diff --git "a/1280.jsonl" "b/1280.jsonl" new file mode 100644--- /dev/null +++ "b/1280.jsonl" @@ -0,0 +1,1181 @@ +{"seq_id":"17323473783","text":"# WAP to input n elements in a list & Display the list in the sorted order without using any built in Function\n# Input : Harsh Bharati Abhimanyu Abhinav\n# Output : Abhimanyu Abhinav Bharati Harsh\ndef quicksort(lst):\n if not lst:\n return []\n return (quicksort([x for x in lst[1:] if x < lst[0]])\n + [lst[0]] +\n quicksort([x for x in lst[1:] if x >= lst[0]]))\n\nn=int(input())\nlst=[]\nfor i in range (n):\n s=input()\n lst.append(s)\nprint(quicksort(lst))\n\n","repo_name":"harshraj24/Code_Practice","sub_path":"Ques_3.py","file_name":"Ques_3.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"71215181140","text":"# string compression\n# assumes original string contains no numbers\n\ndef compress1(string):\n # basic method using new string\n newString = \"\"\n lastChar = None\n lastCount = 0\n for currentChar in string:\n if currentChar != lastChar:\n if lastCount == 1:\n newString += lastChar\n elif lastCount > 1:\n newString += \"%c%d\" % (lastChar, lastCount)\n lastChar = currentChar\n lastCount = 1\n else:\n lastCount += 1\n newString += \"%c\" % lastChar\n if lastCount > 1:\n newString += \"%d\" % lastCount\n return newString\n\ndef expand1(string):\n #basic expansion using new string\n newString = \"\"\n lastChar = None\n for char in string:\n if char.isalpha():\n lastChar = char\n newString += char\n else:\n repeat = int(char)\n newString += lastChar*(repeat-1)\n return newString\n\ndef compress2(string):\n # in-place compress\n # does not work since python strings are immutable, and making\n # a list within this function defeats the purpose...\n pass\n\ntestStrings = [\n (\"abc\", \"abc\"),\n (\"abbc\", \"ab2c\"),\n (\"abbbc\", \"ab3c\"),\n (\"aabbcc\", \"a2b2c2\"),\n (\"aabbbbbc\", \"a2b5c\"),\n ]\n\nfor orig, expected in testStrings:\n compressed = compress1(orig)\n assert compressed == expected\n assert expand1(compressed) == orig\n","repo_name":"JamesWo/Algorithms","sub_path":"string/stringCompress/repeatedCounts.py","file_name":"repeatedCounts.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"8293019957","text":"import seaborn as sns\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import PCA, FastICA\nimport matplotlib as plt\n\ndef scatter_PCA(X, Y, components, alpha):\n \"\"\"\n Description: Creates PCA scatter plot where X is a numpy array of samples and Y contains the corresponding labels. \n\n Args:\n X -- numpy array (Numpy array of data to be plotted)\n Y -- numpy array (Numpy array with labels for data in X)\n components -- int (Number of features of data in X) \n alpha -- double (From [0.0 - 1.0], level of opacity for the dots on the plot)\n\n \"\"\"\n pca = PCA(n_components=components)\n pca_result = pca.fit_transform(X)\n scatter_plot(pca_result, Y, alpha)\n\n\ndef scatter_ICA(X, Y, components, alpha):\n \"\"\"\n Description: Creates ICA scatter plot where X is a numpy array of samples and Y contains the corresponding labels. \n\n Args:\n X -- numpy array (Numpy array of data to be plotted)\n Y -- numpy array (Numpy array with labels for data in X)\n components -- int (Number of features of data in X) \n alpha -- double (From [0.0 - 1.0], level of opacity for the dots on the plot)\n\n \"\"\"\n ica = FastICA(n_components=components)\n ica_result = ica.fit_transform(X)\n scatter_plot(ica_result, Y, alpha)\n\n\ndef scatter_TSNE(X, Y, components, alpha):\n \"\"\"\n Description: Creates t-SNE scatter plot where X is a numpy array of samples and Y contains the corresponding labels. \n\n Args:\n X -- numpy array (Numpy array of data to be plotted)\n Y -- numpy array (Numpy array with labels for data in X)\n components -- int (Number of features of data in X) \n alpha -- double (From [0.0 - 1.0], level of opacity for the dots on the plot)\n\n \"\"\"\n RS = 20150101\n TSNE_proj = TSNE(random_state=RS, n_components=components).fit_transform(X)\n scatter_plot(TSNE_proj, Y, alpha)\n\n\ndef scatter_plot(result, Y, alpha):\n \"\"\"\n Description: Creates scatter plot from output of PCA, ICA, t-SNE functions \n\n Args:\n result -- numpy array (nshape = (n_samples, n_components) Embedding of the training data in low-dimensional space)\n Y -- numpy array (Numpy array with labels for data in X)\n alpha -- double (From [0.0 - 1.0], level of opacity for the dots on the plot)\n\n \"\"\"\n sns.set_style('darkgrid')\n sns.set_palette('muted')\n sns.set_context(\"notebook\", font_scale=1.5,\n rc={\"lines.linewidth\": 1.25})\n df_subset = {\"1\": [], \"2\": []}\n df_subset['1'] = result[:, 0]\n df_subset['2'] = result[:, 1]\n df_subset['y'] = Y\n plt.pyplot.figure(figsize = (10,10))\n sns.scatterplot(\n x=\"2\", y=\"1\",\n hue=\"y\",\n palette=sns.color_palette(\"hls\", 2),\n data=df_subset,\n legend=\"full\",\n alpha=alpha\n )\n","repo_name":"googleinterns/sensor-tools","sub_path":"plotting_util.py","file_name":"plotting_util.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"69816355220","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Nov 10 03:51:41 2021\r\n\r\n@author: ADMIN\r\n\"\"\"\r\n\r\nimport streamlit as st \r\nimport numpy as np \r\nimport pandas as pd \r\nimport plotly.express as px \r\nimport matplotlib.pyplot as plt \r\nimport seaborn as sns \r\nfrom sklearn.linear_model import LinearRegression,Lasso,Ridge \r\nfrom sklearn.model_selection import train_test_split \r\nst.title('Interest rate subsidy') \r\nst.text('Check your eligiblity for availing interest rate subsidy') \r\ndf=pd.read_csv('Training Data.csv') \r\n# The following lines create boxes in which user can enter data required to make prediction\r\nage=st.selectbox (\"Age\",range(21,80,1)) \r\nsex = st.radio(\"Select Gender: \", ('male', 'female')) \r\nincome=st.slider(\"Income\",min_value=0,max_value=10000000,step=10000) \r\nworkex = st.selectbox('Work Experience',range(0,20,1)) \r\nmarital=st.radio('Marital Status',('Yes','No')) \r\nown=st.selectbox('Ownership status',(\"not rented/not owned\",\"rented\",\"owned\")) \r\ndefault = st.radio(\"Have you ever defaulted in past: \", ('yes', 'no')) \r\n# User input \r\nw=0\r\no=0\r\na=0\r\ninc=0\r\nd=0\r\nif 0<= workex <=2:\r\n w = 0 \r\nelif 2 8:\r\n st.text('You are eligible for loans at subsidized interest rates')\r\n elif 6<=user_input<=8:\r\n st.text('You are eligible for loans at normal interest rates') \r\n else:\r\n st.text('You are eligible for loans at high interest rates')\r\n \r\n","repo_name":"swatighiya/Loan","sub_path":"Loan_App.py","file_name":"Loan_App.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"34089046736","text":"x=\"marym\"\nprint (x)\nx=3\nprint(x)\n#python is my life \nx = str(3) # x will be '3’\ny = int(3) # y will be 3\nz = float(3)\nprint(x,y,z)\nu=3\nU='3'\nr=(5) #int\no=(5,) #tuple\nprint(type(u)) # int\nprint(type(U)) #str\nprint(type(r)) \nprint(type(o))\nx=9\ny=1\nprint(x+y)\n#python list \nmylist=['app','banan','cherry','kiwi','zara']\nprint(mylist[:3]) #['app', 'banan', 'cherry']\nprint(mylist[1]) # banan\nprint(mylist[-1]) # zara\nprint(mylist[1:3]) # ['banan', 'cherry']\nprint(mylist[:-1]) # ['app', 'banan', 'cherry', 'kiwi']\nprint(mylist[-1:]) # ['zara']\nprint(mylist[:-2])\nprint(mylist[-2:])\nprint(len(mylist)) #5\nmylist[3]='bbaass'\nprint(mylist) #['app', 'banan', 'cherry', 'bbaass', 'zara']\nlist1=['m','z','a','hh']\nlist2=[9,8,7,6,5,4]\nlist3=[True , False , 6 , 'mmju']\nprint(list1) #['m', 'z', 'a', 'hh']\nprint(list2) #[9, 8, 7, 6, 5, 4]\nprint(list3) #[True, False, 6, 'mmju']\nmylist7 = [\"apple\", \"banana\", \"cherry\"]\nprint(type(mylist))\nmylist7.append('orange') #adding at the end of the list\nprint(mylist7) #['apple', 'banana', 'cherry', 'orange']\nlist11 = list('hello')\nprint(list11)\nlist12=[2445,133,12454,123]\nprint(max(list12))\n# if condation in python\na=8\nb=9\nif a>b:\n print(a)\nelse:\n print(b)\n# while loop \ni=0\nwhile i < 6 :\n i+=1\n if i==3:\n break\n print(i)\n# second code in section \ni=0\nwhile i < 6 :\n i+=1\n if i==4:\n continue\n print(i)\n#for loop\nfmylist=['orange','red','bink','yellow'] \nfor x in fmylist:\n print(x)\nfor y in 'banana' :\n print (y)\n# break in for loop \nfruits = [\"apple\", \"banana\", \"cherry\"]\nfor x in fruits:\n if x == \"banana\":\n break\nprint(x) \n#continue in for loop\nfruits = [\"apple\", \"banana\", \"cherry\"]\nfor x in fruits:\n if x == \"banana\":\n continue\nprint(x) \n#in range in for loop \nfor x in range(6):\n print (x)\nelse:\n print('finally finished!!')\nfor x in range(3,9,2) :\n print(x)\nfor x in range (2,8) :\n print (x)\n#a nested \nlist2=['lk','iu','jj','ff']\nlist4=['k','i','j','f']\nfor x in list2:\n for y in list4:\n print(x,y)\n# a function in python \ndef my_function():\n print(\"my function is a block of code\")\nmy_function() \ndef myfunction(fname):\n print('hello'+\" \"+fname)\nmyfunction(\"marym\")\nmyfunction(\"sara\") \nmyfunction(\"hager\")\ndef myfunction1(fname , lname):\n print(fname +\" \"+lname)\nmyfunction1('mariam' ,' fouda') \n#if the number of arr is unknown\ndef myfunction6(*kids):\n print(\"the youngest child\"+\" \"+kids[2])\nmyfunction6('adam','mostafa','mohamed','ali')\n#the index here not important \ndef my_function(child3, child2, child1):\n print(\"The youngest child is \" + child3)\nmy_function(child1 = \"ali\", child2 = \"anas\", child3 = \"soha\")\ndef my_function(country = \"Egypt\"):\n print(\"I am from \" + country)\nmy_function(\"Sweden\")\nmy_function(\"India\")\nmy_function()\nmy_function(\"Brazil\")\ndef my_function(x):\n return 5 * x\nprint(my_function(3))\nprint(my_function(5))\nprint(my_function(9))\nx= lambda a : a + 10\nprint(x(4))\nx= lambda a,b : a*b\nprint(x(2,3))\nx= lambda a,b,c : a+b+c\nprint(x(1,2,3))\nclass Person:\n def __init__(self, name, age):\n self.name = name\n self.age = age\np1 = Person(\"John\", 36)\nprint(p1.name)\nprint(p1.age)\nclass animals:\n def __init__(self ,kind , color):\n self.kind=kind\n self.color=color\np1=animals('cat','white') \nprint(p1.kind)\nprint(p1.color) \n\n","repo_name":"marymfouda/python-apps","sub_path":"untitled38.py","file_name":"untitled38.py","file_ext":"py","file_size_in_byte":3328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"28346394216","text":"import argparse\nimport cv2\nimport time\nimport math\nimport onnxruntime\nimport numpy as np\nfrom math import cos, sin\nimport mediapipe as mp\nimport os\n\n# HEADPOSE DRAW FUNC\ndef draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size=50, img_size=50):\n # Referenced from HopeNet https://github.com/natanielruiz/deep-head-pose\n if math.isnan(yaw) or math.isnan(pitch):\n return img\n pitch = pitch * np.pi / 180\n yaw = -(yaw * np.pi / 180)\n if tdx != None and tdy != None:\n tdx = tdx\n tdy = tdy\n else:\n height, width = img.shape[:2]\n tdx = width / 2\n tdy = height / 2\n if math.isnan(roll):\n print('roll is nan')\n else:\n roll = roll * np.pi / 180\n # X-Axis pointing to right. drawn in red\n x1 = size * (cos(yaw) * cos(roll)) + tdx\n y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy\n # Y-Axis | drawn in green\n # v\n x2 = size * (-cos(yaw) * sin(roll)) + tdx\n y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy\n cv2.line(img, (int(tdx), int(tdy)), (int(x1), int(y1)), (0, 0, 255), 2)\n cv2.line(img, (int(tdx), int(tdy)), (int(x2), int(y2)), (0, 255, 0), 2)\n # Z-Axis (out of the screen) drawn in blue\n # x3 = size * (sin(yaw)) + tdx\n # y3 = size * (-cos(yaw) * sin(pitch)) + tdy\n x3 = img_size * (sin(yaw)) + tdx\n y3 = img_size * (-cos(yaw) * sin(pitch)) + tdy\n cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(255,0,0),2)\n\n return img\n\n#\ndef plot_pose_cube(img, yaw, pitch, roll, tdx=None, tdy=None, size=150.):\n # Input is a cv2 image\n # pose_params: (pitch, yaw, roll, tdx, tdy)\n # Where (tdx, tdy) is the translation of the face.\n # For pose we have [pitch yaw roll tdx tdy tdz scale_factor]\n if math.isnan(yaw) or math.isnan(pitch):\n return img\n\n p = pitch * np.pi / 180\n y = -(yaw * np.pi / 180)\n r = roll * np.pi / 180\n if tdx != None and tdy != None:\n tdx = tdx\n tdy = tdy\n face_x = tdx - 0.50 * size\n face_y = tdy - 0.50 * size\n else:\n height, width = img.shape[:2]\n tdx = width / 2\n tdy = height / 2\n face_x = width / 2 - 0.5 * size\n face_y = height / 2 - 0.5 * size\n\n x1 = size * (cos(y) * cos(r)) + face_x\n y1 = size * (cos(p) * sin(r) + cos(r) * sin(p) * sin(y)) + face_y\n x2 = size * (-cos(y) * sin(r)) + face_x\n y2 = size * (cos(p) * cos(r) - sin(p) * sin(y) * sin(r)) + face_y\n x3 = size * (sin(y)) + face_x\n y3 = size * (-cos(y) * sin(p)) + face_y\n\n # Draw base in red\n cv2.line(img, (int(face_x), int(face_y)), (int(x1),int(y1)),(0,0,255),3)\n cv2.line(img, (int(face_x), int(face_y)), (int(x2),int(y2)),(0,0,255),3)\n cv2.line(img, (int(x2), int(y2)), (int(x2+x1-face_x),int(y2+y1-face_y)),(0,0,255),3)\n cv2.line(img, (int(x1), int(y1)), (int(x1+x2-face_x),int(y1+y2-face_y)),(0,0,255),3)\n # Draw pillars in blue\n cv2.line(img, (int(face_x), int(face_y)), (int(x3),int(y3)),(255,0,0),2)\n cv2.line(img, (int(x1), int(y1)), (int(x1+x3-face_x),int(y1+y3-face_y)),(255,0,0),2)\n cv2.line(img, (int(x2), int(y2)), (int(x2+x3-face_x),int(y2+y3-face_y)),(255,0,0),2)\n cv2.line(img, (int(x2+x1-face_x),int(y2+y1-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(255,0,0),2)\n # Draw top in green\n cv2.line(img, (int(x3+x1-face_x),int(y3+y1-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(0,255,0),2)\n cv2.line(img, (int(x2+x3-face_x),int(y2+y3-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(0,255,0),2)\n cv2.line(img, (int(x3), int(y3)), (int(x3+x1-face_x),int(y3+y1-face_y)),(0,255,0),2)\n cv2.line(img, (int(x3), int(y3)), (int(x3+x2-face_x),int(y3+y2-face_y)),(0,255,0),2)\n\n return img\n\n# BBOX, HEADPOSE DRAW\ndef draw_bbox_axis(frame, face_pos, add_face, yaw, pitch, roll, draw_bbox=0, draw_cube=1, draw_line=0):\n\n (x, y, w, h) = face_pos\n (x2, y2) = add_face\n w = x2-x\n h = y2-y\n\n # Draw bbox\n if draw_bbox:\n deg_norm = 1.0 - abs(yaw / 180)\n blue = int(255 * deg_norm)\n cv2.rectangle(frame, (int(x), int(y)), (int(x2), int(y2)), color=(blue, 0, 255 - blue), thickness=2)\n\n # Draw pose cube\n if draw_cube:\n frame = plot_pose_cube(frame, yaw, pitch, roll, tdx=x + w / 2, tdy=y + h / 2, size=w)\n\n # Draw pose axis\n if draw_line:\n frame = draw_axis(frame, yaw, pitch, roll, tdx=x + w / 2, tdy=y + h / 2, size=w // 2)\n\n return frame\n\n# ONNX LOAD\ndef load_onnx_model(path, name):\n onnx_model = onnxruntime.InferenceSession(path_or_bytes=os.path.join((os.getcwd() + os.path.sep).split('src')[0], 'models', path))\n globals()['onnx_input_{}'.format(name)] = onnx_model.get_inputs()[0].name\n print(\">>> onnx model load : {}\".format(name))\n print(\">>> input name : {}\".format(onnx_model.get_inputs()[0].name))\n print(\">>> input shape : {}\".format(onnx_model.get_inputs()[0].shape))\n print(\">>> done.\\n\")\n return onnx_model\n\n# 6DREPNET\ndef headpose_6drepnet2(rgb_img, x, y, x2, y2, onnx_input_sixdrepnet, sixdrepnet_model):\n\n face_img = rgb_img[y:y2, x:x2, :]\n\n face_img = cv2.resize(face_img, (256, 256))\n face_img = face_img[16:240,16:240,0:3]\n\n # 공식 깃헙 노말라이즈 구현\n face_img = np.array(face_img, dtype=np.uint8)\n face_img = face_img / 255\n face_img[:,:,0] = (face_img[:,:,0] - 0.485) / 0.229\n face_img[:,:,1] = (face_img[:,:,1] - 0.456) / 0.224\n face_img[:,:,2] = (face_img[:,:,2] - 0.406) / 0.225\n\n face_img = face_img.transpose(2, 0, 1)\n\n face_img = np.expand_dims(face_img, axis=0)\n face_img = np.array(face_img, dtype=np.float32)\n\n st_time = time.time()\n outputs = sixdrepnet_model.run(None, input_feed={onnx_input_sixdrepnet: face_img})[0]\n\n R = outputs\n sy = np.sqrt(R[:, 0, 0] * R[:, 0, 0] + R[:, 1, 0] * R[:, 1, 0])\n singular = sy < 1e-6\n\n x = np.arctan2(R[:, 2, 1], R[:, 2, 2])\n y = np.arctan2(-R[:, 2, 0], sy)\n z = np.arctan2(R[:, 1, 0], R[:, 0, 0])\n xs = np.arctan2(-R[:,1,2], R[:,1,1])\n ys = np.arctan2(-R[:,2,0], sy)\n zs = R[:, 1, 0] * 0\n\n pitch = (x * (1 - singular) + xs * singular)[0] * 180 / np.pi\n yaw = (y * (1 - singular) + ys * singular)[0] * 180 / np.pi\n roll = (z * (1 - singular) + zs * singular)[0] * 180 / np.pi\n\n print(\">>> 6DREPNET Use Time : {}\".format(time.time() - st_time))\n print(yaw, pitch, roll)\n\n return yaw, pitch, roll\n\n# MAIN\ndef main(draw_bbox, draw_cube, draw_line):\n\n # Load 6DPRepNet\n sixdrepnet_model = load_onnx_model(path='sixdrepnet.onnx', name='sixdrepnet')\n\n # Load Mediapipe to predict Face\n face_detection = mp.solutions.face_detection.FaceDetection(min_detection_confidence=0.9)\n\n # Capture\n cap = cv2.VideoCapture(0)\n\n # Start Loop\n while 1:\n ret, frame = cap.read()\n if not ret:\n break\n frame = cv2.flip(frame, 1)\n output_frame = frame.copy()\n rgb_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n start_time, st_time = time.time(), time.time()\n\n # Face Detect (using Mediapipe)\n detected = face_detection.process(rgb_img)\n print(\">>> BlazeFace Use Time : {}\".format(time.time() - st_time))\n\n if detected.detections:\n\n face_pos = detected.detections[0].location_data.relative_bounding_box\n x = int(rgb_img.shape[1] * max(face_pos.xmin, 0))\n y = int(rgb_img.shape[0] * max(face_pos.ymin, 0))\n w = int(rgb_img.shape[1] * min(face_pos.width, 1))\n h = int(rgb_img.shape[0] * min(face_pos.height, 1))\n\n # bbox\n face_plus_scalar = 5\n x2 = min(x + w + face_plus_scalar, rgb_img.shape[1])\n y2 = min(y + h + face_plus_scalar, rgb_img.shape[0])\n x = max(0, x - face_plus_scalar)\n y = max(0, y - face_plus_scalar)\n face_pos = (x, y, w, h)\n\n # headpose\n yaw, pitch, roll = headpose_6drepnet2(rgb_img, x, y, x2, y2, onnx_input_sixdrepnet, sixdrepnet_model)\n\n # draw bbox, axis\n draw_bbox_axis(output_frame, face_pos, (x2, y2), yaw, pitch, roll,\n draw_bbox=draw_bbox, draw_cube=draw_cube, draw_line=draw_line)\n\n print(\">>> Total Loop Time : {}\\n\".format(time.time() - start_time))\n cv2.imshow('demo', output_frame)\n\n if cv2.waitKey(1) == 27:\n cap.release()\n cv2.destroyAllWindows()\n break\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(description='6DRepNet to ONNX')\n parser.add_argument('--draw_bbox', default=1, type=int)\n parser.add_argument('--draw_cube', default=0, type=int)\n parser.add_argument('--draw_line', default=1, type=int)\n args = parser.parse_args()\n\n main(args.draw_bbox, args.draw_cube, args.draw_line)","repo_name":"saeu5407/6drepnet-onnx","sub_path":"src/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":8872,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"72305378900","text":"''' script contains preprocessing pipelines for linear and tree based models, all transformers and helper function using in pipelines ''' \n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.preprocessing import OrdinalEncoder, StandardScaler\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.metrics import accuracy_score, log_loss\n\n''' helper functions '''\n\ndef feature_reduction_pipeline(model, X, y):\n ''' reduce number of features using perturbation techinque'''\n\n model.fit(X,y)\n \n base_acc = accuracy_score(y, model.predict(X))\n base_log_loss= log_loss( y, model.predict_proba(X)[:,1] )\n \n best_features_idx = []\n \n for i in range(X.shape[1]):\n\n hold = X.copy()\n np.random.shuffle(X[:, i])\n\n curr_acc = accuracy_score( y, model.predict(X) )\n diff_acc = curr_acc - base_acc\n\n curr_log_loss = log_loss( y, model.predict_proba(X)[:,1] )\n diff_log_loss = curr_log_loss - base_log_loss\n \n if diff_log_loss > 0: # if diff_acc < 0 and diff_log_loss > 0:\n best_features_idx.append(i)\n\n X = hold\n \n if not best_features_idx:\n best_features_idx = list(range(X.shape[1]))\n \n return np.array(best_features_idx)\n\n\ndef feature_reduction_ann_pipeline(model, X, y):\n ''' reduce number of features for ann using perturbation techinque'''\n \n model.set_params(input_shape=X.shape[1:])\n model.fit(X,y)\n \n base_acc = accuracy_score(y, model.predict(X))\n base_log_loss = log_loss( y, model.predict_proba(X)[:,1] )\n \n best_features_idx = []\n \n for i in range(X.shape[1]):\n\n hold = X.copy()\n np.random.shuffle(X[:, i])\n\n curr_acc = accuracy_score( y, model.predict(X) )\n diff_acc = curr_acc - base_acc\n\n curr_log_loss = log_loss( y, model.predict_proba(X)[:,1] )\n diff_log_loss = curr_log_loss - base_log_loss\n\n if diff_log_loss > 0: # if diff_acc < 0 and diff_log_loss > 0:\n best_features_idx.append(i)\n\n X = hold\n \n if not best_features_idx:\n best_features_idx = list(range(X.shape[1]))\n \n return np.array(best_features_idx)\n\n\ndef feature_reduction_rnn_pipeline(model, X, y):\n ''' reduce number of features for rnn using perturbation techinque'''\n\n X_reshaped = X.reshape(X.shape[0], 1, X.shape[1])\n \n model.set_params(input_shape=X_reshaped.shape[1:])\n model.fit(X_reshaped, y)\n \n base_acc = accuracy_score(y, model.predict(X_reshaped))\n base_log_loss = log_loss( y, model.predict_proba(X_reshaped)[:,1] )\n best_features_idx = []\n \n for i in range(X.shape[1]):\n\n hold = X_reshaped.copy()\n np.random.shuffle(X_reshaped[:, :, i])\n\n curr_acc = accuracy_score( y, model.predict(X_reshaped) )\n diff_acc = curr_acc - base_acc\n curr_log_loss = log_loss( y, model.predict_proba(X_reshaped)[:,1] ) \n diff_log_loss = curr_log_loss - base_log_loss\n\n if diff_log_loss > 0: # if diff_acc < 0 and diff_log_loss > 0:\n best_features_idx.append(i)\n\n X_reshaped = hold\n \n if not best_features_idx:\n best_features_idx = list(range(X.shape[1]))\n \n return np.array(best_features_idx)\n\n \ndef target_mean_encoding(df, cat_name, target, weight=10):\n ''' function return smoothing target mean encoding '''\n\n # Compute the global mean\n mean = df[target].mean()\n\n # Compute the number of values and the mean of each group\n agg = df.groupby(cat_name)[target].agg(['count', 'mean'])\n\n counts = agg['count']\n means = agg['mean']\n\n # Compute the \"smoothed\" means\n smooth = (counts * means + weight * mean) / (counts + weight)\n\n return smooth, mean\n\n\n''' pipeline transformers '''\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n ''' select columns from dataframe and return numpy array '''\n def __init__(self, attribute_names):\n self.attribute_names = attribute_names\n \n def fit(self, X, y=None):\n return self\n \n def transform(self, X, y=None):\n return np.array(X[self.attribute_names])\n\n\nclass TwoColumnScaler(BaseEstimator, TransformerMixin):\n ''' take two columns and scaling it's keeping original ratio between them '''\n def __init__(self, scaler):\n self.scaler = scaler\n \n def fit(self, X, y=None):\n columns_merged = np.concatenate((X[:,0], X[:,1]), axis=0)\n self.scaler.fit(columns_merged.reshape(-1,1))\n return self\n \n def transform(self, X, y=None):\n X1 = self.scaler.transform(X[:, 0].reshape(-1,1))\n X2 = self.scaler.transform(X[:, 1].reshape(-1,1))\n X_new = np.concatenate((X1, X2), axis=1)\n return X_new\n\n \nclass DictionaryEncoder(BaseEstimator, TransformerMixin):\n ''' encoding labels using dictionary '''\n def __init__(self, dictionary):\n self.dictionary = dictionary\n \n def fit(self, X, y=None):\n return self\n \n def transform(self, X, y=None):\n return X.replace(self.dictionary).values\n \n\nclass ToDataFrame(BaseEstimator, TransformerMixin):\n ''' transform numpy array to dataframe '''\n def __init__(self, columns):\n self.columns = columns\n \n def fit(self, X, y=None):\n return self\n \n def transform(self, X, y=None):\n return pd.DataFrame(X, columns=self.columns)\n\n \nclass Array3dTransformer(BaseEstimator, TransformerMixin):\n ''' transform 2d numpy array to 3d numpy array '''\n def fit(self, X, y=None):\n return self\n \n def transform(self, X, y=None):\n return X.reshape(*X.shape,1)\n\n \nclass ImportantFeaturesSelector(BaseEstimator, TransformerMixin):\n ''' select most important features from numpy array'''\n def __init__(self, model, model_type):\n self.model = model\n self.model_type = model_type\n \n def fit(self, X, y=None):\n if self.model_type == 'basic':\n self.important_features = feature_reduction_pipeline(self.model, X, y)\n elif self.model_type == 'ann':\n self.important_features = feature_reduction_ann_pipeline(self.model, X, y)\n elif self.model_type == 'rnn':\n self.important_features = feature_reduction_rnn_pipeline(self.model, X, y)\n else:\n raise TypeError('model_type have to be basic, ann or rnn')\n return self\n \n def transform(self, X, y=None):\n return X[:, self.important_features]\n \n\nclass TargetMeanEncodingTransformer(BaseEstimator, TransformerMixin):\n ''' transform feature using target mean encoding'''\n def __init__(self, cat_name, target):\n self.cat_name = cat_name\n self.target = target\n \n def fit(self, X, y=None):\n self.target_dict, self.global_mean = target_mean_encoding(X, self.cat_name, self.target)\n return self\n\n def transform(self, X, y=None):\n X_arr = np.zeros(len(X)).reshape(-1,1)\n for i in range(len(X_arr)):\n try:\n X_arr[i] = self.target_dict.loc[ X[self.cat_name].iloc[i] ]\n except KeyError: # category doesnt occur in training set\n X_arr[i] = self.global_mean\n return X_arr\n \n \n''' basic pipelines ''' \n\n# read raw data\nX_train_set = pd.read_csv('./preprocessed_data/train_set_stage2.csv', index_col=0)\n\n# create list of team names for ordinal encoder\nhome_team_names = np.unique(X_train_set['HomeTeam'])\naway_team_names = np.unique(X_train_set['AwayTeam'])\nteam_names=[home_team_names, away_team_names]\n\n# assign manually features to the groups\ntarget_col = ['FTR']\n\nteams_cols =['HomeTeam','AwayTeam']\n\nteams_ratio_cols = ['HomeTeamWinRatio', 'AwayTeamWinRatio']\n\nteams_ratio_cat_cols = ['HomeTeamWinRatio_Cat', 'AwayTeamWinRatio_Cat']\n\nlast_year_postion_cols = ['HomeTeamLastYearPosition', 'AwayTeamLastYearPosition']\n\ntotal_cols = ['HomeTeamGoalsScored','AwayTeamGoalsScored','HomeTeamGoalsLost','AwayTeamGoalsLost','HomeTeamShootsMade', \n 'AwayTeamShootsMade','HomeTeamTargetShootsMade','AwayTeamTargetShootsMade','HomeTeamCorners','AwayTeamCorners',\n 'HomeTeamTotalPoints','AwayTeamTotalPoints']\n\ntotal_cat_cols = ['HomeTeamTargetShootsMade_Cat', 'AwayTeamTargetShootsMade_Cat', 'HomeTeamGoalsScored_Cat',\n 'AwayTeamGoalsScored_Cat', 'HomeTeamGoalsLost_Cat','AwayTeamGoalsLost_Cat', 'HomeTeamShootsMade_Cat',\n 'AwayTeamShootsMade_Cat','HomeTeamCorners_Cat', 'AwayTeamCorners_Cat', 'HomeTeamTotalPoints_Cat',\n 'AwayTeamTotalPoints_Cat',]\n\nlast_matches_results_cols = ['HomeTeamLast1Match','AwayTeamLast1Match', 'HomeTeamLast2Match', 'AwayTeamLast2Match',\n 'HomeTeamLast3Match', 'AwayTeamLast3Match', 'HomeTeamLast4Match','AwayTeamLast4Match', \n 'HomeTeamLast5Match', 'AwayTeamLast5Match',]\n\nlast_matches_points_cols = ['HomeTeamPointsFromLast3Matches','AwayTeamPointsFromLast3Matches', \n 'HomeTeamPointsFromLast5Matches','AwayTeamPointsFromLast5Matches', \n 'HomeTeamPointsFromLast10Matches','AwayTeamPointsFromLast10Matches']\n\nbinary_cols = ['HomeTeamWinStreak3', 'HomeTeamWinStreak5', 'HomeTeamLossStreak3','HomeTeamLossStreak5', \n 'AwayTeamWinStreak3', 'AwayTeamWinStreak5','AwayTeamLossStreak3', 'AwayTeamLossStreak5',\n 'IsHomeTeamRegulars', 'IsAwayTeamRegulars', 'IsHomeTeamRookie', 'IsAwayTeamRookie']\n\ndiff_cols = ['HomeTeamGoalsDifference', 'AwayTeamGoalsDifference','TotalGoalsDifference','DifferenceTotalPoints',\n 'Difference1MatchPoints', 'Difference3MatchesPoints','Difference5MatchesPoints','Difference10MatchesPoints',\n 'DifferenceInShoots', 'DifferenceInTargetShoots', 'DifferenceInCorners','DifferenceInLastYearPosition'] \n\ndiff_cat_cols = ['HomeTeamGoalsDifference_Cat','AwayTeamGoalsDifference_Cat', 'TotalGoalsDifference_Cat',\n 'DifferenceTotalPoints_Cat', 'Difference10MatchesPoints_Cat','DifferenceInShoots_Cat',\n 'DifferenceInTargetShoots_Cat','DifferenceInCorners_Cat']\n\n\n''' Base pipeline for tree-based models '''\n\nstandard_scaling_base_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([*binary_cols, *teams_ratio_cols, *last_matches_points_cols, \n *last_matches_results_cols, *last_year_postion_cols, *diff_cols]) ),\n ('standard_scaler', StandardScaler() )\n])\n\n# label enocoding team names\nordinal_encoder_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([*teams_cols]) ),\n ('ordinal_encoder', OrdinalEncoder(categories=team_names) ),\n ('standard_scaler', StandardScaler() )\n])\n\n# process two features to the same scale(leaving dependencies between them)\ngoals_scored_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([total_cols[0], total_cols[1]]) ),\n ('two_column_scaler', TwoColumnScaler(scaler=StandardScaler() ))\n])\n\ngoals_lost_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([total_cols[2], total_cols[3]]) ),\n ('two_column_scaler', TwoColumnScaler(scaler=StandardScaler() ))\n])\n\nshoot_made_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([total_cols[4], total_cols[5]]) ),\n ('two_column_scaler', TwoColumnScaler(scaler=StandardScaler() ))\n])\n\ntotal_shoot_made_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([total_cols[6], total_cols[7]]) ),\n ('two_column_scaler', TwoColumnScaler(scaler=StandardScaler() ))\n])\n\ncorners_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([total_cols[8], total_cols[9]]) ),\n ('two_column_scaler', TwoColumnScaler(scaler=StandardScaler() ))\n])\n\ntotal_points_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([total_cols[10], total_cols[11]]) ),\n ('two_column_scaler', TwoColumnScaler(scaler=StandardScaler() ))\n])\n\nbasic_preprocess_pipeline = FeatureUnion(transformer_list=[\n ('standard_scaling_pipeline', standard_scaling_base_pipeline),\n ('ordinal_encoder_pipeline', ordinal_encoder_pipeline),\n ('goals_scored_pipeline', goals_scored_pipeline),\n ('goals_lost_pipeline', goals_lost_pipeline),\n ('shoot_made_pipeline', shoot_made_pipeline),\n ('total_shoot_made_pipeline', total_shoot_made_pipeline),\n ('corners_pipeline', corners_pipeline),\n ('total_points_pipeline', total_points_pipeline),\n])\n\n\n''' Pipeline for linear models '''\n\nbase_cat_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([*binary_cols]) ),\n ('standard_scaler', StandardScaler() )\n])\n\nhome_team_encoding_pipeline = Pipeline([\n ('encoding', TargetMeanEncodingTransformer(teams_cols[0], *target_col) ),\n ('standard_scaler', StandardScaler() )\n])\n\naway_team_encoding_pipeline = Pipeline([\n ('encoding', TargetMeanEncodingTransformer(teams_cols[1], *target_col) ),\n ('standard_scaler', StandardScaler() )\n])\n\nstandard_scaling_cat_pipeline = Pipeline([\n ('select_cols', DataFrameSelector([*teams_ratio_cat_cols, *last_matches_points_cols, *last_matches_results_cols,\n *last_year_postion_cols, *diff_cat_cols, *total_cat_cols]) ),\n ('standard_scaler', StandardScaler() )\n])\n\ncategorical_preprocess_pipeline = FeatureUnion(transformer_list=[\n ('home_teams_encoding', home_team_encoding_pipeline),\n ('away_teams_encoding', away_team_encoding_pipeline),\n ('base_pipeline ', base_cat_pipeline),\n ('standard_scaling_pipeline', standard_scaling_cat_pipeline),\n])","repo_name":"Cyki89/Predicting_Winning_Team","sub_path":"preprocessing_pipelines.py","file_name":"preprocessing_pipelines.py","file_ext":"py","file_size_in_byte":13959,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"12"} +{"seq_id":"26366476200","text":"from sqlalchemy import Column, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\n\nclass User(Base):\n \"\"\"User contains metadata for a user\"\"\"\n __tablename__ = 'users'\n id = Column(Integer, primary_key=True)\n login = Column(String)\n\n def __init__(self, login):\n self.login = login\n\n def __repr__(self):\n return \"\" % (self.login)\n\n\ndef test():\n\n user1 = User(\"Raph\")\n\n print(user1)\n\n\nif __name__ == '__main__':\n test()\n","repo_name":"tourfl/Apprendre","sub_path":"removed/API/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"23580335597","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Import industry returns.\n\nNotes\n-----\nNever use 'grouped.mean()' in pandas! It leaks memory big time!\n\n\"\"\"\nfrom __future__ import print_function, division\n\nimport os\nimport zipfile\n\nimport pandas as pd\nimport datetime as dt\nimport numpy as np\n\npath = os.getenv(\"HOME\") + '/Dropbox/Research/data/CRSP/data/'\n# __location__ = os.path.realpath(os.path.join(os.getcwd(),\n# os.path.dirname(__file__)))\n# path = os.path.join(__location__, path + 'CRSP/data/')\n\n\ndef convert_dates(string):\n \"\"\"Convert dates from string to Python date format.\n\n \"\"\"\n return dt.datetime.strptime(string, '%d-%m-%Y')\n\n\ndef cum_returns(ret):\n \"\"\"Accumulate returns over time.\n\n \"\"\"\n return np.exp(np.log(1 + ret).sum()) - 1\n\n\ndef import_returns():\n \"\"\"Import raw data.\n\n The file is called industry_returns.zip\n\n Columns:\n DATE : str\n Date in the format 'dd-mm-yyy'\n HSICCD : int\n SIC industry codes\n CUSIP : str\n Firm ID\n PRC : float\n Price\n SHROUT : int\n Shares outstanding\n RETX : float\n Dividend adjusted monthly returns\n\n Typical output:\n Before resampling:\n Date SIC CUSIP Price Shares Return\n 0 1983-01-31 133 06022110 20.250 7074 0.094595\n 1 1983-01-31 174 68417710 2.250 20546 -0.142857\n 2 1983-01-31 179 25660510 9.125 27996 0.028169\n 3 1983-01-31 251 86666510 5.750 614 0.022222\n 4 1983-01-31 752 87831510 9.250 8400 0.088235\n\n After resampling:\n return value\n SIC CUSIP year\n 100 45292410 1995 -23.529461 15334.250\n 1996 -56.982108 15703.750\n 1997 -79.020959 8129.000\n 1998 38.372241 1755.125\n 115 24487820 1988 21.428654 87612.375\n\n \"\"\"\n # Import raw data\n zfile = zipfile.ZipFile(path + 'firm_returns.zip', 'r')\n data = zfile.open(zfile.namelist()[0])\n converters = {'DATE': convert_dates}\n returns = pd.read_csv(data, converters=converters, engine='c')\n # Rename columns\n columns = {'DATE': 'date', 'HSICCD': 'SIC',\n 'PRC': 'price', 'SHROUT': 'shares',\n 'RETX': 'return'}\n returns.rename(columns=columns, inplace=True)\n # Remove incorrect observations\n cond1 = returns['return'] != 'C'\n cond2 = returns['price'] > 0\n cond3 = returns['shares'] > 0\n returns = returns[cond1 & cond2 & cond3]\n # Convert to floats\n returns.loc[:, 'return'] = returns['return'].astype(float)\n\n print(returns.head())\n\n # Resample monthly returns to annual frequency\n returns = resample_returns(returns)\n\n returns.to_hdf(path + 'firm_returns.h5', 'returns')\n\n print(returns.head())\n\n\ndef resample_returns(returns):\n \"\"\"Resample monthly returns to annual frequency.\n\n Typical output:\n return value\n SIC CUSIP year\n 100 45292410 1995 -23.529461 15334.250\n 1996 -56.982108 15703.750\n 1997 -79.020959 8129.000\n 1998 38.372241 1755.125\n 115 24487820 1988 21.428654 87612.375\n\n \"\"\"\n returns.eval('value = shares * price')\n returns.loc[:, 'year'] = returns['date'].apply(lambda x: x.year)\n index = ['SIC', 'CUSIP', 'year']\n returns.set_index(index, inplace=True)\n returns = returns.loc[:, ['return', 'value']]\n returns.sort_index(inplace=True)\n\n grouped = returns.groupby(level=index)\n returns = grouped[['return']].apply(cum_returns)\n returns.loc[:, 'value'] = grouped['value'].first()\n returns.loc[:, 'return'] *= 100\n\n return returns\n\n\ndef load_returns():\n \"\"\"Load data from the disk.\n\n \"\"\"\n return pd.read_hdf(path + 'firm_returns.h5', 'returns')\n\n\nif __name__ == '__main__':\n\n import_returns()\n\n returns = load_returns()\n","repo_name":"khrapovs/datastorage","sub_path":"datastorage/crsp.py","file_name":"crsp.py","file_ext":"py","file_size_in_byte":3880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"72513542101","text":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"multianndata\", # Replace with your own username\n version=\"0.0.4\",\n author=\"Yakir Reshef, Laurie Rumker\",\n author_email=\"yreshef@broadinstitute.org\",\n description=\"Multi-sample version of AnnData\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/yakirr/multianndata\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n install_requires=[\n 'anndata',\n 'numpy',\n ],\n)\n","repo_name":"yakirr/multianndata","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"70716544982","text":"class Solution:\n def minRemoveToMakeValid(self, s: str) -> str:\n invalid_index = set()\n stack = []\n \n for i,c in enumerate(s):\n if c not in '()':\n continue\n elif c == '(':\n stack.append(i)\n elif stack and c == ')':\n stack.pop()\n elif c == ')':\n invalid_index.add(i)\n \n if stack: \n for i in stack: invalid_index.add(i)\n \n res = []\n \n for i in range(len(s)):\n if i not in invalid_index: res.append(s[i])\n \n return ''.join(res)\n ","repo_name":"bvchand/leetcode-problems","sub_path":"1249-minimum-remove-to-make-valid-parentheses/1249-minimum-remove-to-make-valid-parentheses.py","file_name":"1249-minimum-remove-to-make-valid-parentheses.