\n
| \n ||
\n
| \n ||
| Υπηρεσία | \nΑξία (Ευρώ) | \n|
| %s \n (χωρίς ΦΠΑ, άρθρο 22 Κώδικα)\n | \n %s\n | \n|
| \n | ΠΑΡΑΛΑΒΗ ΕΚΔΟΣΗ | \n
| Name | '\n for d in days:\n table += '' + str(d) + ' | '\n table += '|
|---|---|---|
| ' + str(emp.id) + '--' + emp.get_full_name + ' | '\n itr = False\n end_date = -1\n loop = 0\n for d in days:\n if start.get(d) and end.get(d):\n if d >= start.get(d) and d <= end.get(d):\n itr = True\n if end_date == -1:\n end_date = end.get(d) \n print(end_date) \n if end_date >= d and end_date != -1:\n table += ''\n if end_date == d:\n end_date = -1\n else:\n table += ' | '\n end_date = -1\n print(end_date) \n table += ' |
| ',\n ' ',\n name,\n ' ',\n ' | ',\n '
| ',\n data_type,\n ' | '\n '
| ',\n '[%s]' % 'x'.join(shape),\n ' | '\n '
Note content!
\", x1=0.1, y1=0.1, x2=0.2, y2=0.2\n )\n assert len(document.notes.list().results) == 2\n for note in document.notes:\n assert isinstance(note, Annotation)\n note.delete()\n assert len(document.notes.list().results) == 1\n\n def test_create_page_note(self, document):\n note = document.notes.create(\"Test Note\", 0, \"Page note!
\")\n assert note\n note.delete()\n\n def test_create_partial_coords(self, document):\n with pytest.raises(ValueError):\n document.notes.create(\"Test Note\", 0, \"Page note!
\", x1=0.5)\n\n def test_create_invalid_coords(self, document):\n with pytest.raises(ValueError):\n document.notes.create(\n \"Test Note\", 0, \"Page note!
\", x1=0.5, y1=1.5, x2=2, y2=3\n )\n\n def test_str(self, document):\n assert str(document.notes[0])\n\n def test_alias(self, document):\n assert document.notes is document.annotations\n\n def test_location(self, document):\n note = document.notes[0]\n assert note.location.top < note.location.bottom\n assert note.location.left < note.location.right\n","sub_path":"tests/test_annotations.py","file_name":"test_annotations.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"252352368","text":"class Solution:\n def findanagrams(self, s, p):\n n1 = len(s)\n n2 = len(p)\n if n1 == 0 or n1 < n2:\n return []\n res = []\n for i in range(n1-n2+1):\n sub_str = s[i:n2+i]\n if self.isanagrams(sub_str, p):\n res.append(i)\n return res\n\n def count_ord(self, s):\n count = 0\n for item in s:\n count += ord(item)\n return count\n\n def isanagrams(self, s, t):\n count_s = self.count_(s)\n count_t = self.count_(t)\n if count_s == count_t:\n return True\n\n def count_(self, s):\n dict_01 = {item: 0 for item in s}\n for char in s:\n if char in dict_01:\n dict_01[char] += 1\n return dict_01\n\n def findanagrams_01(self, s, p):\n n1 = len(s)\n n2 = len(p)\n if not n1 or n1 < n2:\n return []\n p_list = [0]*26\n s_list = [0]*26\n res = []\n for p1 in p:\n p_list[ord(p1)-97] += 1\n left = 0\n for right in range(n1):\n if right < len(p)-1:\n s_list[ord(s[right])-97] += 1\n continue\n s_list[ord(s[right])-97] += 1\n if p_list == s_list:\n res.append(left)\n s_list[ord(s[left])-97] -= 1\n left += 1\n return res\n\n","sub_path":"Week_02/find_all_anagrams_in_string.py","file_name":"find_all_anagrams_in_string.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"450885970","text":"from django.shortcuts import render\nfrom django.views.generic.list import ListView\nfrom .models import Student\n\n# Create your views here.\nclass StudentList(ListView):\n model = Student # This Single line will perform all below task automatically in Generic ListView:\n # student = Student.object.all()\n # context = {'student_list': student}\n # return render(request, 'App_ListView/student_list.html', context)\n################################## CUSTOMIZING Generic ListView #####################################################\n\n #give your own template name.\n template_name = 'App_ListView/student.html' # If this is not kept then we have to give default template name as 'student_list.html' \n # i.e(moedl_list.html) Model name in small. \n # '_list' is suffix for template in Generic ListView.\n # To give different name to template as compared to default template name we have given this \n #'template_name' attribute.\n\n # ordering = ['name'] #If you want to order all the records by it's name the we can use it as 'ordering'\n\n #give your own context name\n # context_object_name = 'mystudents' # provides our own context object name which can be used in templates.\n # If this is not used then our default context object name will be 'student_list' i.e(modelname_list)\n\n\n #Filter all records queryset as per your needs\n # def get_queryset(self):\n # return Student.objects.filter(course='PHP') \n\n # If you want to pass the context from Generic ListView \n def get_context_data(self, *args, **kwargs):\n context = super().get_context_data(*args, *kwargs)\n context['order_by'] = Student.objects.all().order_by('name')\n return context\n\n # To render different templates depending upon the condition\n #def get_template_names(self): # django ko Generic ListView le provide gareko method ho\n # if self.request.COOKIES['user'] == 'sonam': # If 'cookie' ko 'user' vanni value ma name 'sonam' cha vani sonam.html template render huncha.\n # Cookie value and name has to be set manually by going into browser developer tool.\n # template_name = 'App_ListView/sonam.html' # template_name mathidefine gareko attributes ma sonam.html template haldeko.\n # else:\n # template_name = self.template_name # If cookie ma name sonam chaina vani default template load garauni\n # # i.e in lin no. 16 ('App_ListView/student.html)\n # return [template_name] # this function will return the list so 'template_name' is in type of list.\n\n \n # Another Real life example for 'get_template_name' method\n # def get_template_names(self):\n\n # if self.request.user.is_superuser: # If the user is logged in as 'superuser' then show the 'super.html' template\n # template_name = 'App_ListView/super.html'\n \n # elif self.request.user.is_staff: # If the user is logged in as 'staff' then show the 'staff.html' template\n # template_name = 'App_ListView/staff.html'\n\n # else: # If the user is logged in as normal user then show the 'student.html' template \n # # i.e in line no. 16 ('App_ListView/student.html)\n # template_name = self.template_name\n\n # return [template_name]\n\n\n\n\n\n\n\n\n","sub_path":"06.Generic_ListView/Generic_ListView/App_ListView/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"155842663","text":"\nimport iemdb\nimport mx.DateTime\nIEM = iemdb.connect('iem', bypass=True)\nicursor = IEM.cursor()\n\nicursor.execute(\"\"\"\n\tSELECT id, x(geom) as lon, y(geom) as lat, tmpf from current_log c JOIN stations s on (s.iemid = c.iemid)\n\tWHERE (network ~* 'ASOS' or network = 'AWOS') and country = 'US'\n\tand valid BETWEEN now() - '1500 minutes'::interval \n and now() - '1440 minutes'::interval\n and tmpf > 0 \n\t\"\"\")\ndata = {}\nfor row in icursor:\n\tdata[ row[0] ] = {'lon': row[1], 'lat': row[2], 'val': row[3] }\n\nicursor.execute(\"\"\"\n\tSELECT id, tmpf from current c JOIN stations s ON (s.iemid = c.iemid)\n\tWHERE (network ~* 'ASOS' or network = 'AWOS') and country = 'US' \n and state not in ('AK','HI')\n and tmpf > 0 and valid > now() - '60 minutes'::interval\n\t\"\"\")\n\nobs = []\nlats = []\nlons = []\nmask = []\nfor row in icursor:\n\tif not data.has_key(row[0]):\n\t\tcontinue\n\tv = (row[1] - data[row[0]]['val'])\n\tobs.append( v )\n\t#if v <= -24.:\n\t#\tmask.append( True )\n\t#else:\n\tmask.append( False )\n\tlats.append( data[row[0]]['lat'] )\n\tlons.append( data[row[0]]['lon'] )\n\nimport iemplot\n\ncfg = {\n\t'_conus' : True,\n\t'lbTitleString' : 'F',\n\t 'wkColorMap': 'BlAqGrYeOrRe',\n\t'_valuemask'\t: mask,\n\t'_showvalues'\t: True,\n\t'_format'\t\t: '%.0f',\n\t'_title'\t: '24 Hour Temperature Change',\n\t'_valid'\t: '24 Hour Period Ending %s' % (mx.DateTime.now().strftime(\"%d %b %Y %I %p\"),),\n}\ntmpfp = iemplot.simple_contour(lons, lats, obs, cfg)\niemplot.makefeature(tmpfp)\n","sub_path":"scripts/feature/24h_change.py","file_name":"24h_change.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"487968641","text":"import json\nimport unittest\nfrom unittest.mock import call, MagicMock, patch\n\nimport boto3\nfrom botocore.exceptions import ClientError\nfrom botocore.stub import Stubber, ANY\n\nfrom budget import app\n\n\n@patch.dict('budget.app.configuration', {'account_id': '012345678901'})\nclass TestCreateBudgets(unittest.TestCase):\n\n def test_create_budgets_no_new_users(self):\n no_new_users = []\n teams_by_user_id = {} # this doesn't matter if there are no new users\n result = app.create_budgets(no_new_users, teams_by_user_id)\n # if there are no new users, we expect a message indicating that no\n # new budgets were created\n expected = 'Budgets created for synapse ids: none'\n self.assertEqual(result, expected)\n\n\n @patch('budget.app.create_budget', MagicMock(return_value={}))\n @patch('budget.app.create_budget_notifications', MagicMock(return_value={}))\n @patch.dict('budget.app.TEAM_BUDGET_RULES', {'teams': {'12345': {}}})\n def test_create_budgets_some_users(self):\n new_users = ['3406211', '3388489']\n teams_by_user_id = {'3406211': ['12345'], '3388489': ['12345']}\n result = app.create_budgets(new_users, teams_by_user_id)\n # if there are new users, we expect a message that budgets were\n # created for each of them\n expected = 'Budgets created for synapse ids: 3406211, 3388489'\n self.assertEqual(result, expected)\n\n\n @patch.dict('budget.app.TEAM_BUDGET_RULES', {\n 'teams':{'12345': {'amount': '100','period': 'ANNUALLY'}}\n })\n def test_create_budget(self):\n synapse_id = '3388489'\n team = '12345'\n budgets_client = boto3.client('budgets')\n with Stubber(budgets_client) as stubber:\n app.get_client = MagicMock(return_value=budgets_client)\n expected_params = {\n 'AccountId': '012345678901',\n 'Budget': {\n 'BudgetName': 'service-catalog_3388489',\n 'BudgetLimit': {\n 'Amount': '100',\n 'Unit': 'USD'\n },\n 'CostFilters': {\n 'TagKeyValue': [\n (\n 'aws:servicecatalog:provisioningPrincipalArn$arn:aws:sts::'\n '012345678901:assumed-role/ServiceCatalogEndusers/3388489'\n )\n ]\n },\n 'CostTypes': {\n 'IncludeRefund': False,\n 'IncludeCredit': False\n },\n 'TimeUnit': 'ANNUALLY',\n 'BudgetType': 'COST'\n }\n }\n # verify that the boto3 client will be called with the expected values\n stubber.add_response('create_budget', {}, expected_params)\n result = app.create_budget(synapse_id, team)\n expected = {}\n self.assertEqual(result, expected)\n\n\n def test_create_budget_no_team_rules(self):\n synapse_id = '3388489'\n team = 'foo'\n with self.assertRaises(ValueError) as context_manager:\n app.create_budget(synapse_id, team)\n expected_error = 'No budget rules available for team foo'\n self.assertEqual(str(context_manager.exception), expected_error)\n\n\n @patch.dict('budget.app.TEAM_BUDGET_RULES', {\n 'teams': {\n '12345': {\n 'amount': '100',\n 'period': 'ANNUALLY',\n 'community_manager_emails': []\n }\n }\n })\n def test_create_budget_notifications_makes_expected_call_types(self):\n synapse_id = '3388489'\n team = '12345'\n with patch('budget.app._create_budget_notification') as mock:\n app.create_budget_notifications(synapse_id, team)\n expected = [\n call('3388489', 25.0),\n call('3388489', 50.0),\n call('3388489', 80.0),\n call('3388489', 90.0, admin_emails=[]),\n call('3388489', 100.0, admin_emails=[]),\n call('3388489', 110.0, admin_emails=[])\n ]\n self.assertCountEqual(mock.mock_calls, expected)\n\n\n def test_create_budget_notification_user_only(self):\n budgets_client = boto3.client('budgets')\n fake_topic_arn = 'arn:aws:sns:us-east-1:123456789012:mystack-mytopic-NZJ5JSMVGFIE'\n with Stubber(budgets_client) as stubber, \\\n patch.dict('budget.app.configuration',\n {'notification_topic_arn': fake_topic_arn}):\n app.get_client = MagicMock(return_value=budgets_client)\n # user only\n expected_params = {\n 'AccountId': '012345678901',\n 'BudgetName': 'service-catalog_3388489',\n 'Notification': {\n 'NotificationType': 'ACTUAL',\n 'ComparisonOperator': 'GREATER_THAN',\n 'Threshold': 25.0,\n 'ThresholdType': 'PERCENTAGE',\n 'NotificationState': 'ALARM'\n },\n 'Subscribers': [{\n 'SubscriptionType': 'SNS',\n 'Address': fake_topic_arn\n }]\n }\n # verify that the boto3 client will be called with the expected values\n stubber.add_response('create_notification', {}, expected_params)\n synapse_id = '3388489'\n threshold = 25.0\n result = app._create_budget_notification(synapse_id, threshold)\n expected = {}\n self.assertEqual(result, expected)\n # now with admins\n fake_admin_email = 'jane.doe@sagebase.org'\n expected_params['Subscribers'].insert(0, {\n 'SubscriptionType': 'EMAIL',\n 'Address': fake_admin_email\n })\n stubber.add_response('create_notification', {}, expected_params)\n result = app._create_budget_notification(synapse_id, threshold, admin_emails=[fake_admin_email])\n expected = {}\n self.assertEqual(result, expected)\n","sub_path":"tests/unit/test_create_budgets.py","file_name":"test_create_budgets.py","file_ext":"py","file_size_in_byte":5368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"312121175","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport sys\nimport gc\nimport time\nimport os\nimport shutil\nimport uuid\nimport tempfile\nimport tarfile\nimport copy\nfrom threading import Thread\nfrom termcolor import colored\n\nfrom . import EmbeddingFeedForward, EmbeddingCNN2D5C, EmbeddingCNN3D4C, ProposalNormalNormalMixture, ProposalUniformTruncatedNormalMixture, ProposalCategoricalCategorical, ProposalPoissonTruncatedNormalMixture\nfrom .. import __version__, util, ObserveEmbedding\nfrom ..distributions import Normal, Uniform, Categorical, Poisson\n\n\nclass InferenceNetworkFeedForward(nn.Module):\n # observe_embeddings example: {'obs1': {'embedding':ObserveEmbedding.FEEDFORWARD, 'reshape': [10, 10], 'dim': 32, 'depth': 2}}\n def __init__(self, model, valid_size=64, observe_embeddings={}):\n super().__init__()\n self._model = model\n self._layer_proposal = nn.ModuleDict()\n self._layer_observe_embedding = nn.ModuleDict()\n self._layer_observe_embedding_final = None\n self._layer_hidden_shape = None\n self._infer_observe = None\n self._infer_observe_embedding = {}\n self._optimizer = None\n\n self._total_train_seconds = 0\n self._total_train_traces = 0\n self._total_train_iterations = 0\n self._loss_initial = None\n self._loss_min = float('inf')\n self._loss_max = None\n self._loss_previous = float('inf')\n self._history_train_loss = []\n self._history_train_loss_trace = []\n self._history_valid_loss = []\n self._history_valid_loss_trace = []\n self._history_num_params = []\n self._history_num_params_trace = []\n self._modified = util.get_time_str()\n self._updates = 0\n self._on_cuda = False\n self._device = torch.device('cpu')\n\n self._valid_size = valid_size\n self._observe_embeddings = observe_embeddings\n self._valid_batch = None\n\n def _init_layer_observe_embeddings(self, observe_embeddings):\n if len(observe_embeddings) == 0:\n raise ValueError('At least one observe embedding is needed to initialize inference network.')\n observe_embedding_total_dim = 0\n for name, value in observe_embeddings.items():\n distribution = self._valid_batch.traces[0].named_variables[name].distribution\n if distribution is None:\n raise ValueError('Observable {}: cannot use this observation as an input to the inference network, because there is no associated likelihood.'.format(name))\n else:\n if 'reshape' in value:\n input_shape = torch.Size(value['reshape'])\n else:\n input_shape = distribution.sample().size()\n if 'dim' in value:\n output_shape = torch.Size([value['dim']])\n else:\n print('Observable {}: embedding dim not specified, using the default 256.'.format(name))\n output_shape = torch.Size([256])\n if 'embedding' in value:\n embedding = value['embedding']\n else:\n print('Observable {}: observe embedding not specified, using the default FEEDFORWARD.'.format(name))\n embedding = ObserveEmbedding.FEEDFORWARD\n if embedding == ObserveEmbedding.FEEDFORWARD:\n if 'depth' in value:\n depth = value['depth']\n else:\n print('Observable {}: embedding depth not specified, using the default 2.'.format(name))\n depth = 2\n layer = EmbeddingFeedForward(input_shape=input_shape, output_shape=output_shape, num_layers=depth)\n elif embedding == ObserveEmbedding.CNN2D5C:\n layer = EmbeddingCNN2D5C(input_shape=input_shape, output_shape=output_shape)\n elif embedding == ObserveEmbedding.CNN3D4C:\n layer = EmbeddingCNN3D4C(input_shape=input_shape, output_shape=output_shape)\n else:\n raise ValueError('Unknown embedding: {}'.format(embedding))\n layer.to(device=util._device)\n self._layer_observe_embedding[name] = layer\n observe_embedding_total_dim += util.prod(output_shape)\n self._layer_hidden_shape = torch.Size([observe_embedding_total_dim])\n self._layer_observe_embedding_final = EmbeddingFeedForward(input_shape=self._layer_hidden_shape, output_shape=self._layer_hidden_shape, num_layers=1)\n self._layer_observe_embedding_final.to(device=util._device)\n\n def _save(self, file_name):\n self._modified = util.get_time_str()\n self._updates += 1\n\n data = {}\n data['pyprob_version'] = __version__\n data['torch_version'] = torch.__version__\n # The following is due to a temporary hack related with https://github.com/pytorch/pytorch/issues/9981 and can be deprecated by using dill as pickler with torch > 0.4.1\n data['inference_network'] = copy.copy(self)\n data['inference_network']._model = None\n data['inference_network']._optimizer = None\n\n def thread_save():\n tmp_dir = tempfile.mkdtemp(suffix=str(uuid.uuid4()))\n tmp_file_name = os.path.join(tmp_dir, 'pyprob_inference_network')\n torch.save(data, tmp_file_name)\n tar = tarfile.open(file_name, 'w:gz', compresslevel=2)\n tar.add(tmp_file_name, arcname='pyprob_inference_network')\n tar.close()\n shutil.rmtree(tmp_dir)\n t = Thread(target=thread_save)\n t.start()\n t.join()\n\n @staticmethod\n def _load(file_name):\n try:\n tar = tarfile.open(file_name, 'r:gz')\n tmp_dir = tempfile.mkdtemp(suffix=str(uuid.uuid4()))\n tmp_file = os.path.join(tmp_dir, 'pyprob_inference_network')\n tar.extract('pyprob_inference_network', tmp_dir)\n tar.close()\n if util._cuda_enabled:\n data = torch.load(tmp_file)\n else:\n data = torch.load(tmp_file, map_location=lambda storage, loc: storage)\n shutil.rmtree(tmp_dir)\n except:\n raise RuntimeError('Cannot load inference network.')\n\n if data['pyprob_version'] != __version__:\n print(colored('Warning: different pyprob versions (loaded network: {}, current system: {})'.format(data['pyprob_version'], __version__), 'red', attrs=['bold']))\n if data['torch_version'] != torch.__version__:\n print(colored('Warning: different PyTorch versions (loaded network: {}, current system: {})'.format(data['torch_version'], torch.