModifier.SET_OF: # this is DISTINCT and IF..ELSE args = [a.expr for a in ir.args] else: all_optional = True for arg, typemod in zip(ir.args, ir.params_typemods): if typemod is not qltypes.TypeModifier.SET_OF: all_optional &= typemod is qltypes.TypeModifier.OPTIONAL args.append(arg.expr) if args: card = _common_cardinality( args, scope_tree=scope_tree, singletons=singletons, env=env, ) if all_optional: # An operator that has all optional arguments and # doesn't return a SET OF returns at least ONE result # (we currently don't have operators that return # OPTIONAL). So we upgrade the lower bound. _, upper = _card_to_bounds(card) card = _bounds_to_card(CB_ONE, upper) return card else: return AT_MOST_ONE @_infer_cardinality.register def __infer_const( ir: irast.BaseConstant, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE @_infer_cardinality.register def __infer_param( ir: irast.Parameter, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE if ir.required else AT_MOST_ONE @_infer_cardinality.register def __infer_const_set( ir: irast.ConstantSet, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return ONE if len(ir.elements) == 1 else AT_LEAST_ONE @_infer_cardinality.register def __infer_typecheckop( ir: irast.TypeCheckOp, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.left, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_typecast( ir: irast.TypeCast, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return infer_cardinality( ir.expr, scope_tree=scope_tree, singletons=singletons, env=env, ) def _is_ptr_or_self_ref( ir_expr: irast.Base, result_expr: irast.Set, env: context.Environment, ) -> bool: if not isinstance(ir_expr, irast.Set): return False else: ir_set = ir_expr srccls = env.set_types[result_expr] return ( isinstance(srccls, s_objtypes.ObjectType) and ir_set.expr is None and (env.set_types[ir_set] == srccls or ( ir_set.rptr is not None and srccls.getptr( env.schema, ir_set.rptr.ptrref.shortname.name) is not None )) ) def extract_filters( result_set: irast.Set, filter_set: irast.Set, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, *, # When strict=False, ignore any clauses in the filter_set we don't # care about. If True, return None if any exist. strict: bool = False, ) -> Optional[Sequence[Tuple[s_pointers.Pointer, irast.Set]]]: schema = env.schema scope_tree = _get_set_scope(filter_set, scope_tree) ptr: s_pointers.Pointer expr = filter_set.expr if isinstance(expr, irast.OperatorCall): if expr.func_shortname == 'std::=': left, right = (a.expr for a in expr.args) op_card = _common_cardinality( [left, right], scope_tree=scope_tree, singletons=singletons, env=env, ) result_stype = env.set_types[result_set] if op_card.is_multi(): pass elif _is_ptr_or_self_ref(left, result_set, env): if infer_cardinality( right, scope_tree=scope_tree, singletons=singletons, env=env, ).is_single(): left_stype = env.set_types[left] if left_stype == result_stype: assert isinstance(left_stype, s_objtypes.ObjectType) _ptr = left_stype.getptr(schema, 'id') else: _ptr = env.schema.get(left.rptr.ptrref.name) assert isinstance(_ptr, s_pointers.Pointer) ptr = _ptr return [(ptr, right)] elif _is_ptr_or_self_ref(right, result_set, env): if infer_cardinality( left, scope_tree=scope_tree, singletons=singletons, env=env, ).is_single(): right_stype = env.set_types[right] if right_stype == result_stype: assert isinstance(right_stype, s_objtypes.ObjectType) _ptr = right_stype.getptr(schema, 'id') else: _ptr = env.schema.get(right.rptr.ptrref.name) assert isinstance(_ptr, s_pointers.Pointer) ptr = _ptr return [(ptr, right)] elif expr.func_shortname == 'std::AND': left, right = (a.expr for a in expr.args) left_filters = extract_filters( result_set, left, scope_tree, singletons, env ) right_filters = extract_filters( result_set, right, scope_tree, singletons, env ) ptr_filters: List[Tuple[s_pointers.Pointer, irast.Set]] = [] if left_filters is not None: ptr_filters.extend(left_filters) elif strict: return None if right_filters is not None: ptr_filters.extend(right_filters) elif strict: return None return ptr_filters return None def _analyse_filter_clause( result_set: irast.Set, result_card: qltypes.Cardinality, filter_clause: irast.Set, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: schema = env.schema filtered_ptrs = extract_filters( result_set, filter_clause, scope_tree, singletons, env) if filtered_ptrs: exclusive_constr: s_constr.Constraint = schema.get('std::exclusive') for ptr, _ in filtered_ptrs: ptr = ptr.get_nearest_non_derived_parent(env.schema) is_unique = ( ptr.is_id_pointer(schema) or any(c.issubclass(schema, exclusive_constr) for c in ptr.get_constraints(schema).objects(schema)) ) if is_unique: # Bingo, got an equality filter on a link with a # unique constraint return AT_MOST_ONE return result_card def _infer_stmt_cardinality( result_set: irast.Set, filter_clause: Optional[irast.Set], scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: result_card = infer_cardinality( result_set, scope_tree=scope_tree, singletons=singletons, env=env, ) if result_card.is_single() or filter_clause is None: return result_card return _analyse_filter_clause( result_set, result_card, filter_clause, scope_tree, singletons, env) @_infer_cardinality.register def __infer_select_stmt( ir: irast.SelectStmt, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: if (ir.limit is not None and isinstance(ir.limit.expr, irast.IntegerConstant) and ir.limit.expr.value == '1'): # Explicit LIMIT 1 clause. stmt_card = AT_MOST_ONE else: stmt_card = _infer_stmt_cardinality( ir.result, ir.where, scope_tree=scope_tree, singletons=singletons, env=env, ) if ir.iterator_stmt: iter_card = infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) stmt_card = cartesian_cardinality((stmt_card, iter_card)) return stmt_card @_infer_cardinality.register def __infer_insert_stmt( ir: irast.InsertStmt, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: if ir.iterator_stmt: # XXX: is this branch ever invoked? return infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) else: # INSERT without a FOR is always a singleton. return ONE @_infer_cardinality.register def __infer_update_stmt( ir: irast.UpdateStmt, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: stmt_card = _infer_stmt_cardinality( ir.subject, ir.where, scope_tree=scope_tree, singletons=singletons, env=env, ) if ir.iterator_stmt: iter_card = infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) stmt_card = cartesian_cardinality((stmt_card, iter_card)) return stmt_card @_infer_cardinality.register def __infer_delete_stmt( ir: irast.DeleteStmt, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: stmt_card = _infer_stmt_cardinality( ir.subject, None, singletons=singletons, scope_tree=scope_tree, env=env, ) if ir.iterator_stmt: iter_card = infer_cardinality( ir.iterator_stmt, scope_tree=scope_tree, singletons=singletons, env=env, ) stmt_card = cartesian_cardinality((stmt_card, iter_card)) return stmt_card @_infer_cardinality.register def __infer_stmt( ir: irast.Stmt, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: if ir.cardinality: return ir.cardinality else: return infer_cardinality( ir.result, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_slice( ir: irast.SliceIndirection, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # slice indirection cardinality depends on the cardinality of # the base expression and the slice index expressions args = [ir.expr] if ir.start is not None: args.append(ir.start) if ir.stop is not None: args.append(ir.stop) return _common_cardinality( args, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_index( ir: irast.IndexIndirection, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: # index indirection cardinality depends on both the cardinality of # the base expression and the index expression return _common_cardinality( [ir.expr, ir.index], scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_array( ir: irast.Array, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return _common_cardinality( ir.elements, scope_tree=scope_tree, singletons=singletons, env=env, ) @_infer_cardinality.register def __infer_tuple( ir: irast.Tuple, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId], env: context.Environment, ) -> qltypes.Cardinality: return _common_cardinality( [el.val for el in ir.elements], scope_tree=scope_tree, singletons=singletons, env=env, ) def infer_cardinality( ir: irast.Base, *, scope_tree: irast.ScopeTreeNode, singletons: Collection[irast.PathId] = (), env: context.Environment, ) -> qltypes.Cardinality: result = env.inferred_cardinality.get((ir, scope_tree)) if result is not None: return result result = _infer_cardinality( ir, scope_tree=scope_tree, singletons=singletons, env=env, ) if result not in {AT_MOST_ONE, ONE, MANY, AT_LEAST_ONE}: raise errors.QueryError( 'could not determine the cardinality of ' 'set produced by expression', context=ir.context) env.inferred_cardinality[ir, scope_tree] = result return result def is_subset_cardinality( card0: qltypes.Cardinality, card1: qltypes.Cardinality ) -> bool: '''Determine if card0 is a subset of card1.''' l0, u0 = _card_to_bounds(card0) l1, u1 = _card_to_bounds(card1) return l0 >= l1 and u0 <= u1

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mafia_tally/config.py

auscompgeek/mafia-tally

0

142949

<filename>mafia_tally/config.py import json import os from typing import Dict, Set import werkzeug.security config_dir = os.path.join(os.path.dirname(__file__), '..', 'config') get_path = lambda filename: os.path.join(config_dir, filename) day_config_file = get_path('day.json') access_token_file = get_path('access_token.txt') players_file = get_path('players.txt') admin_passhash_file = get_path('passhash.txt') group_id: int = 328346913872436 # lol let's hardcode this post_id: int day_id: int cutoff: str players: Set[str] pics: Dict[str, str] def get_access_token() -> str: with open(access_token_file) as f: return f.read().strip() def get_passhash() -> str: with open(admin_passhash_file) as f: return f.read().strip() def check_admin_password(password: str) -> bool: return werkzeug.security.check_password_hash(get_passhash(), password) def load_day_config(): global post_id global day_id global cutoff with open(day_config_file) as f: day_info = json.load(f) post_id = day_info['post_id'] day_id = day_info['day_id'] cutoff = day_info['cutoff'] def get_players() -> Set[str]: with open(players_file) as f: return set(map(str.strip, f)) def load_players(): global players players = get_players() def load_pics(): global pics with open(get_path('pics.json')) as f: pics = json.load(f) def load(): load_day_config() load_players() load_pics() def save_day_config(): with open(day_config_file, 'w') as f: json.dump( {'post_id': post_id, 'day_id': day_id, 'cutoff': cutoff}, f, indent='\t' ) def save_players(): with open(players_file, 'w') as f: print(*players, sep='\n', file=f) def save(): save_day_config() save_players() load()

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Algorithms/Opt2_Algorithm/main.py

varshneypratyaksh2017/TSP_Using_AI

1

113215

import time from Algorithms.Opt2_Algorithm.algorithm import Opt2 # 2Opt Algorithm requires no configurations def opt2Algorithm(): opt2 = Opt2() startTime = time.time() route, _ = opt2.findSolution() endTime = time.time() time_taken = endTime - startTime solution = route distance = opt2.getDistance() return solution, distance, time_taken

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wsgi.py

ObjectionTheory/rover-voice-integration

0

1601249

import flask import json import requests import time from rover import Rover application = flask.Flask(__name__) print("Started") rovers = {1: Rover(1)} currentRover = 1 @application.route('/') def getStatus(): print("Hello? Can anyone hear me?") return "Hello, I am running...:" @application.route('/control', methods=['GET']) def controller(): global rovers req = flask.request.args roverid = int(req.get("roverid")) if roverid not in rovers.keys(): rovers[roverid] = Rover(roverid) rovers[roverid].updateData( req.get("left"), req.get("right"), req.get("duration"), req.get("claw") ) res = "Moved!" return res @application.route('/getcommands', methods=['GET']) def returnCommand(): global rovers req = flask.request.args print(req) roverid = int(req.get("roverid")) print(rovers[roverid].postData(reset=False)) if roverid not in rovers.keys(): rovers[roverid] = Rover(roverid) res = rovers[roverid].postData() if req.get("kill"): del rovers[roverid] return return flask.jsonify(res) @application.route('/webhooks', methods=['POST']) def webhook(): req = flask.request.get_json(silent=True, force=True) res = processRequest(req) return flask.jsonify(res) def processRequest(req): global rovers, currentRover print("Request:") print(json.dumps(req, indent=4)) action = req["result"]["action"] if action == "sallyForth": moveRover(req, 255, 255) res = speech("Sallying forth!") elif action == "turn": direction = req["result"]["parameters"]["direction"] if direction == "left": moveRover(req, -255, 255) res = speech("Turning left!") elif direction == "right": moveRover(req, 255, -255) res = speech("Turning right!") elif action == "retreat": moveRover(req, -255, -255) res = speech("Going back!") elif action == "halt": moveRover(req, 0, 0) res = speech("Stopping!") elif action == "openClaw": rovers[currentRover].claw = 0 res = speech("Opening the claw!") elif action == "closeClaw": rovers[currentRover].claw = 1 res = speech("Closing the claw!") elif action == "lightPreset": lights = req.get("result").get("parameters").get("lightSel") rovers[currentRover].lightPreset = int(lights) + 1 res = speech("Lights set to preset "+ lights + ".") elif action == "clearLights": rovers[currentRover].lightPreset = 1 res = speech("Lights cleared.") elif action == "setLights": hue = req.get("results").get("parameters").get("hue") brightness = req.get("results").get("parameters").get("brightness") rovers[currentRover].setLights(hue, brightness) res = speech("Lights configured!") elif action == "setRover": currentRover = req.get("result").get("parameters").get("roverid") if currentRover not in rovers: rovers[currentRover] = Rover(currentRover) print(rovers) res = speech("Set rover " + str(currentRover) + " to be active!") elif action == "numberOfRovers": if len(rovers) == 1: res = speech("There is 1 rover connected to the server.") else: res = speech("There are " + str(len(rovers)) + " connected to the server.") elif action == "activeRovers": activeRovers = 0 for rover in rovers.values(): if rover.isMoving(): activeRovers += 1 if activeRovers == 1: activeSpeech = "There is 1 rover moving at the moment." else: activeSpeech = "There are " + str(activeRovers) + " rovers moving at the moment." if len(rovers)-activeRovers == 1: stoppedSpeech = "There is 1 inactive rover." else: stoppedSpeech = "There are " + str(len(rovers)-activeRovers) + " inactive rovers." res = speech(activeSpeech + "\n" + stoppedSpeech) else: res = speech("Oops, I didn't get that...") return res def moveRover(req, left, right): global rovers, currentRover travelTime = 2 roverid = currentRover if req["result"]["parameters"].get("duration"): duration = req["result"]["parameters"]["duration"] if duration["unit"] == "s": travelTime = duration["amount"] elif duration["unit"] == "min": travelTime = duration["amount"] * 60 print(roverid, rovers.keys()) if roverid in rovers.keys(): print(roverid) rovers[roverid].updateData(left, right, travelTime) def speech(speech): return { "speech": speech, "displayText": speech, "source": "webhookdata" } if __name__ == '__main__': application.run()

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manhattan/backend/rollups.py

cartlogic/manhattan

1

70026

<reponame>cartlogic/manhattan from __future__ import absolute_import, division, print_function """ This module contains some example ``Rollup`` objects, each implementing the interface expected by the Manhattan backend for rollup aggregations. """ import time from datetime import datetime, timedelta import pytz class LocalRollup(object): def __init__(self, tzname): self.tz = pytz.timezone(tzname) def start_date_for(self, timestamp): dt = datetime.utcfromtimestamp(timestamp).replace(tzinfo=pytz.utc) dt_local = dt.astimezone(self.tz).replace(tzinfo=None) return dt_local.date() class LocalDayRollup(LocalRollup): def get_bucket(self, timestamp, history): return time.mktime(self.start_date_for(timestamp).timetuple()) class LocalWeekRollup(LocalRollup): def get_bucket(self, timestamp, history): day = self.start_date_for(timestamp) days_from_sunday = day.isoweekday() % 7 day -= timedelta(days=days_from_sunday) return time.mktime(day.timetuple()) class AllRollup(object): def get_bucket(self, timestamp, history): return 0 class BrowserRollup(object): def browser_from_user_agent(self, user_agent): # FIXME This is a pretty naive and less useful implementation. if 'Chrome' in user_agent: return u'Chrome' elif 'Safari' in user_agent: return u'Safari' elif 'Firefox' in user_agent: return u'Firefox' elif 'MSIE' in user_agent: return u'IE' else: return u'Unknown' def get_bucket(self, timestamp, history): return (history.user_agents and self.browser_from_user_agent(list(history.user_agents)[0]) or u'')

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bluebottle/donations/migrations/0009_auto_20190130_1140.py

jayvdb/bluebottle

0

17281

<reponame>jayvdb/bluebottle<filename>bluebottle/donations/migrations/0009_auto_20190130_1140.py # -*- coding: utf-8 -*- # Generated by Django 1.10.8 on 2019-01-30 10:40 from __future__ import unicode_literals import bluebottle.utils.fields from decimal import Decimal from django.db import migrations, models import django.db.models.deletion import djmoney.models.fields class Migration(migrations.Migration): dependencies = [ ('donations', '0008_auto_20170927_1021'), ] operations = [ migrations.AddField( model_name='donation', name='payout_amount', field=bluebottle.utils.fields.MoneyField(currency_choices="[('EUR', u'Euro')]", decimal_places=2, default=Decimal('0.0'), max_digits=12, verbose_name='Payout amount'), ), migrations.AddField( model_name='donation', name='payout_amount_currency', field=djmoney.models.fields.CurrencyField(choices=[(b'EUR', 'Euro')], default='EUR', editable=False, max_length=3), ), ]

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openid.py

DaniilSezonov/AuthServerClient

0

40561

import jwt import requests from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicNumbers from oauth2 import Oauth2, ResponseObject, RemoteUserProfile, OAuthDiscoveryError class OpenID(Oauth2): state_token: str or None = None well_know_path_part: str = ".well-known" # it is path in url to .well-know https://tools.ietf.org/html/rfc5785 openid_conf_path_part = "openid-configuration" # it is path in url to openid configuration auth_server_domain_name: str or None = None _configuration: dict or None = None # https://openid.net/specs/openid-connect-discovery-1_0.html#ProviderConfig was_load_openid_conf: bool = False audience: str or None = None jwk_cert: dict or None = None def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.openid_conf_path_part = kwargs.get("openid_conf_path_part") or self.openid_conf_path_part self.state_token = kwargs.get('state_token') self.jwk_cert = kwargs.get('jwk_cert') self.audience = kwargs.get('audience') @staticmethod def get_jwt_header(token): """ :param token: the token is a jwt (that has name id_token in google notation) :return: dict with key alg (algorithm), kid (key id), typ (token type(JWT always)) """ return jwt.get_unverified_header(token) @staticmethod def jwk_to_rsa(jwk): """ Decoding jwk format to public rsa key carried out by next steps: (jwk['e'] (RSA exponent) and jwk['n'] (RSA modulus) -> decode from base64url to binary -> encode binary to DEC (десятичная)) :param jwk: JSON Web Key https://tools.ietf.org/html/rfc7517 :return: RSA public key as instance of cryptography.hazmat.backends.openssl.rsa._RSAPublicKey """ e = jwk.get('e') # exponent assert e, "jwk must contains the exponent value by key 'e'" n = jwk.get('n') # modulus assert n, "jwk must contains the modulus value by key 'n'" base64_url_exp = jwt.api_jws.base64url_decode(e) base64_url_modulus = jwt.api_jws.base64url_decode(n) dec64_url_exp = int(base64_url_exp.hex(), base=16) dec64_url_modulus = int(base64_url_modulus.hex(), base=16) return RSAPublicNumbers(e=dec64_url_exp, n=dec64_url_modulus).public_key(default_backend()) def get_user_info_by_jwt(self, token, with_verify=True, **options)-> dict: """ :param token: jwt :param with_verify: verify that response is not compromised :param options: if verify is True we need to pass JWK value for verification :return: """ user_info = {} jwk = options.get("jwk") jwt_decode_options = {} audience = options.get('audience') if audience: jwt_decode_options['audience'] = audience elif self.audience: jwt_decode_options['audience'] = self.audience rsa_key = self.jwk_to_rsa(jwk) if with_verify: user_info = jwt.decode(token, key=rsa_key, **jwt_decode_options) else: user_info = jwt.decode(token, verify=False) return user_info def is_state_token_valid(self, state): # todo return self.state_token == state def get_user_profile(self, exchange_code_response: ResponseObject) -> RemoteUserProfile: id_token = exchange_code_response.get('id_token') assert id_token, "Wrong openid response from auth server. " \ "response must contains id_token (with JWT data) field" options = { 'jwk': self.get_jwk_for_jwt(id_token) } return self.get_user_info_by_jwt(id_token, **options) def load_configuration(self, configuration: dict = None): if configuration is None: configuration = self.get_oauth_conf_by_discovery_doc() self._configuration = configuration self.was_load_openid_conf = True def get_oauth_conf_by_discovery_doc(self): discovery_path = f'https://{self.auth_server_domain_name}/{self.well_know_path_part}/{self.openid_conf_path_part}' response = requests.get(url=discovery_path) if response.status_code != 200: raise OAuthDiscoveryError() configuration = response.json() return configuration def get_jwk_for_jwt(self, token): if self.jwk_cert is None: jwt_header = self.get_jwt_header(token) self.load_jwk_by_remote(jwt_header.get('kid')) return self.jwk_cert def load_jwk_by_remote(self, kid: str): """ :param kid: remote key id :return: jwk """ assert kid, "You cant load jwk if key id (kid) is not set" configuration = self.get_configuration() jwks_uri = configuration.get('jwks_uri') certs_response = requests.get(url=jwks_uri).json() for key in certs_response['keys']: if key['kid'] == kid: self.load_jwk(key) break def load_jwk(self, jwk): self.jwk_cert = jwk

