| English: \" + result.en + \" |
| Spanish: \"+ result.es + \" |
| German: \" + result.de + \" |
| French: \" + result.fr + \" |
| Turkish: \" + result.tr + \" |
' + txt + '
',\n \"timer\": {\n \"hours\": hours,\n \"minutes\": minutes,\n \"seconds\": seconds\n },\n \"completed\": True,\n \"completed_by\": completed_by,\n \"completed_at\": datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n }\n\n steps = json.loads(self.data)\n steps.append(step)\n data = json.dumps(steps)\n response = self._get_or_update(endpoint=self.specific_endpoint, id=self.id, method='PUT', data=data)\n return self.__class__(token=self.token, **response)\n else:\n return []\n\n def add_attachment(self, attachment_id):\n if self.element_type == 'attachments':\n response = self._get_or_update(endpoint=self.add_attachment_endpoint,\n id=attachment_id,\n item={'element_id': self.id},\n method='PUT')\n return self.__class__(token=self.token, **response)\n else:\n return []\n", "repo_name": "BioData/LabguruPython", "sub_path": "labguru/project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 7090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "83", "api": [{"api_name": "response.Response", "line_number": 12, "usage_type": "name"}, {"api_name": "response.Response.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "response.Response", "line_number": 14, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 120, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 123, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 158, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 158, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 161, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 163, "usage_type": "call"}]} +{"seq_id": "29696752160", "text": "import math\r\nimport pygame\r\n\r\n# Initialize Pygame\r\npygame.init()\r\n\r\n# Set the screen dimensions\r\nscreen_width = 800\r\nscreen_height = 600\r\n\r\n# Create the screen\r\nscreen = pygame.display.set_mode((screen_width, screen_height))\r\n\r\n# Set the title of the screen\r\npygame.display.set_caption(\"Two-Link Robot Simulation\")\r\n\r\n# Define the colors\r\nblack = (0, 0, 0)\r\nwhite = (255, 255, 255)\r\nred = (255, 0, 0)\r\ngreen = (0, 255, 0)\r\nblue = (0, 0, 255)\r\n\r\n# Define the link lengths\r\nl1 = 190.0\r\nl2 = 130.0\r\n\r\n# Define the initial angles of the two links\r\nhomepos1=-45.0\r\nhomepos2=-160.0\r\n\r\ntheta1 = homepos1*math.pi/180\r\ntheta2 = homepos2*math.pi/180\r\n\r\nitheta1limitl = -1.0\r\nitheta1limith = 160.0\r\nitheta2limitl = -1.0\r\nitheta2limith = 160.0\r\n\r\ntheta1limitl = math.pi*itheta1limitl/180\r\ntheta1limith = math.pi*itheta1limith/180\r\ntheta2limitl = math.pi*itheta2limitl/180\r\ntheta2limith = math.pi*itheta2limith/180\r\n\r\n\r\n# Define the position of the base of the robot\r\nx0 = screen_width / 2\r\ny0 = screen_height / 2\r\n\r\n# Define the position of the end-effector of the robot\r\nx_end = x0 + l1 * math.cos(theta1) + l2 * math.cos(theta1 + theta2)\r\ny_end = y0 + l1 * math.sin(theta1) + l2 * math.sin(theta1 + theta2)\r\n\r\n# Define the speed of the robot\r\nspeed = 4.9234123490871293874\r\n\r\n# Define the font for displaying the angles\r\nfont = pygame.font.SysFont(None, 25)\r\n\r\n# Define the clock\r\nclock = pygame.time.Clock()\r\n\r\n# Define the main loop of the program\r\n# Define the main loop of the program\r\nwhile True:\r\n # Clear the screen\r\n screen.fill(white)\r\n\r\n # Draw the base of the robot\r\n pygame.draw.circle(screen, black, (int(x0), int(y0)), 10)\r\n\r\n # Draw the first link of the robot\r\n pygame.draw.line(screen, black, (int(x0), int(y0)), (int(x0 + l1 * math.cos(theta1)), int(y0 + l1 * math.sin(theta1))), 5)\r\n\r\n # Draw the second link of the robot\r\n pygame.draw.line(screen, black, (int(x0 + l1 * math.cos(theta1)), int(y0 + l1 * math.sin(theta1))), (int(x_end), int(y_end)), 5)\r\n\r\n # Draw the end-effector of the robot\r\n pygame.draw.circle(screen, blue, (int(x_end), int(y_end)), 10)\r\n\r\n # Display the angles of the two links\r\n theta1_text = font.render(\"Theta1: {:.2f}\".format(math.degrees(theta1)), True, black)\r\n theta2_text = font.render(\"Theta2: {:.2f}\".format(math.degrees(theta2)), True, black)\r\n screen.blit(theta1_text, (10, 10))\r\n screen.blit(theta2_text, (10, 40))\r\n\r\n # Handle events\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n quit()\r\n elif event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_ESCAPE:\r\n pygame.quit()\r\n quit()\r\n\r\n # Update the angles of the two links based on the pressed keys\r\n keys = pygame.key.get_pressed()\r\n\r\n if keys[pygame.K_LEFT] and keys[pygame.K_UP]:\r\n theta1 -= math.radians(speed)\r\n theta2 -= math.radians(speed)\r\n if theta1 < -theta1limith:\r\n theta1 = -theta1limith\r\n if theta2 < -theta2limith:\r\n theta2 = -theta2limith\r\n elif keys[pygame.K_LEFT] and keys[pygame.K_DOWN]:\r\n theta1 -= math.radians(speed)\r\n theta2 += math.radians(speed)\r\n if theta1 < -theta1limith:\r\n theta1 = -theta1limith\r\n if theta2 > theta2limitl:\r\n theta2 = theta2limitl\r\n elif keys[pygame.K_RIGHT] and keys[pygame.K_UP]:\r\n theta1 += math.radians(speed)\r\n theta2 -= math.radians(speed)\r\n if theta1 > theta1limitl:\r\n theta1 = theta1limitl\r\n if theta2 < -theta2limith:\r\n theta2 = -theta2limith\r\n elif keys[pygame.K_RIGHT] and keys[pygame.K_DOWN]:\r\n theta1 += math.radians(speed)\r\n theta2 += math.radians(speed)\r\n if theta1 > theta1limitl:\r\n theta1 = theta1limitl\r\n if theta2 > theta2limitl:\r\n theta2 = theta2limitl\r\n elif keys[pygame.K_LEFT] or keys[pygame.K_RIGHT]:\r\n if keys[pygame.K_LEFT]:\r\n theta1 -= math.radians(speed)\r\n else:\r\n theta1 += math.radians(speed)\r\n if theta1 < -theta1limith:\r\n theta1 = -theta1limith\r\n elif theta1 > theta1limitl:\r\n theta1 = theta1limitl\r\n elif keys[pygame.K_UP] or keys[pygame.K_DOWN]:\r\n if keys[pygame.K_UP]:\r\n theta2 -= math.radians(speed)\r\n else:\r\n theta2 += math.radians(speed)\r\n if theta2 < -theta2limith:\r\n theta2 = -theta2limith\r\n elif theta2 > theta2limitl:\r\n theta2 = theta2limitl\r\n\r\n # Update the position of the end\r\n x_end = x0 + l1 * math.cos(theta1) + l2 * math.cos(theta1 + theta2)\r\n y_end = y0 + l1 * math.sin(theta1) + l2 * math.sin(theta1 + theta2)\r\n # Update the screen\r\n pygame.display.update()\r\n\r\n # Set the FPS\r\n clock.tick(60)\r\n\r\npygame.quit()\r\n\r\n", "repo_name": "tykrus01/TARS", "sub_path": "SCARA_anglecontrol_arm.py", "file_name": "SCARA_anglecontrol_arm.py", "file_ext": "py", "file_size_in_byte": 4864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "83", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 32, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 33, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 40, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 41, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 42, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 43, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 51, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 73, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 73, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 76, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 76, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 79, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 82, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 100, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 101, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.K_LEFT", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 107, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 108, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 109, "usage_type": "call"}, {"api_name": "pygame.K_RIGHT", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 114, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 115, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.K_RIGHT", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 121, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 122, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.K_LEFT", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 129, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 130, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.K_UP", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 138, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 139, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 141, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 148, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 149, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 151, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "9697234232", "text": "import json\nfrom pathlib import Path\nimport argh\n\npositions_dir = Path(\"Manu-Data\") / \"Positions\"\nfit_dir = Path(\"Manu-Data\") / \"LineFit\"\n\ndef main():\n positions_paths = positions_dir.glob(\"*.json\")\n db = {}\n for path in positions_paths:\n with path.open() as f:\n data = json.load(f)\n position_id = path.stem\n\n # Zeroth, remove the big arrays that we have no need for here\n del data[\"mean\"]\n del data[\"std\"]\n del data[\"cont\"]\n del data[\"wavs\"]\n\n # First, get the per-position data such as x, y\n db[position_id] = data\n\n # Second, get the per-(position + line) data\n fit_subdir = fit_dir / position_id\n line_paths = fit_subdir.glob(\"*.json\")\n for linepath in line_paths:\n with linepath.open() as f:\n linedata = json.load(f)\n line_id = linepath.stem\n data[line_id] = linedata\n with open(\"manu_spectral_fit_db.json\", \"w\") as f:\n json.dump(db, f, indent=2)\n\n\n\nif __name__ == \"__main__\":\n argh.dispatch_command(main)\n", "repo_name": "will-henney/orion-tsq", "sub_path": "manu-photom-consolidate.py", "file_name": "manu-photom-consolidate.py", "file_ext": "py", "file_size_in_byte": 1083, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "83", "api": [{"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 34, "usage_type": "call"}, {"api_name": "argh.dispatch_command", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "42579129606", "text": "from django.shortcuts import render\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom django.http import HttpResponse\nimport base64\nfrom PIL import Image\nfrom io import BytesIO\nimport os\n\n# Image Classification Imports\nimport numpy as np\nfrom PIL import Image\nfrom imageio import imread\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\nimport tf_slim as slim\nfrom tf_slim.nets import inception\nimport tf_slim as slim\nimport cv2\n\ncurrent_path = os.path.join(os.getcwd(), 'inception_api')\nckpt_path = os.path.join(current_path, \"input/inception_v3.ckpt\")\nimages_path = os.path.join(current_path, \"images/*\")\nimg_width = 299\nimg_height = 299\nbatch_size = 16\nbatch_shape = [batch_size, img_height, img_width, 3]\nnum_classes = 1001\npredict_output = []\nclass_names_path = os.path.join(current_path, \"input/imagenet_class_names.txt\")\nwith open(class_names_path) as f:\n class_names = f.readlines()\n\n\n# To Load Image\ndef load_images(input_dir):\n global batch_shape\n images = np.zeros(batch_shape)\n filenames = []\n idx = 0\n batch_size = batch_shape[0]\n files = tf.gfile.Glob(input_dir)[:20]\n files.sort()\n for filepath in files:\n with tf.gfile.Open(filepath, \"rb\") as f:\n imgRaw = np.array(Image.fromarray(imread(f, as_gray=False, pilmode=\"RGB\")).resize((299, 299))).astype(np.float) / 255.0\n # Images for inception classifier are normalized to be in [-1, 1] interval.\n images[idx, :, :, :] = imgRaw * 2.0 - 1.0\n filenames.append(os.path.basename(filepath))\n idx += 1\n if idx == batch_size:\n yield filenames, images\n filenames = []\n images = np.zeros(batch_shape)\n idx = 0\n if idx > 0:\n yield filenames, images\n\n\ndef start_prediction():\n predict_output=[]\n return_arr = []\n\n X = tf.placeholder(tf.float32, shape=batch_shape)\n\n with slim.arg_scope(inception.inception_v3_arg_scope()):\n logits, end_points = inception.inception_v3(\n X, num_classes=num_classes, is_training=False, reuse=tf.AUTO_REUSE\n )\n\n predictions = end_points[\"Predictions\"]\n saver = tf.train.Saver(slim.get_model_variables())\n\n\n session_creator = tf.train.ChiefSessionCreator(\n scaffold=tf.train.Scaffold(saver=saver),\n checkpoint_filename_with_path=ckpt_path,\n master='')\n\n with tf.train.MonitoredSession(session_creator=session_creator) as sess:\n for filenames, images in load_images(images_path):\n labels = sess.run(predictions, feed_dict={X: images})\n for filename, label, image in zip(filenames, labels, images):\n predict_output.append([filename, label, image])\n \n for x in predict_output:\n out_list = list(x[1])\n topPredict = sorted(range(len(out_list)), key=lambda i: out_list[i], reverse=True)[:5]\n for p in topPredict:\n return_arr.append(class_names[p-1].strip())\n\n return return_arr\n\n\n\ndef base64_to_image(base64_string):\n # Convert base64 string to PIL Image\n path=os.path.join(os.getcwd(), 'inception_api', 'images', 'example.jpg')\n imgdata = base64.b64decode(base64_string)\n image = Image.open(BytesIO(imgdata))\n image.save(path)\n\n# Create your views here.\n@api_view(['POST'])\ndef get_classification(request):\n rarr = []\n # print(\"Got request- \", request.data)\n image = base64_to_image(request.data['image'])\n prediction_output = start_prediction()\n # print(\"prediction_output: \", prediction_output)\n \n for val in prediction_output:\n rarr.append(' '.join(val.split(' ')[1:]))\n\n # print(\"Rarray- \", rarr)\n return Response({'status': 'success', 'prediction': rarr})\n \ndef index(request):\n return HttpResponse(\"Hello, world. You're at the inception_api index.