__version__), 'red', attrs=['bold']))\n\n ret = data['inference_network']\n if util._cuda_enabled:\n if ret._on_cuda:\n if ret._device != util._device:\n print(colored('Warning: loading CUDA (device {}) network to CUDA (device {})'.format(ret._device, util._device), 'red', attrs=['bold']))\n else:\n print(colored('Warning: loading CPU network to CUDA (device {})'.format(util._device), 'red', attrs=['bold']))\n else:\n if ret._on_cuda:\n print(colored('Warning: loading CUDA (device {}) network to CPU'.format(ret._device), 'red', attrs=['bold']))\n ret.to(device=util._device)\n return ret\n\n def to(self, device=None, *args, **kwargs):\n self._device = device\n self._on_cuda = 'cuda' in str(device)\n super().to(device=device, *args, *kwargs)\n\n def _embed_observe(self, traces=None):\n embedding = []\n for name, layer in self._layer_observe_embedding.items():\n values = torch.stack([util.to_tensor(trace.named_variables[name].value) for trace in traces]).view(len(traces), -1)\n embedding.append(layer(values))\n embedding = torch.cat(embedding, dim=1)\n embedding = self._layer_observe_embedding_final(embedding)\n return embedding\n\n def infer_trace_init(self, observe=None):\n self._infer_observe = observe\n embedding = []\n for name, layer in self._layer_observe_embedding.items():\n value = util.to_tensor(observe[name]).view(1, -1)\n embedding.append(layer(value))\n embedding = torch.cat(embedding, dim=1)\n self._infer_observe_embedding = self._layer_observe_embedding_final(embedding)\n\n def infer_trace_step(self, variable, previous_variable=None):\n success = True\n address = variable.address\n distribution = variable.distribution\n if address not in self._layer_proposal:\n print('Warning: no proposal layer for: {}'.format(address))\n success = False\n\n if success:\n proposal_distribution = self._layer_proposal[address].forward(self._infer_observe_embedding, [variable])\n return proposal_distribution\n else:\n print('Warning: no proposal can be made, prior will be used.')\n return distribution\n\n def _polymorph(self, batch):\n layers_changed = False\n for sub_batch in batch.sub_batches:\n example_trace = sub_batch[0]\n for variable in example_trace.variables_controlled:\n address = variable.address\n distribution = variable.distribution\n variable_shape = variable.value.shape\n if address not in self._layer_proposal:\n print('New proposal layer for address: {}'.format(util.truncate_str(address)))\n if isinstance(distribution, Normal):\n layer = ProposalNormalNormalMixture(self._layer_hidden_shape, variable_shape)\n elif isinstance(distribution, Uniform):\n layer = ProposalUniformTruncatedNormalMixture(self._layer_hidden_shape, variable_shape)\n elif isinstance(distribution, Poisson):\n layer = ProposalPoissonTruncatedNormalMixture(self._layer_hidden_shape, variable_shape)\n elif isinstance(distribution, Categorical):\n layer = ProposalCategoricalCategorical(self._layer_hidden_shape, distribution.num_categories)\n else:\n raise RuntimeError('Distribution currently unsupported: {}'.format(distribution.name))\n layer.to(device=util._device)\n self._layer_proposal[address] = layer\n layers_changed = True\n if layers_changed:\n num_params = sum(p.numel() for p in self.parameters())\n print('Total number of parameters: {:,}'.format(num_params))\n self._history_num_params.append(num_params)\n self._history_num_params_trace.append(self._total_train_traces)\n return layers_changed\n\n def _loss(self, batch):\n gc.collect()\n batch_loss = 0\n for sub_batch in batch.sub_batches:\n example_trace = sub_batch[0]\n observe_embedding = self._embed_observe(sub_batch)\n sub_batch_loss = 0.\n for time_step in range(example_trace.length_controlled):\n address = example_trace.variables_controlled[time_step].address\n variables = [trace.variables_controlled[time_step] for trace in sub_batch]\n values = torch.stack([v.value for v in variables])\n proposal_distribution = self._layer_proposal[address].forward(observe_embedding, variables)\n log_prob = proposal_distribution.log_prob(values)\n if util.has_nan_or_inf(log_prob):\n print(colored('Warning: NaN, -Inf, or Inf encountered in proposal log_prob.', 'red', attrs=['bold']))\n print('proposal_distribution', proposal_distribution)\n print('values', values)\n print('log_prob', log_prob)\n print('Fixing -Inf')\n log_prob = util.replace_negative_inf(log_prob)\n print('log_prob', log_prob)\n if util.has_nan_or_inf(log_prob):\n print(colored('Nan or Inf present in proposal log_prob.', 'red', attrs=['bold']))\n return False, 0\n sub_batch_loss += -torch.sum(log_prob)\n batch_loss += sub_batch_loss\n return True, batch_loss / batch.length\n\n def optimize(self, num_traces, batch_generator, batch_size=64, valid_interval=1000, learning_rate=0.0001, weight_decay=1e-5, auto_save_file_name_prefix=None, auto_save_interval_sec=600, *args, **kwargs):\n if self._valid_batch is None:\n print('Initializing inference network...')\n self._valid_batch = batch_generator.get_batch(self._valid_size, discard_source=True)\n self._init_layer_observe_embeddings(self._observe_embeddings)\n self._polymorph(self._valid_batch)\n\n prev_total_train_seconds = self._total_train_seconds\n time_start = time.time()\n time_loss_min = time.time()\n time_last_batch = time.time()\n last_validation_trace = -valid_interval + 1\n iteration = 0\n trace = 0\n stop = False\n print('Train. time | Trace | Init. loss| Min. loss | Curr. loss| T.since min | Traces/sec')\n max_print_line_len = 0\n loss_min_str = ''\n time_since_loss_min_str = ''\n last_auto_save_time = time.time() - auto_save_interval_sec\n while not stop:\n iteration += 1\n batch = batch_generator.get_batch(batch_size)\n layers_changed = self._polymorph(batch)\n\n if (self._optimizer is None) or layers_changed:\n self._optimizer = optim.Adam(self.parameters(), lr=learning_rate, weight_decay=weight_decay)\n\n self._optimizer.zero_grad()\n success, loss = self._loss(batch)\n if not success:\n print(colored('Cannot compute loss, skipping batch. Loss: {}'.format(loss), 'red', attrs=['bold']))\n else:\n loss.backward()\n self._optimizer.step()\n loss = float(loss)\n\n if self._loss_initial is None:\n self._loss_initial = loss\n self._loss_max = loss\n loss_initial_str = '{:+.2e}'.format(self._loss_initial)\n # loss_max_str = '{:+.3e}'.format(self._loss_max)\n if loss < self._loss_min:\n self._loss_min = loss\n loss_str = colored('{:+.2e}'.format(loss), 'green', attrs=['bold'])\n loss_min_str = colored('{:+.2e}'.format(self._loss_min), 'green', attrs=['bold'])\n time_loss_min = time.time()\n time_since_loss_min_str = colored(util.days_hours_mins_secs_str(0), 'green', attrs=['bold'])\n elif loss > self._loss_max:\n self._loss_max = loss\n loss_str = colored('{:+.2e}'.format(loss), 'red', attrs=['bold'])\n # loss_max_str = colored('{:+.3e}'.format(self._loss_max), 'red', attrs=['bold'])\n else:\n if loss < self._loss_previous:\n loss_str = colored('{:+.2e}'.format(loss), 'green')\n elif loss > self._loss_previous:\n loss_str = colored('{:+.2e}'.format(loss), 'red')\n else:\n loss_str = '{:+.2e}'.format(loss)\n loss_min_str = '{:+.2e}'.format(self._loss_min)\n # loss_max_str = '{:+.3e}'.format(self._loss_max)\n time_since_loss_min_str = util.days_hours_mins_secs_str(time.time() - time_loss_min)\n\n self._loss_previous = loss\n self._total_train_iterations += 1\n trace += batch.length\n self._total_train_traces += batch.length\n total_training_traces_str = '{:9}'.format('{:,}'.format(self._total_train_traces))\n self._total_train_seconds = prev_total_train_seconds + (time.time() - time_start)\n total_training_seconds_str = util.days_hours_mins_secs_str(self._total_train_seconds)\n traces_per_second_str = '{:,.1f}'.format(int(batch.length / (time.time() - time_last_batch)))\n time_last_batch = time.time()\n if num_traces is not None:\n if trace >= num_traces:\n stop = True\n\n self._history_train_loss.append(loss)\n self._history_train_loss_trace.append(self._total_train_traces)\n if trace - last_validation_trace > valid_interval:\n print('\\rComputing validation loss...', end='\\r')\n with torch.no_grad():\n _, valid_loss = self._loss(self._valid_batch)\n valid_loss = float(valid_loss)\n self._history_valid_loss.append(valid_loss)\n self._history_valid_loss_trace.append(self._total_train_traces)\n last_validation_trace = trace - 1\n\n if auto_save_file_name_prefix is not None:\n if time.time() - last_auto_save_time > auto_save_interval_sec:\n last_auto_save_time = time.time()\n file_name = '{}_{}.network'.format(auto_save_file_name_prefix, util.get_time_stamp())\n print('\\rSaving to disk...', end='\\r')\n self._save(file_name)\n\n print_line = '{} | {} | {} | {} | {} | {} | {}'.format(total_training_seconds_str, total_training_traces_str, loss_initial_str, loss_min_str, loss_str, time_since_loss_min_str, traces_per_second_str)\n max_print_line_len = max(len(print_line), max_print_line_len)\n print(print_line.ljust(max_print_line_len), end='\\r')\n sys.stdout.flush()\n print()\n","sub_path":"pyprob/nn/inference_network_feedforward.py","file_name":"inference_network_feedforward.py","file_ext":"py","file_size_in_byte":17995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"321357858","text":"#!/usr/bin/python\n#\n# Copyright 2015 John Kendrick\n#\n# This file is part of PDielec\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the MIT License\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n#\n# You should have received a copy of the MIT License\n# along with this program, if not see https://opensource.org/licenses/MIT\n#\n\"\"\"Read the contents of a QE output file containing QE dynmical matrix\"\"\"\nimport re\nimport math\nimport numpy as np\nfrom Python.Constants import amu, angs2bohr, hartree2ev\nfrom Python.UnitCell import UnitCell\nfrom Python.GenericOutputReader import GenericOutputReader\n\n\nclass QEOutputReader(GenericOutputReader):\n \"\"\"Read the contents of a QE output file containing QE dynmical matrix\"\"\"\n\n def __init__(self, filenames):\n GenericOutputReader.__init__(self, filenames)\n self.type = 'QE output'\n self._alat = None\n return\n\n def _read_output_files(self):\n \"\"\"Read the QE file names\"\"\"\n # Define the search keys to be looked for in the files\n self.manage = {} # Empty the dictionary matching phrases\n self.manage['header'] = (re.compile('Dynamical matrix file'), self._read_header)\n self.manage['lattice'] = (re.compile('Basis vectors'), self._read_lattice_vectors)\n self.manage['lattice2'] = (re.compile('cubic'), self._read_lattice_vectors)\n self.manage['lattice3'] = (re.compile('^ *crystal axes:'), self._read_crystal_axes)\n self.manage['positions'] = (re.compile('ATOMIC_POSITIONS'), self._read_atomic_positions)\n self.manage['dynamical'] = (re.compile(' *Dynamical Matrix in c'), self._read_dynamical)\n self.manage['epsilon'] = (re.compile(' *Dielectric Tensor:'), self._read_epsilon)\n self.manage['charges'] = (re.compile(' *Effective Charges E-U:'), self._read_born_charges)\n self.manage['energy_cutoff'] = (re.compile(' *kinetic-energy cutoff'), self._read_energy_cutoff)\n self.manage['kpoints'] = (re.compile(' *number of k points'), self._read_kpoints)\n self.manage['kpoint_grid'] = (re.compile('K_POINTS automatic'), self._read_kpoint_grid)\n self.manage['electrons'] = (re.compile('^ *number of electrons'), self._read_electrons)\n self.manage['energy'] = (re.compile('^ *total energy *='), self._read_energy)\n self.manage['alat'] = (re.compile('^ *lattice parameter'), self._read_alat)\n self.manage['pressure'] = (re.compile('^ *total *stress *.Ry'), self._read_pressure)\n self.manage['nions'] = (re.compile('^ *number of atoms/cell'), self._read_nions)\n for f in self._outputfiles:\n self._read_output_file(f)\n return\n\n def _read_nions(self, line):\n self.nions = int(line.split()[4])\n\n def _read_pressure(self, line):\n self.pressure = float(line.split()[5])/10.0\n\n def _read_alat(self, line):\n t = float(line.split()[4])\n if abs(t - angs2bohr) < 1.0e-4:\n t = angs2bohr\n # There are rounding errors when reading from the log file\n # So only read if there is no alternative\n if self._alat is None:\n self._alat = t\n\n def _read_electrons(self, line):\n self.electrons = float(line.split()[4])\n\n def _read_energy(self, line):\n self.final_energy_without_entropy = float(line.split()[3]) * hartree2ev / 2.0\n self.final_free_energy = float(line.split()[3]) * hartree2ev / 2.0\n\n def _read_energy_cutoff(self, line):\n self.energy_cutoff = float(line.split()[3]) * hartree2ev / 2.0\n\n def _read_kpoints(self, line):\n self.kpoints = int(line.split()[4])\n\n def _read_kpoint_grid(self, line):\n line = self.file_descriptor.readline()\n self.kpoint_grid = [ float(f) for f in line.split()[0:3] ]\n\n def _read_header(self, line):\n line = self.file_descriptor.readline()\n line = self.file_descriptor.readline()\n self.nspecies = int(line.split()[0])\n self.nions = int(line.split()[1])\n t = float(line.split()[3])\n if abs(t - angs2bohr) < 1.0e-4:\n t = angs2bohr\n self._alat = t\n\n def _read_epsilon(self, line):\n self.file_descriptor.readline()\n linea = self.file_descriptor.readline().split()\n self.zerof_optical_dielectric = []\n self.zerof_optical_dielectric.append([float(f) for f in linea[0:3]])\n linea = self.file_descriptor.readline().split()\n self.zerof_optical_dielectric.append([float(f) for f in linea[0:3]])\n linea = self.file_descriptor.readline().split()\n self.zerof_optical_dielectric.append([float(f) for f in linea[0:3]])\n return\n\n def _read_masses(self):\n self.masses_per_type = []\n self.species = []\n for i in range(self.nspecies):\n linea = self.file_descriptor.readline().replace('\\'', '').split()\n self.species.append(linea[1].capitalize())\n # The factor of two is because au in pwscf are half mass of electron\n self.masses_per_type.append(float(linea[2])*2/amu)\n self._read_fractional_coordinates()\n return\n\n def _read_dynamical(self, line):\n nmodes = self.nions*3\n hessian = np.zeros((nmodes, nmodes))\n self.file_descriptor.readline()\n linea = self.file_descriptor.readline().split()\n # We only want to read the hessian at gamma\n q = [float(q) for q in linea[3:6]]\n qsum = q[0]*q[0] + q[1]*q[1] + q[2]*q[2]\n if qsum > 0.0001:\n return\n # We read the hessian and store the mass weighted matrix\n linea = self.file_descriptor.readline().split()\n for a in range(self.nions):\n for b in range(self.nions):\n self.file_descriptor.readline()\n for ixyz in range(3):\n ipos = a*3 + ixyz\n linea = self.file_descriptor.readline().split()\n for jxyz in range(3):\n jpos = b*3 + jxyz\n # factor of 0.5 'cos of au units in pwscf\n hessian[ipos, jpos] = 0.5*float(linea[2*jxyz])/(amu*math.sqrt(self.masses[a]*self.masses[b]))\n # end for jxyz\n # end for ixyz\n # end for b\n # end for a\n self._dynamical_matrix(hessian)\n\n def _read_born_charges(self, line):\n self.born_charges = []\n line = self.file_descriptor.readline()\n for i in range(self.nions):\n b = []\n line = self.file_descriptor.readline()\n line = self.file_descriptor.readline()\n b.append([float(line.split()[0]), float(line.split()[1]), float(line.split()[2])])\n line = self.file_descriptor.readline()\n b.append([float(line.split()[0]), float(line.split()[1]), float(line.split()[2])])\n line = self.file_descriptor.readline()\n b.append([float(line.split()[0]), float(line.split()[1]), float(line.split()[2])])\n self.born_charges.append(b)\n return\n\n def _read_crystal_axes(self, line):\n linea = self.file_descriptor.readline().split()\n avector = [float(f)*self._alat/angs2bohr for f in linea[3:6]]\n linea = self.file_descriptor.readline().split()\n bvector = [float(f)*self._alat/angs2bohr for f in linea[3:6]]\n linea = self.file_descriptor.readline().split()\n cvector = [float(f)*self._alat/angs2bohr for f in linea[3:6]]\n self.unit_cells.append(UnitCell(avector, bvector, cvector))\n self.ncells = len(self.unit_cells)\n self.volume = self.unit_cells[-1].volume\n return\n\n def _read_lattice_vectors(self, line):\n linea = self.file_descriptor.readline().split()\n avector = [float(f)*self._alat/angs2bohr for f in linea[0:3]]\n linea = self.file_descriptor.readline().split()\n bvector = [float(f)*self._alat/angs2bohr for f in linea[0:3]]\n linea = self.file_descriptor.readline().split()\n cvector = [float(f)*self._alat/angs2bohr for f in linea[0:3]]\n self.unit_cells.append(UnitCell(avector, bvector, cvector))\n self.ncells = len(self.unit_cells)\n self.volume = self.unit_cells[-1].volume\n self._read_masses()\n return\n\n def _read_atomic_positions(self,line):\n # This is a nasty fix for a problem I cant get to the bottom of\n # The coordinates stored in the dynamical matrix file do not seem to be fractional coordinates\n # So I am reading the fractional coordinates here - from the log file\n self.fractional_coordinates = []\n species_list = []\n for i in range(self.nions):\n linea = self.file_descriptor.readline().split()\n self.fractional_coordinates.append([float(linea[1]), float(linea[2]), float(linea[3])])\n \n\n def _read_fractional_coordinates(self):\n self.masses = []\n self.atom_type_list = []\n self.ions_per_type = [ 0 for i in range(self.nspecies) ]\n species_list = []\n self.xyz_coordinates = []\n # It took a long time to work out that alat is in bohr\n const = self._alat/angs2bohr\n for i in range(self.nions):\n linea = self.file_descriptor.readline().split()\n species_index = int(linea[1])\n self.xyz_coordinates.append([const*float(linea[2]), const*float(linea[3]), const*float(linea[4])])\n self.masses.append(self.masses_per_type[species_index-1])\n self.atom_type_list.append(species_index-1)\n self.ions_per_type[species_index-1] += 1\n species_list.append(self.species[species_index-1])\n self.unit_cells[-1].set_xyz_coordinates(self.xyz_coordinates)\n self.unit_cells[-1].set_element_names(species_list)\n return\n","sub_path":"Python/QEOutputReader.py","file_name":"QEOutputReader.py","file_ext":"py","file_size_in_byte":10033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"321456304","text":"\nimport tensorflow as tf\n\nfrom model_deploy import Deploy\nimport tensorflow.contrib.slim as slim\nimport get_inputs\n\ndef _create_losses(input_queue,train_config,model_fn=None):\n dectect_model = model_fn()\n batch_size = train_config[\"batch_size\"]\n input_dict = input_queue.dequeue()\n\n width = []\n height = []\n images = []\n filename = []\n groundtruth_box = []\n groundtruth_class = []\n preprocess_images = []\n preprocess_box = []\n for i in range(batch_size):\n width.append(input_dict[i][\"width\"])\n height.append(input_dict[i][\"height\"])\n images.append(input_dict[i][\"image\"])\n groundtruth_box.append(input_dict[i][\"bbox\"])\n groundtruth_class.append(input_dict[i][\"label\"])\n