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port_scanner.py

Adel-Charef/scripts

0

112754

from queue import Queue import socket import threading target = "127.0.0.1" queue = Queue() open_ports = [] """ port 80 (HTTP) or port 443 (HTTPS) 21 (FTP), 22 (SSH), 25 (SMTP) """ # connect to a target on a specific port def portscan(port): try: sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((target, port)) return True except: return False def get_ports(mode): # for standardized ports if mode == 1: for port in range(1, 1024): queue.put(port) # for reserved ports elif mode == 2: for port in range(1, 49152): queue.put(port) # for the most important ports only elif mode == 3: ports = [20, 21, 22, 23, 25, 53, 80, 110, 443] for port in ports: queue.put(port) # to choose our ports manually elif mode == 4: ports = input("Enter your ports (separate by blank): ") ports = ports.split() ports = list(map(int, ports)) for port in ports: queue.put(port) # get the port numbers from the queue, scan them and print the results def worker(): while not queue.empty(): port = queue.get() if portscan(port): print(f"Port {port} is open!") open_ports.append(port) else: print(f"Port {port} is closed!") # let's create start and manage our threads def main(threads, mode): get_ports(mode) thread_list = [] for t in range(threads): thread = threading.Thread(target=worker) thread_list.append(thread) for thread in thread_list: thread.start() for thread in thread_list: thread.join() print("The open ports are:", open_ports) if __name__ == "__main__": main(100, 4)

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h/streamer/__init__.py

kevinjalbert/h

1

125717

<filename>h/streamer/__init__.py from h.streamer.tweens import close_db_session_tween_factory __all__ = ["close_db_session_tween_factory"] def includeme(config): config.include("h.streamer.views") config.add_subscriber( "h.streamer.streamer.start", "pyramid.events.ApplicationCreated" )

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src/app_contexts/app_factory.py

marcoprado17/flask-bone

0

156656

<filename>src/app_contexts/app_factory.py # !/usr/bin/env python # -*- coding: utf-8 -*- # ====================================================================================================================== # The MIT License (MIT) # ====================================================================================================================== # Copyright (c) 2016 [<NAME> - <EMAIL>] # ====================================================================================================================== import sys if sys.version_info.major < 3: reload(sys) sys.setdefaultencoding('utf8') from flask import Flask, redirect, request, url_for from configs import default_app_config from configs.instance import instance_app_config from configs.instance import unit_test_app_config from extensions import db from flask_bombril.log import log_request def __create_app(configs): static_folder = None for config in configs: if hasattr(config, 'STATIC_FOLDER'): static_folder = config.STATIC_FOLDER app = Flask(__name__, instance_relative_config=True, static_folder=static_folder) for config in configs: app.config.from_object(config) # Initializing extensions from extensions import bcrypt bcrypt.init_app(app) from extensions import db db.init_app(app) from extensions import mail mail.init_app(app) return app def create_app(): app = __create_app([default_app_config, instance_app_config]) @app.route("/") def home_redirect(): return redirect("home") # ================================================================================================================== # # # # # Registering blueprints # ================================================================================================================== # # Components # from blueprints.components.lightly_route_dependent.navbar import navbar_blueprint app.register_blueprint(navbar_blueprint, url_prefix="/navbar") # # Routers # from blueprints.routers.home import home_blueprint app.register_blueprint(home_blueprint, url_prefix="/home") from blueprints.routers.register import register_blueprint app.register_blueprint(register_blueprint, url_prefix="/cadastrar") if app.config["DEBUG"]: from blueprints.routers.debug import debug_blueprint app.register_blueprint(debug_blueprint, url_prefix="/debug") # # Wrappers # from blueprints.wrappers.base import base_blueprint app.register_blueprint(base_blueprint, url_prefix="/base") # # Macros # from macros import macros_blueprint app.register_blueprint(macros_blueprint) # # Email # from email_blueprint import email_blueprint app.register_blueprint(email_blueprint) # ================================================================================================================== # # # # # Registering jinja filters # ================================================================================================================== from flask_bombril.jinja_filters import assert_defined, assert_callable, call, if_filter, is_static, is_toast, get_level app.jinja_env.filters['assert_defined'] = assert_defined app.jinja_env.filters['assert_callable'] = assert_callable app.jinja_env.filters['call'] = call app.jinja_env.filters['if'] = if_filter app.jinja_env.filters['is_static'] = is_static app.jinja_env.filters['is_toast'] = is_toast app.jinja_env.filters['get_level'] = get_level # ================================================================================================================== # # # # # Registering lightly route dependent components context_processors # ================================================================================================================== from blueprints.components.lightly_route_dependent.navbar.navbar_data_provider import navbar_data_provider from r import R @app.context_processor def _(): return dict( get_navbar_data=lambda: navbar_data_provider.get_data(), R=R, ) # ================================================================================================================== # # # # # Configuring Logging # ================================================================================================================== import logging from logging.handlers import TimedRotatingFileHandler handler = TimedRotatingFileHandler( filename=app.config['LOGGING_FILENAME'], when=app.config['LOGGING_WHEN'], interval=app.config['LOGGING_INTERVAL'], backupCount=app.config['LOGGING_BACKUP_COUNT'] ) formatter = logging.Formatter(app.config['LOGGING_FORMAT']) handler.setFormatter(formatter) app.logger.addHandler(handler) # ================================================================================================================== # # # # # Registering 500 error handler # ================================================================================================================== @app.errorhandler(500) def handle_error(error): db.session.rollback() log_request(app.logger.error) return R.string.temp_error_html % dict(href=url_for("home.index")), 500 return app def create_unit_test_app(): app = __create_app([default_app_config, instance_app_config, unit_test_app_config]) return app

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VPLVisitor.py

lamhacker/VPL-Compiler

2

163511

# Generated from VPL.g4 by ANTLR 4.7 from antlr4 import * if __name__ is not None and "." in __name__: from .VPLParser import VPLParser else: from VPLParser import VPLParser # This class defines a complete generic visitor for a parse tree produced by VPLParser. class VPLVisitor(ParseTreeVisitor): # Visit a parse tree produced by VPLParser#program. def visitProgram(self, ctx:VPLParser.ProgramContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#function_declaration. def visitFunction_declaration(self, ctx:VPLParser.Function_declarationContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#parameter. def visitParameter(self, ctx:VPLParser.ParameterContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#parameterName. def visitParameterName(self, ctx:VPLParser.ParameterNameContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#multParameterName. def visitMultParameterName(self, ctx:VPLParser.MultParameterNameContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#variable_declaration. def visitVariable_declaration(self, ctx:VPLParser.Variable_declarationContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#condition. def visitCondition(self, ctx:VPLParser.ConditionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#whileloop. def visitWhileloop(self, ctx:VPLParser.WhileloopContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#assign. def visitAssign(self, ctx:VPLParser.AssignContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#noneStatement. def visitNoneStatement(self, ctx:VPLParser.NoneStatementContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#nest_statement. def visitNest_statement(self, ctx:VPLParser.Nest_statementContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#addExpression. def visitAddExpression(self, ctx:VPLParser.AddExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#minusExpression. def visitMinusExpression(self, ctx:VPLParser.MinusExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#multExpression. def visitMultExpression(self, ctx:VPLParser.MultExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#divExpression. def visitDivExpression(self, ctx:VPLParser.DivExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#minExpression. def visitMinExpression(self, ctx:VPLParser.MinExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#parenthesisExpression. def visitParenthesisExpression(self, ctx:VPLParser.ParenthesisExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#idenetExpression. def visitIdenetExpression(self, ctx:VPLParser.IdenetExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#numExpression. def visitNumExpression(self, ctx:VPLParser.NumExpressionContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#lessThan. def visitLessThan(self, ctx:VPLParser.LessThanContext): return self.visitChildren(ctx) # Visit a parse tree produced by VPLParser#largeThan. def visitLargeThan(self, ctx:VPLParser.LargeThanContext): return self.visitChildren(ctx) del VPLParser

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moodle/mod/workshop/__init__.py

Hardikris/moodlepy

0

1600350

from .base import BaseWorkshop __all__ = [ "BaseWorkshop", ]

[ 1, 515, 869, 3188, 1053, 7399, 5531, 19032, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 376, 5160, 5531, 19032, 613, 13, 29962, 13, 2 ]

rob599_hw3/scripts/noiser.py

4-legends/Software-for-robotics-rob599

0

65983

#!/usr/bin/env python import rospy import math import numpy as np from sensor_msgs.msg import LaserScan ####################################### # Laser Scan: # Header: Seq, Stamp, frame_id # Angle_min, Angle_max, Angle_Increment, Time_Increment # Scan time, range_min, range_max, ranges, intensities ####################################### class Noise_class: def __init__(self): #rospy.on_shutdown(self.save_csv) self.laser_sub = rospy.Subscriber('/base_scan', LaserScan, self.laser_callback) self.scan_pub = rospy.Publisher('/gaus_err_laser_scan', LaserScan, queue_size= 1) def laser_callback(self, msg): filtered_values = LaserScan() distance = np.array(msg.ranges) filtered_values.header = msg.header filtered_values.angle_increment = msg.angle_increment filtered_values.time_increment = msg.time_increment filtered_values.scan_time = msg.scan_time filtered_values.range_min = msg.range_min filtered_values.range_max = msg.range_max filtered_values.intensities = msg.intensities angle = filtered_values.angle_increment min_angle = msg.angle_min max_angle = msg.angle_max laser_noise_variance = rospy.get_param('laser_noise_variance') if laser_noise_variance <= 0: laser_noise_variance = 0.1 filtered_values_ranges = np.zeros(len(distance)) noise_values_ranges = np.random.normal(loc = 0, scale=laser_noise_variance, size=len(distance)) for i in range(len(distance)): filtered_values_ranges[i] = noise_values_ranges[i]+distance[i] filtered_values.ranges = filtered_values_ranges filtered_values.angle_min = min_angle filtered_values.angle_max = max_angle self.scan_pub.publish(filtered_values) if __name__ == '__main__': rospy.init_node('noiser', anonymous=True) noisy = Noise_class() try: rospy.spin() except KeyboardInterrupt: print("Shutting down")

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openGaussBase/testcase/AI/AI_MODEL/Opengauss_Function_Snapshot_Case0007.py

opengauss-mirror/Yat

0

1610319

<reponame>opengauss-mirror/Yat """ Copyright (c) 2022 Huawei Technologies Co.,Ltd. openGauss is licensed under Mulan PSL v2. You can use this software according to the terms and conditions of the Mulan PSL v2. You may obtain a copy of Mulan PSL v2 at: http://license.coscl.org.cn/MulanPSL2 THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. See the Mulan PSL v2 for more details. """ """ Case Type : 智能运维snapshot模块 Case Name : 创建快照后使用linear_regression算子创建model Description : 1.建表并插入数据 2.创建快照 3.创建训练集与测试集 4.查看当前数据表快照 5.查询创建的训练集数据 6.使用linear_regression算子创建model 7.查找系统表 8.预测表数据 9.清理环境 Expect : 1.建表并插入数据成功 2.创建快照成功 3.创建训练集与测试集成功 4.返回当前数据表快照 5.返回创建的训练集数据 6.使用linear_regression算子创建model成功 7.返回创建model的modelname 8.返回预测的表数据 9.清理环境成功 History : """ import os import unittest from testcase.utils.Constant import Constant from testcase.utils.CommonSH import CommonSH from testcase.utils.Logger import Logger class AI(unittest.TestCase): def setUp(self): self.logger = Logger() self.primary_sh = CommonSH('PrimaryDbUser') self.Con = Constant() self.table = 't_snapshot_tab_0007' self.snapshot = '[email protected]' self.snapshot1 = '[email protected]' self.snapshot2 = '[email protected]' self.model = 'm_model_snapshot_0007' def test_ai_snapshot(self): self.logger.info(f'-----{os.path.basename(__file__)} start-----') step = 'step1:建表并插入数据 expect:建表并插入数据成功' self.logger.info(step) create_table = self.primary_sh.execut_db_sql( f'''drop table if exists {self.table}; create table {self.table}(id int,tax int,bedroom int , bath float,price int,size int,lot int ); insert into {self.table} values(1,590,2,1,50000,770,22100), (2,1050,3,2 ,85000,1410,2000),(3,20 ,3,1,22500,1060,3500), (4,870,2,2,90000,1300,17500),(5,1320,3,2,133000,1500,30000), (6,1350,2,1,90500,820,25700),(7,2790,3,2.5,260000,2130,25000), (8,680,2,1,142500,1170,22000),(9,1840,3,2,160000,1500,19000), (10,3680,4,2,240000,2790,20000),(111,660,3,1,87000,1030,17500);''') self.logger.info(create_table) self.assertTrue(self.Con.TABLE_CREATE_SUCCESS in create_table and self.Con.INSERT_SUCCESS_MSG in create_table, "建表并插入数据失败" + step) step = 'step2:创建快照 expect:创建快照成功' self.logger.info(step) create_snapshot = self.primary_sh.execut_db_sql( f'''create snapshot {self.snapshot} as select * from {self.table}; ''') self.logger.info(create_snapshot) self.assertIn(self.snapshot, create_snapshot, "执行失败" + step) step = 'step3:创建训练集与测试集 expect:创建训练集与测试集成功' self.logger.info(step) create_snapshot = self.primary_sh.execut_db_sql( f'''sample snapshot {self.snapshot} stratify by price as _test at ratio .2, as _train at ratio .8 comment is 'training'; ''') self.logger.info(create_snapshot) self.assertIn(self.snapshot1, create_snapshot, "执行失败" + step) self.assertIn(self.snapshot2, create_snapshot, "执行失败" + step) step = 'step4:查看当前数据表快照 expect:返回当前数据表快照' self.logger.info(step) select_snapshot = self.primary_sh.execut_db_sql( f'''select * from db4ai.snapshot;''') self.logger.info(select_snapshot) self.assertIn(self.snapshot, select_snapshot, "执行失败" + step) self.assertIn(self.snapshot1, select_snapshot, "执行失败" + step) self.assertIn(self.snapshot2, select_snapshot, "执行失败" + step) step = 'step5:查询创建的训练集数据 expect:返回创建的训练集数据' self.logger.info(step) select_snapshot = self.primary_sh.execut_db_sql( f'''select * from {self.snapshot1};''') self.logger.info(select_snapshot) self.assertNotIn('(0 rows)', select_snapshot, "执行失败" + step) step = 'step6:使用linear_regression算子创建model;' \ 'expect:使用linear_regression算子创建model成功' self.logger.info(step) create_model = self.primary_sh.execut_db_sql( f'''create model {self.model} using linear_regression features 1,tax,bath,size target price from {self.snapshot1};''') self.logger.info(create_model) self.assertIn('MODEL CREATED. PROCESSED 1', create_model, "执行失败" + step) step = 'step7:查找系统表;expect:返回创建model的modelname' self.logger.info(step) select_model = self.primary_sh.execut_db_sql( f'''select modelname from gs_model_warehouse;''') self.logger.info(select_model) self.assertIn(self.model, select_model, "执行失败" + step) step = 'step8:预测表数据;expect:返回预测的表数据' self.logger.info(step) predict_data = self.primary_sh.execut_db_sql( f'''select predict by {self.model} (features 1,tax,bath,size) from {self.snapshot1};''') self.logger.info(predict_data) self.assertNotIn('(0 rows)', predict_data, "执行失败" + step) def tearDown(self): step = 'step9:清理环境 expect:清理环境成功' self.logger.info(step) clean_environment = self.primary_sh.execut_db_sql(f''' purge snapshot {self.snapshot1}; purge snapshot {self.snapshot2}; purge snapshot {self.snapshot}; drop table {self.table}; drop model {self.model}''') self.logger.info(clean_environment) self.assertIn(self.Con.TABLE_DROP_SUCCESS, clean_environment, "执行失败" + step) self.assertIn(f'{self.snapshot}', clean_environment, "执行失败" + step) self.assertIn(self.snapshot1, clean_environment, "执行失败" + step) self.assertIn(self.snapshot2, clean_environment, "执行失败" + step) self.assertIn('DROP MODEL', clean_environment, "执行失败" + step) self.logger.info(f'-----{os.path.basename(__file__)} end-----')

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yearn/scripts/yearn.py

jonesnc/yearn-client

0

126773

import click from yearn.scripts.load import load as script_load @click.group() def cli(): pass @cli.command() @click.argument('path', default='.') @click.option('--scgi', default=None) @click.option('--mount', default=None) # TODO: support multiple mounts def load(path, scgi, mount): click.echo('Loading torrent files...') click.echo(f'Using SCGI: {scgi}') click.echo(f'Using path: {path}') click.echo(f'Using mount: {mount}') script_load(path, scgi, mount)

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bot coontrol/CollisionControl.py

aritrog/sarathi

3

55412

import BotDecidesPos import numpy as np class Collision_check: def __init__(self): self.m=0.0 self.n=0.0 def load_data(self,bot): tgx,tgy=bot.getTarget() mpx,mpy=bot.getPos() spd=bot.getSpeed() return spd,mpx,mpy,tgx,tgy def checkCollision(self,bot1,bot2): eg = Engine() sp1,x,y,x1,y1=self.load_data(bot1) sp2,a,b,a1,b1=self.load_data(bot2) p=eg.findDist(x,y,x1,y1) q=eg.findDist(a,b,a1,b1) #v1=[sp1*(x-x1)/p,sp1*(y-y1)/p] #v2 = [sp2 * (a - a1) / q, sp2 * (b - b1) / q] #Ax=C, which is the matrix from of the equation on the path of the vehicle s=[[x-x1,y-y1],[a-a1,b-b1]] t=[y*x1-x*y1,b*a1-a*b1] self.m,self.n=eg.eq_StraightLine(s,t) p1=eg.findDist(x,y,self.m,self.n) q1=eg.findDist(a,b,self.m,self.n) eta1=p1/sp1; eta2=q1/sp2; if np.absolute(eta1-eta2)<1 : return True else: return False def getCollisionIndex(self): return self.m,self.n def setCollisionIndex(self,a,b): self.m=a self.n=b

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lib/gstreamer/vaapi/vpp.py

ChipsnMedia/vaapi-fits

0

162450

<filename>lib/gstreamer/vaapi/vpp.py ### ### Copyright (C) 2018-2021 Intel Corporation ### ### SPDX-License-Identifier: BSD-3-Clause ### import os import slash from ....lib.gstreamer.vppbase import BaseVppTest from ....lib.gstreamer.util import have_gst_element from ....lib.gstreamer.vaapi.util import map_best_hw_format, mapformat, mapformatu from ....lib.common import get_media, mapRange, mapRangeWithDefault @slash.requires(*have_gst_element("vaapi")) @slash.requires(*have_gst_element("vaapipostproc")) class VppTest(BaseVppTest): def before(self): super().before() os.environ["GST_VAAPI_DRM_DEVICE"] = get_media().render_device vars(self).update(gstvpp = "vaapipostproc") def map_best_hw_format(self, format, hwformats): return map_best_hw_format(format, hwformats) def map_format(self, format): return mapformat(format) def map_formatu(self, format): return mapformatu(format) def gen_vpp_opts(self): opts = "" procamp = dict( brightness = [ -1.0, 0.0, 1.0], contrast = [ 0.0, 1.0, 2.0], hue = [-180.0, 0.0, 180.0], saturation = [ 0.0, 1.0, 2.0], ) if self.vpp_op in procamp: self.mlevel = mapRangeWithDefault( self.level, [0.0, 50.0, 100.0], procamp[self.vpp_op] ) opts += " {vpp_op}={mlevel}" elif self.vpp_op in ["denoise"]: self.mlevel = mapRange(self.level, [0, 100], [0.0, 1.0]) opts += " denoise={mlevel}" elif self.vpp_op in ["sharpen"]: self.mlevel = mapRange(self.level, [0, 100], [-1.0, 1.0]) opts += " sharpen={mlevel}" elif self.vpp_op in ["deinterlace"]: opts += " deinterlace-mode=1 deinterlace-method={mmethod}" elif self.vpp_op in ["transpose"]: opts += " video-direction={direction}" elif self.vpp_op in ["crop"]: opts += " crop-left={left} crop-right={right} crop-top={top} crop-bottom={bottom}" return opts