\")", "repo_name": "omkarshinde254/Inception_v3-Image-Classification", "sub_path": "mysite/inception_api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3872, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.disable_v2_behavior", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.gfile.Glob", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.gfile", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 43, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.gfile.Open", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.gfile", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "imageio.imread", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 65, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tf_slim.arg_scope", "line_number": 67, "usage_type": "call"}, {"api_name": "tf_slim.nets.inception.inception_v3_arg_scope", "line_number": 67, "usage_type": "call"}, {"api_name": "tf_slim.nets.inception", "line_number": 67, "usage_type": "name"}, {"api_name": "tf_slim.nets.inception.inception_v3", "line_number": 68, "usage_type": "call"}, {"api_name": "tf_slim.nets.inception", "line_number": 68, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.AUTO_REUSE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 69, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.train.Saver", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 73, "usage_type": "name"}, {"api_name": "tf_slim.get_model_variables", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train.ChiefSessionCreator", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 76, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.train.Scaffold", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.train.MonitoredSession", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 81, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 99, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 100, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 101, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 101, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 101, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 117, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 105, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "74552704557", "text": "import os\nimport xlsxwriter\n\ndef readFileName(wb, ws, path):\n\tfilenames = os.listdir(path)\n\tbold = wb.add_format({'bold': True})\n\tws.write('A1', 'Path', bold)\n\tws.write('B1', 'FileName', bold)\n\trow = 1\n\tcol = 0\n\n\tfor filename in filenames:\n\t\tfname, ext = os.path.splitext(filename)\n\t\tif ext.lower() in ['.jpg', '.jpeg','.png','.gif']:\n\t\t\tws.write(row, col, path)\n\t\t\tws.write(row, col+1, filename)\n\t\t\trow += 1\n\t\t#full_filename = os.path.join(path, filename)\n\t\t#print (full_filename)\n\tws.write(row, 0, 'Total', bold)\n\tws.write(row, 1, '=counta(B2:B'+str(row)+')', bold)\n\ndef main():\n\tfor i in os.listdir(os.getcwd()):\n\t\tif os.path.isdir(i):\n\t\t\trf = i + '.xlsx'\n\t\t\tprint(rf)\n\t\t\tworkbook = xlsxwriter.Workbook(rf)\n\t\t\tworksheet = workbook.add_worksheet()\n\t\t\treadFileName(workbook, worksheet, i)\n\t\t\tworkbook.close()\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "bullseye73/python", "sub_path": "fs/recognitionResult.py", "file_name": "recognitionResult.py", "file_ext": "py", "file_size_in_byte": 850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "73", "api": [{"api_name": "os.listdir", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "xlsxwriter.Workbook", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "31440116944", "text": "f\"\"\"\nWalnut Models\n\"\"\"\n\n###\n# Libraries\n###\nimport uuid\n\nfrom django.db import models\nfrom django.utils.translation import ugettext as _\n\n\n###\n# Models\n###\n\nclass VideoManager(models.Manager):\n\n def create(self, **data):\n if data.get('aws_credentials'):\n del data['aws_credentials']\n\n return super().create(**data)\n\nclass Video(models.Model):\n\n class StatusChoices(models.TextChoices):\n SUCCESS = 'Success', _('Success')\n FAILED = 'Failed', _('Failed')\n RUNNING = 'Running', _('Running')\n\n uuid = models.UUIDField(\n verbose_name=_('UUID'),\n default=uuid.uuid4,\n editable=False\n\n )\n\n title = models.CharField(\n verbose_name=_(\"Title\"),\n max_length=300,\n blank=True,\n null=True\n )\n\n description = models.TextField(\n verbose_name=_(\"Description\"),\n blank=True,\n null=True\n )\n\n video_source = models.URLField(\n verbose_name=_(\"Video \"),\n max_length=200\n )\n\n status = models.CharField(\n verbose_name=_(\"Status\"),\n max_length=10,\n choices=StatusChoices.choices,\n default=StatusChoices.RUNNING\n )\n\n user = models.ForeignKey(\n 'accounts.User',\n on_delete=models.CASCADE,\n blank=False,\n null=False\n )\n\n webhook_url = models.URLField(\n verbose_name=_(\"Webhook URL\"),\n max_length=200, \n null=False,\n blank=False\n )\n\n use_dash = models.BooleanField(\n verbose_name=_(\"Use Dash\"),\n default=False,\n null=False\n )\n\n dash_file = models.URLField(\n verbose_name=_(\"Dash file\"),\n max_length=200,\n null=True,\n blank=True,\n help_text=\"This field will be auto generated\"\n )\n\n use_hls = models.BooleanField(\n verbose_name=_(\"Use HLS\"),\n default=False,\n null=False\n )\n\n hls_file = models.URLField(\n verbose_name=_(\"HLS file\"),\n max_length=200,\n null=True,\n blank=True,\n help_text=\"This field will be auto generated\"\n )\n\n \n duration = models.FloatField(\n verbose_name=_(\"Duration\"),\n default=0,\n help_text=\"This field will be auto generated\"\n )\n\n objects = VideoManager()\n \n ", "repo_name": "MarlonCorreia/walnut", "sub_path": "walnut/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2298, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "83", "api": [{"api_name": "django.db.models.Manager", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.TextChoices", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models.UUIDField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 34, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 67, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 93, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 100, "usage_type": "call"}, {"api_name": "django.db.models.FloatField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "256956298", "text": "from collections import OrderedDict\nimport json\nimport inspect\n\nfrom . import base, fields\nfrom .generics import generic, method, call_next_method\n\ndef to_json(schema_family):\n return json.dumps(to_data(schema_family), indent=4, separators=(',', ': '))\n\n@generic\ndef to_data(obj, *args, **kwargs):\n pass\n\n@method(to_data)\ndef schema_family_to_data(family: base.SchemaFamily):\n data = OrderedDict()\n data['title'] = family.name\n data['$schema'] = \"http://json-schema.org/draft-04/schema#\"\n for schema in family.schemas.values():\n data[schema.__name__] = to_data(schema)\n return data\n\n\n@method(to_data)\ndef schema_to_data(schema: base.SchemaMeta):\n data = OrderedDict()\n data['title'] = schema.__name__\n data['description'] = inspect.getdoc(schema)\n for record in schema._meta.records.values():\n data[record.__name__] = to_data(record)\n return data\n\n@method(to_data)\ndef record_to_data(record: base.RecordMeta):\n data = OrderedDict()\n data['title'] = record.__name__\n data['type'] = \"object\"\n data['properties'] = OrderedDict( (field.__name__, to_data(field))\n for field in record._meta.fields.values() )\n\n links = [ to_link_description(link)\n for link in record._meta.fields.values()\n if isinstance(link, fields.Link) ]\n\n if len(links) > 0:\n data['links'] = links\n\n for child in record._meta.children.values():\n prop_name = getattr(child, 'collective_name', child.__name__)\n data['properties'][prop_name] = OrderedDict((\n ('type', 'array'), ('items', to_data(child))))\n\n return data\n\n@method(to_data)\ndef tree_record_to_data(tree: base.TreeMeta):\n data = call_next_method(tree)\n tree_structure = OrderedDict(((rank, {'type': 'string'})\n for rank in tree._ranks))\n data['properties']['tree_structure'] = OrderedDict((\n ('type', 'object'), ('properties', tree_structure)))\n\n return data\n\n@method(to_data)\ndef field_to_data(field: base.Field):\n return OrderedDict()\n\n@method(to_data)\ndef text_field_to_data(field: fields.Text):\n data = call_next_method(field)\n data[\"type\"] = \"string\"\n return data\n\n@method(to_data)\ndef date_field_to_data(field: fields.Date):\n data = call_next_method(field)\n data[\"type\"] = \"string\"\n data['format'] = \"date-time\"\n return data\n\n@method(to_data)\ndef integer_field_to_data(field: fields.Integer):\n data = call_next_method(field)\n data[\"type\"] = \"integer\"\n return data\n\n@method(to_data)\ndef boolean_field_to_data(field: fields.Boolean):\n data = call_next_method(field)\n data[\"type\"] = \"boolean\"\n return data\n\n@method(to_data)\ndef link_to_data(link: fields.Link):\n data = call_next_method(link)\n data[\"type\"] = \"string\"\n data[\"format\"] = \"uuid\"\n return data\n\ndef to_link_description(link):\n target_schema = \"#/%s/%s\" % (link.target._meta.schema.__name__,\n link.target.__name__)\n\n href = \"/%s/%s/{%s}/\" % (link.target._meta.schema.__name__,\n link.target.__name__,\n link.__name__)\n return OrderedDict((\n ('rel', link.__name__),\n ('href', href),\n ('targetSchema', target_schema)))\n", "repo_name": "benanhalt/SchemaTools", "sub_path": "egFish/specify/schema/to_json.py", "file_name": "to_json.py", "file_ext": "py", "file_size_in_byte": 3296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "83", "api": [{"api_name": "json.dumps", "line_number": 9, "usage_type": "call"}, {"api_name": "generics.generic", "line_number": 11, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 17, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 27, "usage_type": "call"}, {"api_name": "inspect.getdoc", "line_number": 29, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 25, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 39, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 51, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 34, "usage_type": "call"}, {"api_name": "generics.call_next_method", "line_number": 58, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 59, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 61, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 56, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 68, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 66, "usage_type": "call"}, {"api_name": "generics.call_next_method", "line_number": 72, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 70, "usage_type": "call"}, {"api_name": "generics.call_next_method", "line_number": 78, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 76, "usage_type": "call"}, {"api_name": "generics.call_next_method", "line_number": 85, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 83, "usage_type": "call"}, {"api_name": "generics.call_next_method", "line_number": 91, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 89, "usage_type": "call"}, {"api_name": "generics.call_next_method", "line_number": 97, "usage_type": "call"}, {"api_name": "generics.method", "line_number": 95, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "31773641981", "text": "###############################################################################\n##### Data Preprocesssing\n###############################################################################\nfrom typing import Tuple\nimport pandas as pd\nimport constants as cst\nfrom datetime import timedelta\nfrom functools import partial\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\n##### Get logger\nimport logging\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.StreamHandler())\nlogger.setLevel(logging.DEBUG)\n\n##### Preprocessor\nclass Preprocessor:\n def __init__(self, val_split_length: int = 90) -> None:\n self.split_day = cst.MAX_DATE - timedelta(val_split_length)\n\n def preprocess(\n self,\n data: pd.DataFrame = None,\n train_val_split: bool = True,\n ) -> pd.DataFrame:\n if train_val_split:\n data = self.enforce_data_quality(data)\n self.get_validation_set(data)\n\n # load train_data\n data = pd.read_csv(cst.TRAIN_TRANSACTION_PATH, index_col=0)\n data[\"date_order\"] = pd.to_datetime(data[\"date_order\"])\n data = data.sort_values(\"date_order\")\n\n data = self.feature_engineering(data)\n data.to_csv(cst.FEATURES_PATH)\n\n labels = self.get_labels(data)\n train_set, test_set = self.get_train_test_sets(\n pd.read_csv(cst.FEATURES_PATH, index_col=0), labels\n )\n\n return train_set, test_set\n\n def enforce_data_quality(self, data: pd.DataFrame) -> pd.DataFrame:\n # remove zero/negative quantities\n data = data[data[\"quantity\"] > 0]\n # remove zero/negative sales\n data = data[data[\"sales_net\"] > 0]\n # enforce date types\n data[\"date_order\"] = pd.to_datetime(data[\"date_order\"])\n data[\"date_invoice\"] = pd.to_datetime(data[\"date_invoice\"])\n\n data = data[\n (data[\"date_order\"].dt.year >= 2015)\n & (data[\"date_invoice\"].dt.year >= 2015)\n ]\n\n logger.info(\"Done performing data quality checks\")\n\n return data\n\n def get_validation_set(self, data: pd.DataFrame) -> None:\n train_set = data.loc[data[\"date_order\"] < self.split_day]\n val_set = data.loc[data[\"date_order\"] >= self.split_day]\n\n logger.info(\"Done splitting validation and train transactions\")\n\n train_set.to_csv(cst.TRAIN_TRANSACTION_PATH)\n val_set.to_csv(cst.VALIDATION_DATA_PATH)\n\n logger.info(\"Done saving validation and train transactions\")\n\n def feature_engineering(self, data: pd.DataFrame) -> pd.DataFrame:\n logger.info(\"Started feature engineering\")\n\n features = data.groupby(by=\"client_id\", as_index=False).agg(\n online_percent=(\"order_channel\", partial(item_freq, item=\"online\")),\n phone_percent=(\"order_channel\", partial(item_freq, item=\"by phone\")),\n store_percent=(\"order_channel\", partial(item_freq, item=\"at the store\")),\n visit_percent=(\n \"order_channel\",\n partial(item_freq, item=\"during the visit of a sales rep\"),\n ),\n other_percent=(\"order_channel\", partial(item_freq, item=\"other\")),\n mean_qty=(\"quantity\", \"mean\"),\n max_qty=(\"quantity\", \"max\"),\n min_qty=(\"quantity\", \"min\"),\n std_qty=(\"quantity\", \"std\"),\n last_qty_1=(\"quantity\", partial(last, n=1)),\n last_qty_2=(\"quantity\", partial(last, n=2)),\n last_qty_3=(\"quantity\", partial(last, n=3)),\n last_qty_4=(\"quantity\", partial(last, n=4)),\n mean_sales=(\"sales_net\", \"mean\"),\n max_sales=(\"sales_net\", \"max\"),\n min_sales=(\"sales_net\", \"min\"),\n std_sales=(\"sales_net\", \"std\"),\n last_sales_1=(\"sales_net\", partial(last, n=1)),\n last_sales_2=(\"sales_net\", partial(last, n=2)),\n last_sales_3=(\"sales_net\", partial(last, n=3)),\n last_sales_4=(\"sales_net\", partial(last, n=4)),\n n_branch=(\"branch_id\", \"nunique\"),\n n_product=(\"product_id\", \"nunique\"),\n purchase_freq=(\"date_order\", purchase_frequency),\n delay_purchase_n1=(\"date_order\", partial(time_delay, n=1)),\n delay_purchase_n2=(\"date_order\", partial(time_delay, n=2)),\n delay_purchase_n3=(\"date_order\", partial(time_delay, n=3)),\n delay_purchase_n4=(\"date_order\", partial(time_delay, n=4)),\n client_age=(\"date_order\", partial(time_from_today, n=0)),\n time_from_last_purchase=(\"date_order\", partial(time_from_today, n=-1)),\n client_lifetime=(\"date_order\", lifetime),\n )\n\n logger.info(\"Done