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"31468330204","text":"# import libraries\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import r2_score\n\n# Importing the dataset\nfrom sklearn.preprocessing import PolynomialFeatures, StandardScaler\nfrom sklearn.svm import SVR\nfrom sklearn.tree import DecisionTreeRegressor\n\ndataset = pd.read_csv(\"Data.csv\")\nx = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, -1].values\n\n# Splitting the dataset into the Training set and Test set\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n\n# Training the Simple Linear Regression model on the Training set\nlinear_regressor = LinearRegression()\nlinear_regressor.fit(x_train, y_train)\n\n# Predicting the Test set results\ny_pred = linear_regressor.predict(x_test)\n\nlr = r2_score(y_test, y_pred)\nprint('R2 for linear regression')\nprint(lr)\n\n# Training the Polynomial Regression model on the whole dataset\npoly_reg = PolynomialFeatures(degree=4)\nx_poly = poly_reg.fit_transform(x_train)\nlin_reg_2 = LinearRegression()\nlin_reg_2.fit(x_poly,y_train)\n\nx_test_poly = poly_reg.transform(x_test)\ny_test_pred_poly = lin_reg_2.predict(x_test_poly)\npr = r2_score(y_test, y_test_pred_poly)\nprint('R2 for polynomial regression')\nprint(pr)\n\n\n# support vector\ny1 = y.reshape(len(y), 1)\nx_train1, x_test1, y_train1, y_test1 = train_test_split(x, y1, test_size=0.2, random_state=0)\n\nsc_x = StandardScaler()\nxt = sc_x.fit_transform(x_train1)\nsc_y = StandardScaler()\nyt = sc_y.fit_transform(y_train1)\nsvr_regressor = SVR(kernel='rbf')\nsvr_regressor.fit(xt, yt)\n\n\nx1t = sc_x.transform(x_test1)\ny1t = svr_regressor.predict(x1t)\ny_pred_svr = sc_y.inverse_transform(y1t)\nsvr_r2 = r2_score(y_test1, y_pred_svr)\nprint('R2 for support vector')\nprint(svr_r2)\n\n\n#decision tree\nregressor = DecisionTreeRegressor(random_state=0)\nregressor.fit(x_train, y_train)\n\n\ny_pred_dcn = regressor.predict(x_test)\ndcn_r2 = r2_score(y_test, y_pred_dcn)\nprint('R2 for decision tree')\nprint(dcn_r2)\n\n# random forest\nregressor = RandomForestRegressor(n_estimators=10, random_state=0)\nregressor.fit(x_train, y_train)\n\n\ny_pred_rndf = regressor.predict(x_test)\nrndf_r2 = r2_score(y_test, y_pred_rndf)\nprint('R2 for random forest')\nprint(rndf_r2)\n","repo_name":"vnc-edu/machine-learning","sub_path":"Regression/AllAtOnce/power.py","file_name":"power.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"35329370595","text":"import numpy as np\r\nimport time\r\nfrom scipy import interp\r\nfrom sklearn.metrics import roc_curve\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.linear_model import LassoCV, LogisticRegression, ElasticNetCV\r\nfrom sklearn.feature_selection import RFECV, SelectFromModel\r\nfrom MLPipe.measures import Measures\r\n\r\nclass LR_Pipeline:\r\n\r\n def __init__(self, x_train=None, y_train=None, x_test=None, y_test=None, itera=None, cv=None, mean_tprr=None, select_feats_logit=False, T=0.1, method='rfc', run=False):\r\n self.measure = Measures(run)\r\n if run:\r\n self.run(x_train, y_train, x_test, y_test, itera, cv, mean_tprr, select_feats_logit=False, T=0.1, method='rfc')\r\n\r\n else:\r\n self.name = 'NONE'\r\n self.clf = 0\r\n\r\n def run_grid(self, x_train, y_train, x_test, y_test, itera, cv, mean_tprr, select_feats_logit=False, T=0.1, method='rfc'):\r\n \r\n self.run = True\r\n self.name = 'LR'\r\n self.measure.run = True\r\n\r\n feats = np.ones(x_train.shape[1])\r\n if select_feats_logit:\r\n feats = self.Feature_Selection(x_train, y_train, T, method, cv)\r\n print(\"Features Selected\", sum(feats))\r\n x_train = x_train[:, feats]\r\n x_test = x_test[:, feats]\r\n\r\n self.clf = self.TestLogistic(x_train, y_train, x_test, y_test, itera, feats)\r\n print(\"Done testing - LR\")\r\n\r\n def Feature_Selection(self, X, y, T, method, cv):\r\n \"\"\"\r\n This functions returns only the features selected by the method using the threshold selected.\r\n We advise to run this function with several thresholds and look for the best, put this function inside a loop and see how it goes\r\n Suggestions for the range of t, thresholds = np.linspace(0.00001, 0.1, num=10)\r\n Input: \r\n X=training set\r\n y=training labels\r\n T=threshold selected\r\n which method= 'rfc', 'lasso', 'elastik'\r\n cv= number of cross validation iterations\r\n Output:\r\n Boolean array with the selected features,with this you can X=X[feats] to select only the relevant features\r\n \"\"\"\r\n alphagrid = np.linspace(0.001, 0.99, num=cv)\r\n\r\n clf = {\r\n 'rfc': RandomForestClassifier(),\r\n 'lasso': LassoCV(), # alphas=alphagrid),\r\n 'elastik': ElasticNetCV(alphas=alphagrid),\r\n 'backward': RFECV(LogisticRegression(), cv=cv, n_jobs=-2)\r\n\r\n }[method]\r\n if method == 'backward':\r\n clf = clf.fit(X, y)\r\n feats = clf.support_\r\n else:\r\n clf.fit(X, y)\r\n sfm = SelectFromModel(clf) # , threshold=T)\r\n print(X.shape)\r\n sfm.fit(X, y)\r\n feats = sfm.get_support()\r\n\r\n return(feats)\r\n\r\n def TestLogistic(self, X_train, Y_train, X_test, Y_test, itera, feats):\r\n\r\n clf = LogisticRegression(C=100000, solver=\"liblinear\")\r\n\r\n clf.fit(X_train, Y_train)\r\n\r\n preds = clf.predict(X_test)\r\n probas = clf.predict_proba(X_test)[:, 1]\r\n\r\n odds = np.exp(clf.coef_)\r\n feats = np.array(feats, dtype='float64')\r\n pos = 0\r\n for i in range(0, feats.shape[0]):\r\n if feats[i] == 1:\r\n feats[i] = odds[0, pos]\r\n # print(odds[0,pos])\r\n pos = pos + 1\r\n # print(feats)\r\n self.measure.feat_imp.append(feats)\r\n # print(\"classes\", clf.classes_)\r\n # name=('Models/RFC'+str(itera)+'.pkl')\r\n # joblib.dump(clf,name)\r\n\r\n self.measure.calculate(Y_test, preds, probas)\r\n\r\n return clf\r\n","repo_name":"rriccilopes/MLPipe","sub_path":"LR.py","file_name":"LR.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"24298202625","text":"#Basic code for receiving serial connection\n\nfrom machine import UART #importing PIN and PWM\n\n#Defining UART channel and Baud Rate\nuart= UART(0,9600)\n\nwhile True:\n if uart.any(): #Checking if data available\n data=uart.read() #Getting data\n #print(data)\n\n # Decodes data and splits string into a list\n data = data.decode(\"ASCII\").split(\",\")\n \n #Spliting array into variables and typecasting to an int\n x = int(data[0])\n y = int(data[1])\n a = int(data[2])\n b = int(data[3])\n rt = int(data[4])\n lt = int(data[5])\n \n print(f\" {x} | {y} | {a} | {b} | {rt} | {lt} | \")\n \n \n ","repo_name":"Waffleer/python-bluetooth-controller","sub_path":"pico/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"8363963415","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 18.06.2018\n\n@author: Kevin\n'''\n\n\n#Module importieren\nimport pandas as pd\nimport scipy.stats as stats\nimport lightgbm\nimport os\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn import preprocessing\nfrom bi2018.BI.data_handler import DataHandler\n\n\n\n\ndef main():\n #Hier werden alle verschiedenen Methoden aufgerufen, da es sonst wirklich ziemlich unübersichtlich wird\n #Einlesen des Files\n df = readF(\"train.csv\", True) # True wenn Index im File vorhanden, wie hier.\n test = readF('test.csv', False)\n #with pd.option_context('display.max_rows', 11, 'display.max_columns', 200):\n #print (df1)\n #print (test)\n cT = ChiSquare(df) #\n useChi(cT) #gibt aus, welche Columns \"important\" sind für \"Category\"; DESCRIPT is most important\n dh = DataHandler()\n dh.load_data(train=df, test=test)\n data_sets = dh.transform_data()\n with pd.option_context('display.max_rows', 11, 'display.max_columns', 200):\n print(\"datasets:\")\n print(data_sets)\n #exit()\n\n resulttrain= lgbm(data_sets)\n print(resulttrain)\n exit()\n\n\n#Data Understanding & Data Preparation von BI_martin.py, dort wird von train.csv die csv \"rewritten.csv\" erstellt, und hier wieder eingelesen zur Auswertung.\ndef readF(path, index): #index == True, wenn Index vorhanden\n print('Reading: ', path)\n if (index == True):\n df = pd.read_csv(path, delimiter= ',', quotechar='\"', header = 0, error_bad_lines=False, dtype={\"AddressSuffix\": str, 'X': float, 'Y': float}) # , dtype={\"Date\": str, \"Time\": str, \"Year\": int, \"Month\": int, \"Day\": int, \"Hour\": int, \"Season\": str, \"Descript\": str, \"DayOfWeek\": str, \"PdDistrict\": str, \"Resolution\": str, \"Address\": str, \"AdressSuffix\": str, \"X\": str, \"Y\": str} columns mit (delimiter\";\"), die headzeile ist die 0., dtype bestimmt datentyp der Columns\n else:\n #df = pd.read_csv(path, header = 0, sep='\\t' )\n #probably not needed anymore since bi_martin is fixed\n df = pd.read_csv(path, delimiter= ',', quotechar='\"', header = 0, error_bad_lines=False, dtype={\"AddressSuffix\": str, 'X': float, 'Y': float}, index_col=0) # , dtype={\"Date\": str, \"Time\": str, \"Year\": int, \"Month\": int, \"Day\": int, \"Hour\": int, \"Season\": str, \"Descript\": str, \"DayOfWeek\": str, \"PdDistrict\": str, \"Resolution\": str, \"Address\": str, \"AdressSuffix\": str, \"X\": str, \"Y\": str} columns mit (delimiter\";\"), die headzeile ist die 0., dtype bestimmt datentyp der Columns\n print('Transforming', path)\n #df['Date'], df['Time'] = df['Dates'].str.split(' ', 1).str\n df['Year'] = df['Dates'].str[:4]\n df['Month'] = df['Dates'].str[5:7]\n df['Day'] = df['Dates'].str[8:10]\n #df['Time'] = df['Dates'].str[11:16] # in stunde und minute aufgesplittet\n df['Hour'] = df['Dates'].str[11:13]\n df['Minute'] = df['Dates'].str[14:16]\n df['Season'] = df.apply(get_season, axis=1)\n #Note the axis=1 specifier, that means that the application is done at a row, rather than a column level.\n #df['AddressSuffix'] = df['Address'].str[-2:]\n df['DayOfWeek'] = df['DayOfWeek'].str.upper()\n #df['Address'] = df['Address'].str.upper()\n df['X'] = df['X'].apply(lambda x: 0 if float(x)>=-122.3649 or float(x)<=-122.5136 else x)\n df['Y'] = df['Y'].apply(lambda y: 0 if float(y)<=37.70788 or float(y)>=37.81998 else y)\n with pd.option_context('display.max_rows', 11, 'display.max_columns', 200):\n print (df)\n df = df.drop('Dates', 1)\n df = df.drop('Address', 1)\n if (path == 'train.csv'):\n df = df.drop('Descript', 1)\n df = df.drop('Resolution', 1)\n\n\n print (df)\n print('Success for ', path)\n\n #with pd.option_context('display.max_rows', 11, 'display.max_columns', 200):\n #print(df.ix[257059]) # --> Einige Zeilen sind abgeschnitten und ergeben nicht immer viel Sinn. So wie diese hier; Excel index + 2 = Python,,, index 257061 = 257059\n #print(df)\n # Abfrage für bestimmten Wert \"NONE\" in Spalte \"Resolution\"\n #print(output.loc[output['Resolution'] == 'NONE'])\n #Entfernt alle Einträge \"NONE\" aus der Spalte \"Resolution\"\n #print(\"Hier werden die zu löschenden Inhalte ausgegeben.\")\n #print(df.loc[~(df['Resolution'] != 'NONE')])\n #Will suchen nach 'OWNING' im Feld 'Descript'; um das zu tun müssen ggf. Descript Felder in Liste umgewandelt werden. oider einzelnd in CSV ausgelesen werden\n #print(df.loc[output['Descript'].isin('OWNING')])\n #Viele kompakte leicht zu verstehende Informationen auf Code Basis sind hier zu finden -v\n #further use: https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/\n #existieren duplicates?\n #print (output.duplicated(subset='Dates', keep=False)) #Keep=False markiert alle Duplikate als True, keep=first, nur den ersten nicht\n #Gebe den Dataframe zurück, da wir nun alle Daten in der CSV wie gewünscht bearbeitet haben\n return df\n\ndef get_season(row):\n if 3 <= int(row['Dates'][5:7]) <= 5:\n return \"SPRING\"\n elif 6 <= int(row['Dates'][5:7]) <= 8:\n return \"SUMMER\"\n elif 9 <= int(row['Dates'][5:7]) <= 11:\n return \"AUTUMN\"\n else: return \"WINTER\"\n\n\n\"\"\"\nFeature Extraction\nFeature Extraction mit ChiSquare Test, welcher Wert nimmt am meisten Einfluß wenn Null Hypothese gilt\nChi-Square Erklärung 5-min YouTube: https://www.youtube.com/watch?v=VskmMgXmkMQ ;; Besser: https://www.youtube.com/watch?v=WXPBoFDqNVk (12 min)\nQuelle: http://www.handsonmachinelearning.com/blog/2AeuRL/chi-square-feature-selection-in-python\n\"\"\"\nclass ChiSquare: #Erstellen von chisquare-Klasse um Werte zu speichern\n def __init__(self, dataframe):\n self.df = dataframe\n self.p = None #P-Value\n self.chi2 = None #Chi Test Statistic\n self.dof = None\n self.dfTabular = None\n self.dfExpected = None\n\n\n # alpha is der Wert, der zur Bestimmung ob Null Hypothese angewendet zutrifft oder nicht\n def _print_chisquare_result(self, colX, alpha):\n result = \"\"\n if self.p calculated by ML soße: \"alpha_range = 10.0**-np.arange(1,7)\" ändert Outcome aber NICHT\n X = self.df[colX].astype(str) #Konvertierung zu String der unabhängigen Features\n Y = self.df[colY].astype(str) #Konvertierung zu String des abhängigen Features\n\n self.dfObserved = pd.crosstab(Y,X) #Anzahl für Observed in Abhängigkeit von Resolution\n chi2, p, dof, expected = stats.chi2_contingency(self.dfObserved.values)\n self.p = p\n self.chi2 = chi2\n self.dof = dof\n #print(\"Observed\")\n #print(self.dfObserved)\n\n self.dfExpected = pd.DataFrame(expected, columns=self.dfObserved.columns, index = self.dfObserved.index)\n #print(\"Expected\")\n #print(self.dfExpected)\n\n self._print_chisquare_result(colX, alpha)\n\n\n\n#Feature Selection\ndef useChi(cT):\n testColumns = ['Year', 'Month', 'Day','Time', 'Season', 'DayOfWeek', 'PdDistrict', 'X', 'Y']\n for var in testColumns: #Für jede einzelne Column wird Chi-Square ausgeführt\n cT.TestIndependence(colX=var,colY=\"Category\") #Aufruf des Chi-Square Test mit Resolution als abhängiges Features\n\ndef lgbm(data_set):\n #categorical_features = ['Year', 'Month', 'Day','Time', 'Season', 'DayOfWeek', 'PdDistrict'] funtzt net so\n params = {}\n params['task'] = 'train'\n params['learning_rate'] = 0.0005\n #params['num_boost_round'] = 'best_iteration'\n params['boosting_type'] = 'goss'\n params['objective'] = 'multiclass'\n params['num_class'] = '39'\n params['metric'] = 'multi_logloss'\n #params['categorical_feature'] = categorical_features\n #params['numerical_feature'] = ['X', 'Y']\n #params['sub_feature'] = 0.5\n\n #OVER/UNDERFITTING\n #params['min_data'] = 50\n params['max_depth'] = 4 # < 0 means no limit; some have 4-6\n params['subsample'] = 0.9\n params['num_leaves'] = 12 #38*2\n #https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst\n #min_data_in_leaf, default = 20, type = int, aliases: min_data_per_leaf, min_data, min_child_samples, constraints: min_data_in_leaf >= 0\n #minimal number of data in one leaf. Can be used to deal with over-fitting\n\n #LAST RESULT: 1: 3.68873 num_leaves = 8\n #LAST RESULT: 1: valid_0's multi_logloss: 3.6638 num_leaves = 12; max_depth = 8\n\n print ('Translating Datasets')\n x_train = data_set['train_X']\n y_train = data_set['train_Y']\n x_test = data_set['test_X']\n\n\n #http://lightgbm.readthedocs.io/en/latest/Python-Intro.html - how it should work\n print ('setup training and eval')\n lgb_train = lightgbm.Dataset(x_train, y_train)\n lgb_eval = lightgbm.Dataset(x_test, reference=lgb_train)\n\n\n print ('trying to perform')\n clf = lightgbm.train(params, lgb_train, 100, valid_sets=lgb_eval)\n print(\"Success, result: \", clf) #hier müsste ein output aus, und zurück gegeben werden\n for keys,values in clf.best_score.items():\n print(keys)\n print(values)\n\n print(\"at iteration: \", clf.best_iteration)\n return clf\n\n\n\n\n\n#Aufrufen der Ausführung, bitte ganz unten\nmain()\n","repo_name":"TiRoX/bi2018","sub_path":"BI/BI.py","file_name":"BI.py","file_ext":"py","file_size_in_byte":9565,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"12635981920","text":"#\n# Freesound is (c) MUSIC TECHNOLOGY GROUP, UNIVERSITAT POMPEU FABRA\n#\n# Freesound is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# Freesound is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n# Authors:\n# See AUTHORS file.\n#\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.http import HttpResponse\nfrom ratings.models import Rating\nfrom utils.cache import invalidate_template_cache\n\n@login_required\ndef add(request, content_type_id, object_id, rating):\n rating = int(rating)\n if rating in range(1,6):\n # in order to keep the ratings compatible with freesound 1, we multiply by two...\n rating = rating*2\n content_type = ContentType.objects.get(id=content_type_id)\n try:\n rating_object = Rating.objects.get(user=request.user, object_id=object_id, content_type=content_type)\n rating_object.rating = rating;\n rating_object.save()\n except Rating.DoesNotExist: #@UndefinedVariable\n rating_object = Rating.objects.create(user=request.user, object_id=object_id, content_type=content_type, rating=rating)\n # make sure the rating is seen on the next page load by invalidating the cache for it.\n ct = ContentType.objects.get(id=content_type_id)\n if ct.name == 'sound':\n # invalidate for logged in/not logged in, only for 'OK' sounds\n invalidate_template_cache(\"sound_header\", object_id, True)\n invalidate_template_cache(\"sound_header\", object_id, False)\n invalidate_template_cache(\"display_sound\", object_id, True, 'OK')\n invalidate_template_cache(\"display_sound\", object_id, False, 'OK')\n # if you want to invalidate some other caches for other content types add them here\n return HttpResponse(Rating.objects.filter(object_id=object_id, content_type=content_type).count())\n","repo_name":"djzikario/freesound","sub_path":"ratings/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"12"} +{"seq_id":"13939025990","text":"\"\"\"Model setup for products.\"\"\"\nfrom django.db import models\nfrom sorl.thumbnail import ImageField\nfrom multiselectfield import MultiSelectField\nfrom taggit.managers import TaggableManager\nfrom django.contrib.auth.models import User\n\nPUB_STATUS = (\n ('PB', 'public'),\n ('PV', 'private'),\n)\n\nLENGTHS = (\n ('4\\\"', '4\\\"'),\n ('5\\\"', '5\\\"'),\n ('6\\\"', '6\\\"'),\n ('7\\\"', '7\\\"'),\n ('8\\\"', '8\\\"'),\n ('9\\\"', '9\\\"'),\n ('10\\\"', '10\\\"'),\n ('11\\\"', '11\\\"'),\n ('12\\\"', '12\\\"'),\n ('13\\\"', '13\\\"'),\n ('14\\\"', '14\\\"'),\n ('15\\\"', '15\\\"'),\n ('16\\\"', '16\\\"'),\n)\n\nDIAMS = (\n ('1/8\\\"', '1/8\\\"'),\n ('1/4\\\"', '1/4\\\"'),\n ('3/8\\\"', '3/8\\\"'),\n ('1/2\\\"', '1/2\\\"'),\n ('5/8\\\"', '5/8\\\"'),\n)\n\n\nclass Product(models.Model):\n \"\"\"Product model for store display.\"\"\"\n\n image = ImageField(upload_to='images')\n published = models.CharField(\n max_length=2,\n choices=PUB_STATUS,\n default='PV')\n date_created = models.DateTimeField(auto_now_add=True)\n date_published = models.DateTimeField(blank=True, null=True)\n name = models.CharField(max_length=100)\n price = models.DecimalField(null=True, max_digits=6, decimal_places=2)\n stock = models.IntegerField(null=True, blank=True)\n length = MultiSelectField(\n max_length=150,\n choices=LENGTHS,\n default='',\n blank=True)\n diameter = MultiSelectField(\n max_length=150,\n choices=DIAMS,\n default='',\n blank=True)\n is_knife = models.BooleanField(default=False)\n creator = models.ForeignKey(User,\n on_delete=models.CASCADE,\n )\n description = models.TextField(default='')\n color = models.TextField(\n max_length=500,\n blank=True)\n extras = models.TextField(\n max_length=500,\n blank=True)\n catagories = TaggableManager(blank=True)\n shipping_length = models.DecimalField(null=True, max_digits=5,\n decimal_places=2)\n shipping_width = models.DecimalField(null=True, max_digits=5,\n decimal_places=2)\n shipping_height = models.DecimalField(null=True, max_digits=5,\n decimal_places=2)\n shipping_weight = models.DecimalField(null=True, max_digits=5,\n decimal_places=2)\n\n def __str__(self):\n \"\"\"Print for admin.\"\"\"\n return self.name\n\n\nclass Service(models.Model):\n \"\"\"Service model for store display.\"\"\"\n\n image = ImageField(upload_to='images')\n published = models.CharField(\n max_length=2,\n choices=PUB_STATUS,\n default='PV')\n date_created = models.DateTimeField(auto_now_add=True)\n date_published = models.DateTimeField(blank=True, null=True)\n name = models.CharField(max_length=100)\n blurb = models.TextField(default='', blank=True)\n description = models.TextField(default='', blank=True)\n commission_fee = models.IntegerField(blank=True, default=0)\n price_range = models.CharField(\n max_length=15,\n default='',\n blank=True)\n limitations = models.TextField(max_length=500, default='', blank=True)\n extras = models.TextField(\n max_length=500,\n blank=True)\n warning = models.TextField(\n max_length=500,\n blank=True)\n\n def __str__(self):\n \"\"\"Print for admin.\"\"\"\n return self.name\n\n\nclass Discount(models.Model):\n \"\"\"Model for discount codes.\"\"\"\n\n code = models.CharField(max_length=30)\n code_type = models.CharField(max_length=20)\n value = models.CharField(max_length=10)\n code_state = models.BooleanField(default=True)\n description = models.CharField(max_length=250)\n prod = models.IntegerField(null=True, blank=True)\n prod_name = models.CharField(null=True, blank=True, max_length=30)\n\n\nclass UserServiceImage(models.Model):\n \"\"\"Model to store images uploaded for a requested service.\"\"\"\n\n image = ImageField(upload_to='service_images')\n used = models.BooleanField(default=False)\n\n def __str__(self):\n \"\"\"Print for admin.\"\"\"\n return str(self.id)\n","repo_name":"cahudson94/Raven-Valley-Forge-Shop","sub_path":"RVFS/catalog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4178,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"31415487840","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# datetime:2020/5/29 17:33\nfrom pyspark.sql import functions as f\nfrom pyspark.sql import SparkSession\nfrom delta.tables import DeltaTable\n\n\ndef merge(spark, update, tableName, cols, key):\n \"\"\"\n 将DataFrame和delta表进行merge操作，insert操作要求DataFrame必须包含delta表所有的列(0.5版本)\n 当我们使用merge操作更新/插入delta表其中几列时，指定在DataFrame中不存在的列的值为null。\n\n 注：DataFrame中要写入delta表的列要和delta表一样\n :param spark,SparkSession实例\n :param update,spark DataFrame\n :param tableName,要更新的delta表\n \"\"\"\n # 如果没有dt列，创建当前日期的dt列\n if \"dt\" not in cols:\n update = update.withColumn(\"dt\", f.current_date())\n cols.append(\"dt\")\n\n # 1.构建merge条件\n mergeExpr = f\"origin.{key}=update.{key}\"\n print(f\"merge expression:{mergeExpr}\")\n\n # 2.构建更新表达式\n updateExpr = {}\n for c in cols:\n updateExpr[c] = f\"update.{c}\"\n\n print(f\"update expression:{updateExpr}\")\n\n origin = DeltaTable.forPath(spark, tableName)\n origin_cols = origin.toDF().columns\n\n # 3.构建插入表达式\n insertExpr = {}\n for origin_col in origin_cols:\n if origin_col in cols:\n insertExpr[origin_col] = f\"update.{origin_col}\"\n else:\n # 不存在,插入null值(不是字符串)\n insertExpr[origin_col] = \"null\"\n\n print(f\"insert expression:{insertExpr}\")\n\n # for origin_col in origin_cols:\n # if origin_col not in cols:\n # update=update.withColumn(origin_col,f.lit(None))\n\n origin.alias(\"origin\") \\\n .merge(update.alias(\"update\"), mergeExpr) \\\n .whenMatchedUpdate(set=updateExpr) \\\n .whenNotMatchedInsert(values=insertExpr) \\\n .execute()\n\nif __name__==\"__main__\":\n deltaTable = \"/user/delta/test\"\n\n spark = SparkSession.builder.appName(\"delta\").master(\"local[2]\").getOrCreate()\n\n #创建delta表\n df = spark.createDataFrame(data=[[None for i in range(8)]],\n schema=\"id long,c0 int,c1 long,c2 float,c3 double,c4 string,c5 date,c6 timestamp\") \\\n .withColumn(\"dt\", f.current_date())\n df.limit(0).write.partitionBy(\"dt\").format(\"delta\").mode(\"append\").save(deltaTable)\n\n #插入数据\n update = spark.range(0, 10)\\\n .withColumn(\"dt\", f.current_date()) \\\n .withColumn(\"c1\", f.lit(0).cast(\"long\"))\n update.write.partitionBy(\"dt\").format(\"delta\").mode(\"append\").save(deltaTable)\n\n\n update = spark.range(5, 15) \\\n .withColumn(\"dt\", f.current_date()) \\\n .withColumn(\"c1\", f.lit(1).cast(\"long\"))\n\n\n merge(spark,update,deltaTable,[\"id\",\"dt\",\"c1\"],\"id\")","repo_name":"ZhiYinZhang/study","sub_path":"pysparkDemo/delta/mergeOpt.py","file_name":"mergeOpt.py","file_ext":"py","file_size_in_byte":2783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"31089306595","text":"prefix = 25\nwith open('input.txt', 'r') as f:\n lines = filter(None, f.read().split('\\n'))\n preamble = {i:int(lines[i]) for i in range(prefix)}\n for i, line in enumerate(lines[prefix:]):\n n = int(line)\n isValid = False\n for preambleNum in preamble.values():\n if n - preambleNum in preamble.values():\n isValid = True\n continue\n if not isValid:\n nonValid = n\n print(n)\n del preamble[i]\n preamble[prefix+i] = n\n# part 2\ntab = [sum([int(line) for line in lines[:j]]) for j in range(2,len(lines))]\nfor i, d in enumerate(lines):\n nonValid += int(d)\n if nonValid in tab:\n print(int(max((lines[i+1:tab.index(nonValid)+1]))) + int(min((lines[i+1:tab.index(nonValid)+1]))))\n","repo_name":"iolloj/advent2020","sub_path":"day9/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"72345009941","text":"import sys, os\n\nCURRENT_TEST_DIR = os.getcwd()\nsys.path.append(CURRENT_TEST_DIR + \"/..\")\n\nfrom datetime import datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport slayerSNN as snn\nfrom learningStats import learningStats\nimport zipfile\nfrom slayer_layer import SlayerLayer\n\nnetParams = snn.params(\"network.yaml\")\n\n\ndef augmentData(event):\n xs = 8\n ys = 8\n xjitter = np.random.randint(2 * xs) - xs\n yjitter = np.random.randint(2 * ys) - ys\n event.x += xjitter\n event.y += yjitter\n return event\n\n\n# Dataset definition\nclass nmnistDataset(Dataset):\n def __init__(\n self, datasetPath, sampleFile, samplingTime, sampleLength, augment=False\n ):\n self.path = datasetPath\n self.samples = np.loadtxt(sampleFile).astype(\"int\")\n self.samplingTime = samplingTime\n self.nTimeBins = int(sampleLength / samplingTime)\n self.augment = augment\n\n def __getitem__(self, index):\n inputIndex = self.samples[index, 0]\n classLabel = self.samples[index, 1]\n\n event = snn.io.read2Dspikes(self.path + str(inputIndex.item()) + \".bs2\")\n if self.augment is True:\n event = augmentData(event)\n inputSpikes = event.toSpikeTensor(\n torch.zeros((2, 34, 34, self.nTimeBins)), samplingTime=self.samplingTime\n )\n\n desiredClass = torch.zeros((10, 1, 1, 1))\n desiredClass[classLabel, ...] = 1\n return inputSpikes, desiredClass, classLabel\n\n def __len__(self):\n return self.samples.shape[0]\n\n\n# Network definition\nclass Network(torch.nn.Module):\n def __init__(self, netParams):\n super(Network, self).__init__()\n # initialize slayer\n slayer = SlayerLayer(netParams[\"neuron\"], netParams[\"simulation\"])\n self.slayer = slayer\n\n # weight normalization\n self.conv1 = torch.nn.utils.weight_norm(\n slayer.conv(2, 16, 5, padding=1), name=\"weight\"\n )\n self.conv2 = torch.nn.utils.weight_norm(\n slayer.conv(16, 32, 3, padding=1), name=\"weight\"\n )\n self.conv3 = torch.nn.utils.weight_norm(\n slayer.conv(32, 64, 3, padding=1), name=\"weight\"\n )\n\n self.pool1 = slayer.pool(2)\n self.pool2 = slayer.pool(2)\n\n self.fc1 = torch.nn.utils.weight_norm(\n slayer.dense((8 * 8 * 64), 512), name=\"weight\"\n )\n self.fc2 = torch.nn.utils.weight_norm(slayer.dense(512, 10), name=\"weight\")\n\n # delays\n self.delay1 = slayer.delay(16)\n self.delay2 = slayer.delay(16)\n self.delay3 = slayer.delay(32)\n self.delay4 = slayer.delay(32)\n self.delay5 = slayer.delay(64 * 8 * 8)\n self.delay6 = slayer.delay(512)\n\n def forward(self, spike):\n # count.append(torch.sum(spike).item())\n\n spike = self.slayer.spike(self.conv1(self.slayer.psp(spike))) # 32, 32, 16\n spike = self.delay1(spike)\n\n spike = self.slayer.spike(self.pool1(self.slayer.psp(spike))) # 16, 16, 16\n spike = self.delay2(spike)\n\n spike = self.slayer.spike(self.conv2(self.slayer.psp(spike))) # 16, 16, 32\n spike = self.delay3(spike)\n\n spike = self.slayer.spike(self.pool2(self.slayer.psp(spike))) # 8, 8, 32\n spike = self.delay4(spike)\n\n spike = self.slayer.spike(self.conv3(self.slayer.psp(spike))) # 8, 8, 64\n spike = spike.reshape((spike.shape[0], -1, 1, 1, spike.shape[-1]))\n spike = self.delay5(spike)\n\n spike = self.slayer.spike(self.fc1(self.slayer.psp(spike))) # 10\n spike = self.delay6(spike)\n\n spike = self.slayer.spike(self.fc2(self.slayer.psp(spike))) # 10\n\n return spike\n\n def clamp(self):\n self.delay1.delay.data.clamp_(0, 64)\n self.delay2.delay.data.clamp_(0, 64)\n self.delay3.delay.data.clamp_(0, 64)\n self.delay4.delay.data.clamp_(0, 64)\n self.delay5.delay.data.clamp_(0, 64)\n self.delay6.delay.data.clamp_(0, 64)\n\n def gradFlow(self, path):\n gradNorm = lambda x: torch.norm(x).item() / torch.numel(x)\n\n grad = []\n grad.append(gradNorm(self.conv1.weight_g.grad))\n grad.append(gradNorm(self.conv2.weight_g.grad))\n grad.append(gradNorm(self.conv3.weight_g.grad))\n grad.append(gradNorm(self.fc1.weight_g.grad))\n grad.append(gradNorm(self.fc2.weight_g.grad))\n\n plt.figure()\n plt.semilogy(grad)\n plt.savefig(path + \"gradFlow.png\")\n plt.close()\n\n\nif __name__ == \"__main__\":\n # # Extract NMNIST samples\n # with zipfile.ZipFile(\"NMNISTsmall.zip\") as zip_file:\n # for member in zip_file.namelist():\n # if not os.path.exists(\"./\" + member):\n # zip_file.extract(member, \"./\")\n\n device = torch.device(\"cuda\")\n net = Network(netParams).to(device)\n error = snn.loss(netParams).to(device)\n\n # Custom NADAM optimizer\n optimizer = snn.utils.optim.Nadam(net.parameters(), lr=0.01, amsgrad=False)\n\n # Dataset and dataLoader instances.\n trainingSet = nmnistDataset(\n datasetPath=netParams[\"training\"][\"path\"][\"in\"],\n sampleFile=netParams[\"training\"][\"path\"][\"train\"],\n samplingTime=netParams[\"simulation\"][\"Ts\"],\n sampleLength=netParams[\"simulation\"][\"tSample\"],\n )\n trainLoader = DataLoader(\n dataset=trainingSet, batch_size=12, shuffle=False, num_workers=4\n )\n\n testingSet = nmnistDataset(\n datasetPath=netParams[\"training\"][\"path\"][\"in\"],\n sampleFile=netParams[\"training\"][\"path\"][\"test\"],\n samplingTime=netParams[\"simulation\"][\"Ts\"],\n sampleLength=netParams[\"simulation\"][\"tSample\"],\n )\n testLoader = DataLoader(\n dataset=testingSet, batch_size=12, shuffle=False, num_workers=4\n )\n\n # Learning stats instance.\n stats = learningStats()\n\n # # Visualize the network.\n # for i in range(5):\n # input, target, label = trainingSet[i]\n # snn.io.showTD(snn.io.spikeArrayToEvent(input.reshape((2, 34, 34, -1)).cpu().data.numpy()))\n\n # training loop\n for epoch in range(200):\n tSt = datetime.now()\n\n # Training loop.\n for i, (input, target, label) in enumerate(trainLoader, 0):\n # Move the input and target to correct GPU.\n input = input.to(device)\n target = target.to(device)\n\n # Forward pass of the network.\n output = net.forward(input)\n\n # Gather the training stats.\n stats.training.correctSamples += torch.sum(\n snn.predict.getClass(output) == label\n ).data.item()\n stats.training.numSamples += len(label)\n\n # Calculate loss.\n loss = error.numSpikes(output, target)\n\n # Reset gradients to zero.\n optimizer.zero_grad()\n\n # Backward pass of the network.\n loss.backward()\n\n # Update weights.\n optimizer.step()\n\n # Clamp delay\n net.clamp()\n\n # Gather training loss stats.\n stats.training.lossSum += loss.cpu().data.item()\n\n # Display training stats.\n stats.print(epoch, i, (datetime.now() - tSt).total_seconds())\n\n # Testing loop.\n # Same steps as Training loops except loss backpropagation and weight update.\n for i, (input, target, label) in enumerate(testLoader, 0):\n input = input.to(device)\n target = target.to(device)\n\n output = net.forward(input)\n\n stats.testing.correctSamples += torch.sum(\n snn.predict.getClass(output) == label\n ).data.item()\n stats.testing.numSamples += len(label)\n\n loss = error.numSpikes(output, target)\n stats.testing.lossSum += loss.cpu().data.item()\n stats.print(epoch, i)\n\n # Update stats.\n stats.update()\n\n # Plot the results.\n plt.figure(1)\n plt.semilogy(stats.training.lossLog, label=\"Training\")\n plt.semilogy(stats.testing.lossLog, label=\"Testing\")\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Loss\")\n plt.legend()\n\n plt.figure(2)\n plt.plot(stats.training.accuracyLog, label=\"Training\")\n plt.plot(stats.testing.accuracyLog, label=\"Testing\")\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Accuracy\")\n plt.legend()\n\n plt.show()\n","repo_name":"synsense/slayer-comparison","sub_path":"archive/reproduce_slayer/nmnist.py","file_name":"nmnist.py","file_ext":"py","file_size_in_byte":8370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"5966533025","text":"import bench\nimport argparse\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n\ndef main():\n from sklearn.manifold import TSNE\n\n # Load and convert data\n X, _, _, _ = bench.load_data(params)\n\n # Create our TSNE model\n tsne = TSNE(n_components=params.n_components, early_exaggeration=params.early_exaggeration,\n learning_rate=params.learning_rate, angle=params.angle,\n min_grad_norm=params.min_grad_norm, random_state=params.random_state)\n\n fit_time, _ = bench.measure_function_time(tsne.fit, X, params=params)\n divergence = tsne.kl_divergence_\n\n bench.print_output(\n library='sklearn',\n algorithm='TSNE',\n stages=['training'],\n params=params,\n functions=['TSNE.fit'],\n times=[fit_time],\n metric_type='divergence',\n metrics=[divergence],\n data=[X],\n alg_instance=tsne,\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='scikit-learn tsne '\n 'regression benchmark')\n\n parser.add_argument('--n-components', type=int, default=2,\n help='The dimension of the embedded space.')\n parser.add_argument('--early-exaggeration', type=float, default=12.0,\n help='This factor increases the attractive forces between points '\n 'and allows points to move around more freely, '\n 'finding their nearest neighbors more easily.')\n parser.add_argument('--learning-rate', type=float, default=200.0,\n help='The learning rate for t-SNE is usually in the range [10.0, 1000.0].')\n parser.add_argument('--angle', type=float, default=0.5,\n help='Angular size. This is the trade-off between speed and accuracy.')\n parser.add_argument('--min-grad-norm', type=float, default=1e-7,\n help='If the gradient norm is below this threshold,'\n 'the optimization is stopped.')\n parser.add_argument('--random-state', type=int, default=1234)\n\n params = bench.parse_args(parser)\n bench.run_with_context(params, main)\n","repo_name":"IntelPython/scikit-learn_bench","sub_path":"sklearn_bench/tsne.py","file_name":"tsne.