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src/start_horovod.py

jakob-ed/pytorch-dist-pos-tagger

0

84265

<reponame>jakob-ed/pytorch-dist-pos-tagger<gh_stars>0 #!/usr/bin/env python import os import random import torch from horovod_adasum.horovod_adasum import HorovodAdaSum MIN_FREQ = int(os.environ['SUSML_MIN_FREQ']) RAND_SEED = int(os.environ['SUSML_RAND_SEED']) NUM_EPOCHS = int(os.environ['SUSML_NUM_EPOCHS']) BATCH_SIZE = int(os.environ['SUSML_BATCH_SIZE']) LR = float(os.environ['SUSML_LR']) PARALLELISM_LEVEL = int(os.environ['SUSML_PARALLELISM_LEVEL']) RAY_PARAMETER_SERVER_STRATEGY = os.environ['RAY_PARAMETER_SERVER_STRATEGY'] random.seed(RAND_SEED) torch.manual_seed(RAND_SEED) torch.backends.cudnn.deterministic = True if __name__ == "__main__": HorovodAdaSum().run()

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myproject/app/serializer.py

xiaoxiansheng19/data_analysis

0

1606430

#! -*- coding:utf-8 -*- from rest_framework import serializers from .models import Job,File from djcelery.models import TaskMeta class JobSerializer(serializers.ModelSerializer): tasks = serializers.SerializerMethodField() def get_tasks(self, row): # role_obj_list = row.taskmeta.to_dict() # ret = [] # # 获取角色的id和名字 # # 以字典的键值对方式显示 # for item in role_obj_list: # ret.append({"id": item.id, "result": item.result}) return role_obj_list class Meta: model = Job fields = ('id','task_id', 'name', 'runtime', 'taskmeta', 'file','create_date','tasks') # depth = 1 # class TaskMetaSerializer(serializers.ModelSerializer): # # job_set = JobSerializer(many=True) # class Meta: # model = TaskMeta # fields = ('id','task_id', 'status', 'result', 'date_done', 'traceback','hidden','meta') # depth =1

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search/serialize/__init__.py

ID2797370/arxiv-search

35

53460

<filename>search/serialize/__init__.py """Provides serialization functions for API responses.""" __all__ = ["JSONSerializer", "as_json", "AtomXMLSerializer", "as_atom"] from search.serialize.json import JSONSerializer, as_json from search.serialize.atom import AtomXMLSerializer, as_atom

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basicThings/frozenLakeTime.py

ASH1998/RLnew

0

112485

# import gym # env = gym.make('FrozenLake8x8-v0') # env.reset() # for _ in range(10): # env.render() # env.step(env.action_space.sample()) # take a random action # env.close() # from gym import envs # import gym # frozen = gym.make('FrozenLake8x8-v0') # numEpisodes = 10 # for episode in range(numEpisodes): # observation = frozen.reset() # for epochs in range(100): # # frozen.render() # # print(observation) # action = frozen.action_space.sample() # # print(action) # obs, reward, done, info = frozen.step(action) # if done: #this is needed to be changed # print("Episode finished after {} timesteps".format(epochs+1)) # break # frozen.close() import gym import numpy as np import matplotlib.pyplot as plt from tqdm import trange ngames = 1000 percentage = [] scores = [] env = gym.make('FrozenLake8x8-v0') for i in trange(ngames): done = False obs = env.reset() score = 0 while not done: action = env.action_space.sample() obs, reward, done, info = env.step(action=action) score+=reward scores.append(score) if i%10==0: average = np.mean(scores[-10:]) percentage.append(average) plt.plot(percentage) plt.show()

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platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/sol/calculators/calc_radio.py

PascalGuenther/gecko_sdk

82

187755

<reponame>PascalGuenther/gecko_sdk from pyradioconfig.parts.ocelot.calculators.calc_radio import CALC_Radio_ocelot class Calc_Radio_Sol(CALC_Radio_ocelot): def calc_txofdm_reg(self, model): #Read in model vars softmodem_modulator_select = model.vars.softmodem_modulator_select.value if softmodem_modulator_select == model.vars.softmodem_modulator_select.var_enum.IQ_MOD: #This method enables OFDM TX txenbbreg = 1 txenmix = 1 else: txenbbreg = 0 txenmix = 0 #Write the registers self._reg_write(model.vars.RAC_TXOFDM_TXENBBREG, txenbbreg) self._reg_write(model.vars.RAC_TXOFDM_TXENMIX, txenmix) def calc_rx_fefiltoutputsel_reg(self, model): #This method calulates the FEFILTOUTPUTSEL field # Read in model vars demod_select = model.vars.demod_select.value # Calculate the field if demod_select == model.vars.demod_select.var_enum.SOFT_DEMOD: fefiltoutputsel = 1 else: fefiltoutputsel = 0 # Write the register self._reg_write(model.vars.RAC_RX_FEFILTOUTPUTSEL, fefiltoutputsel)

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synergy/scheduler/abstract_state_machine.py

mushkevych/scheduler

15

1615931

<reponame>mushkevych/scheduler<filename>synergy/scheduler/abstract_state_machine.py __author__ = '<NAME>' from datetime import datetime from logging import INFO, WARNING, ERROR from synergy.db.model import job from synergy.db.model.job import Job from synergy.db.error import DuplicateKeyError from synergy.db.dao.unit_of_work_dao import UnitOfWorkDao from synergy.db.dao.job_dao import JobDao from synergy.db.model import unit_of_work from synergy.db.model.unit_of_work import UnitOfWork from synergy.system.mq_transmitter import MqTransmitter from synergy.conf import context from synergy.system.decorator import with_reconnect from synergy.system import time_helper from synergy.scheduler.tree_node import DependentOnSummary class AbstractStateMachine(object): """ Abstract state machine used to host common logic for the rest of registered State Machines """ def __init__(self, logger, timetable, name): self.name = name self.logger = logger self.mq_transmitter = MqTransmitter(self.logger) self.timetable = timetable self.uow_dao = UnitOfWorkDao(self.logger) self.job_dao = JobDao(self.logger) @property def run_on_active_timeperiod(self): """ :return: True if given State Machine allows execution on *live* timeperiod, as opposed to the finished one """ raise NotImplementedError(f'property run_on_active_timeperiod must be implemented by {self.__class__.__name__}') def _log_message(self, level:int, process_name:str, timeperiod:str, msg:str): """ method performs logging into log file and Timetable's tree node""" self.timetable.add_log_entry(process_name, timeperiod, msg) self.logger.log(level, msg) @with_reconnect def _insert_uow(self, process_name, timeperiod, start_timeperiod, end_timeperiod, start_id, end_id): """creates unit_of_work and inserts it into the DB :raise DuplicateKeyError: if unit_of_work with given parameters already exists """ uow = UnitOfWork() uow.process_name = process_name uow.timeperiod = timeperiod uow.start_id = str(start_id) uow.end_id = str(end_id) uow.start_timeperiod = start_timeperiod uow.end_timeperiod = end_timeperiod uow.created_at = datetime.utcnow() uow.submitted_at = datetime.utcnow() uow.source = context.process_context[process_name].source uow.sink = context.process_context[process_name].sink uow.state = unit_of_work.STATE_REQUESTED uow.unit_of_work_type = unit_of_work.TYPE_MANAGED uow.number_of_retries = 0 uow.arguments = context.process_context[process_name].arguments uow.db_id = self.uow_dao.insert(uow) msg = 'Created: UOW {0} for {1}@{2} over [{3}:{4}).'\ .format(uow.db_id, process_name, timeperiod, start_timeperiod, end_timeperiod) self._log_message(INFO, process_name, timeperiod, msg) return uow def _publish_uow(self, uow): self.mq_transmitter.publish_managed_uow(uow) msg = 'Published: UOW {0} for {1}@{2} over [{3}:{4}).'\ .format(uow.db_id, uow.process_name, uow.timeperiod, uow.start_timeperiod, uow.end_timeperiod) self._log_message(INFO, uow.process_name, uow.timeperiod, msg) def insert_and_publish_uow(self, job_record, start_id, end_id): """ method creates and publishes a unit_of_work. it also handles DuplicateKeyError and attempts recovery :return: tuple (uow, is_duplicate) :raise UserWarning: if the recovery from DuplicateKeyError was unsuccessful """ process_name = job_record.process_name timeperiod = job_record.timeperiod start_timeperiod = self.compute_start_timeperiod(job_record.process_name, job_record.timeperiod) end_timeperiod = self.compute_end_timeperiod(job_record.process_name, job_record.timeperiod) try: is_duplicate = False uow = self._insert_uow(process_name, timeperiod, start_timeperiod, end_timeperiod, start_id, end_id) except DuplicateKeyError as e: is_duplicate = True msg = 'Catching up with latest UOW {0}@{1} over [{2}:{3}), because of: {4}' \ .format(process_name, timeperiod, start_timeperiod, end_timeperiod, e) self._log_message(WARNING, process_name, timeperiod, msg) uow = self.uow_dao.recover_from_duplicatekeyerror(e) if not uow: msg = 'PERSISTENT TIER ERROR! Unable to locate UOW for {0}@{1} over [{2}:{3})' \ .format(process_name, timeperiod, start_timeperiod, end_timeperiod) self._log_message(WARNING, process_name, timeperiod, msg) raise UserWarning(msg) if uow.is_canceled: # this UOW was marked for re-processing. recycle it uow.created_at = datetime.utcnow() # reset created_at to bypass GC cancellation logic uow.submitted_at = datetime.utcnow() # reset submitted_at to allow 1 hour free of GC resubmitting del uow.started_at del uow.finished_at del uow.number_of_aggregated_documents del uow.number_of_processed_documents uow.state = unit_of_work.STATE_REQUESTED self.uow_dao.update(uow) # publish the created/recovered/recycled unit_of_work self._publish_uow(uow) return uow, is_duplicate def notify(self, uow): """ method is used by StateMachine's users to provide notifications about change in UOW if applicable, method will update job_record state and Timetable tree node state :assumptions: uow is in [STATE_NOOP, STATE_CANCELED, STATE_PROCESSED] """ pass def compute_start_timeperiod(self, process_name, timeperiod): """ computes lowest *inclusive* timeperiod boundary for job to process for process with time_grouping == 1, it returns given timeperiod with no change for process with time_grouping != 1, it computes first timeperiod, not processed by the previous job run For instance: with time_grouping = 3, QUALIFIER_HOURLY, and timeperiod = 2016042018, the start_timeperiod will be = 2016042016 (computed as 2016042018 - 3 + 1) """ time_grouping = context.process_context[process_name].time_grouping if time_grouping == 1: return timeperiod # step1: translate given timeperiod to the time grouped one process_hierarchy = self.timetable.get_tree(process_name).process_hierarchy timeperiod_dict = process_hierarchy[process_name].timeperiod_dict translated_timeperiod = timeperiod_dict._translate_timeperiod(timeperiod) # step 2: compute previous grouped period # NOTICE: simple `time_helper.increment_timeperiod(time_qualifier, timeperiod)` is insufficient # as it does not address edge cases, such as the last day of the month or the last hour of the day # For instance: with time_grouping=3, QUALIFIER_DAILY, and 2016123100 # the `increment_timeperiod` will yield 2016122800 instead of 2016123100 time_qualifier = context.process_context[process_name].time_qualifier for i in range(1, time_grouping + 1): prev_timeperiod = time_helper.increment_timeperiod(time_qualifier, translated_timeperiod, delta=-i) if prev_timeperiod == timeperiod_dict._translate_timeperiod(prev_timeperiod): # prev_timeperiod is currently at the last grouped timeperiod break # step 3: compute first exclusive timeperiod after the *prev_timeperiod*, # which becomes first inclusive timeperiod for this job run over_the_edge_timeperiod = time_helper.increment_timeperiod(time_qualifier, prev_timeperiod, delta=-1) if prev_timeperiod != timeperiod_dict._translate_timeperiod(over_the_edge_timeperiod): # over_the_edge_timeperiod fell into previous day or month or year # *prev_timeperiod* points to the first month, first day of the month or 00 hour start_timeperiod = prev_timeperiod else: start_timeperiod = self.compute_end_timeperiod(process_name, prev_timeperiod) return start_timeperiod def compute_end_timeperiod(self, process_name, timeperiod): """ computes first *exclusive* timeperiod for job to process """ time_qualifier = context.process_context[process_name].time_qualifier end_timeperiod = time_helper.increment_timeperiod(time_qualifier, timeperiod) return end_timeperiod def _is_noop_timeperiod(self, process_name, timeperiod): """ method verifies if the given timeperiod for given process is valid or falls in-between grouping checkpoints :param process_name: name of the process :param timeperiod: timeperiod to verify :return: False, if given process has no time_grouping set or it is equal to 1. False, if time_grouping is custom but the given timeperiod matches the grouped timeperiod. True, if the timeperiod falls in-between grouping cracks """ time_grouping = context.process_context[process_name].time_grouping if time_grouping == 1: return False process_hierarchy = self.timetable.get_tree(process_name).process_hierarchy timeperiod_dict = process_hierarchy[process_name].timeperiod_dict return timeperiod_dict._translate_timeperiod(timeperiod) != timeperiod def _process_noop_timeperiod(self, job_record): """ method is valid for processes having time_grouping != 1. should a job record fall in-between grouped time milestones, its state should be set to STATE_NOOP without any processing """ job_record.state = job.STATE_NOOP self.job_dao.update(job_record) time_grouping = context.process_context[job_record.process_name].time_grouping msg = 'Job {0}@{1} with time_grouping {2} was transferred to STATE_NOOP' \ .format(job_record.process_name, job_record.timeperiod, time_grouping) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) def _process_state_embryo(self, job_record): """ method that takes care of processing job records in STATE_EMBRYO state""" pass def _process_state_in_progress(self, job_record): """ method that takes care of processing job records in STATE_IN_PROGRESS state""" pass def _process_state_final_run(self, job_record): """method takes care of processing job records in STATE_FINAL_RUN state""" uow = self.uow_dao.get_one(job_record.related_unit_of_work) if uow.is_processed: self.update_job(job_record, uow, job.STATE_PROCESSED) elif uow.is_noop: self.update_job(job_record, uow, job.STATE_NOOP) elif uow.is_canceled: self.update_job(job_record, uow, job.STATE_SKIPPED) elif uow.is_invalid: msg = 'Job {0}: UOW for {1}@{2} is in {3}; ' \ 'relying on the Garbage Collector to either recycle or cancel the UOW.' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, uow.state) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) else: msg = 'Suppressed creating UOW for {0}@{1}; Job is in {2}; uow is in {3}' \ .format(job_record.process_name, job_record.timeperiod, job_record.state, uow.state) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) timetable_tree = self.timetable.get_tree(job_record.process_name) timetable_tree.build_tree() def _process_terminal_state(self, job_record): """ method logs a warning message notifying that the job is no longer govern by this state machine """ msg = 'Job {0} for {1}@{2} is in the terminal state {3}, ' \ 'and is no further govern by the State Machine {4}' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, job_record.state, self.name) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) def manage_job_with_blocking_children(self, job_record): """ method will trigger job processing only if: - all children are finished (STATE_PROCESSED, STATE_SKIPPED, STATE_NOOP) - all external dependencies are finalized (STATE_PROCESSED, STATE_SKIPPED, STATE_NOOP) """ is_job_finalizable = self.timetable.is_job_record_finalizable(job_record) if is_job_finalizable: self.manage_job(job_record) else: msg = 'Job {0}@{1} is blocked by unprocessed children/dependencies. Waiting another tick' \ .format(job_record.process_name, job_record.timeperiod) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) def manage_job_with_blocking_dependencies(self, job_record): """ method will trigger job processing only if: - all dependencies are in [STATE_PROCESSED, STATE_NOOP] NOTICE: method will transfer current job into STATE_SKIPPED if any dependency is in STATE_SKIPPED """ depon_summary = self.timetable.dependent_on_summary(job_record) assert isinstance(depon_summary, DependentOnSummary) if depon_summary.all_processed: self.manage_job(job_record) elif depon_summary.skipped_present: # As soon as among <dependent on> periods are in STATE_SKIPPED # there is very little sense in waiting for them to become STATE_PROCESSED # Skip this timeperiod itself job_record.state = job.STATE_SKIPPED self.job_dao.update(job_record) self.mq_transmitter.publish_job_status(job_record) depon_summary.log_skipped(WARNING) msg = 'Job {0}@{1} is blocked by STATE_SKIPPED dependencies. ' \ 'Transferred the job to STATE_SKIPPED'.format(job_record.process_name, job_record.timeperiod) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) else: depon_summary.log_unprocessed(INFO) msg = 'Job {0}@{1} is blocked by unprocessed dependencies. Waiting another tick' \ .format(job_record.process_name, job_record.timeperiod) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg) def manage_job(self, job_record): """ method main duty - is to _avoid_ publishing another unit_of_work, if previous was not yet processed In case the State Machine sees that the unit_of_work already exist it could either: - update boundaries of the processing; republish - wait another tick; idle """ assert isinstance(job_record, Job) try: if self._is_noop_timeperiod(job_record.process_name, job_record.timeperiod): self._process_noop_timeperiod(job_record) return if job_record.is_embryo: self._process_state_embryo(job_record) elif job_record.is_in_progress: self._process_state_in_progress(job_record) elif job_record.is_final_run: self._process_state_final_run(job_record) elif job_record.is_skipped: self._process_terminal_state(job_record) elif job_record.is_processed: self._process_terminal_state(job_record) elif job_record.is_noop: self._process_terminal_state(job_record) else: msg = 'Unknown state {0} of the job {1}'.format(job_record.state, job_record.db_id) self._log_message(ERROR, job_record.process_name, job_record.timeperiod, msg) except LookupError as e: job_record.number_of_failures += 1 self.job_dao.update(job_record) self.timetable.skip_if_needed(job_record) msg = 'Increasing fail counter for Job {0}@{1}, because of: {2}' \ .format(job_record.process_name, job_record.timeperiod, e) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) finally: self.mq_transmitter.publish_job_status(job_record) def reprocess_job(self, job_record): """ method marks given job for reprocessing: - if no UOW was bind with the job record - it is transferred to STATE_EMBRYO only - if related UOW was not finished - we mark the UOW as STATE_CANCELED and pass the job record thru the _process_in_progress routine - otherwise job record is passed thru the _process_in_progress routine """ original_job_state = job_record.state job_record.number_of_failures = 0 if not job_record.related_unit_of_work: job_record.state = job.STATE_EMBRYO self.job_dao.update(job_record) else: uow = self.uow_dao.get_one(job_record.related_unit_of_work) if not uow.is_finished: uow.state = unit_of_work.STATE_CANCELED uow.submitted_at = datetime.utcnow() self.uow_dao.update(uow) self._process_state_in_progress(job_record) msg = 'Reprocessed Job {0} for {1}@{2}: state transfer {3} -> {4};' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, original_job_state, job_record.state) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) def skip_job(self, job_record): """ method transfers: - given job into STATE_SKIPPED if it is not not in finished state - UOW into STATE_CANCELED if it is not in finished state """ original_job_state = job_record.state if not job_record.is_finished: job_record.state = job.STATE_SKIPPED self.job_dao.update(job_record) if job_record.related_unit_of_work: uow = self.uow_dao.get_one(job_record.related_unit_of_work) if not uow.is_finished: uow.state = unit_of_work.STATE_CANCELED uow.submitted_at = datetime.utcnow() self.uow_dao.update(uow) msg = 'Skipped Job {0} for {1}@{2}: state transfer {3} -> {4};' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, original_job_state, job_record.state) self._log_message(WARNING, job_record.process_name, job_record.timeperiod, msg) def create_job(self, process_name, timeperiod): """ method creates a job record in STATE_EMBRYO for given process_name and timeperiod :returns: created job record of type <Job>""" job_record = Job() job_record.state = job.STATE_EMBRYO job_record.timeperiod = timeperiod job_record.process_name = process_name self.job_dao.update(job_record) self.logger.info('Created Job {0} for {1}@{2}' .format(job_record.db_id, job_record.process_name, job_record.timeperiod)) return job_record def update_job(self, job_record, uow, new_state): """ method updates job record with a new unit_of_work and new state""" original_job_state = job_record.state job_record.state = new_state job_record.related_unit_of_work = uow.db_id self.job_dao.update(job_record) msg = 'Updated Job {0} for {1}@{2}: state transfer {3} -> {4};' \ .format(job_record.db_id, job_record.process_name, job_record.timeperiod, original_job_state, new_state) self._log_message(INFO, job_record.process_name, job_record.timeperiod, msg)