feature engineering\")\n\n return features\n\n def get_labels(self, data: pd.DataFrame) -> pd.DataFrame:\n # get purchasing frequencies\n freqs = data.groupby(by=\"client_id\", as_index=False).agg(\n last_purchase=(\"date_order\", \"max\"),\n frequency=(\"date_order\", purchase_frequency),\n )\n\n # setting churn deffinitions\n freq_churn = self.split_day - timedelta(30)\n medium_churn = self.split_day - timedelta(90)\n\n # assiging client categories\n freqs.loc[\n (freqs[\"frequency\"] > 0) & (freqs[\"frequency\"] < 10), \"client_category\"\n ] = \"freq_buyer\"\n freqs.loc[\n (freqs[\"frequency\"] >= 10) & (freqs[\"frequency\"] < 32), \"client_category\"\n ] = \"med_buyer\"\n freqs.loc[\n (freqs[\"frequency\"] >= 32) | (freqs[\"frequency\"] == 0), \"client_category\"\n ] = \"infreq_buyer\"\n\n # assigning labels\n freqs.loc[\n (freqs[\"client_category\"] == \"freq_buyer\")\n & (freqs[\"last_purchase\"] <= freq_churn),\n \"is_churn\",\n ] = 1\n freqs.loc[\n (freqs[\"client_category\"] == \"freq_buyer\")\n & (freqs[\"last_purchase\"] > freq_churn),\n \"is_churn\",\n ] = 0\n freqs.loc[\n (freqs[\"client_category\"] == \"med_buyer\")\n & (freqs[\"last_purchase\"] <= medium_churn),\n \"is_churn\",\n ] = 1\n freqs.loc[\n (freqs[\"client_category\"] == \"med_buyer\")\n & (freqs[\"last_purchase\"] > medium_churn),\n \"is_churn\",\n ] = 0\n\n logger.info(\"Done getting labels\")\n\n freqs.to_csv(cst.TRAIN_TARGET_PATH)\n\n return freqs\n\n def get_train_test_sets(\n self, training_data: pd.DataFrame, training_target: pd.DataFrame\n ) -> Tuple[pd.DataFrame, pd.DataFrame]:\n full_training_data = pd.merge(\n training_data, training_target, on=\"client_id\", how=\"left\"\n )\n full_training_data.dropna(inplace=True)\n train_set, test_set = train_test_split(\n full_training_data, test_size=0.3, random_state=42\n )\n\n logger.info(\"Done splitting train and test set\")\n\n train_set.to_css(cst.TRAIN_SET_PATH)\n test_set.to_css(cst.TEST_SET_PATH)\n\n logger.info(\"Done saving train and test set\")\n\n return train_set, test_set\n\n\ndef purchase_frequency(dates):\n return pd.Timedelta(np.diff(dates.unique()).mean()).total_seconds() / (60 * 60 * 24)\n\n\ndef time_delay(dates, n=1):\n try:\n return pd.Timedelta(dates.iloc[-n] - dates.iloc[-n - 1]).total_seconds() / (\n 60 * 60 * 24\n )\n except IndexError:\n return np.nan\n\n\ndef lifetime(dates):\n return pd.Timedelta(dates.iloc[-1] - dates.iloc[0]).total_seconds() / (60 * 60 * 24)\n\n\ndef time_from_today(dates, n=0):\n return pd.Timedelta(cst.MAX_DATE - dates.iloc[n]).total_seconds() / (60 * 60 * 24)\n\n\ndef last(sequence, n=1):\n try:\n return sequence.iloc[-n]\n except IndexError:\n return np.nan\n\n\ndef item_freq(series, item):\n if not np.isin(item, series):\n return 0\n else:\n return series.value_counts(normalize=True)[item]\n", "repo_name": "henrique-britoleao/churn_prediction", "sub_path": "src/preprocessing.py", "file_name": "preprocessing.py", "file_ext": "py", "file_size_in_byte": 7883, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "83", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 17, "usage_type": "attribute"}, {"api_name": "constants.MAX_DATE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "constants.TRAIN_TRANSACTION_PATH", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 35, "usage_type": "call"}, {"api_name": "constants.FEATURES_PATH", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 43, "usage_type": "call"}, {"api_name": "constants.FEATURES_PATH", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 66, "usage_type": "attribute"}, {"api_name": "constants.TRAIN_TRANSACTION_PATH", "line_number": 72, "usage_type": "attribute"}, {"api_name": "constants.VALIDATION_DATA_PATH", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 81, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 82, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 83, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 86, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 88, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 93, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 94, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 95, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 96, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 101, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 102, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 103, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 104, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 108, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 109, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 110, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 111, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 112, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 113, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 121, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 130, "usage_type": "call"}, {"api_name": "constants.TRAIN_TARGET_PATH", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 172, "usage_type": "attribute"}, {"api_name": "pandas.merge", "line_number": 174, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 178, "usage_type": "call"}, {"api_name": "constants.TRAIN_SET_PATH", "line_number": 184, "usage_type": "attribute"}, {"api_name": "constants.TEST_SET_PATH", "line_number": 185, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 173, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pandas.Timedelta", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 193, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pandas.Timedelta", "line_number": 206, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 210, "usage_type": "call"}, {"api_name": "constants.MAX_DATE", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.isin", "line_number": 221, "usage_type": "call"}]} +{"seq_id": "18212208455", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jan 14 16:02:52 2020\r\n\r\n@author: Harry\r\n\"\"\"\r\n# 1 remeber me \r\nimport json \r\n#如果以前储存了用户名，就加载它\r\n#否则就提示用户输入用户名并储存它\r\nfilename = 'username.json'\r\ntry:\r\n with open(filename) as f_obj:\r\n username = json.load(f_obj)\r\nexcept FileNotFoundError:\r\n username = input(\"What is your name? \")\r\n with open(filename,'w') as f_obj:\r\n json.dump(username,f_obj)\r\n print(\"We will remeber you when you come back, \" +username + \" !\")\r\nelse:\r\n print(\"Welcome back, \"+ username+ '!')\r\n\r\nprint()\r\n# 2 重构\r\nimport json\r\n\r\ndef get_stored_username():\r\n #如果储存了用户名，就获取它\r\n filename = 'username1.json'\r\n try: \r\n with open(filename) as f_obj:\r\n username = json.load(f_obj)\r\n except FileNotFoundError:\r\n return None\r\n else:\r\n return username\r\n \r\n \r\ndef get_new_username():\r\n #提示用户输入用户名\r\n username = input(\"What is your name? \")\r\n filename = 'username1.json'\r\n with open(filename,'w') as f_obj:\r\n json.dump(username,f_obj)\r\n return username\r\n\r\n\r\ndef greet_user():\r\n #问候用户，并指出其名字\r\n username = get_stored_username()\r\n if username:\r\n user_state = input(\"Are you \"+ username+ \"?(Y/N)\")\r\n if user_state =='Y':\r\n print(\"welcome back \"+username+ \"!\")\r\n else:\r\n username = get_new_username()\r\n print(\"We will remeber you when you come back, \"+ username+ '!')\r\n else:\r\n username = get_new_username()\r\n print(\"We will remeber you when you come back, \"+ username+ '!')\r\ngreet_user()\r\n ", "repo_name": "sunstriderLHT/python-start", "sub_path": "chapter 10/remeber_me.py", "file_name": "remeber_me.py", "file_ext": "py", "file_size_in_byte": 1712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 18, "usage_type": "call"}, {"api_name": "json.load", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "23858547974", "text": "import redis\nimport json\n\n\n\nr = redis.StrictRedis(host=\"192.168.1.7\", port=6379, db=0)\ndef add_redis():\n r = redis.Redis(host=\"192.168.1.7\", port=6379, password=\"123\", db=0)\n with r.pipeline(transaction=False) as p:\n\n for value in range(1,13000):\n # print(value)\n mapping = {value:value}\n r.zadd(\"Pp-RelationStoreFans_961948\", mapping)\n p.execute()\n\n\n\ndef get_redis_token(userId):\n token_redis = r.get('pp-user-test:token:app_user:' + userId)\n if token_redis is not None:\n print(type(token_redis))\n print(token_redis)\n str_token = str(token_redis, encoding=\"utf8\")\n dict_token = json.loads(str_token)\n\n token = dict_token[\"token\"]\n print(token)\n return token\n else:\n\n print(\"获取的token没有数据\")\n exit()\n\n\n\n\nif __name__ == '__main__':\n # add_redis()\n userId = input(\"输入userId:\")\n\n get_redis_token(userId)\n\n", "repo_name": "So777888/openstore", "sub_path": "get_redis_token.py", "file_name": "get_redis_token.py", "file_ext": "py", "file_size_in_byte": 952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "redis.StrictRedis", "line_number": 6, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "429471116", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\n\ndef extract(city, page):\n '''Extracting information for jobs city-wise'''\n headers = {\n 'User-Agent':\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:96.0) Gecko/20100101 Firefox/96.0'\n }\n url = f'https://jobs.accaglobal.com/jobs/{city}/{page}'\n r = requests.get(url, headers)\n soup = BeautifulSoup(r.content, 'html.parser')\n return soup\n\n\ndef transform(soup):\n '''Extracting relevant information from the soup object'''\n divs = soup.find_all('div', class_=\"lister__details cf js-clickable\")\n for block in divs:\n title = block.find_all('span')[0].text\n location = block.find_all(\n class_=\"lister__meta-item lister__meta-item--location\")[0].text\n salary = block.find_all(\n class_=\"lister__meta-item lister__meta-item--salary\")[0].text\n recuriter = block.find_all(\n class_=\"lister__meta-item lister__meta-item--recruiter\")[0].text\n a = [\n x['href'].strip()\n for x in block.find_all(class_='js-clickable-area-link', href=True)\n ]\n url = 'https://jobs.accaglobal.com' + str(a).replace(\"['\", '').replace(\n \"']\", '')\n job = {\n 'Title': title,\n 'Location': location,\n 'Salary': salary,\n 'Recuriter': recuriter,\n 'Link': url\n }\n joblist.append(job)\n\n\n# Create and empty job list\njoblist = []\n\n# Creating a writer object for writing data in multiple sheets\nwriter = pd.ExcelWriter('ACCA_jobs.xlsx', engine='xlsxwriter')\nfor city in ['karachi', 'lahore', 'islamabad']:\n for i in range(0, 10):\n print(f'Getting {city.capitalize()} jobs at page, {i}')\n c = extract(city, i)\n transform(c)\ndf = pd.DataFrame(joblist).drop_duplicates()\ndf.to_excel(writer, sheet_name='ACCA_Jobs', index=False)\nwriter.close()\n", "repo_name": "kashifnaz/ACCA_Jobs_Web_Scraping", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "22658617051", "text": "import os\nimport json\nimport parquet\nimport pandas as pd\n\nhome = os.path.expanduser(\"~\")\ndir = \"/media/sumeyer/SSD_2/ML_DATA/\"\nfilename = \"part-r-00000-67ebd6f0-bfb4-42e0-b516-d7aaa77cbcb8.snappy.parquet\"\ndatafile = dir + filename\n\nprint(\"open file : \", datafile)\n\n\n## assuming parquet file with two rows and three columns:\n## foo bar baz\n## 1 2 3\n## 4 5 6\n\nwith open(datafile) as fo:\n # prints:\n # {\"foo\": 1, \"bar\": 2}\n # {\"foo\": 4, \"bar\": 5}\n for row in parquet.DictReader(fo):\n print(json.dumps(row))\n\n\nwith open(datafile) as fo:\n # prints:\n # 1,2\n # 4,5\n for row in parquet.reader(fo):\n print(\",\".join([str(r) for r in row]))\n\nprint(df.info())\nprint(df)", "repo_name": "SvenMeyer/keras", "sub_path": "import_parquet.py", "file_name": "import_parquet.py", "file_ext": "py", "file_size_in_byte": 694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "73", "api": [{"api_name": "os.path.expanduser", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "parquet.DictReader", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 24, "usage_type": "call"}, {"api_name": "parquet.reader", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "11482726919", "text": "def calculate_total_price(N, M, C, D, P):\r\n total_price = 0 # 合計金額を0で初期化\r\n\r\n # 高橋くんが食べた各皿について\r\n for i in range(N):\r\n # その皿の色が価格が定義されている色かどうかを確認\r\n if C[i] in D:\r\n # 定義されている色ならその価格を合計に加える\r\n color_index = D.index(C[i])\r\n total_price += P[color_index + 1] # P[0]は定義されていない色なので +1 する\r\n else:\r\n # 定義されていない色ならP[0]を合計に加える\r\n total_price += P[0]\r\n\r\n return total_price\r\n\r\n\r\n\"\"\"\r\nphp\r\n\r\n\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nf = 800 \r\nhm = 1.5 \r\nhb = [30, 120, 180] \r\nd = np.linspace(1, 10, 100)\r\n\r\ndef okumura_hata(d, f, hb, hm):\r\n ahr = (1.1 * np.log10(f) - 0.7) * hm - (1.56 * np.log10(f) - 0.8)\r\n return 69.55 + 26.16 * np.log10(f) - 13.82 * np.log10(hb) + (44.9 - 6.55 * np.log10(hb)) * np.log10(d) - ahr\r\n\r\n# Calculate loss for each base station antenna height\r\nl = [okumura_hata(d, f, h, hm) for h in hb]\r\n\r\n# Plot\r\nplt.figure(figsize=(10, 6))\r\nfor i, loss in enumerate(l):\r\n plt.plot(d, loss, label=f'hb = {hb[i]} m')\r\nplt.xscale('log')\r\nplt.xlabel('d (km)')\r\nplt.ylabel('Loss (dB)')\r\nplt.title('Loss : d Okumura-Hata')\r\nplt.legend()\r\nplt.grid(True)\r\nplt.show()\r\n", "repo_name": "s1f102103189/Atcorder", "sub_path": "temp/Default Price.py", "file_name": "Default Price.py", "file_ext": "py", "file_size_in_byte": 2141, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "numpy.linspace", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "7424461184", "text": "from io import StringIO\nfrom unittest import mock\n\nimport pytest\nfrom django.core.management import CommandError, call_command\n\nfrom ansible_base.models import Authenticator, AuthenticatorUser\n\n\n@pytest.mark.parametrize(\n \"command_args\",\n [None, \"--list\"],\n)\ndef test_authenticators_cli_list_with_tabulate(command_args, local_authenticator, ldap_authenticator):\n \"\"\"\n When we have tabulate, we have to parse a fancy table.\n\n Ensure that table contains the authenticators we expect.\n \"\"\"\n out = StringIO()\n err = StringIO()\n\n if command_args is None:\n call_command('authenticators', stdout=out, stderr=err)\n else:\n call_command('authenticators', command_args, stdout=out, stderr=err)\n\n lines = out.getvalue().strip().splitlines()\n headers = (\"ID\", \"Enabled\", \"Name\", \"Order\")\n\n for header in headers:\n assert header in lines[0]\n\n for line, authenticator in ((2, local_authenticator), (3, ldap_authenticator)):\n auth_line = lines[line]\n auth_line = auth_line.strip('|')\n (auth_id, enabled, name, order) = auth_line.split(' | ')\n\n assert auth_id.strip() == str(authenticator.id)\n assert enabled.strip() == str(authenticator.enabled)\n assert name.strip() == str(authenticator.name)\n assert order.strip() == str(authenticator.order)\n\n\n@pytest.mark.parametrize(\n \"command_args\",\n [None, \"--list\"],\n)\n@mock.patch(\"ansible_base.management.commands.authenticators.HAS_TABULATE\", False)\ndef test_authenticators_cli_list_without_tabulate(command_args, local_authenticator, ldap_authenticator):\n \"\"\"\n When we don't have tabulate, we have to parse a simple table.