Sign up or login please
Go Home page'\n # check to see if value is empty or not. if not, no need to create anything else.\n # if it is, need to see what it is (assuming a collection of ageGroup records of some sort \n if self.codeTable is not None and isinstance(value, list) :\n for v in value:\n try :\n if isinstance(v, six.text_type):\n e = self.codeTable.objects.get(id=int(v))\n else:\n e = self.codeTable.objects.get(id=v)\n except:\n e = None\n if e is not None:\n try : \n html += '
\"\n html += '' + e.name +'
'\n except (AttributeError, TypeError):\n html += '' + name +'
'\n if value is not None and len(value) > 0 :\n try : \n html += ''\n except (AttributeError, TypeError):\n html += ''\n #end the selected column\n html += \"'\n #iterate over the records in the code table.\n if self.codeTable is not None:\n for e in self.codeTable.objects.all() :\n if value is None or e.id not in value:\n html += '
' + e.name +'
'\n \n html += \"wx.CollapsiblePane
\n\nA collapsable panel is a container with an embedded button-like\ncontrol which can be used by the user to collapse or expand the pane's\ncontents.\n\n\n\"\"\"\n\n\n\nif __name__ == '__main__':\n import sys,os\n import run\n run.main(['', os.path.basename(sys.argv[0])] + sys.argv[1:])\n\n\n","sub_path":"Api/toto.py","file_name":"toto.py","file_ext":"py","file_size_in_byte":5110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"17746267","text":"\"\"\"Krylov iterator solver\"\"\"\n\nimport logging\nimport os\n\nimport numpy as np\n\nfrom .model_config import get_region_cnt\nfrom .model_state_base import lin_comb\nfrom .region_scalars import to_ndarray, to_region_scalar_ndarray\nfrom .solver_state import SolverState, action_step_log_wrap\nfrom .utils import class_name, mkdir_exist_okay\n\n\nclass KrylovSolver:\n \"\"\"\n class for applying a Krylov method to approximate the solution of a system of linear\n equations\n\n The specific Krylov method used is Left-Preconditioned GMRES, algorithm 9.4 of\n 'Iterative Methods for Sparse Linear Systems, 2nd Edition', Yousef Saad, available\n at https://www-users.cs.umn.edu/~saad/books.html.\n\n The solver is applied to A x = -fcn, where A is\n comp_jacobian_fcn_state_prod evaluated at iterate.\n\n Assumes x0 = 0.\n \"\"\"\n\n def __init__(self, iterate, workdir, resume, rewind, hist_fname):\n \"\"\"initialize Krylov solver\"\"\"\n logger = logging.getLogger(__name__)\n logger.debug(\n 'KrylovSolver, workdir=\"%s\", resume=\"%r\", rewind=\"%r\", hist_fname=\"%s\"',\n workdir,\n resume,\n rewind,\n hist_fname,\n )\n\n # ensure workdir exists\n mkdir_exist_okay(workdir)\n\n self._workdir = workdir\n self._solver_state = SolverState(\"Krylov\", workdir, resume, rewind)\n\n iterate.gen_precond_jacobian(\n hist_fname,\n precond_fname=self._fname(\"precond\", iteration=0),\n solver_state=self._solver_state,\n )\n\n def _fname(self, quantity, iteration=None):\n \"\"\"construct fname corresponding to particular quantity\"\"\"\n if iteration is None:\n iteration = self._solver_state.get_iteration()\n return os.path.join(self._workdir, \"%s_%02d.nc\" % (quantity, iteration))\n\n def converged(self):\n \"\"\"is solver converged\"\"\"\n return self._solver_state.get_iteration() >= 3\n\n @action_step_log_wrap(step=\"KrylovSolver._solve0\", per_iteration=False)\n # pylint: disable=unused-argument\n def _solve0(self, fcn, solver_state):\n \"\"\"\n steps of solve that are only performed for iteration 0\n This is step 1 of Saad's alogrithm 9.4.\n \"\"\"\n # assume x0 = 0, so r0 = M.inv*(rhs - A*x0) = M.inv*rhs = -M.inv*fcn\n precond_fcn = fcn.apply_precond_jacobian(\n self._fname(\"precond\", 0), self._fname(\"precond_fcn\"), self._solver_state\n )\n beta = precond_fcn.norm()\n caller = class_name(self) + \"._solve0\"\n (-precond_fcn / beta).dump(self._fname(\"basis\"), caller)\n self._solver_state.set_value_saved_state(\"beta_ndarray\", to_ndarray(beta))\n\n def solve(self, res_fname, iterate, fcn):\n \"\"\"apply Krylov method\"\"\"\n logger = logging.getLogger(__name__)\n logger.debug('res_fname=\"%s\"', res_fname)\n\n self._solve0(fcn, solver_state=self._solver_state)\n\n caller = class_name(self) + \".solve\"\n\n while True:\n j_val = self._solver_state.get_iteration()\n h_mat = to_region_scalar_ndarray(\n np.zeros(\n (\n len(iterate.tracer_modules),\n j_val + 2,\n j_val + 1,\n get_region_cnt(),\n )\n )\n )\n if j_val > 0:\n h_mat[:, :-1, :-1] = to_region_scalar_ndarray(\n self._solver_state.get_value_saved_state(\"h_mat_ndarray\")\n )\n basis_j = type(iterate)(self._fname(\"basis\"))\n w_raw = iterate.comp_jacobian_fcn_state_prod(\n fcn, basis_j, self._fname(\"w_raw\"), self._solver_state\n )\n w_j = w_raw.apply_precond_jacobian(\n self._fname(\"precond\", 0), self._fname(\"w\"), self._solver_state\n )\n h_mat[:, :-1, -1] = w_j.mod_gram_schmidt(j_val + 1, self._fname, \"basis\")\n h_mat[:, -1, -1] = w_j.norm()\n w_j /= h_mat[:, -1, -1]\n h_mat_ndarray = to_ndarray(h_mat)\n self._solver_state.set_value_saved_state(\"h_mat_ndarray\", h_mat_ndarray)\n\n # solve least-squares minimization problem for each tracer module\n coeff_ndarray = self.comp_krylov_basis_coeffs(h_mat_ndarray)\n iterate.log_vals(\"KrylovCoeff\", coeff_ndarray)\n\n # construct approximate solution\n res = lin_comb(\n type(iterate),\n to_region_scalar_ndarray(coeff_ndarray),\n self._fname,\n \"basis\",\n )\n res.dump(self._fname(\"krylov_res\", j_val), caller)\n\n if self.converged():\n break\n\n self._solver_state.inc_iteration()\n w_j.dump(self._fname(\"basis\"), caller)\n\n return res.dump(res_fname, caller)\n\n def comp_krylov_basis_coeffs(self, h_mat_ndarray):\n \"\"\"solve least-squares minimization problem for each tracer module\"\"\"\n h_shape = h_mat_ndarray.shape\n coeff_ndarray = np.zeros((h_shape[0], h_shape[2], h_shape[3]))\n lstsq_rhs = np.zeros(h_shape[1])\n beta_ndarray = self._solver_state.get_value_saved_state(\"beta_ndarray\")\n for tracer_module_ind in range(h_shape[0]):\n for region_ind in range(h_shape[3]):\n lstsq_rhs[0] = beta_ndarray[tracer_module_ind, region_ind]\n coeff_ndarray[tracer_module_ind, :, region_ind] = np.linalg.lstsq(\n h_mat_ndarray[tracer_module_ind, :, :, region_ind],\n lstsq_rhs,\n rcond=None,\n )[0]\n return coeff_ndarray\n","sub_path":"src/krylov_solver.py","file_name":"krylov_solver.py","file_ext":"py","file_size_in_byte":5688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"57019642","text":"import sys\nsys.path[0] += '/../'\n\nfrom collections import defaultdict\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom Grader.helpers import get_threshold\nfrom experiments.helpers import read_training_data\n\n\nPLOT_COLORS = {'aug-gen': 'lightskyblue', 'base': 'lightcoral', 'baseline': 'grey', 'bach': 'steelblue'}\nPLOT_LABELS = {'aug-gen': 'Aug. Gen.', 'base': 'Base', 'baseline': 'Baseline'}\n\naug_gen_dir = 'models/aug-gen_06-04_23:10'\nbase_dir = 'models/base_06-02_06:55'\nbaseline_dir = 'models/base_06-03_00:00'\n\ndef main():\n dir_dict = {'aug-gen': aug_gen_dir, 'base': base_dir, 'baseline': baseline_dir}\n plot_median_grade_per_epoch(dir_dict, num_epochs=40)\n\ndef plot_median_grade_per_epoch(dir_dict, num_epochs):\n median_dict = defaultdict(lambda: [0]*num_epochs)\n for model_label, model_path in dir_dict.items():\n data_dict = read_training_data(data_file=f'{model_path}/grades.csv', feature='grade')\n for epoch, grades in data_dict.items():\n if epoch < num_epochs:\n median_dict[model_label][epoch] = np.median(grades)\n\n plt.figure()\n plt.style.use('seaborn-whitegrid')\n fig, ax = plt.subplots()\n ax.grid(False)\n thres = get_threshold(\n data_file='experiments/ablations/reg_pe_no_oe/bach_grades.csv',\n column='grade',\n aggregate='75p',\n )\n plt.axhline(y=thres, dashes=(2,2), label='Lowest Bach\\ngrade threshold', color=PLOT_COLORS['bach'])\n xlim = range(num_epochs)\n for model_label, median_grades in median_dict.items():\n plt.plot(xlim, median_grades[:num_epochs], label=PLOT_LABELS[model_label], color=PLOT_COLORS[model_label])\n plt.title('Median Grade of Generations During Training')\n ax.set_xticks([i+1 for i in xlim])\n ax.set_xticklabels([str(i) for i in xlim])\n for label in ax.get_xaxis().get_ticklabels()[1::2]:\n label.set_visible(False)\n # plt.legend(loc='right')\n handles, labels = ax.get_legend_handles_labels()\n lgd = ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(-0.2,0.5))\n plt.ylabel('Grade')\n plt.xlabel('Epoch')\n plt.savefig('plots/median_grades_per_epoch.png', bbox_inches='tight')\n\n\nif __name__ == '__main__':\n main()","sub_path":"experiments/compare_training.py","file_name":"compare_training.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"470788237","text":"#\n# Population.py\n#\n#\n\nimport copy\nimport math\nfrom operator import attrgetter\nfrom Individual import *\nfrom Worker import *\nfrom multiprocessing import Pool\n\n\nclass Population:\n \"\"\"\n Population\n \"\"\"\n uniprng=None\n crossoverFraction=None\n \n def __init__(self, populationSize):\n \"\"\"\n Population constructor\n \"\"\"\n self.population=[]\n for i in range(populationSize):\n self.population.append(Individual()) \n\n def __len__(self):\n return len(self.population)\n \n def __getitem__(self,key):\n return self.population[key]\n \n def __setitem__(self,key,newValue):\n self.population[key]=newValue\n \n def copy(self):\n return copy.deepcopy(self)\n \n def evaluateFitness(self,pobj):\n states=[ind.x for ind in self.population]\n fitnesses=pobj.map(Worker.evaluateFitnessPool,states)\n for i in range(len(self.population)): self.population[i].fit=fitnesses[i]\n #for individual in self.population: individual.evaluateFitness() \n \n def mutate(self): \n for individual in self.population:\n individual.mutate()\n \n def crossover(self):\n if (self.startCity==0):\n indexList1=list(range(len(self)))\n indexList2=list(range(len(self)))\n else:\n indexList1=list(range(1,len(self)))\n indexList2=list(range(1,len(self)))\n \n self.uniprng.shuffle(indexList1)\n self.uniprng.shuffle(indexList2)\n\n\n if self.crossoverFraction == 1.0: \n for index1,index2 in zip(indexList1,indexList2):\n self[index1].crossover(self[index2])\n else:\n for index1,index2 in zip(indexList1,indexList2):\n rn=self.uniprng.random()\n if rn < self.crossoverFraction:\n self[index1].crossover(self[index2]) \n\n \n def conductTournament(self):\n # binary tournament\n indexList1=list(range(len(self)))\n indexList2=list(range(len(self)))\n \n self.uniprng.shuffle(indexList1)\n self.uniprng.shuffle(indexList2)\n \n # do not allow self competition\n for i in range(len(self)):\n if indexList1[i] == indexList2[i]:\n temp=indexList2[i]\n if i == 0:\n indexList2[i]=indexList2[-1]\n indexList2[-1]=temp\n else:\n indexList2[i]=indexList2[i-1]\n indexList2[i-1]=temp\n \n #compete\n newPop=[] \n for index1,index2 in zip(indexList1,indexList2):\n if self[index1].fit < self[index2].fit:\n newPop.append(copy.deepcopy(self[index1]))\n elif self[index1].fit > self[index2].fit:\n newPop.append(copy.deepcopy(self[index2]))\n else:\n rn=self.uniprng.random()\n if rn > 0.5:\n newPop.append(copy.deepcopy(self[index1]))\n else:\n newPop.append(copy.deepcopy(self[index2]))\n \n # overwrite old pop with newPop \n self.population=newPop \n\n\n def combinePops(self,otherPop):\n self.population.extend(otherPop.population)\n\n def truncateSelect(self,newPopSize):\n #sort by fitness\n self.population.sort(key=attrgetter('fit'),reverse=False)\n \n #then truncate the bottom\n self.population=self.population[:newPopSize] \n \n def __str__(self):\n s=''\n for ind in self:\n s+=str(ind) + '\\n'\n return s\n\n\n \n \n","sub_path":"Population.py","file_name":"Population.py","file_ext":"py","file_size_in_byte":3861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"369485710","text":"\"\"\"\nAnagram Searcher\n\nFinds anagrams of given input, based on given strings and files. \n\"\"\"\n\n__author__ = \"Nicholas Chua\"\n__docformat__ = 'reStructuredText'\n__since__ = '28/06/2013'\n__modified__ = '11/06/2020'\n\nfrom binary_search_list_tree import BinarySearchListTree\nimport sys\n\ndef read_from_file(filename: str, my_tree: BinarySearchListTree[str, str]) -> None:\n with open(filename, 'r') as f:\n for line in f:\n line = line.strip()\n if line.isalpha():\n sorted_string = \"\".join(sorted(line.lower()))\n print(\"Inserting:\", line, \"->\", sorted_string)\n # TODO: Add word to my_tree\n my_tree[sorted_string] = line\n else:\n print(\"Error: Input string %s from file %s\" % (line, filename))\n\ndef menu() -> None:\n my_tree = BinarySearchListTree()\n quit_program = False\n options = [\"Prac7 Anagram Tree Menu Options:\",\n \"Read String\",\n \"Read File\",\n \"List\",\n \"Search\",\n \"Anagram\",\n \"Quit\"]\n\n while not quit_program:\n print()\n for i, option in enumerate(options):\n optnum = \"%d. \" % i if i > 0 else \"\"\n print(optnum, option, sep=\"\")\n\n command = input(\"Please press a number, then ', ''))\n\n\n@app.route('/latex')\ndef latex():\n return tex.TeX\n\n\nif __name__ == '__main__':\n app.run()","sub_path":"mpapp.py","file_name":"mpapp.