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","stars":102,"dataset":"github-code","pt":"12"} +{"seq_id":"34319458687","text":"from django.conf.urls import url\nfrom . import views\n\n# gDefine which app this URL will find it's patterns\napp_name = 'ims'\nurlpatterns = [\n # URL for a user to input their own qty into a specific store\n url(r'^stores/(?P[0-9]+)/count', views.storerecount, \\\n name='storerecount'),\n \n # Details for a particular store, including inventory totals\n url(r'^stores/(?P[0-9]+)/', views.storedetail, name='storedetail'),\n \n url(r'^stores/$', views.StoreView.as_view(), name='stores'),\n \n # A Debug URL that shows every StoreItem row with a form\n #DEBUG\n url(r'^storeitemsform/$', views.storeitemsform, name='storeitemsform'),\n \n \n url(r'^managers/update/(?P[0-9]+)/', views.edit_manager, name='account_update'),\n \n # URL for a list of all managers\n url(r'^managers/$', views.ManagerView.as_view(), name='managers'),\n \n \n # Details for a particular Item\n url(r'^items/(?P[0-9]+)/', views.itemdetail, name='itemdetail'),\n \n # URL for a list of all items\n url(r'^items/$', views.ItemView.as_view(), name='items'),\n \n # URL for a list of all storeitems\n url(r'^storeitems/$', views.StoreItemView.as_view(), name='storeitems'),\n \n # Front page of IMS, Shows a login page or the user's Store view as default\n url(r'$', views.StoreView.as_view(), name='home_page'),\n]","repo_name":"obl1v1us/MySite","sub_path":"ims/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"25968420844","text":"# encoding=utf-8\r\n\r\nimport time\r\n\r\nimport multiprocessing as mp\r\nimport threading as td\r\n\r\n\r\ncount = 1000000\r\n\r\ndef job(q, name):\r\n t = time.time()\r\n res = 0\r\n for i in range(count):\r\n res += i + i ** 2 + i ** 3\r\n q.put(res)\r\n t2 = time.time()\r\n print(\"%s - %s\" % (name, str(t2 - t)))\r\n\r\n\r\ndef multicore():\r\n q = mp.Queue()\r\n p1 = mp.Process(target=job, args=(q, \"multicore-1\"))\r\n p2 = mp.Process(target=job, args=(q, \"multicore-2\"))\r\n p1.start()\r\n p2.start()\r\n p1.join()\r\n p2.join()\r\n res1 = q.get()\r\n res2 = q.get()\r\n print('multicore:', res1 + res2)\r\n\r\ndef multithread():\r\n q = mp.Queue()\r\n t1 = td.Thread(target=job, args=(q, \"multithread-1\"))\r\n t2 = td.Thread(target=job, args=(q, \"multithread-2\"))\r\n t1.start()\r\n t2.start()\r\n t1.join()\r\n t2.join()\r\n res1 = q.get()\r\n res2 = q.get()\r\n print('multithread:', res1 + res2)\r\n \r\n\r\ndef normal():\r\n res = 0\r\n for _ in range(2):\r\n for i in range(count):\r\n res += i + i ** 2 + i ** 3\r\n print('normal:', res)\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n st = time.time()\r\n normal()\r\n st1 = time.time()\r\n print('normal time:', st1 - st)\r\n multithread()\r\n st2 = time.time()\r\n print('multithread time:', st2 - st1)\r\n multicore()\r\n print('multicore time:', time.time() - st2)\r\n\r\n","repo_name":"liangrengongzuoshi/pythonDemo","sub_path":"com/thread/thread_test.py","file_name":"thread_test.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"28015481345","text":"from rest_framework import serializers\nfrom rest_framework.exceptions import PermissionDenied\nfrom rest_framework.relations import StringRelatedField\n\nfrom building.serializers import BuildingSerializer\nfrom building_post.models import BuildingPost, BuildingPostHistory\n\n\nclass BuildingPostSerializer(serializers.ModelSerializer):\n creator = StringRelatedField()\n\n class Meta:\n model = BuildingPost\n fields = (\n 'building',\n 'creator',\n 'title',\n 'content',\n )\n\n def update(self, instance, validated_data):\n if self.context['request'].user != instance.creator:\n raise PermissionDenied()\n new_instance = super(BuildingPostSerializer, self).update(instance, validated_data)\n BuildingPostHistory.objects.create(\n building_post=instance,\n building=instance.building,\n creator=instance.creator,\n title=instance.title,\n content=instance.content\n )\n return new_instance\n\n\nclass BuildingPostReadSerializer(BuildingPostSerializer):\n building = BuildingSerializer(many=False)\n\n class Meta(BuildingPostSerializer.Meta):\n fields = (\n 'id',\n 'building',\n 'creator',\n 'title',\n 'content',\n 'is_enabled',\n 'created',\n 'updated',\n )\n","repo_name":"trowa88/commstr","sub_path":"building_post/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"70589647383","text":"import gym\nimport math\nimport numpy as np\n# import Expected_Sarsa as Agent\nimport Dyna_Q_plus as Agent\n\nnum_episodes = 500\nbuckets=(1, 1, 6, 12,)\nagent_info = {\"num_actions\": 2, \n \"num_states\": buckets, \n \"epsilon\": 0.1, \n \"step_size\": 0.01, \n \"discount\": 1.0,\n \"kappa\": 0.001,\n \"planning_steps\": 5,\n \"random_seed\": 0,\n \"planning_random_seed\": 0}\n\n# agent = Agent.ExpectedSarsaAgent()\nagent = Agent.DynaQPlusAgent()\n\ndef discretize(obs, env):\n upper_bounds = [env.observation_space.high[0], 0.5, env.observation_space.high[2], math.radians(50)]\n lower_bounds = [env.observation_space.low[0], -0.5, env.observation_space.low[2], -math.radians(50)]\n ratios = [(obs[i] + abs(lower_bounds[i])) / (upper_bounds[i] - lower_bounds[i]) for i in range(len(obs))]\n new_obs = [int(round((buckets[i] - 1) * ratios[i])) for i in range(len(obs))]\n new_obs = [min(buckets[i] - 1, max(0, new_obs[i])) for i in range(len(obs))]\n return tuple(new_obs)\n\nif __name__ == \"__main__\":\n # step_size = [0.01, 0.05, 0.1, 0.5]\n planning_steps = [0, 5, 10, 50]\n\n for step in planning_steps:\n agent_info[\"planning_steps\"] = step\n agent.agent_init(agent_info)\n env = gym.make(\"CartPole-v1\")\n Rewards = []\n \n for ep in range(num_episodes):\n total_rewards = 0\n last_state = discretize(env.reset(), env)\n done = False\n last_action = agent.agent_start(last_state)\n\n agent.epsilon = agent.get_epsilon(ep)\n agent.step_size = agent.get_alpha(ep)\n \n count_steps = 0\n while not done:\n count_steps += 1\n obs, reward, done, _ = env.step(last_action)\n total_rewards += reward\n last_state = discretize(obs, env)\n last_action = agent.agent_step(reward, last_state)\n \n print(\"Episode: {} with {} planning step(s) Total reward: {}\".format(ep, agent.planning_steps, total_rewards))\n Rewards.append(total_rewards)\n \n np.save(\"./DynaQ_plus_results/step_size_0.01/adaptive/planning_step_{}\".format(agent.planning_steps), Rewards)\n ","repo_name":"yanshuolee/RL-implementation","sub_path":"Cartpole/cartpole_game_parameter_search.py","file_name":"cartpole_game_parameter_search.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"22664030228","text":"import subprocess\n\nfrom mock import MagicMock\n\n\n# The process mock can be retrieved by calling PopenMock().mock\nclass PopenMock:\n def __init__(\n self,\n return_code=0,\n poll_result=0,\n communicate_return_value=None,\n communicate_side_effect=None,\n kill_side_effect=None,\n ):\n self.return_code = return_code\n self.poll_result = poll_result\n self.communicate_return_value = communicate_return_value\n self.communicate_side_effect = communicate_side_effect\n self.kill_side_effect = kill_side_effect\n self.mock = self._create_mock()\n\n def _create_mock(self):\n popen_mock = MagicMock()\n if self.communicate_return_value:\n popen_mock.communicate.return_value = self.communicate_return_value\n elif self.communicate_side_effect:\n popen_mock.communicate.side_effect = self.communicate_side_effect\n if self.kill_side_effect:\n popen_mock.kill.side_effect = self.kill_side_effect\n popen_mock.returncode = self.return_code\n popen_mock.poll.return_value = self.poll_result\n return popen_mock\n\n\nDEFAULT_RETRYABLE_FAILURE_POPEN = PopenMock(\n return_code=1,\n poll_result=1,\n communicate_return_value=(b\"\", b\"mount.nfs4: Connection reset by peer\"),\n)\nDEFAULT_NON_RETRYABLE_FAILURE_POPEN = PopenMock(\n return_code=1,\n poll_result=1,\n communicate_return_value=(\n b\"\",\n b\"mount.nfs4: access denied by server while mounting 127.0.0.1:/\",\n ),\n)\nDEFAULT_SUCCESS_POPEN = PopenMock(communicate_return_value=(b\"\", b\"\"))\nDEFAULT_TIMEOUT_POPEN = PopenMock(\n return_code=1,\n poll_result=1,\n communicate_side_effect=subprocess.TimeoutExpired(\"cmd\", timeout=1),\n)\nDEFAULT_UNKNOWN_EXCEPTION_POPEN = PopenMock(\n return_code=1, poll_result=1, communicate_side_effect=Exception(\"Unknown error\")\n)\n","repo_name":"aws/efs-utils","sub_path":"test/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","stars":240,"dataset":"github-code","pt":"12"} +{"seq_id":"34266527551","text":"import numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, optim\n\nimport kornia\n\n\nclass MyHomography(nn.Module):\n def __init__(self, init_homo: torch.Tensor) -> None:\n super().__init__()\n self.homo = nn.Parameter(init_homo.clone().detach())\n\n def forward(self) -> torch.Tensor:\n return torch.unsqueeze(self.homo, dim=0)\n\n\nclass TestWarping:\n # optimization\n lr = 1e-3\n num_iterations = 100\n\n def test_smoke(self, device):\n img_src_t: torch.Tensor = torch.rand(1, 3, 120, 120).to(device)\n img_dst_t: torch.Tensor = torch.rand(1, 3, 120, 120).to(device)\n\n init_homo: torch.Tensor = torch.from_numpy(\n np.array([[0.0415, 1.2731, -1.1731], [-0.9094, 0.5072, 0.4272], [0.0762, 1.3981, 1.0646]])\n ).float()\n\n height, width = img_dst_t.shape[-2:]\n warper = kornia.geometry.transform.HomographyWarper(height, width)\n dst_homo_src = MyHomography(init_homo=init_homo).to(device)\n\n learning_rate = self.lr\n optimizer = optim.Adam(dst_homo_src.parameters(), lr=learning_rate)\n\n for _ in range(self.num_iterations):\n # warp the reference image to the destiny with current homography\n img_src_to_dst = warper(img_src_t, dst_homo_src())\n\n # compute the photometric loss\n loss = F.l1_loss(img_src_to_dst, img_dst_t)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n assert not bool(torch.isnan(dst_homo_src.homo.grad).any())\n","repo_name":"kornia/kornia","sub_path":"test/integration/test_warp.py","file_name":"test_warp.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","stars":8834,"dataset":"github-code","pt":"12"} +{"seq_id":"23821561349","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\ntarea_1.py\n------------\n\nTarea de desarrollo de entornos y agentes\n==========================================\n\n1. Desarrolla un entorno similar al de los dos cuartos (el cual se\n encuentra en el módulo doscuartos_o.py), pero con tres cuartos en\n el primer piso, y tres cuartos en el segundo piso.\n\n El entorno se llamará `SeisCuartos`.\n\n Las acciones totales serán\n\n ```\n [\"ir_Derecha\", \"ir_Izquierda\", \"subir\", \"bajar\", \"limpiar\", \"nada\"]\n ```\n\n La acción de `\"subir\"` solo es legal en el piso de abajo, en los cuartos de los extremos,\n mientras que la acción de `\"bajar\"` solo es legal en el piso de arriba y en el cuarto de el centro (dos\n escaleras para subir, una escalera para bajar).\n\n Las acciones de subir y bajar son mas costosas en término de\n energía que ir a la derecha y a la izquierda, por lo que la función\n de desempeño debe de ser de tener limpios todos los cuartos, con el\n menor numero de acciones posibles, y minimizando subir y bajar en\n relación a ir a los lados. El costo de limpiar es menor a los costos\n   de cualquier acción.\n\n2. Diseña un Agente reactivo basado en modelo para este entorno y\n compara su desempeño con un agente aleatorio despues de 100 pasos\n de simulación.\n\n3. Al ejemplo original de los dos cuartos, modificalo de manera que el\n agente solo pueda saber en que cuarto se encuentra pero no sabe si\n está limpio o sucio.\n\n A este nuevo entorno llamalo `DosCuartosCiego`.\n\n Diseña un agente racional para este problema, pruebalo y comparalo\n con el agente aleatorio.\n\n4. Reconsidera el problema original de los dos cuartos, pero ahora\n modificalo para que cuando el agente decida aspirar, el 80% de las\n veces limpie pero el 20% (aleatorio) deje sucio el cuarto. Igualmente,\n cuando el agente decida cambiar de cuarto, se cambie correctamente de cuarto el 90% de la veces\n y el 10% se queda en su lugar. Diseña\n un agente racional para este problema, pruebalo y comparalo con el\n agente aleatorio.\n\n A este entorno llámalo `DosCuartosEstocástico`.\n\nTodos los incisos tienen un valor de 25 puntos sobre la calificación de\nla tarea.\n\n\"\"\"\n\n##############################################################\n\n__author__ = 'IvanAlejandroMorenoSoto'\n\n##############################################################\n\nimport entornos_o\nfrom random import random, choice\nfrom doscuartos_o import DosCuartos, AgenteReactivoModeloDosCuartos, AgenteAleatorio\n\n##############################################################\n\n# Ejercicio 1.\n\nclass SeisCuartos(entornos_o.Entorno):\n \"\"\"\n Entorno de una casa con seis cuartos: tres en la planta inferior y\n tres en la superior.\n\n Análogamente a DosCuartos, el estado se define como:\n estado := [posición, A, B, C, D, E, F]\n\n D E F\n A B C\n\n Donde A, B, C, son los cuartos inferiores, D, E, F, los superiores,\n y posición puede tomar como valor cualquiera de ellos. Cada cuarto\n puede estar \"limpio\" o \"sucio.\"\n\n Las acciones válidas son:\n acciones = {\"ir_Derecha\", \"ir_Izquierda\", \"subir\", \"bajar\", \"limpiar\", \"nada\"}\n Todas son legales en todos los cuartos excepto por \"subir\" que únicamente es\n legal en A y C, y \"bajar\" que sólo se puede realizar en E.\n\n Los sensores son una tupla que contiene la posición del robot y el estado de\n limpieza del cuarto.\n \"\"\"\n\n def __init__(self, x0=[\"A\", \"sucio\", \"sucio\", \"sucio\", \"sucio\", \"sucio\", \"sucio\"]):\n \"\"\"\n Define el estado inicial de este entorno.\n De forma predeterminada el robot se encuentra en el cuarto inferior izquierdo\n y toda la casa está sucia.\n\n @param x0: Vector con el estado inicial del entorno de la forma\n [posiciónInicial, limpieza_A, limpieza_B, limpieza_C, limpieza_D, limpieza_E, limpieza_F].\n \"\"\"\n self.x = x0[:]\n self.desempeño = 0\n\n def acción_legal(self, acción):\n \"\"\"\n Determina si una acción es legal en el estado actual.\n\n @param acción: Acción que será revisada.\n\n @return True si la acción es legal, False en caso contrario.\n \"\"\"\n # Se separan los casos en: el robot quiere subir o quiere bajar o quiere hacer\n # cualquier otra cosa.\n if acción == \"subir\" and (self.x[0] == \"A\" or self.x[0] == \"C\"):\n return True\n if acción == \"bajar\" and self.x[0] == \"E\":\n return True\n\n return acción in (\"ir_Derecha\", \"ir_Izquierda\", \"limpiar\", \"nada\")\n\n def transición(self, acción):\n \"\"\"\n Transforma al entorno según la acción recibida.\n\n @param acción: Acción de entrada al entorno.\n \"\"\"\n if not self.acción_legal(acción):\n raise ValueError(\"La acción no es legal para este estado\")\n\n posición = self.x[0]\n\n # Se determina el desempeño local.\n if \"sucio\" in self.x or acción == \"limpiar\":\n self.desempeño -= 1\n if acción == \"ir_Derecha\" or acción == \"ir_Izquierda\":\n self.desempeño -= 2\n elif acción == \"subir\" or acción == \"bajar\":\n self.desempeño -= 3\n\n # Se modifica al entorno.\n if acción == \"limpiar\":\n self.x[\" ABCDEF\".find(posición)] = \"limpio\"\n elif acción == \"ir_Derecha\":\n if posición == \"A\":\n self.x[0] = \"B\"\n elif posición == \"B\":\n self.x[0] = \"C\"\n elif posición == \"D\":\n self.x[0] = \"E\"\n elif posición == \"E\":\n self.x[0] = \"F\"\n elif acción == \"ir_Izquierda\":\n if posición == \"B\":\n self.x[0] = \"A\"\n elif posición == \"C\":\n self.x[0] = \"B\"\n elif posición == \"E\":\n self.x[0] = \"D\"\n elif posición == \"F\":\n self.x[0] = \"E\"\n elif acción == \"subir\":\n if posición == \"A\":\n self.x[0] = \"D\"\n else:\n self.x[0] = \"F\"\n elif acción == \"bajar\":\n self.x[0] = \"B\"\n\n def percepción(self):\n \"\"\"\n Regresa la percepción del entorno en el estado actual.\n\n @return Una tupla (posición, limpio?)\n \"\"\"\n return self.x[0], self.x[\" ABCDEF\".find(self.x[0])]\n\n##############################################################\n\n# Ejercicio 2\n\nclass AgenteAleatorioSeisCuartos(AgenteAleatorio):\n \"\"\"\n Define un agente aleatorio que cambia su conjunto de\n posibles acciones dependiendo de lo que sea legal.\n \"\"\"\n\n def programa(self, percepción):\n \"\"\"\n Escoge una acción legal al azar.\n\n @param percepción: Percepción del entorno SeisCuartos.\n\n @return Acción del agente.\n \"\"\"\n return choice(self.calcular_acciones_legales(percepción[0]))\n\n def calcular_acciones_legales(self, posición):\n \"\"\"\n Devuelve una lista de acciones legales en la posición dada.\n\n @param posición: Posición actual del agente.\n\n @return Lista con acciones legales en la posición indicada.\n \"\"\"\n acciones_legales = self.acciones[:]\n\n # Se remueven las acciones ilegales.\n if posición != \"A\" and posición != \"C\":\n acciones_legales.remove(\"subir\")\n if posición != \"E\":\n acciones_legales.remove(\"bajar\")\n\n return acciones_legales\n\nclass AgenteRacionalSeisCuartos:\n \"\"\"\n Agente reactivo basado en modelo para el entorno SeisCuartos.\n Intenta minimizar el costo de sus acciones evitando subir y\n bajar, y prefiriendo moverse a los lados.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Inicializa el modelo interno del agente.\n \"\"\"\n self.modelo = ['A', 'sucio', 'sucio', 'sucio', 'sucio', 'sucio', 'sucio']\n\n def programa(self, percepción):\n \"\"\"\n @param percepción: Percepción del entorno SeisCuartos.\n\n @return Acción del agente.\n \"\"\"\n posición, situación = percepción\n\n # Se actualiza el modelo del agente.\n self.modelo[0] = posición\n self.modelo[' ABCDEF'.find(posición)] = situación\n\n if not 'sucio' in self.modelo:\n return 'nada'\n if situación == 'sucio':\n return 'limpiar'\n\n if posición in ('A', 'B', 'C'):\n if not 'sucio' in self.modelo[1:4]:\n return ('ir_Izquierda' if posición == 'B' else 'subir')\n else:\n return ('ir_Derecha' if posición == 'A' or (posición == 'B' and self.modelo[1] == 'limpio') else\n 'ir_Izquierda')\n else:\n if not 'sucio' in self.modelo[4:]:\n return ('ir_Izquierda' if posición == 'F' else\n 'ir_Derecha' if posición == 'D' else\n 'bajar')\n else:\n return ('ir_Derecha' if posición == 'D' or (posición == 'E' and self.modelo[4] == 'limpio') else\n 'ir_Izquierda')\n\ndef hacerPruebaEjercicio1_2(pasos):\n \"\"\"\n @param pasos: Número de pasos de la simulación.\n \"\"\"\n\n print(\"Prueba en SeisCuartos con un agente aleatorio.\")\n entornos_o.simulador(SeisCuartos(), AgenteAleatorioSeisCuartos(['ir_Derecha', 'ir_Izquierda', 'subir', 'bajar', 'limpiar', 'nada']), pasos)\n\n print(\"Prueba en SeisCuartos con un agente reactivo basado en modelo.\")\n entornos_o.simulador(SeisCuartos(), AgenteRacionalSeisCuartos(), pasos)\n\n##############################################################\n\n# Ejercicio 3.\n\nclass DosCuartosCiego(DosCuartos):\n \"\"\"\n Entorno basado en DosCuartos donde el robot solo tiene\n acceso a su posición actual.\n \"\"\"\n\n def percepción(self):\n \"\"\"\n @return Únicamente la posición actual del robot.\n \"\"\"\n return self.x[0]\n\nclass AgenteDosCuartosCiego(AgenteReactivoModeloDosCuartos):\n \"\"\"\n Agente para el entorno DosCuartosCiego.\n \"\"\"\n\n def programa(self, percepción):\n \"\"\"\n Aquí, el robot decide que acción realizará según su memoria de la\n situación del cuarto donde está.\n\n @param percepción Percepción del entorno en el estado actual.\n\n @return Una de cuatro acciones de ['ir_A', 'ir_B', 'limpiar', 'nada'].\n \"\"\"\n\n # Se actualiza el lugar actual del robot.\n self.modelo[0] = percepción\n\n # Revisa lo que recuerda sobre el cuarto en el que se encuentra.\n situación = self.modelo[' AB'.find(percepción)]\n\n a, b = self.modelo[1], self.modelo[2]\n\n if situación == 'sucio':\n # Antes de regresar la acción, se actualiza la memoria sobre\n # el cuarto actual.\n self.modelo[' AB'.find(percepción)] = 'limpio'\n return 'limpiar'\n else:\n return ('nada' if a == b == 'limpio' else\n 'ir_A' if percepción == 'B' else 'ir_B')\n\ndef hacerPruebaEjercicio3(pasos):\n \"\"\"\n Prueba el AgenteDosCuartosCiego y el AgenteAleatorio (de doscuartos_o)\n en el entorno DosCuartosCiego.\n\n @param pasos: Número de pasos de la simulación.\n \"\"\"\n\n print(\"Prueba en DosCuartosCiego con un agente aleatorio.\")\n entornos_o.simulador(DosCuartosCiego(), AgenteAleatorio(['ir_A', 'ir_B', 'limpiar', 'nada']), pasos)\n\n print(\"Prueba en DosCuartosCiego con un agente racional.\")\n entornos_o.simulador(DosCuartosCiego(), AgenteDosCuartosCiego(), pasos)\n\n##############################################################\n\n# Ejercicio 4.\n\nclass DosCuartosEstocástico(DosCuartos):\n \"\"\"\n Entorno en el cual el agente tiene un 80% de éxito al limpiar un\n cuarto y un 90% al cambiarse de cuarto.\n \"\"\"\n\n def transición(self, acción):\n \"\"\"\n Implementa una transición estocástica del entorno.\n\n @param acción Acción del agente.\n \"\"\"\n if not self.acción_legal(acción):\n raise ValueError(\"La acción no es legal para este estado.\")\n\n robot, a, b = self.x\n\n if acción != \"nada\" or a == \"sucio\" or b == \"sucio\":\n self.desempeño -= 1\n\n if acción == \"limpiar\" and random() <= 0.8:\n self.x[\" AB\".find(self.x[0])] = \"limpio\"\n elif acción == \"ir_A\" and random() <= 0.9:\n self.x[0] = \"A\"\n elif acción == \"ir_B\" and random() <= 0.9:\n self.x[0] = \"B\"\n\nclass AgenteDosCuartosEstocástico(AgenteReactivoModeloDosCuartos):\n \"\"\"\n Agente racional para el entorno DosCuartosEstocástico. Está\n basado en un modelo.\n \"\"\"\n\n def programa(self, percepción):\n \"\"\"\n Funciona igual que el agente reactivo basado en modelo usado\n en DosCuartos, pero al momento de escoger una acción tiene en\n cuenta que puede fallar.\n\n @param percepción: Percepción de DosCuartosEstocástico.\n \"\"\"\n posición, situación = percepción\n\n # Actualiza el modelo interno\n self.modelo[0] = posición\n self.modelo[' AB'.find(posición)] = situación\n\n # Decide sobre el modelo interno y la posibilidad de fallo.\n a, b = self.modelo[1], self.modelo[2]\n éxito = random()\n\n # Si el robot 'siente' que puede fallar, mejor hace nada.\n return ('nada' if a == b == 'limpio' or éxito < 0.2 else\n 'limpiar' if situación == 'sucio' else\n 'ir_B' if posición == 'A' else 'ir_A')\n\ndef hacerPruebaEjercicio4(pasos):\n \"\"\"\n Realiza pruebas con un agente aleatorio y uno reactivo basado en\n modelo en el entorno DosCuartosEstocástico.\n\n @param pasos: Número de pasos de la simulación.\n \"\"\"\n\n print(\"Prueba en DosCuartosEstocástico con un agente aleatorio.\")\n entornos_o.simulador(DosCuartosEstocástico(), AgenteAleatorio(['ir_A', 'ir_B', 'limpiar', 'nada']), pasos)\n\n print(\"Prueba en DosCuartosEstocástico con un agente racional.\")\n entornos_o.simulador(DosCuartosEstocástico(), AgenteDosCuartosEstocástico(), pasos)\n\n##############################################################\n\nif __name__ == \"__main__\":\n hacerPruebaEjercicio1_2(100)\n hacerPruebaEjercicio3(100)\n hacerPruebaEjercicio4(100)\n","repo_name":"rexemin/Material-IA","sub_path":"Tareas/Tarea01-Agentes-Inteligentes/tarea_1.py","file_name":"tarea_1.py","file_ext":"py","file_size_in_byte":14229,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"8066610641","text":"import os\nimport time\n\nimport mysql.connector\nfrom dotenv import load_dotenv\nfrom slack_sdk import WebClient\nfrom slack_sdk.errors import SlackApiError\n\nfrom merchants_data import get_merchants_data\n\nload_dotenv()\nprevious_statuses = {}\n\nMYSQL_HOST = os.environ[\"MYSQL_HOST\"]\nMYSQL_PORT = int(os.environ[\"MYSQL_PORT\"])\nMYSQL_USER = os.environ[\"MYSQL_USER\"]\nMYSQL_PASSWORD = os.environ[\"MYSQL_PASSWORD\"]\nMYSQL_DB_NAME = os.environ[\"MYSQL_DB_NAME\"]\n\nSLACK_BOT_TOKEN = os.environ[\"SLACK_BOT_TOKEN\"]\nSLACK_CHANNEL_ID = os.environ[\"SLACK_CHANNEL_ID\"]\n\ncurrency_decimal_places = {\n 'TRX': 2,\n 'ETH': 6,\n 'BTC': 7,\n 'DOGE': 2,\n 'USDT': 2,\n 'USDC': 2,\n}\n\ndef format_amount(amount, currency):\n decimal_places = currency_decimal_places.get(currency.upper(), 2)\n return f\"{amount:.{decimal_places}f}\"\n\ndef create_db_connection():\n return mysql.connector.connect(\n host=MYSQL_HOST,\n port=MYSQL_PORT,\n user=MYSQL_USER,\n password=MYSQL_PASSWORD,\n database=MYSQL_DB_NAME,\n )\n\nslack_client = WebClient(token=SLACK_BOT_TOKEN)\n\ndef get_status_text(status):\n if status == 'in_progress':\n return ':large_yellow_circle: Transaction in progress'\n elif status == 'success':\n return ':large_green_circle: Transaction success'\n elif status == 'rejected':\n return ':red_circle: Transaction decline'\n elif status == 'pending':\n return ':exclamation: Transaction awaiting provider approval @operations'\n else:\n return status\n\ndef send_slack_message(transaction, project_name, merchant_name):\n amount_from_formatted = format_amount(transaction['amount_from'], transaction['currency_from'])\n amount_to_formatted = format_amount(transaction['amount_to'], transaction['currency_to'])\n\n message_template = f\"\"\">*Exchange*\n:man_in_tuxedo: | \n:currency_exchange: {amount_from_formatted} {transaction['currency_from'].upper()} -> {amount_to_formatted} {transaction['currency_to'].upper()}\n:chart_with_upwards_trend: Rate: {transaction['rate']}\n:money_with_wings: Fee: {transaction['fee_exchange']} {transaction['currency_from'].upper()}\n\n{get_status_text(transaction['status'])}\n\"\"\"\n try:\n response = slack_client.chat_postMessage(\n channel=SLACK_CHANNEL_ID,\n text=message_template\n )\n return response['ts']\n except SlackApiError as e:\n print(f\"Error sending message: {e}\")\n\ndef post_status_in_thread(transaction, ts):\n status_text = get_status_text(transaction['status'])\n\n try:\n slack_client.chat_postMessage(\n channel=SLACK_CHANNEL_ID,\n text=status_text,\n thread_ts=ts\n )\n except SlackApiError as e:\n print(f\"Error posting status in thread: {e}\")\n\ndef update_slack_message(transaction, ts):\n current_status = transaction[\"status\"]\n previous_status = previous_statuses.get(transaction[\"id\"])\n\n if previous_status is None:\n previous_statuses[transaction[\"id\"]] = current_status\n elif current_status != previous_status:\n post_status_in_thread(transaction, ts)\n previous_statuses[transaction[\"id\"]] = current_status\n\ndef get_current_last_id():\n conn = create_db_connection()\n cursor = conn.cursor(dictionary=True)\n\n query = \"SELECT id FROM project_exchange_transactions ORDER BY id DESC LIMIT 1\"\n cursor.execute(query)\n result = cursor.fetchone()\n\n cursor.close()\n conn.close()\n\n if result:\n return result['id']\n return None\n\ndef monitor_transactions():\n merchants = get_merchants_data()\n last_processed_id = get_current_last_id()\n message_ts_map = {}\n\n while True:\n conn = create_db_connection()\n cursor = conn.cursor(dictionary=True)\n\n query = \"SELECT * FROM project_exchange_transactions\"\n if last_processed_id:\n query += f\" WHERE id > {last_processed_id}\"\n query += \" ORDER BY id DESC\"\n\n cursor.execute(query)\n result = cursor.fetchall()\n\n for row in result:\n merchant_name = merchants.get(row['owner_merchant_id'], 'Unknown')\n project_query = f\"SELECT name FROM projects WHERE id = {row['project_id']}\"\n cursor.execute(project_query)\n project = cursor.fetchone()\n project_name = project['name'] if project else 'Unknown'\n\n ts = send_slack_message(row, project_name, merchant_name)\n\n if row['id'] not in message_ts_map:\n message_ts_map[row['id']] = ts\n last_processed_id = row['id']\n else:\n ts = message_ts_map.get(row['id'])\n if ts:\n update_slack_message(row, ts)\n\n for transaction_id, ts in message_ts_map.items():\n query = f\"SELECT * FROM project_exchange_transactions WHERE id = {transaction_id}\"\n cursor.execute(query)\n row = cursor.fetchone()\n\n if row:\n update_slack_message(row, ts)\n\n cursor.close()\n conn.close()\n\n time.sleep(5)\n\nif __name__ == \"__main__\":\n monitor_transactions()\n\n","repo_name":"nodeLogs/notify","sub_path":"exchange_transactions.py","file_name":"exchange_transactions.py","file_ext":"py","file_size_in_byte":5386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"42888480411","text":"import os\nimport re\nimport datetime\n\n#Data Settings\nUSING_WORDS = False\nMIN_UNIT_COUNT = 1\nANALYSIS_TYPE = \"word\" if USING_WORDS else \"character\"\n\n#File Settings\nROOT = \".\"\nDATA_DIR = os.path.join(ROOT, \"shakespeare_data\")\nPRINT_TO_FILE = True\nTIMESTAMP_FILE = False\n\ndef get_time_for_file():\n return datetime.datetime.now().strftime(\"_%m.%d.%y-%H.%M.%S\")\n\nOUTPUT_FILE = ANALYSIS_TYPE + \"_data_analysis\"\nif TIMESTAMP_FILE:\n OUTPUT_FILE += get_time_for_file()\nOUTPUT_FILE += \".txt\"\nOUTPUT_FILE = os.path.join(ROOT, \"data_analysis\", OUTPUT_FILE)\n\nfile_count = 0\ntext = \"\"\nfor file in os.listdir(DATA_DIR):\n if file.endswith(\".txt\"):\n file_count += 1\n text += open(os.path.join(DATA_DIR, file)).read()\n\nif USING_WORDS:\n text = text.lower()\n\nregex = r\"(?:[A-Za-z']*(?:(?| |\\t|\\n\" if USING_WORDS else r\".|\\n\"\nunits = re.findall(regex, text)\nunit_counts = dict()\n\nfor unit in units: #create a dict mapping unit to count\n unit_counts[unit] = unit_counts.get(unit, 0) + 1\n\nunit_counts = sorted(list(unit_counts.items()), key=lambda i: (-i[1], i[0])) #convert dict to list of tuples sort by count then unit\n\ntotal_units = 0\ntotal_top_units = 0\nnum_top_units = 0\nfor i in range(0, len(unit_counts)):\n total_units += unit_counts[i][1]\n if unit_counts[i][1] >= MIN_UNIT_COUNT:\n num_top_units += 1\n total_top_units += unit_counts[i][1]\n\npre_unk_len = len(unit_counts)\n\nless_than_min = 0\nfor i in range(len(unit_counts) - 1, -1, -1):\n if unit_counts[i][1] < MIN_UNIT_COUNT:\n less_than_min += unit_counts[i][1]\n del unit_counts[i]\n\nunit_counts.append((\"\", less_than_min))\n\nnum_top_units_with_unk = num_top_units\nif less_than_min >= MIN_UNIT_COUNT:\n num_top_units_with_unk += 1\n\nunique_percent = num_top_units / pre_unk_len * 100\ntotal_percent = total_top_units / total_units * 100\n\noutput = \"%d files analyzed\\n\\n\" % file_count\noutput += (\"%d unique \" + ANALYSIS_TYPE + \"s\\n%d total \" + ANALYSIS_TYPE + \"s\\n\\n\") % (pre_unk_len, total_units)\noutput += (\"Showing \" + ANALYSIS_TYPE + \"s with count >= %d (top %d)\\n\") % (MIN_UNIT_COUNT, num_top_units)\noutput += \"%.1f%% of unique, %.1f%% of total\\n\\n\" % (unique_percent, total_percent)\nif num_top_units_with_unk > num_top_units:\n output += \"Sum of counts of non-top \" + ANALYSIS_TYPE + \"s included under \\n\"\n output += \" not included in stats, but is ranked\\n\\n\"\noutput += \"%6s%16s%10s\\n\" % (\"Rank:\", \"Word:\", \"Count:\")\noutput += \"--------------------------------\"\n\nunit_counts.sort(key=lambda i: (-i[1], i[0])) #resort for \n\nfor i in range(0, num_top_units_with_unk):\n w = unit_counts[i][0]\n if w == \"\\n\":\n w = \"\"\n elif w == \"\\t\":\n w = \"\"\n elif w == \" \":\n w = \"\"\n output += \"\\n%5d)%16s%10d\" % (i + 1, w, unit_counts[i][1])\n\nif PRINT_TO_FILE:\n with open(OUTPUT_FILE, \"w\") as output_file:\n output_file.write(output)\nelse:\n print(output)","repo_name":"brunofreeman/ShakespeareLSTM","sub_path":"data_analysis.py","file_name":"data_analysis.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"71107620822","text":"from dbpool import DBPool\nfrom genutils import *\nfrom uv_decorators import *\nfrom config import UVConfig\nimport time\nimport re\n\n@singleton\nclass UVNormalizer:\n def __init__(self):\n self.init()\n \n def reload(self):\n self.init()\n\n def init(self):\n self.db_name = UVConfig().get_config_value(\"database\",\"db_name.core\") \n self.rowcount, self.normalize_rules = DBPool().execute_query(\"select id, in_pattern, out_pattern, telco_id, channel, remarks from tb_number_normalizer order by id desc\", self.db_name)\n\n logging.info(\"Normalized rules in search order top to bottom\")\n logging.info(\"id\tin_pattern\tout_pattern\ttelco_id\tchannel\t\tremarks\")\n logging.info(\"-\" * 70)\n for l_row in self.normalize_rules:\n logging.info(\"{0}\\t{1}\\t{2}\\t{3}\\t{4}\\t{5}\".format(l_row['id'], l_row['in_pattern'], l_row['out_pattern'], l_row['telco_id'], l_row['channel'], l_row['remarks']))\n\n def normalize(self, p_msisdn, p_telco_id = \".*\", p_channel = \".*\"):\n logging.debug(\"params - p_msisdn {0}, p_telco_id {1}, p_channel {2}\".format(p_msisdn, p_telco_id, p_channel))\n for l_row in self.normalize_rules:\n if( (None != re.match(l_row['in_pattern'], p_msisdn)) and (None != re.match(l_row['telco_id'], p_telco_id)) and (None != re.match(l_row['channel'], p_channel)) ):\n l_norm_msisdn = re.sub(l_row['in_pattern'], l_row['out_pattern'], p_msisdn)\n logging.info(\"Matchfound. p_msisdn = {0}, l_norm_msisdn = {1}, id = {2}, in_pattern = {3}, out_pattern = {4}, telco_id = {5}, channel = {6}, p_telco_id = {7}. p_channel = {8}\".format(p_msisdn, l_norm_msisdn, l_row['id'], l_row['in_pattern'], l_row['out_pattern'], l_row['telco_id'], l_row['channel'], p_telco_id, p_channel) )\n return True, l_norm_msisdn\n\n #End of for loop. No match found. So return False\n logging.warn(\"No normalizer match not found. p_msisdn = {0}, p_telco_id = {1}. p_channel = {2}\".format(p_msisdn, p_telco_id, p_channel) )\n return False, p_msisdn\n\n#Run unit tests\nif __name__ == \"__main__\":\n init_logging(\"voiceapp.log\")\n conf = UVConfig()\n conf.init(\"/root/ucp/ucp/conf/ucp.conf\")\n\n l_normalizer = UVNormalizer()\n l_found, l_result = l_normalizer.normalize(\"9886161856\")\n l_found, l_result = l_normalizer.normalize(\"9886161856\", p_telco_id = \"91.*\")\n\n\n","repo_name":"govardhan/ucp_beta","sub_path":"ucp/core/number_normalize.py","file_name":"number_normalize.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"29528339946","text":"\"\"\"\nWhat Type of Triangle?\nCreate a function which returns the type of triangle, given the side lengths. Return the following values if they match the criteria.\n\nNo sides equal: \"scalene\"\nTwo sides equal: \"isosceles\"\nAll sides equal: \"equilateral\"\nLess or more than 3 sides given: \"not a triangle\"\n\nExamples\nget_triangle_type([2, 6, 5]) ➞ \"scalene\"\nget_triangle_type([4, 4, 7]) ➞ \"isosceles\"\nget_triangle_type([8, 8, 8]) ➞ \"equilateral\"\nget_triangle_type([3, 5, 5, 2]) ➞ \"not a triangle\"\n\nNotes\nYou will be given a list of positive integers.\nCheck the Resources tab for more information on the types of triangles.\n\n\"\"\"\n\n\n\"\"\"\nSolution 1\n\"\"\"\n\ndef get_triangle_type(lst):\n\tif len(lst) == 3:\n\t\treturn ['equilateral', 'isosceles', 'scalene'][len(set(lst)) - 1]\n\treturn 'not a triangle'\n\n\"\"\"\nSolution 2\n\"\"\"\n\ndef get_triangle_type(lst):\n return ['equilateral','isosceles','scalene'][len(set(lst))-1] if len(lst) == 3 else 'not a triangle'\n\n\"\"\"\nSolution 3\n\"\"\"\n\ndef get_triangle_type(lst):\n\ts = len(set(lst))-1\n\ttriangles = ['equilateral','isosceles','scalene']\n\treturn triangles[s] if len(lst) == 3 else 'not a triangle'\n\n\"\"\"\nSolution 4\n\"\"\"\n\ndef get_triangle_type(lst):\n if len(lst)==3:\n a,b,c=lst[0],lst[1],lst[2]\n if a==b and b==c:\n return 'equilateral'\n elif (a!=b and b==c) or (a==b and b!=c) or (a==c and b!=a):\n return 'isosceles'\n else:\n return 'scalene'\n return 'not a triangle'\n\n\n\n","repo_name":"hoangduy0723/py-programming-excercises","sub_path":"+1500 Python Challenges/V Easy/What Type of Triangle.py","file_name":"What Type of Triangle.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"72917753300","text":"import sys\n# required for when running on a cluster\nsys.path.append('../')\nfrom typing import List\n\nimport sklearn\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\nfrom pathlib import Path\nimport pickle\n\nimport sdem\nfrom sdem import Experiment\nfrom sdem.utils import read_yaml, get_all_permutations, print_dict\n\n# Setup sacred experiment\nex = Experiment(__file__)\n\n@ex.configs\ndef get_config() -> List[dict]:\n configs = {\n 'name': ['linear_model'],\n 'fold': list(range(5))\n }\n return get_all_permutations(configs)\n\ndef get_raw_data():\n np.random.seed(0)\n\n N = 50\n\n x = np.linspace(0, 1, N)\n y = x + 0.1*np.random.randn(N)\n\n return x[:, None], y\n\ndef get_fold(fold):\n X, y = get_raw_data()\n\n kf_gen = sklearn.model_selection.KFold(n_splits=5, shuffle=False).split(X)\n\n # kf is a generator, convert to list so we can index\n kf = [k for k in kf_gen]\n\n train_index, test_index = kf[fold]\n\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n\n return X_train, X_test, y_train, y_test\n\n\n@ex.automain\ndef main(config):\n print_dict(config)\n\n # Output format name. This must match the pattern defined in the experiment config.\n name = '{name}_{_id}'.format(name=config['name'], _id=config['experiment_id'])\n\n # Make sure folder for results exists\n results_root = Path('../results/')\n results_root.mkdir(exist_ok=True)\n\n # Get training data for current fold\n X_train, X_test, y_train, y_test = get_fold(config['fold'])\n\n # Make model\n m = LinearRegression().fit(X_train, y_train)\n\n # Log metrics\n def pred_fn(X):\n return m.predict(X)\n\n train_metrics, pred_train = ex.log_metrics(\n X_train, y_train, pred_fn, var_flag=False, prefix='train'\n )\n test_metrics, pred_test = ex.log_metrics(\n X_test, y_test, pred_fn, var_flag=False, prefix='test'\n )\n \n results = {\n 'metrics': {\n 'train': train_metrics,\n 'test': test_metrics\n },\n 'predictions': {\n 'train': pred_train,\n 'test': pred_test \n }\n }\n\n # save results\n print_dict(results['metrics'])\n\n pickle.dump(results, open(results_root/ f'{name}.pickle', \"wb\" ) )\n ex.add_artifact(results_root/ f'{name}.pickle')\n","repo_name":"defaultobject/sdem","sub_path":"example/example_exp/models/m_model.py","file_name":"m_model.