filename.append(input_dict[i][\"filename\"])\n\n for image in images:\n resize_images = dectect_model.preprocess(image)\n preprocess_images.append(resize_images)\n\n images = tf.concat(preprocess_images, 0)\n\n for indx,box in enumerate(groundtruth_box):\n resize_box = dectect_model.process_gtbox(box,train_config[\"resize\"],width[indx],height[indx])\n preprocess_box.append(resize_box)\n\n\n\n #prediction_dict = dectect_model._predictor(images)\n\n\n dectect_model.provide_groundtruth(preprocess_box, groundtruth_class, filename, width, height)\n\n prediction_dict = dectect_model._predictor(images)\n\n loss = dectect_model._loss(prediction_dict)\n\n for loss_tensor in loss.values():\n tf.losses.add_loss(loss_tensor)\n\n return loss\n\n\ndef train(create_input_dict_fn,\n create_model_fn,\n train_config,\n train_dir,\n task,\n num_clones,\n worker_replicas,\n clone_on_cpu,\n ps_tasks,\n worker_job_name,\n is_chief,):\n\n #模型实例先留着,后面预训练模型时再用\n detection_model = create_model_fn()\n\n with tf.Graph().as_default():\n #配置类\n deploy_config = Deploy.DeploymentConfig(\n num_clones=num_clones,clone_on_cpu=clone_on_cpu,\n replica_id=task,num_replicas=worker_replicas,\n num_ps_tasks=ps_tasks,\n worker_job_name=worker_job_name)\n\n with tf.device(deploy_config.variables_device()):\n global_step = slim.create_global_step()\n\n batch_size = train_config[\"batch_size\"] // num_clones\n\n with tf.device(deploy_config.inputs_device()):\n #从tfrecord读数据，组成batch,生成样本队列\n input_queue =get_inputs.read_and_transform_dataset(per_clone_batch_size=batch_size,\n create_tensor_dict_fn=create_input_dict_fn)\n _create_losses(input_queue,train_config,model_fn=create_model_fn)\n'''\ndef match(gtbox, gtclass, anchors):\n\n batch_cls_target = []\n batch_loc_target = []\n match_list = []\n unmatch_list = []\n batch_iou_max = []\n\n iou_matrix = Iou_interface(anchors, gtbox)\n for i, sclae_iou in enumerate(iou_matrix):\n sclae_match = []\n sclae_unmatch = []\n sclae_cls_target = []\n sclae_loc_target = []\n iou_max = []\n for j, iou in enumerate(sclae_iou):\n tf.summary.histogram(\"scale_iou\", iou)\n\n column_max_indx = tf.argmax(iou, axis=0) # take the per column max value index,index value between[0~M]\n\n tf.summary.histogram(\"indx\", column_max_indx)\n column_max_values = tf.reduce_max(iou, axis=0) # take the per column max value\n\n tf.summary.histogram(\"values\", column_max_values)\n iou_max.append(tf.expand_dims(column_max_values, axis=0))\n # Less than or equal to than threshold position is false,more than the threshold position is true,\n # The false position is no target anchors,true position is targeted anchors\n object_mask = tf.less_equal(0.5, column_max_values)\n tf.summary.histogram(\"obj_mask\", tf.cast(object_mask, dtype=tf.float32))\n sclae_match.append(tf.expand_dims(object_mask, axis=0))\n noobj_mask = 1.0 - tf.cast(object_mask, tf.float32)\n sclae_unmatch.append(tf.expand_dims(noobj_mask, axis=0))\n # gtbox [M,4] column_max_indx's length is [N],broadcast gtbox to [N,4]\n new_box = tf.gather(gtbox[j], column_max_indx)\n sclae_loc_target.append(tf.expand_dims(new_box, axis=0))\n new_class = tf.gather(gtclass[j], column_max_indx)\n sclae_cls_target.append(tf.expand_dims(new_class, axis=0))\n batch_loc_target.append(tf.concat(sclae_loc_target, axis=0))\n batch_cls_target.append(tf.concat(sclae_cls_target, axis=0))\n match_list.append(tf.concat(sclae_match, axis=0))\n unmatch_list.append(tf.concat(sclae_unmatch, axis=0))\n batch_iou_max.append(tf.concat(iou_max, axis=0))\n return batch_cls_target,batch_loc_target,match_list,unmatch_list,batch_iou_max\n\ndef Iou(gtbox,bbox):\n\n area1 = area(bbox)\n area2 = area(gtbox)\n xmin1, ymin1, xmax1, ymax1 = tf.split(gtbox, num_or_size_splits=4, axis=1)\n xmin2, ymin2, xmax2, ymax2 = tf.split(bbox, num_or_size_splits=4, axis=1)\n all_pairs_min_ymax = tf.minimum(ymax1, tf.transpose(ymax2, [1, 0]))\n all_pairs_max_ymin = tf.maximum(ymin1, tf.transpose(ymin2, [1, 0]))\n intersect_heights = tf.maximum(0.0, all_pairs_min_ymax - all_pairs_max_ymin)\n all_pairs_min_xmax = tf.minimum(xmax1, tf.transpose(xmax2, [1, 0]))\n all_pairs_max_xmin = tf.maximum(xmin1, tf.transpose(xmin2, [1, 0]))\n intersect_widths = tf.maximum(0.0, all_pairs_min_xmax - all_pairs_max_xmin)\n innersize = tf.multiply(intersect_heights, intersect_widths)\n uion = tf.transpose(area1, [1, 0]) + area2 - innersize\n iou = tf.where(tf.equal(innersize, 0), tf.zeros_like(innersize),\n tf.truediv(innersize, uion), name=\"iou\")\n\n return iou\n\ndef Iou_interface(bbox,gtbox,name=\"Iou\"):\n #gtbox (N,4)\n # bbox (13,13,3,4),(26,26,3,4),(52,52,3,4) if not tiny_yolo else (13,13,3,4),(26,26,3,4)\n # reshape (507,4) (2028,4)\n all_iou_matrix = []\n with tf.name_scope(name):\n if name==\"Iou\":\n for i,t_bbox in enumerate(bbox):\n iou_matrix = []\n t_bbox = tf.reshape(t_bbox,(-1,4))\n for indx,g_box in enumerate(gtbox):\n iou = Iou(g_box,t_bbox)\n iou_matrix.append(iou)\n all_iou_matrix.append(iou_matrix)\n\n return all_iou_matrix\n\ndef area(box_1):\n if box_1.shape.as_list()[1]==2:\n width,heigth = tf.split(box_1, num_or_size_splits=2, axis=1)\n area = tf.multiply(width,heigth)\n else:\n xmin,ymin,xmax,ymax = tf.split(box_1,num_or_size_splits=4,axis=1)\n area = (xmax-xmin) * (ymax - ymin)\n return area'''","sub_path":"trainer1.py","file_name":"trainer1.py","file_ext":"py","file_size_in_byte":6952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"276277981","text":"import numpy as np\nimport pandas as pd\n\nids = [28, 29, 32, 35, 51, 54, 55]\n\nfeature_importances = np.zeros(shape=(10000, 3))\nfor i in ids:\n a = np.array(pd.read_csv(f'../tree_based_model/features/{i}.csv', header=None))\n feature_importances += a\n\n# load datas \nX = np.load('../../X_train.npz')['arr_0']\ny = np.load('../../Y_train.npz')['arr_0'][:, 2]\nTX = np.load('../../X_test.npz')['arr_0']\nprint('finish loading')\n\n# search params\nparams = {\n 'num_leaves': 180,\n 'min_data_in_leaf': 20,\n 'bagging_fraction': 0.75,\n 'bagging_freq': 7,\n 'learning_rate': 0.1,\n 'boosting': 'dart',\n 'num_iterations': 1000\n}\nprint(params)\n\n# search\n\nimport itertools\nimport lightgbm as lgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_absolute_error\n\nper = 1e-3\n\nX_train = X[:, feature_importances[:, 2] > per]\nX_test = TX[:, feature_importances[:, 2] > per]\nw_train = 1.0 / y\n\nmodel = lgb.LGBMRegressor(**params)\nmodel.fit(X_train, y, sample_weight=w_train)\n\npd.DataFrame(model.predict(X_train).reshape(-1, 1)).to_csv('train_t2y2.csv', index=None, header=None)\npd.DataFrame(model.predict(X_test).reshape(-1, 1)).to_csv('test_t2y2.csv', index=None, header=None)\n\nprint(np.mean(np.abs(y - model.predict(X_train)) / y))\nprint(np.mean(np.abs(y - model.predict(X_train))))\n\n","sub_path":"130/t2y2.py","file_name":"t2y2.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"607442863","text":"import os\nfrom .paths import prefixes, executables, weak_executables, home\nimport dotfiler.logger as log\nimport os.path as path\n\nclass Component(object):\n \"\"\"A base class for component classes which inspect the availability or\n location of softwares installed on system.\n \"\"\"\n def __init__(self):\n super(Component, self).__init__()\n self.exists = False\n self.messages = []\n def mark_found(self):\n self.exists = True\n def log(self, msg):\n self.messages.append(msg)\n def finalize(self):\n if not self.exists:\n log.warn(str(self) + \": The component was not found.\")\n msgs = self.messages\n if len(msgs) > 0:\n log.notice(\" Log messages:\")\n for msg in msgs:\n log.notice(\" \" + msg)\n\n @classmethod\n def get(cls):\n return get_component(cls)\n\n__loaded_components = {}\n\ndef get_component(comp_class):\n inst = __loaded_components.get(comp_class)\n if inst is None:\n inst = comp_class()\n __loaded_components[comp_class] = inst\n inst.finalize()\n return inst\n\nclass Executable(Component):\n def __init__(self, name):\n super(Executable, self).__init__()\n self.path = None\n self.name = name\n\n def search_standard_directories(self):\n for par_dir in executables:\n self.search_directory(par_dir)\n for par_dir in weak_executables:\n self.search_directory(par_dir)\n\n def search_directory(self, par_dir):\n if self.exists:\n return\n p = os.path.join(par_dir, self.name)\n if os.path.exists(p):\n if self.is_valid_file(p):\n self.path = p\n self.mark_found()\n else:\n self.log(\"'%s' was found, but detected as invalid.\" % p)\n else:\n self.log(\"'%s' was not found.\" % p)\n\n def is_valid_file(self, path):\n return True\n\n def __str__(self):\n return \"Executable '%s'\" % self.name\n\npython_vers = (\"2.7\", \"3.4\", \"3.6\")\npython_pkg_dirs = [\n os.path.join(prefix, \"lib\", \"python\" + ver, sd + \"-packages\")\n for sd in (\"site\", \"dist\")\n for ver in python_vers\n for prefix in prefixes]\npython_pkg_dirs += [\n os.path.join(\"/Library/Python/\" + ver, sd + \"-packages\")\n for sd in (\"site\", \"dist\")\n for ver in python_vers]\npython_pkg_dirs += [\n os.path.join(\"/Library/Frameworks/Python.framework/Versions/\" + ver + \"/lib/python\" + ver, sd + \"-packages\")\n for sd in (\"site\", \"dist\")\n for ver in python_vers]\npython_pkg_dirs += [\n os.path.join(\"/opt/local/Library/Frameworks/Python.framework/Versions/\" + ver + \"/lib/python\" + ver, sd + \"-packages\")\n for sd in (\"site\", \"dist\")\n for ver in python_vers]\n\nclass PythonPackage(Component):\n \"\"\"A base class for compoent classes which inspect the availabiliy of\n a python package (2.x or 3.x) located in site-package or dist-package.\n \"\"\"\n def __init__(self, name):\n super(PythonPackage, self).__init__()\n self.path = None\n self.name = name\n\n def search_standard_package_directories(self):\n for pkg_par_dir in python_pkg_dirs:\n self.search_package_directory(pkg_par_dir)\n\n def search_package_directory(self, pkg_par_dir):\n if self.exists:\n return\n pkg_dir = os.path.join(pkg_par_dir, self.name)\n if os.path.exists(pkg_dir) and self.is_valid_package_path(pkg_dir):\n self.path = pkg_dir\n self.mark_found()\n else:\n self.log(\"'%s' was not found.\" % pkg_dir)\n\n def is_valid_package_path(self, pkg_path):\n return True\n\n def __str__(self):\n return \"Python package '%s'\" % self.name\n\nclass TermCap(Component):\n def __init__(self, name):\n super(TermCap, self).__init__()\n self.name = name\n def search_standard_dbs(self):\n for prefix in prefixes:\n self.search_db(path.join(prefix, \"share/misc/termcap\"))\n def search_db(self, termcap_path):\n if self.exists:\n return\n if not os.path.exists(termcap_path):\n self.log(\"'%s' was not found.\" % termcap_path)\n return\n with open(termcap_path, 'r') as f:\n termcap_src = f.read().decode('utf8')\n lines = termcap_src.replace('\\\\\\n', '').splitlines()\n for line in lines:\n line = line.strip()\n if line.startswith('#'):\n continue\n i = line.find(':')\n if i < 1:\n continue\n line = line[0:i].split('|')\n if self.name in line:\n self.mark_found()\n return\n self.log(\"terminal '%s' was not found in '%s'.\" % (self.name, termcap_path))\n\nclass TermInfo(Component):\n def __init__(self, name):\n super(TermInfo, self).__init__()\n self.path = None\n self.name = name\n\n def search_standard_directories(self):\n for prefix in prefixes:\n self.search_directory(path.join(prefix, \"share/terminfo\"))\n self.search_directory(path.join(prefix, \"lib/terminfo\"))\n\n def search_directory(self, par_dir):\n if self.exists:\n return\n name = self.name\n\n fn = os.path.join(par_dir, name[0], name)\n if os.path.exists(fn):\n self.path = fn\n self.mark_found()\n return\n else:\n self.log(\"'%s' was not found.\" % fn)\n\n fn = os.path.join(par_dir, (\"0\" + hex(ord(name[0])))[-2:], name)\n if os.path.exists(fn):\n self.path = fn\n self.mark_found()\n return\n else:\n self.log(\"'%s' was not found.\" % fn)\n def __str__(self):\n return \"Terminfo '%s'\" % self.name\n\nclass Screen256ColorTermCap(TermCap):\n def __init__(self):\n super(Screen256ColorTermCap, self).__init__('screen-256color')\n self.search_standard_dbs()\n\nclass Screen256ColorTermInfo(TermInfo):\n def __init__(self):\n super(Screen256ColorTermInfo, self).__init__('screen-256color')\n self.search_standard_directories()\n\nclass PowerlineDaemon(Executable):\n def __init__(self):\n super(PowerlineDaemon, self).__init__('powerline-daemon')\n self.search_standard_directories()\n\nclass PowerlineStatusPackage(PythonPackage):\n def __init__(self):\n super(PowerlineStatusPackage, self).__init__('powerline')\n self.bindings_path = None\n self.search_standard_package_directories()\n self.search_package_directory(\"/usr/share\")\n\n def is_valid_package_path(self, pkg_path):\n if path.exists(path.join(pkg_path, \"bindings\")):\n self.bindings_path = path.join(pkg_path, \"bindings\")\n return True\n if path.exists(path.join(pkg_path, \"fish\")):\n self.bindings_path = path\n return True\n self.log(\"Bindings were not found for '%s'.\" % pkg_path)\n return False\n\nclass RVM(Component):\n def __init__(self):\n super(RVM, self).__init__()\n\n bash_profile = \"/etc/profile.d/rvm.sh\"\n if path.exists(bash_profile):\n self.bash_profile = bash_profile\n self.mark_found()\n else:\n self.log(\"'%s' was not found.\" % bash_profile)\n def __str__(self):\n return \"RVM\"\n\nclass Pipenv(Executable):\n def __init__(self):\n super(Pipenv, self).__init__('pipenv')\n self.search_standard_directories()\n\nclass OPAM(Executable):\n def __init__(self):\n super(OPAM, self).__init__('opam')\n self.search_standard_directories()\n\nclass PowerlineRS(Executable):\n def __init__(self):\n super(PowerlineRS, self).__init__('powerline-rs')\n self.search_standard_directories()\n\nclass Lsd(Executable):\n \"\"\" The next gen ls command (https://github.com/Peltoche/lsd)\n \"\"\"\n def __init__(self):\n super(Lsd, self).__init__('lsd')\n self.search_standard_directories()\n\nclass SingleUserNix(Executable):\n def __init__(self):\n super(SingleUserNix, self).__init__('nix-shell')\n self.search_directory(path.join(home, '.nix-profile', 'bin'))\n","sub_path":"scripts/dotfiler/components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":8160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"85219356","text":"#!/usr/bin/env python\n__author__ = 'Hristo'\n\nimport os\nimport shutil\n\nprint(\"Cleaning project structure...\")\n\npathName = \".\"\nfileToRemove = \"__pycache__\"\n \nfor subdirectoryPath, subdirectoryName, fileName in os.walk(pathName):\n if fileToRemove in subdirectoryPath:\n print(\"Removing: \" + subdirectoryPath)\n shutil.rmtree(subdirectoryPath)","sub_path":"QtBrowser/cleanup.py","file_name":"cleanup.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"204895656","text":"import tradersbot as tt\r\nimport random\r\n\r\n###########################################################\r\n# Make sure you run pip install tradersbot before running #\r\n###########################################################\r\n\r\n# Make a tradersbot\r\nt = tt.TradersBot(host='127.0.0.1', id='trader0', password='trader0')\r\n\r\n# Constants\r\nPOS_LIMIT = 500\r\nORDER_LIMIT = 100\r\n\r\n# Keeps track of prices\r\nSECURITIES = {}\r\nPREDS = {}\r\ntime = 0;\r\nopen_orders = {};\r\n\r\n# Initializes the prices\r\n# Initializes prediction dictionary\r\ndef ack_register_method(msg, order):\r\n\tglobal SECURITIES, PREDS, time\r\n\tsecurity_dict = msg['case_meta']['securities']\r\n\tfor security in security_dict.keys():\r\n\t\tif not(security_dict[security]['tradeable']): \r\n\t\t\tcontinue\r\n\t\tSECURITIES[security] = security_dict[security]['starting_price']\r\n\r\n\tfor security in security_dict:\r\n\t\tPREDS[security] = {};\r\n\r\n# Updates latest price and time\r\ndef market_update_method(msg, order):\r\n\tglobal SECURITIES, time\r\n\r\n\tsecurity = msg['market_state']['ticker']\r\n\r\n\t# Gets the price by averaging the highest bid (or buy order)\r\n\t# and lowest ask (or sell order)\r\n\tmax_bid = -1;\r\n\tmin_ask = -1;\r\n\tfor bid in msg['market_state']['bids']:\r\n\t\tif float(bid) > max_bid:\r\n\t\t\tmax_bid = float(bid);\r\n\t\r\n\tfor ask in msg['market_state']['asks']:\r\n\t\tif min_ask == -1 or float(ask) < min_ask:\r\n\t\t\tmin_ask =float(ask);\r\n\r\n\tif min_ask == -1 or max_bid == -1:\r\n\t\tprice = msg['market_state']['last_price'];\r\n\telse:\r\n\t\tprice = (min_ask + max_bid) / 2;\r\n\tSECURITIES[security] = price;\r\n\r\n\t# Sets the time\r\n\ttime = msg['elapsed_time']\r\n\r\n# Buys or sells in a random quantity every time it gets an update\r\n# You do not need to buy/sell here\r\n# Checks to make sure does not violate position limits or order limit\r\ndef trader_update_method(msg, order):\r\n\tprint(\"Hello world!\")\r\n\tglobal SECURITIES, POS_LIMIT, open_orders\r\n\r\n\tpositions = msg['trader_state']['positions']\r\n\topen_orders = msg['trader_state']['open_orders']\r\n\r\n\tfor security in positions.keys():\r\n\t\tif len(open_orders) > ORDER_LIMIT:\r\n\t\t\tbreak;\r\n\t\tif abs(positions[security]) >= POS_LIMIT:\r\n\t\t\tcontinue;\r\n\t\tif random.random() < 0.5:\r\n\t\t\tquant = min(10*random.randint(1, 10), POS_LIMIT-positions[security])\r\n\t\t\tif quant < 0:\r\n\t\t\t\tcontinue\r\n\t\t\torder.addBuy(security, quantity=quant,price=SECURITIES[security])\r\n\t\telse:\r\n\t\t\tquant = min(10*random.randint(1, 10), positions[security]+POS_LIMIT)\r\n\t\t\tif quant < 0:\r\n\t\t\t\tcontinue\r\n\t\t\torder.addSell(security, quantity=quant,price=SECURITIES[security])\r\n\r\n# Update store of predictions\r\n# You may want to change the way predictions are stored\r\ndef news_method(msg, order):\r\n\tglobal PREDS\r\n\tinfo = msg['news']['headline'].split()\r\n\tsecurity = info[0]\r\n\tnew_time = float(info[1])\r\n\tprice = float(msg['news']['body']);\r\n\tPREDS[security][new_time] = price;\r\n\r\n\r\n\r\n###############################################\r\n#### You can add more of these if you want ####\r\n###############################################\r\n\r\nt.onAckRegister = ack_register_method\r\nt.onMarketUpdate = market_update_method\r\nt.onTraderUpdate = trader_update_method\r\nt.onNews = news_method\r\n#t.onTrade = trade_method\r\n#t.onAckModifyOrders = ack_modify_orders_method\r\nt.run()","sub_path":"order-execution/ox/simplebot.py","file_name":"simplebot.py","file_ext":"py","file_size_in_byte":3171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"619197899","text":"from ._anvil_designer import HomepageTemplate\nfrom anvil import *\nimport anvil.server\nimport anvil.tables as tables\nimport anvil.tables.query as q\nfrom anvil.tables import app_tables\nfrom ..ArticleEdit import ArticleEdit\n\n\n\nclass Homepage(HomepageTemplate):\n def __init__(self, **properties):\n # Set Form properties and Data Bindings.