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"637148588","text":"import pandas as pd\nimport numpy as np\nimport pickle\nimport os\nimport json\n\nOUT_DIR = os.environ[\"outDir\"]\nCOURSE_VECTOR_DIR = OUT_DIR\nCOURSE_INFO_DIR = '../shared/course_api/outputs'\nINTERMEDIATE_DATA_DIR = './data'\nvectors_path = COURSE_VECTOR_DIR + '/course2vec.npy'\nvec2course_path = COURSE_VECTOR_DIR + '/idx2course.json'\ncourse_info_path = COURSE_INFO_DIR + '/course_description_init.tsv'\n\nprint('[INFO] Begin data preprocessing stage...')\nprint('[INFO] Reading in course vectors...')\n\nvectors = np.load(vectors_path)\n\nwith open(vec2course_path) as json_data:\n vec2course_map = json.load(json_data)\n\nvect_dict = {}\nfor vector_id in vec2course_map.keys():\n vect_dict[int(vector_id)] = vectors[int(vector_id)-1]\n\nvect_df = pd.DataFrame(vect_dict).T\nvect_df.index.name = 'vector_id'\nvect_df.reset_index(inplace = True)\n\ncourse_identifier_list = [vec2course_map[str(vec_id)] for vec_id in vect_df['vector_id']]\nvect_df.insert(loc = 1, column = 'vector_course_identifier', value = course_identifier_list)\n\nprint('[INFO] Transforming vectors done.')\n\nprint('[INFO] Reading in course descriptions...')\n\ndescript_df = pd.read_csv(course_info_path, sep = '\\t', )[['abbr_cid', 'course_num', 'course_subject', 'course_description', 'course_title']]\n\ndescript_df['course_name'] = descript_df['abbr_cid'].str.replace('_', ' ').str.lower().str.capitalize()\ndescript_df['course_alternative_names'] = descript_df['course_subject'] + ' ' + descript_df['course_num']\ndescript_df['course_alternative_names'] = descript_df['course_alternative_names'] + ' ' + descript_df['abbr_cid'].str.replace('_', '')\n# add custom abbreviations here\ndescript_df['vector_course_identifier'] = descript_df['course_subject'] + '_' + descript_df['course_num']\ndescript_df.drop(['course_num', 'abbr_cid'], axis = 1 ,inplace = True)\n\nprint('[INFO] Transforming course descriptions done.')\n\nprint('[INFO] Removing courses with generic descriptions.')\ndescript_df = descript_df[~descript_df.course_description.isna()]\ncourses_to_remove = ['Freshman Seminar', 'Freshman Seminars', 'Freshman/Sophomore Seminar', 'Sophomore Seminar', 'Sophomore Seminars', 'Berkeley Connect']\nremove_regex = '|'.join(courses_to_remove)\ndescript_df = descript_df[~descript_df.course_title.str.contains(remove_regex, regex=True)]\ndescript_df = descript_df[~descript_df.course_name.str.contains('99')]\ndescript_df = descript_df[~descript_df.course_name.str.contains('98')]\ndescript_df = descript_df[~descript_df.course_name.str.contains('97')]\ndescript_df = descript_df[~descript_df.course_title.str.contains('special topics', case=False)]\ndescript_df = descript_df[~descript_df.course_title.str.contains('seminar', case=False)]\ndescript_df = descript_df[~descript_df.course_subject.str.contains('FPF', case=True)]\n\nprint('[INFO] Removing courses with generic descriptions done.')\n\nprint('[INFO] Merging vectors and descriptions...')\n\nvector_course_text_df = pd.merge(vect_df, descript_df, on = 'vector_course_identifier', how = 'left')\nvector_course_text_df.drop('vector_course_identifier', axis = 1, inplace = True)\nvector_course_text_df = vector_course_text_df[vector_course_text_df['course_name'].notnull()]\n\nprint('[INFO] Extract raw course vectors...')\n\nraw_vectors = vector_course_text_df[vector_course_text_df.columns.difference(['course_name', 'course_title', 'course_description', 'course_subject', 'course_alternative_names', 'vector_id'])]\n\nprint('[INFO] Extract corresponding course information...')\nvector_text = vector_course_text_df[['course_name', 'course_title', 'course_description', 'course_subject', 'course_alternative_names']]\n\nprint('[INFO] Writing to tsv files...')\n\nvector_text.to_csv(INTERMEDIATE_DATA_DIR+'/aligned_course_info.tsv', sep='\\t', index = False)\nraw_vectors.to_csv(INTERMEDIATE_DATA_DIR+'/aligned_course_vecs.tsv', sep='\\t', index = False)\n\nprint('[INFO] Data preprocessing complete.')\n","sub_path":"scripts/data_joining.py","file_name":"data_joining.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"401149291","text":"import yaml\nfrom yaml import dump\ntry:\n from yaml import CDumper as Dumper\nexcept ImportError:\n from yaml import Dumper\nimport copy\nfrom collections import OrderedDict\nfrom instance import DockerComponent, MultipleComponent, PrimaryNamenode, SecondaryNamenode, JournalNode, DataNode, \\\n ResourceManager, YarnHistoryServer, ClusterStarter, ClusterDb, ZookeeperNode, HiveServer, HiveMetastore, \\\n SparkHistory, SparkThrift, Hue, PrestoServer, PrestoWorker\nfrom argparse import Namespace\nfrom typing import List\n\n\n\nDOCKER_COMPOSE_YAML = OrderedDict({\n \"version\": \"3\",\n \"services\": {},\n \"networks\": {\n \"hadoop.net\": {\n \"external\": True\n }\n }\n})\n\nyaml.add_representer(type(None), lambda dumper, value: dumper.represent_scalar(u'tag:yaml.org,2002:null', ''),\n Dumper=Dumper)\nyaml.add_representer(OrderedDict, lambda self, data: self.represent_mapping('tag:yaml.org,2002:map', data.items()),\n Dumper=Dumper)\n\n\ndef generate_yaml(instances: List[DockerComponent]):\n compose_yaml = copy.deepcopy(DOCKER_COMPOSE_YAML)\n for instance in instances:\n instance_conf = {\n \"image\": instance.image,\n \"container_name\": instance.name,\n \"networks\": {\n \"hadoop.net\": None\n },\n \"tty\": True\n }\n if getattr(instance, \"ports\") and instance.ports:\n instance_conf[\"ports\"] = list(instance.ports)\n\n if getattr(instance, \"hosts\") and instance.hosts:\n instance_conf[\"networks\"][\"hadoop.net\"] = {\"aliases\": list(instance.hosts)}\n\n if getattr(instance, \"volumes\") and instance.volumes:\n instance_conf[\"volumes\"] = list(instance.volumes)\n\n if getattr(instance, \"environment\") and instance.environment:\n instance_conf[\"environment\"] = instance.environment\n\n if getattr(instance, \"more_options\") and instance.more_options:\n for k, v in instance.more_options.items():\n instance_conf[k] = v\n\n compose_yaml[\"services\"][instance.name] = instance_conf\n return dump(compose_yaml, Dumper=Dumper)\n\n\ndef build_components(args: Namespace) -> List[DockerComponent]:\n components = [ClusterStarter()]\n\n if args.all or args.hive or args.hue:\n components.append(ClusterDb(args))\n primary_nn = [PrimaryNamenode(), JournalNode(1), ZookeeperNode(1), YarnHistoryServer()]\n if args.all or args.hive:\n primary_nn.append(HiveServer())\n primary_nn.append(HiveMetastore())\n\n if args.all or args.presto:\n primary_nn.append(PrestoServer())\n\n components.append(MultipleComponent(\"primary-namenode\", primary_nn))\n\n secondary_nn = [SecondaryNamenode(), JournalNode(2), ZookeeperNode(2), ResourceManager()]\n\n if args.all or args.spark or args.spark_history or args.spark_thrift:\n secondary_nn.append(SparkHistory())\n\n if args.all or args.spark_thrift:\n secondary_nn.append(SparkThrift())\n\n components.append(MultipleComponent(\"secondary-namenode\", secondary_nn))\n\n datanode1 = [DataNode(1), JournalNode(3), ZookeeperNode(3)]\n if args.all or args.presto:\n datanode1.append(PrestoWorker(1))\n components.append(MultipleComponent(\"datanode1\", datanode1))\n\n additional_datanodes = []\n for i in range(2, args.num_datanode + 1):\n additional_datanodes.append(DataNode(i))\n\n # Add presto worker in data node, num of presto worker does not exceed num of datanode\n if (args.all or args.presto) and args.num_presto_worker > 1:\n worker_cnt = 1\n while worker_cnt < args.num_presto_worker and worker_cnt < len(additional_datanodes):\n datanode = additional_datanodes[worker_cnt - 1]\n additional_datanodes[worker_cnt - 1] = MultipleComponent(datanode.name, [datanode,\n PrestoWorker(worker_cnt + 1)])\n worker_cnt += 1\n\n components += additional_datanodes\n\n if args.hue or args.all:\n components.append(Hue(args))\n\n return components\n","sub_path":"docker_compose.py","file_name":"docker_compose.py","file_ext":"py","file_size_in_byte":4096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"431811050","text":"\"\"\"\nDemonstrates the core kwcoco spaces: video, image, and asset space.\n\"\"\"\n\n\ndef setup_data():\n \"\"\"\n This creates aligned video data similar to what might exist in a real world\n use case.\n \"\"\"\n import ubelt as ub\n import kwimage\n\n demo_dpath = ub.Path.appdir('kwcoco/demo/demo_spaces').ensuredir()\n asset_dpath = (demo_dpath / 'assets').ensuredir()\n raw = kwimage.grab_test_image('amazon', dsize=(512, 512))\n raw = kwimage.ensure_float01(raw)\n\n import kwcoco\n dset = kwcoco.CocoDataset()\n dset.fpath = demo_dpath / 'data.kwcoco.json'\n\n video_id = dset.add_video(name='demo_video', width=512, height=512)\n\n observations = [\n {'warp_img_from_vid': kwimage.Affine.coerce(offset=(-32, -32), theta=0.02)},\n {'warp_img_from_vid': kwimage.Affine.coerce(offset=(-64, -64), theta=0.05)},\n {'warp_img_from_vid': kwimage.Affine.coerce(offset=(-128, -128), theta=0.08)},\n ]\n for frame_idx, obs in enumerate(observations):\n warp_img_from_vid = obs['warp_img_from_vid']\n\n ideal_img_frame = kwimage.warp_affine(raw, warp_img_from_vid, dsize='positive', border_value=float('nan'))\n img_h, img_w = ideal_img_frame.shape[0:2]\n\n # pan is shifted a little bit\n pan = kwimage.convert_colorspace(ideal_img_frame, 'rgb', 'gray')\n warp_pan_from_img = kwimage.Affine.coerce(offset=(-2, -3))\n\n image_name = f'frame_{frame_idx:03d}'\n frame_dpath = (asset_dpath / image_name).ensuredir()\n\n pan_fpath = frame_dpath / 'pan.tif'\n red_fpath = frame_dpath / 'red.tif'\n green_fpath = frame_dpath / 'green.tif'\n blue_fpath = frame_dpath / 'blue.tif'\n\n # Some assets are smaller than others\n warp_msi_from_img = kwimage.Affine.scale(0.5)\n msi = kwimage.warp_affine(ideal_img_frame, warp_msi_from_img, dsize='positive', border_value=float('nan'))\n red = msi[..., 0]\n green = msi[..., 1]\n blue = msi[..., 2]\n\n kwimage.imwrite(pan_fpath, pan)\n kwimage.imwrite(red_fpath, red)\n kwimage.imwrite(green_fpath, green)\n kwimage.imwrite(blue_fpath, blue)\n\n gid = dset.add_image(name=image_name, frame_index=frame_idx,\n width=img_w, height=img_h, video_id=video_id,\n warp_img_to_vid=warp_img_from_vid.inv())\n\n coco_img = dset.coco_image(gid)\n\n coco_img.add_asset(pan_fpath, channels='pan', warp_aux_to_img=warp_pan_from_img.inv())\n coco_img.add_asset(red_fpath, channels='red', warp_aux_to_img=warp_msi_from_img.inv())\n coco_img.add_asset(green_fpath, channels='green', warp_aux_to_img=warp_msi_from_img.inv())\n coco_img.add_asset(blue_fpath, channels='blue', warp_aux_to_img=warp_msi_from_img.inv())\n return dset\n\n\ndef main():\n import kwimage\n dset = setup_data()\n\n def build_space_frames(space):\n frame_stack = []\n for coco_img in dset.images().coco_images:\n frame_index = coco_img.img['frame_index']\n asset_stack = []\n for chan in coco_img.channels.fuse().to_list():\n asset_data = coco_img.imdelay(chan, space=space).finalize(nodata_method='float')\n asset_data = kwimage.fill_nans_with_checkers(asset_data)\n asset_data = kwimage.draw_header_text(asset_data, f'T={frame_index} : {chan}')\n asset_stack.append(asset_data)\n asset_row = kwimage.stack_images(asset_stack, axis=1, pad=10, bg_value='kitware_green')\n frame_stack.append(asset_row)\n canvas = kwimage.stack_images(frame_stack, axis=0, pad=10, bg_value='kitware_green')\n return canvas\n\n asset_canvas = build_space_frames(space='asset')\n image_canvas = build_space_frames(space='image')\n video_canvas = build_space_frames(space='video')\n\n import ubelt as ub\n asset_blurb = ub.codeblock(\n '''\n This is a demonstration of \"asset space\".