\n\n Ensure that table contains the authenticators we expect.\n \"\"\"\n out = StringIO()\n err = StringIO()\n\n if command_args is None:\n call_command('authenticators', stdout=out, stderr=err)\n else:\n call_command('authenticators', command_args, stdout=out, stderr=err)\n\n lines = out.getvalue().strip().splitlines()\n headers = (\"ID\", \"Enabled\", \"Name\", \"Order\")\n\n for header in headers:\n assert header in lines[0]\n\n for line, authenticator in ((1, local_authenticator), (2, ldap_authenticator)):\n auth_line = lines[line]\n (auth_id, enabled, name, order) = auth_line.split('\\t')\n\n assert auth_id.strip() == str(authenticator.id)\n assert enabled.strip() == str(authenticator.enabled)\n assert name.strip() == str(authenticator.name)\n assert order.strip() == str(authenticator.order)\n\n\ndef test_authenticators_cli_initialize(django_user_model):\n \"\"\"\n Calling with --initialize will create:\n - An authenticator if there is an admin user\n \"\"\"\n out = StringIO()\n err = StringIO()\n\n # Sanity check:\n assert django_user_model.objects.count() == 0\n\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n call_command('authenticators', \"--initialize\", stdout=out, stderr=err)\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 255\n assert \"No admin user exists\" in err.getvalue()\n\n django_user_model.objects.create(username=\"admin\")\n call_command('authenticators', \"--initialize\", stdout=out, stderr=err)\n assert \"Created default local authenticator\" in out.getvalue()\n\n\ndef test_authenticators_cli_initialize_pre_existing(django_user_model, local_authenticator, admin_user):\n \"\"\"\n What if we already have an admin user?\n\n In this case, the command should do nothing on --initialize.\n \"\"\"\n out = StringIO()\n err = StringIO()\n\n # Sanity check:\n assert django_user_model.objects.count() == 1\n existing_user = django_user_model.objects.first()\n assert AuthenticatorUser.objects.count() == 0\n\n call_command('authenticators', \"--initialize\", stdout=out, stderr=err)\n\n # Make sure no new user got created.\n assert django_user_model.objects.count() == 1\n assert django_user_model.objects.filter(username=\"admin\").count() == 1\n new_user = django_user_model.objects.first()\n\n # Nothing should have changed\n assert existing_user == new_user\n assert existing_user.date_joined == new_user.date_joined\n assert out.getvalue() == \"\"\n assert err.getvalue() == \"\"\n\n # No AuthenticatorUser should get created in this case\n assert AuthenticatorUser.objects.count() == 0\n\n\n@pytest.mark.parametrize(\n \"start_state, flag, end_state, exp_out, exp_err\",\n [\n pytest.param(False, \"--enable\", True, \"\", \"\", id=\"disabled -> enabled\"),\n pytest.param(False, \"--disable\", False, \"\", \"\", id=\"disabled -> disabled\"),\n pytest.param(True, \"--enable\", True, \"\", \"\", id=\"enabled -> enabled\"),\n pytest.param(True, \"--disable\", False, \"\", \"\", id=\"enabled -> disabled\"),\n ],\n)\ndef test_authenticators_cli_enable_disable(local_authenticator, start_state, flag, end_state, exp_out, exp_err):\n \"\"\"\n Test enabling/disabling an authenticator.\n \"\"\"\n local_authenticator.enabled = start_state\n local_authenticator.save()\n\n out = StringIO()\n err = StringIO()\n\n assert Authenticator.objects.get(id=local_authenticator.id).enabled == start_state\n call_command('authenticators', flag, local_authenticator.id, stdout=out, stderr=err)\n assert Authenticator.objects.get(id=local_authenticator.id).enabled == end_state\n\n assert out.getvalue() == exp_out\n assert err.getvalue() == exp_err\n\n\n@pytest.mark.parametrize(\n \"flag\",\n [\"--enable\", \"--disable\"],\n)\n@pytest.mark.django_db\ndef test_authenticators_cli_enable_disable_nonexisting(flag):\n \"\"\"\n Test enabling/disabling a non-existing authenticator.\n \"\"\"\n\n out = StringIO()\n err = StringIO()\n\n with pytest.raises(CommandError) as e:\n call_command('authenticators', flag, 1337, stdout=out, stderr=err)\n\n assert \"Authenticator 1337 does not exist\" in str(e.value)\n", "repo_name": "ansible/django-ansible-base", "sub_path": "ansible_base/tests/management/test_authenticators.py", "file_name": "test_authenticators.py", "file_ext": "py", "file_size_in_byte": 5860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "73", "api": [{"api_name": "io.StringIO", "line_number": 20, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 21, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 26, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 56, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 57, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 60, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 45, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 49, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 49, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 85, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 86, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 91, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 92, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 98, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 108, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 109, "usage_type": "call"}, {"api_name": "ansible_base.models.AuthenticatorUser.objects.count", "line_number": 114, "usage_type": "call"}, {"api_name": "ansible_base.models.AuthenticatorUser.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "ansible_base.models.AuthenticatorUser", "line_number": 114, "usage_type": "name"}, {"api_name": "django.core.management.call_command", "line_number": 116, "usage_type": "call"}, {"api_name": "ansible_base.models.AuthenticatorUser.objects.count", "line_number": 130, "usage_type": "call"}, {"api_name": "ansible_base.models.AuthenticatorUser.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "ansible_base.models.AuthenticatorUser", "line_number": 130, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 149, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 150, "usage_type": "call"}, {"api_name": "ansible_base.models.Authenticator.objects.get", "line_number": 152, "usage_type": "call"}, {"api_name": "ansible_base.models.Authenticator.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "ansible_base.models.Authenticator", "line_number": 152, "usage_type": "name"}, {"api_name": "django.core.management.call_command", "line_number": 153, "usage_type": "call"}, {"api_name": "ansible_base.models.Authenticator.objects.get", "line_number": 154, "usage_type": "call"}, {"api_name": "ansible_base.models.Authenticator.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "ansible_base.models.Authenticator", "line_number": 154, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 133, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pytest.param", "line_number": 136, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 137, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 138, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 139, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 170, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 171, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 173, "usage_type": "call"}, {"api_name": "django.core.management.CommandError", "line_number": 173, "usage_type": "argument"}, {"api_name": "django.core.management.call_command", "line_number": 174, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 160, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 164, "usage_type": "attribute"}]} +{"seq_id": "70909504555", "text": "from django import template\n\nregister = template.Library()\n\n@register.filter(name='dynamic_index')\ndef dynamic_index(list_instance, index):\n \"\"\"\n Accepts a list of weeks materials and return a specific index according to week_number value.\n \"\"\"\n try:\n return list_instance[index]\n except:\n return ''", "repo_name": "Ibrahem3amer/bala7", "sub_path": "cms/templatetags/admin_interface_extras.py", "file_name": "admin_interface_extras.py", "file_ext": "py", "file_size_in_byte": 328, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "django.template.Library", "line_number": 3, "usage_type": "call"}, {"api_name": "django.template", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "29401672532", "text": "#Steuerung SBR-Anlage Kläranlage Altenahr MB2022-06\n#control program for the emergency & temporary wastewater treatment plant in Altenahr, \n#which was installed in April 2022 after the catastrophic flood from Juli 2021.\n#This temporary control program ran successfully by a Raspberry Pi Zero WH \n#connected to a LOW-Triggered relais board from May until July 2022.\n#With many thanks to Ludwig, Calvin, Kurt and Christoph for their hardware support, \n#the friendly commitment and that you tried it with me and this program!\n\nimport time\nimport tkinter\nimport threading\nfrom datetime import datetime, timedelta\nimport RPi.GPIO as GPIO\n\nmain = tkinter.Tk()\nmain.geometry('1000x700')\nmain.title('Steuerung SBR-Anlage Altenahr')\n\ndef ende():\n '''Close and End'''\n global hauptlauf, sbr1_lauf\n hauptlauf = \"AUS\"\n sbr1_lauf = \"AUS\"\n allout()\n GPIO.cleanup()\n main.destroy()\n\ndef tgesberechnen():\n '''calculate total time'''\n global t_d1, t_n1, t_d2, t_n2, t_sed, t_abzug, t_still, t_ges\n t_ges = t_d1 + t_n1 + t_d2 + t_n2 + t_sed + t_abzug + t_still\n ausgabetges[\"text\"] = str(t_ges)\n\ndef uebernehmen(phasenzeit, eingabevariable, ausgabevariable,\n fehlermeldungsvariable):\n '''check input'''\n try:\n ganzzahl = int(eingabevariable.get())\n if 0 <= ganzzahl < 1000:\n phasenzeit = ganzzahl\n ausgabevariable[\"text\"] = str(phasenzeit)\n else:\n t_11 = threading.Thread(target = fehlermeldung,\n args = (fehlermeldungsvariable,))\n t_11.start()\n except:\n t_11 = threading.Thread(target = fehlermeldung,\n args = (fehlermeldungsvariable,))\n t_11.start()\n return(phasenzeit)\n\ndef fehlermeldung(fehlermeldungsv):\n '''error message'''\n fehlermeldungsv[\"text\"] = \"Bitte eine ganze Zahl zwischen 0 und 999 eingeben\"\n time.sleep(3)\n fehlermeldungsv[\"text\"] = \"\"\n\ndef deni1get():\n '''get input for Deni1'''\n global t_d1\n t_d1 = uebernehmen(t_d1, eingabe1, ausgabedeni1, fehlerdeni1)\n tgesberechnen()\n eingabe1.delete(0, 'end')\n\ndef nitri1get():\n '''get input for Nitri1'''\n global t_n1\n t_n1 = uebernehmen(t_n1, eingabe2, ausgabenitri1, fehlernitri1)\n tgesberechnen()\n eingabe2.delete(0, 'end')\n\ndef deni2get():\n '''get input for Deni2'''\n global t_d2\n t_d2 = uebernehmen(t_d2, eingabe3, ausgabedeni2, fehlerdeni2)\n tgesberechnen()\n eingabe3.delete(0, 'end')\n\ndef nitri2get():\n '''get input for Nitri2'''\n global t_n2\n t_n2 = uebernehmen(t_n2, eingabe4, ausgabenitri2, fehlernitri2)\n tgesberechnen()\n eingabe4.delete(0, 'end')\n\ndef sedget():\n '''get input for Sedimentation'''\n global t_sed\n t_sed = uebernehmen(t_sed, eingabe5, ausgabesed, fehlersed)\n tgesberechnen()\n eingabe5.delete(0, 'end')\n\ndef klabzugget():\n '''get input for clarification'''\n global t_abzug\n t_abzug = uebernehmen(t_abzug, eingabe6, ausgabeklabzug, fehlerklabzug)\n tgesberechnen()\n eingabe6.delete(0, 'end')\n\ndef stillget():\n '''get input for waiting time'''\n global t_still\n t_still = uebernehmen(t_still, eingabe7, ausgabestillstand,\n fehlerstillstand)\n tgesberechnen()\n eingabe7.delete(0, 'end')\n\ndef GPIO_initialisieren():\n '''initialise GPIOs'''\n GPIO.setmode(GPIO.BCM)\n\n GPIO.setup(23, GPIO.OUT) #Zulaufpumpe\n GPIO.setup(22, GPIO.OUT) #Ruehrwerk\n GPIO.setup(25, GPIO.OUT) #Beluefter1\n GPIO.setup(24, GPIO.OUT) #Beluefter2\n\ndef allout():\n '''set out all GPIOs'''\n GPIO.output(23, GPIO.HIGH) #Ausschalten Zulaufpumpe\n GPIO.output(22, GPIO.HIGH) #Ausschalten Ruehrwerk\n GPIO.output(25, GPIO.HIGH) #Ausschalten Belüfter1\n GPIO.output(24, GPIO.HIGH) #Ausschalten Belüfter2\n\ndef countdownSBR1():\n '''calculate and show time until next phase starts'''\n global sbr1_auto, sbr1_phaseendezeit\n while sbr1_auto == \"AN\":\n while datetime.now() < sbr1_phaseendezeit:\n restzeit = str((sbr1_phaseendezeit - datetime.now()))\n SBR1Restzeitlabel['text'] = restzeit[0:restzeit.find('.')]