\n self.init_components(**properties)\n\n # Any code you write here will run when the form opens.\n self.refresh_articles()\n \n # Set an event handler on the RepeatingPanel (our 'articles_panel')\n self.articles_panel.set_event_handler('x-delete-article', self.delete_article)\n\n def add_article_button_click(self, **event_args):\n # Initialise an empty dictionary to store the user inputs\n new_article = {}\n # Open an alert displaying the 'ArticleEdit' Form\n save_clicked = alert(\n content=ArticleEdit(item=new_article),\n title=\"Add Article\",\n large=True,\n buttons=[(\"Save\", True), (\"Cancel\", False)],\n )\n # If the alert returned 'True', the save button was clicked.\n if save_clicked:\n anvil.server.call('add_article', new_article)\n \n self.refresh_articles()\n\n def refresh_articles(self):\n # Load existing articles from the Data Table, and display them in the RepeatingPanel\n self.articles_panel.items = anvil.server.call('get_articles') \n \n def delete_article(self, article, **event_args):\n # Delete the article\n anvil.server.call('delete_article', article)\n # Refresh articles to remove the deleted article from the Homepage\n self.refresh_articles()\n","sub_path":"News_app/client_code/Homepage/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"569690251","text":"import pygame, logSystem\nfrom renderGameTest import *\nfrom config import *\nfrom music import *\nimport config\n\nSTEP = 64\n\nWINDOW_HEIGHT = 1024\nWINDOW_WEIGHT = 800\n\nGAME_HEIGHT = 576\nGAME_WEIGHT = 576\n\nINV_HEIGHT = WINDOW_HEIGHT - GAME_HEIGHT\nINV_WEIGHT = GAME_WEIGHT\n\nsurfInv = pygame.Surface((INV_HEIGHT,INV_WEIGHT))\nsurfSelect = pygame.Surface((STEP,STEP))\nsurfInvlog = pygame.Surface((3*STEP, 3*STEP))\nsurfInvitm = pygame.Surface((5*STEP, 3*STEP))\n\nglotok = pygame.mixer.Sound('music/glotok.ogg')\nbrosok = pygame.mixer.Sound('music/brosok.ogg')\n\ndef loadInv():\n\tf = open('inv.txt', 'r')\n\ts = f.read()\n\tm = []\n\ttmp = []\n\tfor x in range(0,len(s)):\n\t\tif s[x]=='\\n':\n\t\t\tm.append(tmp)\n\t\t\ttmp = []\n\t\telse:\n\t\t\ttmp.append(s[x])\n#\tprint(m)\n\treturn m\n\ndef renderInv(surfSelect,a,b,sc):\n\tx = 0\n\ty = 0\n\tfor i in range(0,len(config.inv)):\n\t\tfor j in range(0,len(config.inv[i])):\n\t\t\tif config.inv[i][j]=='0':\n\t\t\t\timg = pygame.image.load('srcBMP/inv/invempty.bmp')\n\t\t\t\timg_rect = img.get_rect(topleft=(x,y))\n\t\t\t\tsurfInvitm.blit(img,img_rect)\n\t\t\t\tx+=STEP\n\t\t\telif config.inv[i][j]=='a':\n\t\t\t\timg = pygame.image.load('srcBMP/inv/invpotion.bmp')\n\t\t\t\timg_rect = img.get_rect(topleft=(x,y))\n\t\t\t\tsurfInvitm.blit(img,img_rect)\n\t\t\t\tx+=STEP\n\t\t\telif config.inv[i][j]=='d':\n\t\t\t\timg = pygame.image.load('srcBMP/inv/invsword.bmp')\n\t\t\t\timg_rect = img.get_rect(topleft=(x,y))\n\t\t\t\tsurfInvitm.blit(img,img_rect)\n\t\t\t\tx+=STEP\n\t\t\telif config.inv[i][j]=='e':\n\t\t\t\timg = pygame.image.load('srcBMP/inv/invspear.bmp')\n\t\t\t\timg_rect = img.get_rect(topleft=(x,y))\n\t\t\t\tsurfInvitm.blit(img,img_rect)\n\t\t\t\tx+=STEP\n\t\t\telif config.inv[i][j]=='i':\n\t\t\t\timg = pygame.image.load('srcBMP/inv/invarmour1.bmp')\n\t\t\t\timg_rect = img.get_rect(topleft=(x,y))\n\t\t\t\tsurfInvitm.blit(img,img_rect)\n\t\t\t\tx+=STEP\n\t\t\telif config.inv[i][j]=='j':\n\t\t\t\timg = pygame.image.load('srcBMP/inv/invarmour2.bmp')\n\t\t\t\timg_rect = img.get_rect(topleft=(x,y))\n\t\t\t\tsurfInvitm.blit(img,img_rect)\n\t\t\t\tx+=STEP\n\t\tx = 0\n\t\ty += STEP\n\tsurfSelect.fill((255, 0, 0))\n\tsurfInvitm.blit(surfSelect,(b*STEP,a*STEP))\n\tsurfInvlog.fill((78, 78, 78))\n\tx = 5\n\tinvlogmsg = ['Уровень брони: '+str(config.player['arm']), 'Сила: '+str(config.player['power'])]\n\tfor s in invlogmsg:\n\t\tfont = pygame.font.SysFont('verdana',20)\n\t\ttext = font.render(s, 1, (255,255,255))\n\t\tsurfInvlog.blit(text,(5,x))\n\t\tx+=20\n\timg = pygame.image.load('srcBMP/inv/invbg.bmp')\n\timg_rect = img.get_rect(topleft=(0,0))\n\tsurfInv.blit(img,img_rect)\n\tsurfInv.blit(surfInvlog, (3*STEP, STEP))\n\tsurfInv.blit(surfInvitm, (1*STEP, 5*STEP))\n\tif config.player['type'] == 0:\n\t\timg = pygame.image.load('srcBMP/inv/invempty.bmp')\n\t\tsurfInv.blit(img, (STEP, STEP))\n\telif config.player['type'] == 1:\n\t\timg = pygame.image.load('srcBMP/inv/invsword.bmp')\n\t\tsurfInv.blit(img, (STEP, STEP))\n\telif config.player['type'] == 2:\n\t\timg = pygame.image.load('srcBMP/inv/invspear.bmp')\n\t\tsurfInv.blit(img, (STEP, STEP))\n\tif config.player['arm'] == 0:\n\t\timg = pygame.image.load('srcBMP/inv/invempty.bmp')\n\t\tsurfInv.blit(img, (STEP, 3*STEP))\n\telif config.player['arm'] == 1:\n\t\timg = pygame.image.load('srcBMP/inv/invarmour1.bmp')\n\t\tsurfInv.blit(img, (STEP, 3*STEP))\n\telif config.player['arm'] == 2:\n\t\timg = pygame.image.load('srcBMP/inv/invarmour2.bmp')\n\t\tsurfInv.blit(img, (STEP, 3*STEP))\n\tsc.blit(surfInv,(GAME_HEIGHT,0))\n\ndef openInv(maps,player,sc):\n\tlogSystem.blitLog('inv',[],sc)\n\ta = 0\n\tb = 0\n\tsurfSelect.set_alpha(127)\n\trenderInv(surfSelect,a,b,sc)\n\twhile True:\n\t\tfor i in pygame.event.get():\n\t\t\tif i.type == pygame.QUIT:\n\t\t\t\texit()\n\t\t\telif i.type == pygame.KEYDOWN:\n\t\t\t\tif i.key == pygame.K_i:\n\t\t\t\t\tsurfSelect.set_alpha(0)\n\t\t\t\t\trenderInv(surfSelect,a,b,sc)\n\t\t\t\t\treturn\n\t\t\t\telif i.key == pygame.K_e:\n\t\t\t\t\t#Использование зелья\n\t\t\t\t\tif config.inv[a][b] == 'a':\n\t\t\t\t\t\tvolume = config.PROCENT / 100\n\t\t\t\t\t\tglotok.set_volume(volume)\n\t\t\t\t\t\topenSound(glotok)\n\t\t\t\t\t\tplayer['hp'] +=2\n\t\t\t\t\t\tif player['hp'] > 6:\n\t\t\t\t\t\t\tplayer['hp'] = 6\n\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\t\t\trenderHP(sc)\n\t\t\t\t\t#Замена экипировки\n\t\t\t\t\telif config.inv[a][b] == 'd':\n\t\t\t\t\t\tif player['type'] == 0:\n\t\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\t\t\telif player['type'] == 1:\n\t\t\t\t\t\t\tconfig.inv[a][b] = 'd'\n\t\t\t\t\t\telif player['type'] == 2:\n\t\t\t\t\t\t\tconfig.inv[a][b] = 'e'\n\t\t\t\t\t\tplayer['type'] = 1\n\t\t\t\t\t\tplayer['power'] = 2\n\t\t\t\t\t\trefreshPlayer(player)\n\t\t\t\t\telif config.inv[a][b] == 'e':\n\t\t\t\t\t\tif player['type'] == 0:\n\t\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\t\t\telif player['type'] == 1:\n\t\t\t\t\t\t\tconfig.inv[a][b] = 'd'\n\t\t\t\t\t\telif player['type'] == 2:\n\t\t\t\t\t\t\tconfig.inv[a][b] = 'e'\n\t\t\t\t\t\tplayer['type'] = 2\n\t\t\t\t\t\tplayer['power'] = 1\n\t\t\t\t\t\trefreshPlayer(player)\n\t\t\t\t\t#Замена брони\n\t\t\t\t\telif config.inv[a][b] == 'i':\n\t\t\t\t\t\tif player['arm'] < 1:\n\t\t\t\t\t\t\tif player['arm'] == 0:\n\t\t\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\t\t\t\telif player['arm'] == 1:\n\t\t\t\t\t\t\t\tconfig.inv[a][b] = 'i'\n\t\t\t\t\t\t\telif player['arm'] == 2:\n\t\t\t\t\t\t\t\tconfig.inv[a][b] = 'j'\n\t\t\t\t\t\t\tplayer['arm'] = 1\n\t\t\t\t\t\t\trefreshPlayer(player)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tlogSystem.blitLog('inv',[0],sc)\n\t\t\t\t\telif config.inv[a][b] == 'j':\n\t\t\t\t\t\tif player['arm'] < 2:\n\t\t\t\t\t\t\tif player['arm'] == 0:\n\t\t\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\t\t\t\telif player['arm'] == 1:\n\t\t\t\t\t\t\t\tconfig.inv[a][b] = 'i'\n\t\t\t\t\t\t\telif player['arm'] == 2:\n\t\t\t\t\t\t\t\tconfig.inv[a][b] = 'j'\n\t\t\t\t\t\t\tplayer['arm'] = 2\n\t\t\t\t\t\t\trefreshPlayer(player)\n\t\t\t\t#Деекипировка\n\t\t\t\telif i.key == pygame.K_r:\n\t\t\t\t\tif config.inv[a][b] == '0':\n\t\t\t\t\t\tif player['type'] == 0:\n\t\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\t\t\telif player['type'] == 1:\n\t\t\t\t\t\t\tconfig.inv[a][b] = 'd'\n\t\t\t\t\t\telif player['type'] == 2:\n\t\t\t\t\t\t\tconfig.inv[a][b] = 'e'\n\t\t\t\t\t\tplayer['type'] = 0\n\t\t\t\t\t\tplayer['power'] = 0.5\n\t\t\t\t\t\trefreshPlayer(player)\n\t\t\t\t#Уничтожение вещи\n\t\t\t\telif i.key == pygame.K_q:\n\t\t\t\t\tif config.inv[a][b] != '0':\n\t\t\t\t\t\tvolume = config.PROCENT / 100\n\t\t\t\t\t\tbrosok.set_volume(volume)\n\t\t\t\t\t\topenSound(brosok)\n\t\t\t\t\t\tconfig.inv[a][b] = '0'\n\t\t\t\telif i.key == pygame.K_UP:\n\t\t\t\t\ta -= 1\n\t\t\t\t\tif a < 0:\n\t\t\t\t\t\ta = 0\n\t\t\t\telif i.key == pygame.K_RIGHT:\t\n\t\t\t\t\tb += 1\n\t\t\t\t\tif b > 4:\n\t\t\t\t\t\tb = 4\n\t\t\t\telif i.key == pygame.K_DOWN:\n\t\t\t\t\ta += 1\n\t\t\t\t\tif a > 2:\n\t\t\t\t\t\ta = 2\n\t\t\t\telif i.key == pygame.K_LEFT:\n\t\t\t\t\tb -= 1\n\t\t\t\t\tif b < 0:\n\t\t\t\t\t\tb = 0\n\t\t\t\telse:\n\t\t\t\t\tprint('ERROR KEY')\n\t\trenderInv(surfSelect,a,b,sc)\n\t\trenderMap(maps,sc)\n\t\tpygame.display.update()\n\t\t\ndef refreshPlayer(player):\n\tdictEnv[2] = 'srcBMP/player/player'+str(player['arm'])+str(player['type'])+'.bmp'\n\t\ndef openChest(maps,sc):\n\tx = config.player['i']\n\ty = config.player['j']\n\tif maps[x-1][y]=='4' or maps[x][y-1]=='4' or maps[x][y+1]=='4' or maps[x+1][y] == '4':\n\t\tfor i in range(x-1, x+2):\n\t\t\tfor j in range(y-1, y+2):\n\t\t\t\tif maps[i][j]=='4':\n\t\t\t\t\tfor a in range(0, len(config.inv)):\n\t\t\t\t\t\tfor b in range(0, len(config.inv[a])):\n\t\t\t\t\t\t\tif config.inv[a][b] == '0':\n\t\t\t\t\t\t\t\taddItem(config.inv,a,b,sc)\n\t\t\t\t\t\t\t\tmaps[i][j] = 'a'\n\t\t\t\t\t\t\t\treturn\n\t\t\t\t\tprint('You are overencumbered')\n\t\t\t\t\tlogSystem.blitLog('game',[1],sc)\n\t\t\t\t\treturn\n\telse:\n\t\tprint('No chest nearby')\n\t\ndef addItem(inv,x,y,sc):\n\tc=random.randint(1,10)\n\tif c<7:\n\t\tconfig.inv[x][y]='a'\n\t\tprint('Potion added')\n\t\tlogSystem.blitLog('game',[2],sc)\n\telif c==7:\n\t\tconfig.inv[x][y]='d'\n\t\tprint('Sword added')\n\t\tlogSystem.blitLog('game',[3],sc)\n\telif c==8:\n\t\tconfig.inv[x][y]='e'\n\t\tprint('Spear & shield added')\n\t\tlogSystem.blitLog('game',[4],sc)\n\telif c==9:\n\t\tconfig.inv[x][y]='i'\n\t\tprint('Leather armour added')\n\t\tlogSystem.blitLog('game',[5],sc)\n\telif c==10:\n\t\tconfig.inv[x][y]='j'\n\t\tprint('Steel armour added')\n\t\tlogSystem.blitLog('game',[6],sc)","sub_path":"renderInv.py","file_name":"renderInv.py","file_ext":"py","file_size_in_byte":7468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"10032628","text":"from aibs.SweepStim import SweepStim\nfrom psychopy import visual, core, event, logging, misc, monitors\nimport itertools\nimport scipy\n\n\"\"\"\nThis is a sample script that sets up a basic white noise experiment. This should be performed by the GUI eventually.\n\"\"\"\n\n\n#GENERIC PARAMETERS (should be passed by GUI, some of which have been read from config file)\nparams = {}\nparams['runs'] = 1 #number of runs\nparams['shuffle'] = True #shuffle sweep tables\nparams['preexpsec'] = 2 #seconds at the start of the experiment\nparams['postexpsec'] = 2 #seconds at the end of the experiment\nparams['sweeplength'] = 2/60.00 #length of sweeps\nparams['postsweepsec'] = 0 #black period after sweeps (foreground remains)\nparams['logdir'] = \"C:\\\\ExperimentLogs\\\\\" #where to put the log\nparams['backupdir'] = None #backup to network\nparams['mouseid'] = \"Spock\" #name of the mouse\nparams['userid'] = \"derricw\" #name of the user\nparams['task'] = \"\" #task type\nparams['stage'] = \"idkwhatthismeans\" #stage\nparams['protocol'] = \"\" #implemented later\nparams['nidevice']='Dev1' #NI device name\nparams['blanksweeps']=0 #blank sweep every x sweeps\nparams['bgcolor']='gray' #background color\nparams['syncsqr']=True #display a flashing square for synchronization\nparams['syncsqrloc']=(-600,-350)\nparams['script']=__file__\n\n\n#SET CONSOLE OUTPUT LEVEL, INITIALIZE WINDOWS\n#logging.console.setLevel(logging.DEBUG) #uncommet for diagnostics\nwindow = visual.Window(units='norm',monitor='testMonitor', fullscr = True, screen = 0, waitBlanking=False)\nwindow.setColor(params['bgcolor'])\n\n#CREATE BACKGROUND STIMULUS\n\nnoise = visual.GratingStim(window,tex=None,mask=\"None\",texRes=64,\n size=[1280,1024], ori = 0, pos = (0,0), name='box', autoLog=False, units = 'pix') \n \n#CREATE BACKGROUND FRAME PARAMETERS (what changes between frames and how much)\nbgFrame = {}\n\n#CREATE BACKGROUND SWEEP PARAMETERS (what changes between sweeps, and in what order)\nbgSweep = {}\n\nnoisematrix = [(scipy.random.randint(2,size = (64,64))*2-1) for x in range(100)]\n\nbgSweep['Tex'] = (noisematrix,0) #each texture we just generated\n\n#CREATE FOREGROUND STIMULUS (none for basic white noise experiment)\n\n\n#CREATE FOREGROUND STIMULUS FRAME PARAMETERS (what changes between frames and how much)\nfgFrame = {}\n\n#CREATE FOREGROUND SWEEP PARAMETERS (what changes between sweeps)\nfgSweep = {}\n\n#CREATE FORAGING CLASS INSTANCE\ng = SweepStim(window = window, params = params, bgStim = noise, bgFrame = bgFrame, bgSweep = bgSweep, fgStim = None)\n#RUN IT\ng.run()","sub_path":"scripts/whitenoiseexp.py","file_name":"whitenoiseexp.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"476779523","text":"import os\nimport datetime\n\nimport yaml\n\n\nclass Config:\n def __init__(self, path):\n self.path = path\n self.data = {}\n\n if os.path.exists(self.path):\n self.data = yaml.safe_load(open(self.path, \"r\"))\n if self.data is None:\n # allow an empty file to start with\n self.data = {}\n\n @property\n def output_path(self):\n return os.path.abspath(self.data.get(\"output_path\", \"output\"))\n\n @property\n def content_paths(self):\n if \"content_paths\" in self.data:\n paths = self.data[\"content_paths\"]\n else:\n paths = [\"content\"]\n if os.path.exists(os.path.join(\"theme\", \"content\")):\n paths.append(os.path.join(\"theme\", \"content\"))\n\n # add the built-in content from combine itself\n paths.append(os.path.join(os.path.dirname(__file__), \"base_content\"))\n\n return [os.path.abspath(x) for x in paths]\n\n @property\n def variables(self):\n variables = self.default_variables\n\n for name, data in self.data.get(\"variables\", {}).items():\n if isinstance(data, dict):\n if \"default\" in data:\n variables[name] = data[\"default\"]\n\n if \"from_env\" in data and data[\"from_env\"] in os.environ:\n variables[name] = os.environ[data[\"from_env\"]]\n else:\n variables[name] = data\n\n return variables\n\n @property\n def default_variables(self):\n return {\"now\": datetime.datetime.now} # as a function\n\n @property\n def steps(self):\n return self.data.get(\"steps\", [])\n","sub_path":"combine/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"414915990","text":"'''\nCreated on 13/04/13\nCode Jam 2013 Qualification Round C\n@author: manolo\n'''\nimport math\nimport sys\nifile = sys.stdin\ndef r():\n return ifile.readline()[:-1]\n\nofile = open('./c-large1.out', 'w')\ndef w(what):\n ofile.write(what + '\\n')\n\ndef is_fair(n):\n return str(n) == str(n)[::-1]\n\n#def is_square(n):\n# sqrt = math.sqrt(n)\n## print 'sqrt = ' + str(sqrt)\n# if sqrt - long(sqrt) > 0:\n# return False\n# else:\n# return is_fair(long(sqrt))\n \n\ndef find(a, b):\n count = 0\n# print '(' + str(a) + ', ' + str(b) + ')'\n\n sqrt_a = math.sqrt(a)\n# print \"sqrt_a: \" + str(sqrt_a)\n long_sqrt_a = long(sqrt_a)\n# print \"long_sqrt_a: \" + str(long_sqrt_a)\n i = long_sqrt_a\n if sqrt_a - long_sqrt_a > 0:\n i += 1\n# print \"i: \" + str(i)\n\n bb = long(math.sqrt(b))\n# print '(' + str(i) + ', ' + str(bb) + ')'\n while (i <= bb):\n if is_fair(i):\n ii = i*i\n if is_fair(ii):\n# print ii\n count += 1\n# else:\n# print str(i) + \" --> NO\"\n i +=1\n return count\n\nt = long(r())\n#print 't: ' + str(t)\n\nfor i in xrange(t):\n (A, B) = r().split(' ')\n a = long(A)\n b = long(B)\n n = find(a, b)\n w('Case #' + str(i+1) + ': ' + str(n))\n\nofile.close\n\n","sub_path":"solutions_python/Problem_118/909.py","file_name":"909.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"475594204","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('userlist/', views.userlist),\n path('addUser/', views.addUser),\n path('addUserHandler/', views.addUserHandler),\n path('editUser/', views.editUser),\n path('editUserHandler/', views.editUserHandler),\n path('login/', views.login),\n path('loginHandler/', views.loginHandler),\n path('loginout/', views.loginout),\n path('homePage/', views.homePage),\n path('menuList/', views.menuList),\n path('addMenu/', views.addMenu),\n path('addMenuHandler/', views.addMenuHandler),\n path('delMenuHandler/', views.delMenuHandler),\n path('editMenu/', views.editMenu),\n path('editMenuHandler/', views.editMenuHandler),\n path('newsList/', views.newsList),\n path('delnews/', views.delnews),\n path('addNews/', views.addNews),\n path('addNewsHandler/', views.addNewsHandler),\n path('editNews/', views.editNews),\n path('editNewsHandler/', views.editNewsHandler),\n path('positionList/', views.positionList),\n path('addPosition/', views.addPosition),\n path('addPositionHandler/', views.addPositionHandler),\n path('editPosition/', views.editPosition),\n path('editPositionHandler/', views.editPositionHandler),\n path('delPosition/', views.delPosition),\n path('poscontent/', views.poscontent),\n path('addPosContent/', views.addPosContent),\n path('delPoscontent/', views.delPoscontent),\n path('editposContent/', views.editposContent),\n path('editposContentHandler/', views.editposContentHandler),\n path('clearFile/', views.clearFile),\n]","sub_path":"admin1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"307695965","text":"#!/bin/python3\n\nimport os\nimport sys\n\n#\n# Complete the countApplesAndOranges function below.\n#\ndef countApplesAndOranges(s, t, a, b, apples, oranges):\n s1=0\n s2=0\n for i in apples:\n if (i in range(s-a,t-a+1)):\n s1+=1\n for j in oranges:\n if (j in range(s-b,t-b+1)):\n s2+=1\n \n print(s1)\n print(s2)\n\nif __name__ == '__main__':\n st = input().split()\n\n s = int(st[0])\n\n t = int(st[1])\n\n ab = input().split()\n\n a = int(ab[0])\n\n b = int(ab[1])\n\n mn = input().split()\n\n m = int(mn[0])\n\n n = int(mn[1])\n\n apple = list(map(int, input().rstrip().split()))\n\n orange = list(map(int, input().rstrip().split()))\n\n countApplesAndOranges(s, t, a, b, apple, orange)\n","sub_path":"HackerRank/Implementation/countApplesAndOranges.py","file_name":"countApplesAndOranges.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"180120226","text":"#\n# Copyright SAS Institute\n#\n# Licensed under the Apache License, Version 2.0 (the License);\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nimport fcntl\nimport os\nimport signal\nimport subprocess\nfrom time import sleep\n\nimport saspy.sascfg as sascfg\n\n\nclass SASconfig:\n def __init__(self, cfgname='', kernel=None, saspath='', options=''):\n # import pdb; pdb.set_trace()\n\n self.configs = []\n self._kernel = kernel\n self.saspath = saspath\n self.options = options\n\n # GET Config options\n try:\n self.cfgopts = getattr(sascfg, \"SAS_config_options\")\n except:\n self.cfgopts = {}\n lock = self.cfgopts.get('lock_down', True)\n # in lock down mode, don't allow runtime overrides of option values from the config file.