\n Each frame is a row, and each channel is a column.\n This is how images are saved on disk.\n Any alignment is delayed until the last possible moment.\n ''')\n image_blurb = ub.codeblock(\n '''\n This is a demonstration of \"image space\".\n Assets are aligned within an image, but are not necesarilly aligned across frames.\n The resolution is typically that of the largest asset in the image, but can be arbitrary.\n ''')\n video_blurb = ub.codeblock(\n '''\n This is a demonstration of \"video space\".\n Assets are aligned within an image and across frames.\n Resolution is usually set to that of some reference image, but it can be arbitrary.\n ''')\n\n import kwplot\n kwplot.autompl()\n kwplot.imshow(asset_canvas, title=asset_blurb, fnum=1)\n kwplot.imshow(image_canvas, title=image_blurb, fnum=2)\n kwplot.imshow(video_canvas, title=video_blurb, fnum=3)\n","sub_path":"examples/demo_kwcoco_spaces.py","file_name":"demo_kwcoco_spaces.py","file_ext":"py","file_size_in_byte":4911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"482005929","text":"\"\"\" Test module for ddpg.model\n\nMostly based on baselines.common.tests.test_identity.py\n\"\"\"\n\nfrom itertools import chain\nimport pytest\nimport numpy as np\nimport tensorflow as tf\nimport logging\nfrom baselines.common.tests.envs.identity_env import BoxIdentityEnv\nfrom ddpg.experience_replay import ExperienceReplay\nfrom ddpg.main import parse_args\nfrom ddpg.monitor import Monitor\nfrom ddpg.model import Model\nfrom ddpg.runner import OrnsteinUhlenbeckNoise, Runner\n\nSEED = 2\nGAMMA = 0.9\nTOTAL_TIMESTEPS = 1100000\n\ndef test_target_update():\n \"\"\" Test if target update is working correctly. \"\"\"\n env = BoxIdentityEnv((1,), episode_len=100)\n kwargs = parse_args()\n kwargs['experience_replay_size'] = 10\n kwargs['minibatch_size'] = 5\n tf.reset_default_graph()\n tf.set_random_seed(SEED)\n np.random.seed(SEED)\n monitor = Monitor(env, None, 100)\n state = env.reset()\n experience_replay = ExperienceReplay(kwargs['experience_replay_size'], kwargs['minibatch_size'],\n state_shape=state.shape,\n action_shape=env.action_space.shape)\n noise = OrnsteinUhlenbeckNoise(np.zeros(env.action_space.shape),\n kwargs['ou_theta'], kwargs['ou_sigma'])\n model = Model(env, **kwargs) # pylint:disable=missing-kwoa\n variables = list(chain(*model._primary_parameters))\n target_variables = list(chain(*model._target_parameters))\n runner = Runner(monitor, model, kwargs['test'], render=kwargs['render'],\n experience_replay=experience_replay,\n ou_noise=noise)\n with tf.Session() as sess:\n model.initialize(sess)\n values = sess.run(variables)\n target_values = sess.run(target_variables)\n for val, target_val, _, _ in zip(values, target_values, variables, target_variables):\n assert np.allclose(val, target_val)\n # Check for soft update\n for _ in range(15):\n runner.step()\n target_values = sess.run(target_variables)\n runner.train()\n for new_var, new_target_var, target_val in zip(variables, target_variables, target_values):\n new_val, new_target_val = sess.run((new_var, new_target_var))\n # new_target_val should be closer to target_val than new_val\n assert np.all(np.abs(new_target_val - target_val) - 1e-5 <= np.abs(new_target_val - new_val) + 1e-5 )\n\ndef test_reuse():\n \"\"\" Test if parameters are reused for primary network.\n\n The parameters of the critic_with_actor network and critic/actor networks must be the same. \"\"\"\n env = BoxIdentityEnv((1,), episode_len=100)\n kwargs = parse_args()\n kwargs['experience_replay_size'] = 10\n kwargs['minibatch_size'] = 5\n tf.reset_default_graph()\n tf.set_random_seed(SEED)\n np.random.seed(SEED)\n monitor = Monitor(env, None, 100)\n state = env.reset()\n experience_replay = ExperienceReplay(kwargs['experience_replay_size'], kwargs['minibatch_size'],\n state_shape=state.shape,\n action_shape=env.action_space.shape)\n noise = OrnsteinUhlenbeckNoise(np.zeros(env.action_space.shape),\n kwargs['ou_theta'], kwargs['ou_sigma'])\n model = Model(env, **kwargs) # pylint:disable=missing-kwoa\n runner = Runner(monitor, model, kwargs['test'], render=kwargs['render'],\n experience_replay=experience_replay,\n ou_noise=noise)\n with tf.Session() as sess:\n model.initialize(sess)\n for _ in range(15):\n runner.step()\n runner.train()\n state = runner._state[np.newaxis, :]\n critic_with_actor = sess.run(model._critic_with_actor, feed_dict={model._state: state,\n model._training_phase: False})\n actor = model.next_action(runner._state)\n critic = sess.run(model._critic, feed_dict={model._state: state,\n model._critic_action: actor,\n model._training_phase: False})\n assert np.all(critic == critic_with_actor)\n\n@pytest.mark.slow\ndef test_discrete_identity():\n \"\"\" Test if it learns BoxIdentityEnv \"\"\"\n import os.path\n env = BoxIdentityEnv((1,), episode_len=100)\n kwargs = parse_args()\n log_level = kwargs['log_level']\n log_dir = kwargs['log_dir']\n log_path = os.path.join(log_dir, 'main.log')\n logging.basicConfig(level=getattr(logging, log_level.upper()), filename=log_path)\n tf.reset_default_graph()\n tf.set_random_seed(SEED)\n monitor = Monitor(env, log_dir, 100)\n state = env.reset()\n experience_replay = ExperienceReplay(kwargs['experience_replay_size'], kwargs['minibatch_size'],\n state_shape=state.shape,\n action_shape=env.action_space.shape)\n noise = OrnsteinUhlenbeckNoise(np.zeros(env.action_space.shape),\n kwargs['ou_theta'], kwargs['ou_sigma'])\n model = Model(env, **kwargs) # pylint:disable=missing-kwoa\n runner = Runner(monitor, model, kwargs['test'], log_dir=log_dir, render=kwargs['render'],\n experience_replay=experience_replay,\n ou_noise=noise)\n with tf.Session() as sess:\n model.initialize(sess)\n for _ in range(30000):\n runner.step()\n runner.train()\n assert np.all(monitor.average > -1),\\\n 'average reward {} is less than -1'.format(monitor.average[0])\n\nif __name__ == '__main__':\n test_discrete_identity()\n","sub_path":"test/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":5761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"636225483","text":"import numpy as np\n\nfrom mushroom.utils.parameters import Parameter\n\n\nclass TDPolicy(object):\n def __init__(self):\n \"\"\"\n Constructor.\n\n \"\"\"\n self._approximator = None\n\n def __call__(self, *args):\n \"\"\"\n Compute the probability of taking action in a certain state following\n the policy.\n\n Args:\n *args (list): list containing a state or a state and an action.\n\n Returns:\n The probability of all actions following the policy in the given\n state if the list contains only the state, else the probability\n of the given action in the given state following the policy.\n\n \"\"\"\n raise NotImplementedError\n\n def draw_action(self, state):\n \"\"\"\n Sample an action in `state` using the policy.\n\n Args:\n state (np.ndarray): the state where the agent is.\n\n Returns:\n The action sampled from the policy.\n\n \"\"\"\n raise NotImplementedError\n\n def set_q(self, approximator):\n \"\"\"\n Args:\n approximator (object): the approximator to use.\n\n \"\"\"\n self._approximator = approximator\n\n def get_q(self):\n \"\"\"\n Returns:\n The approximator used by the policy.\n\n \"\"\"\n return self._approximator\n\n def __str__(self):\n return self.__name__\n\n\nclass EpsGreedy(TDPolicy):\n \"\"\"\n Epsilon greedy policy.\n\n \"\"\"\n def __init__(self, epsilon):\n \"\"\"\n Constructor.\n\n Args:\n epsilon (Parameter): the exploration coefficient. It indicates\n the probability of performing a random actions in the current\n step.\n\n \"\"\"\n self.__name__ = 'EpsGreedy'\n\n super(EpsGreedy, self).__init__()\n\n assert isinstance(epsilon, Parameter)\n self._epsilon = epsilon\n\n def __call__(self, *args):\n state = args[0]\n q = self._approximator.predict(np.expand_dims(state, axis=0)).ravel()\n max_a = np.argwhere(q == np.max(q)).ravel()\n\n p = self._epsilon.get_value(state) / float(self._approximator.n_actions)\n\n if len(args) == 2:\n action = args[1]\n if action in max_a:\n return p + (1. - self._epsilon.get_value(state)) / len(max_a)\n else:\n return p\n else:\n probs = np.ones(self._approximator.n_actions) * p\n probs[max_a] += (1. - self._epsilon.get_value(state)) / len(max_a)\n\n return probs\n\n def draw_action(self, state):\n if not np.random.uniform() < self._epsilon(state):\n q = self._approximator.predict(state)\n max_a = np.argwhere(q == np.max(q)).ravel()\n\n if len(max_a) > 1:\n max_a = np.array([np.random.choice(max_a)])\n\n return max_a\n\n return np.array([np.random.choice(self._approximator.n_actions)])\n\n def set_epsilon(self, epsilon):\n \"\"\"\n Setter.\n\n Args:\n epsilon (Parameter): the exploration coefficient. It indicates the\n probability of performing a random actions in the current step.\n\n \"\"\"\n assert isinstance(epsilon, Parameter)\n\n self._epsilon = epsilon\n\n def update(self, *idx):\n \"\"\"\n Update the value of the epsilon parameter (e.g. in case of different\n values of epsilon for each visited state according to the number of\n visits).\n\n Args:\n idx (int): value to use to update epsilon.\n\n \"\"\"\n self._epsilon.update(*idx)\n\n\nclass Softmax(TDPolicy):\n \"\"\"\n Softmax policy using a Boltzmann distribution.\n\n \"\"\"\n def __init__(self, tau):\n \"\"\"\n Constructor.\n\n Args:\n tau (float): the temperature of the distribution. As the temperature\n approaches infinity, the policy becomes more and more random. As the\n temperature approaches 0.0, the policy becomes more and more greedy.\n\n \"\"\"\n self.__name__ = 'Softmax'\n\n super(Softmax, self).