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"24343857221","text":"import csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import (AutoMinorLocator, MultipleLocator)\n\n# Definitions\nMESSAGE_STATISTICS_FILENAME = './../../data/camstat/message_statistics.csv'\nPLOT_START_TIME = 300.0\nPLOT_END_TIME = 1200.0\n\n# Define data dictionaries\narrInterarrivalTime = [ ]\narrCamSize = [ ]\nmapMaxLatency = { }\nmapMaxDistance = { }\nmapReliableDistance100 = { }\nmapReliableDistance95 = { }\nmapReliableDistance80 = { }\n\n# Read from station statistics file\nline_number = 0\nrow_time = 0\nprev_time = 0\ninterarrival_time = 0\ncam_size = 0\nmax_latency = 0\nmax_distance = 0\nreliable_distance_100_sum = 0\nreliable_distance_95_sum = 0\nreliable_distance_80_sum = 0\nreliable_distance_data_counter = 0\nwith open(MESSAGE_STATISTICS_FILENAME, 'r') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n for row in csv_reader:\n line_number += 1\n if line_number == 1:\n continue\n row_time = int(float(row[0]) / 10) * 10\n if row_time < PLOT_START_TIME or row_time > PLOT_END_TIME:\n continue\n if row_time > prev_time:\n if prev_time > 0:\n mapMaxLatency[prev_time] = max_latency\n mapMaxDistance[prev_time] = max_distance\n if reliable_distance_data_counter > 0:\n mapReliableDistance100[prev_time] = reliable_distance_100_sum / float(reliable_distance_data_counter)\n mapReliableDistance95[prev_time] = reliable_distance_95_sum / float(reliable_distance_data_counter)\n mapReliableDistance80[prev_time] = reliable_distance_80_sum / float(reliable_distance_data_counter)\n prev_time = row_time\n max_latency = 0\n max_distance = 0\n reliable_distance_100_sum = 0\n reliable_distance_95_sum = 0\n reliable_distance_80_sum = 0\n reliable_distance_data_counter = 0\n arrInterarrivalTime.append(float(row[2]))\n arrCamSize.append(int(row[4]))\n if float(row[7]) > max_latency:\n max_latency = float(row[7])\n if float(row[8]) > max_distance:\n max_distance = float(row[8])\n if int(row[5]) > 0 and int(row[6]) > 0:\n reliable_distance_100_sum += float(row[9])\n reliable_distance_95_sum += float(row[10])\n reliable_distance_80_sum += float(row[11])\n reliable_distance_data_counter += 1\n\n# Plot interarrival time\nfigure1, axes1 = plt.subplots(figsize=(8, 6))\nfigure1.tight_layout(pad=5.0)\naxes1.hist(arrInterarrivalTime, bins = np.arange(0.1, 0.51, 0.01) - 0.005, rwidth=0.5)\naxes1.xaxis.set_major_locator(MultipleLocator(0.05))\naxes1.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes1.grid(which='major', color='#CCCCCC', linestyle='--')\naxes1.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes1.set_title('Interarrival Times')\naxes1.set_xlabel('time (s)')\n\n# Plot cam size\nfigure2, axes2 = plt.subplots(figsize=(8, 6))\nfigure2.tight_layout(pad=5.0)\naxes2.hist(arrCamSize)\naxes2.set_title('Cam Lengths')\naxes2.set_xlabel('size (bytes)')\n\n# Plot maximum latency\nX = mapMaxLatency.keys()\nY = mapMaxLatency.values()\nfigure3, axes3 = plt.subplots(figsize=(8, 6))\nfigure3.tight_layout(pad=5.0)\naxes3.set_xlim(PLOT_START_TIME, PLOT_END_TIME)\naxes3.xaxis.set_major_locator(MultipleLocator(300))\naxes3.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes3.set_ylim(0, 0.005)\naxes3.yaxis.set_major_locator(MultipleLocator(0.001))\naxes3.yaxis.set_minor_locator(AutoMinorLocator(5))\naxes3.grid(which='major', color='#CCCCCC', linestyle='--')\naxes3.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes3.set_title('Maximum Latency')\naxes3.set_xlabel('time (s)')\naxes3.set_ylabel('maximum transmission latency\\nmeasured per 10 seconds intervals')\naxes3.plot(X, Y)\n\n# Plot maximum distance\nX = mapMaxDistance.keys()\nY = mapMaxDistance.values()\nfigure4, axes4 = plt.subplots(figsize=(8, 6))\nfigure4.tight_layout(pad=5.0)\naxes4.set_xlim(PLOT_START_TIME, PLOT_END_TIME)\naxes4.xaxis.set_major_locator(MultipleLocator(300))\naxes4.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes4.set_ylim(0, 2500)\naxes4.yaxis.set_major_locator(MultipleLocator(500))\naxes4.yaxis.set_minor_locator(AutoMinorLocator(5))\naxes4.grid(which='major', color='#CCCCCC', linestyle='--')\naxes4.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes4.set_title('Maximum Distance')\naxes4.set_xlabel('time (s)')\naxes4.set_ylabel('maximum transmission distance (meters)\\nmeasured per 10 seconds intervals')\naxes4.plot(X, Y)\n\n# Plot reliable distance 80\nX = mapReliableDistance80.keys()\nY = mapReliableDistance80.values()\nfigure5, axes5 = plt.subplots(figsize=(8, 6))\nfigure5.tight_layout(pad=5.0)\naxes5.set_xlim(PLOT_START_TIME, PLOT_END_TIME)\naxes5.xaxis.set_major_locator(MultipleLocator(300))\naxes5.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes5.set_ylim(0, 250)\naxes5.yaxis.set_major_locator(MultipleLocator(50))\naxes5.yaxis.set_minor_locator(AutoMinorLocator(5))\naxes5.grid(which='major', color='#CCCCCC', linestyle='--')\naxes5.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes5.set_title('80% Distance for Transmitted CAMs')\naxes5.set_xlabel('time (s)')\naxes5.set_ylabel('mean of 80% distances (meters)\\nmeasured per 10 seconds intervals')\naxes5.plot(X, Y)\n\n# Plot reliable distance 95\nX = mapReliableDistance95.keys()\nY = mapReliableDistance95.values()\nfigure6, axes6 = plt.subplots(figsize=(8, 6))\nfigure6.tight_layout(pad=5.0)\naxes6.set_xlim(PLOT_START_TIME, PLOT_END_TIME)\naxes6.xaxis.set_major_locator(MultipleLocator(300))\naxes6.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes6.set_ylim(0, 250)\naxes6.yaxis.set_major_locator(MultipleLocator(50))\naxes6.yaxis.set_minor_locator(AutoMinorLocator(5))\naxes6.grid(which='major', color='#CCCCCC', linestyle='--')\naxes6.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes6.set_title('95% Distance for Transmitted CAMs')\naxes6.set_xlabel('time (s)')\naxes6.set_ylabel('mean of 95% distances (meters)\\nmeasured per 10 seconds intervals')\naxes6.plot(X, Y)\n\n# Plot reliable distance 100\nX = mapReliableDistance100.keys()\nY = mapReliableDistance100.values()\nfigure7, axes7 = plt.subplots(figsize=(8, 6))\nfigure7.tight_layout(pad=5.0)\naxes7.set_xlim(PLOT_START_TIME, PLOT_END_TIME)\naxes7.xaxis.set_major_locator(MultipleLocator(300))\naxes7.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes7.set_ylim(0, 250)\naxes7.yaxis.set_major_locator(MultipleLocator(50))\naxes7.yaxis.set_minor_locator(AutoMinorLocator(5))\naxes7.grid(which='major', color='#CCCCCC', linestyle='--')\naxes7.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes7.set_title('100% Distance for Transmitted CAMs')\naxes7.set_xlabel('time (s)')\naxes7.set_ylabel('mean of 100% distances (meters)\\nmeasured per 10 seconds intervals')\naxes7.plot(X, Y)\n\n# Plot reliable distances all in one\nX100 = mapReliableDistance100.keys()\nY100 = mapReliableDistance100.values()\nX95 = mapReliableDistance95.keys()\nY95 = mapReliableDistance95.values()\nX80 = mapReliableDistance80.keys()\nY80 = mapReliableDistance80.values()\nfigure8, axes8 = plt.subplots(figsize=(8, 6))\nfigure8.tight_layout(pad=5.0)\naxes8.set_xlim(PLOT_START_TIME, PLOT_END_TIME)\naxes8.xaxis.set_major_locator(MultipleLocator(300))\naxes8.xaxis.set_minor_locator(AutoMinorLocator(5))\naxes8.set_ylim(0, 250)\naxes8.yaxis.set_major_locator(MultipleLocator(50))\naxes8.yaxis.set_minor_locator(AutoMinorLocator(5))\naxes8.grid(which='major', color='#CCCCCC', linestyle='--')\naxes8.grid(which='minor', color='#CCCCCC', linestyle=':')\naxes8.set_title('Comparison of XY% Distances for Transmitted CAMs')\naxes8.set_xlabel('time (s)')\naxes8.set_ylabel('mean of XY% distances (meters)\\nmeasured per 10 seconds intervals')\naxes8.plot(X100, Y100)\naxes8.plot(X95, Y95)\naxes8.plot(X80, Y80)\naxes8.legend(['100% Distance', '95% Distance', '80% Distance'], loc =\"lower right\")\n\n\n# Show plotted figures\nplt.show()","repo_name":"kctnky/v2x-work","sub_path":"code/python/plotMessageStatistics.py","file_name":"plotMessageStatistics.py","file_ext":"py","file_size_in_byte":7891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"33109695653","text":"import Utilidades\n\nclass Aula():\n '''Un aula tendrá un código, un conjunto de alumnos y un conjunto de asignaturas(módulos) que se imparten'''\n def __init__(self, codigo):\n self.codigo = codigo\n self.__alumnos = []\n self.__asignaturas = []\n self.__profesores = []\n\n \"\"\"Getters y setters\"\"\"\n\n @property\n def codigo(self):\n return self.__codigo\n\n @property\n def asignaturas(self):\n return self.__asignaturas\n\n @property\n def alumnos(self):\n return self.__alumnos\n\n @property\n def profesores(self):\n return self.__profesores\n\n @codigo.setter\n def codigo(self, codigo):\n self.__codigo = codigo\n\n \"\"\"Gestión de asignaturas\"\"\"\n\n def add_asignatura(self, asignatura):\n '''Añade una asignatura a la colección de asignaturas'''\n # Sólo se añade la asignatura si el profesor está registrado en el curso\n if asignatura.profesor not in self.__profesores:\n raise ValueError(\"No podemos añadir la asignatura porque el profesor no está registrado en el equipo educativo \"+str(asignatura))\n error_msg = \"Asignatura previamente registrada. Asignatura: \"+str(asignatura)\n Utilidades.add_elemento(asignatura, self.__asignaturas,error_msg)\n\n def asignatura_en_curso(self, asignatura):\n '''Nos indica si la asignatura se encuentra en las que se dan en el aula'''\n return Utilidades.esta_elemento(asignatura, self.__asignaturas)\n\n def remove_asignatura(self, asignatura):\n '''Elimina la asignatura del conjunto de asignaturas que se imparten en el aula. Si no se lanzará una\n excepción'''\n # No podemos eliminar una asignatura si hay alumnos matriculados en ella\n if self.hay_alumnos_matriculados(asignatura):\n raise ValueError(\"Asignatura no eliminada. Alumnos matriculados.\")\n error_msg = \"No se imparte la asignatura. Asignatura \" + asignatura.codigo\n Utilidades.remove_elemento(asignatura, self.__asignaturas, error_msg)\n\n def get_asignatura(self, asignatura):\n '''Devuelve la asignatura de la lista de asignaturas registradas. Se basa internamente en buscarlo en su código.\n Los datos reales de las asignaturas están en la lista de asignaturas del aula'''\n if not self.asignatura_en_curso(asignatura):\n raise ValueError(\"Asignatura no registrada. Asignatura \"+str(asignatura))\n\n index = self.asignaturas.index(asignatura)\n return self.asignaturas[index]\n\n def hay_asignaturas_impartidas_por(self, profesor):\n \"\"\"Devuelve Trye si en el curso hay asignaturas impartidas por dicho profesor\"\"\"\n impartidas = False\n contador = 0\n while not impartidas and contador < len(self.__asignaturas):\n if self.__asignaturas[contador].profesor == profesor:\n impartidas = True\n else:\n contador += 1\n return impartidas\n\n \"\"\"Gestión de alumnos\"\"\"\n\n def add_alumno(self, alumno):\n '''Añade un alumno a la colección de alumnos'''\n # El alumno sólo se debe añadir si las asignaturas están registradas en el sistema\n asignatura_registrada = True\n contador = 0\n while asignatura_registrada and contador < len(alumno.asignaturas_matricula):\n if alumno.asignaturas_matricula[contador] not in self.__asignaturas:\n asignatura_registrada = False\n contador+=1\n if not asignatura_registrada:\n raise ValueError(\"Alumno con asignaturas no registradas. \"+str(alumno.asignaturas_matricula[contador-1]))\n error_msg = \"Alumno previamente registrado. Alumno: \"+str(alumno)\n Utilidades.add_elemento(alumno, self.__alumnos, error_msg)\n\n def esta_alumno_en_curso(self, alumno):\n '''Nos indica si el alumno está asignado al curso'''\n return Utilidades.esta_elemento(alumno, self.__alumnnos)\n\n def remove_alumno(self, alumno):\n '''Elimina la asignatura del conjunto de asignaturas que se imparten en el aula. Si no se lanzará una\n excepción'''\n error_msg = \"Alumno no matriculado. Alumno: \"+str(alumno)\n Utilidades.remove_elemento(alumno, self.__alumnnos, error_msg)\n\n def hay_alumnos_matriculados(self, asignatura):\n \"\"\"Devuelve True si en el curso hay alumnos matriculados de dicha asignatura\"\"\"\n matriculados = False\n contador = 0\n while not matriculados and contador < len(self.__alumnos):\n for a in self.__alumnos[contador].asignaturas_matricula:\n if a == asignatura:\n matriculados = True\n if not matriculados:\n contador += 1\n return matriculados\n\n \"\"\"Gestión de profesores\"\"\"\n\n def add_profesor(self, profesor):\n '''Añade un profesor a la colección de profesores'''\n error_msg = \"Profesor previamente registrado. Profesor: \" + str(profesor)\n Utilidades.add_elemento(profesor, self.__profesores, error_msg)\n\n def esta_profesor_en_curso(self, profesor):\n '''Nos indica si el profesor está asignado al curso'''\n return Utilidades.esta_elemento(profesor, self.__profesores)\n\n def remove_profesor(self, profesor):\n '''Elimina el profesor del conjunto de profesores que imparten en el curso. Si no existe se lanzará una\n excepción'''\n # No podemos eliminar un profesor si hay asignaturas que son impartidas por él\n if self.hay_asignaturas_impartidas_por(profesor):\n raise ValueError(\"Profesor no eliminado. Asignaturas impartidas por él.\")\n error_msg = \"Profesor no registrado. Profesor: \" + profesor.dni\n Utilidades.remove_elemento(profesor, self.__profesores, error_msg)\n\n def get_profesor(self, profesor):\n '''Devuelve el profesor de la lista de profesores registrados. Se basa internamente en buscarlo en su DNI. Los\n datos reales de los profesores están en la lista de profesores del aula'''\n if not self.esta_profesor_en_curso(profesor):\n raise ValueError(\"Profesor no registrado. Profesor \"+str(profesor))\n\n index = self.profesores.index(profesor)\n return self.profesores[index]\n\n def __eq__(self, aula):\n return self.codigo == aula.codigo\n\n def __str__(self):\n clase = type(self).__name__\n msg = \"{0} => Código: {1}\\nProfesores: \\n\"\n\n for profesor in self.__profesores:\n msg += str(profesor) + \"\\n\"\n\n msg += \"Asignaturas:\\n\"\n\n for asignatura in self.__asignaturas:\n msg += str(asignatura) + \"\\n\"\n\n msg += \"Alumnos:\\n\"\n\n for alumno in self.alumnos:\n msg += str(alumno)+\"\\n\"\n\n return msg.format(clase, self.codigo)\n\n def to_dictionary(self):\n # Convierte la información del aula en un diccionario\n aula_dict = {}\n aula_dict.setdefault(self.codigo, {})\n datos_aula = aula_dict[self.codigo]\n asignaturas = {}\n alumnos = {}\n profesores = {}\n\n for profesor in self.__profesores:\n profesores.update(profesor.to_dictionary())\n datos_aula[\"profesores\"] = profesores\n\n for asignatura in self.__asignaturas:\n asignaturas.update(asignatura.to_dictionary())\n datos_aula[\"asignaturas\"] = asignaturas\n\n for alumno in self.__alumnos:\n alumnos.update(alumno.to_dictionary())\n datos_aula[\"alumnos\"]=alumnos\n\n aula_dict[self.codigo] = datos_aula\n return aula_dict","repo_name":"ICoelloC/Sistemas-de-Gestion-Empresarial-2020-2021","sub_path":"Python/Recuperacion/02-Aulas/Aula.py","file_name":"Aula.py","file_ext":"py","file_size_in_byte":7552,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"12248266838","text":"def cansum_tabulated(targetsum,arr):\r\n new_arr=[False for i in range(targetsum+1)]\r\n new_arr[0]=True\r\n for i in range(targetsum+1):\r\n if new_arr[i]==True:\r\n for j in arr:\r\n if i+j<=targetsum:\r\n new_arr[i+j]=True\r\n \r\n return new_arr[-1]\r\n\r\n\r\nprint(cansum_tabulated(8,[5,6,7]))\r\n","repo_name":"satishkumarsajjan/standard_dynamicprogramming_problems","sub_path":"cansum_tabulated.py","file_name":"cansum_tabulated.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"14543797169","text":"class Solution:\n \n def matrixReshape(self, mat, r: int, c: int):\n m, n = len(mat), len(mat[0])\n if m*n != r*c: return mat\n ans = []\n cum = 0\n for i in range(r):\n r1, c1 = divmod(cum, n)\n \n count, res = 0, []\n print(cum, r1, c1, ans)\n for row in range(r1, m):\n for j in range(c1, n):\n if count == c:\n break\n res.append(mat[row][j])\n count += 1\n \n c1 = 0\n \n ans.append(res)\n cum += c\n return ans\n \n\nprint(Solution().matrixReshape(\n[[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16],[17,18,19,20]]\n,4\n,5\n))","repo_name":"qianOU/leetcode","sub_path":"566. 重塑矩阵.py","file_name":"566. 重塑矩阵.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"30788241887","text":"def KMP_matching(cadena, patron):\n\t'''\n\tRecibe dos cadenas no vacías, una de ellas es un patrón a buscar\n\tde la segunda (de menos caracteres). Devuelve True si el patrón \n\tse encontraba en la cadena\n\t\n\tImplementáción de decisión en base a la versión en pseudocódigo del\n\talgoritmo que figura en: \n\t\"Introduction to Algorithms - Second Edition\", \n\tT.H. Cormen, C.E. Leiserson, R.L. Rivest y C. Stein.\n\tPágina 926 (capítulo 32).\n\t'''\n\tn = len(cadena)\n\tm = len(patron)\n\tfuncionDePrefijos = calcularFuncionDePrefijos(patron)\n\tnumCoincidencias = 0\n\t\n\tfor i in range(n):\n\t\twhile (numCoincidencias > 0) and (patron[numCoincidencias] != cadena[i]):\n\t\t\tnumCoincidencias = funcionDePrefijos[numCoincidencias - 1]\n\t\t\n\t\tif (patron[numCoincidencias] == cadena [i]):\n\t\t\tnumCoincidencias += 1\n\t\n\t\tif (numCoincidencias == m):\n\t\t\treturn True\n\t\n\treturn False\n\n\ndef calcularFuncionDePrefijos(patron):\n\t'''\n\tFunción auxiliar de KMP_matching, utilizada para devolver\n\tla función (lista) de los prefijos del patrón.\n\t'''\n\tm = len(patron)\n\tfuncionDePrefijos = []\n\tfuncionDePrefijos.insert(0, 0)\n\tk = 0\n\t\n\tfor q in range(1, m):\n\t\twhile (k > 0) and (patron[k] != patron[q]):\n\t\t\tk = funcionDePrefijos[k - 1]\n\t\t\n\t\tif (patron[k] == patron[q]):\n\t\t\tk += 1\n\t\t\t\n\t\tfuncionDePrefijos.insert(q,k)\n\n\treturn funcionDePrefijos\n","repo_name":"FdelMazo/7529rw-TDA","sub_path":"TP2/2. StringRotation/KMP_matching.py","file_name":"KMP_matching.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"29148676037","text":"from send_key import PressKey, ReleaseKey\nimport time\nimport threading\n\n# Hash code for key we may use: https://docs.microsoft.com/en-us/windows/win32/inputdev/virtual-key-codes?redirectedfrom=MSDN\nW = 0x57 # jump\nS = 0x23 # down\nA = 0x41 # left\nD = 0x44 # right\nR = 0x52 # interact\n\nL_SHIFT = 0xA0 # roll\nJ = 0x4A # main weapon attack\nK = 0x4B # second weapon attack\n\nESC = 0x1B # pause\n\n\n# characters will equip a short range weapon(and fast attack speed) and a shield\n# no healing allowed, no left nor right skills\n# since jump dash can not dizzy boss, it is skipped\n\n# move actions\n# 0\ndef Nothing():\n ReleaseKey(W)\n ReleaseKey(S)\n ReleaseKey(A)\n ReleaseKey(D)\n ReleaseKey(J)\n ReleaseKey(K)\n ReleaseKey(L_SHIFT)\n pass\n\n\n# Move\n# 0\ndef Move_Left():\n PressKey(A)\n time.sleep(0.01)\n\n\n# 1\ndef Move_Right():\n PressKey(D)\n time.sleep(0.01)\n\n\n# 2\ndef Single_Jump():\n PressKey(W)\n time.sleep(0.02)\n ReleaseKey(W)\n Nothing()\n time.sleep(0.01)\n\n\n# 3\ndef Double_Jump():\n PressKey(W)\n time.sleep(0.02)\n ReleaseKey(W)\n time.sleep(0.02)\n PressKey(W)\n time.sleep(0.02)\n ReleaseKey(W)\n Nothing()\n\n\n# ----------------------------------------------------------------------\n\n#\n# 0\ndef Attack():\n PressKey(J)\n time.sleep(0.15)\n ReleaseKey(J)\n Nothing()\n time.sleep(0.01)\n\n\n# 1\ndef Shield():\n PressKey(K)\n time.sleep(0.1)\n ReleaseKey(K)\n time.sleep(0.01)\n\n\n# 2\ndef Roll():\n PressKey(L_SHIFT)\n time.sleep(0.01)\n ReleaseKey(L_SHIFT)\n Nothing()\n time.sleep(0.02)\n\n\n# Restart function\n# it restart a new game\n# it is not in actions space\n\n# when boss fight is finished\n# the character will be sent to entrance and x location is 123 (this is fixed)\n# we will start at x = 20 at boss room\n# not until x = 46 will the boss fight start\ndef restart(location):\n while 120 < location <= 129:\n Move_Right()\n PressKey(R)\n time.sleep(0.02)\n ReleaseKey(R)\n time.sleep(1)\n while location <= 46:\n Move_Right()\n\n\n# List for action functions\nActions = [Attack, Shield, Roll]\nDirections = [Move_Left, Move_Right, Turn_Left, Turn_Right]\n\n\n# Run the action\ndef take_action(action):\n Actions[action]()\n\n\ndef take_direction(direc):\n Directions[direc]()\n\n\nclass TackAction(threading.Thread):\n def __init__(self, threadID, name, direction, action):\n threading.Thread.__init__(self)\n self.threadID = threadID\n self.name = name\n self.direction = direction\n self.action = action\n\n def run(self):\n take_direction(self.direction)\n take_action(self.action)\n","repo_name":"WuhaoStatistic/Dead-Cell-Reinforcement-Learning","sub_path":"tools/do_actions.py","file_name":"do_actions.py","file_ext":"py","file_size_in_byte":2627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"40566779247","text":"from collections import defaultdict\ndef solution(s):\n a=defaultdict(list)\n answer = []\n for idx in range(len(s)):\n if len(a[s[idx]])==0:\n answer.append(-1)\n a[s[idx]].append(idx)\n continue\n a[s[idx]].append(idx)\n answer.append(idx-a[s[idx]][-2])\n return answer","repo_name":"gudals-kim/Studyroom","sub_path":"프로그래머스/unrated/142086. 가장 가까운 같은 글자/가장 가까운 같은 글자.py","file_name":"가장 가까운 같은 글자.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"23560749711","text":"import discord\nfrom discord.ext import commands\nimport os\nimport random\nfrom discord.ext.commands.core import command\nimport praw\n\nreddit = praw.Reddit(client_id=\"5-_GzjyTOOhukQ\",\n client_secret=os.environ['REDDIT_SECRET'],\n username=\"idioticspaceman\",\n password=os.environ['REDDIT_PASS'],\n user_agent=\"Economy-BOT\")\n\nclass Images(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command(help=\"Use the command to get a meme\", usage=\"`#meme`\", aliases=['memes'])\n async def meme(self, ctx):\n async with ctx.typing():\n subreddit = reddit.subreddit(\"memes\")\n all_subs = []\n top = subreddit.top(limit=50)\n\n for submission in top:\n all_subs.append(submission)\n random_sub = random.choice(all_subs)\n name = random_sub.title\n url = random_sub.url\n meme_embed = discord.Embed(title=name, colour=discord.Colour.blue())\n meme_embed.set_image(url=url)\n await ctx.send(embed=meme_embed)\n\n @commands.command(aliases=['dogs', 'bark'], help=\"Use the command to see cute pictures of dogs!\", usage=\"`#dog`\")\n async def dog(self, ctx):\n async with ctx.typing():\n subreddit = reddit.subreddit(\"dogs\")\n all_subs = []\n top = subreddit.top(limit=50)\n\n for submission in top:\n all_subs.append(submission)\n random_sub = random.choice(all_subs)\n name = random_sub.title\n url = random_sub.url\n dog_embed = discord.Embed(title=name, colour=discord.Colour.teal())\n dog_embed.set_image(url=url)\n await ctx.send(embed=dog_embed)\n\n @commands.command(aliases=['cats', 'meow'], help=\"Use the command to see cats! MEOWWWW!\", usage=\"`#cat`\")\n async def cat(self, ctx):\n async with ctx.typing():\n subreddit = reddit.subreddit(\"cats\")\n all_subs = []\n top = subreddit.top(limit=50)\n\n for submission in top:\n all_subs.append(submission)\n random_sub = random.choice(all_subs)\n name = random_sub.title\n url = random_sub.url\n cat_embed = discord.Embed(title=name, colour=discord.Colour.teal())\n cat_embed.set_image(url=url)\n await ctx.send(embed=cat_embed)\n\n @commands.command(aliases=['hoot', 'owls'], help=\"Use the command to see owls! HOOT HOOT\", usage=\"`#owl`\")\n async def owl(self, ctx):\n async with ctx.typing():\n subreddit = reddit.subreddit(\"owls\")\n all_subs = []\n top = subreddit.top(limit=50)\n\n for submission in top:\n all_subs.append(submission)\n random_sub = random.choice(all_subs)\n name = random_sub.title\n url = random_sub.url\n owl_embed = discord.Embed(title=name, colour=discord.Colour.teal())\n owl_embed.set_image(url=url)\n await ctx.send(embed=owl_embed)\n\n @commands.command(aliases=['foxxy'], help=\"Use the command to see foxes\", usage=\"`#fox`\")\n async def fox(self, ctx):\n async with ctx.typing():\n subreddit = reddit.subreddit(\"foxes\")\n all_subs = []\n top = subreddit.top(limit=50)\n\n for submission in top:\n all_subs.append(submission)\n random_sub = random.choice(all_subs)\n name = random_sub.title\n url = random_sub.url\n fox_embed = discord.Embed(title=name, colour=discord.Colour.teal())\n fox_embed.set_image(url=url)\n await ctx.send(embed=fox_embed)\n \n @commands.command(aliases=['lizzards', 'lizzard'], help=\"Use the command to see a lizzard\", usage=\"`#lizziboi`\")\n async def lizziboi(self, ctx):\n async with ctx.typing():\n subreddit = reddit.subreddit(\"lizards\")\n all_subs = []\n top = subreddit.top(limit=50)\n\n for submission in top:\n all_subs.append(submission)\n random_sub = random.choice(all_subs)\n name = random_sub.title\n url = random_sub.url\n liz_embed = discord.Embed(title=name, colour=discord.Colour.teal())\n liz_embed.set_image(url=url)\n await ctx.send(embed=liz_embed)\n\n\n\ndef setup(bot):\n bot.add_cog(Images(bot))\n","repo_name":"nothingButSyntaxError/DisBot","sub_path":"cogs/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":4458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"43892770","text":"import serial\nimport serial.tools.list_ports\nfrom datetime import datetime\nimport calendar\n\nSETTIME = 6\n\n\nclass IMU_Watch(object):\n\n def __init__(self, serialrate=115200):\n # Initialise serial payload\n self.count = 0\n self.plSz = 0\n self.payload = bytearray()\n\n # Looks for a watch until it finds one\n running = False\n while True:\n ports = list(serial.tools.list_ports.comports())\n ports = [str(p.device) for p in ports if str(p.hwid).find('9D0F') > 0]\n if len(ports) == 0:\n print('Watch not found')\n else:\n break\n self.serialport = ports[0]\n # Initialise serial port\n self.ser = serial.Serial(self.serialport, serialrate)\n while not running:\n if self.ser.isOpen():\n print('Watch found at ', self.serialport)\n running = True\n else:\n print('Cannot open %s. Trying again...', self.serialport)\n self.ser.open()\n\n def serial_write(self, command, string=''):\n # Format:\n # | 255 | 255 | no. of bytes | command | filename/time | checksum |\n\n header = [255, 255]\n chksum = 254\n\n payload_size = len(string) + 1\n\n chksum += payload_size + command\n\n self.ser.write(bytes([header[0]]))\n self.ser.write(bytes([header[1]]))\n self.ser.write(bytes([payload_size]))\n\n self.ser.write(bytes([command]))\n\n if string != '':\n for i in range(len(string)):\n self.ser.write(bytes([ord(string[i])]))\n chksum += ord(string[i])\n\n self.ser.write(bytes([chksum % 256]))\n\n def serial_read(self):\n if (self.ser.read() == b'\\xff') and (self.ser.read() == b'\\xff'):\n self.count += 1\n chksum = 255 + 255\n\n sz = self.ser.read(2)\n self.plSz = int.from_bytes(sz, 'little')\n chksum += sum(sz)\n\n self.payload = self.ser.read(self.plSz)\n chksum += sum(self.payload)\n chksum = bytes([chksum % 256])\n _chksum = self.ser.read()\n\n return _chksum == chksum\n return False\n\n def set_time(self):\n # Sends current time from PC and reads the time set on the IMU watch\n unix = calendar.timegm(datetime.now().timetuple())\n self.serial_write(SETTIME, string=str(unix))\n # self.statusBar().showMessage(\"Initialized IMU watch\")\n print('Command sent: SETTIME - ', datetime.utcfromtimestamp(unix).strftime('%Y-%m-%d %H:%M:%S'))\n if self.serial_read():\n unix = int(self.payload.decode('utf-8'))\n a = ('Time on Watch: ' + datetime.utcfromtimestamp(unix).strftime('%Y-%m-%d %H:%M:%S'))\n return a","repo_name":"SujithChristopher/MIRA","sub_path":"support_py/timeset.py","file_name":"timeset.py","file_ext":"py","file_size_in_byte":2800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"37883993823","text":"import math\na, b, c = map(int, input().split())\ni = 0\nwhile True:\n i += 1\n if i % 15 == a-1 and i % 28 == b-1 and i % 19 == c-1:\n if i == 7980:\n i = 0\n print(i+1)\n break\n","repo_name":"JaeHyunL/Python-Alg","sub_path":"Backup/2020.07~/1476.py","file_name":"1476.py","file_ext":"py","file_size_in_byte":208,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"12"} +{"seq_id":"37288123686","text":"import cv2\nimport numpy as np\n\nclass ImageHandler:\n \n @staticmethod\n def crop(np_image, points):\n return np_image[points[0]:points[1], points[2]:points[3]]\n\n @staticmethod\n def write_to_file(filename, np_image):\n return cv2.imwrite(filename, np_image)\n\n @staticmethod\n def draw_vehicle_shape(np_image, points, color=(255,0,0), thickness=1):\n top_left = (points[2], points[0])\n bottom_right = (points[3], points[1])\n cv2.rectangle(np_image, top_left, bottom_right, color, thickness=thickness)\n\n @staticmethod\n def draw_losangle(np_image, points, color=(1.,1.,1.), thickness=1):\n for i in range(4):\n pt1 = tuple(points[:,i].astype(int).tolist())\n pt2 = tuple(points[:,(i+1)%4].astype(int).tolist())\n cv2.line(np_image,pt1,pt2,color,thickness)\n\n @staticmethod\n def write2img(np_image,points,strg,txt_color=(0,0,0),bg_color=(255,255,255),font_size=1):\n wh_img = np.array(np_image.shape[1::-1])\n \n font = cv2.FONT_HERSHEY_SIMPLEX\n\n wh_text,v = cv2.getTextSize(strg, font, font_size, 3)\n rpoints = points / np.array(wh_img, dtype=float).reshape(2,1)\n \n bl_corner = rpoints.min(1) * wh_img\n tl_corner = np.array([bl_corner[0],bl_corner[1]-wh_text[1]])/wh_img\n br_corner = np.array([bl_corner[0]+wh_text[0],bl_corner[1]])/wh_img\n bl_corner /= wh_img\n\n if (tl_corner < 0.).any():\n delta = 0. - np.minimum(tl_corner,0.)\n elif (br_corner > 1.).any():\n delta = 1. - np.maximum(br_corner,1.)\n else:\n delta = 0.\n\n tl_corner += delta\n br_corner += delta\n bl_corner += delta\n\n tpl = lambda x: tuple((x*wh_img).astype(int).tolist())\n\n cv2.rectangle(np_image, tpl(tl_corner), tpl(br_corner), bg_color, -1)\t\n cv2.putText(np_image,strg,tpl(bl_corner),font,font_size,txt_color,3) \n\n ","repo_name":"knetto/Pakeerplaats-lp-scanner-knetto-main","sub_path":"alpr-unconstrained-master2/classes/ImageHandler.py","file_name":"ImageHandler.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"26457729160","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jun 1 13:34:43 2022\r\n\r\n\"\"\"\r\n\r\n#skrypt wykonuje zewnetrzny SQL na bazie oracle i mierzy czas\r\n\r\nimport os\r\nimport pandas as pd\r\nimport cx_Oracle\r\nimport time\r\n\r\nos.chdir(\"c:/python/bazy\") # format:'C:/folder/folder'\r\nfile='p.txt'\r\n\r\nline = []\r\nwith open(file, \"r\") as file:\r\n line = file.readlines()\r\n\r\n#with open as\r\nuserpwd=line[0]\r\n \r\nconnection = cx_Oracle.connect(\"N1400274\", password=userpwd, dsn=\"KMB_PRE\") #\r\nquery = connection.cursor()\r\n\r\n\r\nwith open(\"c:/python/bazy/sql.txt\") as file_in:\r\n lines = []\r\n for line in file_in:\r\n lines.append(line)\r\n\r\nprint(lines)\r\nsql_text=[]\r\n\r\nfor i in lines:\r\n a=str(i)\r\n sql_text.append(a)\r\n \r\nsql = ''.join(sql_text)\r\n\r\nprint(sql)\r\n\r\n# rs=query.execute(sql)\r\nrs=query.execute(\"select max(end_dt) as end_Dt, min(end_dt) as end_dt from kpr.cust_ent_dim \\\r\n union all \\\r\n select max(end_dt) as end_Dt, min(end_dt) as end_dt from kpr.cust_ent_dim \")\r\nprint(\"Fetching data: started\")\r\nstart_time = time.time()\r\ndata=pd.DataFrame(rs.fetchall())\r\nprint(\"Fetching data: finished. time: {} s\".format(time.time() - start_time))\r\nprint(len(query.description))\r\nprint(data)\r\ncol_names=[]\r\nprint(query.description)\r\nfor i in range(0, len(query.description)):\r\n col_names.append(query.description[i][0])\r\nprint(col_names)\r\nprint(data.columns)\r\ndata.columns=col_names\r\nprint(data)\r\nconnection.close()\r\nend_time = time.time()\r\nprint (end_time - start_time)","repo_name":"krzysiekgluch/python_test","sub_path":"KPR_02.py","file_name":"KPR_02.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"33881553415","text":"#!/bin/python3.8\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\n\nfrom src.ai.architectural_components import ResidualBlock\nfrom src.ai.architectures.bc_deeply_supervised_auto_encoder import Net as BaseNet\n#from src.ai.architectures.auto_encoder_deeply_supervised_share_weights import Net as BaseNet\nfrom src.ai.base_net import ArchitectureConfig\nfrom src.ai.utils import mlp_creator, conv_creator\nfrom src.core.data_types import Action\nfrom src.core.logger import get_logger, cprint\nfrom src.core.utils import get_filename_without_extension\n\n\"\"\"\nDeep Supervision net with discriminator.\nDiscriminator is used to improve the predictions from the network on unlabeled real data.\nDiscriminator discriminates between simulated (training) data prediction (0) and real (test) data prediction (1).\nThe main network can then be trained also on unlabeled real data to minimize the discriminators output.\n\"\"\"\n\n\nclass Net(BaseNet):\n\n def __init__(self, config: ArchitectureConfig, quiet: bool = False):\n super().__init__(config=config, quiet=True)\n self._deeply_supervised_parameter_names = [name for name, _ in self.named_parameters()]\n self._discriminator = conv_creator(channels=[1, 3, 6, 9],\n kernel_sizes=[5, 5, 5],\n strides=[3, 3, 3],\n activation=nn.LeakyReLU(),\n output_activation=nn.LeakyReLU(),\n batch_norm=self._config.batch_normalisation)\n self._discriminator_decision = mlp_creator([9*6*6, 1], output_activation=nn.Sigmoid(),\n bias_in_last_layer=False)\n if not quiet:\n self._logger = get_logger(name=get_filename_without_extension(__file__),\n output_path=config.output_path,\n quiet=False)\n self.initialize_architecture()\n cprint(f'Started.', self._logger)\n\n def deeply_supervised_parameters(self, recurse=True):\n for name, param in self.named_parameters(recurse=recurse):\n if name in self._deeply_supervised_parameter_names:\n yield param\n\n def discriminator_parameters(self, recurse=True):\n for p in self._discriminator.parameters(recurse=recurse):\n yield p\n for p in self._discriminator_decision.parameters(recurse=recurse):\n yield p\n\n def forward_with_all_outputs(self, inputs, train: bool = False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor,\n torch.Tensor, torch.Tensor]:\n for p in self.deeply_supervised_parameters():\n p.requires_grad = train\n return super().forward_with_all_outputs(inputs, train=train)\n\n def discriminate(self, predictions, train: bool = False) -> torch.Tensor:\n \"\"\"\n Evaluate predictions on whether they come from simulated (0) or real (1) data\n :param predictions: NxCxHxW with CxHxW corresponding to the output size\n :param train: train the discriminator part or evaluate\n :return: output 0 --> simulated, 1 --> real\n \"\"\"\n self._discriminator.train(train)\n for p in self.discriminator_parameters():\n p.requires_grad = train\n feature = self._discriminator(predictions).view(-1, 9*6*6)\n return self._discriminator_decision(feature)\n","repo_name":"kkelchte/imitation-learning-codebase","sub_path":"src/ai/architectures/auto_encoder_deeply_supervised_with_discriminator.py","file_name":"auto_encoder_deeply_supervised_with_discriminator.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"69814813783","text":"from bs4 import BeautifulSoup\nimport requests\n\n\ndef get_soup(url):\n html_soup = requests.get(url)\n soup = BeautifulSoup(html_soup.text, 'lxml')\n return soup.find_all(class_=\"message-userContent\")\n\n\ndef get_plans(posts):\n for el in posts:\n el = str(el.text)\n return el.split(\"\\n\")\n\n\ndef plan_counts(arr, plan_count):\n\n for i in arr:\n if i.startswith(\"[X]\") or i.startswith(\"[x]\"):\n if i in plan_count:\n plan_count[i] += 1\n else:\n plan_count[i] = 1\n return plan_count\n\n\ndef send_plan(plans, url):\n for plan in plans:\n data ={\"plan\":next(iter(plan)), \"votes\":plan.next(iter(plan))}\n requests.post(url, data)","repo_name":"Hazel-J-Nova/discord-Bot","sub_path":"python/soup_test.py","file_name":"soup_test.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"28867057337","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# @Time : 2020/04/12 \n# @Author : XU Liu\n# @FileName: 1408.String Matching in an Array.py\n\n'''\n周赛\n关键点： \n\na = 'abcc'\nc = 'ab'\nif c in a:\n print('OK')\n\n'''\nclass Solution:\n def stringMatching(self, words: List[str]) -> List[str]:\n if words == []:\n return []\n \n res = []\n for w1 in words:\n for w2 in words:\n if w1 != w2:\n if w1 in w2:\n if w1 not in res:\n res.append(w1)\n return res\n","repo_name":"Leahxuliu/Data-Structure-And-Algorithm","sub_path":"Python/每周竞赛/1408.String Matching in an Array.py","file_name":"1408.String Matching in an Array.