\n if lock:\n if len(saspath) > 0 or len(options) > 0:\n print(\"Parameters passed to SAS_session were ignored due to configuration restriction.\")\n saspath = ''\n options = ''\n\n # GET Config names\n self.configs = getattr(sascfg, \"SAS_config_names\")\n\n if len(cfgname) == 0:\n if len(self.configs) == 0:\n print(\"No SAS Configuration names found in saspy.sascfg\")\n return\n else:\n if len(self.configs) == 1:\n cfgname = self.configs[0]\n if kernel is None:\n print(\"Using SAS Config named: \" + cfgname)\n else:\n cfgname = self._prompt(\n \"Please enter the name of the SAS Config you wish to run. Available Configs are: \" + str(\n self.configs) + \" \")\n\n while cfgname not in self.configs:\n cfgname = self._prompt(\n \"The SAS Config name specified was not found. Please enter the SAS Config you wish to use. Available Configs are: \" + str(\n self.configs) + \" \")\n\n self.name = cfgname\n cfg = getattr(sascfg, cfgname)\n if len(saspath) == 0:\n self.saspath = cfg.get('saspath', '/opt/sasinside/SASHome/SASFoundation/9.4/sas')\n if len(options) == 0:\n self.options = cfg.get('options', '')\n\n def _prompt(self, prompt, pw=False):\n if self._kernel is None:\n if not pw:\n try:\n return input(prompt)\n except (KeyboardInterrupt):\n return ''\n else:\n try:\n return getpass.getpass(prompt)\n except (KeyboardInterrupt):\n return ''\n else:\n try:\n return self._kernel._input_request(prompt, self._kernel._parent_ident, self._kernel._parent_header,\n password=pw)\n except (KeyboardInterrupt):\n return ''\n\n\nclass SASsession:\n def __init__(self, cfgname='', kernel=None, saspath='', options=''):\n self.pid = None\n self.stdin = None\n self.stderr = None\n self.stdout = None\n\n self.sascfg = SASconfig(cfgname, kernel, saspath, options)\n self._log_cnt = 0\n self._log = \"\"\n self._logr = \"\"\n\n self._startsas(self.sascfg)\n\n def __del__(self):\n if self.pid:\n self._endsas()\n self.pid = None\n\n def _logcnt(self, nxt=True):\n if nxt:\n self._log_cnt += 1\n return '%08d' % self._log_cnt\n\n def _startsas(self, sasconfig):\n if self.pid:\n return self.pid\n\n pgm = sasconfig.saspath\n parms = [pgm]\n parms += sasconfig.options\n parms += [\"-pagesize\", \"MAX\"]\n parms += [\"-nodms\"]\n parms += [\"-stdio\"]\n parms += [\"-terminal\"]\n parms += [\"-nosyntaxcheck\"]\n parms += ['']\n\n pipe_read = 0\n pipe_write = 1\n\n pin = os.pipe()\n pout = os.pipe()\n perr = os.pipe()\n\n pidpty = os.forkpty()\n if pidpty[0]:\n # we are the parent\n\n pid = pidpty[0]\n os.close(pin[pipe_read])\n os.close(pout[pipe_write])\n os.close(perr[pipe_write])\n\n else:\n # we are the child\n signal.signal(signal.SIGINT, signal.SIG_DFL)\n\n os.close(0)\n os.close(1)\n os.close(2)\n\n os.dup2(pin[pipe_read], 0)\n os.dup2(pout[pipe_write], 1)\n os.dup2(perr[pipe_write], 2)\n\n os.close(pin[pipe_read])\n os.close(pin[pipe_write])\n os.close(pout[pipe_read])\n os.close(pout[pipe_write])\n os.close(perr[pipe_read])\n os.close(perr[pipe_write])\n\n try:\n os.execv(pgm, parms)\n except:\n os._exit(-6)\n\n self.pid = pidpty[0]\n self.stdin = os.fdopen(pin[pipe_write], mode='wb')\n self.stderr = os.fdopen(perr[pipe_read], mode='rb')\n self.stdout = os.fdopen(pout[pipe_read], mode='rb')\n\n fcntl.fcntl(self.stdout, fcntl.F_SETFL, os.O_NONBLOCK)\n fcntl.fcntl(self.stderr, fcntl.F_SETFL, os.O_NONBLOCK)\n\n self.submit(\"options svgtitle='svgtitle'; options validvarname=any; ods graphics on;\", \"text\")\n\n return self.pid\n\n def _endsas(self):\n rc = 0\n if self.pid:\n code = b\";*\\';*\\\";*/;\\n;quit;endsas;\\n\"\n self.stderr.read1(4096)\n self.stdin.write(code)\n self.stdin.flush()\n sleep(1)\n try:\n rc = os.waitid(os.P_PID, self.pid, os.WEXITED | os.WNOHANG)\n except subprocess.TimeoutExpired:\n print(\"SAS didn't shutdown w/in 5 seconds; killing it to be sure\")\n os.kill(self.pid, signal.SIGKILL)\n self.pid = None\n return rc\n\n def submit(self, code, results=\"html\"):\n odsopen = b\"ods listing close;ods html5 file=stdout options(bitmap_mode='inline') device=png; ods graphics on / outputfmt=png;\\n\"\n odsclose = b\"ods html5 close;ods listing;\\n\"\n ods = True\n htm = \"html HTML\"\n mj = b\";*\\';*\\\";*/;\"\n lstf = '' \n logf = '' \n bail = False\n eof = 5\n bc = False\n done = False\n logn = self._logcnt()\n logcodei = \"%put E3969440A681A24088859985\" + logn + \";\"\n logcodeo = \"\\nE3969440A681A24088859985\" + logn\n\n if self.pid is None:\n return dict(LOG=\"No SAS process attached. SAS process has terminated unexpectedly.\", LST='')\n\n rc = os.waitid(os.P_PID, self.pid, os.WEXITED | os.WNOHANG)\n if rc is not None:\n self.pid = None\n return dict(LOG='SAS process has terminated unexpectedly. Pid State= ' + str(rc), LST='')\n\n if htm.find(results) < 0:\n ods = False\n\n if ods:\n self.stdin.write(odsopen)\n\n out = self.stdin.write(mj + b'\\n' + code.encode() + b'\\n' + mj)\n\n if ods:\n self.stdin.write(odsclose)\n\n out = self.stdin.write(b'\\n' + logcodei.encode() + b'\\n')\n self.stdin.flush()\n\n while not done:\n try:\n while True:\n rc = os.waitid(os.P_PID, self.pid, os.WEXITED | os.WNOHANG)\n if rc is not None:\n self.pid = None\n return dict(LOG='SAS process has terminated unexpectedly. Pid State= ' +\n str(rc), LST='')\n if bail:\n eof -= 1\n if eof < 0:\n break\n lst = self.stdout.read1(4096).decode()\n if len(lst) > 0:\n lstf += lst\n else:\n log = self.stderr.read1(4096).decode() \n if len(log) > 0:\n logf += log\n if logf.count(logcodeo) >= 1:\n bail = True\n if not bail and bc:\n self.stdin.write(odsclose+logcodei.encode() + b'\\n')\n self.stdin.flush()\n bc = False\n done = True\n\n except (KeyboardInterrupt, SystemExit):\n print('Exception caught!')\n ll = self._breakprompt(logcodeo)\n\n if ll.get('ABORT', False):\n return ll\n\n logf += ll['LOG']\n lstf += ll['LST']\n bc = ll['BC']\n\n if not bc:\n print('Exception handled :)\\n')\n else:\n print('Exception ignored, continuing to process...\\n')\n\n self.stdin.write(odsclose+logcodei.encode()+b'\\n')\n self.stdin.flush()\n\n trip = lstf.rpartition(\"/*]]>*/\") \n if len(trip[1]) > 0 and len(trip[2]) < 100:\n lstf = ''\n\n self._log += logf\n self._logr = logf\n final = logf.partition(logcodei)\n z = final[0].rpartition(chr(10))\n prev = '%08d' % (self._log_cnt - 1)\n zz = z[0].rpartition(\"\\nE3969440A681A24088859985\" + prev +'\\n')\n logd = zz[2].replace(mj.decode(), '')\n\n lstd = lstf.replace(chr(12), chr(10)).replace('',\n '').replace(\"font-size: x-small;\",\n \"font-size: normal;\")\n return dict(LOG=logd, LST=lstd)\n\n def _breakprompt(self, eos):\n found = False\n logf = ''\n lstf = ''\n bc = False\n\n if self.pid is None:\n return dict(LOG=b\"No SAS process attached. SAS process has terminated unexpectedly.\", LST=b'', ABORT=True)\n\n interrupt = signal.SIGINT\n os.kill(self.pid, interrupt)\n sleep(.25)\n\n while True:\n rc = os.waitid(os.P_PID, self.pid, os.WEXITED | os.WNOHANG)\n if rc is not None:\n self.pid = None\n outrc = str(rc)\n return dict(LOG=b'SAS process has terminated unexpectedly. Pid State= ' +\n outrc.encode(), LST=b'',ABORT=True)\n\n lst = self.stdout.read1(4096).decode()\n lstf += lst\n if len(lst) > 0:\n lsts = lst.rpartition('Select:')\n if lsts[0] != '' and lsts[1] != '':\n found = True\n query = lsts[1] + lsts[2].rsplit('\\n?')[0] + '\\n'\n print('Processing interrupt\\nAttn handler Query is\\n\\n' + query)\n response = self.sascfg._prompt(\"Please enter your Response: \")\n self.stdin.write(response.encode() + b'\\n')\n self.stdin.flush()\n if (response == 'C' or response == 'c') and query.count(\"C. Cancel\") >= 1:\n bc = True\n break\n else:\n lsts = lst.rpartition('Press')\n if lsts[0] != '' and lsts[1] != '':\n query = lsts[1] + lsts[2].rsplit('\\n?')[0] + '\\n'\n print('Secondary Query is:\\n\\n' + query)\n response = self.sascfg._prompt(\"Please enter your Response: \")\n self.stdin.write(response.encode() + b'\\n')\n self.stdin.flush()\n if (response == 'N' or response == 'n') and query.count(\"N to continue\") >= 1:\n bc = True\n break\n else:\n #print(\"******************No 'Select' or 'Press' found in lst=\")\n pass\n else:\n log = self.stderr.read1(4096).decode()\n logf += log\n self._log += log\n self._logr = log\n\n if log.count(eos) >= 1:\n print(\"******************Found end of step. No interrupt processed\")\n found = True\n\n if found:\n break\n\n sleep(.25)\n\n lstr = lstf\n logr = logf\n\n return dict(LOG=logr, LST=lstr, BC=bc)\n\n\"\"\"\nif __name__ == \"__main__\":\n self._startsas()\n\n ll = self.submit(sys.argv[1], \"text\")\n\n print(ll['LOG'])\n print(ll['LST'])\n\n self.endsas()\n\"\"\"\n","sub_path":"saspy/sasbase.py","file_name":"sasbase.py","file_ext":"py","file_size_in_byte":12946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"223035957","text":"\"\"\"Unit tests for API components.\"\"\"\nimport json\nimport unittest\nimport requests\n\n\nclass TestApi(unittest.TestCase):\n \"\"\"Unit tests for API components.\"\"\"\n\n @classmethod\n def setUpClass(cls):\n \"\"\"Execute this before the tests.\"\"\"\n cls.base_url = 'http://api:5434/mobydq/api/v1'\n\n def test_get_health(self):\n \"\"\"Unit tests endpoint get /health.\"\"\"\n\n url = self.base_url + '/health'\n headers = {'Content-Type': 'application/json'}\n response = requests.get(url, headers=headers)\n status = response.status_code\n body = json.loads(response.text)\n\n # Assert http status code is 200\n self.assertEqual(status, 200)\n self.assertIsNotNone(body['message'])\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test/test_api/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"269637778","text":"import numpy as np\n#import math\n#import scipy.ndimage\nfrom .frequency import frequency\n\n\ndef ridge_freq(\n im,\n mask,\n orient,\n blksze,\n windsze,\n minWaveLength,\n maxWaveLength):\n rows, cols = im.shape\n freq = np.zeros((rows, cols))\n\n for r in range(0, rows - blksze, blksze):\n for c in range(0, cols - blksze, blksze):\n blkim = im[r:r + blksze][:, c:c + blksze]\n blkor = orient[r:r + blksze][:, c:c + blksze]\n\n freq[r:r + blksze][:,\n c:c + blksze] = frequency(blkim,\n blkor,\n windsze,\n minWaveLength,\n maxWaveLength)\n\n freq = freq * mask\n freq_1d = np.reshape(freq, (1, rows * cols))\n ind = np.where(freq_1d > 0)\n\n ind = np.array(ind)\n ind = ind[1, :]\n\n non_zero_elems_in_freq = freq_1d[0][ind]\n\n meanfreq = np.mean(non_zero_elems_in_freq)\n # does not work properly\n medianfreq = np.median(non_zero_elems_in_freq)\n return(freq, meanfreq)\n","sub_path":"enhance/ridge_frequency.py","file_name":"ridge_frequency.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"8677763","text":"# 2263 <트리의 순회>\n\nimport sys\nsys.setrecursionlimit(10**6)\ninput = lambda: sys.stdin.readline()\n\nn=int(input())\ninorder=list(map(int, input().split()))\npostorder=list(map(int, input().split()))\nroot=postorder[-1]\n# inorder 원소들 어딨는지 알기 쉽게 하기 위해 따로 저장\npos=[0]*(n+1)\n\nfor i in range(n):\n pos[inorder[i]]=i\n\ndef divide(instart, inend, poststart, postend):\n if instart > inend or poststart>postend:\n return\n root=postorder[postend]\n print(root, end=' ')\n root_inorder=pos[root]\n left=root_inorder-instart\n\n divide(instart, root_inorder-1, poststart, poststart+left-1)\n divide(root_inorder+1, inend, poststart+left, postend-1)\n return\n\ndivide(0,n-1,0,n-1)","sub_path":"week2/hyungjoon/2263.py","file_name":"2263.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"635571669","text":"import cv2\r\nimport numpy as np\r\ntracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW']\r\ntracker_type = tracker_types[3]\r\n\r\ndef imageCopy(src):\r\n return np.copy(src)\r\n \r\n \r\ndef convertColor(image, flag=None):\r\n if flag is None:\r\n return image\r\n else:\r\n return cv2.cvtColor(image, flag)\r\n\r\n\r\ndef rangeColor(image, lower, upper):\r\n return cv2.inRange(image, lower, upper)\r\n\r\n\r\ndef splitColor(image, lower, upper, flag=None):\r\n converted = convertColor(image, flag)\r\n mask = rangeColor(converted, lower, upper)\r\n return cv2.bitwise_and(image, image, mask=mask)\r\n\r\n\r\ndef polyROI(image, points):\r\n if len(image.shape) == 2:\r\n channels = 1\r\n else:\r\n channels = image.shape[2]\r\n mask = np.zeros_like(image)\r\n ignore_mask_color = (255,) * channels\r\n cv2.fillPoly(mask, points, ignore_mask_color)\r\n return cv2.bitwise_and(image, mask)\r\n\r\n\r\ndef convertColor(image, flag=None):\r\n if flag is None:\r\n return image\r\n else:\r\n return cv2.cvtColor(image, flag)\r\n\r\n\r\ndef cannyEdge(image, threshold1=100, threshold2=200):\r\n return cv2.Canny(image, threshold1, threshold2)\r\n\r\n\r\ndef addWeightedImage(image1, w1, imagw2, w2=None):\r\n if w2 is None:\r\n return cv2.addWeighted(image1, float(w1) * 0.01, imagw2, float(100 - w1) * 0.01, 0)\r\n else:\r\n return cv2.addWeighted(image1, w1 * 0.01, imagw2, w2 * 0.01, 0)\r\n\r\n\r\ndef houghLinesP(image, rho=1.0, theta=np.pi/180, threshold=100, minLineLength=10, maxLineGap=100):\r\n return cv2.HoughLinesP(image, rho, theta, threshold, minLineLength=minLineLength, maxLineGap=maxLineGap)\r\n\r\n\r\ndef drawHoughLinesP(image, lines):\r\n result = imageCopy(image)\r\n if len(image.shape) == 2:\r\n result = convertColor(image, cv2.COLOR_GRAY2BGR)\r\n for i in range(len(lines)):\r\n for x1, y1, x2, y2 in lines[i]:\r\n cv2.line(result, (x1, y1), (x2, y2), (0, 0, 255), 3)\r\n return result\r\n\r\n\r\ndef splitLines(lines):\r\n left_x = []\r\n left_y = []\r\n right_x = []\r\n right_y = []\r\n for line in lines:\r\n x1 = line[0,0]\r\n y1 = line[0,1]\r\n x2 = line[0,2]\r\n y2 = line[0,3]\r\n slope = (float)(y2-y1)/(float)(x2-x1)\r\n if abs(slope) < 0.5:\r\n continue\r\n if slope <= 0:\r\n left_x.append([x1, x2])\r\n left_y.append([y1, y2])\r\n else:\r\n right_x.append([x1, x2])\r\n right_y.append([y1, y2])\r\n return left_x, left_y, right_x, right_y\r\n\r\n\r\ndef meanPoint(x):\r\n sum1 = 0\r\n sum2 = 0\r\n for x1, x2 in x:\r\n sum1 += x1\r\n sum2 += x2\r\n sum1 = int(float(sum1)/float(len(x)))\r\n sum2 = int(float(sum2)/float(len(x)))\r\n return [sum1, sum2]\r\n\r\ndef medianPoint(x):\r\n xx = sorted(x)\r\n return xx[(int)(len(xx)/2)]\r\n \r\n\r\ndef interpolate(list_x, list_y, y):\r\n x1 = list_x[0]\r\n x2 = list_x[1]\r\n y1 = list_y[0]\r\n y2 = list_y[1]\r\n return int(float(y - y1) * float(x2-x1) / float(y2-y1) + x1)\r\n\r\n \r\ndef lineFitting(image, left_x, left_y, right_x, right_y):\r\n result = imageCopy(image)\r\n height = image.shape[0]\r\n lx = meanPoint(left_x)\r\n ly = meanPoint(left_y)\r\n rx = meanPoint(right_x)\r\n ry = meanPoint(right_y)\r\n min_y = int(height*0.6)\r\n max_y = height\r\n min_x_left = interpolate(lx, ly, min_y)\r\n max_x_left = interpolate(lx, ly, max_y)\r\n min_x_right = interpolate(rx, ry, min_y)\r\n max_x_right = interpolate(rx, ry, max_y)\r\n cv2.line(result, (min_x_left, min_y), (max_x_left, max_y), (0, 0, 255), 3)\r\n cv2.line(result, (min_x_right, min_y), (max_x_right, max_y), (0, 0, 255), 3)\r\n return result\r\n\r\n\r\ndef frameProcessing(frame):\r\n result = imageCopy(frame)\r\n height, width = result.shape[:2]\r\n pt1 = (width*0.45, height*0.6)\r\n pt2 = (width*0.55, height*0.6)\r\n pt3 = (width*0.95, height*1.0)\r\n pt4 = (width*0.05, height*1.0)\r\n roi_corners = np.array([[pt1, pt2, pt3, pt4]], dtype=np.int32)\r\n result = polyROI(result, roi_corners)\r\n\r\n lower_white_hls = np.array([0, 200, 0])\r\n upper_white_hls = np.array([179, 255, 255])\r\n lower_yellow_hls = np.array([15, 30, 115])\r\n upper_yellow_hls = np.array([35, 200, 255])\r\n W_hls = splitColor(result, lower_white_hls, upper_white_hls, cv2.COLOR_BGR2HLS)\r\n Y_hls = splitColor(result, lower_yellow_hls, upper_yellow_hls, cv2.COLOR_BGR2HLS)\r\n result = W_hls+Y_hls\r\n result = cannyEdge(result, 100, 200)\r\n result = houghLinesP(result, 1, np.pi/180, 10, 10)\r\n lx, ly, rx, ry = splitLines(result)\r\n result = lineFitting(frame, lx, ly, rx, ry)\r\n return result\r\n\r\npath = \"/home/nw/Desktop/OpenCV_in_Ubuntu/Data/Lane_Detection_Videos/\"\r\nroadVideo_01 = \"solidYellowLeft.mp4\"\r\nroadVideo_02 = \"solidWhiteRight.mp4\"\r\n\r\nopenPath = path+roadVideo_02\r\n\r\ncap = cv2.VideoCapture(openPath)\r\n\r\n# Get frame per second information\r\nfps = cap.get(cv2.CAP_PROP_FPS)\r\nprint(\"Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}\".format(fps))\r\n# Get width and height information\r\nwidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\nprint(width)\r\nheight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\nprint(height)\r\n# Define the codec and create VideoWriter object\r\nfourcc = int(cv2.VideoWriter_fourcc(*'DIVX'))\r\nprint(fourcc)\r\nout = cv2.VideoWriter('output_Single_Object_Tracker.avi', fourcc, fps, (width, height), True)\r\n\r\nif tracker_type == 'BOOSTING':\r\n tracker = cv2.TrackerBoosting_create()\r\nif tracker_type == 'MIL':\r\n tracker = cv2.TrackerMIL_create()\r\nif tracker_type == 'KCF':\r\n tracker = cv2.TrackerKCF_create()\r\nif tracker_type == 'TLD':\r\n tracker = cv2.TrackerTLD_create()\r\nif tracker_type == 'MEDIANFLOW':\r\n tracker = cv2.TrackerMedianFlow_create()\r\n \r\ncv2.namedWindow(\"Input\", cv2.WINDOW_GUI_EXPANDED)\r\ncv2.namedWindow(\"Output\", cv2.WINDOW_GUI_EXPANDED)\r\nret, frame = cap.read()\r\nif(ret):\r\n bbox = cv2.selectROI(frame, False)\r\n ok = tracker.init(frame, bbox)\r\n\r\nwhile cap.isOpened():\r\n # Capture frame-by-frame\r\n ret, frame = cap.read()\r\n if ret:\r\n # Our operations on the frame come here\r\n ret, bbox = tracker.update(frame)\r\n \r\n output = frameProcessing(frame)\r\n if ret:\r\n # Tracking success\r\n p1 = (int(bbox[0]), int(bbox[1]))\r\n p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))\r\n cv2.rectangle(output, p1, p2, (255,0,0), 2, 1)\r\n else :\r\n # Tracking failure\r\n cv2.putText(output, \"Tracking failure detected\", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)\r\n # Display tracker type on frame\r\n cv2.putText(output, tracker_type + \" Tracker\", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);\r\n \r\n # Display FPS on frame\r\n cv2.putText(output, \"FPS : \" + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);\r\n # Write frame-by-frame\r\n out.write(output)\r\n # Display the resulting frame\r\n cv2.imshow(\"Input\", frame)\r\n cv2.imshow(\"Output\", output)\r\n\r\n else:\r\n break\r\n\r\n # waitKey(int(1000.0/fps)) for matching fps of video\r\n if cv2.waitKey(int(1000.0/fps)) & 0xFF == ord('q'):\r\n break\r\n\r\n# When everything done, release the capture\r\ncap.release()\r\nout.release()\r\n\r\ncv2.destroyAllWindows()\r\n","sub_path":"courses/w10_opencv/source/OpenCV_in_Ubuntu/Python/2nd_07H/52_Video_Single_Object_tracking.py","file_name":"52_Video_Single_Object_tracking.py","file_ext":"py","file_size_in_byte":7280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"3018777","text":"import pathlib\nimport webbrowser\nfrom tests import BaseTestStoreMethods\nfrom store import DropboxStore\n\n\nclass TestDropboxStore(BaseTestStoreMethods.TestStoreMethods):\n def setUp(self):\n self.store_class = DropboxStore\n self.store_name = 'test-dropbox'\n self.store = self.store_class(self.config, self.store_name, self.tokenbox)\n if self.store.authorized() is False:\n webbrowser.open(self.store.get_authorization_url())\n res = input('response url :')\n self.store.fetch_token(res)\n\n def tearDown(self):\n test_dir = pathlib.PurePath('/')\n entries = self.store.get_list(test_dir)\n for entry in entries:\n self.store.remove(test_dir / entry.name)\n del self.store\n","sub_path":"tests/test_dropbox_store.py","file_name":"test_dropbox_store.