__init__()\n self._tau = tau\n\n def __call__(self, *args):\n state = args[0]\n qs = np.ones(self._approximator.n_actions)\n for a in xrange(self._approximator.n_actions):\n qs[a] = (np.e**(self._approximator.predict(state, a) / self._tau))\n\n if len(args) == 2:\n action = args[1]\n\n return qs[action] / np.sum(qs)\n else:\n p = np.ones(qs.size)\n for i in xrange(p.size):\n p[i] = qs[i] / np.sum(qs)\n\n return p\n\n def draw_action(self, state):\n return np.array([np.random.choice(self._approximator.n_actions,\n p=self(state))])\n","sub_path":"mushroom/policy/td_policy.py","file_name":"td_policy.py","file_ext":"py","file_size_in_byte":4782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"144856982","text":"# -*- coding: utf-8 -*-\nimport socket\n\nfrom tenhou.client import TenhouClient\nfrom utils.settings_handler import settings\n\n\ndef connect_and_play():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((settings.TENHOU_HOST, settings.TENHOU_PORT))\n\n client = TenhouClient(s)\n was_auth = client.authenticate()\n\n if was_auth:\n client.start_the_game()\n else:\n client.end_the_game()\n","sub_path":"project/tenhou/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"222558973","text":"# pylint: disable=missing-docstring, invalid-name\nimport unittest\nimport numpy as np\nimport obsoper\nfrom obsoper import (exceptions,\n ObservationOperator)\n\n\nclass TestOperator(unittest.TestCase):\n def setUp(self):\n # Model grid\n self.grid_lons = [[0, 1],\n [3, 2]]\n self.grid_lats = [[0, 1],\n [0, 1]]\n self.grid_depths = None\n\n # Observation positions\n self.obs_lons = [1.5]\n self.obs_lats = [0.5]\n self.obs_depths = None\n\n # Forecasts/results\n self.analysis = [[1, 2],\n [3, 4]]\n self.counterparts = [2.5]\n\n def test_interpolate_given_tripolar_grid(self):\n fixture = obsoper.Operator(self.grid_lons,\n self.grid_lats,\n self.obs_lons,\n self.obs_lats,\n layout=\"tripolar\")\n result = fixture.interpolate(self.analysis)\n expect = self.counterparts\n np.testing.assert_array_almost_equal(expect, result, decimal=4)\n\n def test_interpolate_given_regular_grid(self):\n fixture = obsoper.Operator([100, 200],\n [10, 20],\n [120],\n [12],\n layout=\"regular\")\n result = fixture.interpolate(self.analysis)\n expect = [1.6]\n np.testing.assert_array_almost_equal(expect, result)\n\n def test_interpolate_given_regional_grid(self):\n fixture = obsoper.Operator(self.grid_lons,\n self.grid_lats,\n self.obs_lons,\n self.obs_lats,\n layout=\"regional\")\n result = fixture.interpolate(self.analysis)\n expect = self.counterparts\n np.testing.assert_array_equal(expect, result)\n\n def test_constructor_given_unknown_layout_raises_exception(self):\n with self.assertRaises(exceptions.UnknownLayout):\n obsoper.Operator(None, None, None, None, layout=\"not a layout\")\n\n\nclass TestIncompatibleGrids(unittest.TestCase):\n def setUp(self):\n self.obs_shape = 7\n self.grid_shape = 3, 3\n self.different_grid_shape = 5, 5\n\n def test_interpolate_given_incompatible_shape_raises_exception(self):\n operator = obsoper.Operator(\n np.zeros(self.grid_shape),\n np.zeros(self.grid_shape),\n np.zeros(self.obs_shape),\n np.zeros(self.obs_shape))\n with self.assertRaises(exceptions.IncompatibleGrid):\n operator.interpolate(np.zeros(self.different_grid_shape))\n\n def test_interpolate_tripolar_given_incompatible_shape_raises_exception(self):\n operator = obsoper.Operator(\n np.zeros(self.grid_shape),\n np.zeros(self.grid_shape),\n np.zeros(self.obs_shape),\n np.zeros(self.obs_shape),\n layout=\"tripolar\")\n with self.assertRaises(exceptions.IncompatibleGrid):\n operator.interpolate(np.zeros(self.different_grid_shape))\n\n\nclass TestOperatorRegularGrid(unittest.TestCase):\n def setUp(self):\n self.grid_lons = np.array([0, 1])\n self.grid_lats = np.array([0, 1])\n self.s_levels = np.array([[[0, 1],\n [0, 1]],\n [[0, 1],\n [0, 2]]])\n self.z_levels = np.array([0, 1])\n self.field = np.array([[[11, 12],\n [13, 14]],\n [[21, 22],\n [23, 24]]])\n self.z_counterparts = 17.5\n self.s_counterparts = 17.4\n\n def make_operator(self, levels):\n obs_lons, obs_lats, obs_depths = [0.5], [0.5], [0.5]\n return obsoper.Operator(self.grid_lons,\n self.grid_lats,\n obs_lons,\n obs_lats,\n observed_depths=obs_depths,\n grid_depths=levels)\n\n def test_vertical_interpolation_given_regular_layout_and_s_levels(self):\n self.check_interpolate(self.s_levels, self.s_counterparts)\n\n def test_vertical_interpolation_given_regular_layout_and_z_levels(self):\n self.check_interpolate(self.z_levels, self.z_counterparts)\n\n def check_interpolate(self, levels, expect):\n fixture = self.make_operator(levels)\n result = fixture.interpolate(self.field)\n np.testing.assert_array_almost_equal(expect, result)\n\n\nclass TestObservationOperator(unittest.TestCase):\n def setUp(self):\n self.longitudes = np.arange(3)\n self.latitudes = np.arange(3)\n self.depths = np.tile([10, 20, 30, 40], (3, 3, 1))\n self.fixture = ObservationOperator(self.longitudes, self.latitudes,\n self.depths)\n\n # Pseudo-model field\n self.surface_field = np.ones((3, 3))\n self.full_field = np.ones((3, 3, 4))\n\n # Sample coordinates\n self.inside_lons = np.array([0.9])\n self.inside_lats = np.array([1.5])\n self.outside_lons = np.array([-0.1])\n self.outside_lats = np.array([3.1])\n self.nan_lons_masked = np.ma.MaskedArray([np.nan], mask=[True])\n self.lons_masked = np.ma.MaskedArray([999., 999.], mask=[False, True])\n self.lats_masked = np.ma.MaskedArray([999., 999.], mask=[False, True])\n\n def test_interpolate_given_coordinates_and_depths(self):\n observed_depths = np.array([[15]])\n result = self.fixture.interpolate(self.full_field, self.inside_lons,\n self.inside_lats, observed_depths)\n expect = np.array([[1]])\n np.testing.assert_array_almost_equal(expect, result)\n\n def test_horizontal_interpolate(self):\n observed_lats, observed_lons = np.array([1]), np.array([1])\n result = self.fixture.horizontal_interpolate(self.surface_field,\n observed_lons,\n observed_lats)\n expect = [1]\n np.testing.assert_array_almost_equal(expect, result)\n\n def test_vertical_interpolate_given_section(self):\n model_section = np.array([[1, 2, 3, 4],\n [5, 6, 7, 8]])\n model_depths = np.array([[10, 20, 30, 40],\n [10, 20, 30, 40]])\n observed_depths = np.array([[15],\n [35]])\n result = self.fixture.vertical_interpolate(model_section,\n model_depths,\n observed_depths)\n expect = np.array([[1.5],\n [7.5]])\n np.testing.assert_array_almost_equal(expect, result)\n\n def test_horizontal_interpolate_given_data_outside_returns_masked(self):\n result = self.fixture.horizontal_interpolate(self.surface_field,\n self.outside_lons,\n self.outside_lats)\n expect = np.ma.masked_all((1,))\n self.assertMaskedArrayEqual(expect, result)\n\n def test_horizontal_interpolate_given_masked_lons_nan(self):\n result = self.fixture.horizontal_interpolate(self.surface_field,\n self.nan_lons_masked,\n self.outside_lats)\n expect = np.ma.masked_all((1,))\n self.assertMaskedArrayEqual(expect, result)\n\n def test_horizontal_interpolate_given_masked_positions(self):\n result = self.fixture.horizontal_interpolate(self.full_field,\n self.lons_masked,\n self.lats_masked)\n expect = np.ma.masked_all((2, 4))\n self.assertMaskedArrayEqual(expect, result)\n\n def assertMaskedArrayEqual(self, expect, result):\n self.assertEqual(expect.shape, result.shape)\n np.testing.assert_array_almost_equal(expect.compressed(),\n result.compressed())\n","sub_path":"obsoper/test/test_obsoper.py","file_name":"test_obsoper.py","file_ext":"py","file_size_in_byte":8422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"411949328","text":"\"\"\"\nHigh level STIX data products created from single stand alone packets or a sequence of packects.\n\"\"\"\nfrom datetime import timedelta, datetime\nfrom itertools import chain\n\nimport astropy.units as u\nimport numpy as np\nfrom astropy.io import fits\nfrom astropy.table import QTable, vstack, unique\nfrom astropy.time import Time\n\nfrom stix.fits.calibration.integer_compression import decompress\nfrom stix.core.stix_datetime import scet_to_datetime\nfrom stix.fits.products.common import _get_detector_mask, _get_pixel_mask, _get_energy_bins, \\\n _get_compression_scheme, _get_num_energies, _get_sub_spectrum_mask\n\n# __all__ = ['LightCurve', 'Background', 'Spectra', 'Variance', 'CalibrationSpectra',\n# 'FlareFlagAndLocation', 'TMManagementAndFlareList', 'get_energies_from_mask']\n\nENERGY_CHANNELS = {\n 0: {'channel_edge': 0, 'energy_edge': 0, 'e_lower': 0.0, 'e_upper': 4.0, 'bin_width': 4.0,\n 'dE_E': 2.000, 'ql_channel': None},\n 1: {'channel_edge': 1, 'energy_edge': 4, 'e_lower': 4.0, 'e_upper': 5.0, 'bin_width': 1.0,\n 'dE_E': 0.222, 'ql_channel': 0},\n 2: {'channel_edge': 2, 'energy_edge': 5, 'e_lower': 5.0, 'e_upper': 6.0, 'bin_width': 1.0,\n 'dE_E': 0.182, 'ql_channel': 0},\n 3: {'channel_edge': 3, 'energy_edge': 6, 'e_lower': 6.0, 'e_upper': 7.0, 'bin_width': 1.0,\n 'dE_E': 0.154, 'ql_channel': 0},\n 4: {'channel_edge': 4, 'energy_edge': 7, 'e_lower': 7.0, 'e_upper': 8.0, 'bin_width': 1.0,\n 'dE_E': 0.133, 'ql_channel': 0},\n 5: {'channel_edge': 5, 'energy_edge': 8, 'e_lower': 8.0, 'e_upper': 9.0, 'bin_width': 1.0,\n 'dE_E': 0.118, 'ql_channel': 0},\n 6: {'channel_edge': 6, 'energy_edge': 9, 'e_lower': 9.0, 'e_upper': 10.0, 'bin_width': 1.0,\n 'dE_E': 0.105, 'ql_channel': 0},\n 7: {'channel_edge': 7, 'energy_edge': 10, 'e_lower': 10.0, 'e_upper': 11.0, 'bin_width': 1.0,\n 'dE_E': 0.095, 'ql_channel': 1},\n 8: {'channel_edge': 8, 'energy_edge': 11, 'e_lower': 11.0, 'e_upper': 12.0, 'bin_width': 1.0,\n 'dE_E': 0.087, 'ql_channel': 1},\n 9: {'channel_edge': 9, 'energy_edge': 12, 'e_lower': 12.0, 'e_upper': 13.0, 'bin_width': 1.0,\n 'dE_E': 0.080, 'ql_channel': 1},\n 10: {'channel_edge': 10, 'energy_edge': 13, 'e_lower': 13.0, 'e_upper': 14.0, 'bin_width': 1.0,\n 'dE_E': 0.074, 'ql_channel': 1},\n 11: {'channel_edge': 11, 'energy_edge': 14, 'e_lower': 14.0, 'e_upper': 15.0, 'bin_width': 1.0,\n 'dE_E': 0.069, 'ql_channel': 1},\n 12: {'channel_edge': 12, 'energy_edge': 15, 'e_lower': 15.0, 'e_upper': 16.0, 'bin_width': 1.0,\n 'dE_E': 0.065, 'ql_channel': 2},\n 13: {'channel_edge': 13, 'energy_edge': 16, 'e_lower': 16.0, 'e_upper': 18.0, 'bin_width': 1.0,\n 'dE_E': 0.061, 'ql_channel': 2},\n 14: {'channel_edge': 14, 'energy_edge': 18, 'e_lower': 18.0, 'e_upper': 20.0, 'bin_width': 2.0,\n 'dE_E': 0.105, 'ql_channel': 2},\n 15: {'channel_edge': 15, 'energy_edge': 20, 'e_lower': 20.0, 'e_upper': 22.0, 'bin_width': 2.0,\n 'dE_E': 0.095, 'ql_channel': 2},\n 16: {'channel_edge': 16, 'energy_edge': 22, 'e_lower': 22.0, 'e_upper': 25.0, 'bin_width': 3.0,\n 'dE_E': 0.128, 'ql_channel': 2},\n 17: {'channel_edge': 17, 'energy_edge': 25, 'e_lower': 25.0, 'e_upper': 28.0, 'bin_width': 3.0,\n 'dE_E': 0.113, 'ql_channel': 3},\n 18: {'channel_edge': 18, 'energy_edge': 28, 'e_lower': 28.0, 'e_upper': 32.0, 'bin_width': 4.0,\n 'dE_E': 0.133, 'ql_channel': 3},\n 19: {'channel_edge': 19, 'energy_edge': 32, 'e_lower': 32.0, 'e_upper': 36.0, 'bin_width': 4.0,\n 'dE_E': 0.118, 'ql_channel': 3},\n 20: {'channel_edge': 20, 'energy_edge': 36, 'e_lower': 36.0, 'e_upper': 40.0, 'bin_width': 4.0,\n 'dE_E': 0.105, 'ql_channel': 3},\n 21: {'channel_edge': 21, 'energy_edge': 40, 'e_lower': 40.0, 'e_upper': 45.0, 'bin_width': 5.0,\n 'dE_E': 0.118, 'ql_channel': 3},\n 22: {'channel_edge': 22, 'energy_edge': 45, 'e_lower': 45.0, 'e_upper': 50.0, 'bin_width': 5.0,\n 'dE_E': 0.105, 'ql_channel': 3},\n 23: {'channel_edge': 23, 'energy_edge': 50, 'e_lower': 50.0, 'e_upper': 56.0, 'bin_width': 6.0,\n 'dE_E': 0.113, 'ql_channel': 4},\n 24: {'channel_edge': 24, 'energy_edge': 56, 'e_lower': 56.0, 'e_upper': 63.0, 'bin_width': 7.0,\n 'dE_E': 0.118, 'ql_channel': 4},\n 25: {'channel_edge': 25, 'energy_edge': 63, 'e_lower': 63.0, 'e_upper': 70.0, 'bin_width': 7.0,\n 'dE_E': 0.105, 'ql_channel': 4},\n 26: {'channel_edge': 26, 'energy_edge': 70, 'e_lower': 70.0, 'e_upper': 76.0, 'bin_width': 6.0,\n 'dE_E': 0.082, 'ql_channel': 4},\n 27: {'channel_edge': 27, 'energy_edge': 76, 'e_lower': 76.0, 'e_upper': 84.0, 'bin_width': 8.0,\n 'dE_E': 0.100, 'ql_channel': 4},\n 28: {'channel_edge': 28, 'energy_edge': 84, 'e_lower': 84.0, 'e_upper': 100.0,\n 'bin_width': 16.0, 'dE_El': 0.174, 'ql_channel': 4},\n 29: {'channel_edge': 29, 'energy_edge': 100, 'e_lower': 100.0, 'e_upper': 120.0,\n 'bin_width': 20.0, 'dE_El': 0.182, 'ql_channel': 4},\n 30: {'channel_edge': 30, 'energy_edge': 120, 'e_lower': 120.0, 'e_upper': 150.0,\n 'bin_width': 30.0, 'dE_El': 0.222, 'ql_channel': 4},\n 31: {'channel_edge': 31, 'energy_edge': 150, 'e_lower': 150.0, 'e_upper': np.inf,\n 'bin_width': np.inf, 'dE_E': np.inf, 'ql_channel': None}\n}\n\n\nclass Control(QTable):\n\n def __repr__(self):\n return f'<{self.