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"27954757199","text":"from setuptools import setup\n\ntry:\n with open('requirements.txt', 'r') as file:\n requirements = file.read()\nexcept FileNotFoundError:\n requirements = []\n\nsetup(name='gym_image_maze',\n version='0.0.1',\n install_requires=requirements)","repo_name":"thanakorn/gym-image-maze","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"20314177956","text":"from os import path, listdir\n\nfruit_type = \"pequis\"\n\nfilenames = listdir(path.join(path.curdir, \"..\", \"databases\", fruit_type))\nfilenames = list(filter(lambda filename: filename.endswith(\".png\"), filenames))\n\ntry:\n with open(path.join(path.curdir, \"classification\", f\"{fruit_type}.csv\"), \"x\") as csv:\n csv.write(\"image,label\\n\" + \"\\n\".join((filename + \",\" for filename in sorted(filenames))))\n\nexcept FileExistsError:\n with open(path.join(path.curdir, \"classification\", f\"{fruit_type}.csv\"), \"a\") as csv:\n csv.write(\"\\n\" + \"\\n\".join((filename + \",\" for filename in sorted(filenames))))\n","repo_name":"henrique-tavares/Identificacao-Defeitos-Frutas","sub_path":"organization/csv-generator.py","file_name":"csv-generator.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"26482356105","text":"import time\n\nimport mh_z19\nfrom prometheus_client import Gauge\nfrom prometheus_client import start_http_server, Summary\n\nREQUEST_TIME = Summary('request_processing_seconds',\n 'Time spent processing request')\ngauge_co2 = Gauge('atmosphere_mainroom_co2', \"Main room CO2 level\")\ngauge_temperature = Gauge('atmosphere_mainroom_temperature',\n \"Main room temperature\")\n\n\n@REQUEST_TIME.time()\ndef process_request(wait_second: int):\n sensor_values = mh_z19.read_all()\n gauge_co2.set(sensor_values['co2'])\n gauge_temperature.set(sensor_values['temperature'])\n\n time.sleep(wait_second)\n\n\nif __name__ == '__main__':\n start_http_server(8000)\n while True:\n process_request(300)\n","repo_name":"MizunoYouki/MHZ19C-Prometheus-Exporter","sub_path":"mh19c_prometheus_exporter.py","file_name":"mh19c_prometheus_exporter.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"3865586536","text":"def select_view_module(name):\n global selectedview\n if name == 'pygame':\n import pgview\n selectedview = pgview\n\n elif name == 'pyqt':\n import qt4view\n selectedview = qt4view\n\n elif name == 'none':\n import noview\n selectedview = noview\n\ndef get_view_module():\n global selectedview\n return selectedview\n","repo_name":"CodingRobots/CodingRobots","sub_path":"viewselect.py","file_name":"viewselect.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"12"} +{"seq_id":"20785533287","text":"#!/usr/bin/env python\n\nimport os\nimport sys\nimport base64\nimport textwrap\nimport time\nfrom os.path import join as pjoin\n\nimport requests\nimport numpy as np\nimport pandas as pd\nimport yaml\nfrom yaml import Loader\nfrom regions import Regions\nfrom astropy.table import Table\n\nfrom baselayer.app.env import load_env, parser\n\nfrom skyportal.tests import api\nfrom skyportal.tests.patch_requests import patch_requests\n\n\npatch_requests()\n\n\nif __name__ == \"__main__\":\n parser.description = 'Load data into SkyPortal'\n parser.add_argument(\n 'data_files', type=str, nargs='+', help='YAML files with data to load'\n )\n parser.add_argument(\n '--host',\n help=textwrap.dedent(\n '''Fully specified URI of the running SkyPortal instance.\n E.g., https://myserver.com:9000.\n\n Defaults to http://localhost on the port specified\n in the SkyPortal configuration file.'''\n ),\n )\n parser.add_argument(\n '--token',\n help=textwrap.dedent(\n '''Token required for accessing the SkyPortal API.\n\n By default, SkyPortal produces a token that is\n written to .tokens.yaml. If no token is specified\n here, that token will be used.'''\n ),\n )\n parser.add_argument(\n '--create_tables',\n action='store_true',\n help=\"Set to create the SkyPortal database tables before inserting data.\",\n )\n\n env, cfg = load_env()\n\n # TODO: load multiple files\n if len(env.data_files) > 1:\n raise NotImplementedError(\"Cannot yet handle multiple data files\")\n\n fname = env.data_files[0]\n src = yaml.load(open(fname), Loader=Loader)\n src_path = os.path.dirname(fname)\n\n if env.create_tables:\n from baselayer.app.model_util import create_tables\n from skyportal.models import init_db\n\n RETRIES = 6\n timeout = 3\n for i in range(RETRIES):\n try:\n print(f\"Connecting to database {cfg['database']['database']}\")\n init_db(**cfg['database'])\n except TimeoutError:\n if i == RETRIES - 1:\n print('FAIL')\n print()\n print(\n f'Error: Could not connect to SkyPortal database; trying again in {timeout}s'\n )\n sys.exit(-1)\n else:\n time.sleep(timeout)\n timeout = max(timeout * 2, 30)\n print('Retrying connection...')\n\n print(\"Creating tables\")\n create_tables()\n\n def get_token():\n if env.token:\n return env.token\n\n try:\n token = yaml.load(open('.tokens.yaml'), Loader=yaml.Loader)['INITIAL_ADMIN']\n return token\n except (FileNotFoundError, TypeError, KeyError):\n return None\n\n print('Testing connection...', end='')\n\n RETRIES = 30\n timeout = 3\n admin_token = None\n status = None\n for i in range(RETRIES):\n try:\n previous_admin_token = admin_token\n admin_token = get_token()\n if admin_token != previous_admin_token:\n print('Loaded token from SkyPortal provisioned .tokens.yaml')\n\n def get(endpoint, token=admin_token):\n response_status, data = api(\"GET\", endpoint, token=token, host=env.host)\n return response_status, data\n\n def post(endpoint, data, token=admin_token):\n response_status, data = api(\n \"POST\", endpoint, data=data, token=token, host=env.host\n )\n return response_status, data\n\n def assert_post(endpoint, data, token=admin_token):\n response_status, data = post(endpoint, data, token)\n if not response_status == 200 and data[\"status\"] == \"success\":\n raise RuntimeError(\n f'API call to {endpoint} failed with status {status}: {data[\"message\"]}'\n )\n return data\n\n if admin_token:\n status, data = get('sysinfo')\n else:\n print('No token specified; reading from ', end='')\n print('SkyPortal generated .tokens.yaml')\n time.sleep(timeout)\n continue\n\n if status == 200 and data['status'] == 'success':\n break\n else:\n if i == RETRIES - 1:\n print('FAIL')\n else:\n time.sleep(timeout)\n print(f'Expected HTTP 200, received {status}. Trying again.')\n continue\n except requests.exceptions.ConnectionError:\n host = env.host or f'http://localhost:{cfg[\"ports.app\"]}'\n if i == RETRIES - 1:\n print('FAIL')\n print()\n print('Error: Could not connect to SkyPortal instance; please ensure ')\n print(f' it is running at the given host/port [{host}]')\n sys.exit(-1)\n else:\n time.sleep(timeout)\n print(f'Could not connect to {host}. Trying again.')\n\n if status not in (200, 400):\n print(f'Fatal: could not connect to server (HTTP status {status})')\n sys.exit(-1)\n\n if data['status'] != 'success':\n print(\n 'Error: Could not authenticate against SkyPortal; please specify a valid token.'\n )\n sys.exit(-1)\n\n status, response = get('groups/public')\n if status != 200 or response['status'] != 'success':\n print('Error: no public group found; aborting')\n sys.exit(-1)\n public_group_id = response['data']['id']\n\n error_log = []\n\n references = {'public_group_id': public_group_id}\n\n def inject_references(obj):\n if isinstance(obj, dict):\n if 'file' in obj:\n filename = pjoin(src_path, obj['file'])\n if filename.endswith('csv'):\n df = pd.read_csv(filename).replace({np.nan: None})\n obj.pop('file')\n obj.update(df.to_dict(orient='list'))\n elif filename.endswith('.png'):\n return base64.b64encode(open(filename, 'rb').read())\n elif filename.endswith('xml'):\n with open(filename, 'rb') as fid:\n payload = fid.read()\n return payload\n elif filename.endswith('reg'):\n return Regions.read(filename).serialize(format='ds9')\n elif filename.endswith('h5') or filename.endswith('hdf5'):\n try:\n payload = (\n Table.read(filename)\n .to_pandas()\n .replace({np.nan: None})\n .to_dict(orient='list')\n )\n except Exception as e:\n # sometimes we save HDF5 files using an HDFStore.\n # in this case we read it as a binary file and return it as \"data\"\n if 'values_block_0' in str(e):\n with open(filename, 'rb') as fid:\n payload = base64.b64encode(fid.read())\n else:\n raise e\n return payload\n elif filename.endswith('bz2'):\n payload = (\n pd.read_csv(filename, compression='bz2')\n .replace({np.nan: None})\n .to_dict(orient='list')\n )\n return payload\n elif filename.endswith('log'):\n with open(filename) as f:\n return f.read()\n else:\n raise NotImplementedError(\n f'{filename}: Only CSV, PNG, xml, reg, and hdf5 files '\n 'currently supported for extending individual objects'\n )\n\n for k, v in obj.items():\n obj[k] = inject_references(v)\n return obj\n elif isinstance(obj, str) and obj.startswith('='):\n try:\n return references[obj[1:]]\n except KeyError:\n print(\n f'\\nReference {obj[1:]} not found while posting to {endpoint}; skipping'\n )\n raise\n elif isinstance(obj, list):\n return [inject_references(item) for item in obj]\n else:\n return obj\n\n ENDPOINT_RETRIES = 3\n\n for endpoint, to_post in src.items():\n # Substitute references in path\n endpoint_parts = endpoint.split('/')\n try:\n for i, part in enumerate(endpoint_parts):\n if part.startswith('='):\n endpoint_parts[i] = str(references[part[1:]])\n except KeyError:\n print(\n f'\\nReference {part[1:]} not found while interpolating endpoint {endpoint}; skipping'\n )\n continue\n\n endpoint = '/'.join(endpoint_parts)\n\n print(f'Posting to {endpoint}: ', end='')\n if 'file' in to_post:\n filename = pjoin(src_path, to_post['file'])\n post_objs = yaml.load(open(filename), Loader=yaml.Loader)\n else:\n post_objs = to_post\n\n for obj in post_objs:\n # Fields that start with =, such as =id, get saved for using as\n # references later on\n saved_fields = {v: k[1:] for k, v in obj.items() if k.startswith('=')}\n\n # Remove all such fields from the object to be posted\n obj = {k: v for k, v in obj.items() if not k.startswith('=')}\n\n # Replace all references of the format field: =key or [=key, ..]\n # with the appropriate reference value\n try:\n inject_references(obj)\n except KeyError:\n continue\n\n if \"payload\" in obj:\n date_keys = [\"start_date\", \"end_date\"]\n for key in date_keys:\n if key in obj[\"payload\"]:\n obj[\"payload\"][key] = obj[\"payload\"][key].isoformat()\n\n ntries = 0\n posted_success = False\n while (ntries < ENDPOINT_RETRIES) and not posted_success:\n status, response = post(endpoint, data=obj)\n\n print('.' if status == 200 else 'X', end='')\n if status != 200:\n ntries = ntries + 1\n continue\n else:\n posted_success = True\n\n if status != 200:\n error_log.append(\n f\"/{endpoint}: {response['message'] if response else None}\"\n )\n else:\n # Save all references from the response\n for target, field in saved_fields.items():\n references[target] = response['data'][field]\n\n print()\n\n if error_log:\n print(\"\\nError log:\")\n print(\"----------\")\n print(\"\\n\".join(error_log))\n\n sys.exit(-1)\n","repo_name":"skyportal/skyportal","sub_path":"tools/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":11403,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"12"} +{"seq_id":"12052106573","text":"import os\nimport requests\n\nfrom functools import lru_cache\n\nfrom . import BaseBackend\n\nISSUE_BACKEND_URL = os.environ[\"ISSUE_BACKEND_URL\"]\nISSUE_BACKEND_ENDPOINT = \"/issues/{issue}.json\"\nISSUE_BACKEND_API_KEY = os.environ[\"ISSUE_BACKEND_API_KEY\"]\n\n\nclass Backend(BaseBackend):\n @property\n @lru_cache()\n def session(self):\n s = requests.Session()\n s.headers.update({\"X-Redmine-API-Key\": ISSUE_BACKEND_API_KEY})\n\n return s\n\n @property\n @lru_cache()\n def issue(self):\n full_url = \"{}{}\".format(ISSUE_BACKEND_URL, ISSUE_BACKEND_ENDPOINT).format(\n issue=self.issue_number\n )\n\n response = self.session.get(full_url)\n\n response.raise_for_status()\n\n return response.json()[\"issue\"]\n\n @property\n def subject(self):\n return self.issue[\"subject\"]\n","repo_name":"rca/issuebranch","sub_path":"src/issuebranch/backends/redmine.py","file_name":"redmine.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"28156695411","text":"import tensorflow as tf\nfrom keras import backend as K\nimport numpy as np\nfrom tensorflow.keras import datasets, layers, models\nfrom copy import copy\nimport os,sys,inspect\nimport time\nimport math\nfrom tqdm import tqdm\nfrom tqdm import trange\ncurrent_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparent_dir = os.path.dirname(current_dir)\nsys.path.insert(0, parent_dir)\n\nfrom Models.test_model import make_prediction\n\ndef zip_and_shuffle(img, lab):\n if len(img) != len(lab):\n raise IndexError(\"The image list and lable list does not have the same length\")\n try:\n zip_test = [(img[i], lab[i]) for i in range(len(img))]\n np.random.shuffle(zip_test)\n img = [zip_test[i][0] for i in range(len(zip_test))] \n lab = [zip_test[i][1] for i in range(len(zip_test))] \n except Exception as e:\n print(f\"ERROR: {e}\")\n raise Exception\n return img, lab\n\ndef get_batch(images, lables, batch_size, noises, noise_method, shuffle=True,\n drop_last=True, augmentation=False):\n idx = len(images)\n \n \n if drop_last:\n n_batches = idx // batch_size\n else:\n n_batches = np.ceil(idx / batch_size).astype(np.int32)\n \n if shuffle:\n images, lables = zip_and_shuffle(images, lables)\n \n for b in range(n_batches):\n left_idx = b * batch_size\n right_idx = min((b+1)*batch_size, idx)\n img_batch, lab_batch = images[left_idx:right_idx], lables[left_idx:right_idx]\n\n if augmentation:\n try:\n img_batch = noise_method(img_batch, noises, batch_size)\n except Exception as e:\n print(f\"ERROR: {e}\")\n raise Exception\n\n yield img_batch, lab_batch\n\n\n\ndef apply_noise_evenly(img_batch, noises, batch_size):\n global_idx = 0\n aug_bs = batch_size // len(noises)\n \n for i, noise in enumerate(noises):\n for img in img_batch[i*aug_bs:i+1*aug_bs]:\n img_batch[global_idx] = noise + img\n global_idx += 1\n\n return img_batch\n\ndef should_early_stop(best_epoch, epoch, patience):\n return best_epoch + patience <= epoch\n\ndef sum_accuracy(right, wrong):\n if len(right) != len(wrong):\n raise IndexError(\"The list 'right' and 'wrong' are not the same lenght\")\n \n return [100*(right[i]/(right[i]+wrong[i])) for i in range(len(right))]\n\ndef lr_exp_decay(epoch, lr):\n k = 0.1\n return lr * math.exp(-k*epoch)\n\ndef validate_monitor(monitor, best_accuracy, accuracy, best_loss, loss):\n if monitor == 'val_loss':\n return loss < best_loss or best_loss == -1\n elif monitor == 'val_acc':\n return accuracy > best_accuracy or best_accuracy == -1\n else:\n raise TypeError(f'{monitor} is not a valid evaluation monitor')\n\ndef calc_accuracy(right, wrong):\n return right / (right + wrong)\n\ndef average(tim):\n return sum(tim) / len(tim)\n\ndef fit_model(model, train_img, train_lab, val_img, val_lab, filter_names, apply_noise_method, monitor='val_loss',\n delta_value=None, patience=10, epochs=100, restore_weights=False, augmentation=False\n ):\n scce = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n history_monitor = []\n batch_size = 32\n \n best_epoch = -1\n best_loss = -1\n best_accuracy = -1\n best_model = copy(model)\n \n current_learning_rate = 0.001\n \n right, wrong = [], []\n \n done = epochs\n progress = trange(done, desc='epoch stuff', leave=True)\n loss = 0\n \n times = []\n tik = time.perf_counter()\n for epoch in progress:\n progress.set_description(f\"E = {epoch}, LR = {current_learning_rate}, LOSS = {loss}\")\n progress.refresh()\n \n if epoch != 0:\n current_learning_rate = lr_exp_decay(epoch, current_learning_rate)\n K.set_value(model.optimizer.learning_rate, current_learning_rate)\n \n for (xb, yb) in get_batch(train_img, train_lab, batch_size, filter_names, apply_noise_method, augmentation=augmentation):\n \n xb = np.array(xb)\n yb = np.array(yb)\n try:\n _ = model.train_on_batch(tf.convert_to_tensor(xb) , tf.convert_to_tensor(yb))\n except Exception as e:\n print(f\"ERROR: {e}\")\n raise Exception\n \n img_predict = []\n img_true = []\n \n right.append(0)\n wrong.append(0)\n \n for xb, yb in get_batch(val_img, val_lab, batch_size, filter_names, apply_noise_method, augmentation=augmentation):\n for i in range(len(xb)):\n prediction = make_prediction(model, xb[i], (52, 52, 3)).numpy()[0] #TODO resolution is hard coded. pls fix\n img_predict.append(prediction)\n img_true.append(int(yb[i]))\n \n predicted_label = np.argmax(prediction)\n \n if predicted_label == int(yb[i]):\n right[-1] += 1\n else:\n wrong[-1] += 1\n\n loss = scce(img_true, img_predict).numpy()\n history_monitor.append(loss)\n \n current_accuracy = calc_accuracy(right[-1], wrong[-1])\n if validate_monitor(monitor, best_accuracy, current_accuracy, best_loss, loss) and should_early_stop:\n # if loss < best_loss or best_loss == -1 and should_early_stop:\n best_epoch = epoch\n best_loss = loss\n best_accuracy = calc_accuracy(right[-1], wrong[-1])\n best_model = copy(model)\n elif should_early_stop(best_epoch, epoch, patience) and should_early_stop:\n return best_model, history_monitor, sum_accuracy(right, wrong)\n\n\n tok = time.perf_counter()\n print(f\"EPOCH TIME: {tok-tik}\")\n \n return_acuracy = sum_accuracy(right, wrong)\n \n if should_early_stop:\n return best_model, history_monitor, return_acuracy\n else:\n return model, history_monitor, return_acuracy\n \n \n # return best_model, history_monitor, return_acuracy if should_early_stop else model, history_monitor, return_acuracy\n","repo_name":"Biksbois/BiksTurePy","sub_path":"phase_one/fit_model_on_batch.py","file_name":"fit_model_on_batch.py","file_ext":"py","file_size_in_byte":6136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"7128639925","text":"from telegram import Update, ReplyKeyboardRemove\nfrom telegram.ext import CallbackContext\n\nfrom tgbot.conversations import states\nfrom tgbot.conversations.core import JSON\nfrom tgbot.conversations.core import main_keyboard, siren_keyboard\nfrom tgbot.conversations.api import client as api\n\n\ndef district_choise(update: Update, context: CallbackContext[JSON, JSON, JSON]) -> int:\n question = 'В каком районе нужно найти РСУ?'\n context.user_data['choice'] = 'district'\n update.message.reply_text(question, reply_markup=ReplyKeyboardRemove())\n\n return states.DISTRICT_STATS\n\n\ndef district_stats(update: Update, context: CallbackContext[JSON, JSON, JSON]) -> int:\n if not isinstance(update.message.text, str):\n update.message.reply_text('Input text')\n return states.DISTRICT_STATS\n\n selected_district = update.message.text\n districts = api.districts.get_by_name(selected_district)\n\n if not districts:\n update.message.reply_text('В Татарстане нет такого района, проверьте правильность ввода')\n return states.DISTRICT_STATS\n\n district = districts[0]\n district_sirens = api.districts.get_for_district(district.uid)\n siren_name = [siren.name for siren in district_sirens]\n\n update.message.reply_text(f'{district.name} район:', reply_markup=siren_keyboard(siren_name))\n\n context.user_data['district_id'] = district.uid\n\n return states.SIREN_STATS\n","repo_name":"mchs-rsu/tgbot","sub_path":"tgbot/conversations/districts.py","file_name":"districts.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"71388473941","text":"#!/bin/env python\n\n'''pyats_ios_example_job.py\n\nThis is an easypy job example intended to run the pyATS IOS example testscript.\n\n\nArguments:\n This script requires one script argument (testbed_file) and two optional\n script argument (ios1 and ios2) to be passed in when run under easypy for\n demonstration purposes.\n testbed_file: the path to testbed yaml file\n ios1: the device name defined in the testbed yaml file, if modified\n ios2: the device name defined in the testbed yaml file, if modified\n\nExamples:\n # to run under easypy\n bash$ easypy pyats_ios_example_job.py -testbed_file pyats_ios_example.yaml\n\nReferences:\n For the complete and up-to-date user guide on pyATS, visit:\n https://developer.cisco.com/site/pyats/docs/\n'''\n\n#\n# optional author information\n#\n__author__ = 'Wei Chen '\n__copyright__ = 'Copyright 2017, Cisco Systems'\n__email__ = 'pyats-support@cisco.com'\n__date__= 'Nov 15, 2017'\n\n\n#\n# import statements\n#\nimport os\nimport logging\nimport argparse\n\nfrom ats.easypy import run\n\n# easypy allows argument propagations\n# any unrecognized is left behind to allow custom parsers to handle\nparser = argparse.ArgumentParser()\nparser.add_argument('--ios1', dest = 'ios1_name', type = str, default = 'ios1')\nparser.add_argument('--ios2', dest = 'ios2_name', type = str, default = 'ios2')\n\ndef main():\n\n # parse args\n args, unknown = parser.parse_known_args()\n\n # configure your log outputs level\n #logging.getLogger('ats.connections').setLevel('DEBUG')\n\n # Find the location of the script in relation to the job file\n test_path = os.path.dirname(os.path.abspath(__file__))\n testscript = os.path.join(test_path, 'pyats_ios_example.py')\n\n # run it\n run(testscript, **vars(args))\n","repo_name":"CiscoDevNet/pyats-ios-sample","sub_path":"pyats_ios_example_job.py","file_name":"pyats_ios_example_job.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"12"} +{"seq_id":"23377684540","text":"import os\n\nimport pandas as pd\n\nfrom chatbot.entity_helpers import get_dimension_names\nfrom chatbot.helper_methods import apply_condition\nfrom settings import DATA_PATH, COL_MAPPING, TABLE_MAPPING\n\n\ndef get_columns_from_rls(rls_json):\n\taccess_filters = eval(rls_json)\n\tcolumns = [col['col_name'] for col in access_filters]\n\treturn columns\n\n\ndef get_columns_from_entities(raw_entities):\n\tcolumns = []\n\tfor entity in raw_entities:\n\t\tif entity['entity'] in ['dim', 'fact', 'adject', 'filter']:\n\t\t\tcolumns.append(entity['value'])\n\t\t# entity time for duckling pipeline\n\t\telif entity['entity'] == 'time':\n\t\t\tcolumns.append('CalendarDate')\n\t\telif entity['entity'] in get_dimension_names():\n\t\t\tcolumns.append(entity['entity'])\n\t\t# Load extra Columns for Business logic\n\t\telif entity['entity'] == 'logic':\n\t\t\tcolumns += ['SalesAmount', 'TargetAmount', 'CalendarDate', 'Month', 'Year', 'MonthYear', 'Quarter',\n\t\t\t\t\t\t'QuarterYear']\n\treturn columns\n\n\ndef apply_row_level_security(df, access_json):\n\taccess_filters = eval(access_json)\n\tfor condition in access_filters:\n\t\tdf = apply_condition(df=df, col_name=condition['col_name'], condition=condition[\n\t\t\t'operator_choice'], condition_value=condition['value'])\n\n\treturn df\n\n\ndef load_data(columns):\n\tif not columns:\n\t\treturn pd.DataFrame()\n\tmapping_col_df = pd.read_csv(os.path.join(DATA_PATH, COL_MAPPING))\n\ttable_mapping = pd.read_csv(os.path.join(DATA_PATH, TABLE_MAPPING))\n\ttables = list(mapping_col_df[mapping_col_df['column'].isin(columns)]['table'].unique())\n\t# first load fact table i.e., tables[0]\n\tl_table = tables[0:1]\n\tdate_col = mapping_col_df[mapping_col_df['table'] == l_table[0]]['date_col']\n\tdate_col = eval(date_col.iloc[0]) if date_col.any() else False\n\tdf = pd.read_csv(os.path.join(DATA_PATH, l_table[0]), parse_dates=date_col)\n\ttables = tables[1:]\n\tfor table in tables:\n\t\tdate_col = mapping_col_df[mapping_col_df['table'] == table]['date_col']\n\t\tdate_col = eval(date_col.iloc[0]) if date_col.any() else False\n\t\ttemp_df = pd.read_csv(os.path.join(DATA_PATH, table), parse_dates=date_col)\n\t\tleft_on = eval(table_mapping[(table_mapping['l_table'].isin(l_table)) & (table_mapping['r_table'] == table)][\n\t\t\t\t\t\t 'l_columns'].iloc[0])\n\t\tright_on = eval(table_mapping[(table_mapping['l_table'].isin(l_table)) & (table_mapping['r_table'] == table)][\n\t\t\t\t\t\t\t'r_columns'].iloc[0])\n\t\tdf = pd.merge(df, temp_df, left_on=left_on, right_on=right_on, how='left')\n\t\tl_table.append(table)\n\tcolumns = [x for x in columns if x in df.columns]\n\tdf = df[columns]\n\treturn df\n\n\ndef load_table_rls_filtered(intent, entities, rls_json):\n\tcolumns = get_columns_from_rls(rls_json) + get_columns_from_entities(entities)\n\tisLineTotaltoload = False\n\tfor ent in entities:\n\t\tif ent['value'] in ('ProductName', 'ProductNumber', 'ProductSubcategoryName', 'ProductCategoryName'):\n\t\t\tisLineTotaltoload = True\n\tif intent == 'POHeaderDetails':\n\t\tcolumns = get_columns_from_rls(rls_json) + get_columns_from_entities(entities) + [\n\t\t\t'PurchaseOrderID', 'EmployeeFirstName', 'EmployeeLastName', 'EmployeeName', 'JobTitle', 'DepartmentName',\n\t\t\t'VendorAccountNumber', 'VendorName', 'VendorCreditRating', 'Status', 'ShipMethodName', 'OrderDate',\n\t\t\t'ShipDate', 'SubTotal', 'TaxAmt', 'Freight', 'TotalDue', 'ProductName', 'ProductNumber',\n\t\t\t'ProductSubcategoryName', 'ProductCategoryName', 'OrderQty', 'UnitPrice', 'LineTotal', 'ReceivedQty',\n\t\t\t'RejectedQty']\n\n\telif intent == 'POHeader':\n\t\tcolumns = get_columns_from_rls(rls_json) + get_columns_from_entities(entities) + [\n\t\t\t'PurchaseOrderID', 'EmployeeFirstName', 'EmployeeLastName', 'EmployeeName', 'JobTitle', 'DepartmentName',\n\t\t\t'VendorAccountNumber', 'VendorName', 'VendorCreditRating', 'Status', 'ShipMethodName', 'OrderDate',\n\t\t\t'ShipDate', 'SubTotal', 'TaxAmt', 'Freight', 'TotalDue']\n\t\tif isLineTotaltoload:\n\t\t\tcolumns += ['OrderQty', 'UnitPrice', 'LineTotal', 'ReceivedQty', 'RejectedQty']\n\n\telif intent == 'PODetails':\n\t\tcolumns = get_columns_from_rls(rls_json) + get_columns_from_entities(entities) + [\n\t\t\t'PurchaseOrderID', 'OrderDate', 'ShipDate', 'SubTotal', 'TaxAmt', 'Freight', 'TotalDue',\n\t\t\t'OrderQty', 'UnitPrice', 'LineTotal', 'ReceivedQty', 'RejectedQty']\n\t\tif 'EmployeeName' in columns:\n\t\t\tcolumns += ['EmployeeFirstName', 'EmployeeLastName']\n\n\telif intent == 'ProductDescription':\n\t\tcolumns = get_columns_from_rls(rls_json) + get_columns_from_entities(entities) + [\n\t\t\t'PurchaseOrderID', 'OrderDate', 'Month', 'Status', 'ProductID', 'ProductName', 'ProductNumber',\n\t\t\t'ProductSubcategoryName',\n\t\t\t'ProductCategoryName', 'OrderQty', 'UnitPrice', 'LineTotal', 'ReceivedQty', 'RejectedQty']\n\n\t\tif 'EmployeeName' in columns:\n\t\t\tcolumns += ['EmployeeFirstName', 'EmployeeLastName']\n\n\tunique_columns = []\n\tfor col in columns:\n\t\tif col not in unique_columns:\n\t\t\tunique_columns.append(col)\n\tcolumns = unique_columns\n\tdf = load_data(columns)\n\tdf = apply_row_level_security(df, access_json=rls_json)\n\treturn df\n\n\ndef get_unique_dim_value(dimension):\n\ttable = load_data([dimension])\n\tvalues = table[dimension].sort_values().unique().tolist()\n\treturn values\n","repo_name":"igupta967937/Purchase_Chatbot","sub_path":"chatbot/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":5041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"37032162107","text":"import time\n\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom temba.contacts.models import ContactGroup\nfrom temba.mailroom import queue_populate_dynamic_group\n\n\nclass Command(BaseCommand):\n help = \"Re-evaluates a smart group\"\n\n def add_arguments(self, parser):\n parser.add_argument(\"group_uuid\", help=\"UUID of contact group to re-evaluate.\")\n\n def handle(self, group_uuid: str, *args, **kwargs):\n group = ContactGroup.objects.filter(uuid=group_uuid, group_type=ContactGroup.TYPE_SMART).first()\n if not group:\n raise CommandError(\"no such group\")\n\n self.stdout.write(\n f\"Queueing re-evaluation for group {group.name} with query '{group.query}' \"\n f\"and {group.get_member_count()} members...\"\n )\n\n # mark group as evaluating\n group.status = ContactGroup.STATUS_EVALUATING\n group.save(update_fields=(\"status\",))\n\n queue_populate_dynamic_group(group)\n\n while True:\n time.sleep(2)\n\n group.refresh_from_db()\n if group.status == ContactGroup.STATUS_READY:\n break\n\n self.stdout.write(f\" > {group.get_member_count()} members...\")\n\n self.stdout.write(f\"Re-evaluation complete with {group.get_member_count()} members.\")\n","repo_name":"rapidpro/rapidpro","sub_path":"temba/contacts/management/commands/reeval_group.py","file_name":"reeval_group.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":832,"dataset":"github-code","pt":"12"} +{"seq_id":"14172429135","text":"import copy\nimport random\nfrom collections import Counter\n\n'''\n\nTOMBS - Python Version\n\nInput example: [0,1,5] or 0,1,5\nPlaces a Blade at (0,1)\n\nActivating Thorn example: [0,1,2,1,2] or 0,1,2,1,2\nPlaces a Thorn at (0,1) while removing target at (1,2)\n\nDiscard input example: 3\nDiscards an Ombra from hand\n\nQuit input: [0]\n\nPlaying board (x,y):\n\n 00 | 10 | 20\n--------------\n 01 | 11 | 21\n--------------\n 02 | 12 | 22\n\nTile types:\n0 - Empty\n1 - Tombstone\n2 - Thorn (old name: priest)\n3 - Ombra (old name: rogue)\n4 - Magus (old name: mage)\n5 - Blade (old name: warrior)\n\nTile owner:\n0 - neutral (for empty tiles and tombstones)\n1-n: Player 1-n\n\nWin conditions:\n1. Field-lock\n2. Kill-count\n3. Unit-count\n\nTo do: GUI\n\n'''\n\n\nclass Tile:\n\n # ex: Tile(0, 1, 5, 1): Player 1's Blade at position (x,y) = (0,1)\n\n def __init__(self, x, y, type, owner):\n self.x = x\n self.y = y\n self.type = type\n self.owner = owner\n\n def __str__(self):\n return \"(\" + str(self.type) + \" \" + str(self.owner) + \")\"\n\n\nclass Error(Exception):\n pass\n\n\nclass Deck:\n\n # nb: number of cards -> evenly distributed between the 4 character types\n\n def __init__(self, nb):\n self.nb = nb\n self.cards = []\n for x in range(nb//4):\n self.cards += [2, 3, 4, 5]\n\n def __str__(self):\n return str(self.cards)\n\n def shuffle(self):\n random.shuffle(self.cards)\n\n # take1: returns first card of deck, which is then removed from it\n\n def take1(self):\n drawn = self.cards[0]\n self.cards = self.cards[1:]\n return drawn\n\n\nclass Player:\n\n # index: player's identification\n # d: deck\n\n def __init__(self, index, d):\n self.index = index\n self.hand = []\n self.draw(d)\n self.draw(d)\n\n def draw(self, d):\n self.hand += [d.take1()]\n\n # undraw: in case of error in input -> rollback\n\n def undraw(self, d):\n d.cards = [self.hand[-1]] + d.cards\n self.hand = self.hand[0:-1]\n\n # play1: removes a card of unit u from hand\n\n def play1(self, u):\n if u in self.hand:\n self.hand.remove(u)\n else:\n raise Error(\"Card unavailable.\")\n\n\nclass Game:\n\n def __init__(self, numP):\n\n self.numP = numP # number of players\n self.currentP = 1 # current player\n self.board = [[Tile(i, j, 0, 0) for j in range(3)] for i in range(3)]\n # initialize an empty board\n\n '''\n\n For testing purposes\n\n self.board = [[Tile(0, 0, 3, 1), Tile(0, 1, 3, 2), Tile(0, 2, 3, 3)],\n [Tile(1, 0, 3, 4), Tile(1, 1, 3, 1), Tile(1, 2, 3, 2)],\n [Tile(2, 0, 0, 0), Tile(2, 1, 0, 0), Tile(2, 2, 0, 0)]]\n\n '''\n \n self.turnCount = 1\n self.stuckCount = 0 # to check for Field-lock win condition\n\n try:\n if numP == 2:\n\n self.score = [0, 0]\n self.safeTurns = 3 # no violence during first 3 turns\n self.killWin = 8 # need 8 kills to win\n self.deck = Deck(32) # deck of 32 cards\n self.lastDraw = self.deck.nb - 2*numP # turn on which the last card is drawn\n self.deck.shuffle()\n self.players = [Player(1, self.deck), Player(2, self.deck)] # initialize 2 players\n\n elif numP == 3:\n\n self.score = [0, 0, 0]\n self.safeTurns = 2\n self.killWin = 7\n self.deck = Deck(36)\n self.lastDraw = self.deck.nb - 2*numP\n self.deck.shuffle()\n self.players = [Player(1, self.deck), Player(2, self.deck), Player(3, self.deck)]\n\n elif numP == 4:\n\n self.score = [0, 0, 0, 0]\n self.safeTurns = 3\n self.killWin = 6\n self.deck = Deck(40)\n self.lastDraw = self.deck.nb - 2*numP\n self.deck.shuffle()\n self.players = [Player(1, self.deck), Player(2, self.deck),\n Player(3, self.deck), Player(4, self.deck)]\n\n else:\n\n raise Error(\"Game is unsupported for \" + str(numP) + \" players.\")\n\n except Error as msg:\n print(msg)\n raise SystemExit\n\n # printBoard: prints the board state and other info\n\n def printBoard(self, b):\n\n for j in range(3):\n print(\"\\n\")\n for i in range(3):\n print(b[int(i)][int(j)], end=\" \")\n print(\"\\n-----------------\")\n\n print(\"Score:\", self.score, \"\\n\")\n\n '''\n\n # for testing purposes\n\n for x in self.players:\n print(x.hand)\n\n print(self.deck.cards, len(self.deck.cards))\n\n '''\n\n # btile: board x-coord y-coord -> list of tiles\n # returns tiles in the threat zones of a Blade at (x,y)\n\n @staticmethod\n def btile(b, x, y):\n lst = []\n if y < 2:\n lst = [b[x][y+1]] + lst # down\n if x < 2:\n lst = [b[x+1][y]] + lst # right\n if x > 0:\n lst = [b[x-1][y]] + lst # left\n if y > 0:\n lst = [b[x][y-1]] + lst # up\n return lst\n\n # mtile1: board x-coord y-coord -> list of tiles\n # returns adjacent diagonal tiles to a character at (x,y)\n\n @staticmethod\n def mtile1(b, x, y):\n lst = []\n if x < 2 and y < 2:\n lst = [b[x+1][y+1]] + lst # dr\n if x > 0 and y < 2:\n lst = [b[x-1][y+1]] + lst # dl\n if x < 2 and y > 0:\n lst = [b[x+1][y-1]] + lst # tr\n if x > 0 and y > 0:\n lst = [b[x-1][y-1]] + lst # tl\n return lst\n\n # mtile: board x-coord y-coord -> list of tiles\n # returns tiles in the threat zones of a Magus at (x,y)\n\n @staticmethod\n def mtile(b, x, y):\n lst = Game.mtile1(b, x, y)\n if x == 0 and y == 0:\n lst += [b[2][2]]\n elif x == 2 and y == 0:\n lst += [b[0][2]]\n elif x == 0 and y == 2:\n lst = [b[2][0]] + lst\n elif x == 2 and y == 2:\n lst = [b[0][0]] + lst\n return lst\n\n # ttile: board x-coord y-coord -> list of tiles\n # returns tiles in the threat zones of a Thorn at (x,y)\n\n @staticmethod\n def ttile(b, x, y):\n return Game.btile(b, x, y) + Game.mtile1(b, x, y)\n\n # enemy: tile list-of-tiles unit -> boolean\n # returns whether there is a specific enemy unit to the given tile in the list of tiles\n\n @staticmethod\n def enemy(t, lst, u):\n truth = False\n for x in lst:\n if x.type == u and x.owner != t.owner:\n truth = True\n return truth\n\n # target: tile list-of-tiles -> boolean\n # returns whether there is an enemy to the given tile in the list of tiles\n\n @staticmethod\n def target(t, lst):\n truth = False\n for x in lst:\n if not (x.owner == t.owner or x.owner == 0):\n truth = True\n return truth\n\n # singletarget: tile1 tile2 -> boolean\n # returns whether tile2 is an enemy to tile1\n\n @staticmethod\n def singletarget(t1, t2):\n if not (t2.owner == t1.owner or t2.owner == 0):\n truth = True\n else:\n truth = False\n return truth\n\n '''\n\n Checking process:\n\n 1. check1\n 2. place (maybe thornkill)\n 3. check2\n 4. movef (maybe killupdate)\n\n '''\n\n # check1: board tile -> boolean\n # checks if the tile can be placed* on the board\n\n def check1(self, b, t):\n\n if self.turnCount == 1 and t.x == 1 and t.y == 1:\n truth = False # turn 1: cannot place in middle tile\n\n elif self.turnCount <= self.safeTurns \\\n and ((t.type == 5 and Game.target(t, Game.btile(b, t.x, t.y)))\n or (t.type == 4 and Game.target(t, Game.mtile(b, t.x, t.y)))):\n truth = False # no violence turns\n\n elif t.type == 2 and b[t.x][t.y].type == 1 and Game.target(t, Game.ttile(b, t.x, t.y)) \\\n and self.turnCount > self.safeTurns and not (Game.enemy(t, Game.ttile(b, t.x, t.y), 2)):\n truth = True # activated Thorn on tombstone with target to kill and no adjacent enemy Thorn\n\n elif t.type == 3 and Game.target(t, [b[t.x][t.y]]) and b[t.x][t.y].type != 3 \\\n and self.turnCount > self.safeTurns:\n truth = True # Ombra assassinating target\n\n elif b[t.x][t.y].type == 0:\n truth = True # normal placement on empty space\n\n else:\n truth = False\n\n return truth\n\n # thornkill: board tile x-coord y-coord -> board\n # updates the board by removing Thorn's target at (i, j)\n\n @staticmethod\n def thornkill(b, t, i, j):\n\n new = []\n\n for e in Game.ttile(b, t.x, t.y):\n if (e.x == i and e.y == j) and (not (e.owner == t.owner or e.owner == 0)):\n new = copy.deepcopy(b)\n new[i][j] = Tile(i, j, 0, 0)\n new[t.x][t.y] = t\n\n if not new:\n raise Error(\"Invalid move.