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"391557769","text":"\r\n# 面积图--DTM训练结果总图\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n#中文及负号处理，字体\r\nplt.rcParams['font.sans-serif'] = 'Microsoft YaHei'\r\nplt.rcParams['axes.unicode_minus'] = False\r\nfont1 = {'family': 'Times New Roman',\r\n 'weight': 'normal',\r\n 'size': 8,\r\n }\r\nfont2 = {'family': 'Times New Roman',\r\n 'weight': 'normal',\r\n 'size': 10,\r\n }\r\n\r\ndef plotDTMTotal():\r\n # figure\r\n fig = plt.figure(figsize=(6, 6))\r\n x = np.arange(2010, 2020, dtype=int) # x坐标年份\r\n # data\r\n for i in range(1,7):\r\n sheetname = 'Sheet'+str(i)\r\n tmpdf = pd.read_excel(r'D:\\tmpWORKSTATION\\testdtm.xlsx', sheet_name=sheetname)\r\n y = tmpdf.transpose().values\r\n ax = fig.add_subplot(2,3,i)\r\n labels = list(tmpdf.keys())\r\n plt.xlim(2010, 2019) # 设置x的范围\r\n ax.stackplot(x, y, labels=labels) # 堆积面积图\r\n ax.set_xticks(range(2010, 2020, 3)) # 横坐标标签\r\n plt.xlabel('', fontdict=font2) # 横纵坐标\r\n plt.ylabel('', fontdict=font2)\r\n if i == 3: # 添加图例\r\n plt.legend(labels=labels,\r\n prop=font1, # Title for the legend\r\n bbox_to_anchor=(1.05, 1),\r\n loc='upper left',\r\n borderaxespad=0\r\n )\r\n\r\n # fig.tight_layout()#调整整体空白\r\n plt.subplots_adjust(wspace=0.3, hspace=0.2)#调整子图间距,wspace:横向空白,hspace:纵向空白\r\n plt.show()\r\n\r\n# 各省子图\r\ndef plotDTMProvincial():\r\n facetnum = 3 # 设置将要绘制的相关主题个数\r\n fig = plt.figure(figsize=(6, 4))\r\n x = np.arange(2010, 2020, dtype=int) # x坐标年份\r\n markes = ['-o', '-s', '-^', '-p', '-^', '-v', '-p', '-d', '-h', '-2', '-8', '-6'] #折线图标记\r\n for i in range(1, facetnum+1):\r\n sheetname = 'Sheet' + str(i)\r\n tmpdf = pd.read_excel(r'D:\\tmpWORKSTATION\\testdtm_prov1.xlsx', sheet_name=sheetname)\r\n labels = list(tmpdf.keys())\r\n y = tmpdf.transpose().values\r\n ax = fig.add_subplot(facetnum, 1, i)\r\n for j, yval in enumerate(y.tolist()):\r\n plt.xlim(2009.5, 2019.5) # 设置x的范围\r\n ax.plot(x, yval, markes[j])\r\n ax.set_xticks(range(2010, 2020, 1)) # 横坐标标签\r\n plt.legend(labels,\r\n loc='upper left',\r\n bbox_to_anchor=(1.01,1),\r\n borderaxespad=0,\r\n labelspacing=0.2)# vertical space between legend entries\r\n\r\n plt.subplots_adjust(wspace=0.3, hspace=0.3) # 调整子图间距,wspace:横向空白,hspace:纵向空白\r\n fig.subplots_adjust(right=0.8)\r\n fig.savefig('D:/test.png', dpi=600)\r\n # fig.show()\r\n\r\nif __name__ == '__main__':\r\n # plotDTMProvincial()\r\n plotDTMTotal()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Visualization/dtmVis.py","file_name":"dtmVis.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"44223711","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport csv\nfrom datetime import datetime\n\ndate = []\nclose_values = []\naverage_of_last_365_days = []\nstart = 0\nend = 366\nav = 0\nsecond_x_values = []\n\nwith open('BTC-USD.csv') as csv_file:\n reader = csv.reader(csv_file)\n for row in reader:\n if 'Date' in row or 'null' in row:\n pass\n else:\n date.append(row[0])\n close_values.append(float(row[4]))\n\n\nwhile end != len(close_values)-1:\n av = 0\n for i in range(start,end):\n av += close_values[i]\n average_of_last_365_days.append(av/365)\n start += 1\n end += 1\n\n\nx_values = [datetime.strptime(d,'%d/%m/%Y').strftime('%m/%d/%Y') for d in date]\ny_values = close_values\n\ndel date[:367]\nsecond_x_values=[datetime.strptime(d,'%d/%m/%Y').strftime('%m/%d/%Y') for d in date]\nsecond_y_values=average_of_last_365_days\n\n\ndata = {'x':x_values,\n 'y':y_values\n }\n\nsecond_line = {'x':second_x_values,\n 'y':second_y_values}\n\n\ndf = pd.DataFrame({'x_value':x_values,'y_value':y_values})\nplt.plot('x_value','y_value',data=df,marker='',color='black',linewidth=2)\n\ndf = pd.DataFrame({'x_value':second_x_values,'y_value':second_y_values})\nplt.plot('x_value','y_value',data=df,marker='',color='red',linewidth=2)\n\nplt.show()\n","sub_path":"Data_analysis/plot_data_from_file_using_pandas.py","file_name":"plot_data_from_file_using_pandas.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"432223003","text":"from constants import *\r\nfrom ZurichInstruments_UHFLI import ZurichInstruments_UHFLI\r\nimport sys\r\nimport os\r\nimport shutil\r\nimport qt\r\nimport progressbar\r\nimport numpy as np\r\nimport time\r\n\r\nuhf = ZurichInstruments_UHFLI('dev2232')\r\nrte = qt.instruments.create('RTE1104', 'RhodeSchwartz_RTE1104', address = 'TCPIP0::192.168.1.6::INSTR')\r\n# aps = qt.instruments.create('APSYN420',\t'AnaPico_APSYN420',\t\t\taddress = APSYN420_ADDRESS)\r\n# fsv = qt.instruments.create('FSV', 'RhodeSchwartz_FSV', address = FSV_ADDRESS)\r\n# smf = qt.instruments.create('SMF100', 'RhodeSchwartz_SMF100', address = SMF100_ADDRESS)\r\n# aps = qt.instruments.create('APSYN420',\t'AnaPico_APSYN420',\t\t\taddress = APSYN420_ADDRESS)\r\n\r\n\r\n# center_freq = 7.82067* GHz\r\n# span = 70*MHz\r\n# fsv.set_centerfrequencyc(enter_freq)\r\n# fsv.set_span(span)\r\n# fsv.set_bandwidth(10*Hz)\r\n\r\ndelay_length_start = 0\r\ndelay_length_stop = 0\r\ndelay_length_numpoints = 10\r\ndelay_length_array = np.linspace(delay_length_start, delay_length_stop, delay_length_numpoints)\r\n\r\ncontrol_start = 5.6365*GHz - 20*MHz\r\ncontrol_stop = 5.6365*GHz + 20*MHz\r\n# control_start = 5.6*GHz\r\n# control_stop = 5.68*GHz\r\ncontrol_numpoints = 11\r\ncontrol_array = np.linspace(control_start, control_stop, control_numpoints)\r\n\r\npower_start = 120\r\npower_stop = 120 #at SA -36.35 dBm, after 4dB cable and 20dB directional coupler\r\npower_numpoints = 1\r\npower_array = np.linspace(power_start, power_stop, power_numpoints)\r\n\r\ngauss_width = 32 #in sample points\r\n\r\n\r\ndelay_start = 0\r\ndelay_stop = 10*1800\r\ndelay_inc = 32\r\ndelay_array = np.arange(delay_start,delay_stop,delay_inc)\r\ndelay_numpoints = len(delay_array)#int((pulse_stop-pulse_start)/pulse_inc)\r\n# pulse_array = np.linspace(pulse_start, pulse_stop, pulse_numpoints+1)\r\n\r\n\r\ndef generate_meta_file(no_of_delay, delay):\r\n\tmetafile = open('%s.meta.txt' % data.get_filepath()[:-4], 'w')\r\n\tmetafile.write('#inner loop\\n%s\\n%s\\n%s\\n%s\\n'%\r\n\t\t\t(record_length, start_time/us, stop_time/us, 'Time(us)'))\r\n\tmetafile.write('#outer loop\\n%s\\n%s\\n%s\\n%s\\n'%\r\n\t\t\t(no_of_delay, delay, delay_array[0], 'Delay Length'))\r\n\tmetafile.write('#outermost loop (unused)\\n1\\n0\\n1\\nNothing\\n')\r\n\r\n\tmetafile.write('#for each of the values\\n')\r\n\tvalues = data.get_values()\r\n\ti=0\r\n\twhile i{' '.join(map(str, encrypted_flag))}\"]\n }, sys.stdout)\n\n\nif __name__ == \"__main__\":\n generate()\n","sub_path":"tasks/powerful/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"382098801","text":"\"\"\"All classes/methods/tools to help to adapt to v2 `preview`\"\"\"\n\nimport uuid\n\n\ndef adapt_preview_data(uid,\n connection_id,\n table,\n ds_id,\n preview_data):\n \"\"\"\n Adapts vnext data table preview data to v2.\n\n Args:\n uid (str): UID.\n connection_id (str): Connection id.\n table (dict): Table object.\n ds_id (int): Datasource ID.\n preview_data (dict): Preview data payload.\n\n Returns:\n dict: Adapted data.\n \"\"\"\n\n database = table['database']\n table_id = table['id']\n table_name = table['name']\n\n # 'columns' are table columns defined by user when\n # a table is saved.\n #\n # When this function is called at preview time (before\n # table is saved, 'columsn' is None; otherwise it\n # contains definitions provided by user.\n #\n # The adaptation code deals with both cases: when 'columns'\n # are available, type and formatting options are got from it,\n # otherwise it's got from fields of the preview_data (which\n # determines the default type of columns).\n\n columns = table.get('columns')\n\n rows = preview_data['data']\n rows.insert(0, [fld['id'] for fld in preview_data['fields']])\n data = {\n 'data': preview_data['data'],\n 'table': _adapt_table_structure(table_id, table_name, columns, preview_data['fields']),\n 'tableId': table_name,\n 'timezone': None,\n 'colSum': len(preview_data['data']),\n 'connectionId': connection_id,\n 'createTime': None,\n 'creatorId': None,\n 'dataBaseName': database,\n 'dsId': ds_id,\n 'lastModifiedDate': None,\n 'lastUpdateTime': None,\n 'name': table_name,\n 'operateType': '',\n 'remotePath': '',\n 'remoteStatus': '1',\n 'rowCount': None,\n 'sourceId': '',\n 'uid': uid,\n 'updateFrequency': None,\n 'updateHour': None,\n 'updateStatus': None,\n 'updateTime': None\n }\n return data\n\n\ndef _adapt_table_structure(table_id, table_name, table_columns, data_columns):\n\n if not table_columns:\n # populate dummy table columns for easier processing\n table_columns = [{}] * len(data_columns)\n\n assert len(table_columns) == len(data_columns)\n\n def _get_custom_name(table_column, data_column): # pylint: disable=unused-argument\n return table_column.get('customName')\n\n def _get_column_type(table_column, data_column):\n if table_column:\n return table_column.get('dataType').upper()\n return data_column['type'].upper()\n\n def _get_data_format(table_column, data_column):\n if table_column:\n return table_column.get('formatOptions', {}).get('dateFormat', '')\n return 'yyyy-MM-dd' if data_column['type'] == 'date' else ''\n\n def _get_date_front(table_column, data_column):\n if table_column:\n return table_column.get('formatOptions', {}).get('dateStartsWith', None)\n return 'year' if data_column['type'] == 'date' else None\n\n def _get_data_format_type(table_column, data_column): # pylint: disable=unused-argument\n if table_column:\n currency_symbol = table_column.get('formatOptions', {}).get('currencySymbol')\n if currency_symbol:\n return f'{currency_symbol}###'\n return ''\n\n return {\n 'code': table_name,\n 'colSum': str(len(data_columns)),\n 'columnCount': len(data_columns),\n 'columns': [\n {\n 'code': data_column['name'],\n 'columnType': _get_column_type(table_column, data_column),\n 'customDateColumn': False,\n 'customName': _get_custom_name(table_column, data_column),\n 'dataType': None,\n\n 'dataFormat': _get_data_format(table_column, data_column),\n 'dataFormatType': _get_data_format_type(table_column, data_column),\n 'dateFront': _get_date_front(table_column, data_column),\n\n 'display': table_column.get('include', True),\n 'id': str(uuid.uuid1()),\n 'index': index,\n 'isCustom': None,\n 'name': data_column['name'],\n 'ordinalPosition': 0,\n 'primaryKey': False,\n 'remarks': None,\n 'separator': None,\n\n # TODO: check if this is needed\n 'type': 'metrics' if data_column['type'] == 'number' else 'dimension'\n }\n for index, (table_column, data_column) in enumerate(zip(table_columns, data_columns))],\n 'headIndex': 0,\n 'headMode': 'assign',\n 'headType': 'row',\n 'id': table_id,\n 'ignoreCol': [],\n 'ignoreColEnd': None,\n 'ignoreColStart': None,\n 'ignoreRow': [],\n 'ignoreRowEnd': 0,\n 'ignoreRowStart': 1,\n 'rowSum': None,\n 'tableName': table_name,\n 'tableType': None\n }\n","sub_path":"backend-master/src/api-gateway/main/services/adaptors/preview.py","file_name":"preview.py","file_ext":"py","file_size_in_byte":5038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"357013626","text":"from flask import Flask\nfrom web.public import course\nfrom api.item import i_user\nfrom ext import db\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True\n\ndb.init_app(app)\n\n@app.before_first_request\ndef create_tables():\n db.create_all()\n\n@app.route('/')\ndef hello():\n return \"Hello\"\n\napp.register_blueprint(course, url_prefix = \"/api/v1/course\")\n\napp.register_blueprint(i_user, url_prefix = \"/api/v1/i_user\")\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"321764438","text":"# 태극기 그리는 파이썬 그래픽 예제\r\n# 고급예제6.py\r\n# ver 1.0 by Won, Jongho\r\n# ver 2.0 by Oh, Sangmoon\r\n\r\nimport turtle as t\r\nimport math as m\r\n\r\nwidth = 1200 # 태극기 밑변 (높이의 3/2)\r\nheight = 800 # 태극기 높이 (밑변의 2/3)\r\ndiameter = height / 2 # 태극원 지름 = 높이 / 2\r\nradius = height / 4 # 태극원 반지름 = 높이 / 4\r\n\r\n# 대각선 길이 구하기 (= 1432)\r\ndiagonal_length = m.sqrt(width*width + height*height)\r\n# 대각선 각도 구하기 (= 33.69도)\r\ndiagonal_degree = m.atan2(height,width) * 180/m.pi\r\n\r\nt.title(\"Korean Flag\") # 그래픽 창 제목 지정\r\nt.setup(width, height) # 그래픽 창 크기 지정\r\nt.bgcolor(\"white\") # 배경색 지정\r\n\r\n# 괘의 한 선 그리기 \r\ndef draw_k():\r\n n = [radius/2,radius/6,radius,radius/6,radius/2]\r\n t.pendown()\r\n t.color(\"black\", \"black\")\r\n t.begin_fill()\r\n for size in n:\r\n t.left(90)\r\n t.forward(size)\r\n t.end_fill()\r\n t.penup()\r\n\r\n# 괘 한 선의 중간을 흰색으로 그리기 (분리된 괘)\r\ndef draw_k_white():\r\n n = [radius/24+1,radius/6+2,radius/12+2,radius/6+2,radius/24+1] \r\n t.color(\"white\", \"white\")\r\n t.begin_fill()\r\n t.right(90)\r\n t.forward(1)\r\n for size in n:\r\n t.left(90)\r\n t.forward(size)\r\n t.end_fill() \r\n\r\n# 건괘 그리기 \r\ndef draw_gun():\r\n for r in range(20, 27, 3):\r\n t.home() \r\n t.right(diagonal_degree+180)\r\n t.forward(radius*r/12)\r\n draw_k()\r\n\r\n# 리괘 그리기\r\ndef draw_ri():\r\n for r in range(20, 27, 3):\r\n t.home() \r\n t.right(-diagonal_degree+180)\r\n t.forward(radius*r/12)\r\n draw_k()\r\n if( r == 23 ):\r\n draw_k_white()\r\n\r\n# 감괘 그리기\r\ndef draw_kam():\r\n for r in range(20, 27, 3):\r\n t.home() \r\n t.left(diagonal_degree)\r\n t.forward(radius*r/12)\r\n draw_k()\r\n if( r != 23 ):\r\n draw_k_white()\r\n\r\n# 곤괘 그리기\r\ndef draw_kon():\r\n for r in range(20, 27, 3):\r\n t.home() \r\n t.left(-diagonal_degree)\r\n t.forward(radius*r/12)\r\n draw_k()\r\n draw_k_white()\r\n\r\n# 4괘 모두 그리기 \r\ndef draw_4k():\r\n t.penup() \r\n draw_gun() # 건\r\n draw_ri() # 리\r\n draw_kam() # 감\r\n draw_kon() # 곤 \r\n\r\n# 태극 원형 모양 그리기\r\ndef draw_taegeuk():\r\n t.home()\r\n t.right(diagonal_degree)\r\n t.forward(radius)\r\n t.left(90)\r\n t.color(\"#C60C30\", \"#C60C30\") # 붉은색\r\n t.begin_fill()\r\n t.circle(radius, 180)\r\n t.end_fill()\r\n t.color(\"#003478\", \"#003478\") # 청색\r\n t.begin_fill()\r\n t.circle(radius, 180)\r\n t.end_fill()\r\n t.home()\r\n t.left(90 - diagonal_degree)\r\n t.begin_fill()\r\n t.color(\"#C60C30\", \"#C60C30\") # 붉은색\r\n t.circle(radius / 2)\r\n t.end_fill()\r\n t.home()\r\n t.right(90 + diagonal_degree)\r\n t.begin_fill()\r\n t.color(\"#003478\", \"#003478\") # 청색\r\n t.circle(radius / 2)\r\n t.end_fill()\r\n\r\n#---------------------------------------------\r\n# 메인 처리 \r\n#---------------------------------------------\r\n\r\n# 태극 모양 그리기 \r\ndraw_taegeuk()\r\n\r\n# 4괘 그리기\r\ndraw_4k()\r\n\r\n# 거북이 커서 숨김\r\nt.hideturtle() \r\n","sub_path":"고급예제6.py","file_name":"고급예제6.py","file_ext":"py","file_size_in_byte":3276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"296655825","text":"#!/usr/bin/env python3\n\nimport claims\nimport tabulate\n\nreports = claims.Report()\n\nstat_all = len(reports)\nreports_fails = [i for i in reports if i['status'] in claims.Case.FAIL_STATUSES]\nstat_failed = len(reports_fails)\nreports_claimed = [i for i in reports_fails if i['testActions'][0].get('reason')]\nstat_claimed = len(reports_claimed)\n\nprint(\"\\nOverall stats\")\nprint(tabulate.tabulate(\n [[stat_all, stat_failed, stat_claimed]],\n headers=['all reports', 'failures', 'claimed failures']))\n\nrules = claims.Ruleset()\nrules_reasons = [r['reason'] for r in rules]\nreports_per_reason = {'UNKNOWN': stat_failed-stat_claimed}\nreports_per_reason.update({r:0 for r in rules_reasons})\nfor report in reports_claimed:\n reason = report['testActions'][0]['reason']\n if reason not in reports_per_reason:\n reports_per_reason[reason] = 0\n reports_per_reason[reason] += 1\n\nprint(\"\\nHow various reasons for claims are used\")\nreports_per_reason = sorted(reports_per_reason.items(), key=lambda x: x[1], reverse=True)\nreports_per_reason = [(r, c, r in rules_reasons) for r, c in reports_per_reason]\nprint(tabulate.tabulate(\n reports_per_reason,\n headers=['claim reason', 'number of times', 'is it in current knowleadgebase?']))\n\nreports_per_class = {}\nfor report in reports:\n class_name = report['className']\n if class_name not in reports_per_class:\n reports_per_class[class_name] = {'all': 0, 'failed': 0}\n reports_per_class[class_name]['all'] += 1\n if report in reports_fails:\n reports_per_class[class_name]['failed'] += 1\n\nprint(\"\\nHow many failures are there per class\")\nprint(tabulate.tabulate(\n sorted([(c, r['all'], r['failed'], float(r['failed'])/r['all']) for c,r in reports_per_class.items()],\n key=lambda x: x[3], reverse=True),\n headers=['class name', 'number of reports', 'number of failures', 'failures ratio'],\n floatfmt=\".3f\"))\n\nreports_per_method = {}\nfor report in reports:\n method = report['className'].split('.')[2]\n if method not in reports_per_method:\n reports_per_method[method] = {'all': 0, 'failed': 0}\n reports_per_method[method]['all'] += 1\n if report in reports_fails:\n reports_per_method[method]['failed'] += 1\n\nprint(\"\\nHow many failures are there per method (CLI vs. API vs. UI)\")\nprint(tabulate.tabulate(\n sorted([(c, r['all'], r['failed'], float(r['failed'])/r['all']) for c,r in reports_per_method.items()],\n key=lambda x: x[3], reverse=True),\n headers=['method', 'number of reports', 'number of failures', 'failures ratio'],\n floatfmt=\".3f\"))\n","sub_path":"claimstats.py","file_name":"claimstats.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"455706815","text":"import time\nfrom os import environ\nimport resources\nfrom sys import stdout, exit\nfrom typing import Any\nimport boto3\nimport logging\nimport pytest\nimport subprocess\n\n# mypy later maybe\nS3ServiceResource = Any\nSqsServiceResource = Any\n\nBUCKET_PREFIX = environ[\"BUCKET_PREFIX\"]\nassert BUCKET_PREFIX == \"local-grapl\"\n\n\ndef _upload_analyzers(s3_client: S3ServiceResource) -> None:\n \"\"\"\n Basically reimplementing upload_local_analyzers.sh\n Janky, since Jesse will have an analyzer-uploader service pretty soon.