\n time.sleep(1)\n SBR1Restzeitlabel['text'] = ''\n\ndef SBR1an():\n '''run SBR1'''\n global t_d1, t_n1, t_d2, t_n2, t_sed, t_abzug, t_still, sbr1_lauf, sbr1_phaseendezeit, sbr1_auto, sbr1_count\n sbr1_auto = \"AN\"\n t_5 = threading.Thread(target = countdownSBR1)\n t_5.start()\n while sbr1_lauf == \"AN\":\n #1. Deniphase 1: Ruehrwerk an\n GPIO.output(22, GPIO.LOW) #Einschalten Ruehrwerk\n sbr1phase[\"text\"] = \"Denitrifikation 1\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_d1)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n\n #2. Nitriphase 1: Beluefter an\n GPIO.output(25, GPIO.LOW) #Einschalten Belüfter1\n GPIO.output(24, GPIO.LOW) #Einschalten Belüfter2\n sbr1phase[\"text\"] = \"Nitrifikation 1\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_n1)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n\n #3. Deniphase 2: Beluefter aus\n GPIO.output(25, GPIO.HIGH) #Ausschalten Belüfter1\n GPIO.output(24, GPIO.HIGH) #Ausschalten Belüfter2\n sbr1phase[\"text\"] = \"Denitrifikation 2\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_d2)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n\n #4. Nitriphase 2: Beluefter an\n GPIO.output(25, GPIO.LOW) #Einschalten Belüfter1\n GPIO.output(24, GPIO.LOW) #Einschalten Belüfter2\n sbr1phase[\"text\"] = \"Nitrifikation 2\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_n2)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n\n #5. Sedimentations-/Absetzphase: Ruehrwerk und Belüfter aus\n GPIO.output(22, GPIO.HIGH) #Ausschalten Ruehrwerk\n GPIO.output(25, GPIO.HIGH) #Ausschalten Belüfter1\n GPIO.output(24, GPIO.HIGH) #Ausschalten Belüfter2\n sbr1phase[\"text\"] = \"Sedimentation\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_sed)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n\n #7. Klarwasserabzugs- und Zulaufphase\n GPIO.output(23, GPIO.LOW) #Einschalten Zulaufpumpe\n sbr1phase[\"text\"] = \"Klarwasserabzug, Zulauf\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_abzug)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n GPIO.output(23, GPIO.HIGH) #Ausschalten Zulaufpumpe\n\n #9. Stillstandszeit\n sbr1phase[\"text\"] = \"Stillstandszeit\"\n sbr1phasestart[\"text\"] = time.strftime(\"%H:%M:%S\",time.localtime())\n sbr1_phaseendezeit = datetime.now()+timedelta(minutes = t_still)\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n while datetime.now() < sbr1_phaseendezeit and sbr1_lauf == \"AN\":\n time.sleep(1)\n\n if sbr1_lauf == \"AN\":\n sbr1_count += 1\n SBR1Durchganglabel[\"text\"] = str(sbr1_count)\n\n sbr1phase[\"text\"] = \"Pause\"\n sbr1_phaseendezeit = datetime.now()\n sbr1phaseende[\"text\"] = sbr1_phaseendezeit.strftime('%H:%M:%S')\n sbr1phasestart[\"text\"] = \"\"\n sbr1_auto = \"AUS\"\n\ndef schalten1():\n '''put SBR1 on or off'''\n global sbr1_lauf\n if sbr1_lauf == \"AUS\" and sbr1phase[\"text\"] == \"Pause\":\n sbr1_lauf = \"AN\"\n schalter1['bg'] = 'lime'\n schalter1['text'] = 'An'\n t_1 = threading.Thread(target = SBR1an)\n t_1.start()\n else:\n sbr1_lauf = \"AUS\"\n schalter1['bg'] = 'red'\n schalter1['text'] = 'Aus'\n\ndef zeitstempelaktualisieren():\n '''update time stamp'''\n global hauptlauf\n while hauptlauf == \"AN\":\n zeitjetzt['text'] = time.strftime(\"%H:%M:%S\",time.localtime())\n time.sleep(1)\n\ndef cputempaktualisieren():\n '''update temperatur for CPU'''\n global hauptlauf\n while hauptlauf == \"AN\":\n tempData = \"/sys/class/thermal/thermal_zone0/temp\"\n dateilesen = open(tempData, \"r\")\n temperatur = dateilesen.readline(5)\n dateilesen.close()\n temperatur = round(float(temperatur)/1000,1)\n cputemp['text'] = temperatur\n time.sleep(10)\n\n#Programm Start\nGPIO_initialisieren()\nallout()\nhauptlauf = \"AN\"\nsbr1_lauf = \"AN\"\nsbr1_auto = \"AUS\"\nt_d1 = 0#30 #Min\nt_n1 = 240#210 #Min\nt_d2 = 0 #Min\nt_n2 = 0 #Min\nt_sed = 30#60 #Min\nt_abzug = 5#60 #Min\nt_still = 15#30 Min\nt_ges = t_d1 + t_n1 + t_d2 + t_n2 + t_sed + t_abzug + t_still\nsbr1_count = 0\nsbr1_phaseendezeit = datetime.now()\n\n#Überschrift\ntkinter.Label(main, text = 'Kläranlage Altenahr ', font = ('arial', 11,'bold')\n ).place(x = 10, y = 10)\ntkinter.Label(main, text = 'SBR ', font = ('arial', 20, 'bold')\n ).place(x = 10, y = 50, anchor = 'w')\ntkinter.Label(main, text = '1', font = ('arial', 20, 'bold')\n ).place(x = 250, y = 50, anchor = 'center')\n\n#Schalter\nschalter1 = tkinter.Button(main, width = 8, text = 'Schalten',\n command = schalten1, cursor = 'tcross', bg = 'lime',\n font = ('arial', 10, 'bold'))\nschalter1.place(x = 250, y = 85, anchor = 'center')\n\n#Phase\ntkinter.Label(main, text = 'Aktuelle Phase:', font = ('arial', 11, 'bold')\n ).place(x = 10, y = 120, anchor = 'w')\nsbr1phase = tkinter.Label(main, text = 'Pause', font = ('arial', 11, 'bold'))\nsbr1phase.place(x = 250, y = 120, anchor = 'center')\n\ntkinter.Label(main, text = 'Start Phase:', font = ('arial', 11, 'bold')\n ).place(x = 10, y = 140, anchor = 'w')\nsbr1phasestart = tkinter.Label(main, text = '-', font = ('arial', 11, 'bold'))\nsbr1phasestart.place(x = 250, y = 140, anchor = 'center')\n\ntkinter.Label(main, text = 'Ende Phase:', font = ('arial', 11, 'bold')\n ).place(x = 10, y = 160, anchor = 'w')\nsbr1phaseende = tkinter.Label(main, text = '-', font = ('arial', 11, 'bold'))\nsbr1phaseende.place(x = 250, y = 160, anchor = 'center')\n\ntkinter.Label(main, text = 'Restzeit:', font = ('arial', 11, 'bold')\n ).place(x = 10, y = 180, anchor = 'w')\nSBR1Restzeitlabel = tkinter.Label(main, text = '-',\n font = ('arial', 11, 'bold'))\nSBR1Restzeitlabel.place(x = 250, y = 180, anchor = 'center')\n\ntkinter.Label(main, text = 'Durchgang:', font = ('arial', 11, 'bold')\n ).place(x = 10, y = 200, anchor = 'w')\nSBR1Durchganglabel = tkinter.Label(main, text = str(sbr1_count),\n font = ('arial', 11, 'bold'))\nSBR1Durchganglabel.place(x = 250, y = 200, anchor = 'center')\n\n#Deni1\ntkinter.Label(main, text = 'Denitrifikation 1: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 250, anchor = 'e')\nausgabedeni1 = tkinter.Label(main, text = str(t_d1),\n font = ('arial', 11, 'bold'))\nausgabedeni1.place(x = 245, y = 240, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 240, anchor = 'w')\neingabe1 = tkinter.Entry(main, width = 5)\neingabe1.place(x = 245, y = 260, anchor = 'e')\neingabe1but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = deni1get)\neingabe1but.place(x = 250, y = 260, anchor = 'w')\nfehlerdeni1 = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlerdeni1.place(x = 160, y = 260, anchor = 'w')\n\n#Nitri1\ntkinter.Label(main, text = 'Nitrifikation 1: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 310, anchor = 'e')\nausgabenitri1 = tkinter.Label(main, text = str(t_n1)\n , font = ('arial', 11, 'bold'))\nausgabenitri1.place(x = 245, y = 300, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 300, anchor = 'w')\neingabe2 = tkinter.Entry(main, width = 5)\neingabe2.place(x = 245, y = 320, anchor = 'e')\neingabe2but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = nitri1get)\neingabe2but.place(x = 250, y = 320, anchor = 'w')\nfehlernitri1 = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlernitri1.place(x = 160, y = 320, anchor = 'w')\n\n#Deni2\ntkinter.Label(main, text = 'Denitrifikation 2: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 370, anchor = 'e')\nausgabedeni2 = tkinter.Label(main, text = str(t_d2),\n font = ('arial', 11, 'bold'))\nausgabedeni2.place(x = 245, y = 360, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 360, anchor = 'w')\neingabe3 = tkinter.Entry(main, width = 5)\neingabe3.place(x = 245, y = 380, anchor = 'e')\neingabe3but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = deni2get)\neingabe3but.place(x = 250, y = 380, anchor = 'w')\nfehlerdeni2 = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlerdeni2.place(x = 160, y = 380, anchor = 'w')\n\n#Nitri2\ntkinter.Label(main, text = 'Nitrifikation 2: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 430, anchor = 'e')\nausgabenitri2 = tkinter.Label(main, text = str(t_n2),\n font = ('arial', 11, 'bold'))\nausgabenitri2.place(x = 245, y = 420, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 420, anchor = 'w')\neingabe4 = tkinter.Entry(main, width = 5)\neingabe4.place(x = 245, y = 440, anchor = 'e')\neingabe4but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = nitri2get)\neingabe4but.place(x = 250, y = 440, anchor = 'w')\nfehlernitri2 = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlernitri2.place(x = 160, y = 440, anchor = 'w')\n\n#Absetz-/Sedimentation\ntkinter.Label(main, text = 'Sedimentation: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 500, anchor = 'e')\nausgabesed = tkinter.Label(main, text = str(t_sed), font = ('arial', 11,\n 'bold'))\nausgabesed.place(x = 245, y = 490, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 490, anchor = 'w')\neingabe5 = tkinter.Entry(main, width = 5)\neingabe5.place(x = 245, y = 510, anchor = 'e')\neingabe5but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = sedget)\neingabe5but.place(x = 250, y = 510, anchor = 'w')\nfehlersed = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlersed.place(x = 160, y = 510, anchor = 'w')\n\n#Klarwasserabzug, Zulauf\ntkinter.Label(main, text = 'Klarwasserabzug: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 560, anchor = 'e')\nausgabeklabzug = tkinter.Label(main, text = str(t_abzug),\n font = ('arial', 11, 'bold'))\nausgabeklabzug.place(x = 245, y = 550, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 550, anchor = 'w')\neingabe6 = tkinter.Entry(main, width = 5)\neingabe6.place(x = 245, y = 570, anchor = 'e')\neingabe6but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = klabzugget)\neingabe6but.place(x = 250, y = 570, anchor = 'w')\nfehlerklabzug = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlerklabzug.place(x = 160, y = 570, anchor = 'w')\n\n#Stillstandszeit\ntkinter.Label(main, text = 'Stillstandszeit: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 620, anchor = 'e')\nausgabestillstand = tkinter.Label(main, text = str(t_still),\n font = ('arial', 11, 'bold'))\nausgabestillstand.place(x = 245, y = 610, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 610, anchor = 'w')\neingabe7 = tkinter.Entry(main, width = 5)\neingabe7.place(x = 245, y = 630, anchor = 'e')\neingabe7but = tkinter.Button(main, text = \"OK\", font = ('arial', 8, 'bold'),\n width = 1, command = stillget)\neingabe7but.place(x = 250, y = 630, anchor = 'w')\nfehlerstillstand = tkinter.Label(main, font = ('arial', 11, 'bold'), fg = 'red')\nfehlerstillstand.place(x = 160, y = 630, anchor = 'w')\n\n#Zyklus/ Gesamtzeit\ntkinter.Label(main, text = 'Zykluszeit: ', font = ('arial', 11, 'bold')\n ).place(x = 150, y = 670, anchor = 'e')\nausgabetges = tkinter.Label(main, text = str(t_ges), font = ('arial', 11, 'bold'))\nausgabetges.place(x = 245, y = 670, anchor = 'e')\ntkinter.Label(main, text = 'Min.', font = ('arial', 11, 'bold')\n ).place(x = 250, y = 670, anchor = 'w')\n\n#Zeitstempel anzeigen und aktualisieren\nzeitjetzt = tkinter.Label(main, text = '-', font = ('arial', 11, 'bold'))\nzeitjetzt.place(x = 625, y = 50, anchor = 'w')\nthreading.Thread(target = zeitstempelaktualisieren).start()\n\n#bei Programmstart SBR-Steuerung anschalten\nthreading.Thread(target = SBR1an).start()\n\n#CPU-Temperatur anzeigen und aktualisieren\ntkinter.Label(main, text = 'CPU-Temp.: ', font = ('arial', 11, 'bold')\n ).place (x = 625, y = 70, anchor = 'w')\ntkinter.Label(main, text = '°C', font = ('arial', 11, 'bold')\n ).place (x = 750, y = 70, anchor = 'w')\ncputemp = tkinter.Label(main, text = '-', font = ('arial', 11, 'bold'))\ncputemp.place(x = 750, y = 70, anchor = 'e')\nthreading.Thread(target = cputempaktualisieren).start()\n\n#Beenden-Schalter anordnen\nendeschalt = tkinter.Button(main, text = 'Beenden', command = ende,\n cursor = 'tcross', bg= 'white',\n font = ('arial', 11, 'bold'))\nendeschalt.place(x = 625, y = 20, anchor = 'w')\n\n# Programm auch beenden wenn das Fenster geschlossen wird\nmain.protocol(\"WM_DELETE_WINDOW\", ende)\n\n#loop\nmain.mainloop()\n", "repo_name": "markusbombeck/wastewatertreatment", "sub_path": "sbr_altenahr.py", "file_name": "sbr_altenahr.py", "file_ext": "py", "file_size_in_byte": 19136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "83", "api": [{"api_name": "tkinter.Tk", "line_number": 15, "usage_type": "call"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 25, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 25, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 43, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 110, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 110, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 110, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 112, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 112, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 112, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 113, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 113, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 113, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 114, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 114, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 114, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 115, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 115, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 119, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 119, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 119, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 120, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 120, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 120, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 121, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 121, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 121, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 122, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 122, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 122, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 129, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 138, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 142, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 142, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 142, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 144, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 144, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 145, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 148, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 151, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 151, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 151, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 152, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 152, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 152, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 154, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 155, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 157, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 157, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 158, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 161, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 161, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 161, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 162, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 162, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 162, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 164, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 165, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 165, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 165, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 168, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 171, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 171, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 171, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 172, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 172, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 172, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 174, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 174, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 175, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 177, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 177, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 178, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 181, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 181, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 181, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 182, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 182, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 182, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 183, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 183, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 183, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 185, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 185, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 186, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 186, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 186, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 188, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 192, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 192, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 192, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 194, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 195, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 195, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 195, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 197, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 198, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 199, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 199, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 199, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 203, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 203, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 204, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 206, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 207, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 214, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 226, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 237, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 237, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 238, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 250, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 267, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 267, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 270, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 272, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 274, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 278, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 284, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 286, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 289, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 291, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 294, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 296, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 299, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 301, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 305, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 307, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 312, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 314, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 317, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 319, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 321, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 324, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 328, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 330, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 333, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 335, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 337, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 340, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 344, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 346, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 349, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 351, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 353, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 356, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 360, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 362, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 365, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 367, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 369, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 372, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 376, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 378, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 381, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 383, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 385, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 388, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 392, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 394, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 397, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 399, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 401, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 404, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 408, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 410, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 413, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 415, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 417, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 420, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 424, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 426, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 428, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 432, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 434, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 437, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 440, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 442, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 444, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 446, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 449, "usage_type": "call"}]} +{"seq_id": "26856437298", "text": "from cloudvolume import CloudVolume, Storage\nfrom shapely.geometry import Polygon, box\nimport numpy as np\nfrom io import BytesIO\nfrom PIL import Image, ImageDraw\n\nmip = 6\nmask = Storage('gs://neuroglancer/pinky100_v0/edge_mask')\norder = Storage('gs://neuroglancer/pinky100_v0/z_order_corrected')\nout = CloudVolume('gs://neuroglancer/pinky100_v0/image_single_slices/roi', \n\t\t\t\t\t\t\t\t\t\t\t\t\tcdn_cache=True, mip=mip)\n\n# Get bounding box of a total slice\noffset = out.voxel_offset\nsize = tuple(out.shape[:2])\nx_slice = slice(offset[0], offset[0]+size[0])\ny_slice = slice(offset[1], offset[1]+size[1])\n\n# Build z remap dict\nf = order.get_file('dst_to_src.csv')\norder_arr = np.genfromtxt(BytesIO(f), dtype=np.int, \n\t\t\t\t\t\t\t\t\t\t\tdelimiter=',', skip_header=1)\ndst_to_src = {order_arr[i,0]:order_arr[i,1] \n\t\t\t\t\t\t\t\t\tfor i in range(order_arr.shape[0])}\n\n# Compile all ROI mask polygons (indexed by src_z)\n# ROI is translated by offset\nsrc_z_range = dst_to_src.values()\nsrc_z_filenames = list(map(str, src_z_range))\nmask_files = mask.get_files(src_z_filenames)\nmask_polygons = {}\nfor f in mask_files:\n\tif f['content'] is not None:\n\t\tpts = np.genfromtxt(BytesIO(f['content']), \n\t\t\t\t\t\t\t\tdtype=np.float, delimiter=',')\n\t\tpoly = Polygon(map(tuple, pts[:,:2] / 2**mip))\n\t\tpts = [(int(round(a[0]))-offset[0], int(round(a[1]))-offset[1]) for a \n\t\t\t\t\t\t\t\t\t\t\t\tin list(poly.exterior.coords)]\n\t\tmask_polygons[int(f['filename'])] = pts\n\n# Create mask image for each dst_z\ndst_z_range = dst_to_src.keys()\nfor dst_z in dst_z_range:\n\tsrc_z = dst_to_src[dst_z]\n\tpts = mask_polygons[src_z]\n\timg_mask = Image.new('1', size, 0)\n\tImageDraw.Draw(img_mask).polygon(pts, outline=1, fill=1)\n\timg_mask = np.transpose(np.array(img_mask)).astype(np.uint8)\n\tout[x_slice, y_slice, dst_z] = np.reshape(img_mask, img_mask.shape+(1,))\n", "repo_name": "seung-lab/Alembic", "sub_path": "src/tasks/python/preprocessing/ingest_masks.py", "file_name": "ingest_masks.py", "file_ext": "py", "file_size_in_byte": 1784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "73", "api": [{"api_name": "cloudvolume.Storage", "line_number": 8, "usage_type": "call"}, {"api_name": "cloudvolume.Storage", "line_number": 9, "usage_type": "call"}, {"api_name": "cloudvolume.CloudVolume", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 21, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 34, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 35, "usage_type": "attribute"}, {"api_name": "shapely.geometry.Polygon", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 46, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "31862844722", "text": "import os\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime as dt\nfrom tqdm import tqdm\nimport sys\n##needed for debugging\nsys.path.append('.')\nfrom Database.db_api import db_api\n\n\ndef renaming_fun(x):\n #this fuction renames columns in df to better match db for upload later\n name_dict = {\n 'p_name':'name',\n 'M':'meters_made',\n 'C':'carries',\n 'P':'passes_made',\n 'T':'tackles_made',\n 'MT':'missed_tackles',\n 'TW':'turnovers_won',\n 'TC':'turnovers_conceded',\n 'DB':'defenders_beaten',\n 'TA':'try_assists',\n 'O':'offloads',\n 'CB':'clean_breaks',\n 'LW':'lineouts_won',\n 'LS':'lineouts_stolen',\n }\n try:\n return name_dict[x]\n except KeyError:\n return x\n\ndef check_for_duplicates(master_dict):\n#duplicates from scraping can only appear sequentially\n for i in range(1,158):\n if isinstance(master_dict[i], str):\n print(i, master_dict[i], sep=': ')\n continue\n if isinstance(master_dict[i-1], str):\n continue\n\n if master_dict[i]['FT_Score']==master_dict[i-1]['FT_Score']:\n print(i, 'Possible Dupe', sep=': ')\n\ndef extract_sub_data(Pos_Player_Array):\n #https://www.premiershiprugby.com/match-report/match-report-leicester-tigers-34-19-exeter-chiefs#report\n #weird inconsistancy in subbing on site double entries.\n #so we will always take last entry as those line up correctly\n #possible given issue where player will be subbed and not captured on site\n sub_flag = False\n p_name = []\n pos_num = []\n min_played = []\n is_sub = []\n for pos, player in Pos_Player_Array:\n #check if we are in replacements section\n if player == 'Replacements':\n sub_flag = True\n p_name.append(player)\n min_played.append(-1)\n is_sub.append(-1)\n pos_num.append(-1)\n continue\n\n subs = player.split(' ')\n\n #check if there are any subs\n if len(subs) == 1: #no subs\n p_name.append(subs[0])\n min_played.append(80)\n\n #issue where some subs do not have replacements data on site\n #Note on site they will populate sub data with players not subbed\n # when there is no sub. These players will be caught here and removed\n # by clean_df at end\n if sub_flag:\n #this -1 signifies not subbed in player to be removed\n is_sub.append(-1)\n pos_num.append(-1)\n continue\n\n is_sub.append(0)\n pos_num.append(int(pos))\n continue\n\n if sub_flag:\n p_name.append(subs[0])\n min_played.append(80 - int(subs[-1].strip(\"'\")))\n is_sub.append(1)\n #find position of sub\n try:\n pos_num.append(pos_num[p_name.index(subs[-2])])\n except: #in the case of a resub\n pos_num.append([i for i,item in enumerate(Pos_Player_Array) if subs[-2] in item[1]][0])\n continue\n\n p_name.append(subs[0])\n #second split here since website has some small inconcistancies in formatting\n min_played.append(int(subs[-1].split(' ')[-1].strip(\"'\")))\n is_sub.append(0)\n pos_num.append(int(pos))\n\n return(p_name, min_played, is_sub, pos_num)\n\ndef lookup_PlayGuid_list(p_names_df, list_ids):\n p_names, player_ids = zip(*list_ids)\n PlayGuids = []\n for p_name in p_names_df:\n try:\n PlayGuid_link = player_ids[p_names.index(p_name)]\n PlayGuid = PlayGuid_link.split('=')[1]\n except ValueError: #take player from relitave positon when lookup fails\n PlayGuid_link = player_ids[np.where(p_names_df==p_name)[0][0]]\n PlayGuid = PlayGuid_link.split('=')[1]\n\n PlayGuids.append(PlayGuid)\n return(PlayGuids)\n\ndef create_dict_special(special_list, at_home):\n headers = special_list[2]\n side_details = special_list[at_home]\n dict_lst = {h: d for h, d in zip(headers, side_details)}\n return dict_lst\n\ndef add_target_values(dict_key, dict_lst, df_col, df):\n #dictonary of known player miss spelling\n spelling = { 'Sam Lewis': 'Samuel Lewis',\n 'Melani Nanai Vai': 'Melani Nanai',\n 'Rus Tuima': 'Rusiate Tuima',\n 'Matty Proctor': 'Matt Proctor',\n 'Dan Thomas': 'Daniel Thomas',\n 'Dan du Preez': 'Daniel du Preez',\n 'Dom Morris': 'Dominic Morris',\n 'Jamie Shillcock': 'James Shillcock',\n 'Theo McFarland': 'Theodore McFarland',\n 'Matt Cornish': 'Matthew Cornish',\n 'Val Rapava Ruskin': 'Val Rapava-Ruskin',\n 'Seb Atkinson': 'Sebastien Atkinson',\n 'Elliott Obatoyinbo': 'Elliot Obatoyinbo',\n 'Semi Radradra Waqavatu': 'Semi Radradra',\n 'Ash Challenger': 'Ashley Challenger'\n }\n #penos\n try:\n players = [[p.split(',')[0], len(p.split(','))-1]for p in dict_lst[dict_key]]\n\n for p in players:\n #player name not recorded \n #TYPO HERE MAY NEED TO TEST\n if len(p[0])<=3:\n continue\n\n #player name not found\n if p[0] not in df['name'].values:\n try:\n df.loc[df['name'] == spelling[p[0]], df_col] = p[1]\n except:\n print('Cannot find player {0}'.format(p[0]))\n else:\n df.loc[df['name'] == p[0], df_col] = p[1]\n except:\n #if no penalty goals in game\n pass\n\ndef clean_df(df, list_ids, at_home, target_details):\n df['at_home'] = at_home\n\n #determine minutes played and subs and add to dataframe\n p_name, min_played, is_sub, pos_num = extract_sub_data(df[['Pos','Player']].values)\n df['name'] = p_name\n df['mins_played'] = min_played\n df['is_sub'] = is_sub\n df['position_num'] = pos_num \n\n #remove replacements column\n df.drop([p_name.index('Replacements')], inplace=True)\n\n #add in player ids\n df['playguid'] = lookup_PlayGuid_list(df['name'].values, list_ids)\n\n #drop reformatted columns\n df.drop(['Pos', 'Player'], axis=1, inplace=True)\n\n #fill in 0s\n df = df.replace('-',0)\n\n #rename columns for database\n df.columns = map(renaming_fun, df.columns)\n\n #add target detaild for penos, tries and convos\n dict_lst = create_dict_special(target_details, at_home)\n\n df['penalty_goals'] = 0\n