__class__.__name__} \\n {super().__repr__()}>'\n\n def _get_time(self):\n # Replicate packet time for each sample\n base_times = Time(list(chain(\n *[[scet_to_datetime(f'{self[\"scet_coarse\"][i]}:{self[\"scet_fine\"][i]}')]\n * n for i, n in enumerate(self['num_samples'])])))\n # For each sample generate sample number and multiply by duration and apply unit\n start_delta = np.hstack(\n [(np.arange(ns) * it) for ns, it in self[['num_samples', 'integration_time']]])\n # hstack op loses unit\n start_delta = start_delta.value * self['integration_time'].unit\n\n duration = np.hstack([np.ones(num_sample) * int_time for num_sample, int_time in\n self[['num_samples', 'integration_time']]])\n duration = duration.value * self['integration_time'].unit\n\n # TODO Write out and simplify\n end_delta = start_delta + duration\n\n # Add the delta time to base times and convert to relative from start time\n times = base_times + start_delta + (end_delta - start_delta) / 2\n # times -= times[0]\n return times, duration\n\n @classmethod\n def from_packets(cls, packets):\n # Header\n control = cls()\n # self.energy_bin_mask = None\n # self.samples = None\n control['scet_coarse'] = np.array(packets['NIX00445'], np.uint32)\n # Not all QL data have fine time in TM default to 0 if no present\n scet_fine = packets.get('NIX00446')\n if scet_fine:\n control['scet_fine'] = np.array(scet_fine, np.uint32)\n else:\n control['scet_fine'] = np.zeros_like(control['scet_coarse'], np.uint32)\n\n integration_time = packets.get('NIX00405')\n if integration_time:\n control['integration_time'] = (np.array(integration_time, np.float) + 1) * 0.1 * u.s\n else:\n control['integration_time'] = np.zeros_like(control['scet_coarse'], np.float) * u.s\n\n # control = unique(control)\n control['index'] = np.arange(len(control))\n\n return control\n\n\nclass Data(QTable):\n def __repr__(self):\n return f'<{self.__class__.__name__} \\n {super().__repr__()}>'\n\n @classmethod\n def from_packets(cls, packets):\n raise NotImplementedError\n\n\nclass Product:\n def __init__(self, control, data):\n \"\"\"\n Generic product compose of control and data\n Parameters\n ----------\n control : stix_parser.products.quicklook.Control\n Table containing control information\n data : stix_parser.products.quicklook.Data\n Table containing data\n \"\"\"\n self.type = 'ql'\n self.control = control\n self.data = data\n\n self.obs_beg = self.data['time'][0] - self.control['integration_time'][0] / 2\n self.obs_end = self.data['time'][-1] + self.control['integration_time'][-1] / 2\n self.obs_avg = self.obs_beg + (self.obs_end - self.obs_beg) / 2\n\n def __add__(self, other):\n \"\"\"\n Combine two products stacking data along columns and removing duplicated data using time as\n the primary key.\n\n Parameters\n ----------\n other : A subclass of stix_parser.products.quicklook.Product\n\n Returns\n -------\n A subclass of stix_parser.products.quicklook.Product\n The combined data product\n \"\"\"\n if not isinstance(other, type(self)):\n raise TypeError(f'Products must of same type not {type(self)} and {type(other)}')\n\n # TODO reindex and update data control_index\n other.control['index'] = other.control['index'] + self.control['index'].max() + 1\n control = vstack((self.control, other.control))\n # control = unique(control, keys=['scet_coarse', 'scet_fine'])\n # control = control.group_by(['scet_coarse', 'scet_fine'])\n\n other.data['control_index'] = other.data['control_index'] + self.control['index'].max() + 1\n data = vstack((self.data, other.data))\n data = unique(data, keys='time')\n # data = data.group_by('time')\n unique_control_inds = np.unique(data['control_index'])\n control = control[np.isin(control['index'], unique_control_inds)]\n\n return type(self)(control, data)\n\n def __repr__(self):\n return f'<{self.__class__.__name__}\\n' \\\n f' {self.control.__repr__()}\\n' \\\n f' {self.data.__repr__()}\\n' \\\n f'>'\n\n def to_days(self):\n days = set([(t.year, t.month, t.day) for t in self.data['time'].to_datetime()])\n date_ranges = [(datetime(*day), datetime(*day) + timedelta(days=1)) for day in days]\n for dstart, dend in date_ranges:\n i = np.where((self.data['time'] >= dstart) &\n (self.data['time'] < dend))\n\n data = self.data[i]\n control_indices = np.unique(data['control_index'])\n control = self.control[np.isin(self.control['index'], control_indices)]\n control_index_min = control_indices.min()\n\n data['control_index'] = data['control_index'] - control_index_min\n control['index'] = control['index'] - control_index_min\n yield type(self)(control=control, data=data)\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n control = Control.from_packets(packets)\n data = Data.from_packets(packets)\n return cls(control, data)\n\n @classmethod\n def from_fits(cls, fitspath):\n header = fits.getheader(fitspath)\n control = QTable.read(fitspath, hdu='CONTROL')\n data = QTable.read(fitspath, hdu='DATA')\n obs_beg = Time(header['DATE_OBS'])\n data['time'] = (data['time'] + obs_beg)\n return cls(control=control, data=data)\n\n def get_energies(self):\n if 'energy_bin_edge_mask' in self.control.colnames:\n energies = get_energies_from_mask(self.control['energy_bin_edge_mask'][0])\n elif 'energy_bin_mask' in self.control.colnames:\n energies = get_energies_from_mask(self.control['energy_bin_mask'][0])\n else:\n energies = get_energies_from_mask()\n\n return energies\n\n\nclass LightCurve(Product):\n \"\"\"\n Quick Look Light Curve data product.\n \"\"\"\n def __init__(self, control=None, data=None):\n super().__init__(control=control, data=data)\n self.name = 'lightcurve'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n control = Control.from_packets(packets)\n control['detector_mask'] = _get_detector_mask(packets)\n control['pixel_mask'] = _get_pixel_mask(packets)\n control['energy_bin_edge_mask'] = _get_energy_bins(packets, 'NIX00266', 'NIXD0107')\n control['compression_scheme_counts_skm'] = \\\n _get_compression_scheme(packets, 'NIXD0101', 'NIXD0102', 'NIXD0103')\n control['compression_scheme_triggers_skm'] = \\\n _get_compression_scheme(packets, 'NIXD0104', 'NIXD0105', 'NIXD0106')\n control['num_energies'] = _get_num_energies(packets)\n control['num_samples'] = np.array(packets['NIX00271'])[\n np.cumsum(control['num_energies']) - 1]\n\n time, duration = control._get_time()\n # Map a given entry back to the control info through index\n control_indices = np.hstack([np.full(ns, cind) for ns, cind in\n control[['num_samples', 'index']]])\n\n cs, ck, cm = control['compression_scheme_counts_skm'][0]\n counts, counts_var = decompress(packets['NIX00272'], s=cs, k=ck, m=cm, return_variance=True)\n\n ts, tk, tm = control['compression_scheme_triggers_skm'][0]\n triggers, triggers_var = decompress(packets['NIX00274'], s=ts, k=tk, m=tm,\n return_variance=True)\n\n flat_indices = np.hstack((0, np.cumsum([*control['num_samples']]) *\n control['num_energies'])).astype(int)\n counts_reformed = [\n np.array(counts[flat_indices[i]:flat_indices[i + 1]]).reshape(n_eng, n_sam)\n for i, (n_sam, n_eng) in enumerate(control[['num_samples', 'num_energies']])]\n\n counts_var_reformed = [\n np.array(counts_var[flat_indices[i]:flat_indices[i + 1]]).reshape(n_eng, n_sam)\n for i, (n_sam, n_eng) in enumerate(control[['num_samples', 'num_energies']])]\n\n counts = np.hstack(counts_reformed).T\n counts_var = np.hstack(counts_var_reformed).T\n\n data = Data()\n data['control_index'] = control_indices\n data['time'] = time\n data['timedel'] = duration\n data['triggers'] = triggers\n data['triggers_err'] = np.sqrt(triggers_var)\n data['rcr'] = packets['NIX00276']\n data['counts'] = counts * u.ct\n data['counts_err'] = np.sqrt(counts_var) * u.ct\n\n return cls(control=control, data=data)\n\n def __repr__(self):\n return f'{self.name}, {self.level}\\n' \\\n f'{self.obs_beg.fits}, {self.obs_end}\\n ' \\\n f'{len(self.control)}, {len(self.data)}'\n\n\nclass Background(Product):\n \"\"\"\n Background product.\n \"\"\"\n\n def __init__(self, control, data):\n super().__init__(control=control, data=data)\n self.name = 'background'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n control = Control.from_packets(packets)\n\n # Control\n control['energy_bin_mask'] = _get_energy_bins(packets, 'NIX00266', 'NIXD0111')\n control['compression_scheme_background_skm'] = _get_compression_scheme(packets, 'NIXD0108',\n 'NIXD0109',\n 'NIXD0110')\n control['compression_scheme_triggers_skm'] = _get_compression_scheme(packets, 'NIXD0112',\n 'NIXD0113', 'NIXD0114')\n\n control['num_energies'] = _get_num_energies(packets)\n control['num_samples'] = np.array(packets['NIX00277'])[\n np.cumsum(control['num_energies']) - 1]\n\n time, duration = control._get_time()\n # Map a given entry back to the control info through index\n control_indices = np.hstack([np.full(ns, cind) for ns, cind in\n control[['num_samples', 'index']]])\n\n # Data\n bs, bk, bm = control['compression_scheme_background_skm'][0]\n counts, counts_var = decompress(packets['NIX00278'], s=bs, k=bk, m=bm, return_variance=True)\n\n flat_indices = np.hstack((0, np.cumsum([*control['num_samples']]) *\n control['num_energies'])).astype(int)\n\n counts_reformed = [\n np.array(counts[flat_indices[i]:flat_indices[i + 1]]).reshape(n_eng, n_sam)\n for i, (n_sam, n_eng) in enumerate(control[['num_samples', 'num_energies']])]\n\n counts_var_reformed = [\n np.array(counts_var[flat_indices[i]:flat_indices[i + 1]]).reshape(n_eng, n_sam)\n for i, (n_sam, n_eng) in enumerate(control[['num_samples', 'num_energies']])]\n\n counts = np.hstack(counts_reformed).T\n counts_var = np.hstack(counts_var_reformed).T\n\n ts, tk, tm = control['compression_scheme_triggers_skm'][0]\n triggers, triggers_var = decompress(packets['NIX00274'], s=ts, k=tk, m=tm,\n return_variance=True)\n\n data = Data()\n data['control_index'] = control_indices\n data['time'] = time\n data['timedel'] = duration\n data['background'] = counts * u.ct\n data['background_err'] = np.sqrt(counts_var) * u.ct\n data['triggers'] = triggers\n data['triggers_err'] = np.sqrt(triggers_var)\n\n return cls(control=control, data=data)\n\n\nclass Spectra(Product):\n \"\"\"\n Spectra product.\n \"\"\"\n\n def __init__(self, control, data):\n super().