\n \"\"\"\n to_upload = [\n (\n \"/home/grapl/etc/local_grapl/suspicious_svchost/main.py\",\n \"analyzers/suspicious_svchost/main.py\",\n ),\n (\n \"/home/grapl/etc/local_grapl/unique_cmd_parent/main.py\",\n \"analyzers/unique_cmd_parent/main.py\",\n ),\n ]\n bucket = f\"{BUCKET_PREFIX}-analyzers-bucket\"\n for (local_path, s3_key) in to_upload:\n logging.info(f\"S3 uploading {local_path}\")\n with open(local_path, \"r\") as f:\n s3_client.put_object(Body=f.read(), Bucket=bucket, Key=s3_key)\n\n\ndef _upload_test_data(s3_client: S3ServiceResource) -> None:\n logging.info(f\"Running upload-sysmon-logs\")\n\n # i hate this lol\n # but it's probably better than mucking with path and importing that module...\n subprocess.run(\n [\n \"python3\",\n \"/home/grapl/etc/local_grapl/bin/upload-sysmon-logs.py\",\n \"--bucket_prefix\",\n BUCKET_PREFIX,\n \"--logfile\",\n \"/home/grapl/etc/sample_data/eventlog.xml\",\n \"--use-links\",\n \"True\",\n ]\n )\n\n\ndef _create_s3_client() -> S3ServiceResource:\n return boto3.client(\n \"s3\",\n endpoint_url=\"http://s3:9000\",\n aws_access_key_id=\"minioadmin\",\n aws_secret_access_key=\"minioadmin\",\n )\n\n\ndef _create_sqs_client() -> SqsServiceResource:\n # mostly cribbed from upload-sysmon-logs\n return boto3.client(\n \"sqs\",\n endpoint_url=\"http://sqs:9324\",\n region_name=\"us-east-1\",\n aws_access_key_id=\"minioadmin\",\n aws_secret_access_key=\"minioadmin\",\n )\n\n\ndef main() -> int:\n logging.basicConfig(stream=stdout, level=logging.INFO)\n\n s3_client = _create_s3_client()\n sqs_client = _create_sqs_client()\n\n resources.wait_for(\n [\n # for uploading analyzers\n resources.WaitForS3Bucket(s3_client, f\"{BUCKET_PREFIX}-analyzers-bucket\"),\n # for upload-sysmon-logs.py\n resources.WaitForS3Bucket(s3_client, f\"{BUCKET_PREFIX}-sysmon-log-bucket\"),\n resources.WaitForSqsQueue(sqs_client, \"grapl-sysmon-graph-generator-queue\"),\n ]\n )\n\n _upload_analyzers(s3_client)\n _upload_test_data(s3_client)\n\n logging.info(\"Waiting 60s for the pipeline to do its thing...\")\n time.sleep(60)\n\n return pytest.main(\n [\n \"-s\", # disable stdout capture\n ]\n )\n\n\nif __name__ == \"__main__\":\n exit(main())\n","sub_path":"src/python/grapl_e2e_tests/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"367500427","text":"from django.shortcuts import render, get_object_or_404, get_list_or_404\nfrom django.views import View\nfrom django.utils import timezone\n\nfrom .models import Organizer, Conference, Event\n\n\nclass IndexView(View):\n template_name = 'CFP/index.html'\n def get(self, request):\n upcomming_deadline_list = Event.objects.order_by('submission_due').filter(submission_due__gte = timezone.now())[:5]\n upcomming_conference_list = Event.objects.order_by('start_date').filter(start_date__gte = timezone.now())[:4]\n context = {\n 'upcomming_deadline_list': upcomming_deadline_list,\n 'upcomming_conference_list': upcomming_conference_list\n }\n return render(request, self.template_name, context)\n\n\nclass OrganizerView(View):\n template_name = 'CFP/organizer.html'\n def get(self, request, *args, **kwargs):\n organizer = get_object_or_404(Organizer, abbreviation__iexact = self.kwargs['organizer'])\n conference_list = get_list_or_404(Conference, organizer = organizer)\n for conference in conference_list:\n event_list = get_list_or_404(Event, conference = conference)\n conference.event_list = event_list\n context = {\n 'organizer': organizer,\n 'conference_list': conference_list,\n }\n return render(request, self.template_name, context)\n\n\nclass ConferenceView(View):\n template_name = 'CFP/conference.html'\n def get(self, request, *args, **kwargs):\n organizer = get_object_or_404(Organizer, abbreviation__iexact = self.kwargs['organizer'])\n conference = get_object_or_404(Conference, abbreviation__iexact = self.kwargs['conference'])\n event_list = get_list_or_404(Event, conference = conference)\n context = {\n 'organizer': organizer,\n 'conference': conference,\n 'event_list': event_list\n }\n return render(request, self.template_name, context)\n\n\nclass EventView(View):\n template_name = 'CFP/event.html'\n def get(self, request, *args, **kwargs):\n organizer = get_object_or_404(Organizer, abbreviation__iexact = self.kwargs['organizer'])\n conference = get_object_or_404(Conference, abbreviation__iexact = self.kwargs['conference'])\n event = get_object_or_404(Event, conference = conference, start_date__year = self.kwargs['year'])\n context = {\n 'organizer': organizer,\n 'conference': conference,\n 'event': event\n }\n return render(request, self.template_name, context)\n","sub_path":"CFP/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"483641308","text":"__author__ = 'gaa8664'\nimport openpyxl\n\n\ndef get_new_code(sheet, old_code):\n new_code = None\n for row in range(sheet.max_row):\n if row <= 1:\n continue\n code = sheet.cell(row=row, column=2)\n if old_code == code.value:\n try:\n new_code = sheet.cell(row=row, column=3)\n new_code = new_code.value\n\n break\n except Exception as ex:\n new_code = ''\n return new_code\n\n\ndef update_lic():\n code_file = openpyxl.load_workbook(\"D:\\Project\\excel\\Employee Wise Templates Allocation.xlsx\")\n lic_file = openpyxl.load_workbook(\"D:\\Project\\excel\\LIC Detail.xlsx\",read_only=False)\n code_sheet = code_file.get_sheet_by_name(\"Template Allocation\")\n lic_sheet = lic_file.get_sheet_by_name(\"LIC Detail\")\n\n for row in range(lic_sheet.max_row):\n if row <= 1:\n continue\n old_code = lic_sheet.cell(row=row, column=1)\n new_code = lic_sheet.cell(row=row, column=2)\n val = get_new_code(code_sheet, old_code.value)\n new_code.value = val\n lic_file.save(\"D:\\Project\\excel\\LIC Detail1.xlsx\")\n\nif __name__ == '__main__':\n update_lic()\n\n\n\n\n\n\n\n","sub_path":"LearningPython/excel/LICNewCodeMapping.py","file_name":"LICNewCodeMapping.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"571796752","text":"import math\n\ndef median(n):\n s = len(n)\n m = math.floor(s/2)\n if (s % 2 == 0):\n return ((n[m]+n[m-1])/2)\n else:\n return n[m]\n\ndef q1(n):\n s = len(n)\n m = math.floor(s/2)\n return median(n[0:m])\n\ndef q2(n):\n return median(n)\n\ndef q3(n):\n s = len(n)\n m = math.floor(s/2)\n if (s % 2 == 0):\n return median(n[m:s])\n else:\n return median(n[m+1:s])\n\n# main \nn = input()\nx = [int(a) for a in input().split()]\nf = [int(a) for a in input().split()]\n\nar = []\n\nfor i, e in enumerate(x):\n ar.extend([x[i]] * f[i])\n\nar = sorted(ar)\n\nprint(\"{:.1f}\".format(q3(ar) - q1(ar)))","sub_path":"10_days_of_statistics/01_interquartile_range.py","file_name":"01_interquartile_range.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"2933866","text":"import numpy as np\nfrom mode_transmission_network.autoregression import extract_autoregression_feature\n\ndef get_all_fluctuation_modes(time_series_dict, window_length):\n timestamp_list = time_series_dict['timestamp_list']\n idx_to_timestamp_dict = time_series_dict['idx_to_timestamp_dict']\n data_list = time_series_dict['data_list']\n time_series_length = len(timestamp_list)\n fluctuation_mode_list = []\n # 0,1,2,...,\n for i in range(time_series_length - window_length):\n this_data = data_list[i: i + window_length]\n this_timestamp = timestamp_list[i: i + window_length]\n this_data_np_array = np.asarray(this_data)\n print(extract_autoregression_feature(this_data_np_array, this_timestamp))\n\n\n\n","sub_path":"mode_transmission_network/extract_fluctuation_modes.py","file_name":"extract_fluctuation_modes.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"519165022","text":"from dataclasses import dataclass, field\nfrom typing import List, Optional\nfrom .annotation import (\n AdminData,\n Annotation,\n DocumentationBlock,\n VariationPoint,\n)\nfrom .boolean import Boolean\nfrom .can_nm_node import CanNmNode\nfrom .category_string import CategoryString\nfrom .communication_cluster_subtypes_enum import CommunicationClusterSubtypesEnum\nfrom .flexray_nm_node import FlexrayNmNode\nfrom .identifier import Identifier\nfrom .integer import Integer\nfrom .j_1939_nm_node import J1939NmNode\nfrom .multi_language_overview_paragraph import MultiLanguageOverviewParagraph\nfrom .multilanguage_long_name import MultilanguageLongName\nfrom .positive_integer import PositiveInteger\nfrom .ref import Ref\nfrom .short_name_fragment import ShortNameFragment\nfrom .time_value import TimeValue\nfrom .udp_nm_node import UdpNmNode\n\n__NAMESPACE__ = \"http://autosar.org/schema/r4.0\"\n\n\n@dataclass\nclass CanNmCluster:\n \"\"\"\n Can specific NmCluster attributes.\n\n :ivar short_name: This specifies an identifying shortName for the\n object. It needs to be unique within its context and is intended\n for humans but even more for technical reference.\n :ivar short_name_fragments: This specifies how the\n Referrable.shortName is composed of several shortNameFragments.\n :ivar long_name: This specifies the long name of the object. Long\n name is targeted to human readers and acts like a headline.\n :ivar desc: This represents a general but brief (one paragraph)\n description what the object in question is about. It is only one\n paragraph! Desc is intended to be collected into overview\n tables. This property helps a human reader to identify the\n object in question. More elaborate documentation, (in particular\n how the object is built or used) should go to \"introduction\".\n :ivar category: The category is a keyword that specializes the\n semantics of the Identifiable. It affects the expected existence\n of attributes and the applicability of constraints.\n :ivar admin_data: This represents the administrative data for the\n identifiable object.\n :ivar introduction: This represents more information about how the\n object in question is built or is used. Therefore it is a\n DocumentationBlock.\n :ivar annotations: Possibility to provide additional notes while\n defining a model element (e.g. the ECU Configuration Parameter\n Values). These are not intended as documentation but are mere\n design notes.\n :ivar communication_cluster_ref: Association to a\n CommunicationCluster in the topology description.\n :ivar nm_channel_id: This attribute has the status \"removed\" and\n shall not be used any longer. Old description: Channel\n identification number of the corresponding channel. Must be\n unique over all NmClusters.\n :ivar nm_channel_sleep_master: This parameter shall be set to\n indicate if the sleep of this network can be absolutely decided\n by the local node only and that no other nodes can oppose that\n decision.\n :ivar nm_nodes: The upper multiplicity of this role has been\n increased to * due to resolving an atpVariation stereotype. The\n previous value was -1.\n :ivar nm_node_detection_enabled: Enables the Request Repeat Message\n Request support. Only valid if nmNodeIdEnabled is set to true.\n :ivar nm_node_id_enabled: Enables the source node identifier.\n :ivar nm_pnc_participation: Defines whether this NmCluster\n contributes to the partial network mechanism.\n :ivar nm_repeat_msg_ind_enabled: Switch for enabling the Repeat\n Message Bit Indication.\n :ivar nm_synchronizing_network: If this parameter is true, then this\n network is a synchronizing network for the NM coordination\n cluster which it belongs to. The network is expected to call\n Nm_SynchronizationPoint() at regular intervals.\n :ivar variation_point: This element was generated/modified due to an\n atpVariation stereotype.\n :ivar nm_busload_reduction_active: It determines if bus load\n reduction for the respective CanNm channel is active or not.\n :ivar nm_car_wake_up_bit_position: Specifies the bit position of the\n CarWakeUp within the NmPdu.\n :ivar nm_car_wake_up_filter_enabled: Please note that this parameter\n is deprecated and will be removed in future. Old description: If\n this attribute is set to true the CareWakeUp filtering is\n supported. In this case only the CarWakeUp bit within the NmPdu\n with source node identifier nmCarWakeUpFilterNodeId is\n considered as CarWakeUp request.\n :ivar nm_car_wake_up_filter_node_id: Source node identifier for\n CarWakeUp filtering.\n :ivar nm_car_wake_up_rx_enabled: Please note that this parameter is\n deprecated and will be removed in future. Old description: If\n set to true this attribute enables the support of CarWakeUp bit\n evaluation in received NmPdus.\n :ivar nm_cbv_position: Defines the position of the control bit\n vector within the NmPdu (Byte position). If this attribute is\n not configured, the Control Bit Vector is not used.\n :ivar nm_channel_active: Please note that this attribute is\n deprecated and will be removed in future. This switch determines\n if the respective CanNm channel is active or not. Indicates\n whether a particular CanNm channel shall be initialized (TRUE)\n or not (FALSE). If this parameter is set to FALSE the respective\n NM instance shall not be used during runtime.\n :ivar nm_immediate_nm_cycle_time: Defines the immediate NmPdu cycle\n time in seconds which is used for nmImmediateNmTransmissions\n NmPdu transmissions. This parameter is only valid if\n CanNmImmediateNmTransmissions is greater one.\n :ivar nm_immediate_nm_transmissions: Defines the number of immediate\n NmPdus which shall be transmitted. If the value is zero no\n immediate NmPdus are transmitted. The cycle time of immeditate\n NmPdus is defined by nmImmediateNmCycleTime.\n :ivar nm_message_timeout_time: Timeout of an NmPdu in seconds. It\n determines how long the NM shall wait with notification of\n transmission failure while communication errors occur on the\n bus.\n :ivar nm_msg_cycle_time: Period of a NmPdu in seconds. It determines\n the periodic rate in the periodic transmission mode with bus\n load reduction and is the basis for transmit scheduling in the\n periodic transmission mode without bus load reduction.\n :ivar nm_network_timeout: Network Timeout for NmPdus in seconds It\n denotes the time how long the CanNm shall stay in the Network\n Mode before transition into Prepare Bus-Sleep Mode shall take\n place.\n :ivar nm_nid_position: Defines the byte position of the source node\n identifier within the NmPdu. If this attribute is not\n configured, the Node Identification is not used.\n :ivar nm_remote_sleep_indication_time: Timeout for Remote Sleep\n Indication in seconds. It defines the time how long it shall\n take to recognize that all other nodes are ready to sleep.\n :ivar nm_repeat_message_time: Timeout for Repeat Message State in\n seconds. Defines the time how long the NM shall stay in the\n Repeat Message State.\n :ivar nm_user_data_length: Defines the length of the user data\n contained in the NmPdu. Please note that this attribute is\n deprecated and will be removed in future.\n :ivar nm_wait_bus_sleep_time: Timeout for bus calm down phase in\n seconds. It denotes the time how long the CanNm shall stay in\n the Prepare Bus-Sleep Mode before transition into Bus-Sleep Mode\n shall take place.\n :ivar s: Checksum calculated by the user's tool environment for an\n ArObject. May be used in an own tool environment to determine if\n an ArObject has changed. The checksum has no semantic meaning\n for an AUTOSAR model and there is no requirement for AUTOSAR\n tools to manage the checksum.\n :ivar t: Timestamp calculated by the user's tool environment for an\n ArObject. May be used in an own tool environment to determine\n the last change of an ArObject. The timestamp has no semantic\n meaning for an AUTOSAR model and there is no requirement for\n AUTOSAR tools to manage the timestamp.\n :ivar uuid: The purpose of this attribute is to provide a globally\n unique identifier for an instance of a meta-class. The values of\n this attribute should be globally unique strings prefixed by the\n type of identifier. For example, to include a DCE UUID as\n defined by The Open Group, the UUID would be preceded by \"DCE:\".\n The values of this attribute may be used to support merging of\n different AUTOSAR models. The form of the UUID (Universally\n Unique Identifier) is taken from a standard defined by the Open\n Group (was Open Software Foundation). This standard is widely\n used, including by Microsoft for COM (GUIDs) and by many\n companies for DCE, which is based on CORBA. The method for\n generating these 128-bit IDs is published in the standard and\n the effectiveness and uniqueness of the IDs is not in practice\n disputed. If the id namespace is omitted, DCE is assumed. An\n example is \"DCE:2fac1234-31f8-11b4-a222-08002b34c003\". The uuid\n attribute has no semantic meaning for an AUTOSAR model and there\n is no requirement for AUTOSAR tools to manage the timestamp.