add_target_values('Penalties',dict_lst, 'penalty_goals', df)\n\n df['tries'] = 0\n add_target_values('Tries',dict_lst, 'tries', df)\n #add_target_values('Penalty Tries',dict_lst, 'tries', df)\n\n df['conversions'] = 0\n add_target_values('Conversions',dict_lst, 'conversions', df)\n\n #remove players who did not play or we dont know for how long\n df = df[df['is_sub'] >= 0]\n\n #reset index\n df = df.reset_index(drop=True)\n return(df)\n\ndef load_to_db(year, loaded_dict):\n #connect to db\n db_tool = db_api()\n\n #legacy\n list_of_dfs = []\n\n #create compitition record\n table = 'Comps' \n insert_dict = {'name': 'Premiership',\n 'year': int(year)}\n comp = db_tool.insert(table, **insert_dict)\n\n for i in tqdm(range(0,len(loaded_dict))):\n #skip empty game data\n if isinstance(loaded_dict[i], str):\n print(i, loaded_dict[i], sep=': ')\n continue\n\n #Match Details\n match_dict = dict((k, loaded_dict[i][k]) for k in ('match_date', 'home_team', 'away_team', 'FT_Score', 'HT_Score'))\n table = 'Matches' \n p_table = 'Players'\n pm_table = 'Player_Matches'\n insert_dict = {'idComp': comp[0]['idComp'],\n 'date': dt.strptime(match_dict['match_date'], '%A %d %B %Y'),\n 'home': match_dict['home_team'],\n 'away': match_dict['away_team'],\n 'FT_Score': match_dict['FT_Score'],\n 'HT_Score': match_dict['HT_Score'],\n }\n match = db_tool.insert(table, **insert_dict)\n\n #home\n df1 = clean_df(loaded_dict[i]['home_df'], loaded_dict[i]['home_player_ids'],\n 1, loaded_dict[i]['target_details'])\n\n player_dict_list = df1[['playguid','name']].T.to_dict()\n player_match_dict_list = df1.drop(['name', 'playguid'], axis=1).T.to_dict()\n\n for j in range(0,len(player_dict_list)):\n player = db_tool.insert(p_table, **player_dict_list[j])\n #player match id\n id_dict = { 'idPlayer': player[0]['idPlayer'] , 'idMatch': match[0]['idMatch'] }\n insert_dict = {**id_dict, **player_match_dict_list[j]}\n db_tool.insert(pm_table, **insert_dict)\n \n #legacy\n list_of_dfs.append(df1)\n\n #away\n df2 = clean_df(loaded_dict[i]['away_df'], loaded_dict[i]['away_player_ids'], \n 0, loaded_dict[i]['target_details'])\n\n player_dict_list = df2[['playguid','name']].T.to_dict()\n player_match_dict_list = df2.drop(['name','playguid'], axis=1).T.to_dict()\n\n for j in range(0,len(player_dict_list)):\n player = db_tool.insert(p_table, **player_dict_list[j])\n #player match id\n id_dict = { 'idPlayer': player[0]['idPlayer'] , 'idMatch': match[0]['idMatch'] }\n insert_dict = {**id_dict, **player_match_dict_list[j]}\n db_tool.insert(pm_table, **insert_dict)\n\n #legacy\n list_of_dfs.append(df2)\n\n #legacy\n master_df = pd.concat(list_of_dfs)\n master_df.reset_index(inplace = True, drop=True)\n\n return master_df\n\n\npickle_path = os.getcwd() + '\\\\Scrapers\\\\Scraped Data\\\\premiership_matches.pkl'\nwith open(pickle_path, 'rb') as f:\n loaded_dict = pickle.load(f)\n\nmaster_df = load_to_db(2022, loaded_dict)\n\nsave_path = os.getcwd() + '\\\\Data Cleaners\\\\Cleaner_Data\\\\premiership_matches.csv'\n\nmaster_df.to_csv(save_path)\n\nloaded_dict[1]['home_df'].columns\n\n", "repo_name": "Aidzillafont/Rugby-Recruitment", "sub_path": "Data Cleaners/premiership_games_cleaner.py", "file_name": "premiership_games_cleaner.py", "file_ext": "py", "file_size_in_byte": 10143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 116, "usage_type": "call"}, {"api_name": "Database.db_api.db_api", "line_number": 215, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 226, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 238, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 238, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 281, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 287, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 289, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 293, "usage_type": "call"}]} +{"seq_id": "9778380578", "text": "import sys, os, yaml, re, json\nimport numpy as np, torch as th\nfrom lib import reorder\nimport lib\nimport argparse, collections\nfrom termcolor import cprint, colored\nimport statistics\nfrom PIL import Image\nfrom torchvision.transforms import functional as transfunc\nimport rich\nc = rich.get_console()\n\n\ndef PracticalAttack(argv):\n '''\n Attack a pre-trained model\n '''\n ag = argparse.ArgumentParser()\n ag.add_argument('-A', '--attack', type=str, default='SPSA')\n ag.add_argument('-e', '--epsilon', default=1./255., type=float)\n '''\n A NOTE ON SELECTION OF EPSILON (For Attacking JDModel)\n\n 0.062 (16/255) -> top5 go out of sight with little exception\n 0.031 ( 8/255) -> 1-of-top5 does not go out of sight\n 0.015 ( 4/255) -> 3-of-top5 does not go out of sight\n 0.008 ( 2/255) -> top5 within sight but not close to each other\n 0.004 ( 1/255) -> quite good. (and cannot be lower)\n\n For BingModel\n 1/255 -> topk very persistent\n 2/255 -> top3 very persistent\n 4/255 -> top1 starts to vary\n 8/255 -> looks appropriate.\n '''\n ag.add_argument('-M', '--model', type=str, choices=['JDModel', 'BingModel'])\n ag.add_argument('-v', '--verbose', action='store_true', help='verbose?')\n ag.add_argument('-Q', '--qbudget', type=int, default=500, help='query budget')\n ag.add_argument('-k', '--topk', type=int, default=5, help='generate permutation for topk')\n ag.add_argument('-c', '--canseek', type=int, default=50, help='length of returned ranking list')\n ag.add_argument('-l', '--payload', type=str, required=True, help='path to the payload image')\n ag.add_argument('-V', '--visualize', action='store_true')\n ag.add_argument('-O', '--oneshot', action='store_true')\n ag.add_argument('--randperm', action='store_true', help='use a random permutation instead')\n ag = ag.parse_args(argv)\n cprint(json.dumps(vars(ag), indent=4), 'yellow')\n\n # Process the arguments\n if ag.epsilon > 1.0:\n ag.epsilon = ag.epsilon / 255.\n assert(ag.topk > 1)\n\n # Load the payload image\n image = Image.open(ag.payload, mode='r').resize((224,224), Image.ANTIALIAS)\n query = transfunc.to_tensor(image).clone().unsqueeze(0)\n print(f'* Payload Image Info: shape={query.shape}')\n #tmp = transfunc.to_pil_image(query.squeeze(), mode='RGB')\n #tmp.show()\n #input('2')\n\n # Load the target model\n cprint(f'Setting up the \"{ag.model}\" Model')\n if ag.model == 'JDModel':\n model = getattr(lib.snapshop, ag.model)(canseek=ag.canseek)\n elif ag.model == 'BingModel':\n model = getattr(lib.bing, ag.model)(canseek=ag.canseek)\n else:\n raise ValueError('unsupported model')\n print(model)\n\n # Start attacking\n cprint(f'>_< Starting {ag.attack} Attack with Epsilon = {ag.epsilon:.3f}',\n 'red', None, ['bold', 'underline'])\n argsort, _ = model(query, id='init')\n orig_argsort = argsort.clone().detach()\n if not ag.randperm:\n rperm = th.LongTensor([1, 5, 4, 3, 2]) - 1 # manually specified order\n else:\n rperm = np.arange(ag.topk)\n np.random.shuffle(rperm)\n rperm = th.from_numpy(rperm)\n otopk = argsort[:len(rperm)]\n rtopk = otopk[rperm]\n cprint(f'> Original CanSee\\n {argsort.tolist()}', 'cyan')\n cprint(f'> Original TopK {otopk}', 'green')\n cprint(f'> Attacker Rperm {rperm}', 'yellow')\n cprint(f'> Expected TopK {rtopk}', 'red')\n if ag.oneshot:\n print('Exiting as requested oneshot mode.')\n exit(0)\n\n qr, r, score, mrank, aux = getattr(reorder, ag.attack)(model, query, rperm,\n eps=ag.epsilon, parallel=1, maxprobe=ag.qbudget, verbose=True)\n #argsort, _ = model(query, id='final')\n #cprint(f'> FINAL TopK', 'red')\n #cprint(argsort.tolist(), 'cyan')\n c.print('Final score:', score)\n\n\nif __name__ == '__main__':\n PracticalAttack(sys.argv[1:])\n", "repo_name": "cdluminate/advorder", "sub_path": "PracticalOA.py", "file_name": "PracticalOA.py", "file_ext": "py", "file_size_in_byte": 3874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "73", "api": [{"api_name": "rich.get_console", "line_number": 11, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 46, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 54, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 55, "usage_type": "name"}, {"api_name": "termcolor.cprint", "line_number": 62, "usage_type": "call"}, {"api_name": "lib.snapshop", "line_number": 64, "usage_type": "attribute"}, {"api_name": "lib.bing", "line_number": 66, "usage_type": "attribute"}, {"api_name": "termcolor.cprint", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 81, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 84, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 85, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 86, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 87, "usage_type": "call"}, {"api_name": "lib.reorder", "line_number": 92, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}]} +{"seq_id": "8839949787", "text": "# USAGE\n# python pi_detect_drowsiness.py --cascade haarcascade_frontalface_default.xml --shape-predictor shape_predictor_68_face_landmarks.dat\n# python pi_detect_drowsiness.py --cascade haarcascade_frontalface_default.xml --shape-predictor shape_predictor_68_face_landmarks.dat --alarm 1\n\n# import the necessary packages\nimport sys\nsys.path.insert(0, \"/usr/local/lib/python3.5/dist-packages\")\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nfrom imutils.video import VideoStream\nfrom imutils import face_utils\nimport numpy as np\nimport argparse\nimport imutils\nimport time\nimport dlib\nimport cv2\nimport RPi.GPIO as GPIO\nimport time\nimport threading\n\ndef pin_int():\n\tglobal speaker,led_system,led_status,chair_mode1,chair_mode2\n\tspeaker=11\n\tled_system=33\n\tled_status=13\n\tchair_mode1=29\n\tchair_mode2=31\n\tGPIO.setmode(GPIO.BOARD)\n\tGPIO.setup(speaker,GPIO.OUT, initial = 0)\n\tGPIO.setup(led_system,GPIO.OUT, initial = 0)\n\tGPIO.setup(led_status,GPIO.OUT, initial = 1)\n\tGPIO.setup(chair_mode1,GPIO.OUT, initial = 0)\n\tGPIO.setup(chair_mode2,GPIO.OUT, initial = 0)\n#==========Define actions function==========================\nglobal count\nglobal x,y,detect_mode,old_status\ncount=0\ndetect_mode=0\nold_status=0\ndef led_blinking(count,pulse):\n\tfor i in range(count):\n\t\ttime.sleep(pulse)\n\t\tGPIO.output(led_system,GPIO.HIGH)\n\t\ttime.sleep(pulse)\n\t\tGPIO.output(led_system,GPIO.LOW)\ndef status_blinking():\n\t#detect_mode=0\n\t#if (detect_mode==0):\n\ttime.sleep(0.5)\n\tGPIO.output(led_status,GPIO.LOW)\n\ttime.sleep(0.5)\n\tGPIO.output(led_status,GPIO.HIGH)\ndef speaker_alert(count,pulse):\n\tfor i in range(count):\n\t\ttime.sleep(pulse)\n\t\tGPIO.output(speaker,GPIO.HIGH)\n\t\ttime.sleep(pulse+0.3)\n\t\tGPIO.output(speaker,GPIO.LOW)\ndef led_blinking(count,pulse):\n\tfor i in range(count):\n\t\ttime.sleep(pulse)\n\t\tGPIO.output(led_system,GPIO.HIGH)\n\t\ttime.sleep(pulse)\n\t\tGPIO.output(led_system,GPIO.LOW)\n\n\n\ndef drownside_alert():\n\tglobal old_status\n\tprint (\"=========Starting Alert System=============\")\n\twhile True:\n\t\tif (old_status == 1):\n\t\t\tprint ( \"=Downside detected==\" )\n\t\t\tprint ( \"======>Triggered Speaker==\")\n\t\t\tGPIO.output(led_status,GPIO.LOW)\n\t\t\tGPIO.output(chair_mode2,GPIO.HIGH)\n\t\t\tGPIO.output(chair_mode1,GPIO.LOW)\n\t\t\tfor i in range(3):\n\t\t\t\ttime.sleep(0.2)\n\t\t\t\tGPIO.output(speaker,GPIO.HIGH)\n\t\t\t\tGPIO.output(led_system,GPIO.HIGH)\n\t\t\t\ttime.sleep(0.2+0.3)\n\t\t\t\tGPIO.output(led_system,GPIO.LOW)\n\t\t\t\tGPIO.output(speaker,GPIO.LOW)\n\t\t\tGPIO.output(chair_mode2,GPIO.LOW)\n\t\t#time.sleep(5)\n\t\tif (old_status == 2):\n \tprint ( \"==== No Downside detected ========\" )\n \t\tprint ( \"==== >Alert System Stopped\" )\n \tGPIO.output(speaker,GPIO.LOW)\n \tGPIO.output(led_system,GPIO.LOW)\n \tGPIO.output(led_status,GPIO.HIGH)\n \tGPIO.output(chair_mode1,GPIO.LOW)\n \tGPIO.output(chair_mode2,GPIO.LOW)\n\t\tif (old_status==0):\n\t\t\tstatus_blinking()\n\n#===========================================================\ndef euclidean_dist(ptA, ptB):\n\t# compute and return the euclidean distance between the two\n\t# points\n\treturn np.linalg.norm(ptA - ptB)\n\ndef eye_aspect_ratio(eye):\n\t# compute the euclidean distances between the two sets of\n\t# vertical eye landmarks (x, y)-coordinates\n\tA = euclidean_dist(eye[1], eye[5])\n\tB = euclidean_dist(eye[2], eye[4])\n\n\t# compute the euclidean distance between the horizontal\n\t# eye landmark (x, y)-coordinates\n\tC = euclidean_dist(eye[0], eye[3])\n\n\t# compute the eye aspect ratio\n\tear = (A + B) / (2.0 * C)\n\n\t# return the eye aspect ratio\n\treturn ear\n \n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-c\", \"--cascade\", required=True,\n\thelp = \"path to where the face cascade resides\")\nap.add_argument(\"-p\", \"--shape-predictor\", required=True,\n\thelp=\"path to facial landmark predictor\")\nap.add_argument(\"-a\", \"--alarm\", type=int, default=0,\n\thelp=\"boolean used to indicate if TraffHat should be used\")\nargs = vars(ap.parse_args())\n\n# check to see if we are using GPIO/TrafficHat as an alarm\n#if args[\"alarm\"] > 0:\n#\tfrom gpiozero import TrafficHat\n#\tth = TrafficHat()\n#\tprint(\"[INFO] using TrafficHat alarm...\")\n \n# define two constants, one for the eye aspect ratio to indicate\n# blink and then a second constant for the number of consecutive\n# frames the eye must be below the threshold for to set off the\n# alarm\nEYE_AR_THRESH = 0.3 # do nhay cua mat , mat nho thi tang len mat to thi giam xuong tu 0.2 den 0.33\nEYE_AR_CONSEC_FRAMES = 4 # so lan do mat nham, muon nhanh thi giam ve 2 muon cham thi tang len 8 -10 \n# xong nho save file roi ra desktop chay\n\n# initialize the frame counter as well as a boolean used to\n# indicate if the alarm is going off\nglobal COUNTER,ALARM_ON,status_ret\nCOUNTER=0\nALARM_ON= False\nstatus_ret=0\n# load OpenCV's Haar cascade for face detection (which is faster than\n# dlib's built-in HOG detector, but less accurate), then create the\n# facial landmark predictor\nprint(\"[INFO] loading facial landmark predictor...\")\ndetector = cv2.CascadeClassifier(args[\"cascade\"])\npredictor = dlib.shape_predictor(args[\"shape_predictor\"])\n\n# grab the indexes of the facial landmarks for the left and\n# right eye, respectively\n(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"left_eye\"]\n(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"right_eye\"]\n\n# initialize the camera and grab a reference to the raw camera capture\n#camera = PiCamera()\n#camera.resolution = (640, 480)\n#camera.framerate = 32\n#vs= PiRGBArray(camera, size=(640, 480))\n#=============intit GPIO PIN================\npin_int()\nprint(\"[INFO] GPIO Init...