__init__(control=control, data=data)\n self.name = 'spectra'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n # Header\n control = Control.from_packets(packets)\n\n # Control\n control['pixel_mask'] = _get_pixel_mask(packets)\n control['compression_scheme_spectra_skm'] = _get_compression_scheme(packets, 'NIXD0115',\n 'NIXD0116', 'NIXD0117')\n control['compression_scheme_triggers_skm'] = _get_compression_scheme(packets, 'NIXD0112',\n 'NIXD0113', 'NIXD0114')\n # Fixed for spectra\n num_energies = 32\n control['num_energies'] = num_energies\n control['num_samples'] = np.array(packets['NIX00089'])\n\n # Due to the way packets are split up full contiguous block of detector 1-32 are not always\n # down-linked to the ground so need to pad the array to write to table and later fits\n total_samples = control['num_samples'].sum()\n full, partial = divmod(total_samples, 32)\n pad_after = 0\n if partial != 0:\n pad_after = 32 - partial\n\n control_indices = np.pad(np.hstack([np.full(ns, cind) for ns, cind in\n control[['num_samples', 'index']]]), (0, pad_after),\n constant_values=-1)\n control_indices = control_indices.reshape(-1, 32)\n\n duration, time = cls._get_time(control, num_energies, packets, pad_after)\n\n # sample x detector x energy\n # counts = np.array([eng_packets.get('NIX00{}'.format(i)) for i in range(452, 484)],\n # np.uint32).T * u.ct\n ss, sk, sm = control['compression_scheme_spectra_skm'][0]\n counts, counts_var = zip(\n *[decompress(packets.get('NIX00{}'.format(i)), s=ss, k=sk, m=sm, return_variance=True)\n for i in range(452, 484)])\n counts = np.vstack(counts).T\n counts_var = np.vstack(counts_var).T\n\n counts = np.pad(counts, ((0, pad_after), (0, 0)), constant_values=0)\n counts_var = np.pad(counts_var, ((0, pad_after), (0, 0)), constant_values=0)\n\n ts, tk, tm = control['compression_scheme_triggers_skm'][0]\n triggers, triggers_var = decompress(packets.get('NIX00484'), s=ts, k=tk, m=tm,\n return_variance=True)\n\n triggers = np.pad(triggers, (0, pad_after), constant_values=0)\n triggers_var = np.pad(triggers_var, (0, pad_after), constant_values=0)\n\n detector_index = np.pad(np.array(packets.get('NIX00100'), np.int16), (0, pad_after),\n constant_values=-1)\n num_integrations = np.pad(np.array(packets.get('NIX00485'), np.uint16), (0, pad_after),\n constant_values=0)\n\n # Data\n data = Data()\n data['control_index'] = control_indices[:, 0]\n data['time'] = time[:, 0]\n data['timedel'] = duration[:, 0]\n data['detector_index'] = detector_index.reshape(-1, 32) * u.ct\n data['spectra'] = counts.reshape(-1, 32, num_energies) * u.ct\n data['spectra_err'] = np.sqrt(counts_var.reshape(-1, 32, num_energies))\n data['triggers'] = triggers.reshape(-1, num_energies)\n data['triggers_err'] = np.sqrt(triggers_var.reshape(-1, num_energies))\n data['num_integrations'] = num_integrations.reshape(-1, num_energies)\n\n return cls(control=control, data=data)\n\n @classmethod\n def _get_time(cls, control, num_energies, packets, pad_after):\n times = []\n durations = []\n start = 0\n for i, (ns, it) in enumerate(control['num_samples', 'integration_time']):\n off_sets = np.array(packets.get('NIX00485')[start:start + ns]) * it\n base_time = Time(scet_to_datetime(\n f'{control[\"scet_coarse\"][i]}:{control[\"scet_fine\"][i]}'))\n start_times = base_time + off_sets\n end_times = base_time + off_sets + it\n cur_time = start_times + (end_times - start_times) / 2\n times.extend(cur_time)\n durations.extend([it]*ns)\n start += ns\n time = Time(times)\n time = Time(np.pad(time.datetime64, (0, pad_after), constant_values=time[-1].datetime64))\n time = time.reshape(-1, num_energies)\n duration = np.pad(np.hstack(durations), (0, pad_after)).reshape(-1, num_energies) * it.unit\n return duration, time\n\n\nclass Variance(Product):\n \"\"\"\n Variance product.\n \"\"\"\n def __init__(self, control, data):\n super().__init__(control=control, data=data)\n self.name = 'variance'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n # Header\n control = Control.from_packets(packets)\n\n # Control\n control['samples_per_variance'] = np.array(packets.get('NIX00279'), np.ubyte)\n control['pixel_mask'] = _get_pixel_mask(packets)\n control['detector_mask'] = _get_detector_mask(packets)\n control['compression_scheme_variance_skm'] = _get_compression_scheme(packets, 'NIXD0118',\n 'NIXD0119', 'NIXD0120')\n energy_masks = np.array([\n [bool(int(x)) for x in format(packets.get('NIX00282')[i], '032b')]\n for i in range(len(packets.get('NIX00282')))])\n\n control['energy_bin_mask'] = energy_masks\n control['num_energies'] = 1\n control['num_samples'] = packets.get('NIX00280')\n\n time, duration = control._get_time()\n # Map a given entry back to the control info through index\n control_indices = np.hstack([np.full(ns, cind) for ns, cind in\n control[['num_samples', 'index']]])\n\n vs, vk, vm = control['compression_scheme_variance_skm'][0]\n variance, variance_var = decompress(packets.get('NIX00281'), s=vs, k=vk, m=vm,\n return_variance=True)\n\n # Data\n data = Data()\n data['time'] = time\n data['timedel'] = duration\n data['control_index'] = control_indices\n data['variance'] = variance\n data['variance_err'] = np.sqrt(variance_var)\n\n return cls(control=control, data=data)\n\n\nclass CalibrationSpectra(Product):\n \"\"\"\n Calibration Spectra data product.\n \"\"\"\n def __init__(self, control, data):\n super().__init__(control=control, data=data)\n self.name = 'calibration_spectrum'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n control = Control.from_packets(packets)\n\n control['integration_time'] = (np.array(packets['NIX00122'], np.uint32) + 1) * 0.1 * u.s\n # control['obs_beg'] = control['obs_utc']\n # control['.obs_end'] = control['obs_beg'] + timedelta(seconds=control['duration'].astype('float'))\n # control['.obs_avg'] = control['obs_beg'] + (control['obs_end'] - control['obs_beg']) / 2\n\n # Control\n control['quiet_time'] = np.array(packets['NIX00123'], np.uint16)\n control['live_time'] = np.array(packets['NIX00124'], np.uint32)\n control['average_temperature'] = np.array(packets['NIX00125'], np.uint16)\n control['detector_mask'] = _get_detector_mask(packets)\n control['pixel_mask'] = _get_pixel_mask(packets)\n control['subspectrum_mask'] = _get_sub_spectrum_mask(packets)\n control['compression_scheme_counts_skm'] = _get_compression_scheme(packets, 'NIXD0126',\n 'NIXD0127', 'NIXD0128')\n subspec_data = {}\n j = 129\n for subspec, i in enumerate(range(300, 308)):\n subspec_data[subspec + 1] = {'num_points': packets.get(f'NIXD0{j}')[0],\n 'num_summed_channel': packets.get(f'NIXD0{j + 1}')[0],\n 'lowest_channel': packets.get(f'NIXD0{j + 2}')[0]}\n j += 3\n\n control['num_samples'] = np.array(packets.get('NIX00159'), np.uint16)\n # control.remove_column('index')\n # control = unique(control)\n # control['index'] = np.arange(len(control))\n\n control['subspec_num_points'] = np.array(\n [v['num_points'] for v in subspec_data.values()]).reshape(1, -1)\n control['subspec_num_summed_channel'] = np.array(\n [v['num_summed_channel'] for v in subspec_data.values()]).reshape(1, -1)\n control['subspec_lowest_channel'] = np.array(\n [v['lowest_channel'] for v in subspec_data.values()]).reshape(1, -1)\n\n subspec_index = np.argwhere(control['subspectrum_mask'][0].flatten() == 1)\n num_sub_spectra = control['subspectrum_mask'].sum(axis=1)\n sub_channels = [np.arange(control['subspec_num_points'][0, index] + 1)\n * (control['subspec_num_summed_channel'][0, index] + 1)\n + control['subspec_lowest_channel'][0, index] for index in subspec_index]\n channels = list(chain(*[ch.tolist() for ch in sub_channels]))\n control['num_channels'] = len(channels)\n\n # Data\n data = Data()\n data['control_index'] = [0]\n data['time'] = (Time(scet_to_datetime(f\"{control['scet_coarse'][0]}\"\n f\":{control['scet_fine'][0]}\"))\n + control['integration_time'][0]/2).reshape(1)\n data['timedel'] = control['integration_time'][0]\n # data['detector_id'] = np.array(packets.get('NIXD0155'), np.ubyte)\n # data['pixel_id'] = np.array(packets.get('NIXD0156'), np.ubyte)\n # data['subspec_id'] = np.array(packets.get('NIXD0157'), np.ubyte)\n num_spec_points = np.array(packets.get('NIX00146'))\n\n cs, ck, cm = control['compression_scheme_counts_skm'][0]\n counts, counts_var = decompress(packets.get('NIX00158'), s=cs, k=ck, m=cm,\n return_variance=True)\n\n\n counts_rebinned = np.apply_along_axis(rebin_proportional, 1,\n counts.reshape(-1, len(channels)), channels,\n np.arange(1025))\n\n counts_var_rebinned = np.apply_along_axis(rebin_proportional, 1,\n counts_var.reshape(-1, len(channels)), channels,\n np.arange(1025))\n\n dids = np.array(packets.get('NIXD0155'), np.ubyte).reshape(-1, num_sub_spectra[0])[:, 0]\n pids = np.array(packets.get('NIXD0156'), np.ubyte).reshape(-1, num_sub_spectra[0])[:, 0]\n\n full_counts = np.zeros((32, 12, 1024))\n full_counts[dids, pids] = counts_rebinned\n full_counts_var = np.zeros((32, 12, 1024))\n full_counts_var[dids, pids] = counts_var_rebinned\n data['counts'] = full_counts.reshape((1, *full_counts.shape))\n data['counts_err'] = np.sqrt(full_counts_var).reshape((1, *full_counts_var.shape))\n\n return cls(control=control, data=data)\n\n\nclass FlareFlagAndLocation(Product):\n \"\"\"\n Flare flag and location product\n \"\"\"\n def __init__(self, control, data):\n super().__init__(control=control, data=data)\n self.name = 'flareflag'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets):\n control = Control.from_packets(packets)\n control['num_samples'] = packets.get('NIX00089')\n\n control_indices = np.hstack([np.full(ns, cind) for ns, cind in\n control[['num_samples', 'index']]])\n\n time, duration = control._get_time()\n\n # DATA\n data = Data()\n data['control_index'] = control_indices\n data['time'] = time\n data['duration'] = duration\n data['loc_z'] = np.array(packets['NIX00283'], np.int16)\n data['loc_y'] = np.array(packets['NIX00284'], np.int16)\n data['thermal_index'] = np.array(packets['NIXD0061'], np.uint16)\n data['non_thermal_index'] = np.array(packets['NIXD0060'], np.uint16)\n data['location_status'] = np.array(packets['NIXD0059'], np.uint16)\n\n return cls(control=control, data=data)\n\n\nclass TMManagementAndFlareList(Product):\n \"\"\"\n TM Management and Flare list product.\n \"\"\"\n def __init__(self, control, data):\n super().__init__(control=control, data=data)\n self.name = 'flareflag'\n self.level = 'L1A'\n\n @classmethod\n def from_packets(cls, packets, eng_packets):\n tmp = QTable()\n tmp['scet_coarse'] = packets['coarse_time']\n tmp['scet_fine'] = packets['coarse_time']\n control = Control(tmp)\n data = Data()\n if 'parameters' in packets:\n\n control['ubsd_counter'] = packets.get('NIX00285')[0]\n control['pald_counter'] = packets.get('NIX00286')[0]\n control['num_samples'] = packets.get('NIX00286')[0]\n\n # DATA\n data['start_scet_coarse'] = packets.get('NIX00287')\n data['end_scet_coarse'] = packets.get('NIX00287')\n data['obs_utc'] = scet_to_datetime(f\"{data['start_scet_coarse']}:0\")\n data['highest_flareflag'] = packets.get('NIX00289')[0]\n data['tm_byte_volume'] = packets.get('NIX00290')[0]\n data['average_z_loc'] = packets.get('NIX00291')[0]\n data['average_y_loc'] = packets.get('NIX00292')[0]\n data['processing_mask'] = packets.get('NIX00293')[0]\n\n return cls(control=control, data=data)\n else:\n return None\n\n\ndef get_energies_from_mask(mask=None):\n \"\"\"\n Return energy channels for\n Parameters\n ----------\n mask : list or array\n Energy bin mask\n\n Returns\n -------\n tuple\n Lower and high energy edges\n \"\"\"\n\n if mask is None:\n low = [ENERGY_CHANNELS[edge]['e_lower'] for edge in range(32)]\n high = [ENERGY_CHANNELS[edge]['e_upper'] for edge in range(32)]\n elif len(mask) == 33:\n edges = np.where(np.array(mask) == 1)[0]\n channel_edges = [edges[i:i + 2].tolist() for i in range(len(edges) - 1)]\n low = []\n high = []\n for edge in channel_edges:\n l, h = edge\n low.append(ENERGY_CHANNELS[l]['e_lower'])\n high.append(ENERGY_CHANNELS[h - 1]['e_upper'])\n elif len(mask) == 32:\n edges = np.where(np.array(mask) == 1)\n low_ind = np.min(edges)\n high_ind = np.max(edges)\n low = [ENERGY_CHANNELS[low_ind]['e_lower']]\n high = [ENERGY_CHANNELS[high_ind]['e_upper']]\n else:\n raise ValueError(f'Energy mask or edges must have a length of 32 or 33 not {len(mask)}')\n\n return low, high\n\n\ndef rebin_proportional(y1, x1, x2):\n x1 = np.asarray(x1)\n y1 = np.asarray(y1)\n x2 = np.asarray(x2)\n\n # the fractional bin locations of the new bins in the old bins\n i_place = np.interp(x2, x1, np.arange(len(x1)))\n\n cum_sum = np.r_[[0], np.cumsum(y1)]\n\n # calculate bins where lower and upper bin edges span\n # greater than or equal to one original bin.\n # This is the contribution from the 'intact' bins (not including the\n # fractional start and end parts.\n whole_bins = np.floor(i_place[1:]) - np.ceil(i_place[:-1]) >= 1.\n start = cum_sum[np.ceil(i_place[:-1]).astype(int)]\n finish = cum_sum[np.floor(i_place[1:]).astype(int)]\n\n y2 = np.where(whole_bins, finish - start, 0.)\n\n bin_loc = np.clip(np.floor(i_place).astype(int), 0, len(y1) - 1)\n\n # fractional contribution for bins where the new bin edges are in the same\n # original bin.