\")\n\n return new\n\n # check2: board tile -> boolean\n # checks if the tile would be threatened by something after it is placed on the board\n\n @staticmethod\n def check2(b, t):\n if Game.enemy(t, Game.btile(b, t.x, t.y), 5):\n truth = False # threatened by enemy Blade\n elif Game.enemy(t, Game.mtile(b, t.x, t.y), 4):\n truth = False # threatened by enemy Magus\n else:\n truth = True\n return truth\n\n # maketomb: tile list-of-tiles -> list-of-tiles\n # checks for the tile's kills in the input list and returns a list of tombstone tiles\n\n @staticmethod\n def maketomb(t, lst):\n lst1 = []\n for e in lst:\n if Game.singletarget(t, e):\n lst1 = [Tile(e.x, e.y, 1, 0)] + lst1\n return lst1\n\n # update: board list-of-tiles -> board\n # returns board updated with tiles from the list\n\n @staticmethod\n def update(b, lst):\n new = copy.deepcopy(b)\n for e in lst:\n new[e.x][e.y] = e\n return new\n\n # killupdate: board tile -> board\n # returns board after checking for tombstones created\n\n @staticmethod\n def killupdate(b, t):\n if t.type == 5:\n new = Game.update(b, Game.maketomb(t, Game.btile(b, t.x, t.y)))\n elif t.type == 4:\n new = Game.update(b, Game.maketomb(t, Game.mtile(b, t.x, t.y)))\n else:\n new = copy.deepcopy(b)\n return new\n\n # place: board tile x-coord y-coord -> board\n # returns board with the tile placed on it, and maybe call thornkill\n # (i,j) only matters when activating a Thorn\n # otherwise: (1,1) by default\n\n def place(self, b, t, i, j):\n if (t.type == 2 and b[t.x][t.y].type == 1) and self.check1(b, t):\n new = Game.thornkill(b, t, i, j)\n elif self.check1(b, t):\n new = copy.deepcopy(b)\n new[t.x][t.y] = t\n else:\n raise Error(\"Cannot place here.\")\n return new\n\n # movef: board tile x-coord y-coord -> board\n # purely functional one-turn action\n\n def movef(self, b, t, i, j):\n if Game.check2(self.place(b, t, i, j), t):\n new = Game.killupdate(self.place(b, t, i, j), t)\n else:\n raise Error(\"Zone is threatened.\")\n return new\n\n # nodupe: list -> list\n # removes duplicates from the list\n\n @staticmethod\n def nodupe(lst):\n seen = set()\n seen_add = seen.add\n return [x for x in lst if not (x in seen or seen_add(x))]\n\n # choices: board player -> list-of-moves\n # returns all legal moves the player can make\n\n def choices(self, b, p):\n\n sol = []\n for u in Game.nodupe(p.hand):\n for x in range(3):\n for y in range(3):\n\n if u == 2 and b[int(x)][int(y)].type == 1:\n\n for i in range(3):\n for j in range(3):\n\n try:\n self.movef(b, Tile(x, y, u, p.index), i, j)\n except:\n pass\n else: # moves involving activating Thorn\n sol += [[x, y, u, i, j]]\n\n else:\n\n try:\n self.movef(b, Tile(x, y, u, p.index), 1, 1)\n except:\n pass\n else: # normal moves\n sol += [[x, y, u]]\n\n return sol\n\n # move: tile x-coord y-coord\n # non-functional version of movef, changes the board\n # example: move(Tile(0, 1, 2, 4), 1, 1)\n\n def move(self, t, i, j):\n\n temp = copy.deepcopy(self.board)\n\n self.board = self.movef(self.board, t, i, j)\n\n # score incrementation\n\n if t.type == 5:\n self.score[t.owner-1] += len(Game.maketomb(t, Game.btile(temp, t.x, t.y)))\n elif t.type == 4:\n self.score[t.owner-1] += len(Game.maketomb(t, Game.mtile(temp, t.x, t.y)))\n\n # hire: x-coord y-coord owner type\n # shortcut for move: normal character at (x,y)\n\n def hire(self, x, y, o, t):\n self.move(Tile(x, y, t, o), 1, 1)\n\n # drain: x-coord y-coord owner x-coord y-coord\n # shortcut for move: activating Thorn at (x,y) with target at (i,j)\n\n def drain(self, x, y, o, i, j):\n self.move(Tile(x, y, 2, o), i, j)\n\n # countUnits: board -> list-of-owners\n # returns list of owners who have the most units on the board\n\n @staticmethod\n def countUnits(b):\n\n lst = []\n for i in range(3):\n for j in range(3):\n lst += [b[i][j].owner]\n\n lst = list(filter((0).__ne__, lst))\n count = Counter(lst)\n freq = count.values()\n total = list(freq).count(max(freq))\n\n return [elem[0] for elem in count.most_common(total)]\n\n # compare: list-of-owners -> owner or list-of-owners\n # takes list from countUnits and compares the owners' scores if their unit count ties\n # returns winner(s)\n\n def compare(self, lst):\n\n if len(lst) == 1:\n winner = lst[0]\n\n else:\n kills = []\n for i in lst:\n for x in range(self.score[i-1]):\n kills += [i]\n\n count = Counter(kills)\n freq = count.values()\n total = list(freq).count(max(freq))\n top = [elem[0] for elem in count.most_common(total)]\n\n if len(top) == 1:\n winner = top[0]\n else:\n winner = top\n\n return winner\n\n # main code for one turn's actions\n\n def turn(self):\n\n # (for testing purposes)\n # print(\"Possible moves: \", self.choices(self.board, self.players[self.currentP-1]))\n\n self.printBoard(self.board)\n\n if not self.choices(self.board, self.players[self.currentP - 1]):\n\n discard = eval(input(\"No possible moves for Player \" + str(self.currentP) +\n \", please discard a card.\\nYour hand is: \" +\n str(self.players[self.currentP-1].hand) + \"\\n\"))\n\n self.players[self.currentP-1].play1(discard)\n self.stuckCount += 1\n\n else:\n\n try:\n command = list(eval(input(\"Show me your move, Player \" +\n str(self.currentP) + \"!\\nYour hand is: \" +\n str(self.players[self.currentP-1].hand) + \"\\n\")))\n except:\n raise Error(\"Unknown command.\")\n\n if len(command) == 1 and command[0] == 0:\n raise SystemExit\n\n elif len(command) == 3:\n self.hire(command[0], command[1], self.currentP, command[2])\n self.players[self.currentP-1].play1(command[2])\n\n elif len(command) == 5 and command[2] == 2:\n self.drain(command[0], command[1], self.currentP, command[3], command[4])\n self.players[self.currentP-1].play1(command[2])\n\n else:\n raise Error(\"Unknown command.\")\n\n self.stuckCount = 0\n\n # main code for the game's execution\n # includes the 3 win conditions\n # recursion of turns until a win condition is met (or an error occurs)\n\n def play(self):\n\n try:\n if self.turnCount <= self.lastDraw:\n self.players[self.currentP-1].draw(self.deck)\n\n self.turn()\n\n # win by kills\n\n if self.score[self.currentP-1] >= self.killWin:\n print(\"Player \" + str(self.currentP) + \" wins!\")\n raise SystemExit\n\n # win by units\n\n if self.turnCount == self.deck.nb:\n win = self.compare(Game.countUnits(self.board))\n\n if isinstance(win, list):\n print(\"It's a tie between Players \" + str(win) + \"!\")\n else:\n print(\"Player \" + str(win) + \" wins!\")\n raise SystemExit\n\n self.currentP += 1\n self.turnCount += 1\n\n if self.currentP > self.numP:\n self.currentP = 1\n\n except Error as msg:\n if self.turnCount <= self.lastDraw:\n self.players[self.currentP-1].undraw(self.deck)\n print(msg)\n\n # win by field lock\n\n if self.stuckCount == self.numP - 1:\n print(\"Player \" + str(self.currentP) + \" wins!\")\n raise SystemExit\n\n self.play()\n\nstart = eval(input(\"Number of players:\\n\"))\nGame(start).play()\n","repo_name":"Waznop/Tombs","sub_path":"tombs.py","file_name":"tombs.py","file_ext":"py","file_size_in_byte":17761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"42266291565","text":"import pygame\r\nfrom pygame.sprite import Sprite\r\n\r\n\r\nclass Bullet(Sprite):\r\n \"\"\"A Class to manage bullet from ship\"\"\"\r\n\r\n def __init__(self, game_settings, screen, ship):\r\n super().__init__()\r\n\r\n self.screen = screen\r\n # Create a bullet rect at (0, 0) and then set correct position.\r\n self.rect = pygame.Rect(0, 0, game_settings.bullet_width, game_settings.bullet_height)\r\n\r\n self.rect.centerx = ship.rect.centerx\r\n\r\n self.rect.top = ship.rect.top\r\n\r\n # Store the bullet's position as a decimal value.\r\n self.y = float(self.rect.y)\r\n\r\n self.color = game_settings.bullet_color\r\n self.speed_factor = game_settings.bullet_speed_factor\r\n\r\n def update(self):\r\n \"\"\"Move bullet upperward in the screen \"\"\"\r\n\r\n self.y -= self.speed_factor # Update decimal position of the bullet.\r\n self.rect.y = self.y # Update rect position.\r\n\r\n def draw_bullet(self):\r\n \"\"\"Draw the bullet to the screen.\"\"\"\r\n\r\n pygame.draw.rect(self.screen, self.color, self.rect)\r\n\r\n\r\nif __name__ == '__main__':\r\n print(\"Go to main file and run from there.\")\r\n","repo_name":"skinan/Alien-Shooter-Game","sub_path":"bullet.py","file_name":"bullet.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"32130576011","text":"import time\nimport pandas as pd\nfrom mpi4py import MPI\n\nstart = time.time()\ncomm = MPI.COMM_WORLD\nsize = comm.Get_size()\nrank = comm.Get_rank()\ndataset = 'datasets/Combined_Flights_2021.csv'\n\n\n# find_max fn. iterates over the aggregated input and picks up the key corresponding to the highest integer value\ndef find_max(final_out: dict):\n most_cancelled = \"\"\n maximum = 0\n for k, val in final_out.items():\n if final_out.get(k) > maximum:\n most_cancelled = k\n maximum = val\n\n print(f'{most_cancelled} had the most canceled flights in September 2021')\n end = time.time()\n print(f'time taken with (MPI execution): {round(end - start, 2)} second(s)')\n\n\nif rank == 0:\n start = time.time()\n \"\"\"\n Master worker (with rank 0) is responsible for distributes the workload evenly \n between slave workers.\n \"\"\"\n\n def distribute_rows(n_rows: int, n_processes):\n reading_info = []\n skip_rows = 1\n reading_info.append([n_rows - skip_rows, skip_rows])\n skip_rows = n_rows\n\n for _ in range(1, n_processes - 1):\n reading_info.append([n_rows, skip_rows])\n skip_rows = skip_rows + n_rows\n reading_info.append([None, skip_rows])\n return reading_info\n\n\n slave_workers = size - 1\n # distributing data among 4 slaves\n chunk_distribution = distribute_rows(n_rows=1600000, n_processes=slave_workers)\n\n # distribute tasks to slaves\n for worker in range(1, size):\n chunk_to_process = worker - 1\n comm.send(chunk_distribution[chunk_to_process], dest=worker)\n\n # receive and aggregate results from slave\n results = []\n for worker in (range(1, size)): # receive\n result = comm.recv(source=worker)\n results.append(result)\n\n out = {}\n for r in results:\n for key, value in r.to_dict().items():\n if key in out:\n out[key] = out[key] + value\n else:\n out[key] = value\n find_max(out)\n\n\n# All workers perform processing on the given chunk of data and return the output to master\nelif rank > 0:\n chunk_to_process = comm.recv()\n inp = pd.read_csv(dataset, nrows=chunk_to_process[0], skiprows=chunk_to_process[1], header=None)\n # In order to filter out date values using \".dt.month\" and \".dt.year\" changing datatype of FlightDate column to\n # datetime\n inp.isetitem(0, pd.to_datetime(inp.iloc[:, 0]))\n # filtering dataframe to fetch cancelled flights in Sep. 2021\n filtered_data = inp[(inp.iloc[:, 4] == True) & (inp.iloc[:, 0].dt.month == 9) & (inp.iloc[:, 0].dt.year == 2021)]\n # calculating number of flights cancelled per airline\n result = filtered_data.iloc[:, 1].value_counts()\n # sending processed result to master\n comm.send(result, dest=0)\n\n","repo_name":"msrana25/Distributed-System-Concepts-1","sub_path":"Implementation/Q1/T3.py","file_name":"T3.py","file_ext":"py","file_size_in_byte":2797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"37828958941","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import api, fields, models\n\n\nclass Followers(models.Model):\n \"\"\" mail_followers holds the data related to the follow mechanism inside\n Odoo. Partners can choose to follow documents (records) of any kind\n that inherits from mail.thread. Following documents allow to receive\n notifications for new messages. A subscription is characterized by:\n\n :param: res_model: model of the followed objects\n :param: res_id: ID of resource (may be 0 for every objects)\n \"\"\"\n _name = 'mail.followers'\n _rec_name = 'partner_id'\n _log_access = False\n _description = 'Document Followers'\n\n res_model = fields.Char(\n 'Related Document Model', required=True, index=True, help='Model of the followed resource')\n res_id = fields.Integer(\n 'Related Document ID', index=True, help='Id of the followed resource')\n partner_id = fields.Many2one(\n 'res.partner', string='Related Partner', ondelete='cascade', index=True)\n channel_id = fields.Many2one(\n 'mail.channel', string='Listener', ondelete='cascade', index=True)\n subtype_ids = fields.Many2many(\n 'mail.message.subtype', string='Subtype',\n help=\"Message subtypes followed, meaning subtypes that will be pushed onto the user's Wall.\")\n\n @api.model\n def _add_follower_command(self, res_model, res_ids, partner_data, channel_data, force=True):\n \"\"\" Please upate me\n :param force: if True, delete existing followers before creating new one\n using the subtypes given in the parameters\n \"\"\"\n force_mode = force or (all(data for data in partner_data.values()) and all(data for data in channel_data.values()))\n generic = []\n specific = {}\n existing = {} # {res_id: follower_ids}\n p_exist = {} # {partner_id: res_ids}\n c_exist = {} # {channel_id: res_ids}\n\n followers = self.sudo().search([\n '&',\n '&', ('res_model', '=', res_model), ('res_id', 'in', res_ids),\n '|', ('partner_id', 'in', partner_data.keys()), ('channel_id', 'in', channel_data.keys())])\n\n if force_mode:\n followers.unlink()\n else:\n for follower in followers:\n existing.setdefault(follower.res_id, list()).append(follower)\n if follower.partner_id:\n p_exist.setdefault(follower.partner_id.id, list()).append(follower.res_id)\n if follower.channel_id:\n c_exist.setdefault(follower.channel_id.id, list()).append(follower.res_id)\n\n default_subtypes = self.env['mail.message.subtype'].search([\n ('default', '=', True),\n '|', ('res_model', '=', res_model), ('res_model', '=', False)])\n external_default_subtypes = default_subtypes.filtered(lambda subtype: not subtype.internal)\n\n if force_mode:\n employee_pids = self.env['res.users'].sudo().search([('partner_id', 'in', partner_data.keys()), ('share', '=', False)]).mapped('partner_id').ids\n for pid, data in partner_data.iteritems():\n if not data:\n if pid not in employee_pids:\n partner_data[pid] = external_default_subtypes.ids\n else:\n partner_data[pid] = default_subtypes.ids\n for cid, data in channel_data.iteritems():\n if not data:\n channel_data[cid] = default_subtypes.ids\n\n # create new followers, batch ok\n gen_new_pids = [pid for pid in partner_data.keys() if pid not in p_exist]\n gen_new_cids = [cid for cid in channel_data.keys() if cid not in c_exist]\n for pid in gen_new_pids:\n generic.append([0, 0, {'res_model': res_model, 'partner_id': pid, 'subtype_ids': [(6, 0, partner_data.get(pid) or default_subtypes.ids)]}])\n for cid in gen_new_cids:\n generic.append([0, 0, {'res_model': res_model, 'channel_id': cid, 'subtype_ids': [(6, 0, channel_data.get(cid) or default_subtypes.ids)]}])\n\n # create new followers, each document at a time because of existing followers to avoid erasing\n if not force_mode:\n for res_id in res_ids:\n command = []\n doc_followers = existing.get(res_id, list())\n\n new_pids = set(partner_data.keys()) - set([sub.partner_id.id for sub in doc_followers if sub.partner_id]) - set(gen_new_pids)\n new_cids = set(channel_data.keys()) - set([sub.channel_id.id for sub in doc_followers if sub.channel_id]) - set(gen_new_cids)\n\n # subscribe new followers\n for new_pid in new_pids:\n command.append((0, 0, {\n 'res_model': res_model,\n 'partner_id': new_pid,\n 'subtype_ids': [(6, 0, partner_data.get(new_pid) or default_subtypes.ids)],\n }))\n for new_cid in new_cids:\n command.append((0, 0, {\n 'res_model': res_model,\n 'channel_id': new_cid,\n 'subtype_ids': [(6, 0, channel_data.get(new_cid) or default_subtypes.ids)],\n }))\n if command:\n specific[res_id] = command\n return generic, specific\n\n #\n # Modifying followers change access rights to individual documents. As the\n # cache may contain accessible/inaccessible data, one has to refresh it.\n #\n @api.multi\n def _invalidate_documents(self):\n \"\"\" Invalidate the cache of the documents followed by ``self``. \"\"\"\n for record in self:\n if record.res_id:\n self.env[record.res_model].invalidate_cache(ids=[record.res_id])\n\n @api.model\n def create(self, vals):\n res = super(Followers, self).create(vals)._check_rights()\n res._invalidate_documents()\n return res\n\n @api.multi\n def write(self, vals):\n if 'res_model' in vals or 'res_id' in vals:\n self._invalidate_documents()\n res = super(Followers, self).write(vals)\n self._check_rights()\n self._invalidate_documents()\n return res\n\n @api.multi\n def unlink(self):\n self._invalidate_documents()\n return super(Followers, self).unlink()\n\n @api.multi\n def _check_rights(self):\n user_partner = self.env.user.partner_id\n for record in self:\n obj = self.env[record.res_model].browse(record.res_id)\n if record.channel_id or record.partner_id != user_partner:\n obj.check_access_rights('write')\n obj.check_access_rule('write')\n subject = record.channel_id or record.partner_id\n subject.check_access_rights('read')\n subject.check_access_rule('read')\n else:\n obj.check_access_rights('read')\n obj.check_access_rule('read')\n return self\n\n _sql_constraints = [\n ('mail_followers_res_partner_res_model_id_uniq', 'unique(res_model,res_id,partner_id)', 'Error, a partner cannot follow twice the same object.'),\n ('mail_followers_res_channel_res_model_id_uniq', 'unique(res_model,res_id,channel_id)', 'Error, a channel cannot follow twice the same object.'),\n ('partner_xor_channel', 'CHECK((partner_id IS NULL) != (channel_id IS NULL))', 'Error: A follower must be either a partner or a channel (but not both).')\n ]\n","repo_name":"AwesomeFoodCoops/odoo-production","sub_path":"odoo/addons/mail/models/mail_followers.py","file_name":"mail_followers.py","file_ext":"py","file_size_in_byte":7521,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"12"} +{"seq_id":"21758253341","text":"import os \nfrom glob import glob\nfrom setuptools import setup\n\npackage_name = 'robot_spawner_pkg'\ncur_directory_path = os.path.abspath(os.path.dirname(__file__))\n\nsetup(\n name=package_name,\n version='0.0.0',\n packages=[package_name],\n data_files=[\n ('share/ament_index/resource_index/packages',\n ['resource/' + package_name]),\n ('share/' + package_name, ['package.xml']),\n (os.path.join('share', package_name,'launch'), glob('launch/*.launch.py')),\n (os.path.join('share', package_name,'worlds/'), glob('./worlds/*')),\n (os.path.join('share', package_name,'models/Maze_ql_1/'), glob('./models/Maze_ql_1/*')),\n (os.path.join('share', package_name,'models/basic_robot'), glob('./models/basic_robot/*')),\n (os.path.join('share', package_name,'models/globe'), glob('./models/globe/*'))\n\n ],\n install_requires=['setuptools'],\n zip_safe=True,\n maintainer='ubuntu',\n maintainer_email='ubuntu@todo.todo',\n description='TODO: Package description',\n license='TODO: License declaration',\n tests_require=['pytest'],\n entry_points={\n 'console_scripts': [\n 'spawn_demo = robot_spawner_pkg.spawn_demo:main',\n 'spawn_scenario = robot_spawner_pkg.spawn_scenario:main'\n\n ],\n },\n)\n","repo_name":"laurencourtney/Robotics","sub_path":"test_final/src/template/robot_spawner_pkg/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"16800830903","text":"import codecs\nimport time\nimport json\nimport threading\nfrom os import listdir, SEEK_CUR\nfrom os.path import join, isdir, basename\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\n\nimport events\nfrom config import config\n\n\nclass JournalHandler(FileSystemEventHandler):\n\n def __init__(self):\n self.journal_dir = config['journal_dir']\n self.logfile = None\n self.loghandle = None\n self.observer = None\n self.thread = None\n self.event_queue = []\n self.state = {\n 'Commander': None,\n 'Ship_Localised': None,\n 'ShipName': None,\n 'ShipIdent': None,\n 'FuelLevel': None,\n 'FuelCapacity': None,\n 'GameMode': None,\n 'Credits': None,\n\n 'Docked': None,\n 'StarSystem': None,\n 'StarSystemBodies': {},\n 'SystemSecurity_Localised': None,\n 'Population': 0,\n 'Body': None,\n 'BodyType': None,\n\n 'Latitude': None,\n 'Longitude': None,\n\n 'StationName': None,\n 'StationType': None,\n\n 'Raw': {},\n 'Manufactured': {},\n 'Encoded': {},\n }\n\n def start(self):\n if not self.journal_dir or not isdir(self.journal_dir):\n self.stop()\n return False\n\n try:\n logfiles = sorted(\n [f for f in listdir(self.journal_dir)\n if f.startswith('Journal') and f.endswith('.log')],\n key=lambda x: x.split('.')[1:]\n )\n if logfiles:\n self.logfile = join(self.journal_dir, logfiles[-1]) or None\n except OSError:\n self.logfile = None\n return False\n\n self.observer = Observer()\n self.observer.daemon = True\n self.observer.schedule(self, self.journal_dir)\n self.observer.start()\n\n if not self.running():\n self.thread = threading.Thread(\n target=self.worker,\n name='Journal worker')\n self.thread.daemon = True\n self.thread.start()\n\n return True\n\n def stop(self):\n self.thread = None\n if self.observer:\n self.observer.stop()\n self.observer.join()\n self.observer = None\n\n def running(self):\n return self.thread and self.thread.is_alive()\n\n def on_created(self, event):\n cond1 = not event.is_directory\n cond2 = basename(event.src_path).startswith('Journal')\n cond3 = basename(event.src_path).endswith('.log')\n if cond1 and cond2 and cond3:\n newlogfile = event.src_path\n\n if self.loghandle:\n self.loghandle.close()\n\n self.logfile = newlogfile\n self.loghandle = open(newlogfile, 'r')\n\n print(self.logfile)\n\n def worker(self):\n if not self.logfile:\n return\n self.loghandle = codecs.open(join(self.journal_dir, self.logfile), 'r', encoding='utf-8')\n\n while True:\n loghandle = self.loghandle\n if loghandle:\n loghandle.seek(0, SEEK_CUR)\n for line in loghandle:\n self.parse(line)\n\n time.sleep(1)\n\n if threading.current_thread() != self.thread:\n return\n\n def parse(self, line):\n entry = json.loads(line)\n event = entry['event']\n\n if event == 'FSDJump':\n self.state['BodyType'] = 'Star'\n for k, v in entry.items():\n if k in self.state:\n self.state[k] = v\n entry.update({'FuelCapacity': self.state['FuelCapacity']})\n line = json.dumps(entry, separators=(', ', ':'))\n\n elif event == 'FuelScoop':\n self.state['FuelLevel'] = entry['Total']\n\n elif event in ['RefuelAll', 'RefuelPartial']:\n self.state['FuelLevel'] += entry['Amount']\n\n elif event == 'Scan':\n body_scan = events.Scan(entry).body_scan\n self.state['StarSystemBodies'].update({\n (self.state['StarSystem'], entry['BodyID']): body_scan,\n })\n\n elif event == 'SupercruiseEntry':\n self.state['BodyType'] = 'Null'\n\n elif event == 'SupercruiseExit':\n for k, v in entry.items():\n if k in self.state:\n self.state[k] = v\n\n elif event == 'ApproachBody':\n self.state['BodyType'] = 'Planet'\n for k, v in entry.items():\n if k in self.state:\n self.state[k] = v\n\n elif event == 'LeaveBody':\n self.state['BodyType'] = 'Null'\n for k, v in entry.items():\n if k in self.state:\n self.state[k] = v\n\n elif event == 'Touchdown':\n self.state['Latitude'] = entry.get('Latitude')\n self.state['Longitude'] = entry.get('Longitude')\n\n elif event == 'Liftoff':\n self.state['Latitude'] = None\n self.state['Longitude'] = None\n\n elif event == 'Materials':\n for category in ['Raw', 'Manufactured', 'Encoded']:\n for material in entry.get(category, []):\n count = material['Count']\n name = material.get('Name_Localised')\n if not name:\n name = material['Name']\n self.state[category].update({name: count})\n\n elif event in ['MaterialCollected', 'MaterialDiscarded']:\n category = entry['Category']\n count = entry['Count']\n name = entry.get('Name_Localised')\n if not name:\n name = entry['Name']\n\n if event == 'MaterialCollected':\n total = self.state[category].get(name, 0) + count\n elif event == 'MaterialDiscarded':\n total = self.state[category][name] - count\n\n self.state[category].update({name: total})\n\n entry.update({'Total': total})\n line = json.dumps(entry, separators=(', ', ':'))\n\n elif event == 'Docked':\n self.state['Docked'] = True\n self.state['StationName'] = entry['StationName']\n self.state['StationType'] = entry['StationType']\n\n elif event == 'Undocked':\n self.state['Docked'] = False\n self.state['StationName'] = None\n self.state['StationType'] = None\n\n elif event == 'SetUserShipName':\n self.state['ShipName'] = entry['UserShipName']\n self.state['ShipIdent'] = entry['UserShipId']\n\n elif event in ['ShipyardNew', 'ShipyardSwap']:\n self.state['Ship_Localised'] = entry.get('ShipType')\n self.state['ShipName'] = None\n self.state['ShipIdent'] = None\n\n elif event == 'Commander':\n self.state['Commander'] = entry['Name']\n\n elif event in ['LoadGame', 'Location']:\n for k, v in entry.items():\n if k in self.state:\n self.state[k] = v\n\n elif (event == 'Loadout' and\n not entry['Ship'].lower().endswith('fighter')):\n self.state['ShipName'] = entry['ShipName']\n self.state['ShipIdent'] = entry['ShipIdent']\n fuel_capacity = 0\n for module in entry['Modules']:\n if module['Item'].lower().find('fueltank') > -1:\n item = module['Item'].split('_')\n size = int(item[2][-1])\n fuel_capacity += 2 ** size\n self.state['FuelCapacity'] = fuel_capacity\n\n elif event == 'NewCommander':\n self.state['Commander'] = entry['Name']\n\n self.event_queue.append(line)\n\n def get_entry(self):\n if not self.event_queue:\n return None\n\n entry = self.event_queue.pop(0)\n\n return entry\n\n\nmonitor = JournalHandler()\n","repo_name":"alturus/EDLogPrint","sub_path":"monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":8009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"12865535760","text":"import re\n\nfrom harrier.build import slugify\nfrom harrier.extensions import modify\n\n\n@modify.pages('/index.md')\ndef modify_foo(page, config):\n content = page['content']\n headings = re.findall('^(#{1,2}) (.*)', page['content'], re.M)\n headings = [(f'{len(p)}-{slugify(h)}', h) for p, h in headings]\n for slug, heading in headings:\n observer = (\n f''\n f''\n )\n content = re.sub('^(#{1,2} ' + heading + ')$', observer + r'\\n\\n\\1', content, flags=re.M)\n page.update(\n headings=headings,\n content=content,\n )\n return page\n","repo_name":"samuelcolvin/harrier","sub_path":"docs/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"12"} +{"seq_id":"15699515317","text":"import os\nimport re\n\nfrom sqlalchemy import text\nfrom sqlalchemy.exc import OperationalError\n\nfrom pilotscope.Common.Index import Index\nfrom pilotscope.Common.SSHConnector import SSHConnector\nfrom pilotscope.DBController.BaseDBController import BaseDBController\nfrom pilotscope.Exception.Exception import DBStatementTimeoutException, DatabaseCrashException, DatabaseStartException, \\\n PilotScopeInternalError\nfrom pilotscope.PilotConfig import PostgreSQLConfig\n\n\nclass PostgreSQLController(BaseDBController):\n _instances = set()\n\n def __new__(cls, *args, **kwargs):\n instance = super().__new__(cls)\n cls._instances.add(instance)\n return instance\n\n def __del__(self):\n self._disconnect()\n type(self)._instances.remove(self)\n\n def __init__(self, config: PostgreSQLConfig, echo=True, enable_simulate_index=False):\n super().__init__(config, echo)\n self.config: PostgreSQLConfig = config\n\n self.enable_simulate_index = enable_simulate_index\n self._add_extension()\n if self.enable_simulate_index:\n self.simulate_index_visitor = SimulateIndexVisitor(self)\n for index in super().get_all_indexes():\n sql = f\"SELECT hypopg_hide_index('{index.index_name}'::REGCLASS)\"\n self.execute(sql)\n\n def _add_extension(self):\n extensions = self.get_available_extensions()\n if \"pg_buffercache\" not in extensions:\n self.execute(\"create extension pg_buffercache\")\n if \"pg_hint_plan\" not in extensions:\n self.execute(\"create extension pg_hint_plan\")\n if self.enable_simulate_index and \"hypopg\" not in extensions:\n self.execute(\"create extension hypopg\")\n\n def get_available_extensions(self):\n \"\"\"\n Get all extensions that have installed in the connected database\n :return: the list of extension names\n \"\"\"\n sql = (\"SELECT name, default_version, installed_version FROM\"\n \" pg_available_extensions WHERE installed_version is not NULL ORDER BY name;\")\n res = self.execute(sql, fetch=True)\n extensions = []\n for row in res:\n extensions.append(row[0])\n return extensions\n\n def _create_conn_str(self):\n return \"{}://{}:{}@{}:{}/{}?{}\".format(\"postgresql\", self.config.db_user, self.config.db_user_pwd,\n self.config.db_host,\n self.config.db_port, self.config.db, \"connect_timeout=2\")\n\n def execute(self, sql, fetch=False, fetch_column_name=False):\n \"\"\"\n Execute a SQL query.\n\n :param sql: the SQL query to execute\n :param fetch: it indicates whether to fetch the result of the query\n :param fetch_column_name: it indicates whether to fetch the column names of the result.\n :return: the result of the query if fetch is True, otherwise None\n \"\"\"\n row = None\n try:\n self._connect_if_loss()\n conn = self._get_connection()\n result = conn.execute(text(sql) if isinstance(sql, str) else sql)\n if fetch:\n row = result.all()\n if fetch_column_name:\n row = [tuple(result.keys()), *row]\n except OperationalError as e:\n if \"canceling statement due to statement timeout\" in str(e):\n raise DBStatementTimeoutException(str(e))\n else:\n raise e\n except Exception as e:\n if \"Can not find the corresponding sub-plan query in push anchor\" in str(e):\n raise PilotScopeInternalError(str(e))\n if \"PilotScopePullEnd\" not in str(e):\n raise e\n return row\n\n def set_hint(self, key, value):\n \"\"\"\n Set the value of each hint (i.e., the run-time config) when execute SQL queries.\n The hints can be used to control the behavior of the database system in a session.\n For PostgreSQL, you can find all valid hints in https://www.postgresql.org/docs/13/runtime-config.html.\n\n :param key: the name of the hint\n :param value: the value of the hint\n \"\"\"\n sql = \"SET {} TO {}\".format(key, value)\n self.execute(sql)\n\n def create_index(self, index: Index):\n \"\"\"\n Create an index on columns `index.columns` of table `index.table` with name `index.index_name`.\n\n :param index: a Index object including the information of the index\n \"\"\"\n if self.enable_simulate_index:\n self.simulate_index_visitor.create_index(index)\n else:\n column_names = index.joined_column_names()\n sql = f\"create index {index.index_name} on {index.table} ({column_names});\"\n self.execute(sql, fetch=False)\n\n def drop_index(self, index: Index):\n \"\"\"\n Drop an index by its index name.\n\n :param index: an index that will be dropped\n \"\"\"\n if self.enable_simulate_index:\n self.simulate_index_visitor.drop_index(index)\n else:\n statement = (\n f\"DROP INDEX IF EXISTS {index.index_name};\"\n )\n self.execute(statement, fetch=False)\n\n def drop_all_indexes(self):\n \"\"\"\n Drop all indexes across all tables in the database. This will not delete the system indexes and unique indexes.\n \"\"\"\n if self.enable_simulate_index:\n self.simulate_index_visitor.drop_all_indexes()\n else:\n indexes = self.get_all_indexes()\n for index in indexes:\n self.drop_index(index)\n\n def get_all_indexes_byte(self):\n \"\"\"\n Get the size of all indexes across all tables in the database in bytes.\n This will include the system indexes and unique indexes.\n\n :return: the size of all indexes in bytes\n \"\"\"\n if self.enable_simulate_index:\n result = self.simulate_index_visitor.get_all_indexes_byte()\n else:\n sql = (\"select sum(pg_indexes_size(table_name::text)) from \"\n \"(select table_name from information_schema.tables \"\n \"where table_schema='public') as all_tables;\")\n result = float(self.execute(sql, fetch=True)[0][0])\n return result\n\n def get_table_indexes_byte(self, table_name):\n \"\"\"\n Get the size of all indexes on a table in bytes.\n This will include the system indexes and unique indexes.\n\n :param table_name: a table name that the indexes belong to\n :return: the size of all indexes on the table in bytes\n \"\"\"\n if self.enable_simulate_index:\n result = self.simulate_index_visitor.get_table_indexes_byte(table_name)\n else:\n sql = f\"select pg_indexes_size('{table_name}');\"\n result = float(self.execute(sql, fetch=True)[0][0])\n return result\n\n def get_index_byte(self, index: Index):\n \"\"\"\n Get the size of an index in bytes by its index name.\n\n :param index: the index to get size\n :return: the size of the index in bytes\n \"\"\"\n if self.enable_simulate_index:\n return self.simulate_index_visitor.get_index_byte(index)\n sql = f\"select pg_table_size('{index.get_index_name()}');\"\n result = int(self.execute(sql, fetch=True)[0][0])\n return result\n\n def get_existed_indexes(self, table):\n if self.enable_simulate_index:\n return self.simulate_index_visitor.get_existed_index(table)\n else:\n return super().get_existed_indexes(table)\n\n def get_all_indexes(self):\n \"\"\"\n Get all indexes across all tables in the database.\n\n :return: A collection containing the details of all indexes.\n \"\"\"\n if self.enable_simulate_index:\n return self.simulate_index_visitor.get_all_indexes()\n else:\n return super().get_all_indexes()\n\n def get_index_number(self, table):\n \"\"\"\n Get the number of indexes built on the specified table.\n\n :param table: The name of the table for which to count indexes.\n :return: The number of indexes on the specified table.\n \"\"\"\n if self.enable_simulate_index:\n return self.simulate_index_visitor.get_index_number(table)\n else:\n return super().get_index_number(table)\n\n def explain_physical_plan(self, sql, comment=\"\"):\n \"\"\"\n Get the physical plan from database's optimizer of a SQL query.\n\n :param sql: The SQL query to be explained.\n :param comment: A SQL comment will be added to the beginning of the SQL query.\n :return: The physical plan of the SQL query.\n \"\"\"\n return self._explain(sql, comment, False)\n\n def explain_execution_plan(self, sql, comment=\"\"):\n \"\"\"\n Get the execution plan from database's optimizer of a SQL query.\n\n :param sql: The SQL query to be explained.\n :param comment: A SQL comment will be added to the beginning of the SQL query.\n :return: The execution plan of the SQL query.\n \"\"\"\n return self._explain(sql, comment, True)\n\n def _explain(self, sql, comment, execute: bool):\n return self.execute(text(self.get_explain_sql(sql, execute, comment)), True)[0][0][0]\n\n def get_estimated_cost(self, sql, comment=\"\"):\n \"\"\"\n Get an estimated cost of a SQL query.\n\n :param sql: The SQL query for which to estimate the cost.\n :param comment: A SQL comment will be added to the beginning of the SQL query.\n :return: The estimated total cost of executing the SQL query.\n \"\"\"\n plan = self.explain_physical_plan(sql, comment=comment)\n return plan[\"Plan\"][\"Total Cost\"]\n\n def get_explain_sql(self, sql, execute: bool, comment=\"\"):\n \"\"\"\n Constructs an EXPLAIN SQL statement for a given SQL query.\n\n :param sql: The SQL query to explain.\n :param execute: A boolean flag indicating whether to execute the query plan.\n :param comment: A SQL comment will be added to the beginning of the SQL query.\n :return: The result of executing the `EXPLAIN` SQL statement.\n \"\"\"\n return \"{} explain ({} VERBOSE, SETTINGS, SUMMARY, FORMAT JSON) {}\".format(comment,\n \"ANALYZE,\" if execute else \"\",\n sql)\n\n def get_buffercache(self):\n \"\"\"\n Get the numbers of buffer per table in the shared buffer cache in real time.\n\n :return: a dict, where keys are the names of table and values are the numbers of buffer per table\n \"\"\"\n sql = \"\"\"\n SELECT c.relname, count(*) AS buffers\n FROM pg_buffercache b JOIN pg_class c\n ON b.relfilenode = pg_relation_filenode(c.oid) AND\n b.reldatabase IN (0, (SELECT oid FROM pg_database\n WHERE datname = current_database()))\n JOIN pg_namespace n ON n.oid = c.relnamespace\n GROUP BY c.relname;\n \"\"\"\n res = self.execute(sql, fetch=True)\n return {k: v for k, v in res if not k.startswith(\"pg_\")}\n\n def shutdown(self):\n \"\"\"\n Shutdown the database\n \"\"\"\n\n self._check_enable_deep_control()\n\n for instance in type(self)._instances:\n # if hasattr(instance, \"engine\"):\n instance._disconnect() # to set DBController's self.connection_thread.conn is None\n instance.engine.dispose(close=True)\n # del instance.engine\n self._surun(\"{} stop -P {} -D {} 2>&1 > /dev/null\".format(self.config.pg_ctl, self.config.db_port, self.config.pgdata))\n\n def start(self):\n \"\"\"\n Try to start DBMS. If fails the first time, recover config to self.config.backup_db_config_path and raise DatabaseStartException.