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reconstruction_depth_to_3d/open3d/create_point_cloud.py

iwatake2222/opencv_sample

35

160485

import open3d as o3d image_rgb = o3d.io.read_image("image.png") image_depth = o3d.io.read_image("image_depth.png") width = int(image_rgb.get_max_bound()[0]) height = int(image_rgb.get_max_bound()[1]) camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(width=width, height=height, fx=500, fy=500,cx=width/2, cy=height/2) image_rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(image_rgb, image_depth, convert_rgb_to_intensity=False) pcd = o3d.geometry.PointCloud.create_from_rgbd_image(image_rgbd, camera_intrinsic) pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)) # Flip it, otherwise the pointcloud will be upside down pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) o3d.visualization.draw_geometries([pcd], width=640, height=480) o3d.io.write_point_cloud("output.ply", pcd)

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geolocator.py

Kugeleis/TeslaInventoryChecker

7

18850

<reponame>Kugeleis/TeslaInventoryChecker import http.client import json from types import SimpleNamespace def get_token(): conn = http.client.HTTPSConnection("www.tesla.com") payload = { "resource": "geocodesvc", "csrf_name": "", "csrf_value": "" } headers = { 'Content-Type': 'application/json' } conn.request("POST", "/inventory/api/v1/refresh_token", json.dumps(payload), headers) res = conn.getresponse() data = res.read() auth = json.loads(data, object_hook=lambda d: SimpleNamespace(**d)) return auth.token def decode_zip(token, zip_code, country_code): conn = http.client.HTTPSConnection("www.tesla.com") payload = { "token": token, "postal_code": zip_code, "country_code": country_code, "csrf_name": "", "csrf_value": "" } headers = { 'Content-Type': 'application/json' } conn.request("POST", "/inventory/api/v1/address", json.dumps(payload), headers) res = conn.getresponse() data = res.read() geo_result = json.loads(data, object_hook=lambda d: SimpleNamespace(**d)) # Example Data: # { # "city": "Montreal", # "stateProvince": "Quebec", # "postalCode": "H1K 3T2", # "countryCode": "CA", # "countryName": "Canada", # "longitude": -73.5614205, # "latitude": 45.60802700000001, # "county": "Montreal", # "stateCode": "QC" # } return geo_result.data

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tests/test_create_account.py

moctarjallo/ebank

0

152881

<filename>tests/test_create_account.py import unittest import pythonapi as api from banking import CreateAccount class TestCreateAccount(unittest.TestCase): def setUp(self): self.create_account = CreateAccount() def test_normal(self): request = api.Request({ 'firstname': 'moctar', 'lastname': 'diallo', 'address': 'medina', 'balance': 400 }) account = self.create_account.execute(request.data) response = api.Response(account) self.assertLess(response.data['code'], 10000) self.assertGreaterEqual(response.data['code'], 1) del response.data['code'] self.assertEqual(response.data, { 'client':{ 'firstname': 'moctar', 'lastname': 'diallo', 'address': 'medina', }, 'balance': 400, }) if __name__ == '__main__': unittest.main()

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future_annotations.py

asottile/future-annotations

21

114123

import argparse import ast import codecs import encodings import io import sys import tokenize import warnings from typing import Match from typing import Optional from typing import Sequence from typing import Set from typing import Tuple import tokenize_rt def _ast_parse(contents_text: str) -> ast.Module: # intentionally ignore warnings, we might be fixing warning-ridden syntax with warnings.catch_warnings(): warnings.simplefilter('ignore') return ast.parse(contents_text.encode()) def _ast_to_offset(node: ast.expr) -> tokenize_rt.Offset: return tokenize_rt.Offset(node.lineno, node.col_offset) class Visitor(ast.NodeVisitor): def __init__(self) -> None: self.offsets: Set[tokenize_rt.Offset] = set() def visit_AnnAssign(self, node: ast.AnnAssign) -> None: self.offsets.add(_ast_to_offset(node.annotation)) self.generic_visit(node) def visit_FunctionDef(self, node: ast.FunctionDef) -> None: args = [] if hasattr(node.args, 'posonlyargs'): # pragma: no cover (py38+) args.extend(node.args.posonlyargs) args.extend(node.args.args) if node.args.vararg is not None: args.append(node.args.vararg) args.extend(node.args.kwonlyargs) if node.args.kwarg is not None: args.append(node.args.kwarg) for arg in args: if arg.annotation is not None: self.offsets.add(_ast_to_offset(arg.annotation)) if node.returns is not None: self.offsets.add(_ast_to_offset(node.returns)) self.generic_visit(node) utf_8 = encodings.search_function('utf8') def _new_coding_cookie(match: Match[str]) -> str: s = match[0] i = 0 while s[i].isspace(): i += 1 ret = f'{s[:i]}# {"*" * (len(s) - 2 - i)}' assert len(ret) == len(s), (len(ret), len(s)) return ret def decode(b: bytes, errors: str = 'strict') -> Tuple[str, int]: u, length = utf_8.decode(b, errors) # replace encoding cookie so there isn't a recursion problem lines = u.splitlines(True) for idx in (0, 1): if idx >= len(lines): break lines[idx] = tokenize.cookie_re.sub(_new_coding_cookie, lines[idx]) u = ''.join(lines) visitor = Visitor() visitor.visit(_ast_parse(u)) tokens = tokenize_rt.src_to_tokens(u) for i, token in tokenize_rt.reversed_enumerate(tokens): if token.offset in visitor.offsets: # look forward for a `:`, `,`, `=`, ')' depth = 0 j = i + 1 while depth or tokens[j].src not in {':', ',', '=', ')', '\n'}: if tokens[j].src in {'(', '{', '['}: depth += 1 elif tokens[j].src in {')', '}', ']'}: depth -= 1 j += 1 j -= 1 # look backward to delete whitespace / comments / etc. while tokens[j].name in tokenize_rt.NON_CODING_TOKENS: j -= 1 quoted = repr(tokenize_rt.tokens_to_src(tokens[i:j + 1])) tokens[i:j + 1] = [tokenize_rt.Token('STRING', quoted)] return tokenize_rt.tokens_to_src(tokens), length class IncrementalDecoder(codecs.BufferedIncrementalDecoder): def _buffer_decode(self, input, errors, final): # pragma: no cover if final: return decode(input, errors) else: return '', 0 class StreamReader(utf_8.streamreader): """decode is deferred to support better error messages""" _stream = None _decoded = False @property def stream(self): if not self._decoded: text, _ = decode(self._stream.read()) self._stream = io.BytesIO(text.encode('UTF-8')) self._decoded = True return self._stream @stream.setter def stream(self, stream): self._stream = stream self._decoded = False # codec api codec_map = { name: codecs.CodecInfo( name=name, encode=utf_8.encode, decode=decode, incrementalencoder=utf_8.incrementalencoder, incrementaldecoder=IncrementalDecoder, streamreader=StreamReader, streamwriter=utf_8.streamwriter, ) for name in ('future-annotations', 'future_annotations') } def register() -> None: # pragma: no cover codecs.register(codec_map.get) def main(argv: Optional[Sequence[str]] = None) -> int: parser = argparse.ArgumentParser(description='Prints transformed source.') parser.add_argument('filename') args = parser.parse_args(argv) with open(args.filename, 'rb') as f: text, _ = decode(f.read()) getattr(sys.stdout, 'buffer', sys.stdout).write(text.encode('UTF-8')) return 0 if __name__ == '__main__': raise SystemExit(main())

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Python Files/preprocessing.py

errohankumar/thesis

0

45163

import os import pandas as pd from datetime import datetime from preprocessing_service import PreProcessingService class PreProcessing(PreProcessingService): """ A class used to automatically scrape CSV files from ENTSOE Transparecny Platform ... Attributes ---------- _preProcessing : str A PreProcessingService instance for handling CSV files _preProcessing2: str A PreProcessingService instance for handling CSV files Methods ------- find_time_column(df) A function to find the timestamp column for a given CSV files start_preprocess() Initializes pre-processing """ def __init__(self): super().__init__() self._preProcessing = PreProcessingService() self._preProcessing2 = PreProcessingService() @staticmethod def find_time_column(df): """Finds the timestamp column for a given dataset. Parameters ---------- df : DataFrame The dataset containing historical-observations Raises ------ ValueError If no timestamp column is found. """ for column in df.get_columns(): try: found = datetime.strptime(df.df[column][0][:10], "%d.%m.%Y") return column except: continue raise ValueError("Timestamp column not found!") @staticmethod def last_date_for_column(df, column_name): """ Return last date of a specific column """ return df[column_name].dropna().index[-1:][0] def start_preprocess(self): """Initiates a sequence of pre-processing tasks """ # Get all the CSV files in the directory file_directory = os.getcwd() + "/download/" file_names = os.listdir(file_directory) actual_columns = ['Solar - Actual Aggregated [MW]', 'Wind Offshore - Actual Aggregated [MW]', 'Wind Onshore - Actual Aggregated [MW]', 'Actual Total Load [MW] - BZN|DE-LU'] forecast_columns = ['Generation - Solar [MW] Day Ahead/ BZN|DE-LU', 'Generation - Wind Offshore [MW] Day Ahead/ BZN|DE-LU', 'Generation - Wind Onshore [MW] Day Ahead/ BZN|DE-LU', 'Day-ahead Total Load Forecast [MW] - BZN|DE-LU'] filter_columns = ['Day-ahead Price [EUR/MWh]', 'Generation - Solar [MW] Day Ahead/ BZN|DE-LU', 'Generation - Wind Offshore [MW] Day Ahead/ BZN|DE-LU', 'Generation - Wind Onshore [MW] Day Ahead/ BZN|DE-LU', 'Solar - Actual Aggregated [MW]', 'Wind Offshore - Actual Aggregated [MW]', 'Wind Onshore - Actual Aggregated [MW]', 'Day-ahead Total Load Forecast [MW] - BZN|DE-LU', 'Actual Total Load [MW] - BZN|DE-LU'] for i in range(0, len(file_names)): # First CSV is used as the main file to combine the rest of the files if i == 0: self._preProcessing.open_csv(file_directory + file_names[i]) # Find time column and set it as index time_column = self.find_time_column(self._preProcessing) self._preProcessing.reformat_time(colname=time_column) self._preProcessing.rename_colname(time_column, "cet_timestamp") self._preProcessing.set_index(colname="cet_timestamp") # Convert arguments (non numeric values) to invalid NAN for j in self._preProcessing.get_columns(): self._preProcessing.df[j] = pd.to_numeric(self._preProcessing.df[j], errors='coerce') # Drop first duplicate self._preProcessing.drop_duplicates(keep="first", inplace=True) # Resample the quarterly values in the 'Total Load' and 'Actual Generation' to hourly if ("Actual" or "Load" or "Forecasts") in file_names[i]: self._preProcessing = self._preProcessing.resample_rows() else: self._preProcessing2.open_csv(file_directory + file_names[i]) # Find time column and set it as index time_column = self.find_time_column(self._preProcessing2) self._preProcessing2.reformat_time(colname=time_column) self._preProcessing2.rename_colname(time_column, "cet_timestamp") self._preProcessing2.set_index(colname="cet_timestamp") # Drop all the columns that are not numeric for column_index in self._preProcessing2.get_columns(): try: float(self._preProcessing2.df[column_index][0]) except: self._preProcessing2.df.drop(columns=column_index, inplace=True) continue # Convert the non-numeric items to Nans # If ‘coerce’, then invalid parsing will be set as NaN. for column_name in self._preProcessing2.get_columns(): self._preProcessing2.df[column_name] = pd.to_numeric(self._preProcessing2.df[column_name], errors='coerce') # To resample quarter hour to hour if present self._preProcessing2 = self._preProcessing2.resample_rows() # concatenate all the columns in one single dataset print(self._preProcessing2.df) self._preProcessing.df = pd.concat([self._preProcessing.df, self._preProcessing2.df], axis=1, sort=False) # Filter and drop all rows with four NAN values (to remove NAN values beyond latest time of available # day-ahead price) self._preProcessing.df = self._preProcessing.df[filter_columns].dropna(thresh=4, axis=0) print(self._preProcessing.df.columns) # Get three month of past observations if len(self._preProcessing.df) >= 2160: self._preProcessing.df = self._preProcessing.df[-2160:] # the missing values in the Actuals can be replaced by the generation forecast # the last date of the actuals column just before the missing values can be replaced with the values from the # last date of the forecast columns for column_name, forecast_column_name in zip(actual_columns, forecast_columns): last_date = self.last_date_for_column(self._preProcessing.df, column_name) self._preProcessing.df[column_name][last_date:] = self._preProcessing.df[forecast_column_name][ last_date:].values # Generate and add 'WeekDays' feature week_day_col = self._preProcessing.df.index.weekday self._preProcessing.df["WeekDays"] = week_day_col # Imputation through a interpolation method called 'cubic' to fulfill forecasting method criteria self._preProcessing = self._preProcessing.interpolate_ts() # Remove forecast columns, since they are not needed anymore self._preProcessing.remove_columns(cols = ['Generation - Solar [MW] Day Ahead/ BZN|DE-LU', 'Generation - Wind Offshore [MW] Day Ahead/ BZN|DE-LU', 'Generation - Wind Onshore [MW] Day Ahead/ BZN|DE-LU', 'Day-ahead Total Load Forecast [MW] - BZN|DE-LU']) self._preProcessing.df.to_csv(os.getcwd() + "/download/final_dataset_kafka.csv")

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ai2business/kpi_collector/test/test_finance_collector.py

Anselmoo/ai2business

0

147504

"""Test-Enviroment for finance_collector.py""" import pandas as pd from ai2business.kpi_collector import finance_collector as fnc ticker = fnc.FinanceCollector() builder = fnc.DesignerFinanceCollector(["SPY", "AAPL", "MSFT"]) ticker.builder = builder def test_find_chart_history(): ticker.find_chart_histogram() assert type(builder.return_dataframe) == type(pd.DataFrame()) def test_find_isin_code(): ticker.find_isin_code() assert type(builder.return_dict) == type(dict()) def test_find_major_holders_dict(): ticker.find_major_holders() assert type(builder.return_dict) == type(dict()) def test_find_institutional_holders(): ticker.find_institutional_holders() assert type(builder.return_dict) == type(dict()) def test_find_mutualfund_holders(): ticker.find_mutualfund_holders() assert type(builder.return_dict) == type(dict()) def test_find_dividends(): ticker.find_dividends() assert type(builder.return_dict) == type(dict()) def test_find_splits(): ticker.find_splits() assert type(builder.return_dict) == type(dict()) def test_find_actions(): ticker.find_actions() assert type(builder.return_dict) == type(dict()) def test_find_info(): ticker.find_info() assert type(builder.return_dict) == type(dict()) def test_find_calendar(): ticker.find_calendar() assert type(builder.return_dict) == type(dict()) def test_find_recommendation(): ticker.find_recommendations() assert type(builder.return_dict) == type(dict()) def test_find_earnings(): ticker.find_earnings() assert type(builder.return_dict) == type(dict()) def test_find_quarterly_earnings(): ticker.find_quarterly_earnings() assert type(builder.return_dict) == type(dict()) def test_find_financials(): ticker.find_financials() assert type(builder.return_dict) == type(dict()) def test_find_quarterly_financials(): ticker.find_quarterly_financials() assert type(builder.return_dict) == type(dict()) def test_find_balancesheet(): ticker.find_balancesheet() assert type(builder.return_dict) == type(dict()) def test_find_quarterly_balancesheet(): ticker.find_quarterly_balancesheet() assert type(builder.return_dict) == type(dict()) def test_find_cashflow(): ticker.find_cashflow() assert type(builder.return_dict) == type(dict()) def test_find_quarterly_cashflow(): ticker.find_quarterly_cashflow() assert type(builder.return_dict) == type(dict()) def test_find_sustainability(): ticker.find_sustainability() assert type(builder.return_dict) == type(dict()) def test_find_options(): ticker.find_options() assert type(builder.return_dict) == type(dict())

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venv/Lib/site-packages/pandas/core/array_algos/transforms.py

arnoyu-hub/COMP0016miemie

0

4945

""" transforms.py is for shape-preserving functions. """ import numpy as np def shift(values: np.ndarray, periods: int, axis: int, fill_value) -> np.ndarray: new_values = values if periods == 0 or values.size == 0: return new_values.copy() # make sure array sent to np.roll is c_contiguous f_ordered = values.flags.f_contiguous if f_ordered: new_values = new_values.T axis = new_values.ndim - axis - 1 if new_values.size: new_values = np.roll( new_values, np.intp(periods), axis=axis, ) axis_indexer = [slice(None)] * values.ndim if periods > 0: axis_indexer[axis] = slice(None, periods) else: axis_indexer[axis] = slice(periods, None) new_values[tuple(axis_indexer)] = fill_value # restore original order if f_ordered: new_values = new_values.T return new_values

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fixture/soap.py

AlekseyVR/Python_mantis

0

44631

from suds.client import Client from suds import WebFault from model.project import Project class SoapHelper: def __init__(self, app): self.app = app def can_login(self, username, password): client = Client(self.app.base_url + "api/soap/mantisconnect.php?wsdl") try: client.service.mc_login(username, password) return True except WebFault: return False def get_project_list_user(self, username, password): self.can_login(username, password) client = Client(self.app.base_url + "api/soap/mantisconnect.php?wsdl") try: response = client.service.mc_projects_get_user_accessible(username, password) project_list = [] for element in response: project = Project(id=element.id, name_project=element.name, description_project=element.description) project_list.append(project) return project_list except WebFault: return False

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my_weather_api/admin.py

swingthrough/weather-api-task-django

0

155557

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

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d_dataprocess.py

adamyalei/Property-Graphs-Lab-Assignment

0

164617

import re regex_str = [ r'<[^>]+>', # HTML tags r'(?:@[\w_]+)', # @-mentions r"(?:\#+[\w_]+[\w\'_\-]*[\w_]+)", # hash-tags r'http[s]?://(?:[a-z]|[0-9]|[$-_@.&+]|[!*,]|(?:%[0-9a-f][0-9a-f]))+', # URLs r'(?:(?:\d+,?)+(?:\.?\d+)?)', # numbers r"(?:[a-z][a-z'\-_]+[a-z])", # words with - and ' r'(?:[\w_]+)', # other words r'(?:\S)' # anything else ] tokens_re = re.compile(r'('+'|'.join(regex_str)+')', re.VERBOSE | re.IGNORECASE) def tokenize(s): return tokens_re.findall(s) def preprocess(s, lowercase=False): tokens = tokenize(s) if lowercase: tokens = [token.lower() for token in tokens] return tokens def extract_keyword_from_title(title): keywords = [] title = str(title) for token in preprocess(title): keywords.append(token) ALL_KEYWORDS.extend(keywords) return keywords ALL_KEYWORDS = [] articles = pd.read_csv('dblp_dump/output_article.csv', delimiter=';', usecols=['key','author','title','year','ee', 'cite', 'mdate','pages','journal','volume'], dtype=str) # Extract Keyword articles['keyword'] = articles['title'].apply(extract_keyword_from_title).str.join('|') # Store keyword ALL_KEYWORDS = pd.DataFrame(ALL_KEYWORDS, columns=["keyword"]).drop_duplicates() articles.to_csv('out/papers.csv', index=None, header=True) ALL_KEYWORDS.to_csv('out/keywords.csv', index=None, header=True)

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examples/rsa_client.py

datalogics/flask-oauthprovider

1

170757

from os.path import join, dirname from client import app def fread(fn): with open(join(dirname(__file__), fn), 'r') as f: return f.read().decode("utf-8") app.config["OAUTH_CREDENTIALS"] = { u"rsa_key": fread("mykey.pem"), u"signature_method": u"RSA-SHA1", "signature_type": "body" } app.config["CLIENT_KEY"] = u"<KEY>" app.run(debug=True, port=5001)

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library/cogs/rss_feed.py

og2701/C-3PO

3

183898

<reponame>og2701/C-3PO<filename>library/cogs/rss_feed.py<gh_stars>1-10 from discord.ext.commands import Cog, command from discord.ext import tasks from discord import Embed, Client from feedparser import parse client = Client() class RSS(Cog): def __init__(self, bot): self.bot = bot self.SWnews.start() @tasks.loop(seconds=3600) async def SWnews(self): nf = parse("https://www.starwars.com/news/feed").entries[0] with open("./data/rss_feed.txt","r") as f: old_feed = f.read() if old_feed != str(nf): with open("./data/rss_feed.txt","w") as f: f.write(str(nf)) Mbed = Embed(colour=0xE5B233, description=nf.summary+f"\n[Link to article]({nf.link})") Mbed.set_author(name=nf.title) Mbed.set_thumbnail(url=nf.media_thumbnail[0]["url"]) Mbed.set_footer(text='Author: '+nf.author+' | Published: '+nf.published) try: await self.stdout.send(embed=Mbed) except AttributeError as exc: print(f"[!] {exc}") @Cog.listener() async def on_ready(self): if not self.bot.ready: self.stdout = self.bot.get_channel(797266342822412338) self.bot.cogs_ready.ready_up("RSS") self.SWnews.start() def setup(bot): bot.add_cog(RSS(bot))