\n same_cell = np.floor(i_place[1:]) == np.floor(i_place[:-1])\n frac = i_place[1:] - i_place[:-1]\n contrib = (frac * y1[bin_loc[:-1]])\n y2 += np.where(same_cell, contrib, 0.)\n\n # fractional contribution for bins where the left and right bin edges are in\n # different original bins.\n different_cell = np.floor(i_place[1:]) > np.floor(i_place[:-1])\n frac_left = np.ceil(i_place[:-1]) - i_place[:-1]\n contrib = (frac_left * y1[bin_loc[:-1]])\n\n frac_right = i_place[1:] - np.floor(i_place[1:])\n contrib += (frac_right * y1[bin_loc[1:]])\n\n y2 += np.where(different_cell, contrib, 0.)\n\n return y2\n","sub_path":"stix/fits/products/quicklook.py","file_name":"quicklook.py","file_ext":"py","file_size_in_byte":33879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"20083283","text":"import gi\n\ngi.require_version('Gtk','3.0')\nfrom gi.repository import Gtk\n\nclass MiVentana(Gtk.Window):\n\n\tdef __init__(self,*args,**kwargs):\n\t\tsuper(MiVentana,self).__init__(*args,**kwargs)\n\t\tself.set_default_size(500,500)\n\t\tself.connect('delete-event',Gtk.main_quit)\n\t\tself.contenedor()\n\t\tself.agregar_wigets()\n\n\n\n\tdef contenedor(self):\n\t\tself.contenedor = Gtk.Grid()\n\t\tself.contenedor.set_column_homogeneous(True)\n\t\tself.add(self.contenedor)\n\n\n\tdef agregar_wigets(self):\n\t\tself.label_user = Gtk.Label('Victoria Usuario ')\n\t\tself.contenedor.attach(self.label_user,0,0,1,1)\n\t\tself.label_maquina = Gtk.Label('Victoria Maquina')\n\t\tself.contenedor.attach(self.label_maquina,0,0,3,1)\n\n\nif __name__ == '__main__':\n\n\tventana = MiVentana()\n\tventana.show_all()\n\tGtk.main()","sub_path":"Python-gtk/juego.py","file_name":"juego.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"74790354","text":"import telebot\nimport pyowm\n\ntoken_bot_Shevnin=\"902452228:AAFxwaA2yilyGl7JppE3pwoe8545SDJ88Ns\"\nAPI_key_Shevnin='7c83d4d6fc0856c7a2ed516b47ebbabd'\n\ntoken_bot_Sim=\"971477626:AAHYok-sna2mJ-UnkEYGGLlyNhcZBZ_1h-o\"\nAPI_key_Sim='9262eaa5e82b734436f39f3e9c42f0bd'\n\nbot = telebot.TeleBot(token_bot_Shevnin)\nowm = pyowm.OWM(API_key_Shevnin, language='ru')\ndef t_9(string):\n city = string\n with open(\"citys.txt\", 'r', encoding='utf8') as citys:\n towns_list = []\n index_list = []\n maximum = 0\n for line in citys:\n towns_list.append(str(line))\n city1 = 0\n city2 = 0\n index = 0\n for i in city:\n if city[city1] == line[city2]:\n index += 1\n city1 += 1\n city2 += 1\n index_list.append(index)\n\n for i in range(len(index_list)):\n if maximum < index_list[i]:\n maximum = index_list[i]\n index = i\n towns_list = [line.rstrip() for line in towns_list]\n string_ret = towns_list[index]\n\n return string_ret\n\n@bot.message_handler(content_types=['text'])\ndef send_weather(message):\n #answer = \"Добро пожаловать в weatherbot, напиши название города в котором ты находишься \\n\"\n inputs = message.text.lower()\n inputs = inputs.title()\n print(inputs)\n inputs = t_9(inputs)\n observation = owm.weather_at_place(inputs)\n status = observation.get_weather()\n wind = status.get_wind()[\"speed\"]\n humidity = status.get_humidity()\n temp = status.get_temperature('celsius')[\"temp\"]\n if status.get_detailed_status() ==\"ясно\":\n emoji = \"☀\"\n elif status.get_detailed_status() ==\"слегка облачно\":\n emoji = \"🌤️\"\n elif status.get_detailed_status() ==\"облачно\":\n emoji = \"🌥️\"\n elif status.get_detailed_status() == \"пасмурно\":\n emoji = \"☁\"\n elif status.get_detailed_status() == \"гроза\":\n emoji = \"🌩️\"\n elif status.get_detailed_status() == \"дождь\":\n emoji = \"🌧️\"\n elif status.get_detailed_status() == \"снег\":\n emoji = \"🌨️\"\n else:\n emoji = \"🌦️\"\n\n answer = \"В городе \" + inputs + \" сейчас: \"+status.get_detailed_status() + emoji +\"\\n\"\n answer += \"Температура в районе: \" + str(round(temp)) + \"℃\"+\"\\n\"\n answer += \"Скорость ветра: \" + str(wind) + \"м/с\"+\"\\n\"\n answer += \"Влажность: \" + str(humidity) + \"%\"\n\n bot.send_message(message.chat.id, answer)\n\n\nbot.polling(none_stop = True)\n","sub_path":"teleBot.py","file_name":"teleBot.py","file_ext":"py","file_size_in_byte":2683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"616628803","text":"from flask import render_template, redirect, url_for, request, session, flash\nfrom flask import current_app as app\n\nfrom app.api.user_manager import UserManager\nfrom app.forms.checkin import CheckInForm, CheckOutForm\nfrom app.api.checkin_manager import CheckInManager\n\n\n@app.route('/checkin_and_out/checkin', methods=['GET', 'POST'])\ndef checkin():\n if 'booking_user_id' not in session or 'booking_credit_num' not in session:\n return redirect(url_for('checkin_form'))\n\n user = UserManager.get_user_by_id(session['booking_user_id'])\n credit_num = session['booking_credit_num']\n\n # An exit button should clear the session cookies!!!\n # del session['booking_user_id']\n # del session['booking_credit_num']\n\n bookings = CheckInManager.getBookings(user.details.first_name, user.details.last_name, credit_num)\n\n if request.method == 'POST':\n var = int(request.form['r_num'])\n CheckInManager.check_in(var)\n\n return render_template('checkin_and_out/AcceptCheckIn.html', bookings=bookings, user=user)\n\n\n@app.route('/checkin_and_out/checkinform', methods=['GET', 'POST'])\ndef checkin_form():\n form = CheckInForm()\n if form.validate_on_submit():\n first_name = form.first_name.data\n last_name = form.last_name.data\n credit_num = form.credit_num.data\n\n # This implementation (for unauthenticated users works)\n # An alternatice could be added for logged in users too\n bookings = CheckInManager.getBookings(first_name, last_name, credit_num)\n\n if (bookings):\n user = bookings[0].user\n session['booking_user_id'] = user.id\n session['booking_credit_num'] = credit_num\n return redirect(url_for('checkin'))\n else:\n flash('Sorry, the Name or Credit Card number is wrong or no bookings could be found\\n', 'danger')\n\n return render_template('checkin_and_out/CheckIn.html', fm=form)\n\n\n@app.route('/checkin_and_out/checkoutform', methods=['GET', 'POST'])\ndef checkout():\n form = CheckOutForm()\n if form.validate_on_submit():\n credit_num = form.credit_num.data\n\n if CheckInManager.check_out(credit_num):\n return render_template('/checkin_and_out/AcceptCheckOut.html')\n else:\n flash('Your credit card number is incorrect or you have no records to checkout', 'danger')\n\n return render_template('checkin_and_out/CheckOut.html', fm=form)\n","sub_path":"code/app/controllers/frontend/checkin_and_out.py","file_name":"checkin_and_out.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"178549858","text":"import MapReduce\nimport sys\n\n\"\"\"\nWord Count Example in the Simple Python MapReduce Framework\n\"\"\"\n\nmr = MapReduce.MapReduce()\n\n# =============================\n# Do not modify above this line\n\ndef mapper(record):\n # key: friend A\n # value: friend B\n key = record[0]\n value = record[1]\n pair_key = hash(key) * hash(value)\n mr.emit_intermediate(pair_key, record)\n\ndef reducer(key, list_of_values):\n # key: word\n # value: list of occurrence counts\n if len(list_of_values) < 2: \n fa = list_of_values[0][0] \n fb = list_of_values[0][1] \n mr.emit((fa,fb))\n mr.emit((fb,fa))\n\n\n# Do not modify below this line\n# =============================\nif __name__ == '__main__':\n inputdata = open(sys.argv[1])\n mr.execute(inputdata, mapper, reducer)\n","sub_path":"jsmapreduce/asymmetric_friendship.py","file_name":"asymmetric_friendship.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"559736793","text":"import pandas as pd\nimport numpy as np\n\ndatos = pd.read_csv(\"datosR.csv\")\n\ndatos2 = pd.read_csv(\"test_data_1.csv\")\n\ndef get_first_n_rows(dataset, n):\n \"\"\"Retorna un DataFrame que contiene las primeras
Enter the week numbers each of the two points')\nmarkup_start = widgets.Text(description='Point 1', value='')\nmarkup_end = widgets.Text(description='Point 2', value='')\nmarkup_draw = widgets.Button(description='Draw')\nmarkup_warning = widgets.HTML('')\n\nrectangle = widgets.VBox(children=[markup_intro, markup_start, markup_end, markup_draw, markup_warning])\n\n# Line draw form\nline_intro = widgets.HTML('Trend Line Drawer
Enter the week numbers of two points on the line and indicate whether it is an upper or lower trendline.')\nline_start = widgets.Text(description='Point 1', value='')\nline_end = widgets.Text(description='Point 2', value='')\nline_hilo = widgets.Dropdown(options=['High', 'Low'], value='High')\nline_button = widgets.Button(description='Draw')\nline_warning = widgets.HTML('')\n\nline = widgets.VBox(children=[line_intro, line_start, line_end, line_hilo, line_button, line_warning])\n\nmarkup = widgets.HBox(children=[rectangle, line])\n\ntabs = widgets.Tab(children=[fetch, markup])\n\ntabs.set_title(0, 'Graph')\ntabs.set_title(1, 'Markup')\n\ndisplay(tabs)\n\ndef get_data():\n global ticker, exchange, weeks, start, end\n \n ticker = ticker_dropdown.value\n if ticker == 'Other':\n ticker = ticker_in.value\n \n exchange = exchange_in.value\n if exchange == 'TSE':\n ticker = ticker + \".TO\"\n \n start = False\n end = False\n \n try:\n start_valid.value = ''\n start = datetime.strptime(start_in.value, '%Y-%m-%d')\n except:\n start = False\n start_valid.value = 'Invalid Start Date'\n \n try:\n end_valid.value = ''\n end = datetime.strptime(end_in.value, '%Y-%m-%d')\n except:\n end = False\n end_valid.value = 'Invalid End Date'\n \n if start == False or end == False:\n return\n \n url = \"http://real-chart.finance.yahoo.com/table.csv?s=\"\n url = url + ticker\n url = url + str(\"&a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&g=d&ignore=.csv\" % (start.month - 1, start.day, start.year, end.month-1, end.day, end.year))\n \n try:\n query_valid.value = ''\n data = pandas.read_csv(url, parse_dates=True)\n data = data.reindex(index = data.index[::-1]).reset_index()\n data[data.columns[1]] = pandas.to_datetime(data[data.columns[1]])\n if data.empty:\n raise Exception(\"Stuff didn't happen\")\n try:\n cursor.execute(\"SELECT * FROM \" + ticker.replace('.', '_'))\n cursor.fetchall()\n cursor.execute(\"UPDATE \" + ticker.replace('.', '_') + \" SET volume = volume + 1 WHERE date = '\" + freq.strftime('%Y-%m-%d') + \"'\")\n cnx.commit()\n\n except:\n e = sys.exc_info()[0]\n write_to_page( \"
Error: %s