\n If fails again after recovering config, raise DatabaseCrashException.\n\n :raises DatabaseStartException\n \"\"\"\n\n self._check_enable_deep_control()\n\n self._surun(\"{} start -P {} -D {} 2>&1 > /dev/null\".format(self.config.pg_ctl, self.config.db_port, self.config.pgdata))\n if not self.is_running():\n raise DatabaseCrashException\n\n for instance in type(self)._instances:\n instance._connect_if_loss()\n\n def is_running(self):\n \"\"\"\n Check whether the database is running.\n\n :return: True if the database is running, False otherwise.\n \"\"\"\n self._check_enable_deep_control()\n\n check_db_running_cmd = \"echo {} | su {} -c '{} status -P {} -D {}'\".format(self.config.db_host_pwd, self.config.db_host_user, \n self.config.pg_ctl, self.config.db_port, self.config.pgdata)\n if self.config._is_local:\n with os.popen(check_db_running_cmd) as res:\n status = res.read()\n else:\n ssh_conn = SSHConnector(self.config.db_host, self.config.db_host_user, self.config.db_host_pwd,\n self.config.db_host_port)\n ssh_conn.connect()\n res_out, res_err = ssh_conn.remote_exec_cmd(check_db_running_cmd)\n ssh_conn.close()\n status = \"{},{}\".format(res_out, res_err)\n\n return \"server is running\" in status\n\n def write_knob_to_file(self, key_2_value_knob: dict):\n \"\"\"\n Write knobs to config file, you should restart database to make it work.\n\n :param key_2_value_knob: a dict with keys as the names of the knobs and values as the values to be set.\n \"\"\"\n\n self._check_enable_deep_control()\n\n with open(self.config.db_config_path, \"a\") as f:\n f.write(\"\\n\")\n for k, v in key_2_value_knob.items():\n f.write(\"{} = {}\\n\".format(k, v))\n\n def recover_config(self):\n \"\"\"\n Recover config file of database to the lasted saved config file by `backup_config()`\n \"\"\"\n\n self._check_enable_deep_control()\n\n with open(self.config.backup_db_config_path, \"r\") as f:\n db_config_file = f.read()\n with open(self.config.db_config_path, \"w\") as f:\n f.write(db_config_file)\n\n def backup_config(self):\n \"\"\"\n Creates a backup of the database configuration file.\n \"\"\"\n\n self._check_enable_deep_control()\n\n with open(self.config.db_config_path, \"r\") as f:\n with open(self.config.backup_db_config_path, \"w\") as w:\n w.write(f.read())\n\n def get_table_columns(self, table_name, enable_all_schema=False):\n \"\"\"\n Retrieves all column names for a given table. If enable_all_schema is true,\n Pilotscope will search it across all schemas in the database.\n Otherwise, Pilotscope will only search it in the public schema.\n\n :param table_name: The name of the table for which to retrieve column names.\n :param enable_all_schema:\n :return: A list of column names for the specified table.\n \"\"\"\n if enable_all_schema:\n sql = \"SELECT column_name FROM information_schema.columns WHERE table_name = '{}';\".format(table_name)\n else:\n sql = \"SELECT column_name FROM information_schema.columns WHERE table_name = '{}' and table_schema='public';\".format(\n table_name)\n return [x[0] for x in self.execute(sql, fetch=True)]\n\n def get_number_of_distinct_value(self, table_name, column_name):\n \"\"\"\n Get the number of distinct value of a column\n\n :param table_name: the name of the table that the column belongs to\n :param column_name: the name of the column\n :return: the number of distinct value, type of which is same as the data of the column\n \"\"\"\n return self.execute(f\"select count(distinct {column_name}) from {table_name};\", True)[0][0]\n\n # switch user and run\n def _surun(self, cmd):\n su_and_cmd = \"echo {} | su {} -c '{}'\".format(self.config.db_host_pwd, self.config.db_host_user, cmd)\n if self.config._is_local:\n return os.system(su_and_cmd)\n else:\n ssh_conn = SSHConnector(self.config.db_host, self.config.db_host_user, self.config.db_host_pwd,\n self.config.db_host_port)\n ssh_conn.connect()\n ssh_conn.remote_exec_cmd(su_and_cmd)\n ssh_conn.close()\n\n\nclass SimulateIndexVisitor:\n\n def __init__(self, db_controller: PostgreSQLController):\n super().__init__()\n self.db_controller = db_controller\n\n def create_index(self, index: Index):\n columns = index.joined_column_names()\n statement = (\n \"select * from hypopg_create_index( \"\n f\"'create index on {index.table} \"\n f\"({columns})')\"\n )\n result = self.db_controller.execute(statement, fetch=True)[0]\n index.hypopg_oid = result[0]\n index.hypopg_name = result[1]\n\n def _get_oid_by_indexname(self, index_name):\n sql = f\"SELECT indexrelid FROM hypopg_list_indexes WHERE index_name like '%{index_name}%'\"\n res = self.db_controller.execute(sql, fetch=True)\n assert len(res) == 1, f\"No oid or more than one oid named like '%{index_name}%'\"\n return res[0][0]\n\n def _get_oid_of_index(self, index: Index):\n if index.hypopg_oid is not None:\n return index.hypopg_oid\n elif index.hypopg_name is not None:\n return self._get_oid_by_indexname(index_name=index.hypopg_name)\n else:\n return self._get_oid_by_indexname(index_name=index.index_name)\n\n def drop_index(self, index: Index):\n oid = self._get_oid_of_index(index)\n statement = f\"select * from hypopg_drop_index({oid})\"\n result = self.db_controller.execute(statement, fetch=True)\n assert result[0][0] is True, f\"Could not drop simulated index with oid = {oid}.\"\n\n def drop_all_indexes(self):\n sql = \"select hypopg_reset()\"\n self.db_controller.execute(sql)\n\n def get_all_indexes_byte(self):\n return self.get_table_indexes_byte(\"1' or '1'='1\")\n\n def get_table_indexes_byte(self, table):\n sql = f\"SELECT sum(hypopg_relation_size(h.indexrelid)) from hypopg() h left join pg_class t on h.indrelid=t.oid where t.relname = '{table}'\"\n res = self.db_controller.execute(sql, fetch=True)[0][0]\n return 0 if res is None else float(res)\n\n def get_index_byte(self, index: Index):\n try:\n oid = self._get_oid_of_index(index)\n statement = f\"select hypopg_relation_size({oid})\"\n result = self.db_controller.execute(statement, fetch=True)[0][0]\n assert result > 0, \"Hypothetical index does not exist.\"\n return float(result)\n except:\n raise RuntimeError\n\n def get_index_number(self, table):\n sql = f\"SELECT COUNT(*) from hypopg() h left join pg_class t on h.indrelid=t.oid where t.relname = '{table}'\"\n return int(self.db_controller.execute(sql, fetch=True)[0][0])\n\n def get_all_indexes(self):\n return self.get_existed_index(\"1' or '1'='1\")\n\n def get_existed_index(self, table):\n sql = f\"SELECT h.indexrelid, h.indexname, hypopg_get_indexdef(h.indexrelid), t.relname from hypopg() h left join pg_class t on h.indrelid=t.oid where t.relname = '{table}'\"\n res = self.db_controller.execute(sql, fetch=True)\n indexes = []\n for indexrelid, indexname, indexdef, relname in res:\n col = [col.strip() for col in re.search(r\"\\([\\S\\s]*\\)\", indexdef).group(0)[1:-1].split(\",\")]\n index = Index(columns=col, table=relname, index_name=None)\n index.hypopg_name = indexname\n index.hypopg_oid = indexrelid\n indexes.append(index)\n return indexes\n","repo_name":"alibaba/pilotscope","sub_path":"pilotscope/DBController/PostgreSQLController.py","file_name":"PostgreSQLController.py","file_ext":"py","file_size_in_byte":20047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"26638704288","text":"# create model that classifies the data in data/winequality.csv\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn import metrics\nimport pickle\nimport numpy as np\n\ndef create_model():\n # import the data from data/winequality.csv\n data = pd.read_csv('model/data/winequality.csv')\n\n # split the data into training and test sets\n train, test = train_test_split(data, test_size=0.2, random_state=42)\n\n # separate the features from the labels\n train_features = train.drop('quality', axis=1)\n train_labels = train['quality']\n\n test_features = test.drop('quality', axis=1)\n test_labels = test['quality']\n\n # save test data in file test_wine.csv\n test.to_csv('model/data/test_wine.csv', index=False)\n sc = StandardScaler()\n\n train_features = sc.fit_transform(train_features)\n test_features = sc.transform(test_features)\n\n # create a model\n model = GradientBoostingClassifier()\n\n # train the model\n model.fit(train_features, train_labels)\n\n # evaluate the model\n predictions = model.predict(test_features)\n\n # save the metrics in file metrics.txt\n with open('model/metrics.txt', 'w') as f:\n f.write('For the accuracy:' + str(metrics.accuracy_score(test_labels, predictions)) + '\\n')\n f.write('For the recision: ' + str(metrics.precision_score(test_labels, predictions, average='weighted')) + '\\n')\n f.write('For the recall:' + str(metrics.recall_score(test_labels, predictions, average='weighted')) + '\\n')\n f.write('For the F1 score:' + str(metrics.f1_score(test_labels, predictions, average='weighted')))\n\n # save the model in file model.pkl\n pickle.dump(model, open('model/model.pkl', 'wb'))\n","repo_name":"Unikarah/Wine-MLOps-project","sub_path":"src/model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"19787091459","text":"import sys\nimport random\n\ndef findMaxCrossingSubarray(A, low, mid, high):\n leftSum = -sys.maxsize\n total = 0\n maxLeft = 0\n for i in range(mid, low, -1):\n total += A[i]\n if(total > leftSum):\n leftSum = total\n maxLeft = i\n rightSum = -sys.maxsize\n total = 0\n maxRight = 0\n for j in range(mid+1, high):\n total += A[j]\n if(total > rightSum):\n rightSum = total\n maxRight = j\n return (maxLeft, maxRight, leftSum + rightSum)\n\ndef findMaximumSubarray(A, low, high):\n if(high == low):\n return (low, high, A[low])\n else:\n mid = int((low + high) / 2)\n (leftLow, leftHigh, leftSum) = findMaximumSubarray(A, low, mid)\n (rightLow, rightHigh, rightSum) = findMaximumSubarray(A, mid+1, high)\n (crossLow, crossHigh, crossSum) = findMaxCrossingSubarray(A, low, mid, high)\n if(leftSum >= rightSum and leftSum >= crossSum):\n return (leftLow, leftHigh, leftSum)\n elif(rightSum >= leftSum and rightSum >= crossSum):\n return (rightLow, rightHigh, rightSum)\n else:\n return (crossLow, crossHigh, crossSum)\n\ndef getNumbers(numbers, length):\n for i in range(length):\n numbers.append(random.randrange(-50, 50))\n\ndef main():\n numbers = []\n getNumbers(numbers, 20)\n (low, high, total) = findMaximumSubarray(numbers, 0, len(numbers)-1)\n print(\"List: {}\".format(numbers))\n print(\"Low: {}, High: {}, Sum: {}\".format(low, high, total))\n\nmain()","repo_name":"andrest50/algorithms","sub_path":"maximum-subarray.py","file_name":"maximum-subarray.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"3046715734","text":"def checkio(test):\n test = test.lower()\n result = [0]\n num = 0\n for i in range(0, len(test)):\n if test[i].isalpha():\n if test.count(test[i], i, len(test)) > num:\n result = [test[i]]\n num = test.count(test[i], i, len(test))\n elif test.count(test[i], i, len(test)) == num:\n result.append(test[i])\n \n result.sort()\n return result[0]\n\nif __name__ == '__main__':\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio(\"Hello World!\") == \"l\", \"Hello test\"\n assert checkio(\"How do you do?\") == \"o\", \"O is most wanted\"\n assert checkio(\"One\") == \"e\", \"All letter only once.\"\n assert checkio(\"Oops!\") == \"o\", \"Don't forget about lower case.\"\n assert checkio(\"AAaooo!!!!\") == \"a\", \"Only letters.\"\n assert checkio(\"abe\") == \"a\", \"The First.\"\n print(\"Start the long test\")\n assert checkio(\"a\" * 9000 + \"b\" * 1000) == \"a\", \"Long.\"\n print(\"The local tests are done.\")\n\n\n\n\ndef checkio(text):\n charDict = dict() # A dictionnary is used so that characters and their number of appearance of the given string text are stored while traversing the string. \n text = text.lower() # The text is put to lower case.\n \n for char in text: # The text is traversed,\n if char.isalpha(): # to check if it is a letter,\n if not char in charDict.keys(): # and in the case where it is not already in the dict,\n charDict[char] = 1 # it is added with count 1,\n else:\n charDict[char] += 1 # otherwise counted one more time.\n \n tmpMax = 0\n maxChar = chr(255)\n \n for char in charDict.keys(): # Then the character with the maximum count is determined, also respecting the instruction of returning the lowest in the alphabet.\n if charDict[char] > tmpMax or charDict[char] == tmpMax and ord(char) < ord(maxChar):\n tmpMax = charDict[char]\n maxChar = char\n \n return maxChar\n\n\n\ndef checkio(text):\n text = text.lower().strip('!').strip('?').replace(' ', '')\n most_wanted = []\n result = {}\n chs = set(text)\n for c in chs:\n if c.isalpha():\n result[c] = text.count(c)\n highest = max(result.values())\n for k, v in result.items():\n if v == highest:\n most_wanted.append(k)\n\n return sorted(most_wanted)[0]\n\nif __name__ == '__main__':\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio(\"Hello World!\") == \"l\", \"Hello test\"\n assert checkio(\"How do you do?\") == \"o\", \"O is most wanted\"\n assert checkio(\"One\") == \"e\", \"All letter only once.\"\n assert checkio(\"Oops!\") == \"o\", \"Don't forget about lower case.\"\n assert checkio(\"AAaooo!!!!\") == \"a\", \"Only letters.\"\n assert checkio(\"abe\") == \"a\", \"The First.\"\n print(\"Start the long test\")\n assert checkio(\"a\" * 9000 + \"b\" * 1000) == \"a\", \"Long.\"\n print(\"The local tests are done.\")\n\n\n\n\ndef checkio(text):\n text = text.lower()\n letters_dict = {t: text.count(t) for t in text if t.isalpha()}\n letters_dict = [v[0] for v in sorted(letters_dict.items(), key=lambda kv: (-kv[1], kv[0]))]\n return letters_dict[0][0]\n\n return \n\nif __name__ == '__main__':\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio(\"Hello World!\") == \"l\", \"Hello test\"\n assert checkio(\"How do you do?\") == \"o\", \"O is most wanted\"\n assert checkio(\"One\") == \"e\", \"All letter only once.\"\n assert checkio(\"Oops!\") == \"o\", \"Don't forget about lower case.\"\n assert checkio(\"AAaooo!!!!\") == \"a\", \"Only letters.\"\n assert checkio(\"abe\") == \"a\", \"The First.\"\n print(\"Start the long test\")\n assert checkio(\"a\" * 9000 + \"b\" * 1000) == \"a\", \"Long.\"\n print(\"The local tests are done.\")\n\n\n\n\n\n\ndef checkio(text):\n order = sorted(text.lower())\n new_order = [(i, order.count(i)) for i in order if i.isalpha()]\n max_value = max([i[1] for i in new_order])\n all_get = [i[0] for i in new_order if i[1] == max_value]\n a = sorted(all_get)[0]\n\n #replace this for solution\n return a\n\nif __name__ == '__main__':\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio(\"Hello World!\") == \"l\", \"Hello test\"\n assert checkio(\"How do you do?\") == \"o\", \"O is most wanted\"\n assert checkio(\"One\") == \"e\", \"All letter only once.\"\n assert checkio(\"Oops!\") == \"o\", \"Don't forget about lower case.\"\n assert checkio(\"AAaooo!!!!\") == \"a\", \"Only letters.\"\n assert checkio(\"abe\") == \"a\", \"The First.\"\n print(\"Start the long test\")\n assert checkio(\"a\" * 9000 + \"b\" * 1000) == \"a\", \"Long.\"\n print(\"The local tests are done.\")","repo_name":"DennTerentyev/CheckIO","sub_path":"Most wanted letter.py","file_name":"Most wanted letter.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"2342193889","text":"__author__ = 'Brown'\nclass Solution(object):\n def hIndex(self, citations):\n \"\"\"\n :type citations: List[int]\n :rtype: int\n \"\"\"\n # Solution 1\n # citations.sort(reverse=True)\n # for i,N in enumerate(citations):\n # if i>=N:\n # return i\n # Solution 2\n N=len(citations)\n list=[0]*(N+1)\n for i in citations:\n if i>N:\n list[N]+=1\n else:\n list[i]+=1\n sum=0\n for h in range(N,-1,-1):\n if sum+list[h]>=h:\n return h\n sum+=list[h]\n return 0","repo_name":"Brownxin/Alogrithm_learning","sub_path":"leetcode/H_Index.py","file_name":"H_Index.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"14987232999","text":"from typing import List\nfrom pydantic import BaseModel\n\n\nclass CreateSchool(BaseModel):\n school_name: str\n address: str\n\n\nclass CreateStudent(BaseModel):\n first_name: str\n last_name: str\n address: str\n school: int\n email: str\n\n\nclass UserInfoBase(BaseModel):\n username: str\n fullname: str\n\n\nclass UserCreate(UserInfoBase):\n password: str\n\n\nclass UserInfo(UserInfoBase):\n id: int\n username: str\n fullname: str\n\n class Config:\n orm_mode = True\n\n\nclass SchoolInfo(CreateSchool):\n id: int\n school_name: str\n address: str\n\n class Config:\n orm_mode = True\n\n\nclass StudentInfo(CreateStudent):\n id: int\n\n class Config:\n orm_mode = True\n","repo_name":"Allwin12/student-management-system-using-fast-api","sub_path":"sql_app/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"41344382840","text":"from flask import Blueprint, request, jsonify\n\nfrom myshop.controllers import basket as basket_ctrl\nfrom myshop.exceptions import BadRequest, NotFound\nfrom myshop.libs import auth\n\n\nbp = Blueprint(__name__, \"basket\")\n\n@bp.route(\"/basket/create_or_add\", methods=[\"POST\"])\ndef basket_create():\n product_id = request.form.get(\"product_id\")\n total = request.form.get(\"total\")\n\n product_ids = []\n # product_id separate with comma if more than one\n for i in product_id.split(\",\"):\n product_ids.append(int(i))\n\n totals = []\n # total separate with comma if more than one\n for i in total.split(\",\"):\n totals.append(int(i))\n\n if None in (product_id, total):\n raise BadRequest(\"terdapat komponen yang kosong\")\n\n basket = basket_ctrl.create(\n user_id=auth.user.id,\n product_ids=product_ids,\n totals=totals,\n )\n\n response = {\n \"status\": 200,\n \"id\": basket.id,\n }\n\n return jsonify(response)\n\n\n@bp.route(\"/basket/user/\", methods=[\"GET\"])\ndef basket_by_user(user_id):\n \"\"\"Get basket\n\n \"\"\"\n basket = basket_ctrl.get_by_user(\n user_id=user_id\n )\n\n if not basket:\n response = {\n \"status\": 204,\n \"message\": \"Keranjang tidak ditemukan\"\n }\n\n else:\n response = {\n \"status\": 200,\n \"id\": basket.id,\n \"user\": basket.user_json,\n \"basket_product\": basket.basket_product_json,\n \"total_product\": basket.total_product,\n \"sub_total\": basket.sub_total,\n \"created_on\": basket.created_on.timestamp(),\n }\n\n return jsonify(response)\n\n\n@bp.route(\"/basket/item/delete\", methods=[\"POST\"])\ndef basket_delete():\n basket_id = request.form.get(\"basket_id\")\n product_id = request.form.get(\"product_id\")\n\n product_ids = []\n # product_id separate with comma if more than one\n for i in product_id.split(\",\"):\n product_ids.append(int(i))\n\n if None in (basket_id, product_id):\n raise BadRequest(\"terdapat komponen yang kosong\")\n\n basket = basket_ctrl.item_delete(\n basket_id=basket_id,\n product_ids=product_ids,\n )\n\n response = {\n \"status\": 200,\n \"id\": basket.id,\n \"user\": basket.user_json,\n \"basket_product\": basket.basket_product_json,\n \"updated_on\": basket.updated_on.timestamp(),\n }\n\n return jsonify(response)","repo_name":"IsnandaZain/e-commerce-api","sub_path":"myshop/routes/v1/basket.py","file_name":"basket.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"16516879805","text":"from imdb import IMDb\nfrom pyrogram import Client, filters\nfrom pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup\n\nfrom Stark import error_handler\n\nia = IMDb()\n\n\n@Client.on_message(filters.command([\"imdb\", \"IMDb\"]))\n@error_handler\nasync def search_movie(client, message):\n if len(message.command) < 2:\n await client.send_message(\n chat_id=message.chat.id,\n text=\"`Please provide a movie or TV series name after the /imdb command.`\"\n )\n return\n # Get the movie name from the user's message \n movie_name = message.text.split(\" \", 1)[1]\n if len(movie_name) < 1:\n await client.send_message(\n chat_id=message.chat.id,\n text=\"`Please provide a movie or TV series name after the /imdb command.`\"\n )\n return\n if len(str(movie_name)) > 40:\n await client.send_message(\n chat_id=message.chat.id,\n text=\"`Please provide a movie or TV series name. Not a paragraph! :)`\"\n )\n return\n mv = await message.reply_photo(\"https://exchange4media.gumlet.io/news-photo/123661-93930-IMDbAmazon.jpg\", caption=f\"`Searching for {movie_name}`\")\n movies = ia.search_movie(movie_name, results=10)\n if len(movies) == 0:\n await mv.edit(\"**__No movies found with that name!__**\")\n return\n button_list = []\n for i, movie in enumerate(movies[:10]):\n button_list.append([InlineKeyboardButton(text=movie['title'], callback_data=f\"{message.from_user.id}.more_details {movie.movieID} :{movie_name}:\")])\n # button_list.append([InlineKeyboardButton(text=\"\", callback_data=f\"more_details {movie.movieID}\")])\n # Add the buttons to an InlineKeyboardMarkup object\n keyboard = InlineKeyboardMarkup(button_list)\n\n # Send a message to the user with the search results and buttons\n message_text = f\"Found {len(movies)} results. Please select a movie:\"\n await mv.edit(\n text=message_text,\n reply_markup=keyboard,\n disable_web_page_preview=True\n )\n","repo_name":"Naveen-X/Mr.Stark","sub_path":"Stark/Plugins/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"12"} +{"seq_id":"11888102651","text":"from pynwb.form.build import GroupBuilder, DatasetBuilder\n\nfrom pynwb import TimeSeries\n\nfrom . import base\n\n\n@base.container_test(TimeSeries)\nclass TestTimeSeriesIO(base.TestMapRoundTrip):\n\n def setUpContainer(self):\n return TimeSeries('test_timeseries', 'example_source', list(range(100, 200, 10)),\n 'SIunit', timestamps=list(range(10)), resolution=0.1)\n\n def setUpBuilder(self):\n return GroupBuilder('test_timeseries',\n attributes={'source': 'example_source',\n 'namespace': base.CORE_NAMESPACE,\n 'neurodata_type': 'TimeSeries',\n 'description': 'no description',\n 'comments': 'no comments',\n 'help': 'General time series object'},\n datasets={'data': DatasetBuilder('data', list(range(100, 200, 10)),\n attributes={'unit': 'SIunit',\n 'conversion': 1.0,\n 'resolution': 0.1}),\n 'timestamps': DatasetBuilder('timestamps', list(range(10)),\n attributes={'unit': 'Seconds', 'interval': 1})})\n","repo_name":"q0j0p/pynwb","sub_path":"tests/integration/ui_write/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"3965654702","text":"'''In the timetracker we sometimes require writers to interface with other\nsystems. Here are any writers which are required for these purposes.\n'''\n\ntry: # pragma: no cover\n from cStringIO import StringIO\nexcept ImportError:\n from StringIO import StringIO\n\nimport csv, codecs\n\nclass UnicodeWriter(object): # pragma: no cover\n \"\"\"\n A CSV writer which will write rows to CSV file \"f\",\n which is encoded in the given encoding.\n\n Taken from the python documentation.\n \"\"\"\n\n def __init__(self, fio, dialect=csv.excel, encoding=\"utf-8\", **kwds):\n '''\n The constructor for our Unicode compliant csv writer.\n\n :param f: This is anything which has the interface of a file. I.e. it\n has both the read and write methods.\n :param dialect: The dialect of the csv file you're using, you can find\n a selection of these in the CSV package. Defaults to\n excel's dialect.\n :param encoding: The encoding of the document which you are creating.\n Defaults to UTF-8.\n '''\n self.queue = StringIO()\n self.writer = csv.writer(self.queue, dialect=dialect, **kwds)\n self.stream = fio\n self.encoder = codecs.getincrementalencoder(encoding)()\n\n def writerow(self, row):\n '''Implements the writerow function as a csv writer would do so.'''\n self.writer.writerow([unicode(s).encode(\"utf-8\") for s in row])\n # Fetch UTF-8 output from the queue ...\n data = self.queue.getvalue()\n data = data.decode(\"utf-8\")\n # ... and reencode it into the target encoding\n data = self.encoder.encode(data)\n # write to the target stream\n self.stream.write(data)\n # empty queue\n self.queue.truncate(0)\n\n def writerows(self, rows):\n '''Implements the writerows function as a csv writer would do so.'''\n for row in rows:\n self.writerow(row)\n","repo_name":"AeroNotix/django-timetracker","sub_path":"utils/writers.py","file_name":"writers.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"12"} +{"seq_id":"26780749827","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport socket\nimport os\nimport sys\nimport time\nimport select\n\nMYSOCKET=\"/tmp/TelldusClient\"\n\ngetNDev = \"20:tdGetNumberOfDevices\"\n\nprint(\"Connecting...\")\nif os.path.exists(MYSOCKET):\n client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n client.connect(MYSOCKET)\n print(\"Ready.\")\n print(\"Ctrl-C to quit.\")\n print(\"Sending 'DONE' shuts down the server and quits.\")\n client.setblocking(0)\n\n try:\n while True:\n try:\n \n # Send data\n message = getNDev\n ready = select.select([], [client], [], 1)\n if ready[1]:\n print('sending \"{0:s}\"'.format(message))\n client.sendall(bytearray(message,'utf8'))\n\n amount_received = 0\n ready = select.select([client], [], [], 1)\n if ready[0]:\n data = client.recv(4096)\n amount_received += len(data)\n print('received \"{0:s}\"'.format(data.decode('utf8')))\n time.sleep(3)\n except BrokenPipeError as bp:\n client.close()\n # time.sleep(.1)\n client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n client.connect(MYSOCKET)\n client.setblocking(0)\n print(\"bp\")\n #ready = select.select([client], [], [], 1)\n #if ready[0]:\n # data = client.recv(4096)\n # amount_received += len(data)\n # print('received \"{0:s}\"'.format(data.decode('utf8')))\n\n except KeyboardInterrupt as k:\n print(\"Shutting down.\")\n finally:\n print('closing socket')\n client.close()\nelse:\n print(\"Couldn't Connect!\")\n print(\"Done\")\n","repo_name":"saitta/saserver","sub_path":"telldus/socket_test.py","file_name":"socket_test.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"12"} +{"seq_id":"72414764820","text":"# firestore test\nfrom google.cloud import firestore\nfrom google.cloud.firestore_v1beta1 import GeoPoint\nimport json\n\n\n# 모든 waypoints를 업로드한다. \ndef upload_all_waypoints(db):\n upload_subway_stations(db)\n upload_subway_gates(db)\n upload_bus_stations(db)\n upload_bicycle_stations(db)\n upload_car_stations(db)\n\n\n# 지하철 역 waypoints\ndef upload_subway_stations(db):\n with open('./data/subway_station.json', 'r') as f:\n subway_station = json.loads(f.read())\n\n ref = db.collection('waypoints')\n for n in subway_station:\n lat = subway_station[n]['location']['latitude']\n lon = subway_station[n]['location']['longitude']\n point = GeoPoint(lat, lon)\n metadata = {}\n metadata.update({\n 'fr_code': subway_station[n]['fr_code'],\n 'line_num': subway_station[n]['line_num']\n })\n\n ref.document(n).set({\n 'type': subway_station[n]['type'],\n 'name': subway_station[n]['name'],\n 'address': subway_station[n]['address'],\n 'location': point,\n 'metadata': metadata\n })\n\n\n# 지하철 출구 waypoints\ndef upload_subway_gates(db):\n with open('./data/subway_gate.json', 'r') as f:\n subway_gates = json.loads(f.read())\n\n ref = db.collection('waypoints')\n for n in subway_gates:\n lat = subway_gates[n]['location']['latitude']\n lon = subway_gates[n]['location']['longitude']\n point = GeoPoint(lat, lon)\n\n ref.document(n).set({\n 'type': subway_gates[n]['type'],\n 'name': subway_gates[n]['name'],\n 'address': subway_gates[n]['address'],\n 'location': point\n })\n\n\n# 버스 정류장 waypoints\ndef upload_bus_stations(db):\n with open('./data/bus_station.json', 'r') as f:\n bus_stations = json.loads(f.read())\n\n ref = db.collection('waypoints')\n for n in bus_stations:\n lat = bus_stations[n]['location']['latitude']\n lon = bus_stations[n]['location']['longitude']\n point = GeoPoint(lat, lon)\n metadata = {}\n metadata.update({\n 'ars_id': bus_stations[n]['id']\n })\n\n ref.document(n).set({\n 'type': bus_stations[n]['type'],\n 'name': bus_stations[n]['name'],\n 'address': bus_stations[n]['address'],\n 'location': point,\n 'metadata': metadata\n })\n\n\n# 자전거 정류장 waypoints\ndef upload_bicycle_stations(db):\n with open('./data/bicycle_node.json', 'r') as f:\n bicycle_stations = json.loads(f.read())\n\n ref = db.collection('nodes')\n for n in bicycle_stations:\n lat = bicycle_stations[n]['location']['latitude']\n lon = bicycle_stations[n]['location']['longitude']\n point = GeoPoint(lat, lon)\n metadata = {}\n metadata.update({\n 'id': bicycle_stations[n]['id']\n })\n\n ref.document(n).set({\n 'type': bicycle_stations[n]['type'],\n 'name': bicycle_stations[n]['name'],\n 'address': bicycle_stations[n]['address'],\n 'location': point,\n 'metadata': metadata\n })\n\n\n# 나눔카 정류장 waypoints\ndef upload_car_stations(db):\n with open('./data/car_node.json', 'r') as f:\n car_stations = json.loads(f.read())\n\n ref = db.collection('waypoints')\n for n in car_stations:\n lat = car_stations[n]['location']['latitude']\n lon = car_stations[n]['location']['longitude']\n point = GeoPoint(lat, lon)\n metadata = {}\n metadata.update({\n 'id': car_stations[n]['id']\n })\n\n ref.document(n).set({\n 'type': car_stations[n]['type'],\n 'name': car_stations[n]['name'],\n 'address': car_stations[n]['address'],\n 'location': point,\n 'metadata': metadata\n })\n\n\n# 모든 라인을 업로드\ndef upload_all_lines(db):\n upload_bus_lines(db)\n upload_subway_lines(db)\n\n\ndef upload_bus_lines(db):\n with open('./data/bus_line.json', 'r') as f:\n bus_lines = json.loads(f.read())\n\n ref = db.collection('lines')\n for n in bus_lines:\n ref.document(n).set({\n 'type': bus_lines[n]['type'],\n 'name': bus_lines[n]['name'],\n 'id': bus_lines[n]['id'],\n })\n\n\ndef upload_subway_lines(db):\n with open('./data/subway_line.json', 'r') as f:\n subway_lines = json.loads(f.read())\n\n ref = db.collection('lines')\n for n in subway_lines:\n ref.document(n).set({\n 'type': subway_lines[n]['type'],\n 'name': subway_lines[n]['name'],\n 'id': subway_lines[n]['id'],\n })\n\n\n# 모든 directions를 업로드\ndef upload_all_directions(db):\n # 지하철 링크\n upload_subway_links(db)\n # 지하철 환승\n upload_subway_transfer_links(db)\n # 지하철역 - 출구\n upload_gate_links(db)\n # 지전거\n upload_bicycle_links(db)\n # 버스\n upload_bus_links(db)\n # 걸어서 닿을 수 있는 거리\n upload_bus_walk_link(db)\n\n\n# 지하철 링크\ndef upload_subway_links(db):\n with open('./data/subway_link.json', 'r') as f:\n subway_links = json.loads(f.read())\n\n ref = db.collection('directions')\n for n in subway_links:\n ref.document(n).set(subway_links[n])\n\n\n# 지하철 환승\ndef upload_subway_transfer_links(db):\n with open('./data/subway_transfer.json', 'r') as f:\n subway_transfer_links = json.loads(f.read())\n\n ref = db.collection('directions')\n for n in subway_transfer_links:\n ref.document(n).set(subway_transfer_links[n])\n\n\n# 지하철역 - 출구\ndef upload_gate_links(db):\n with open('./data/gate_link.json', 'r') as f:\n gate_links = json.loads(f.read())\n\n ref = db.collection('directions')\n for n in gate_links:\n ref.document(n).set(gate_links[n])\n\n\n# 지전거\ndef upload_bicycle_links(db):\n with open('./data/bicycle_link.json', 'r') as f:\n bicycle_links = json.loads(f.read())\n\n ref = db.collection('directions')\n for n in bicycle_links:\n ref.document(n).set(bicycle_links[n])\n\n\n# 버스\ndef upload_bus_links(db):\n with open('./data/bus_link.json', 'r') as f:\n bus_links = json.loads(f.read())\n\n ref = db.collection('directions')\n for n in bus_links:\n ref.document(n).set(bus_links[n])\n\n\n# 걸어서 닿을 수 있는 거리\ndef upload_bus_walk_link(db):\n with open('./data/walk_link.json', 'r') as f:\n walk_links = json.loads(f.read())\n\n ref = db.collection('directions')\n for n in walk_links:\n ref.document(n).set(walk_links[n])\n\n\ndef run():\n db = firestore.Client()\n upload_all_waypoints(db)\n upload_all_lines(db)\n upload_all_directions(db)\n","repo_name":"notesquare/zigmap-tool","sub_path":"proj/upload/firestore.py","file_name":"firestore.py","file_ext":"py","file_size_in_byte":6760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"74629136020","text":"import pandas as pd\nimport pyglet\nimport animation_manager\nimport data_functions\nimport headings\nimport read_database\nimport readout\nimport trace\nimport minimap\n\n\npyglet.font.add_directory(\"fonts\")\n\nwindow = pyglet.window.Window(640, 640)\nbatch = pyglet.graphics.Batch()\ngroups = [\n \"background\",\n \"midground\",\n \"foreground\",\n \"overlay\",\n \"GUI_back\",\n \"GUI_mid\",\n \"GUI_front\"\n]\ngroup_dict = {}\nfor i, group_name in enumerate(groups):\n group_dict[group_name] = pyglet.graphics.OrderedGroup(i)\n\nanimation_manager = animation_manager.AnimationManager(window)\n\nstatic_elements = []\n\n\ndef full_lap_follow(session_date, session_name, driver_lap_tcam_tracked_tuples, heading1, heading2, buffer_seconds, master_lap_index=0):\n raw_frames = []\n smooth_frames = []\n tracked_traces = []\n\n # Driver traces\n for driver, lap, tcam, tracked in driver_lap_tcam_tracked_tuples:\n # Get frames\n frame = read_database.read_lap_samples(session_date, session_name, driver, lap, buffer_seconds)\n raw_frame = frame.copy()\n raw_frame = data_functions.interpolate_gaps(raw_frame)\n raw_frame = data_functions.add_animation_time(raw_frame)\n raw_frames.append(raw_frame)\n\n smooth_frame = frame.copy()\n smooth_frame = data_functions.interpolate_gaps(smooth_frame)\n smooth_frame = data_functions.sample_smoothing(smooth_frame)\n smooth_frame = data_functions.add_animation_time(smooth_frame)\n smooth_frames.append(smooth_frame)\n\n tracking_window = data_functions.get_tracking_window(smooth_frame)\n\n # Make traces\n raw_trace = trace.Trace(\n batch=batch, \n group_dict=group_dict, \n radius=5, \n frame=raw_frame, \n animation_manager=animation_manager, \n tracking_window=None, \n tla=False, \n tcam=tcam, \n tail=True\n )\n smooth_trace = trace.Trace(\n batch=batch,\n group_dict=group_dict,\n radius=10,\n frame=smooth_frame,\n animation_manager=animation_manager,\n tracking_window=tracking_window,\n tla=True,\n tcam=tcam,\n tail=False\n )\n\n if tracked: tracked_traces.append(smooth_trace)\n\n animation_manager.tracked_traces = tracked_traces\n\n # Racing line and start/finish marker based on master lap\n trace.RollingRacingLine(\n batch=batch, \n group_dict=group_dict, \n width=3, \n frame=raw_frames[master_lap_index], \n rolling_samples=50, \n animation_manager=animation_manager\n )\n start_finish_point = data_functions.make_start_finish_point(raw_frames[master_lap_index])\n trace.StartFinishPoint(\n world_point=start_finish_point, \n radius=5, \n color=(0, 0, 0), \n batch=batch, \n group_dict=group_dict, \n animation_manager=animation_manager\n )\n\n # Minimap, headings, etc.\n minimap.Minimap((20, 20), 180, raw_frames[master_lap_index], batch, group_dict, animation_manager)\n\n h1 = headings.Heading(window, window.height - 40, 40, heading1, 18, (255, 255, 255, 255), (255, 30, 0), batch, group_dict)\n h2 = headings.Heading(window, window.height - 70, 30, heading2, 14, (255, 255, 255, 255), (0, 0, 0), batch, group_dict)\n for h in (h1, h2): static_elements.append(h)\n\n note_text = \"Note: This animation contains imprecisions due to source telemetry's low sample rate (~5Hz) and significant jitter.\" \\\n \"Small markers follow an interpolated version of the raw signal. Large markers represent a smooth, heavily filtered signal.