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cplcom/moa/device/mcdaq.py

matham/cplcom

0

23791

'''Barst Measurement Computing DAQ Wrapper ========================================== ''' from functools import partial from pybarst.mcdaq import MCDAQChannel from kivy.properties import NumericProperty, ObjectProperty from moa.threads import ScheduledEventLoop from moa.device.digital import ButtonViewPort from cplcom.moa.device import DeviceExceptionBehavior __all__ = ('MCDAQDevice', ) class MCDAQDevice(DeviceExceptionBehavior, ButtonViewPort, ScheduledEventLoop): '''A :class:`moa.device.digital.ButtonViewPort` wrapper around a :class:`pybarst.mcdaq.MCDAQChannel` instance which controls a Switch and Sense 8/8. For this class, :class:`moa.device.digital.ButtonViewPort.dev_map` must be provided upon creation and it's a dict whose keys are the property names and whose values are the Switch and Sense 8/8 channel numbers that the property controls. E.g. for a light switch connected to channel 3 on the Switch and Sense 8/8 output port define the class:: class MyMCDAQDevice(MCDAQDevice): light = BooleanProperty(False) And then create the instance with:: dev = MyMCDAQDevice(dev_map={'light': 3}) And then we can set it high by calling e.g.:: dev.set_state(high=['light']) For an input devices it can defined similarly and the state of the property reflects the value of the port. A switch and sense which has both a input and output device still needs to create two device for each. ''' __settings_attrs__ = ('SAS_chan', ) _read_event = None def _write_callback(self, value, mask, result): self.timestamp = result for idx, name in self.chan_dev_map.iteritems(): if mask & (1 << idx): setattr(self, name, bool(value & (1 << idx))) self.dispatch('on_data_update', self) def _read_callback(self, result, **kwargs): t, val = result self.timestamp = t for idx, name in self.chan_dev_map.iteritems(): setattr(self, name, bool(val & (1 << idx))) self.dispatch('on_data_update', self) def set_state(self, high=[], low=[], **kwargs): if self.activation != 'active': raise TypeError('Can only set state of an active device. Device ' 'is currently "{}"'.format(self.activation)) if 'o' not in self.direction: raise TypeError('Cannot write state for a input device') dev_map = self.dev_map mask = 0 val = 0 for name in high: idx = dev_map[name] val |= (1 << idx) mask |= (1 << idx) for name in low: mask |= (1 << dev_map[name]) self.request_callback( self.chan.write, callback=partial(self._write_callback, val, mask), mask=mask, value=val) def get_state(self): if self.activation != 'active': raise TypeError('Can only read state of an active device. Device ' 'is currently "{}"'.format(self.activation)) if 'i' in self.direction: # happens anyway return self._read_event = self.request_callback( self.chan.read, callback=self._read_callback) def activate(self, *largs, **kwargs): kwargs['state'] = 'activating' if not super(MCDAQDevice, self).activate(*largs, **kwargs): return False self.start_thread() self.chan = MCDAQChannel(chan=self.SAS_chan, server=self.server.server) def finish_activate(*largs): self.activation = 'active' if 'i' in self.direction: self._read_event = self.request_callback( self.chan.read, repeat=True, callback=self._read_callback) self.request_callback(self._start_channel, finish_activate) return True def _start_channel(self): chan = self.chan chan.open_channel() if 'o' in self.direction: chan.write(mask=0xFF, value=0) def deactivate(self, *largs, **kwargs): kwargs['state'] = 'deactivating' if not super(MCDAQDevice, self).deactivate(*largs, **kwargs): return False self.remove_request(self.chan.read, self._read_event) self._read_event = None def finish_deactivate(*largs): self.activation = 'inactive' self.stop_thread() self.request_callback(self._stop_channel, finish_deactivate) return True def _stop_channel(self): if 'o' in self.direction: self.chan.write(mask=0xFF, value=0) if 'i' in self.direction and self.chan.continuous: self.chan.cancel_read(flush=True) chan = ObjectProperty(None) '''The internal :class:`pybarst.mcdaq.MCDAQChannel` instance. It is read only and is automatically created. ''' server = ObjectProperty(None, allownone=True) '''The internal barst :class:`pybarst.core.server.BarstServer`. It must be provided to the instance. ''' SAS_chan = NumericProperty(0) '''The channel number of the Switch & Sense 8/8 as configured in InstaCal. Defaults to zero. '''

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Lib/hTools2/modules/unicode.py