\" % e )\n make_table = (\"CREATE TABLE \" + ticker.replace('.', '_') + \" (date datetime, high float, low float, close float, volume bigint)\")\n cursor.execute(make_table)\n cnx.commit()\n add_data = (\"INSERT INTO \" + ticker.replace('.', '_') + \" (date, high, low, close, volume) VALUES (%s, %s, %s, %s, %s)\")\n cursor.execute(add_data, (datetime.strptime('1024-01-01', '%Y-%m-%d'), 0, 0, 0, 1))\n cnx.commit()\n except:\n try:\n query_valid.value = \"Can't access Yahoo Finance. Connecting to database.\"\n get_data = (\"SELECT * FROM \" + ticker.replace('.', '_') + \" WHERE date BETWEEN %s AND %s\")\n cursor.execute(get_data, (start, end))\n data = pandas.DataFrame(columns=('Date', 'High', 'Low', 'Close', 'Volume'))\n index = 0\n for s in cursor.fetchall():\n data.loc[index] = (s[0].strftime('%Y-%m-%d'), s[1], s[2], s[3], s[4])\n index = index + 1\n data = data.reindex(index = data.index[::1]).reset_index()\n except:\n query_valid.value = 'Invalid Query'\n return False\n data[data.columns[1]] = pandas.to_datetime(data[data.columns[1]])\n data['Cumulative'] = data['Volume'].cumsum() - data['Volume']\n \n # Create weekly data\n weeks = pandas.DataFrame(columns=['High', 'Low', 'Volume', 'Prev', 'Close', 'Days', 'Start', 'End'])\n index = 0\n weeks.loc[0] = (0, 0, 0, 0, 0, 0, 0, 0)\n yesterday = 6\n for i, row in data.iterrows():\n if row[1].weekday() < yesterday:\n index = index + 1\n weeks.loc[index] = (row['High'],\n row['Low'],\n 0,\n weeks.loc[index - 1][3] + weeks.loc[index - 1][2],\n 0,\n 0,\n row[1],\n row[1])\n yesterday = row[1].weekday()\n weeks.loc[index] = (max(row['High'], weeks.loc[index][0]),\n min(row['Low'], weeks.loc[index][1]),\n row['Volume'] + weeks.loc[index][2],\n weeks.loc[index][3],\n row['Close'],\n weeks.loc[index][5] + 1,\n weeks.loc[index][6],\n row[1])\n\n weeks = weeks[1:]\n\ndef draw_graph():\n # refresh the graph\n plt.close()\n dis.clear_output()\n fig, ax = plt.subplots()\n \n # draw bar graph\n ax.set_title(ticker + \": \" + start.strftime(\"%b %d, %Y\") + \" - \" + end.strftime(\"%b %d, %Y\"), fontsize=15)\n bars = ax.bar(weeks['Prev']/1000000.0,\n weeks['High'] - weeks['Low'],\n width=weeks['Volume']/1000000.0,\n bottom=weeks['Low'])\n ax.plot((weeks['Prev'] + weeks['Volume'])/1000000.0, weeks['Close'], '_', mew = 2, ms = 4, color='r')\n \n ylims = ax.get_ylim()\n \n # draw markups\n for mk in marks:\n x1 = weeks.loc[mk[0]][3]/1000000.0\n y = max(weeks.loc[mk[0]][0], weeks.loc[mk[1]][0])\n x2 = weeks.loc[mk[1]][3]/1000000.0\n ax.plot([x1, x1], [0, y], linestyle = '-', linewidth = 2, color = 'k')\n ax.plot([x2, x2], [0, y], linestyle = '-', linewidth = 2, color = 'k')\n \n x3 = max(x1, x2) + abs(x1 - x2)\n ax.plot([x3, x3], [0, y], linestyle = '-', linewidth = 2, color = 'k')\n ax.plot([min(x1, x2), x3], [y, y], linestyle = '-', linewidth = 2, color = 'k')\n \n xlims = ax.get_xlim()\n \n # draw lines\n for ln in lines:\n x1 = weeks.loc[ln[0]][3]/1000000.0\n x2 = weeks.loc[ln[2]][3]/1000000.0\n if ln[1] == 'High':\n y1 = weeks.loc[ln[0]][0]\n y2 = weeks.loc[ln[2]][0]\n else:\n y1 = weeks.loc[ln[0]][1]\n y2 = weeks.loc[ln[2]][1]\n minx = min(x1, x2)\n if x1 < x2:\n miny = y1\n else:\n miny = y2\n ax.plot([minx, xlims[1]], [miny, (xlims[1] - minx)*(y2-y1)/(x2-x1)+miny], linestyle = '-', linewidth = 2, color = 'm')\n \n ax.set_xlim(xlims)\n ax.set_ylim(ylims)\n ax.set_ylabel(\"Price\", fontsize=14,labelpad = 10)\n ax.set_xlabel(\"Volume (millions)\", fontsize=14,labelpad = 10)\n plt.locator_params(axis='x',nbins=4)\n ax.grid(True, alpha=0.3)\n \n \n # add annotations\n for j, bar in enumerate(bars.get_children()):\n popup=str('| Week # | %d | \\\n
|---|---|
| Start | %s | \\\n
| End | %s | \\\n
| High | %.2f | \\\n
| Low | %.2f | \\\n
| Close | %.2f | \\\n
| Volume | %s | \\\n
\\n' + about + '\\n
'\n \n #create a Html widget\n class MyHTML(v.VuetifyTemplate):\n template = traitlets.Unicode(about).tag(sync=True)\n \n \n content = MyHTML()\n \n super().__init__('about_widget', 'About', inputs=[content], **kwargs)\n \nclass TileDisclaimer(Tile):\n \"\"\"\n create a about tile using a md file. This tile will have the \"about_widget\" id and \"About\" title.\"\"\"\n \n def __init__(self, **kwargs):\n \n pathname = os.path.join(os.path.dirname(__file__), 'scripts', 'disclaimer.md')\n \n #read the content and transform it into a html\n f = open(pathname, 'r')\n if f.mode == 'r':\n about = f.read()\n else :\n about = '**No Disclaimer File**'\n \n about = markdown(about, extensions=['fenced_code','sane_lists'])\n \n #need to be nested in a div to be displayed\n about = '\\n' + about + '\\n
'\n \n #create a Html widget\n class MyHTML(v.VuetifyTemplate):\n template = traitlets.Unicode(about).tag(sync=True)\n \n \n content = MyHTML()\n \n super().__init__('about_widget', 'Disclaimer', inputs=[content], **kwargs)\n\n \nclass DownloadBtn(v.Btn, SepalWidget):\n \"\"\"Create a green downloading button with the user text\"\"\"\n \n def __init__(self, text, path='#', **kwargs):\n \n #create the url\n if utils.is_absolute(path):\n url = path\n else: \n url = utils.create_download_link(path)\n \n super().__init__(\n class_='ma-2',\n xs5=True,\n color='success',\n href=url,\n children=[\n v.Icon(left=True, children=['mdi-download']),\n text\n ],\n **kwargs\n )\n \nclass DatePicker(v.Layout, SepalWidget):\n \n def __init__(self, label=\"Date\", **kwargs):\n \n date_picker = v.DatePicker(\n no_title=True, \n v_model=None, \n scrollable=True\n )\n\n date_text = v.TextField(\n v_model=None,\n label=label,\n hint=\"YYYY-MM-DD format\",\n persistent_hint=True, \n prepend_icon=\"event\",\n readonly=True,\n v_on='menuData.on'\n )\n\n menu = v.Menu(\n transition=\"scale-transition\",\n offset_y=True, \n v_slots=[{\n 'name': 'activator',\n 'variable': 'menuData',\n 'children': date_text,\n }], \n children=[date_picker]\n )\n\n super().__init__(\n v_model=None,\n row=True,\n class_='pa-5',\n align_center=True,\n children=[v.Flex(xs10=True, children=[menu])],\n **kwargs\n )\n\n jslink((date_picker, 'v_model'), (date_text, 'v_model'))\n jslink((date_picker, 'v_model'), (self, 'v_model'))\n \nclass FileInput(v.Layout, SepalWidget, HasTraits):\n\n \n file = Unicode('')\n \n def __init__(self, \n extentions=['.txt'], \n folder=os.path.expanduser('~'), \n label='select file', \n **kwargs):\n\n self.extentions = extentions\n self.folder = folder\n \n self.selected_file = v.TextField(\n label='file', \n v_model=self.file\n )\n \n self.file_list = v.List(\n dense=True, \n color='grey lighten-4',\n flat=True,\n children=[\n v.ListItemGroup(\n children=self.get_items(),\n v_model=''\n )\n ]\n )\n \n self.file_menu = v.Menu(\n min_width=300,\n children=[self.file_list], \n close_on_content_click=False,\n max_height='300px', \n v_slots=[{\n 'name': 'activator',\n 'variable': 'x',\n 'children': v.Btn(v_model=False, v_on='x.on', children=[label])\n }])\n \n super().__init__(\n row=True,\n class_='pa-5',\n align_center=True,\n children=[\n v.Flex(xs12=True, children=[self.selected_file]),\n v.Flex(xs12=True, children=[self.file_menu])\n ],\n **kwargs\n )\n \n link((self.selected_file, 'v_model'), (self, 'file'))\n\n def on_file_select(change):\n\n new_value = change['new']\n if new_value:\n if os.path.isdir(new_value):\n self.folder = new_value\n self.change_folder()\n \n elif os.path.isfile(new_value):\n self.file = new_value\n\n self.file_list.children[0].observe(on_file_select, 'v_model')\n \n def change_folder(self):\n \"\"\"change the target folder\"\"\"\n #reset files \n self.file_list.children[0].children = self.get_items()\n \n\n def get_items(self):\n \"\"\"return the list of items inside the folder\"\"\"\n \n list_dir = glob(os.path.join(self.folder, '*/'))\n \n for extention in self.extentions:\n list_dir.extend(glob(os.path.join(self.folder, '*' + extention)))\n \n folder_list = []\n file_list = []\n\n for el in list_dir:\n extention = Path(el).suffix\n if extention == '':\n icon = 'mdi-folder-outline'\n color = 'amber'\n elif extention in ['.csv', '.txt']:\n icon = 'mdi-border-all'\n color = 'green accent-4'\n elif extention in ['.tiff', '.tif']:\n icon = \"mdi-image-outline\"\n color = \"deep-purple\"\n else:\n icon = 'mdi-file-outline'\n color = 'light-blue'\n \n children = [\n v.ListItemAction(children=[v.Icon(color= color,children=[icon])]),\n v.ListItemContent(children=[v.ListItemTitle(children=[Path(el).stem + Path(el).suffix])])\n ]\n\n if os.path.isdir(el): \n folder_list.append(v.ListItem(value=el, children=children))\n else:\n file_list.append(v.ListItem(value=el, children=children))\n \n\n folder_list = sorted(folder_list, key=lambda x: x.value)\n file_list = sorted(file_list, key=lambda x: x.value)\n\n parent_path = str(Path(self.folder).parent)\n parent_item = v.ListItem(value=parent_path, children=[\n v.ListItemAction(children=[v.Icon(color='black',children=['mdi-folder-upload-outline'])]),\n v.ListItemContent(children=[v.ListItemTitle(children=[f'..{parent_path}'])])\n ])\n\n folder_list.extend(file_list)\n folder_list.insert(0,parent_item)\n\n return folder_list\n \n def get_parent_path(self):\n \"\"\"return the list of all the parents of a given path\"\"\"\n path_list = [self.folder]\n path = Path(self.folder)\n\n while str(path.parent) != path_list[-1]:\n path = path.parent\n path_list.append(str(path))\n \n return path_list\n \nclass Markdown(v.Layout, SepalWidget):\n \"\"\"create a v.layout based on the markdown text given\"\"\"\n \n def __init__(self, mkd_str=\"\", **kwargs):\n \n mkd = markdown(mkd_str, extensions=['fenced_code','sane_lists'])\n \n #need to be nested in a div to be displayed\n mkd = '\\n' + mkd + '\\n
'\n \n #create a Html widget\n class MyHTML(v.VuetifyTemplate):\n template = traitlets.Unicode(mkd).tag(sync=True)\n \n content = MyHTML()\n \n super().__init__(\n row=True,\n class_='pa-5',\n align_center=True,\n children=[v.Flex(xs12=True, children=[content])],\n **kwargs\n )","sub_path":"sepal_ui/sepalwidgets_ui.py","file_name":"sepalwidgets_ui.py","file_ext":"py","file_size_in_byte":22373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"578813976","text":"\n\nimport random\n\nclass node:\n def __init__(self, data, next = 0):\n self.data = data\n self.next = next\n\ndef genlist(n, min = 1, max = 10):\n result = []\n for i in range(n):\n c = random.randint(min, max)\n result.append(c)\n return result\n\ndef linklist(ll):\n ll = ll[::-1]\n count = len(ll)\n head = 0\n for i in range(count):\n n = node(ll[i], head)\n head = n\n return head\n\ndef printlist(head):\n n = head\n while(n != 0):\n print(n.data)\n n = n.next\n\ndef removeDuplicate(head):\n n1 = head\n while(n1 != 0):\n v = n1.data\n n2 = n1.next\n prev = n1\n while(n2 != 0):\n if (n2.data == v):\n prev.next = n2.next\n else:\n prev = n2\n n2 = prev.next\n n1 = n1.next\n\n return head\n\ndef removeDuplicateNew(head):\n n = head\n values = []\n while(n != 0):\n if (not n.data in values):\n values.append(n.data)\n prev = n\n else:\n prev.next = n.next\n\n n = n.next\n\n return head\n\n#method = removeDuplicate\nmethod = removeDuplicateNew\n\n#values = [ 2, 2, 2, 3, 5, 2, 1, 4, 5, 2 ]\nvalues = genlist(10, 1, 5)\nlinkhead = linklist(values)\nprintlist(linkhead)\nprint(\"====================\")\nnewhead = method(linkhead)\nprintlist(newhead)\n","sub_path":"Cracking_the_Coding_Interview/2-1.py","file_name":"2-1.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"464921456","text":"from collections import defaultdict\n\n\nclass Solution:\n def areSentencesSimilar(self, words1, words2, pairs):\n \"\"\"\n :type words1: List[str]\n :type words2: List[str]\n :type pairs: List[List[str]]\n :rtype: bool\n \"\"\"\n if len(words1) != len(words2):\n return False\n pairDict = defaultdict(set)\n for pair in pairs:\n pairDict[pair[0]].add(pair[1])\n pairDict[pair[1]].add(pair[0])\n for i in range(len(words1)):\n if words1[i] != words2[i] and (not words1[i] in pairDict or not words2[i] in pairDict[words1[i]]):\n return False\n return True\n","sub_path":"src/sentence-similarity.py","file_name":"sentence-similarity.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"573086103","text":"from PIL import Image\n\ndef fit(image, size, method=Image.NEAREST, centering=(0.0, 0.0)):\n if not isinstance(centering, list):\n centering = [centering[0], centering[1]]\n\n liveArea = (0, 0, image.size[0], image.size[1])\n liveSize = (liveArea[2] - liveArea[0], liveArea[3] - liveArea[1])\n liveAreaAspectRatio = float(liveSize[0])/float(liveSize[1])\n aspectRatio = float(size[0]) / float(size[1])\n\n if liveAreaAspectRatio >= aspectRatio:\n cropWidth = int((aspectRatio * float(liveSize[1])) + 0.5)\n cropHeight = liveSize[1]\n else:\n cropWidth = liveSize[0]\n cropHeight = int((float(liveSize[0])/aspectRatio) + 0.5)\n\n leftSide = int(liveArea[0] + (float(liveSize[0]-cropWidth) * centering[0]))\n topSide = int(liveArea[1] + (float(liveSize[1]-cropHeight) * centering[1]))\n \n if leftSide < 0:\n leftSide = 0\n \n if topSide < 0:\n topSide = 0\n\n out = image.crop((leftSide, topSide, leftSide + cropWidth, topSide + cropHeight))\n\n return out.resize(size, method)\n\ndef squaremaker(image):\n im = image\n \n w = im.size[0]\n h = im.size[1]\n \n if w