\n \"\"\"\n class Meta:\n name = \"CAN-NM-CLUSTER\"\n\n short_name: Optional[Identifier] = field(\n default=None,\n metadata={\n \"name\": \"SHORT-NAME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n \"required\": True,\n }\n )\n short_name_fragments: Optional[\"CanNmCluster.ShortNameFragments\"] = field(\n default=None,\n metadata={\n \"name\": \"SHORT-NAME-FRAGMENTS\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n long_name: Optional[MultilanguageLongName] = field(\n default=None,\n metadata={\n \"name\": \"LONG-NAME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n desc: Optional[MultiLanguageOverviewParagraph] = field(\n default=None,\n metadata={\n \"name\": \"DESC\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n category: Optional[CategoryString] = field(\n default=None,\n metadata={\n \"name\": \"CATEGORY\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n admin_data: Optional[AdminData] = field(\n default=None,\n metadata={\n \"name\": \"ADMIN-DATA\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n introduction: Optional[DocumentationBlock] = field(\n default=None,\n metadata={\n \"name\": \"INTRODUCTION\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n annotations: Optional[\"CanNmCluster.Annotations\"] = field(\n default=None,\n metadata={\n \"name\": \"ANNOTATIONS\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n communication_cluster_ref: Optional[\"CanNmCluster.CommunicationClusterRef\"] = field(\n default=None,\n metadata={\n \"name\": \"COMMUNICATION-CLUSTER-REF\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_channel_id: Optional[PositiveInteger] = field(\n default=None,\n metadata={\n \"name\": \"NM-CHANNEL-ID\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_channel_sleep_master: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-CHANNEL-SLEEP-MASTER\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_nodes: Optional[\"CanNmCluster.NmNodes\"] = field(\n default=None,\n metadata={\n \"name\": \"NM-NODES\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_node_detection_enabled: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-NODE-DETECTION-ENABLED\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_node_id_enabled: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-NODE-ID-ENABLED\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_pnc_participation: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-PNC-PARTICIPATION\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_repeat_msg_ind_enabled: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-REPEAT-MSG-IND-ENABLED\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_synchronizing_network: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-SYNCHRONIZING-NETWORK\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n variation_point: Optional[VariationPoint] = field(\n default=None,\n metadata={\n \"name\": \"VARIATION-POINT\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_busload_reduction_active: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-BUSLOAD-REDUCTION-ACTIVE\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_car_wake_up_bit_position: Optional[PositiveInteger] = field(\n default=None,\n metadata={\n \"name\": \"NM-CAR-WAKE-UP-BIT-POSITION\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_car_wake_up_filter_enabled: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-CAR-WAKE-UP-FILTER-ENABLED\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_car_wake_up_filter_node_id: Optional[PositiveInteger] = field(\n default=None,\n metadata={\n \"name\": \"NM-CAR-WAKE-UP-FILTER-NODE-ID\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_car_wake_up_rx_enabled: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-CAR-WAKE-UP-RX-ENABLED\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_cbv_position: Optional[Integer] = field(\n default=None,\n metadata={\n \"name\": \"NM-CBV-POSITION\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_channel_active: Optional[Boolean] = field(\n default=None,\n metadata={\n \"name\": \"NM-CHANNEL-ACTIVE\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_immediate_nm_cycle_time: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-IMMEDIATE-NM-CYCLE-TIME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_immediate_nm_transmissions: Optional[PositiveInteger] = field(\n default=None,\n metadata={\n \"name\": \"NM-IMMEDIATE-NM-TRANSMISSIONS\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_message_timeout_time: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-MESSAGE-TIMEOUT-TIME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_msg_cycle_time: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-MSG-CYCLE-TIME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_network_timeout: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-NETWORK-TIMEOUT\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_nid_position: Optional[Integer] = field(\n default=None,\n metadata={\n \"name\": \"NM-NID-POSITION\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_remote_sleep_indication_time: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-REMOTE-SLEEP-INDICATION-TIME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_repeat_message_time: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-REPEAT-MESSAGE-TIME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_user_data_length: Optional[Integer] = field(\n default=None,\n metadata={\n \"name\": \"NM-USER-DATA-LENGTH\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n nm_wait_bus_sleep_time: Optional[TimeValue] = field(\n default=None,\n metadata={\n \"name\": \"NM-WAIT-BUS-SLEEP-TIME\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n s: Optional[str] = field(\n default=None,\n metadata={\n \"name\": \"S\",\n \"type\": \"Attribute\",\n }\n )\n t: Optional[str] = field(\n default=None,\n metadata={\n \"name\": \"T\",\n \"type\": \"Attribute\",\n \"pattern\": r\"([0-9]{4}-[0-9]{2}-[0-9]{2})(T[0-9]{2}:[0-9]{2}:[0-9]{2}(Z|([+\\-][0-9]{2}:[0-9]{2})))?\",\n }\n )\n uuid: Optional[str] = field(\n default=None,\n metadata={\n \"name\": \"UUID\",\n \"type\": \"Attribute\",\n }\n )\n\n @dataclass\n class ShortNameFragments:\n short_name_fragment: List[ShortNameFragment] = field(\n default_factory=list,\n metadata={\n \"name\": \"SHORT-NAME-FRAGMENT\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n\n @dataclass\n class Annotations:\n annotation: List[Annotation] = field(\n default_factory=list,\n metadata={\n \"name\": \"ANNOTATION\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n\n @dataclass\n class CommunicationClusterRef(Ref):\n dest: Optional[CommunicationClusterSubtypesEnum] = field(\n default=None,\n metadata={\n \"name\": \"DEST\",\n \"type\": \"Attribute\",\n \"required\": True,\n }\n )\n\n @dataclass\n class NmNodes:\n can_nm_node: List[CanNmNode] = field(\n default_factory=list,\n metadata={\n \"name\": \"CAN-NM-NODE\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n flexray_nm_node: List[FlexrayNmNode] = field(\n default_factory=list,\n metadata={\n \"name\": \"FLEXRAY-NM-NODE\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n j_1939_nm_node: List[J1939NmNode] = field(\n default_factory=list,\n metadata={\n \"name\": \"J-1939-NM-NODE\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n udp_nm_node: List[UdpNmNode] = field(\n default_factory=list,\n metadata={\n \"name\": \"UDP-NM-NODE\",\n \"type\": \"Element\",\n \"namespace\": \"http://autosar.org/schema/r4.0\",\n }\n )\n","sub_path":"autosar/models/can_nm_cluster.py","file_name":"can_nm_cluster.py","file_ext":"py","file_size_in_byte":21112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"460727962","text":"#\n# Copyright (C) 2016 Yi-Wei Ci\n#\n# Distributed under the terms of the MIT license.\n#\n\nimport os\nfrom base64 import b64encode\nfrom vdtools.dev.driver import Driver\nfrom vdtools.lib.util import readlink\nfrom vdtools.lib.modes import MODE_OVP, MODE_SWITCH\n\nHOME = '~/vdev/dev/fileloader'\n\nclass FileLoader(Driver):\n def __init__(self, name=None):\n Driver.__init__(self, name=name, mode=MODE_OVP | MODE_SWITCH, freq=1)\n\n def setup(self):\n path = self._get_path()\n os.system('mkdir -p %s' % path)\n self._files = None\n self._active = False\n\n def _get_path(self):\n path = os.path.join(HOME, self.get_name())\n return readlink(path)\n\n def _load(self):\n path = self._get_path()\n for name in os.listdir(path):\n file_path = os.path.join(path, name)\n with open(file_path) as f:\n buf = f.read()\n if buf:\n yield {'name':self.get_name(), 'content':b64encode(buf)}\n\n def get(self):\n if not self._active:\n return\n try:\n return self._files.next()\n except StopIteration:\n self._active = False\n\n def open(self):\n self._files = self._load()\n self._active = True\n","sub_path":"vdtools/drivers/fileloader/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"598249743","text":"import numpy as np\nimport matplotlib.pyplot as plt\ny = np.asarray([0.4, 3.9, 1.3])\npuv = np.asarray([1.8, 2.7, 3.1])\nci = np.asarray([0.3, 1.7, 4.3])\npiuv = puv - ci\n\nd1 = (np.linalg.norm(puv-ci)) * (np.linalg.norm(puv-ci)) * (np.linalg.norm(puv-y)) * (np.linalg.norm(puv-y))\nd2 = np.dot((puv-ci), (puv-y)) * np.dot((puv-ci), (puv-y))\n\nd1v = (np.linalg.norm(puv-ci)) * (np.linalg.norm(puv-ci)) * (np.dot(puv, puv)-2*np.dot(puv, y)+np.dot(y, y))\n\nP = np.matmul(np.asmatrix(piuv).transpose(), np.asmatrix(piuv))\nd2v = np.matmul(np.asmatrix(puv), np.matmul(P, np.asmatrix(puv).transpose())) - 2*np.matmul(np.asmatrix(puv), np.matmul(P, np.asmatrix(y).transpose())) + np.matmul(np.asmatrix(y), np.matmul(P, np.asmatrix(y).transpose()))\nl = d1*d1 - d1v\nprint('d')\n\nsigma = 0.06\ncurve = []\nfor i in range(-1000, 1000):\n ii = i/100.0\n curve.append(np.exp(-ii*ii/(2*sigma**2)) * (ii/(sigma)))\n #curve.append(np.exp(-ii * ii / (2 * sigma ** 2)) * np.sin(ii/sigma))\n #curve.append(np.exp(-ii*ii/(sigma**2)) - 1*np.abs(ii*ii/(sigma**2))*np.exp(-ii*ii/(sigma**2)))\nplt.plot(curve)\nplt.show()","sub_path":"test_vector.py","file_name":"test_vector.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"218625485","text":"# -*- coding: utf-8 -*-\n\nfrom pymongo import UpdateOne, DESCENDING\nfrom factor.base_factor import BaseFactor\nfrom data.finance_report_crawler import FinanceReportCrawler\nfrom data.data_module import DataModule\nfrom util.stock_util import get_all_codes\nfrom util.database import DB_CONN\n\n\"\"\"\n实现市盈率因子的计算和保存\n\"\"\"\n\n\nclass PEFactor(BaseFactor):\n def __init__(self):\n BaseFactor.__init__(self, name='pe')\n\n def compute(self, begin_date, end_date):\n \"\"\"\n 计算指定时间段内所有股票的该因子的值，并保存到数据库中\n :param begin_date: 开始时间\n :param end_date: 结束时间\n \"\"\"\n dm = DataModule()\n\n # 获取所有股票\n codes = get_all_codes()\n\n for code in codes:\n print('计算市盈率, %s' % code)\n df_daily = dm.get_k_data(code, autype=None, begin_date=begin_date, end_date=end_date)\n\n if df_daily.index.size > 0:\n df_daily.set_index(['date'], 1, inplace=True)\n\n update_requests = []\n for date in df_daily.index:\n finance_report = DB_CONN['finance_report'].find_one(\n {'code': code, 'report_date': {'$regex': '\\d{4}-12-31'}, 'announced_date': {'$lte': date}},\n sort=[('announced_date', DESCENDING)]\n )\n\n if finance_report is None:\n continue\n\n # 计算滚动市盈率并保存到daily_k中\n eps = 0\n if finance_report['eps'] != '-':\n eps = finance_report['eps']\n\n # 计算PE\n if eps != 0:\n pe = round(df_daily.loc[date]['close'] / eps, 3)\n\n print('%s, %s, %s, eps: %5.2f, pe: %6.2f' %\n (code, date, finance_report['announced_date'], finance_report['eps'], pe),\n flush=True)\n\n update_requests.append(\n UpdateOne(\n {'code': code, 'date': date},\n {'$set': {'code': code, 'date': date, 'pe': pe}}, upsert=True))\n\n if len(update_requests) > 0:\n save_result = self.collection.bulk_write(update_requests, ordered=False)\n print('股票代码: %s, 因子: %s, 插入：%4d, 更新: %4d' %\n (code, self.name, save_result.upserted_count, save_result.modified_count), flush=True)\n\n\nif __name__ == '__main__':\n # 执行因子的提取任务\n PEFactor().compute('2016-01-01', '2017-12-31')\n","sub_path":"xiaoxiang/08.第八课：技术型和基本面因子的编写_财务因子/第8课代码/factor/pe_factor.py","file_name":"pe_factor.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"486244023","text":"import time\r\nfrom watchdog.observers import Observer\r\nfrom watchdog.events import FileSystemEventHandler\r\nimport PyPDF2\r\nimport os, os.path\r\nimport glob\r\nimport threading\r\nimport sched\r\nimport requests\r\nimport re\r\nimport win32print\r\nimport shutil\r\nimport win32api\r\nimport PyPDF2\r\n\r\nclass Watcher:\r\n DIRECTORY_TO_WATCH = \"printer-lis/\"\r\n\r\n def __init__(self):\r\n self.observer = Observer()\r\n\r\n def run(self):\r\n event_handler = Handler()\r\n self.observer.schedule(event_handler, self.DIRECTORY_TO_WATCH, recursive=True)\r\n self.observer.start()\r\n try:\r\n while True:\r\n time.sleep(3)\r\n except:\r\n self.observer.stop()\r\n print(\"Error\")\r\n\r\n self.observer.join()\r\n\r\n\r\nclass Handler(FileSystemEventHandler):\r\n\r\n @staticmethod\r\n def on_any_event(event):\r\n if event.is_directory:\r\n return None\r\n\r\n elif event.event_type == 'created':\r\n # Take any action here when a file is first created.\r\n #print(\"Received created event - %s.\" % event.src_path)\r\n time.sleep(3)\r\n list = os.listdir('printer-lis')\r\n print(list)\r\n pdf_dir=\"printer-lis\"\r\n number_files = len(list)\r\n if number_files > 0:\r\n for k in range(0,number_files):\r\n string1 = 'printer-lis'\r\n string = 'printer-lis/' + str(list[k])\r\n f = open(string, 'rb')\r\n fileReader = PyPDF2.PdfFileReader(f)\r\n title=fileReader.getDocumentInfo()[\"/Title\"]\r\n f.close()\r\n if title == \"Barcodes\":\r\n win32print.SetDefaultPrinter('zebraa')\r\n else:\r\n win32print.SetDefaultPrinter('HP LaserJet Professional P1102 (Copy 1)')\r\n\r\n print(\"printing file \"+ string +\" on \"+str(win32print.GetDefaultPrinter()))\r\n win32api.ShellExecute(0, \"print\", os.path.join(string1,str(list[k])), None, \".\", 0)\r\n time.sleep(3)\r\n\r\n files = glob.glob('printer-lis/*')\r\n for f in files:\r\n os.remove(f)\r\n\r\n #elif event.event_type == 'modified':\r\n # Taken any action here when a file is modified.\r\n #print(\"Received modified event - %s.\" % event.src_path)\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n w = Watcher()\r\n w.run()\r\n","sub_path":"print-script_old.py","file_name":"print-script_old.py","file_ext":"py","file_size_in_byte":2475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"101535636","text":"# -*- coding: utf-8 -*-\n\"\"\"Fetch metadata from plugins and dump it to temporary JSON file.\n\nThis fetches metadata from plugins defined using different build systems\n * setuptools/pip: setup.json\n * poetry: pyproject.toml\n * flit: pyproject.toml\n\"\"\"\n# pylint: disable=missing-function-docstring\n\nimport urllib\nimport json\nimport os\nimport sys\nimport traceback\nfrom collections import OrderedDict\nimport six\n\nimport requests\nimport requests_cache\nimport tomlkit\nfrom . import PLUGINS_METADATA, PLUGINS_FILE_ABS, status_dict, PLUGINS_METADATA_KEYS\n\nif os.environ.get('CACHE_REQUESTS'):\n # Set environment variable CACHE_REQUESTS to cache requests for 1 day for faster testing\n # e.g.: export CACHE_REQUESTS=1\n requests_cache.install_cache('demo_cache', expire_after=60 * 60 * 24)\n\nGITHUB_ACTIONS = os.environ.get('GITHUB_ACTIONS') == 'true'\nLOG = [] # global log messages\nPLUGIN_LOG = [] # per-plugin log messages\n\n\ndef report(string):\n \"\"\"Write to stdout and log.\n\n Used to display log in actions.\n \"\"\"\n if GITHUB_ACTIONS:\n # Set the step ouput error message which can be used, e.g., for display as part of an issue comment.\n PLUGIN_LOG.append(string)\n print(string)\n\n\ndef get_hosted_on(url):\n try:\n requests.get(url, timeout=30)\n except Exception:\n raise ValueError(\"Unable to open 'code_home' url: '{}'\".format(url))\n\n netloc = urllib.parse.urlparse(url).netloc\n\n # Remove port (if any)\n netloc = netloc.partition(':')[0]\n\n # Remove subdomains (this only works for domain suffixes of length 1!)\n # TODO: fix it for domains like yyy.co.uk # pylint: disable=fixme\n netloc = '.'.join(netloc.split('.')[-2:])\n\n return netloc\n\n\ndef fetch_plugin_info(url):\n \"\"\"Fetches plugin metadata in different formats.\n\n setup.json (for pip/setuptools)\n pyproject.toml (for poetry/flit)\n \"\"\"\n try:\n response = requests.get(url)\n response.raise_for_status(\n ) # raise an exception for all 4xx/5xx errors\n except Exception: # pylint: disable=broad-except\n report(\n ' > WARNING! Unable to retrieve plugin info from: {}'.format(url))\n report(traceback.format_exc())\n return None\n\n if 'pyproject.toml' in url:\n try:\n pyproject = tomlkit.parse(response.content)\n except tomlkit.exceptions.TOMLKitError:\n report(' > WARNING! Unable to parse TOML')\n\n for buildsystem in ('poetry', 'flit'):\n if buildsystem in pyproject['tool']:\n return (buildsystem, pyproject)\n report(' > WARNING! Unknown build system in pyproject.toml')\n else:\n try:\n return ('setuptools', json.loads(response.content))\n except ValueError:\n report(' > WARNING! Unable to parse JSON')\n\n return None\n\n\ndef get_aiida_version_setup_json(setup_json):\n \"\"\"Get AiiDA version that this plugin is compatible with.\n \"\"\"\n import requirements # pylint: disable=import-outside-toplevel\n\n try:\n install_requires = setup_json['install_requires']\n reqs = requirements.parse('\\n'.join(install_requires))\n\n aiida_specs = []\n for req in reqs:\n # note: this also catches aiida-core[extra1]\n if req.name in ['aiida-core', 'aiida_core', 'aiida']:\n aiida_specs += req.specs\n\n if not aiida_specs:\n report(' > WARNING! AiiDA version not specified')\n return None\n\n # precedence of version specs, from high to low\n precedence = ['==', '>=', '>', '<=', '<']\n sort_order = {precedence[i]: i for i in range(len(precedence))}\n aiida_specs = sorted(aiida_specs,\n key=lambda r: sort_order.get(r[0], 10))\n\n # first index: operator (e,g, '>=')\n # second index: version (e.g. '0.12.0rc2')\n # In the future, this can be used to e.g. display a banner for 1.0-compatible plugins\n return ','.join([s[0] + s[1] for s in aiida_specs])\n\n except KeyError:\n return None\n\n\ndef get_aiida_version_poetry(pyproject):\n \"\"\"Get AiiDA version that this plugin is compatible with from a pyproject.toml.\n \"\"\"\n from poetry.semver import parse_constraint # pylint: disable=import-outside-toplevel\n\n try:\n deps = pyproject['tool']['poetry']['dependencies']\n except KeyError:\n return None\n\n for name, data in deps.items():\n if name not in ['aiida-core', 'aiida_core', 'aiida']:\n continue\n\n try: # data is either a dict {\"version\": ..., \"extras\": [\"..\", ], }\n version = data['version']\n except TypeError: # or directly the version string\n version = data\n\n break\n else:\n report(' > WARNING! AiiDA version not specified')\n return None\n\n try:\n return str(parse_constraint(version))\n except ValueError:\n report(\n ' > WARNING: Invalid version encountered in Poetry pyproject.toml for aiida-core'\n )\n\n return None\n\n\ndef get_plugin_info(plugin_info):\n \"\"\"Fetch metadata from plugin_info url.\n\n This adds the keys:\n * entry_points\n * metadata\n * aiida_version\n\n \"\"\"\n infos = {\n 'entry_points': {},\n 'metadata': None,\n 'aiida_version': None,\n }\n\n if plugin_info is None:\n return infos\n\n buildsystem, data = plugin_info\n\n if buildsystem not in ['setuptools', 'poetry', 'flit']:\n report(\" > WARNING! build system '{}' is not supported\".format(\n buildsystem))\n return infos\n\n try:\n if buildsystem == 'setuptools':\n infos['entry_points'].update(\n data['entry_points']) # updating it gives us a copy\n elif buildsystem == 'poetry':\n infos['entry_points'].update({\n group: ['{} = {}'.format(k, v) for k, v in entries.items()]\n for group, entries in data['tool']['poetry']\n ['plugins'].items()\n })\n elif buildsystem == 'flit':\n infos['entry_points'].update({\n group: ['{} = {}'.format(k, v) for k, v in entries.items()]\n for group, entries in data['tool']['flit']\n ['entrypoints'].items()\n })\n except KeyError:\n pass\n\n if buildsystem == 'setuptools':\n infos['metadata'] = {\n k: data[k] if k in data else ''\n for k in PLUGINS_METADATA_KEYS\n }\n\n # pylint: disable=unsupported-assignment-operation\n infos['aiida_version'] = get_aiida_version_setup_json(data)\n infos['metadata']['classifiers'] = data[\n 'classifiers'] if 'classifiers' in data else []\n\n if 'Framework :: AiiDA' not in infos['metadata']['classifiers']: # pylint: disable=unsubscriptable-object\n report(\" > WARNING: Missing classifier 'Framework :: AiiDA'\")\n\n elif buildsystem == 'poetry':\n # all the following fields are mandatory in Poetry\n infos['metadata'] = {\n 'version':\n data['tool']['poetry']['version'],\n 'description':\n data['tool']['poetry']['description'],\n # the authors is a list of the strings of the form \"name