gferreira/hTools2

11

67418

<gh_stars>10-100 # [h] hTools2.modules.unicode from collections import OrderedDict '''Tools to work with Unicode, convert glyph names to hex/unicode etc.''' def clear_unicodes(font): '''Remove unicodes from all glyphs in the font.''' for g in font: g.unicodes = [] font.update() def auto_unicodes(font, custom_unicodes={}): '''Automatically set unicode values for all glyphs in the font.''' clear_unicodes(font) for g in font: if g is not None: auto_unicode(g, custom_unicodes) font.update() def auto_unicode(g, custom_unicodes={}): ''' Automatically set unicode value(s) for the specified glyph. The method uses RoboFab's ``glyph.autoUnicodes()`` function for common glyphs, and complements it with additional values from ``unicodes_extra``. ''' if g.name is not None: # handle 'uni' names if g.name[:3] == "uni" and len(g.name) in [7, 8]: c = g.name g.unicode = int(c.split('uni')[1], 16) # handle extra cases elif g.name in unicodes_extra.keys(): uString = 'uni%s' % unicodes_extra[g.name] g.unicode = unicode_hexstr_to_int(uString) # use auto unicode for everything else elif custom_unicodes.get(g.name) is not None: uString = 'uni%s' % custom_unicodes[g.name] g.unicode = unicode_hexstr_to_int(uString) else: g.autoUnicodes() g.update() #------------------------------ # unicode-to-string conversion #------------------------------ def unicode_int_to_hexstr(intUnicode, _0x=False, uni=False): ''' Converts unicode integers to hexadecimal. See also the reverse function ``unicode_hexstr_to_int``. Note that ``glyph.unicodes`` is a list (a glyph can have many unicodes), so we need to pass the first value only. The optional parameters ``uni`` and ``_0x`` add the respective prefixes. ''' hexUnicode = "%X".lstrip("0x") % intUnicode hexUnicode = "0" * (4 - len(hexUnicode)) + hexUnicode if _0x: return "0x%s" % hexUnicode elif uni: return "uni%s" % hexUnicode return hexUnicode def unicode_hexstr_to_int(hexUnicode, replaceUni=True): ''' Converts a unicode hexadecimal value into an integer. It does exactly the reverse of ``unicode_int_to_hexstr``. ''' if replaceUni: return int(hexUnicode.replace("uni",""), 16) return int(hexUnicode.lstrip("x"), 16) #----------------------------- # additional unicode mappings #----------------------------- #: A dict containing additional `glyphName` to `unicode` mappings. unicodes_extra = { # extended latin lc 'aemacron' : '01E3', 'dotlessj' : '0237', 'schwa' : '0259', 'ymacron' : '0233', 'eszett' : '00DF', # extended latin uc 'AEmacron' : '01E2', 'Schwa' : '018F', 'Uppercaseeszett' : '1E9E', # ligatures 'fi' : 'FB01', 'fl' : 'FB02', 'f_f' : 'FB00', 'f_f_i' : 'FB03', 'f_f_l' : 'FB04', # greek exceptions 'Delta' : '2206', # 0394 'Omega' : '2126', # 03A9 'mu' : '00B5', # 03BC # superiors 'zerosuperior' : '2070', 'onesuperior' : '00B9', 'twosuperior' : '00B2', 'threesuperior' : '00B3', 'foursuperior' : '2074', 'fivesuperior' : '2075', 'sixsuperior' : '2076', 'sevensuperior' : '2077', 'eightsuperior' : '2078', 'ninesuperior' : '2079', # inferiors 'zeroinferior' : '2080', 'oneinferior' : '2081', 'twoinferior' : '2082', 'threeinferior' : '2083', 'fourinferior' : '2084', 'fiveinferior' : '2085', 'sixinferior' : '2086', 'seveninferior' : '2087', 'eightinferior' : '2088', 'nineinferior' : '2089', # spaces 'enspace' : '2002', 'emspace' : '2003', 'nbspace' : '00A0', 'hairspace' : '200A', 'thinspace' : '2009', 'thickspace' : '2004', 'figurespace' : '2007', 'zerowidthspace' : '200B', # combining accents 'gravecomb' : '0300', 'acutecomb' : '0301', 'circumflexcomb' : '0302', 'tildecomb' : '0303', 'dieresiscomb' : '0308', 'dotbelowcomb' : '0323', 'cedillacomb' : '0327', # arrows 'arrowleft' : '2190', 'arrowup' : '2191', 'arrowright' : '2192', 'arrowdown' : '2193', 'arrowleftright' : '2194', 'arrowupdown' : '2195', 'arrowupleft' : '2196', 'arrowupright' : '2197', 'arrowdownright' : '2198', 'arrowdownleft' : '2199', # latin accented lc 'adotbelow' : '1EA1', 'aringacute' : '01FB', 'edotbelow' : '1EB9', 'etilde' : '1EBD', 'gcaron' : '01E7', 'idotbelow' : '1ECB', 'ndotbelow' : '1E47', 'nhookleft' : '0272', 'odotbelow' : '1ECD', 'oogonek' : '01EB', 'sdotbelow' : '1E63', 'udotbelow' : '1EE5', 'ymacron' : '0233', 'ytilde' : '1EF9', # latin accented uc 'Adotbelow' : '1EA0', 'Aringacute' : '01FA', 'Edotbelow' : '1EB8', 'Etilde' : '1EBC', 'Gcaron' : '01E6', 'Idotbelow' : '1ECA', 'Ndotbelow' : '1E46', 'Nhookleft' : '019D', 'Odotbelow' : '1ECC', 'Oogonek' : '01EA', 'Sdotbelow' : '1E62', 'Udotbelow' : '1EE4', 'Ymacron' : '0232', 'Ytilde' : '1EF8', # symbols etc 'bulletoperator' : '2219', 'florin' : '0192', } #----------------------------- # unicode to psNames mappings #----------------------------- #: A dictionary mapping unicode values to psNames. unicode2psnames = { None : '.notdef', 32 : 'space', 33 : 'exclam', 34 : 'quotedbl', 35 : 'numbersign', 36 : 'dollar', 37 : 'percent', 38 : 'ampersand', 39 : 'quotesingle', 40 : 'parenleft', 41 : 'parenright', 42 : 'asterisk', 43 : 'plus', 44 : 'comma', 45 : 'hyphen', 46 : 'period', 47 : 'slash', 48 : 'zero', 49 : 'one', 50 : 'two', 51 : 'three', 52 : 'four', 53 : 'five', 54 : 'six', 55 : 'seven', 56 : 'eight', 57 : 'nine', 58 : 'colon', 59 : 'semicolon', 60 : 'less', 61 : 'equal', 62 : 'greater', 63 : 'question', 64 : 'at', 65 : 'A', 66 : 'B', 67 : 'C', 68 : 'D', 69 : 'E', 70 : 'F', 71 : 'G', 72 : 'H', 73 : 'I', 74 : 'J', 75 : 'K', 76 : 'L', 77 : 'M', 78 : 'N', 79 : 'O', 80 : 'P', 81 : 'Q', 82 : 'R', 83 : 'S', 84 : 'T', 85 : 'U', 86 : 'V', 87 : 'W', 88 : 'X', 89 : 'Y', 90 : 'Z', 91 : 'bracketleft', 92 : 'backslash', 93 : 'bracketright', 94 : 'asciicircum', 95 : 'underscore', 96 : 'grave', 97 : 'a', 98 : 'b', 99 : 'c', 100 : 'd', 101 : 'e', 102 : 'f', 103 : 'g', 104 : 'h', 105 : 'i', 106 : 'j', 107 : 'k', 108 : 'l', 109 : 'm', 110 : 'n', 111 : 'o', 112 : 'p', 113 : 'q', 114 : 'r', 115 : 's', 116 : 't', 117 : 'u', 118 : 'v', 119 : 'w', 120 : 'x', 121 : 'y', 122 : 'z', 123 : 'braceleft', 124 : 'bar', 125 : 'braceright', 126 : 'asciitilde', 161 : 'exclamdown', 162 : 'cent', 163 : 'sterling', 164 : 'currency', 165 : 'yen', 166 : 'brokenbar', 167 : 'section', 168 : 'dieresis', 169 : 'copyright', 170 : 'ordfeminine', 171 : 'guillemotleft', 172 : 'logicalnot', 174 : 'registered', 175 : 'macron', 176 : 'degree', 177 : 'plusminus', 178 : 'twosuperior', 179 : 'threesuperior', 180 : 'acute', 181 : 'mu', 182 : 'paragraph', 183 : 'periodcentered', 184 : 'cedilla', 185 : 'onesuperior', 186 : 'ordmasculine', 187 : 'guillemotright', 188 : 'onequarter', 189 : 'onehalf', 190 : 'threequarters', 191 : 'questiondown', 192 : 'Agrave', 193 : 'Aacute', 194 : 'Acircumflex', 195 : 'Atilde', 196 : 'Adieresis', 197 : 'Aring', 198 : 'AE', 199 : 'Ccedilla', 200 : 'Egrave', 201 : 'Eacute', 202 : 'Ecircumflex', 203 : 'Edieresis', 204 : 'Igrave', 205 : 'Iacute', 206 : 'Icircumflex', 207 : 'Idieresis', 208 : 'Eth', 209 : 'Ntilde', 210 : 'Ograve', 211 : 'Oacute', 212 : 'Ocircumflex', 213 : 'Otilde', 214 : 'Odieresis', 215 : 'multiply', 216 : 'Oslash', 217 : 'Ugrave', 218 : 'Uacute', 219 : 'Ucircumflex', 220 : 'Udieresis', 221 : 'Yacute', 222 : 'Thorn', 223 : 'germandbls', 224 : 'agrave', 225 : 'aacute', 226 : 'acircumflex', 227 : 'atilde', 228 : 'adieresis', 229 : 'aring', 230 : 'ae', 231 : 'ccedilla', 232 : 'egrave', 233 : 'eacute', 234 : 'ecircumflex', 235 : 'edieresis', 236 : 'igrave', 237 : 'iacute', 238 : 'icircumflex', 239 : 'idieresis', 240 : 'eth', 241 : 'ntilde', 242 : 'ograve', 243 : 'oacute', 244 : 'ocircumflex', 245 : 'otilde', 246 : 'odieresis', 247 : 'divide', 248 : 'oslash', 249 : 'ugrave', 250 : 'uacute', 251 : 'ucircumflex', 252 : 'udieresis', 253 : 'yacute', 254 : 'thorn', 255 : 'ydieresis', 256 : 'Amacron', 257 : 'amacron', 258 : 'Abreve', 259 : 'abreve', 260 : 'Aogonek', 261 : 'aogonek', 262 : 'Cacute', 263 : 'cacute', 264 : 'Ccircumflex', 265 : 'ccircumflex', 266 : 'Cdotaccent', 267 : 'cdotaccent', 268 : 'Ccaron', 269 : 'ccaron', 270 : 'Dcaron', 271 : 'dcaron', 272 : 'Dcroat', 273 : 'dcroat', 274 : 'Emacron', 275 : 'emacron', 276 : 'Ebreve', 277 : 'ebreve', 278 : 'Edotaccent', 279 : 'edotaccent', 280 : 'Eogonek', 281 : 'eogonek', 282 : 'Ecaron', 283 : 'ecaron', 284 : 'Gcircumflex', 285 : 'gcircumflex', 286 : 'Gbreve', 287 : 'gbreve', 288 : 'Gdotaccent', 289 : 'gdotaccent', 290 : 'Gcommaaccent', 291 : 'gcommaaccent', 292 : 'Hcircumflex', 293 : 'hcircumflex', 294 : 'Hbar', 295 : 'hbar', 296 : 'Itilde', 297 : 'itilde', 298 : 'Imacron', 299 : 'imacron', 300 : 'Ibreve', 301 : 'ibreve', 302 : 'Iogonek', 303 : 'iogonek', 304 : 'Idotaccent', 305 : 'dotlessi', 306 : 'IJ', 307 : 'ij', 308 : 'Jcircumflex', 309 : 'jcircumflex', 310 : 'Kcommaaccent', 311 : 'kcommaaccent', 312 : 'kgreenlandic', 313 : 'Lacute', 314 : 'lacute', 315 : 'Lcommaaccent', 316 : 'lcommaaccent', 317 : 'Lcaron', 318 : 'lcaron', 319 : 'Ldot', 320 : 'ldot', 321 : 'Lslash', 322 : 'lslash', 323 : 'Nacute', 324 : 'nacute', 325 : 'Ncommaaccent', 326 : 'ncommaaccent', 327 : 'Ncaron', 328 : 'ncaron', 329 : 'napostrophe', 330 : 'Eng', 331 : 'eng', 332 : 'Omacron', 333 : 'omacron', 334 : 'Obreve', 335 : 'obreve', 336 : 'Ohungarumlaut', 337 : 'ohungarumlaut', 338 : 'OE', 339 : 'oe', 340 : 'Racute', 341 : 'racute', 342 : 'Rcommaaccent', 343 : 'rcommaaccent', 344 : 'Rcaron', 345 : 'rcaron', 346 : 'Sacute', 347 : 'sacute', 348 : 'Scircumflex', 349 : 'scircumflex', 350 : 'Scedilla', 351 : 'scedilla', 352 : 'Scaron', 353 : 'scaron', 354 : 'Tcommaaccent', 355 : 'tcommaaccent', 356 : 'Tcaron', 357 : 'tcaron', 358 : 'Tbar', 359 : 'tbar', 360 : 'Utilde', 361 : 'utilde', 362 : 'Umacron', 363 : 'umacron', 364 : 'Ubreve', 365 : 'ubreve', 366 : 'Uring', 367 : 'uring', 368 : 'Uhungarumlaut', 369 : 'uhungarumlaut', 370 : 'Uogonek', 371 : 'uogonek', 372 : 'Wcircumflex', 373 : 'wcircumflex', 374 : 'Ycircumflex', 375 : 'ycircumflex', 376 : 'Ydieresis', 377 : 'Zacute', 378 : 'zacute', 379 : 'Zdotaccent', 380 : 'zdotaccent', 381 : 'Zcaron', 382 : 'zcaron', 402 : 'florin', 508 : 'AEacute', 509 : 'aeacute', 510 : 'Oslashacute', 511 : 'oslashacute', 536 : 'Scommaaccent', 537 : 'scommaaccent', 710 : 'circumflex', 711 : 'caron', 728 : 'breve', 729 : 'dotaccent', 730 : 'ring', 731 : 'ogonek', 732 : 'tilde', 733 : 'hungarumlaut', 894 : 'uni037E', 900 : 'tonos', 901 : 'dieresistonos', 902 : 'Alphatonos', 903 : 'anoteleia', 904 : 'Epsilontonos', 905 : 'Etatonos', 906 : 'Iotatonos', 908 : 'Omicrontonos', 910 : 'Upsilontonos', 911 : 'Omegatonos', 912 : 'iotadieresistonos', 913 : 'Alpha', 914 : 'Beta', 915 : 'Gamma', # 916 : 'Delta', 917 : 'Epsilon', 918 : 'Zeta', 919 : 'Eta', 920 : 'Theta', 921 : 'Iota', 922 : 'Kappa', 923 : 'Lambda', 924 : 'Mu', 925 : 'Nu', 926 : 'Xi', 927 : 'Omicron', 928 : 'Pi', 929 : 'Rho', 931 : 'Sigma', 932 : 'Tau', 933 : 'Upsilon', 934 : 'Phi', 935 : 'Chi', 936 : 'Psi', 937 : 'Omega', 938 : 'Iotadieresis', 939 : 'Upsilondieresis', 940 : 'alphatonos', 941 : 'epsilontonos', 942 : 'etatonos', 943 : 'iotatonos', 944 : 'upsilondieresistonos', 945 : 'alpha', 946 : 'beta', 947 : 'gamma', 948 : 'delta', 949 : 'epsilon', 950 : 'zeta', 951 : 'eta', 952 : 'theta', 953 : 'iota', 954 : 'kappa', 955 : 'lambda', 956 : 'uni03BC', 957 : 'nu', 958 : 'xi', 959 : 'omicron', 960 : 'pi', 961 : 'rho', 962 : 'uni03C2', 963 : 'sigma', 964 : 'tau', 965 : 'upsilon', 966 : 'phi', 967 : 'chi', 968 : 'psi', 969 : 'omega', 970 : 'iotadieresis', 971 : 'upsilondieresis', 972 : 'omicrontonos', 973 : 'upsilontonos', 974 : 'omegatonos', 1025 : 'afii10023', 1026 : 'afii10051', 1027 : 'afii10052', 1028 : 'afii10053', 1029 : 'afii10054', 1030 : 'afii10055', 1031 : 'afii10056', 1032 : 'afii10057', 1033 : 'afii10058', 1034 : 'afii10059', 1035 : 'afii10060', 1036 : 'afii10061', 1038 : 'afii10062', 1039 : 'afii10145', 1040 : 'afii10017', 1041 : 'afii10018', 1042 : 'afii10019', 1043 : 'afii10020', 1044 : 'afii10021', 1045 : 'afii10022', 1046 : 'afii10024', 1047 : 'afii10025', 1048 : 'afii10026', 1049 : 'afii10027', 1050 : 'afii10028', 1051 : 'afii10029', 1052 : 'afii10030', 1053 : 'afii10031', 1054 : 'afii10032', 1055 : 'afii10033', 1056 : 'afii10034', 1057 : 'afii10035', 1058 : 'afii10036', 1059 : 'afii10037', 1060 : 'afii10038', 1061 : 'afii10039', 1062 : 'afii10040', 1063 : 'afii10041', 1064 : 'afii10042', 1065 : 'afii10043', 1066 : 'afii10044', 1067 : 'afii10045', 1068 : 'afii10046', 1069 : 'afii10047', 1070 : 'afii10048', 1071 : 'afii10049', 1072 : 'afii10065', 1073 : 'afii10066', 1074 : 'afii10067', 1075 : 'afii10068', 1076 : 'afii10069', 1077 : 'afii10070', 1078 : 'afii10072', 1079 : 'afii10073', 1080 : 'afii10074', 1081 : 'afii10075', 1082 : 'afii10076', 1083 : 'afii10077', 1084 : 'afii10078', 1085 : 'afii10079', 1086 : 'afii10080', 1087 : 'afii10081', 1088 : 'afii10082', 1089 : 'afii10083', 1090 : 'afii10084', 1091 : 'afii10085', 1092 : 'afii10086', 1093 : 'afii10087', 1094 : 'afii10088', 1095 : 'afii10089', 1096 : 'afii10090', 1097 : 'afii10091', 1098 : 'afii10092', 1099 : 'afii10093', 1100 : 'afii10094', 1101 : 'afii10095', 1102 : 'afii10096', 1103 : 'afii10097', 1105 : 'afii10071', 1106 : 'afii10099', 1107 : 'afii10100', 1108 : 'afii10101', 1109 : 'afii10102', 1110 : 'afii10103', 1111 : 'afii10104', 1112 : 'afii10105', 1113 : 'afii10106', 1114 : 'afii10107', 1115 : 'afii10108', 1116 : 'afii10109', 1118 : 'afii10110', 1119 : 'afii10193', 1168 : 'afii10050', 1169 : 'afii10098', 1241 : 'afii10846', 7808 : 'Wgrave', 7809 : 'wgrave', 7810 : 'Wacute', 7811 : 'wacute', 7812 : 'Wdieresis', 7813 : 'wdieresis', 7922 : 'Ygrave', 7923 : 'ygrave', 8211 : 'endash', 8212 : 'emdash', 8216 : 'quoteleft', 8217 : 'quoteright', 8218 : 'quotesinglbase', 8220 : 'quotedblleft', 8221 : 'quotedblright', 8222 : 'quotedblbase', 8224 : 'dagger', 8225 : 'daggerdbl', 8226 : 'bullet', 8230 : 'ellipsis', 8240 : 'perthousand', 8249 : 'guilsinglleft', 8250 : 'guilsinglright', 8260 : 'fraction', 8304 : 'zerosuperior', 8308 : 'foursuperior', 8309 : 'fivesuperior', 8310 : 'sixsuperior', 8311 : 'sevensuperior', 8312 : 'eightsuperior', 8313 : 'ninesuperior', 8317 : 'parenleftsuperior', 8318 : 'parenrightsuperior', 8320 : 'zeroinferior', 8321 : 'oneinferior', 8322 : 'twoinferior', 8323 : 'threeinferior', 8324 : 'fourinferior', 8325 : 'fiveinferior', 8326 : 'sixinferior', 8327 : 'seveninferior', 8328 : 'eightinferior', 8329 : 'nineinferior', 8333 : 'parenleftinferior', 8334 : 'parenrightinferior', 8364 : 'Euro', 8467 : 'afii61289', 8470 : 'afii61352', 8482 : 'trademark', 8494 : 'estimated', 8706 : 'partialdiff', 8710 : 'uni2206', 8719 : 'product', 8721 : 'summation', 8722 : 'minus', 8730 : 'radical', 8734 : 'infinity', 8747 : 'integral', 8776 : 'approxequal', 8800 : 'notequal', 8804 : 'lessequal', 8805 : 'greaterequal', 9674 : 'lozenge', 57345 : 'f_f_j', 57346 : 'f_j', 61421 : 'uniEFED', 61422 : 'uniEFEE', 61425 : 'uniEFF1', 61426 : 'uniEFF2', 61427 : 'uniEFF3', 61429 : 'uniEFF5', 61431 : 'uniEFF7', 63017 : 'uniF629', 63018 : 'uniF62A', 63019 : 'uniF62B', 63020 : 'uniF62C', 63021 : 'uniF62D', 63022 : 'uniF62E', 63023 : 'uniF62F', 63024 : 'uniF630', 63025 : 'uniF631', 63026 : 'uniF632', 63027 : 'uniF633', 63028 : 'uniF634', 63032 : 'uniF638', 63033 : 'uniF639', 63034 : 'uniF63A', 63035 : 'uniF63B', 63036 : 'uniF63C', 63037 : 'uniF63D', 63038 : 'uniF63E', 63039 : 'uniF63F', 63040 : 'uniF640', 63041 : 'uniF641', 63042 : 'uniF642', 63043 : 'uniF643', 63044 : 'uniF644', 63045 : 'uniF645', 63046 : 'uniF646', 63047 : 'uniF647', 63048 : 'uniF648', 63049 : 'uniF649', 63050 : 'uniF64A', 63051 : 'uniF64B', 63052 : 'uniF64C', 63054 : 'uniF64E', 63055 : 'uniF64F', 63056 : 'uniF650', 63057 : 'uniF651', 63058 : 'uniF652', 63059 : 'uniF653', 63060 : 'uniF654', 63061 : 'uniF655', 63062 : 'uniF656', 63063 : 'uniF657', 63064 : 'uniF658', 63065 : 'uniF659', 63066 : 'uniF65A', 63067 : 'uniF65B', 63068 : 'uniF65C', 63069 : 'uniF65D', 63070 : 'uniF65E', 63071 : 'uniF65F', 63072 : 'uniF660', 63073 : 'uniF661', 63074 : 'uniF662', 63075 : 'uniF663', 63076 : 'uniF664', 63077 : 'uniF665', 63078 : 'uniF666', 63079 : 'uniF667', 63080 : 'uniF668', 63081 : 'uniF669', 63082 : 'uniF66A', 63083 : 'uniF66B', 63084 : 'uniF66C', 63150 : 'uniF6AE', 63151 : 'uniF6AF', 63152 : 'uniF6B0', 63153 : 'uniF6B1', 63154 : 'uniF6B2', 63155 : 'uniF6B3', 63156 : 'uniF6B4', 63157 : 'uniF6B5', 63158 : 'uniF6B6', 63159 : 'uniF6B7', 63160 : 'uniF6B8', 63161 : 'uniF6B9', 63162 : 'uniF6BA', 63163 : 'uniF6BB', 63164 : 'uniF6BC', 63165 : 'uniF6BD', 63171 : 'commaaccent', 63177 : 'Acute', 63178 : 'Caron', 63179 : 'Dieresis', 63180 : 'DieresisAcute', 63181 : 'DieresisGrave', 63182 : 'Grave', 63183 : 'Hungarumlaut', 63184 : 'Macron', 63185 : 'cyrBreve', 63186 : 'cyrFlex', 63187 : 'dblGrave', 63188 : 'cyrbreve', 63189 : 'cyrflex', 63190 : 'dblgrave', 63191 : 'dieresisacute', 63192 : 'dieresisgrave', 63196 : 'uniF6DC', 63199 : 'centinferior', 63200 : 'centsuperior', 63201 : 'commainferior', 63202 : 'commasuperior', 63203 : 'dollarinferior', 63204 : 'dollarsuperior', 63205 : 'hypheninferior', 63206 : 'hyphensuperior', 63207 : 'periodinferior', 63208 : 'periodsuperior', 63280 : 'zerooldstyle', 63281 : 'oneoldstyle', 63282 : 'twooldstyle', 63283 : 'threeoldstyle', 63284 : 'fouroldstyle', 63285 : 'fiveoldstyle', 63286 : 'sixoldstyle', 63287 : 'sevenoldstyle', 63288 : 'eightoldstyle', 63289 : 'nineoldstyle', 64256 : 'ff', 64257 : 'fi', 64258 : 'fl', 64259 : 'ffi', 64260 : 'ffl', } #: A dictionary mapping psNames to unicode values. psnames2unicodes = dict([[v, k] for k, v in unicode2psnames.items()]) #---------------- # unicode ranges #---------------- # https://www.microsoft.com/typography/otspec/os2.htm OS2_unicode_ranges_src = '''\ 0; Basic Latin; 0000; 007F; 1; Latin-1 Supplement; 0080; 00FF; 2; Latin Extended-A; 0100; 017F; 3; Latin Extended-B; 0180; 024F; 4; IPA Extensions; 0250; 02AF; 4; Phonetic Extensions; 1D00; 1D7F; 4; Phonetic Extensions Supplement; 1D80; 1DBF; 5; Spacing Modifier Letters; 02B0; 02FF; 5; Modifier Tone Letters; A700; A71F; 6; Combining Diacritical Marks; 0300; 036F; 6; Combining Diacritical Marks Supplement; 1DC0; 1DFF; 7; Greek and Coptic; 0370; 03FF; 8; Coptic; 2C80; 2CFF; 9; Cyrillic; 0400; 04FF; 9; Cyrillic Supplement; 0500; 052F; 9; Cyrillic Extended-A; 2DE0; 2DFF; 9; Cyrillic Extended-B; A640; A69F; 10; Armenian; 0530; 058F; 11; Hebrew; 0590; 05FF; 12; Vai; A500; A63F; 13; Arabic; 0600; 06FF; 13; Arabic Supplement; 0750; 077F; 14; NKo; 07C0; 07FF; 15; Devanagari; 0900; 097F; 16; Bengali; 0980; 09FF; 17; Gurmukhi; 0A00; 0A7F; 18; Gujarati; 0A80; 0AFF; 19; Oriya; 0B00; 0B7F; 20; Tamil; 0B80; 0BFF; 21; Telugu; 0C00; 0C7F; 22; Kannada; 0C80; 0CFF; 23; Malayalam; 0D00; 0D7F; 24; Thai; 0E00; 0E7F; 25; Lao; 0E80; 0EFF; 26; Georgian; 10A0; 10FF; 26; Georgian Supplement; 2D00; 2D2F; 27; Balinese; 1B00; 1B7F; 28; Hangul Jamo; 1100; 11FF; 29; Latin Extended Additional; 1E00; 1EFF; 29; Latin Extended-C; 2C60; 2C7F; 29; Latin Extended-D; A720; A7FF; 30; Greek Extended; 1F00; 1FFF; 31; General Punctuation; 2000; 206F; 31; Supplemental Punctuation; 2E00; 2E7F; 32; Superscripts And Subscripts; 2070; 209F; 33; Currency Symbols; 20A0; 20CF; 34; Combining Diacritical Marks For Symbols; 20D0; 20FF; 35; Letterlike Symbols; 2100; 214F; 36; Number Forms; 2150; 218F; 37; Arrows; 2190; 21FF; 37; Supplemental Arrows-A; 27F0; 27FF; 37; Supplemental Arrows-B; 2900; 297F; 37; Miscellaneous Symbols and Arrows; 2B00; 2BFF; 38; Mathematical Operators; 2200; 22FF; 38; Supplemental Mathematical Operators; 2A00; 2AFF; 38; Miscellaneous Mathematical Symbols-A; 27C0; 27EF; 38; Miscellaneous Mathematical Symbols-B; 2980; 29FF; 39; Miscellaneous Technical; 2300; 23FF; 40; Control Pictures; 2400; 243F; 41; Optical Character Recognition; 2440; 245F; 42; Enclosed Alphanumerics; 2460; 24FF; 43; Box Drawing; 2500; 257F; 44; Block Elements; 2580; 259F; 45; Geometric Shapes; 25A0; 25FF; 46; Miscellaneous Symbols; 2600; 26FF; 47; Dingbats; 2700; 27BF; 48; CJK Symbols And Punctuation; 3000; 303F; 49; Hiragana; 3040; 309F; 50; Katakana; 30A0; 30FF; 50; Katakana Phonetic Extensions; 31F0; 31FF; 51; Bopomofo; 3100; 312F; 51; Bopomofo Extended; 31A0; 31BF; 52; Hangul Compatibility Jamo; 3130; 318F; 53; Phags-pa; A840; A87F; 54; Enclosed CJK Letters And Months; 3200; 32FF; 55; CJK Compatibility; 3300; 33FF; 56; Hangul Syllables; AC00; D7AF; 57; Non-Plane 0 *; D800; DFFF; 58; Phoenician; 10900; 1091F; 59; CJK Unified Ideographs; 4E00; 9FFF; 59; CJK Radicals Supplement; 2E80; 2EFF; 59; Kangxi Radicals; 2F00; 2FDF; 59; Ideographic Description Characters; 2FF0; 2FFF; 59; CJK Unified Ideographs Extension A; 3400; 4DBF; 59; CJK Unified Ideographs Extension B; 20000; 2A6DF; 59; Kanbun; 3190; 319F; 60; Private Use Area; E000; F8FF; 61; CJK Strokes; 31C0; 31EF; 61; CJK Compatibility Ideographs; F900; FAFF; 61; CJK Compatibility Ideographs Supplement; 2F800; 2FA1F; 62; Alphabetic Presentation Forms; FB00; FB4F; 63; Arabic Presentation Forms-A; FB50; FDFF; 64; Combining Half Marks; FE20; FE2F; 65; Vertical Forms; FE10; FE1F; 65; CJK Compatibility Forms; FE30; FE4F; 66; Small Form Variants; FE50; FE6F; 67; Arabic Presentation Forms-B; FE70; FEFF; 68; Halfwidth And Fullwidth Forms; FF00; FFEF; 69; Specials; FFF0; FFFF; 70; Tibetan; 0F00; 0FFF; 71; Syriac; 0700; 074F; 72; Thaana; 0780; 07BF; 73; Sinhala; 0D80; 0DFF; 74; Myanmar; 1000; 109F; 75; Ethiopic; 1200; 137F; 75; Ethiopic Supplement; 1380; 139F; 75; Ethiopic Extended; 2D80; 2DDF; 76; Cherokee; 13A0; 13FF; 77; Unified Canadian Aboriginal Syllabics; 1400; 167F; 78; Ogham; 1680; 169F; 79; Runic; 16A0; 16FF; 80; Khmer; 1780; 17FF; 80; Khmer Symbols; 19E0; 19FF; 81; Mongolian; 1800; 18AF; 82; Braille Patterns; 2800; 28FF; 83; Yi Syllables; A000; A48F; 83; Yi Radicals; A490; A4CF; 84; Tagalog; 1700; 171F; 84; Hanunoo; 1720; 173F; 84; Buhid; 1740; 175F; 84; Tagbanwa; 1760; 177F; 85; Old Italic; 10300; 1032F; 86; Gothic; 10330; 1034F; 87; Deseret; 10400; 1044F; 88; Byzantine Musical Symbols; 1D000; 1D0FF; 88; Musical Symbols; 1D100; 1D1FF; 88; Ancient Greek Musical Notation; 1D200; 1D24F; 89; Mathematical Alphanumeric Symbols; 1D400; 1D7FF; 90; Private Use (plane 15); FF000; FFFFD; 90; Private Use (plane 16); 100000; 10FFFD; 91; Variation Selectors; FE00; FE0F; 91; Variation Selectors Supplement; E0100; E01EF; 92; Tags; E0000; E007F; 93; Limbu; 1900; 194F; 94; Tai Le; 1950; 197F; 95; New Tai Lue; 1980; 19DF; 96; Buginese; 1A00; 1A1F; 97; Glagolitic; 2C00; 2C5F; 98; Tifinagh; 2D30; 2D7F; 99; Yijing Hexagram Symbols; 4DC0; 4DFF; 100; Syloti Nagri; A800; A82F; 101; Linear B Syllabary; 10000; 1007F; 101; Linear B Ideograms; 10080; 100FF; 101; Aegean Numbers; 10100; 1013F; 102; Ancient Greek Numbers; 10140; 1018F; 103; Ugaritic; 10380; 1039F; 104; Old Persian; 103A0; 103DF; 105; Shavian; 10450; 1047F; 106; Osmanya; 10480; 104AF; 107; Cypriot Syllabary; 10800; 1083F; 108; Kharoshthi; 10A00; 10A5F; 109; Tai Xuan Jing Symbols; 1D300; 1D35F; 110; Cuneiform; 12000; 123FF; 110; Cuneiform Numbers and Punctuation; 12400; 1247F; 111; Counting Rod Numerals; 1D360; 1D37F; 112; Sundanese; 1B80; 1BBF; 113; Lepcha; 1C00; 1C4F; 114; <NAME>; 1C50; 1C7F; 115; Saurashtra; A880; A8DF; 116; <NAME>; A900; A92F; 117; Rejang; A930; A95F; 118; Cham; AA00; AA5F; 119; Ancient Symbols; 10190; 101CF; 120; Phaistos Disc; 101D0; 101FF; 121; Carian; 102A0; 102DF; 121; Lycian; 10280; 1029F; 121; Lydian; 10920; 1093F; 122; <NAME>; 1F030; 1F09F; 122; <NAME>; 1F000; 1F02F; ''' OS2_unicode_ranges = OrderedDict() for line in OS2_unicode_ranges_src.split('\n'): if len(line.split(';')) == 5: bit, unicode_range, block_start, block_end = line.split(';')[:4] OS2_unicode_ranges[unicode_range.strip()] = [int(bit), (block_start.strip(), block_end.strip())]

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darling_ansible/python_venv/lib/python3.7/site-packages/oci/os_management/models/installable_package_summary.py

revnav/sandbox

0

75731

<gh_stars>0 # coding: utf-8 # Copyright (c) 2016, 2020, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class InstallablePackageSummary(object): """ A software package available for install on a managed instance """ def __init__(self, **kwargs): """ Initializes a new InstallablePackageSummary object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param display_name: The value to assign to the display_name property of this InstallablePackageSummary. :type display_name: str :param name: The value to assign to the name property of this InstallablePackageSummary. :type name: str :param type: The value to assign to the type property of this InstallablePackageSummary. :type type: str :param version: The value to assign to the version property of this InstallablePackageSummary. :type version: str :param architecture: The value to assign to the architecture property of this InstallablePackageSummary. :type architecture: str :param software_sources: The value to assign to the software_sources property of this InstallablePackageSummary. :type software_sources: list[SoftwareSourceId] """ self.swagger_types = { 'display_name': 'str', 'name': 'str', 'type': 'str', 'version': 'str', 'architecture': 'str', 'software_sources': 'list[SoftwareSourceId]' } self.attribute_map = { 'display_name': 'displayName', 'name': 'name', 'type': 'type', 'version': 'version', 'architecture': 'architecture', 'software_sources': 'softwareSources' } self._display_name = None self._name = None self._type = None self._version = None self._architecture = None self._software_sources = None @property def display_name(self): """ **[Required]** Gets the display_name of this InstallablePackageSummary. Package name :return: The display_name of this InstallablePackageSummary. :rtype: str """ return self._display_name @display_name.setter def display_name(self, display_name): """ Sets the display_name of this InstallablePackageSummary. Package name :param display_name: The display_name of this InstallablePackageSummary. :type: str """ self._display_name = display_name @property def name(self): """ **[Required]** Gets the name of this InstallablePackageSummary. Unique identifier for the package. NOTE - This is not an OCID :return: The name of this InstallablePackageSummary. :rtype: str """ return self._name @name.setter def name(self, name): """ Sets the name of this InstallablePackageSummary. Unique identifier for the package. NOTE - This is not an OCID :param name: The name of this InstallablePackageSummary. :type: str """ self._name = name @property def type(self): """ **[Required]** Gets the type of this InstallablePackageSummary. Type of the package :return: The type of this InstallablePackageSummary. :rtype: str """ return self._type @type.setter def type(self, type): """ Sets the type of this InstallablePackageSummary. Type of the package :param type: The type of this InstallablePackageSummary. :type: str """ self._type = type @property def version(self): """ **[Required]** Gets the version of this InstallablePackageSummary. Version of the package :return: The version of this InstallablePackageSummary. :rtype: str """ return self._version @version.setter def version(self, version): """ Sets the version of this InstallablePackageSummary. Version of the package :param version: The version of this InstallablePackageSummary. :type: str """ self._version = version @property def architecture(self): """ Gets the architecture of this InstallablePackageSummary. The architecture for which this package was built :return: The architecture of this InstallablePackageSummary. :rtype: str """ return self._architecture @architecture.setter def architecture(self, architecture): """ Sets the architecture of this InstallablePackageSummary. The architecture for which this package was built :param architecture: The architecture of this InstallablePackageSummary. :type: str """ self._architecture = architecture @property def software_sources(self): """ Gets the software_sources of this InstallablePackageSummary. list of software sources that provide the software package :return: The software_sources of this InstallablePackageSummary. :rtype: list[SoftwareSourceId] """ return self._software_sources @software_sources.setter def software_sources(self, software_sources): """ Sets the software_sources of this InstallablePackageSummary. list of software sources that provide the software package :param software_sources: The software_sources of this InstallablePackageSummary. :type: list[SoftwareSourceId] """ self._software_sources = software_sources def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