\")\nx = threading.Thread(target=drownside_alert)\nx.start()\n#===========================================\n# start the video stream thread\n#==============================================\nprint(\"[INFO] starting video stream thread...\")\n#vs = VideoStream(src=0).start()\nvs = VideoStream(usePiCamera=True,resolution=(640,480),framerate=32).start()\n#vs= PiRGBArray(camera, size=(640, 480))\ntime.sleep(1.0)\n\n# loop over frames from the video stream\nwhile True:\n\t# grab the frame from the threaded video file stream, resize\n\t# it, and convert it to grayscale\n\t# channels)\n\t\n\tframe = vs.read()\n\tframe = imutils.resize(frame, width=450)\n\tgray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n\t# detect faces in the grayscale frame\n\trects = detector.detectMultiScale(gray, scaleFactor=1.1, \n\t\tminNeighbors=5, minSize=(30, 30),\n\t\tflags=cv2.CASCADE_SCALE_IMAGE)\n\n\t# loop over the face detections\n\tfor (x, y, w, h) in rects:\n\t\t# construct a dlib rectangle object from the Haar cascade\n\t\t# bounding box\n\t\trect = dlib.rectangle(int(x), int(y), int(x + w),\n\t\t\tint(y + h))\n\n\t\t# determine the facial landmarks for the face region, then\n\t\t# convert the facial landmark (x, y)-coordinates to a NumPy\n\t\t# array\n\t\tshape = predictor(gray, rect)\n\t\tshape = face_utils.shape_to_np(shape)\n\n\t\t# extract the left and right eye coordinates, then use the\n\t\t# coordinates to compute the eye aspect ratio for both eyes\n\t\tleftEye = shape[lStart:lEnd]\n\t\trightEye = shape[rStart:rEnd]\n\t\tleftEAR = eye_aspect_ratio(leftEye)\n\t\trightEAR = eye_aspect_ratio(rightEye)\n\n\t\t# average the eye aspect ratio together for both eyes\n\t\tear = (leftEAR + rightEAR) / 2.0\n\n\t\t# compute the convex hull for the left and right eye, then\n\t\t# visualize each of the eyes\n\t\tleftEyeHull = cv2.convexHull(leftEye)\n\t\trightEyeHull = cv2.convexHull(rightEye)\n\t\tcv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)\n\t\tcv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)\n\n\t\t# check to see if the eye aspect ratio is below the blink\n\t\t# threshold, and if so, increment the blink frame counter\n\t\tif ear < EYE_AR_THRESH:\n\t\t\tCOUNTER += 1\n\n\t\t\t# if the eyes were closed for a sufficient number of\n\t\t\t# frames, then sound the alarm\n\t\t\tif COUNTER >= EYE_AR_CONSEC_FRAMES:\n\t\t\t\t# if the alarm is not on, turn it on\n#\t\t\t\tif not ALARM_ON:\n#\t\t\t\t\tALARM_ON = True\n#\n#\t\t\t\t\t# check to see if the TrafficHat buzzer should\n#\t\t\t\t\t# be sounded\n#\t\t\t\t\tif args[\"alarm\"] > 0:\n#\t\t\t\t\t\tth.buzzer.blink(0.1, 0.1, 10,\n#\t\t\t\t\t\t\tbackground=True)\n\t\t\t\t# draw an alarm on the frame\n\t\t\t\t#ALARM_ON = True\n\t\t\t\tdetect_mode=1\n\t\t\t\tstatus_ret=1\n\t\t\t\tcv2.putText(frame, \"DROWSINESS ALERT!\", (10, 30),\n\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n\t\t\t\tif (detect_mode==1):\n\t\t\t\t\tif (detect_mode != old_status):\n\t\t\t\t\t\told_status=detect_mode\n\t\t\t\t\t\t#x = threading.Thread(target=drownside_alert, args=[1,0,])\n\t\t\t\t\t\t#x.start()\n\t\t\t\t\t\t#COUNTER = 0\n\t\t# otherwise, the eye aspect ratio is not below the blink\n\t\t# threshold, so reset the counter and alarm\n\t\telse:\n\t\t\tCOUNTER = 0\n\t\t\tif (detect_mode==1):\n\t\t\t\t#ALARM_ON = False\n\t\t\t\tdetect_mode=3\n\t\t\t\told_status=0\n\t\t\t\t#x = threading.Thread(target=drownside_alert, args=[2,0,])\n\t\t\t\t#x.start()\n\t\t\t\tdetect_mode=0\n\t\t# draw the computed eye aspect ratio on the frame to help\n\t\t# with debugging and setting the correct eye aspect ratio\n\t\t# thresholds and frame counter\n\t\tcv2.putText(frame, \"EAR: {:.3f}\".format(ear), (300, 30),\n\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n \n\t# show the frame\n\tcv2.imshow(\"Frame\", frame)\t\n\t#if (detect_mode==0):\n\t\t#status_blinking()\n\t#GPIO.output(led_status,GPIO.LOW)\n\t#time.sleep(0.5)\n\tkey = cv2.waitKey(1) & 0xFF\n\t#GPIO.output(led_status,GPIO.HIGH)\n\t# if the `q` key was pressed, break from the loop\n\t#if key == ord(\"q\"):\n\t#\tbreak\n\n# do a bit of cleanup\n#cv2.destroyAllWindows()\nvs.stop()\n\n", "repo_name": "LuizKun/Drownside_detection", "sub_path": "FaBo9AXIS-MPU9250-Python/example/pi_detect_drowsiness.py", "file_name": "pi_detect_drowsiness.py", "file_ext": "py", "file_size_in_byte": 9337, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "sys.path.insert", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setmode", "line_number": 29, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 29, "usage_type": "name"}, {"api_name": "RPi.GPIO.BOARD", "line_number": 29, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 30, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 30, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 31, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 31, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 32, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 32, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 32, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 33, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 33, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 34, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 44, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 44, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 44, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 46, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 46, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 46, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 51, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 51, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 51, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 53, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 53, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 53, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 57, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 57, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 57, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 59, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 59, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 59, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 63, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 63, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 65, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 65, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 65, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 76, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 76, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 76, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 77, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 77, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 77, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 78, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 78, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 78, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 80, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 81, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 81, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 81, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 82, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 82, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 82, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 84, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 84, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 84, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 85, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 85, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 85, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 86, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 86, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 86, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 91, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 91, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 91, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 92, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 92, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 92, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 93, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 93, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 93, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 94, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 94, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 94, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 95, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 95, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 103, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 155, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 156, "usage_type": "call"}, {"api_name": "imutils.face_utils.FACIAL_LANDMARKS_IDXS", "line_number": 160, "usage_type": "attribute"}, {"api_name": "imutils.face_utils", "line_number": 160, "usage_type": "name"}, {"api_name": "imutils.face_utils.FACIAL_LANDMARKS_IDXS", "line_number": 161, "usage_type": "attribute"}, {"api_name": "imutils.face_utils", "line_number": 161, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 171, "usage_type": "call"}, {"api_name": "imutils.video.VideoStream", "line_number": 178, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 180, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 189, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 190, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 190, "usage_type": "attribute"}, {"api_name": "cv2.CASCADE_SCALE_IMAGE", "line_number": 195, "usage_type": "attribute"}, {"api_name": "dlib.rectangle", "line_number": 201, "usage_type": "call"}, {"api_name": "imutils.face_utils.shape_to_np", "line_number": 208, "usage_type": "call"}, {"api_name": "imutils.face_utils", "line_number": 208, "usage_type": "name"}, {"api_name": "cv2.convexHull", "line_number": 222, "usage_type": "call"}, {"api_name": "cv2.convexHull", "line_number": 223, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 224, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 225, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 248, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 249, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 271, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 274, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 279, "usage_type": "call"}]} +{"seq_id": "43964646389", "text": "import pygame\nfrom objects.ball import Ball\nfrom objects.square import Square\nfrom funcs import *\nimport random\nballs_sp = []\n\n\ndef draw_line(pos):\n if pos[1] >= screen_height - 100 or pos == [10000000000, 10000000]:\n return\n k, b = get_k_and_b((x0, y0), pos)\n final = (-b // k, 0)\n pygame.draw.line(screen, pygame.Color(\"white\"), (x0, y0), final, width=1)\n\n\ndef spawn_balls(pos):\n balls_sp.append(Ball((x0, y0), pos, screen, screen_size))\n\n\ndef draw_lines_among_balls():\n if len(balls_sp) >= 2:\n for ball in balls_sp[1:]:\n for ball2 in balls_sp:\n pygame.draw.line(screen, pygame.Color('white'), ball2.get_coords(), ball.get_coords())\n\n\nif __name__ == '__main__':\n pygame.init()\n screen_size = screen_width, screen_height = 1000, 1000\n screen = pygame.display.set_mode(screen_size)\n screen.fill(pygame.Color('black'))\n running = True\n clock = pygame.time.Clock()\n pos = [10000000000, 10000000]\n x0 = screen_width // 2\n y0 = screen_height - 100\n need_draw_lines_among_balls = False\n need_draw_poligon = False\n need_draw_blocks = False\n squares = []\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n if event.type == pygame.MOUSEMOTION:\n pos = event.pos\n if event.type == pygame.MOUSEBUTTONDOWN and pos[1] < y0:\n spawn_balls(pos)\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_UP or event.key == pygame.K_w:\n y0 -= 100\n y0 -= 100\n if y0 <= 0:\n y0 = 0\n balls_sp.clear()\n elif event.key == pygame.K_DOWN or event.key == pygame.K_s:\n y0 += 100\n if y0 >= screen_height:\n y0 = screen_height\n elif event.key == pygame.K_c:\n balls_sp.clear()\n elif event.key == pygame.K_g:\n need_draw_lines_among_balls = True if need_draw_lines_among_balls is False else False\n elif event.key == pygame.K_t:\n need_draw_poligon = True if need_draw_poligon is False else False\n elif event.key == pygame.K_b:\n squares = []\n need_draw_blocks = True if need_draw_blocks is False else False\n\n clock.tick(600)\n pygame.display.flip()\n screen.fill(pygame.Color('black'))\n pygame.draw.line(screen, pygame.Color('white'), (0, y0), (screen_width, y0), width=3)\n if need_draw_blocks and len(squares) == 0:\n squares = []\n for _ in range(10):\n squares.append(Square((random.randint(0, 700), random.randint(0, 600)), 100))\n if need_draw_lines_among_balls:\n draw_lines_among_balls()\n if need_draw_poligon and len(balls_sp) >= 3:\n pygame.draw.polygon(screen, pygame.Color('white'), [ball.get_coords() for ball in balls_sp])\n for square in squares:\n pygame.draw.rect(screen, pygame.Color('white'), square.rect)\n if pos[1] < y0:\n draw_line(pos)\n if balls_sp:\n for ball in balls_sp:\n ball.move(y0, squares)\n ball.render()\n pygame.quit()", "repo_name": "DulmievMusa/Balls_shooter", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "73", "api": [{"api_name": "pygame.draw.line", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 14, "usage_type": "call"}, {"api_name": "objects.ball.Ball", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEMOTION", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.K_c", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.K_g", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.K_t", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.K_b", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 74, "usage_type": "call"}, {"api_name": "objects.square.Square", "line_number": 78, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.draw.polygon", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "74792454635", "text": "from flask import Flask,jsonify,request\r\n\r\napp = Flask(__name__)\r\nalunos = []\r\nprofessorres = []\r\ndisciplinas = []\r\nofertadas = []\r\n@app.route('/alunos')\r\ndef retorna_alunos():\r\n return jsonify(alunos)\r\n@app.route('/alunos', methods=['POST'])\r\ndef add_aluno():\r\n new = request.json\r\n if 'nome' not in new.keys():\r\n return jsonify({'erro':'aluno sem nome'}),400\r\n for aluno in alunos:\r\n if aluno['id'] == new['id']:\r\n return jsonify({'erro':'id ja utilizada'}),400\r\n \r\n alunos.append(request.json)\r\n return jsonify(alunos),200\r\n \r\n@app.route('/alunos/