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ngspy/Promotor.py

manuSrep/ngspy

0

42304

#!/usr/bin/python # -*- coding: utf8 -*- """ Functions and classes for handling next generation sequencing data. :author: <NAME> :license: FreeBSD License ---------- Copyright (c) 2016, <NAME> All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ from __future__ import division, absolute_import, unicode_literals, print_function import datetime from .Formats import GFF, GFF3Entry def extractProm(inGFF, name, path, upstram=499, downstream=100, filter={}): """ Extract promotor regions from GFF file as provided by GENCODE: http://www.gencodegenes.org Genes can be filtered by GFF fields and attributes. Results are stored in a new GFF file. Parameters --------- inGFF : GFF The GFF object with a filled dataset. The entries describe the gene regions. From these regions the promoter is extracted. name : basestring The filename of the resulting GFF file. path : basestring The path where to save the results upstream : int Determines how many genes upstream of the TSS shall regarded as promoter. (in front of the gene) downstream : int Determines how many genes downstream of the TSS shall regarded as promoter. (inside the gene) filter : dict Regions in the GFF are only considerd if they match the filter entries. Keys can be any attribute in the GFFEntry. For most tags it is checked if the attribute value is in the filter. For "start" it is checked if the value is higher than the filter. The same is for score. For "end" it is checked if the value is lower than the filter. """ def check_attr(filter_attr, item_attr): for key in filter_attr.keys(): if not item_attr[key] in filter_attr[key]: return False return True if len(inGFF) == 0: raise ValueError("No data in input GFF") outGFF = GFF() outGFF._header_general.append("## Promotors extracted at: " + str(datetime.date.today()) + "\n") for gff in inGFF: # filter if item shall be extracted # shall we filer for seqid if "seqid" in filter: if not gff.seqid in filter["seqid"]: continue if "source" in filter: if not gff.source in filter["source"]: continue if "type" in filter: if not gff.type in filter["type"]: continue if "start" in filter: if not gff.start > filter["start"]: continue if "end" in filter: if not gff.end < filter["end"]: continue if "score" in filter: if gff.score < filter["score"]: continue if "strand" in filter: if not gff.strand in filter["strand"]: continue if "phase" in filter: if not gff.phase in filter["phase"]: continue if "attributes" in filter: if not check_attr(filter["attributes"], gff.attributes): continue seqid = gff.seqid source = "ROI" type = "promotor" strand = gff.strand attributes = {} attributes["ID"] = gff.attributes["ID"] # TSS varies from + and - strands if gff.strand == '-': start = gff.end - downstream end = gff.end + upstram else: # = is assumed for '.' start = gff.start - upstram end = gff.start + downstream entry = GFF3Entry(seqid, source, type, start, end, strand=strand, attributes=attributes) outGFF.append(entry) outGFF.sort() outGFF.write(name, path)

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ircclient.py

and3rson/notify

0

90703

import socket from select import select from time import sleep class IRCClient(object): def __init__(self, server, port, username, password): # super(Client, self).__init__() self.server = server self.port = port self.username = username self.password = password self.alive = False def send(self, message): self.conn.send(message + '\r\n') def start(self): self.alive = True while self.alive: while not self.connect(): pass self.process() def process(self): while self.alive: i, o, e = select([self.conn], [], [], 1) if self.conn in i: data = filter(None, self.conn.recv(1024).split('\r\n')) if not len(data): return for line in data: self.on_recv(line) def connect(self): try: self.conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.conn.connect((self.server, self.port)) self.send('NICK {}'.format(self.username)) self.send('PASS {}'.format(self.password)) self.send('USER {u} {u} {u} NotifyClient {u}'.format(u=self.username)) self.alive = True except: sleep(1) return False return True def on_recv(self, line): parts = line.split(' ') if parts[0].lower() == 'ping': self.send('PONG {}'.format(parts[1])) elif parts[1] == '001': self.on_connect() else: self.on_message(line) def on_message(self, line): raise NotImplementedError() def join(self, channel): self.send('JOIN #{}'.format(channel)) def privmsg(self, channel, message): self.send('PRIVMSG #{} {}'.format(channel, message)) def stop(self): self.alive = False print 'See ya!' self.send('QUIT')

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test/testDecorators.py

redhog/ferenda

18

87187

# -*- coding: utf-8 -*- from __future__ import (absolute_import, division, print_function, unicode_literals) from builtins import * import sys import os import datetime import shutil import tempfile from ferenda.compat import unittest, Mock, MagicMock, patch from ferenda import util from ferenda import DocumentRepository, DocumentStore, Document, DocumentEntry from ferenda.errors import DocumentRemovedError, ParseError # SUT from ferenda.decorators import (timed, parseifneeded, ifneeded, render, handleerror, makedocument, recordlastdownload, downloadmax) class Decorators(unittest.TestCase): def test_timed(self): # Test that a wrapped method... @timed def testfunc(repo,doc): pass # ...passed a particular docrepo and doc mockrepo = Mock() mockdoc = Mock() mockdoc.basefile = "1234" # ...has it's instances logger called... testfunc(mockrepo,mockdoc) call_args = mockrepo.log.info.call_args # ...with the correct method and arguments self.assertEqual(len(call_args[0]), 2) self.assertEqual(call_args[0][0], 'parse OK (%.3f sec)') def test_parseifneeded(self): @parseifneeded def testfunc(repo, basefile): repo.called = True # mockdoc = Mock() # mockdoc.basefile="1234" mockbasefile = "1234" mockrepo = Mock() mockrepo.store.needed = DocumentStore(datadir='fake').needed mockrepo.called = False mockrepo.config.force = False # test 1: Outfile is newer - the parseifneeded decorator # should make sure the actual testfunc code is never reached with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older with patch('ferenda.util.outfile_is_newer', return_value=False): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False # test 3: Outfile is newer, but the global force option was set mockrepo.config.force = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False # test 4: Outfile is newer, but the module parseforce option was set mockrepo.config.parseforce = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False def test_ifneeded_parse(self): @ifneeded("parse") def testfunc(repo, basefile): repo.called = True # mockdoc = Mock() # mockdoc.basefile="1234" mockbasefile = "1234" mockrepo = Mock() mockrepo.store.needed = DocumentStore(datadir='fake').needed mockrepo.called = False mockrepo.config.force = False # test 1: Outfile is newer - the ifneeded decorator should # make sure the actual testfunc code is never reached with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older with patch('ferenda.util.outfile_is_newer', return_value=False): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False # test 3: Outfile is newer, but the global force option was set mockrepo.config.force = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False def test_ifneeded_relate(self): @ifneeded("relate") def testfunc(repo, basefile, needed): repo.called = True repo.needed = needed try: datadir = tempfile.mkdtemp() mockbasefile = "1234" mockrepo = Mock() mockrepo.store = DocumentStore(datadir=datadir) mockrepo.called = False mockrepo.config.force = False # create some docentry file in a good place de = DocumentEntry(mockrepo.store.documententry_path("1234")) now = datetime.datetime.now() if hasattr(now, 'timestamp'): # py3 timestamp = now.timestamp() else: import time # this isn't as good as the python3 timestamp() method # because this loses fractions of a second, but it'll # do. timestamp = time.mktime(now.timetuple()) de.indexed_ts = now + datetime.timedelta(seconds=3600) de.indexed_ft = now + datetime.timedelta(seconds=-3600) de.indexed_dep = now + datetime.timedelta(seconds=-3600) de.save() # test 1: Outfile is newer - the ifneeded decorator should # make sure the actual testfunc code is never reached # NOTE: the "relate" branch of DocumentStore.needed # doesn't use outfile_is_newer, so we can't patch that, we # have to create actual files parsedpath = mockrepo.store.parsed_path("1234") util.writefile(parsedpath, "dummy") os.utime(parsedpath, (timestamp, timestamp - 7200)) testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older than the information in the documententry file os.utime(parsedpath, (timestamp, timestamp)) testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) self.assertTrue(mockrepo.needed) self.assertFalse(mockrepo.needed.triples) self.assertFalse(mockrepo.needed.dependencies) self.assertTrue(mockrepo.needed.fulltext) mockrepo.called = False # test 3: Outfile is newer, but the global force option was set os.utime(parsedpath, (timestamp, timestamp - 7200)) mockrepo.config.force = True testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False finally: if os.path.exists(datadir): shutil.rmtree(datadir) def test_ifneeded_generate(self): @ifneeded("generate") def testfunc(repo, basefile): repo.called = True mockbasefile = "1234" mockrepo = Mock() mockrepo.store.needed = DocumentStore(datadir='fake').needed mockrepo.called = False mockrepo.config.force = False # test 1: Outfile is newer - the ifneeded decorator should # make sure the actual testfunc code is never reached with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertFalse(mockrepo.called) mockrepo.called = False # test 2: Outfile is older than source file with patch('ferenda.util.outfile_is_newer', return_value=False): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.called = False # FIXME: we could add more tests, eg create a dependency file # and make sure an arbitrary file named in that depfile is # newer then outfile. but the tests in testDocStore.Needed # should cover that pretty well. # test 3: Outfile is newer, but the global force option was set mockrepo.config.force = True with patch('ferenda.util.outfile_is_newer', return_value=True): testfunc(mockrepo, mockbasefile) self.assertTrue(mockrepo.called) mockrepo.config.force = None mockrepo.called = False @patch('ferenda.documentrepository.Graph') def test_render(self,mock_graph): @render def testfunc(repo,doc): pass mockdoc = Mock() mockrepo = MagicMock() mockrepo.store.parsed_path.return_value = "parsed_path.xhtml" with open("parsed_path.xhtml", "w") as fp: fp.write("""<?xml version="1.0" encoding="utf-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML+RDFa 1.0//EN" "http://www.w3.org/MarkUp/DTD/xhtml-rdfa-1.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" xmlns:dcterms="http://purl.org/dc/terms/"> <head about="http://example.org/doc"> <title property="dcterms:title">Document title</title> </head> <body> <h1>Hello!</h1> </body> </html>""") with open("entry_path.json", "w") as fp: fp.write("""{ "id": "https://lagen.nu/concept/St\\u00e5ende_anbud", "title": "St\\u00e5ende anbud", }""") mockrepo.store.distilled_path.return_value = "distilled_path.xhtml" mockrepo.get_globals.return_value = {'symbol table':'fake'} mockrepo.required_predicates = [] mockrepo.config = MagicMock() mockdoc.meta = MagicMock() # need Magicmock which supports magic funcs like __iter__ bodypart = MagicMock() bodypart.meta = MagicMock() mockdoc.body = [bodypart] mockdoc.meta.__iter__.return_value = [] mockdoc.uri = "http://example.org/doc" mockrepo.store.documententry_path.return_value = None with patch('ferenda.util.ensure_dir', return_value=True): testfunc(mockrepo, mockdoc) # 1 ensure that DocumentRepository.render_xhtml is called with # four arguments mockrepo.render_xhtml.assert_called_with(mockdoc, "parsed_path.xhtml") # 2 ensure that DocumentRepository.create_external_resources # is called with 1 argument mockrepo.create_external_resources.assert_called_with(mockdoc) # 3 ensure that a Graph object is created, its parse and # serialize methods called # FIXME: Why doesn't the patching work?! # self.assertTrue(mock_graph().parse.called) # self.assertTrue(mock_graph().serialize.called) # (4. ensure that a warning gets printed if doc.meta and # distilled_graph do not agree) mock_graph().__iter__.return_value = ['a','b'] mockdoc.meta.__iter__.return_value = ['a','b','c'] mockdoc.meta.serialize.return_value = b"<c>" with patch('ferenda.util.ensure_dir', return_value=True): testfunc(mockrepo, mockdoc) self.assertTrue(mockrepo.log.warning.called) os.remove("parsed_path.xhtml") os.remove("distilled_path.xhtml") os.remove("entry_path.json") def test_handleerror(self): @handleerror def testfunc(repo, doc): if doc.exception: raise doc.exception else: return True mockrepo = Mock() mockrepo.config = MagicMock() # must support __in__() mockdoc = Mock() # 1. should not raise an exception (but should call log.info # and util.robust_remove, and return false) with patch('ferenda.util.robust_remove') as robust_remove: mockdoc.exception = DocumentRemovedError self.assertFalse(testfunc(mockrepo, mockdoc)) self.assertTrue(mockrepo.log.info.called) self.assertTrue(robust_remove.called) # 2. should raise the same exception mockdoc.exception = KeyboardInterrupt with self.assertRaises(KeyboardInterrupt): testfunc(mockrepo, mockdoc) # 3.1 Should raise the same exeption mockdoc.exception = ParseError mockrepo.config.fatalexceptions = True with self.assertRaises(ParseError): testfunc(mockrepo, mockdoc) mockrepo.config.fatalexceptions = None # 3.2 Should not raise an exception (but should call log.error and return false) mockdoc.exception = ParseError self.assertFalse(testfunc(mockrepo, mockdoc)) self.assertTrue(mockrepo.log.error.called) # 4.1 Should raise the same exception mockdoc.exception = Exception mockrepo.config.fatalexceptions = True with self.assertRaises(Exception): testfunc(mockrepo, mockdoc) mockrepo.config.fatalexceptions = None # 4.2 Should not raise an exception mockdoc.exception = Exception self.assertFalse(testfunc(mockrepo, mockdoc)) self.assertTrue(mockrepo.log.error.called) # 5. No exceptions - everything should go fine mockdoc.exception = None self.assertTrue(testfunc(mockrepo, mockdoc)) def test_makedocument(self): @makedocument def testfunc(repo, doc): return doc doc = testfunc(DocumentRepository(), "base/file") self.assertIsInstance(doc, Document) self.assertEqual(doc.basefile, "base/file") def test_recordlastdownload(self): @recordlastdownload def testfunc(repo): pass mockrepo = Mock() with patch('ferenda.decorators.LayeredConfig.write') as mockconf: testfunc(mockrepo) # check that config.lastdownload has been set to a datetime self.assertIsInstance(mockrepo.config.lastdownload, datetime.datetime) # and that LayeredConfig.write has been called self.assertTrue(mockconf.called) def test_downloadmax(self): @downloadmax def testfunc(repo, source): for x in range(100): yield x mockrepo = MagicMock() mockrepo.config.downloadmax = None self.assertEqual(100, len(list(testfunc(mockrepo, None)))) mockrepo.config.downloadmax = 10 mockrepo.config.__contains__.return_value = True self.assertEqual(10, len(list(testfunc(mockrepo, None))))

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076_Minimum_Window_Substring.py

joshlyman/Josh-LeetCode

0

6090

# Other solution # V2 def minWindow(s, t): need = collections.Counter(t) #hash table to store char frequency missing = len(t) #total number of chars we care start, end = 0, 0 i = 0 for j, char in enumerate(s, 1): #index j from 1 if need[char] > 0: missing -= 1 need[char] -= 1 if missing == 0: #match all chars while i < j and need[s[i]] < 0: #remove chars to find the real start need[s[i]] += 1 i += 1 need[s[i]] += 1 #make sure the first appearing char satisfies need[char]>0 missing += 1 #we missed this first char, so add missing by 1 if end == 0 or j-i < end-start: #update window start, end = i, j i += 1 #update i to start+1 for next window return s[start:end] # Time: O(|S|+|T|) # Space:O(|S|+|T|) # Refer from: # https://leetcode.com/problems/minimum-window-substring/solution/ # Sliding Window # We start with two pointers, leftleft and rightright initially pointing to the first element of the string S. # We use the rightright pointer to expand the window until we get a desirable window i.e. a window that contains all of the characters of T. # Once we have a window with all the characters, we can move the left pointer ahead one by one. If the window is still a desirable one we keep on updating the minimum window size. # If the window is not desirable any more, we repeat step 2 onwards. # The current window is s[i:j] and the result window is s[I:J]. In need[c] I store how many times I # need character c (can be negative) and missing tells how many characters are still missing. # In the loop, first add the new character to the window. Then, if nothing is missing, # remove as much as possible from the window start and then update the result. class Solution: def minWindow(self, s: str, t: str) -> str: m = len(s) n = len(t) if m < n: return '' lt = {} # put t into dict (lt) and count how many # for each char for i in t: if i not in lt: lt[i] = 1 else: lt[i] += 1 # missing is to count how many remaining char needed from substring # finally get candidate substring which satisfy need of t missing = n i = I = J = 0 for j, c in enumerate(s, 1): if c in lt and lt[c] > 0: missing -= 1 if c in lt: # lt can be negative lt[c] -= 1 # i is index of candidate substring, remove as many as char from candidate while i < j and not missing: if not J or j-i < J-I: I, J = i, j if s[i] not in lt: i += 1 continue else: # if lt contains s[i], then # of s[i] +1, might reach to 0 lt[s[i]] += 1 # if > 0, means we need more, then missing +1 if lt[s[i]] > 0: missing += 1 i += 1 return s[I:J] # Time: O(|S|+|T|) # Space:O(|S|+|T|) # Optimized Sliding Window # A small improvement to the above approach can reduce the time complexity of the algorithm to O(2*∣filtered_S∣+∣S∣+∣T∣), # where filtered(S) is the string formed from S by removing all the elements not present in T

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coreapi/models/company.py

recentfahim/smartbusinessbd

0

191965

<gh_stars>0 from generic.models import BaseModel from django.db import models from .city import City from .country import Country class Company(BaseModel): name = models.CharField(max_length=55) website = models.CharField(max_length=50, null=True, blank=True) email = models.EmailField(max_length=30, null=True, blank=True) address = models.CharField(max_length=100, blank=True, null=True) city = models.ForeignKey(City, on_delete=models.CASCADE, blank=True, null=True) region = models.CharField(max_length=20, blank=True, null=True) postcode = models.IntegerField(blank=True, null=True) country = models.ForeignKey(Country, on_delete=models.CASCADE, blank=True, null=True) phone = models.CharField(max_length=15, null=True, blank=True) fax = models.CharField(max_length=15, null=True, blank=True) image = models.CharField(max_length=50, blank=True, null=True) logo = models.CharField(max_length=50, blank=True, null=True) class Meta: db_table = 'company' verbose_name_plural = 'companies' def __str__(self): return self.name

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circuit_maintenance_parser/data.py

pke11y/circuit-maintenance-parser

0

78155

<gh_stars>0 """Definition of Data classes.""" import logging from typing import List, NamedTuple, Optional, Type, Set import email from pydantic import BaseModel, Extra from circuit_maintenance_parser.constants import EMAIL_HEADER_SUBJECT, EMAIL_HEADER_DATE logger = logging.getLogger(__name__) class DataPart(NamedTuple): """Simplest data unit to be parsed.""" # type is an arbitrary string that is used to match the DataPart to the Parser class, that contains _data_types type: str content: bytes class NotificationData(BaseModel, extra=Extra.forbid): """Base class for Notification Data types.""" data_parts: List[DataPart] = [] def add_data_part(self, data_type: str, data_content: bytes): """Add a DataPart element into the instance data_parts.""" self.data_parts.append(DataPart(data_type, data_content)) @classmethod def init_from_raw( cls: Type["NotificationData"], data_type: str, data_content: bytes ) -> Optional["NotificationData"]: """Initialize the data_parts with only one DataPart object.""" try: return cls(data_parts=[DataPart(data_type, data_content)]) except Exception: # pylint: disable=broad-except logger.exception("Error found initializing data raw: %s, %s", data_type, data_content) return None @classmethod def init_from_email_bytes(cls: Type["NotificationData"], raw_email_bytes: bytes) -> Optional["NotificationData"]: """Initialize the data_parts from an email defined as raw bytes..""" try: raw_email_string = raw_email_bytes.decode("utf-8") email_message = email.message_from_string(raw_email_string) return cls.init_from_emailmessage(email_message) except Exception: # pylint: disable=broad-except logger.exception("Error found initializing data from email raw bytes: %s", raw_email_bytes) return None @classmethod def walk_email(cls, email_message, data_parts): """Recursive walk_email using Set to not duplicate data entries.""" for part in email_message.walk(): if "image" in part.get_content_type(): # Not interested in parsing images/QRs yet continue if "multipart" in part.get_content_type(): for inner_part in part.get_payload(): if isinstance(inner_part, email.message.Message): cls.walk_email(inner_part, data_parts) elif "message/rfc822" in part.get_content_type(): if isinstance(part.get_payload(), email.message.Message): cls.walk_email(part.get_payload(), data_parts) else: data_parts.add(DataPart(part.get_content_type(), part.get_payload(decode=True))) @classmethod def init_from_emailmessage(cls: Type["NotificationData"], email_message) -> Optional["NotificationData"]: """Initialize the data_parts from an email.message.Email object.""" try: data_parts: Set[DataPart] = set() cls.walk_email(email_message, data_parts) # Adding extra headers that are interesting to be parsed data_parts.add( DataPart( EMAIL_HEADER_SUBJECT, # decode_header() handles conversion from RFC2047 ASCII representation of non-ASCII content to # a list of (string, charset) tuples. # make_header() merges these back into a single Header object containing this text # str() gets the simple Unicode representation of the Header. str(email.header.make_header(email.header.decode_header(email_message["Subject"]))).encode(), ) ) data_parts.add(DataPart(EMAIL_HEADER_DATE, email_message["Date"].encode())) # Ensure the data parts are processed in a consistent order return cls(data_parts=sorted(data_parts, key=lambda part: part.type)) except Exception: # pylint: disable=broad-except logger.exception("Error found initializing data from email message: %s", email_message) return None

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doc/epsilon_pretty_printing.py

Jojendersie/Epsilon-Intersection

14

119848

<filename>doc/epsilon_pretty_printing.py import gdb.printing import gdb class EiMatrixPrinter: "Prints ei::Matrix<> type" def __init__(self, val): self.val = val # Does not work: https://stackoverflow.com/questions/26472066/gdb-pretty-printing-returning-string-from-a-childrens-iterator-but-displaye/29752860 def children(self): rows = self.val.type.template_argument(1) cols = self.val.type.template_argument(2) data = self.val["m_data"] for y in range(rows): line = '' for x in range(cols): idx = x+y*cols yield (str(idx), data[idx]) def to_string(self): return 'Matrix' #rows = self.val.type.template_argument(1) #cols = self.val.type.template_argument(2) #ps = '[' #data = self.val["m_data"] #if rows == 1 or cols == 1: # for i in range(rows * cols): # ps += str(data[i]) # if (i+1) != rows*cols: ps += ', ' # if rows > 1: ps += "]'" # else: ps += ']' #else: # for y in range(rows): # for x in range(cols): # ps += "%12.6g" % self.val["m_data"][x+y*cols] # Multiversion line, looks good on hover, but fails in oneline views # if x != cols-1: ps += ' ' # if y != rows-1: ps +=']\n[' # else: ps += ']' #return ps def build_pretty_printer(): pp = gdb.printing.RegexpCollectionPrettyPrinter( "Epsilon Intersection") pp.add_printer('ei::Matrix', '^ei::Matrix.*$', EiMatrixPrinter) return pp

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tests/unit/props/types/test_number.py

manoadamro/jason

0

193348

import pytest from jason import props def test_validate(): props.Number().load(5) def test_loads_int_from_string(): assert props.Number().load("5") == 5 def test_loads_float_from_string(): assert props.Number().load("5.5") == 5.5 def test_not_allow_strings(): with pytest.raises(props.PropertyValidationError): assert props.Number(allow_strings=False).load("5") == 5 def test_accepts_int(): assert props.Number().load(5) == 5 def test_accepts_float(): assert props.Number().load(5.5) == 5.5 def test_too_low(): with pytest.raises(props.PropertyValidationError): props.Number(min_value=10).load(5) def test_too_high(): with pytest.raises(props.PropertyValidationError): props.Number(max_value=10).load(20) def test_nullable(): props.Number(nullable=True).load(None) def test_not_nullable(): with pytest.raises(props.PropertyValidationError): props.Number().load(None) def test_wrong_type(): with pytest.raises(props.PropertyValidationError): props.Number().load("nope") def test_default(): assert props.Number(default=123).load(None) == 123

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outerJoin.py

eyalsus/python-fun

0

43738

import pandas as pd def leftOuterJoin(left_df, right_df, key): right_df['tmp'] = '@' join_df = left_df.merge(right_df[['tmp', key]], how='left', on=key) join_df = join_df[pd.isnull(join_df['tmp'])] join_df.drop('tmp', axis=1, inplace=True) right_df.drop('tmp', axis=1, inplace=True) return join_df def rightOuterJoin(left_df, right_df, key): return leftOuterJoin(right_df, left_df, key) existing_df = pd.DataFrame.from_dict([{'a': 1, 'b':2}, {'a': 11, 'b':22}, {'a': 111, 'b':222}]) new_df = pd.DataFrame.from_dict([{'a': 11, 'c':33}, {'a': 111, 'c':333}, {'a': 1111, 'c':3333}]) key = 'a' print leftOuterJoin(existing_df, new_df, key) print rightOuterJoin(existing_df, new_df, key)

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prml/nn/math/product.py

brunomaga/PRML

11,017

48233

import numpy as np from prml.nn.function import Function class Product(Function): def __init__(self, axis=None, keepdims=False): if isinstance(axis, int): axis = (axis,) elif isinstance(axis, tuple): axis = tuple(sorted(axis)) self.axis = axis self.keepdims = keepdims def _forward(self, x): self.output = np.prod(x, axis=self.axis, keepdims=True) if not self.keepdims: return np.squeeze(self.output) else: return self.output def backward(self, delta, x): if not self.keepdims and self.axis is not None: for ax in self.axis: delta = np.expand_dims(delta, ax) dx = delta * self.output / x return dx def prod(x, axis=None, keepdims=False): """ product of all element in the array Parameters ---------- x : tensor_like input array axis : int, tuple of ints axis or axes along which a product is performed keepdims : bool keep dimensionality or not Returns ------- product : tensor_like product of all element """ return Product(axis=axis, keepdims=keepdims).forward(x)

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.history/app/scrape_20201229173546.py

Super-Web112/python-scraping-for-hosting

0

47370

from django.contrib.auth.decorators import login_required from django.shortcuts import render, get_object_or_404, redirect from django.http import HttpResponse, JsonResponse from selenium import webdriver from webdriver_manager.chrome import ChromeDriverManager from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By from selenium.webdriver.remote.webelement import WebElement from selenium.webdriver.common.action_chains import ActionChains from app.models import ItalianName, City import csv import os @login_required(login_url="/login/") def scrape_data(request): scrape_name = request.GET.get('scrape_name', None) start_id = request.GET.get('start_id', None) end_id = request.GET.get('end_id', None) option = webdriver.ChromeOptions() option.add_argument("window-size=1280,800") # option.add_argument("--headless") # Setup wait for later # wait = WebDriverWait(driver, 10) driver = '' url = '' id = int(start_id) while(id <= int(end_id)): csv_file = "csv_files/"+scrape_name+"_"+str(id)+".csv" if os.path.exists(csv_file): os.remove(csv_file) try: CLICK_NEXT_BUTTON = True CLICK_NEXT_BUTTON_NUM = 0; aaaaaaaaaa = 0 while (CLICK_NEXT_BUTTON): driver = webdriver.Chrome(ChromeDriverManager().install(), options=option) driver.set_window_position(0, 0) # driver.set_window_size(0, 0) url = 'https://sfera.sferabit.com/servizi/alboonlineBoot/index.php?id='+str(id) driver.get(url) original_window = driver.current_window_handle # Check we don't have other windows open already assert len(driver.window_handles) == 1 driver.switch_to.window(driver.window_handles[0]) driver.implicitly_wait(10) name_input = driver.find_element(By.ID, 'filtroRagioneSociale') name_input.send_keys(scrape_name) # ActionChains(driver).move_to_element(name_input).key_up('Francesco', name_input).perform() # ActionChains(driver).move_to_element(name_input).send_keys(name_input, 'Francesco').perform() # ActionChains(driver).move_to_element(name_input).key_down(Keys.CONTROL).send_keys('Francesco').key_up(Keys.CONTROL).perform() send_button = driver.find_element(By.CSS_SELECTOR, 'button.btn-primary') ActionChains(driver).move_to_element(send_button).click(send_button).perform() driver.implicitly_wait(10) if (CLICK_NEXT_BUTTON): for i in range(CLICK_NEXT_BUTTON_NUM): next_button = driver.find_elements(By.CSS_SELECTOR, '#risultatoRicerca a')[2] ActionChains(driver).move_to_element(next_button).click(next_button).perform() driver.implicitly_wait(10) pros_num_table = driver.find_element(By.CSS_SELECTOR, '#risultatoRicerca>table') pros_num_td = pros_num_table.find_elements(By.TAG_NAME, 'td')[1].get_attribute('innerHTML').strip().split(' ') pros_now_num = pros_num_td[0].strip() pros_total_num = pros_num_td[2].strip() print(pros_now_num) print(pros_total_num) if(pros_total_num in pros_now_num): CLICK_NEXT_BUTTON = False else: CLICK_NEXT_BUTTON = True modal_buttons = driver.find_elements(By.CSS_SELECTOR, 'button.buttonAnagrafica') for modal_button in modal_buttons: ActionChains(driver).move_to_element(modal_button).click(modal_button).perform() driver.implicitly_wait(30) fonts = driver.find_elements(By.CSS_SELECTOR, '#modalPersona td') address = name = birth = email = pec = tel = phone = '' if(len(fonts) > 5): use_nums = [2, 3, 5] first_pros = [] result_pros = [] for use_num in use_nums: first_pros.extend(fonts[use_num].get_attribute("innerHTML").strip().split("<br>")) for first_pro in first_pros: temp = first_pro.replace(" ", " ").strip() temp1 = temp.lower() temp_flag = ('foro di appartenenza' in temp1) or (('data' in temp1) and ('nascita' in temp1)) or ('email' in temp1) or ('pec' in temp1) or ('tel' in temp1) or ('cell' in temp1) if(temp_flag): result_pros.append(temp) for result_pro in result_pros: if(":" in result_pro): rows = result_pro.split(":") temp = rows[0].lower() if("foro di appartenenza" in temp): address = rows[1].strip() if("data" in temp): birth = rows[1].strip() if("email" in temp): email = result_pro.split(">")[1].strip()[:-3] if("pec" in temp): pec = result_pro.split(">")[1].strip()[:-3] if("tel" in temp): tel = rows[1].strip() if("cell" in temp): phone = rows[1].strip() name = fonts[1].get_attribute("innerHTML").split("<b>")[1].strip().split("</b>")[0].strip() print(address) print(name) print(birth) print(email) print(pec) print(tel) print(phone) with open(csv_file, mode='a') as employee_file: employee_writer = csv.writer(employee_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) employee_writer.writerow([address, name, birth, email, pec, tel, phone]) aaaaaaaaaa = aaaaaaaaaa + 1 print(aaaaaaaaaa) close_buttons = driver.find_element(By.CSS_SELECTOR, "button.btn-secondary") driver.implicitly_wait(10) ActionChains(driver).move_to_element(close_buttons).click(close_buttons).perform() driver.implicitly_wait(10) CLICK_NEXT_BUTTON_NUM = CLICK_NEXT_BUTTON_NUM + 1 print(CLICK_NEXT_BUTTON) print(CLICK_NEXT_BUTTON_NUM) driver.implicitly_wait(10) driver.quit() except: driver.quit() print("id="+str(id)+" is not support.") continue finally: # driver.implicitly_wait(10) # driver.quit() id = id + 1 print(str(id)) data = { 'is_taken': 'sdfsdfsdfsdfsdf' } return JsonResponse(data) @login_required(login_url="/login/") def scrape_aaa(request): ItalianName.objects.all().delete() # username = request.GET.get('username', None) option = webdriver.ChromeOptions() option.add_argument("window-size=1280,800") # option.add_argument("--headless") driver = webdriver.Chrome(ChromeDriverManager().install(), options=option) driver.set_window_position(0, 0) driver.get('https://www.nomix.it/nomi-italiani-maschili-e-femminili.php') urls = {'https://www.nomix.it/nomi-italiani-maschili-e-femminili.php', 'https://www.nomix.it/nomi-italiani-lettera-B.php', 'https://www.nomix.it/nomi-italiani-lettera-C.php', 'https://www.nomix.it/nomi-italiani-lettera-D.php', 'https://www.nomix.it/nomi-italiani-lettera-E.php', 'https://www.nomix.it/nomi-italiani-lettera-F.php', 'https://www.nomix.it/nomi-italiani-lettera-G.php', 'https://www.nomix.it/nomi-italiani-lettera-I.php', 'https://www.nomix.it/nomi-italiani-lettera-L.php', 'https://www.nomix.it/nomi-italiani-lettera-M.php', 'https://www.nomix.it/nomi-italiani-lettera-NO.php', 'https://www.nomix.it/nomi-italiani-lettera-PQ.php', 'https://www.nomix.it/nomi-italiani-lettera-R.php', 'https://www.nomix.it/nomi-italiani-lettera-S.php', 'https://www.nomix.it/nomi-italiani-lettera-TUV.php', 'https://www.nomix.it/nomi-italiani-lettera-WZ.php'} for url in urls: driver.get(url) driver.implicitly_wait(10) original_window = driver.current_window_handle # Check we don't have other windows open already assert len(driver.window_handles) == 1 driver.switch_to.window(driver.window_handles[0]) tables = driver.find_elements(By.TAG_NAME, 'table') male_tds = tables[2].find_elements(By.TAG_NAME, 'td') for male_td in male_tds: td_content = male_td.get_attribute('innerHTML') if('<div' not in td_content): if('strong' in td_content): td_content = td_content.replace(' ', '') td_content = td_content.replace('<strong>', '') td_content = td_content.replace('</strong>', '').strip() else: td_content = td_content.replace(' ', '') td_content = td_content.strip() print(td_content) italy_name = ItalianName(names_col=td_content, gender_col='male') italy_name.save() # italy_name.names_col = td_content # italy_name.gender_col = 'male' # italy_name.save() male_tds = tables[3].find_elements(By.TAG_NAME, 'td') for male_td in male_tds: td_content = male_td.get_attribute('innerHTML') if('<div' not in td_content): if('strong' in td_content): td_content = td_content.replace(' ', '') td_content = td_content.replace('<strong>', '') td_content = td_content.replace('</strong>', '').strip() else: td_content = td_content.replace(' ', '') td_content = td_content.strip() print(td_content) italy_name = ItalianName(names_col=td_content, gender_col='male') italy_name.save() driver.implicitly_wait(10) driver.quit() data = { 'is_taken': 'sdfsdfsdfsdfsdf' } return JsonResponse(data) @login_required(login_url="/login/") def scrape_name(request): # username = request.GET.get('username', None) i = 1080 driver = '' while(i<=1086): try: option = webdriver.ChromeOptions() option.add_argument("window-size=1280,800") # option.add_argument("--headless") driver = webdriver.Chrome(ChromeDriverManager().install(), options=option) driver.get('https://sfera.sferabit.com/servizi/alboonlineBoot/index.php?id='+str(i)) original_window = driver.current_window_handle # Check we don't have other windows open already assert len(driver.window_handles) == 1 driver.switch_to.window(driver.window_handles[0]) name_input = driver.find_element(By.ID, 'filtroRagioneSociale') name_input.send_keys('an') send_button = driver.find_element(By.CSS_SELECTOR, 'button.btn-primary') ActionChains(driver).move_to_element(send_button).click(send_button).perform() driver.implicitly_wait(10) td = driver.find_element(By.CSS_SELECTOR, '#risultatoRicerca>table.table tr>td:last-child').get_attribute('innerHTML') td = td.split(" ")[-2].strip() city = City(url_id=i, city_name=td) city.save() except: print(str(i)+" does not support") finally: i = i + 1 driver.quit() data = { 'is_taken': 'sdfsdfsdfsdfsdf' } return JsonResponse(data)

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normalizer/__init__.py

TCC-ADS-2020/Normalizador

7

110900

from .normalizer import *

[ 1, 515, 869, 8945, 3950, 1053, 334, 13, 2 ]

nels-test-integration/api/job.py

elixir-no-nels/nels-core

3

69426

# -*- coding: utf-8 -*- ''' Created on Sep 2, 2016 @author: Kidane ''' import requests import sys import config from utils import feed_utils STORAGE_COPY = 100 STORAGE_MOVE = 101 SBI_PUSH = 106 #norstore: 102 SBI_PULL = 107 #norstore: 103 TSD_PUSH = 104 TSD_PULL = 105 def add_job(job_type_id, nels_id, src_items, dest_folder): try: url = config.api_url("jobs/add") if job_type_id == STORAGE_COPY or job_type_id == STORAGE_MOVE: params = {"nels_id": nels_id, "job_type_id": job_type_id, "parameters": {"source": src_items, "destination": dest_folder}} response = requests.post(url, json=params, auth=(config.API_KEY, config.API_SECRET)) if (response.status_code == requests.codes.created): return response.json()['job_id'] except: feed_utils.error(sys.exc_info()[0]) return -1 def add_sbi_job(job_type_id, nels_id, files_list, folders_list, remote_host, user_name, ssh_key, dataset_id, dataset, subtype, subtype_id, parent_path_of_source, destination_path): try: url = config.api_url("jobs/add") if job_type_id == SBI_PUSH or job_type_id == SBI_PULL: params = {"nels_id": nels_id, "job_type_id": job_type_id, "parameters": {"remote_host": remote_host, "user_name": user_name, "ssh_key": ssh_key, "dataset_id": dataset_id, "dataset": dataset, "subtype": subtype, "subtype_id": subtype_id, "parent_path_of_source": parent_path_of_source, "destination_path": destination_path, "files": files_list, "folders": folders_list }} feed_utils.info("Job type id: %s" % params["job_type_id"]) response = requests.post(url, json=params, auth=(config.API_KEY, config.API_SECRET)) if (response.status_code == requests.codes.created): feed_utils.info(response.text) return response.json()['job_id'] else: feed_utils.info(response.text) except: feed_utils.error(sys.exc_info()[0]) return -1 def get_job_info(job_id): try: url = config.api_url("jobs/%s" % job_id) params = {"jobId": job_id} response = requests.get(url, json=params, auth=(config.API_KEY, config.API_SECRET)) if (response.status_code == requests.codes.ok): return response.json(); except: feed_utils.error(sys.exc_info()[0]) return None def delete_job(job_id): try: url = config.api_url("jobs/%s" % job_id) response = requests.delete(url, auth=(config.API_KEY, config.API_SECRET)) return (response.status_code == requests.codes.no_content) except: feed_utils.error(sys.exc_info()[0]) return False def wait_for_job(job_id): completion = -1 job_info = get_job_info(job_id) while (job_info["completion"] != 100): if (job_info["completion"] != completion): completion = job_info["completion"] feed_utils.info("waiting for job to finish: completion %s" % completion) job_info = get_job_info(job_id) feed_utils.info("job completed")

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urlunescape.py

chys87/scripts

0

172960

<reponame>chys87/scripts #! /usr/bin/env python3 # -*- coding: utf-8 -*- # vim:ts=8 sts=4 sw=4 expandtab ft=python # # Copyright (c) 2014, chys <<EMAIL>> # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # Neither the name of chys <<EMAIL>> nor the names of other # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # import sys from urllib.parse import unquote DEFAULT_CHARSETS = ['utf-8', 'gb18030'] def unescape(url, attempt_charsets=DEFAULT_CHARSETS): for enc in attempt_charsets: try: return unquote(url, encoding=enc, errors='strict') except UnicodeDecodeError: pass return url def main(): if len(sys.argv) > 1: for url in sys.argv[1:]: print(unescape(url)) else: while True: try: url = input() except EOFError: break print(unescape(url)) if __name__ == '__main__': main()

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