\"\n \n note_doc = pyglet.text.document.UnformattedDocument(note_text)\n note_doc.set_style(0, 100, attributes={\n \"font_name\": \"TitilliumWeb-Regular\",\n \"font_size\": 9,\n \"color\": (21, 21, 30, 255)\n })\n note_layout = pyglet.text.layout.TextLayout(note_doc, 350, 60, True, batch=batch, group=group_dict[\"GUI_front\"], wrap_lines=True)\n note_layout.position = (250, 10)\n\n static_elements.append(note_layout)\n\n # Lap/sector time readouts\n readout_frames = []\n for driver, lap, tcam, tracked in driver_lap_tcam_tracked_tuples:\n readout_data = read_database.read_times(session_date, session_name, driver, lap)\n readout_data = data_functions.add_readout_animation_times(readout_data)\n readout_frames.append(readout_data)\n readout_frame = pd.concat(readout_frames)\n readout_frame.reset_index(inplace=True, drop=True)\n readout_frame = data_functions.add_readout_deltas(readout_frame)\n\n readout.Readout(readout_frame, (420, 540), animation_manager, batch, group_dict)\n\n\ndriver_lap_tcam_tracked_tuples = [\n (16, 12, False, True),\n (1, 14, False, True),\n (55, 11, True, True),\n (11, 14, True, False),\n (44, 16, True, False),\n (63, 14, False, False),\n (4, 18, True, False),\n (3, 19, False, False)\n]\nfull_lap_follow(\"2022-09-10\", \"Qualifying\", driver_lap_tcam_tracked_tuples, \"Italian Grand Prix 2022\", \"Final Qualifying Laps\", 3, 0)\n\n\npyglet.options[\"vsync\"] = False\npyglet.gl.glClearColor(247/255, 244/255, 241/255, 1)\n\npyglet.clock.schedule(animation_manager.update_traces)\n\n@window.event\ndef on_draw():\n window.clear()\n batch.draw()\n\n\nif __name__ == \"__main__\":\n animation_manager.run()\n pyglet.app.run()","repo_name":"FraserTarbet/F1Tracer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5369,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"12"} +{"seq_id":"9347863104","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[2]:\n\n\n#โปรแกรมที่คำนวณหาผลรวมของจำนวนเต็มบวก\nnums = [3, 5]\nmax = int(input(\"count: \"))\n\nresult = 0\nfor i in range(0,max):\n if i%3 == 0 or i%5 == 0:\n result += i\n\nprint(result)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"pangfoon/pangfoon","sub_path":"Multi3or5.py","file_name":"Multi3or5.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"th","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"42606081524","text":"'''\n不要自己给自己加戏好吗？递归的都没写就去写迭代版本的。没有看到有题解是用迭代实现的。\n最初写了一个迭代的。不符合题意。理解错了。后面又想不出来。\n\nclass Solution:\n def lexicalOrder(self, n: int) -> List[int]:\n if n < 1:\n return []\n res = []\n\n def dfs(cur):\n if cur > n:\n return\n res.append(cur)\n for j in range(10):# 遍历0 ~ 9\n dfs(cur * 10 + j)\n\n for i in range(1, 10):# 遍历1 ~ 9\n dfs(i)\n return res\n看了递归版本的发现你居然不想用栈，来写非递归。哦，简直离谱。\n这题看了下，速度快的提交是用sort做的。这就是个nt题。\n'''\nfrom typing import List\n\n\nclass Solution:\n def lexicalOrder(self, n: int) -> List[int]:\n ans = [1] * n\n fir, x, idx = 1, 1, 0\n if n < 10:\n return [i + 1 for i in range(n)]\n while idx < n:\n while x*10 <= n:\n ans[idx] = x\n idx += 1\n x *= 10\n\n for i in range(x, min(x+11, n+1)):\n ans[idx] = i\n idx += 1\n x += 1\n while x % 10 == 0:\n x //= 10\n return ret\n\nmt = [34, 121]\nbug = [100]\nfor i in mt+bug:\n print('input:', i)\n print(Solution().lexicalOrder(i))\n\n","repo_name":"z472/ProblemLeecode","sub_path":"350-399/386. 字典序排数.py","file_name":"386. 字典序排数.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"191560319","text":"\"\"\"Hacky Methods\n\nThis script contains ehm... hacks that should normally not be used but\nwe use anyways because reasons.\n\"\"\"\n\n\ndef stringify_residue_args(\n _locals: dict, args_name: str = \"args\", kwargs_name: str = \"kwargs\"\n) -> str:\n \"\"\"Stringify and concatenate all *args and **kwargs arguments\n\n Parameters\n ----------\n _locals : dict\n The dictionary obtained by calling locals() inside a function,\n the * and ** args should be named as *args and **kwargs for it\n to work properly.\n\n Note that whatever objects in the *args and **kwargs must be\n have implemented the __str__ methods.\n\n args_name : str, OPTIONAL\n Defaults to 'args', this needs to be assigned if your *args are\n called something else\n\n kwargs_name : str, OPTIONAL\n Defaults to 'kwargs', this needs to be assigned if your **kwargs\n are called something else\n\n Returns\n -------\n str\n A string containing all *args and **kwargs separared with a\n space\n \"\"\"\n\n _args = list(_locals.get(args_name, ()))\n _kwargs = list(_locals.get(kwargs_name, {}).values())\n\n all_args = _args + _kwargs\n\n return \" \".join(map(lambda i: str(i), all_args))\n","repo_name":"Puh00/toru-bot","sub_path":"util/hacks.py","file_name":"hacks.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"18244718086","text":"from datetime import datetime, timedelta\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\nfrom sklearn.base import clone\nfrom graph_traffic.model_selection import timeseries_cv\nfrom graph_traffic.custom_transformer import transform_df\nfrom graph_traffic.config import project_path\nfrom graph_traffic.merge_data import merge_data\nfrom graph_traffic.get_data import get_mmagns\nimport itertools\nfrom time import time\nimport pickle\nimport matplotlib as mpl\nimport numpy as np\n\nmpl.rcParams['axes.grid'] = False\n\n\n\ndef get_combinations(dict_possible):\n keys, values = zip(*dict_possible.items())\n return [dict(zip(keys, v)) for v in itertools.product(*values)]\n\n\ndef try_combinations(data_dict, meteo_combinations, temporal_combinations, pipeline):\n training_datetime = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n training_folder = f\"{project_path}/training_history/regression\"\n\n meteo_values = {}\n temporal_values = {}\n results = {}\n training_time = {}\n alpha = {}\n\n for i, meteo_dict in enumerate(meteo_combinations):\n print(f\"\\n{i}\")\n meteo_values[i] = meteo_dict\n\n mmagns = get_mmagns(meteo_dict)\n\n df = merge_data(data_dict[\"ids_list\"][0], data_dict[\"from_date\"], data_dict[\"to_date\"], data_dict[\"target\"], mmagns)\n\n with open(f\"{training_folder}/{training_datetime}_meteo_values.pkl\", \"wb\") as f:\n pickle.dump(meteo_values, f)\n\n for j, temporal_dict in enumerate(temporal_combinations):\n df_t = transform_df(df, meteo_dict, temporal_dict, data_dict[\"interactions\"], data_dict[\"target\"])\n\n data_size = df_t.shape[0]\n\n train_x = df_t[:int(0.8 * data_size):11, 1:]\n train_y = df_t[:int(0.8 * data_size):11, 0].ravel()\n\n if np.linalg.matrix_rank(train_x) != train_x.shape[1]:\n continue\n\n temporal_values[j] = temporal_dict\n print(j, end=\"\\r\")\n\n start_time = time()\n _, _, results[(i, j)], alpha[(i, j)] = timeseries_cv(pipeline, train_x, train_y, with_previous_timesteps=False,\n with_alpha=True)\n training_time[(i, j)] = time() - start_time\n\n if i == 0:\n with open(f\"{training_folder}/{training_datetime}_temporal_values.pkl\", \"wb\") as f:\n pickle.dump(temporal_values, f)\n\n with open(f\"{training_folder}/{training_datetime}_results.pkl\", \"wb\") as f:\n pickle.dump(results, f)\n\n with open(f\"{training_folder}/{training_datetime}_times.pkl\", \"wb\") as f:\n pickle.dump(training_time, f)\n\n with open(f\"{training_folder}/{training_datetime}_alphas.pkl\", \"wb\") as f:\n pickle.dump(alpha, f)\n\n\ndef train_with_args(data_dict, meteo_dict, temporal_dict, pipeline_class, train_until=None):\n mmagns = get_mmagns(meteo_dict)\n #dates = pd.date_range(data_dict[\"from_date\"], data_dict[\"to_date\"], freq=\"15min\")\n dfs_dict = {}\n ids_used = []\n train_sizes = {}\n test_sizes = {}\n for i in data_dict[\"ids_list\"]:\n print(i, end=\"\\r\")\n dfs_dict[i] = merge_data(i, data_dict[\"from_date\"], data_dict[\"to_date\"], data_dict[\"target\"], mmagns)\n if train_until is None:\n train_sizes[i] = int(0.8 * dfs_dict[i].shape[0])\n test_sizes[i] = int(0.2 * dfs_dict[i].shape[0])\n else:\n train_sizes[i] = len(dfs_dict[i][dfs_dict[i].date <= train_until])\n test_sizes[i] = len(dfs_dict[i][(dfs_dict[i].date > train_until) &\n (dfs_dict[i].date <= train_until + timedelta(days=30))])\n #if dates.intersection(dfs_dict[i].date).empty:\n # continue\n #dates = dates.intersection(dfs_dict[i].date)\n #ids_used.append(i)\n\n for i in data_dict[\"ids_list\"]:\n df = dfs_dict[i]\n #df = df[df.date.isin(dates)]\n dfs_dict[i] = transform_df(df, meteo_dict, temporal_dict, data_dict[\"interactions\"], data_dict[\"target\"])\n\n #data_size = dfs_dict[i].shape[0]\n\n #all_hours = dates.hour + dates.minute / 60\n\n #test_dates = all_hours.values[int(0.8 * data_size):]\n\n estimators = {}\n maes = {}\n mses = {}\n for sensor_id in data_dict[\"ids_list\"]:\n print(sensor_id)\n train_x = dfs_dict[sensor_id][:train_sizes[sensor_id], 1:]\n train_y = dfs_dict[sensor_id][:train_sizes[sensor_id], 0].ravel()\n\n test_x = dfs_dict[sensor_id][train_sizes[sensor_id]:train_sizes[sensor_id]+test_sizes[sensor_id], 1:]\n test_y = dfs_dict[sensor_id][train_sizes[sensor_id]:train_sizes[sensor_id]+test_sizes[sensor_id], 0].ravel()\n pipeline = clone(pipeline_class)\n print(\"Shape of train predictors and labels:\", train_x.shape, train_y.shape)\n pipeline.fit(train_x, train_y)\n\n estimators[sensor_id] = pipeline\n\n test_pred = pipeline.predict(test_x)\n maes[sensor_id] = mean_absolute_error(test_y, test_pred)\n mses[sensor_id] = mean_squared_error(test_y, test_pred)\n print(\"MAE:\", maes[sensor_id])\n print(\"MSE:\", mses[sensor_id])\n\n return ids_used, estimators, dfs_dict, maes, mses\n\n\ndef coefs_plot(ids_used, estimators, column_names, title=\"Model coefficients\"):\n fig, axs = plt.subplots(1, len(ids_used), figsize=(8, 10), sharey=True)\n for j, i in enumerate(ids_used):\n ax = axs[j]\n coefs = estimators[i][-1].coef_\n pd.DataFrame(zip(coefs, column_names)).iloc[::-1].rename(columns={0: \"importances\", 1: \"features\"}).plot.barh(\n x=1, ax=ax, legend=False)\n ax.set_title(f\"{i}\")\n fig.suptitle(title)\n plt.show()\n\n","repo_name":"elena-sg/madrid-traffic","sub_path":"graph_traffic/graph_traffic/regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":5712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"21109982174","text":"'''\n문자열 자료형\n\n하나의 문자, 혹은 여러개의 문자가 결합된 문자열을 포함\n\n문자열은 4개의 표현 방식이 존재\n1. '' 작은 따옴표\n2. \"\" 큰 따옴표\n3. ''' ''' 작은 따옴표 3개를 이용하여 둘러싸기\n4. \"\"\" \"\"\" 큰 따옴표 3개를 이용하여 둘러싸기\n\n'''\n\nsentence = 'my name is eddie'\nsentence = \"my name is eddie\"\n\n#따옴표를 중복 시키고 싶은 경우는 \\(백슬래시)를 붙혀 사용\nsentence = 'it\\'s a beautiful day'\nsentence = \"he said \\\"hello\\\" to me\"\n\n#여러 줄을 입력하고 싶으면 \\n을 사용\nsentence = 'my\\n name\\n is\\n eddie'\nsentence = \"my\\n name\\n is\\n eddie\"\n\n#혹은 3개를 겹쳐 쓰자\nsentence = '''my\nname\nis\neddie'''\nsentence = \"\"\"my\nname\nis\neddie\"\"\"\n\n'''\n문자열 더하기\n\n문자열은 + 기호를 사용하여 서로 붙힐 수 있고,\n * 기호를 써서 여러번 출력하는것도 가능하다\n'''\n\nhead = 'hello'\ntail = 'world'\nhead + tail\ntail + head\nhead * 2\n\n'''\n콘솔 창에 문자열을 출력 하기 위해서 \nprint() 함수를 이용한다\n\n'''\n\n#print 사용 예\nprint('my name is eddie')\n\nprint('=' * 20)\nprint('program start')\nprint('=' * 20)\n\n'''\n문자열 슬라이싱\n\n문자열은 순서를 갖는다\nhello 와 같은 문자열이 있을때 \nh = 0번째 <---> -5번째\ne = 1번째 <---> -4번째\nl = 2번째 <---> -3번째\nl = 3번째 <---> -2번째\no = 4번째 <---> -1번째\n와 같이 순서는 항상 0번 부터 시작하고,\n뒤에서 부터 셀 때는 -1부터 시작한다\n'''\n\na = 'hello'\nprint(a[2])\nprint(a[-2])\n\n'''\n: 을 이용하여 슬라이싱\n\na[시작 번호:끝 번호] 를 이용하여 슬라이싱 가능\na[0:3] 으로 슬라이싱 하면\n0 <= a < 3 의 범위로 슬라이싱됨\n\na[시작 번호:] 인 경우 시작 부터 마지막까지\na[:끝 번호] 인 경우 처음부터 끝 번호 까지\n'''\n\na = 'hello world'\nprint(a[0:5])\nprint(a[5:])\nprint(a[:5])\n\n'''\n포맷팅\n\n문자열 내에 값을 삽입하는것\n예를 들어 오늘의 날짜를 출력하는 경우\n매일 날짜가 변하는데 이를 전부 다른 문자열로 하는것은 비효율적\n따라서 포맷팅을 사용하는 것이 편리\n'''\n\n#변수 day에 요일만 바꿔주면 된다\nday = 'monday'\nstring = f\"today is {day}\"\nprint(string)\n\n#여러개의 변수를 대입하는 방법\n\nmonth = 3\nday = 5\nstring = f\"오늘은 {month}월 {day}일 입니다\"\nprint(string)\n\n'''\n리스트\n\n리스트란 자료들의 모음\n\n예를 들어 학생들의 이름을 한데 묶어 처리하고 싶을때 리스트를 만들어 처리할 수 있다\nname = [andy, barbie, cathie, danny, eddie]처럼 이름을 하나의 리스트에 담을 수 있다\n\n문자열과 동일하게 요소는 0부터 시작, 마직막에서 셀때는 -1에서 시작한다\n\n리스트에는 리스트를 넣을 수도 있는데 그것을 다중 리스트라 한다\n예를 들어 학생들의 이름을 넣고 싶은데 학급마다 다르게 저장하고 싶은 경우\n\nname = [['andy', 'barbie'],['cathie', 'danny'], ['eddie']]\n처럼 리스트 안에 리스트를 넣을 수 있다\n이때 name 이라는 리스트의 0번째 요소는 ['andy', 'barbie'] 두명의 이름을 담고있는 리스트이다\n그럼 andy의 이름 만을 뽑고 싶은 경우는 어떡하면 될까\nname[0][0] 과 같이 불러주면 name이라는 0번재 요소의 리스트에서 0번째 요소를 불러오게 된다\n'''\n\nname = [['andy', 'barbie'],['cathie', 'danny'], ['eddie']]\nprint(name[0])\nprint(name[0][0])\n\n'''\n리스트 활용하기\n\n리스트끼리 더하기\n\n리스트는 문자열과 마찬가지로 서로 합칠 수 있다\n예를 들어 \na=[1,2,3]\nb=[4,5.6]\n과 같이 두개의 리스트가 존재할때 a+b를 하면\n[1,2,3,4,5,6]처럼 두 리스트를 합치게 된다\n\n또 *를 써서 같은 리스트를 반복시키는 것도 가능하다\n'''\n\na=[1,2,3]\nb=[4,5,6]\nprint(a+b)\nprint(a*2)\n\n#슬라이싱은 문자열과 동일\na=[1,2,3]\nprint(a[2])\na[2] = 4\nprint(a)\n\n#1:2 인 경우 2는 포함 되지 않으므로 1번 요소만 바뀐다\n#그러나 1:2 로 슬라이싱 하는것과 a[1]로 슬라이싱 하는것은 결과가 다르다\na[1:2] = ['a', 'b', 'c']\nprint(a)\na=[1,2,4]\na[1] = ['a', 'b', 'c']\nprint(a)\n\n'''\n리스트의 요소 삭제, 추가\n\n삭제 하는 경우 : 빈 리스트를 이용하거나 del이라는 함수를 사용\n추가 하는 경우 : append 라는 함수를 이용\n'''\n\na=[1,2,3]\na[1:2] = []\nprint(a)\nb=[1,2,3]\ndel b[1]\nprint(b)\n\n#append는 마직 요소에 값을 삽입\n#pop은 마지막 요소를 제거\nc=[1,2,3]\nc.append(4)\nprint(c)\nc.pop()\nprint(c)\n\n'''\n튜플 \n\n튜플은 리스트와 동일하나 안에 값을 바꿀 수 없다\n'''\n\n#이러면 에러남\na=(1,2,3)\ndel a[1]\n","repo_name":"wiggleji/python-basics","sub_path":"variables/var_type_day2.py","file_name":"var_type_day2.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"7418309882","text":"import logging\nimport sys\nfrom typing import Optional\n\nimport click\n\nsys.path.append(\"..\")\n\nfrom pulse_jig.config import settings\nfrom lib.jig_client import JigClient\nfrom lib.ui.jig_gui import JigGUI\nfrom lib.provisioner.provisioner import Provisioner\nfrom lib.registrar import Registrar\nfrom lib.pulse_manager import PulseManager\n\n\ndef _configure_logging(debug):\n logging.basicConfig(\n level=logging.DEBUG if debug else logging.INFO,\n format=\"[%(asctime)s] [%(levelname)-5s] [%(name)s.%(funcName)s:%(lineno)d] %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n )\n logging.getLogger(\"transitions\").setLevel(logging.INFO if debug else logging.ERROR)\n logging.getLogger(\"botocore\").setLevel(logging.WARN if debug else logging.ERROR)\n\n\n@click.command()\n@click.option(\"--dev\", default=lambda: JigClient.find_device())\n@click.option(\"--reset-pin\", default=6)\n@click.option(\"--pcb-sense-pin\", default=5)\n@click.option(\"--xdot-volume\", default=\"/media/pi/XDOT\")\ndef main(dev: Optional[str], reset_pin: int, pcb_sense_pin: int, xdot_volume: str):\n if dev is None:\n print(\"Could not detect device\")\n exit(1)\n\n _configure_logging(settings.app.debug)\n\n registrar = Registrar()\n registrar.network_check()\n\n pulse_manager = PulseManager(reset_pin, pcb_sense_pin, xdot_volume)\n provisioner_factory = Provisioner.build_factory(registrar, pulse_manager, dev)\n\n app = JigGUI()\n app.run(provisioner_factory, registrar)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"syamkg/pulse-production-jig-app","sub_path":"pulse_jig/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"69911399063","text":"def primtal(talet):\n är_primtal = True\n for k in range(2, talet):\n if talet % k == 0:\n är_primtal = False\n return är_primtal\n\n# Test\nt = int(input('Skriv ett tal: '))\nif primtal(t):\n print('Primtal')\nelse:\n print('Ej primtal') ","repo_name":"Anton-L-GitHub/Learning","sub_path":"Python/1_PROJECTS/Python_bok/Prov (övningar)/Kap8/ovn8-4.py","file_name":"ovn8-4.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"sv","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"39186427177","text":"#!/usr/bin/env python3\n# -*- encoding=utf-8 -*-\n\n# description:\n# author:jack\n# create_time: 2018/1/2\n\nfrom dueros.directive.BaseDirective import BaseDirective\nimport logging\n\nclass LaunchApp(BaseDirective):\n \"\"\"\n 用于调用app的指令类\n \"\"\"\n\n def __init__(self, appName='', packageName='', deepLink=''):\n '''\n 三者必须传一个\n :param appName: 应用名称\n :param packageName: 应用包\n :param deepLink: 打开应用指定功能\n '''\n\n super(LaunchApp, self).__init__('AppLauncher.LaunchApp')\n if not appName and not packageName and not deepLink:\n print('appName packageName deepLink 必须要有一个')\n else:\n self.data = dict({\n 'appName': appName,\n 'packageName': packageName,\n 'deepLink': deepLink,\n 'token': self.genToken()\n },**self.data)\n\n def setAppName(self, appName):\n\n if appName:\n self.data['appName'] = appName\n return self\n\n def setPackageName(self, packageName):\n\n if packageName:\n self.data['packageName'] = packageName\n return self\n\n def setDeepLink(self, deepLink):\n\n if deepLink:\n self.data['deepLink'] = deepLink\n return self\n\nif __name__ == '__main__':\n\n launchApp = LaunchApp('', '', '2')\n launchApp.setDeepLink('dd')\n print(launchApp.data)\n\n","repo_name":"Totlehuang/bot-sdk-python","sub_path":"dueros/directive/AppLauncher/LaunchApp.py","file_name":"LaunchApp.py","file_ext":"py","file_size_in_byte":1450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"12"} +{"seq_id":"5384605499","text":"import tqdm\nimport os\nimport numpy as np\nimport random\nimport pandas as pd\nimport random\n\n# DiveFace data are divided in six folders (demographic groups)\nfolders = ['AM4K', 'AW4K', 'BM4K', 'BW4K', 'CM4K', 'CW4K' ]\n\n\ndef _get_label_coded(label):\n\tsex = 0 if label[1] == 'M' else 1\n\teth = 0 if label[0] == 'A' else 1 if label[0] == 'B' else 2\n\treturn [sex, None, eth]\n\n\ndef get_embeddings(embeddings_path):\n\tembeddings = []\n\tlabels = []\n\tfilenames = []\n\tusers = []\n\t# return embeddings and the list of filenames, in the same df\n\tfor path, subdirs, files in tqdm.tqdm(os.walk(embeddings_path)):\n\t\tfor name in [f for f in files if f.find('.npy') >= 0]:\n\t\t\t# store embeddings normalized with L2-norm\n\t\t\tembedding = np.load(os.path.join(path, name))\n\t\t\tembedding = embedding / np.linalg.norm(embedding, ord=2)\n\t\t\tembeddings.append(embedding)\n\t\t\t# get the user name\n\t\t\tuser = path.split('\\\\')[-1]\n\t\t\tusers.append(user)\n\t\t\t# get the labels (contained in the name of the second last folder)\n\t\t\tlabel = _get_label_coded(path.split('\\\\')[-2])\n\t\t\tlabels.append(label)\n\t\t\t# remove file extension (npy)\n\t\t\tfilename = name[:-4]\n\t\t\tfilenames.append(filename)\n\n\t# create DataFrame with filenames, embeddings, users, and files\n\tembeddings = np.array(embeddings)\n\tfiles_embeddings_df = pd.DataFrame(embeddings, columns=['f'+str(i) for i in range(len(embedding))])\n\tfiles_embeddings_df['filename'] = filenames\n\tfiles_embeddings_df['users'] = users\n\n\tlabels = np.array(labels)\n\tlabels_df = pd.DataFrame(labels, columns=['sex', 'age', 'ethnicity'])\n\tfiles_embeddings_df = pd.concat([files_embeddings_df, labels_df], axis=1)\n\n\treturn files_embeddings_df, len(embedding)\n\n\ndef get_diveface_df(embeddings_path, seed, save_files=False, limit_size=False):\n\tordered_filenames_lab_df, length_embeddings = get_embeddings(embeddings_path)\n\tif limit_size:\n\t\trandom.seed(seed)\n\t\tpats_to_keep = []\n\t\t# specific for diveface\n\t\tfor sex_code in range(2):\n\t\t\tfor eth_code in range(3):\n\t\t\t\ttmp = ordered_filenames_lab_df.loc[(ordered_filenames_lab_df['sex'] == sex_code) &\n\t\t\t\t\t\t\t\t\t\t\t\t (ordered_filenames_lab_df['ethnicity'] == eth_code)]\n\t\t\t\tpats = list(tmp['users'].unique())\n\t\t\t\trandom.shuffle(pats)\n\t\t\t\tpats_to_keep += pats[:1000]\n\n\t\tordered_filenames_lab_df = ordered_filenames_lab_df[ordered_filenames_lab_df['users'].isin(pats_to_keep)]\n\n\tordered_filenames_lab_df = ordered_filenames_lab_df.sample(frac=1, random_state=seed).reset_index(drop=True)\n\n\tif save_files:\n\t\tordered_filenames_lab_df.to_csv('data/diveface_df.csv', index=False)\n\n\treturn ordered_filenames_lab_df, length_embeddings\n\n\ndef get_sb_train_test_indexes(diveface_df, seed, length_embedding, spl=0.7):\n\ttrain_indexes = []\n\ttest_indexes = []\n\trandom.seed(seed)\n\t# here we do not need the embeddings\n\tdiveface_df = diveface_df.drop(['f' + str(i) for i in range(length_embedding)], axis=1)\n\t# to maintain the original indexes\n\tdiveface_df['initialIndex'] = diveface_df.index.values\n\t# consider one random sample for each subject\n\tdf_gby = diveface_df.groupby('users').apply(lambda x: x.sample(1, random_state=seed)).reset_index(drop=True)\n\t# specific for diveface\n\tfor sex_code in range(2):\n\t\tfor eth_code in range(3):\n\t\t\tref = df_gby.loc[(df_gby['sex']==sex_code) & (df_gby['ethnicity']==eth_code)]\n\t\t\tindexes = list(ref['initialIndex'])\n\t\t\trandom.shuffle(indexes)\n\t\t\ttrain_indexes += indexes[:int(spl * len(indexes))]\n\t\t\ttest_indexes += indexes[int(spl * len(indexes)):]\n\n\trandom.shuffle(train_indexes)\n\treturn train_indexes, test_indexes\n\n\ndef get_verification_indexes(diveface_df, seed, length_embedding, genuine=3):\n\tdict_verification_indexes = {}\n\trandom.seed(seed)\n\t# here we do not need the embeddings\n\tdiveface_df = diveface_df.drop(['f' + str(i) for i in range(length_embedding)], axis=1)\n\t# consider three random sample for each subject\n\tdf_gby = diveface_df.groupby('users').apply(lambda x: x.sample(min(genuine, len(x)), random_state=seed))\n\n\tfor ax, _ in df_gby.iterrows():\n\t\tuser = ax[0]\n\t\tinitial_index = ax[1]\n\t\tif user not in list(dict_verification_indexes.keys()):\n\t\t\tdict_verification_indexes[user] = []\n\t\tdict_verification_indexes[user].append(initial_index)\n\n\treturn dict_verification_indexes\n\n\ndef get_x_ready(df, length_embeddings):\n\t# get the embeddings\n\tx = df[['f'+str(i) for i in range(length_embeddings)]]\n\tx = x.to_numpy()\n\treturn x\n\n\ndef get_y_ready(df, labels):\n\t# get the labels\n\ty = df[labels]\n\ty = y.to_numpy()\n\treturn y\n","repo_name":"otroshi/multi-ive","sub_path":"evaluation/load_diveface.py","file_name":"load_diveface.py","file_ext":"py","file_size_in_byte":4389,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"71907552982","text":"import os\nimport crud, models, schemas\n\nfrom database import SessionLocal\nfrom fastapi import FastAPI, Depends, HTTPException, Request, Form\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.responses import HTMLResponse\nfrom sqlalchemy.orm import Session\nfrom typing import List\n\n\napp = FastAPI(root_path=os.environ['ROOT_PATH'])\n\norigins = ['*']\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=['*'],\n allow_headers=['*']\n)\n\ndef get_db():\n db = SessionLocal()\n try:\n yield db\n finally:\n db.close()\n\n\n# MAIN\n\n@app.get(\"/\")\ndef root():\n return {\"message\": \"Welcome to Smart Inventory\"}\n\n# USERS\n\n@app.get(\"/users/\", response_model=List[schemas.User])\ndef read_all_users(db: Session = Depends(get_db)):\n return crud.get_all_users(db)\n\n@app.get(\"/user/{uid}/\", response_model=schemas.User)\ndef read_user_by_uid(uid: str, db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, uid)\n if db_user is None:\n raise HTTPException(status_code=404, detail=\"User not found\")\n return db_user\n\n@app.post(\"/user/\", response_model=schemas.User)\ndef create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, user.uid)\n if db_user:\n raise HTTPException(status_code=400, detail=\"User already exists\")\n return crud.create_user(db=db, user=user)\n\n@app.delete(\"/user/{uid}/\")\ndef delete_user_by_uid(uid: str, db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, uid)\n if db_user is None:\n raise HTTPException(status_code=404, detail=\"User not found\")\n db.delete(db_user)\n db.commit()\n return {'Deleted user with uid': uid}\n\n# CABINETS\n\n@app.get(\"/cabinets/\", response_model=List[schemas.Cabinet])\ndef read_all_cabinets(db: Session = Depends(get_db)):\n return crud.get_all_cabinets(db)\n\n@app.get(\"/cabinet/{id}/\", response_model=schemas.Cabinet)\ndef read_cabinet_by_id(id: str, db: Session = Depends(get_db)):\n db_cabinet = crud.get_cabinet_by_id(db, id)\n if db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"Cabinet not found\")\n return db_cabinet\n\n@app.post(\"/cabinet/\", response_model=schemas.Cabinet)\ndef create_cabinet(cabinet: schemas.CabinetCreate, db: Session = Depends(get_db)):\n db_cabinet = crud.get_cabinet_by_id(db, cabinet.id)\n if db_cabinet:\n raise HTTPException(status_code=400, detail=\"Cabinet already exists\")\n return crud.create_cabinet(db, cabinet)\n\n@app.delete(\"/cabinet/{id}/\")\ndef delete_cabinet_by_id(id: str, db: Session = Depends(get_db)):\n db_cabinet = crud.get_cabinet_by_id(db, id)\n if db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"Cabinet not found\")\n db.delete(db_cabinet)\n db.commit()\n return {'Deleted cabinet with id': id}\n\n# CATEGORIES\n\n@app.get(\"/categories/\", response_model=List[schemas.Category]) # reads all categories\ndef read_all_categories(db: Session = Depends(get_db)):\n return crud.get_all_categories(db)\n\n@app.get(\"/categories/root/\", response_model=List[schemas.Category]) # reads all root categories\ndef read_root_categories(db: Session = Depends(get_db)):\n return crud.get_root_categories(db)\n\n@app.get(\"/category/{id}/\", response_model=schemas.Category)\ndef read_category_by_id(id: str, db: Session = Depends(get_db)):\n db_category = crud.get_category_by_id(db, id)\n if db_category is None:\n raise HTTPException(status_code=404, detail=\"Category not found\")\n return db_category\n\n@app.get(\"/categories/subcategories/{parent_id}/\", response_model=List[schemas.Category]) # reads all sub-categories of a category\ndef read_sub_categories(parent_id: int, db: Session = Depends(get_db)):\n parent_category = crud.get_category_by_id(db, parent_id)\n if not parent_category:\n raise HTTPException(status_code=404, detail=\"Parent category not found\")\n return crud.get_sub_categories(db, parent_id)\n\n@app.post(\"/category/\", response_model=schemas.Category)\ndef create_category(category: schemas.CategoryCreate, db: Session = Depends(get_db)):\n db_category = crud.get_category_by_title(db, category.title)\n if db_category:\n raise HTTPException(status_code=400, detail=\"Category already exists\")\n if category.parent_id is not None:\n db_parent_category = crud.get_category_by_id(db, category.parent_id)\n if db_parent_category is None:\n raise HTTPException(status_code=404, detail=\"Parent category not found\")\n return crud.create_category(db, category)\n\n@app.delete(\"/category/{id}/\")\ndef delete_category_by_id(id: int, db: Session = Depends(get_db)):\n db_category = crud.get_category_by_id(db, id)\n if db_category is None:\n raise HTTPException(status_code=404, detail=\"Category not found\")\n db.delete(db_category)\n db.commit()\n return {'Deleted category with id': id}\n\n# ITEMS\n\n@app.get(\"/items/\", response_model=List[schemas.Item])\ndef read_all_items(db: Session = Depends(get_db)):\n return crud.get_all_items(db)\n\n@app.get(\"/item/{id}/\", response_model=schemas.Item)\ndef read_item_by_id(id: int, db: Session = Depends(get_db)):\n db_item = crud.get_item_by_id(db, id)\n if db_item is None:\n raise HTTPException(status_code=404, detail=\"Item not found\")\n return db_item\n\n@app.get(\"/categories/{category_id}/items/\", response_model=List[schemas.Item]) # reads all items under a category\ndef read_all_items(category_id: int, db: Session = Depends(get_db)):\n category = crud.get_category_by_id(db, category_id)\n if not category:\n raise HTTPException(status_code=404, detail=\"Category not found\")\n return crud.get_items_by_category_id(db, category_id)\n\n@app.post(\"/item/\", response_model=schemas.Item)\ndef create_item(item: schemas.ItemCreate, db: Session = Depends(get_db)):\n if item.category_id is not None:\n db_category = crud.get_category_by_id(db, item.category_id)\n if not db_category:\n raise HTTPException(status_code=404, detail=\"Category not found\")\n db_item = crud.get_item_by_title(db, item.title)\n if db_item:\n raise HTTPException(status_code=400, detail=\"Item already exists\")\n return crud.create_item(db, item)\n\n@app.delete(\"/item/{id}/\")\ndef delete_item_by_id(id: int, db: Session = Depends(get_db)):\n db_item = crud.get_item_by_id(db, id)\n if db_item is None:\n raise HTTPException(status_code=404, detail=\"Item not found\")\n db.delete(db_item)\n db.commit()\n return {'Deleted item with id': id}\n\n# ORDER REQUESTS\n\n@app.get(\"/order-requests/\", response_model=List[schemas.OrderRequest])\ndef read_all_order_requests(db: Session = Depends(get_db)):\n return crud.get_all_order_requests(db)\n\n@app.get(\"/order-requests/item/{id}/\", response_model=List[schemas.OrderRequest])\ndef read_order_requests_by_item_id(id: int, db: Session = Depends(get_db)):\n db_item = crud.get_item_by_id(db, id)\n if db_item is None:\n raise HTTPException(status_code=404, detail=\"Item not found\")\n return crud.get_order_requests_by_item_id(db, id)\n\n@app.get(\"/order-requests/user/{uid}/\", response_model=List[schemas.OrderRequest])\ndef read_order_requests_by_user_id(uid: str, db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, uid)\n if db_user is None:\n raise HTTPException(status_code=404, detail=\"User not found\")\n return crud.get_order_requests_by_user_id(db, uid)\n\n@app.get(\"/order-requests/state/{state}/\", response_model=List[schemas.OrderRequest])\ndef read_order_requests_by_state(state: int, db: Session = Depends(get_db)):\n return crud.get_order_requests_by_state(db, state)\n\n@app.post(\"/order-request/\", response_model=schemas.OrderRequest)\ndef create_order_request(order_request: schemas.OrderRequestCreate, db: Session = Depends(get_db)):\n db_item = crud.get_item_by_id(db, order_request.item_id)\n db_user = crud.get_user_by_uid(db, order_request.user_id)\n if db_item is None or db_user is None:\n raise HTTPException(status_code=404, detail=\"Item or user not found\")\n db_order_request = crud.get_order_requests_by_item_and_user_id(db, order_request.item_id, order_request.user_id)\n if db_order_request:\n raise HTTPException(status_code=400, detail=\"Order already requested by this user\")\n return crud.create_order_request(db, order_request)\n\n@app.delete(\"/order-request/{id}/\")\ndef delete_order_request_by_id(id: int, db: Session = Depends(get_db)):\n db_order_request = crud.get_order_request_by_id(db, id)\n if db_order_request is None:\n raise HTTPException(status_code=404, detail=\"Order request not found\")\n db.delete(db_order_request)\n db.commit()\n return {'Deleted order request with id': id}\n\n# STORAGE UNITS\n\n@app.get(\"/storage-units/\", response_model=List[schemas.StorageUnit])\ndef read_all_storage_units(db: Session = Depends(get_db)):\n return crud.get_all_storage_units(db)\n\n@app.get(\"/storage-unit/{id}/\", response_model=schemas.StorageUnit)\ndef read_storage_unit_by_id(id: int, db: Session = Depends(get_db)):\n db_storage_unit = crud.get_storage_unit_by_id(db, id)\n if db_storage_unit is None:\n raise HTTPException(status_code=404, detail=\"Storage unit not found\")\n return db_storage_unit\n\n@app.get(\"/storage-units/cabinet/{cabinet_id}/\", response_model=List[schemas.StorageUnit])\ndef read_storage_units_by_cabinet_id(cabinet_id: str, db: Session = Depends(get_db)):\n db_cabinet = crud.get_cabinet_by_id(db, cabinet_id)\n if db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"Cabinet not found\")\n return crud.get_storage_units_by_cabinet_id(db, cabinet_id) \n\n@app.post(\"/storage-unit/\", response_model=schemas.StorageUnit)\ndef create_storage_unit(storage_unit: schemas.StorageUnitCreate, db: Session = Depends(get_db)):\n db_item = crud.get_item_by_id(db, storage_unit.item_id)\n if db_item is None:\n raise HTTPException(status_code=404, detail=\"Item not found\")\n if storage_unit.cabinet_id is not None:\n db_cabinet = crud.get_cabinet_by_id(db, storage_unit.cabinet_id)\n if db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"Cabinet not found\")\n db_storage_unit = crud.get_storage_unit_by_id(db, storage_unit.id)\n if db_storage_unit:\n raise HTTPException(status_code=400, detail=\"Storage unit ID already assigned\")\n return crud.create_storage_unit(db, storage_unit)\n\n@app.delete(\"/storage-unit/{id}/\")\ndef delete_storage_unit_by_id(id: int, db: Session = Depends(get_db)):\n db_storage_unit = crud.get_storage_unit_by_id(db, id)\n if db_storage_unit is None:\n raise HTTPException(status_code=404, detail=\"Storage unit not found\")\n db.delete(db_storage_unit)\n db.commit()\n return {'Deleted storage unit with id': id}\n\n# CABINETS UNLOCK ATTEMPTS\n\n@app.get(\"/unlock-attempts/\", response_model=List[schemas.CabinetUnlockAttempt])\ndef read_all_unlock_attempts(db: Session = Depends(get_db)):\n return crud.get_all_unlock_attempts(db)\n\n@app.get(\"/unlock-attempts/cabinet/{cabinet_id}/\", response_model=List[schemas.CabinetUnlockAttempt])\ndef read_unlock_attempts_by_cabinet_id(cabinet_id: str, db: Session = Depends(get_db)):\n db_cabinet = crud.get_cabinet_by_id(db, cabinet_id)\n if db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"Cabinet not found\")\n return crud.get_unlock_attempts_by_cabinet_id(db, cabinet_id)\n\n@app.get(\"/unlock-attempts/user/{uid}/\", response_model=List[schemas.CabinetUnlockAttempt])\ndef read_unlock_attempts_by_user_id(uid: str, db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, uid)\n if db_user is None:\n raise HTTPException(status_code=404, detail=\"User not found\")\n return crud.get_unlock_attempts_by_user_id(db, uid)\n\n@app.get(\"/unlock-attempts/cabinet/{cabinet_id}/user/{uid}/\", response_model=List[schemas.CabinetUnlockAttempt])\ndef read_unlock_attempts_by_cabinet_and_user_id(cabinet_id, uid: str, db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, uid)\n db_cabinet = crud.get_cabinet_by_id(db, cabinet_id)\n if db_user is None or db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"User or cabinet not found\")\n return crud.get_unlock_attempts_by_cabinet_and_user_id(db, cabinet_id, uid)\n\n@app.post(\"/unlock-attempt/\", response_model=schemas.CabinetUnlockAttempt)\ndef create_unlock_attempt(unlock_attempt: schemas.CabinetUnlockAttemptCreate , db: Session = Depends(get_db)):\n db_user = crud.get_user_by_uid(db, unlock_attempt.user_id)\n db_cabinet = crud.get_cabinet_by_id(db, unlock_attempt.cabinet_id)\n if db_user is None or db_cabinet is None:\n raise HTTPException(status_code=404, detail=\"User or cabinet not found\")\n return crud.create_unlock_attempt(db, unlock_attempt)\n\n@app.delete(\"/unlock-attempts/days/{n}/\")\ndef delete_unlock_attempts_older_than(n: int, db: Session = Depends(get_db)):\n db.execute(f\"delete from cabinets_unlock_attempts where date < now() - interval '{n} days';\")\n db.commit()\n return {'Deleted all cabinets unlock attempts older than number of days': n}","repo_name":"DeVinci-Innovation-Center/SMART-INVENTORY-DB-API","sub_path":"API/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"3269883429","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport time\nimport hashlib\nimport requests\n\n\nclass AttendanceUtil(object):\n\n def calculate_sign(self):\n \"\"\"\n 获取md5加密签名\n :return: 返回签名\n \"\"\"\n timestamp = self.get_timestamp()\n app_secret, app_key = \"47F9B660196F0F23B55908786E8A327B\", \"E1B559D014E90F7EF8047949A7440F3E\"\n md5_val = hashlib.md5((app_key + timestamp + app_secret).lower().encode(\"utf-8\")).hexdigest()\n return md5_val, timestamp\n\n def app_auth(self, host, path):\n \"\"\"\n 获取token\n :param host:请求域名\n :param path:请求路径\n :return: 返回token\n \"\"\"\n url = host + path\n data = {\n 'app_id': '15676497800668552d',\n 'app_key': 'E1B559D014E90F7EF8047949A7440F3E',\n 'timestamp': self.calculate_sign()[-1],\n 'sign': self.calculate_sign()[0]\n }\n rs = requests.post(url, json=data)\n return rs.json()['data']\n\n def get_timestamp(self):\n \"\"\"\n 获取时间戳\n :return: 返回时间戳\n \"\"\"\n timestamp = str(round(time.time() * 1000))\n return timestamp\n\n\nif __name__ == '__main__':\n host = 'http://attendance.yooticloud.cn/api/v1/'\n path = 'app/auth'\n au = AttendanceUtil()\n token = au.app_auth(host, path)\n print(token)\n","repo_name":"caijianwei01/tlischool_robotframework_api","sub_path":"verification_library/attendance_util.py","file_name":"attendance_util.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"19832896604","text":"import tkinter as tk\nfrom tkinter.filedialog import askopenfilename, asksaveasfilename\nfrom tkinter import messagebox\nfrom PIL import ImageTk, Image\n\n#### Globals\nNRM = \"normal\"\nVRT = \"vertical\"\nHOR = \"horizontal\"\n\nclass Playground:\n ##### Playground class, duhh\n \n def __init__(self, master) -> None:\n ##### Init\n \n ##### Save the reference to master\n self.master = master\n\n ##### Ah, yes, this too\n self.isPressed = False\n self.mode = NRM\n\n ##### Make and Pack the import button\n self.btn_import = tk.Button(master, text=\"Import\", command=self.import_)\n self.btn_import.configure(font=(\"Arial\", 15), width=17, bg=\"#1f1f1f\", fg=\"#f0f0f0\")\n self.btn_import.pack()\n \n def resize(self, img):\n #### This function resizes the original object which is passed!\n\n screen_width = self.master.winfo_screenwidth()\n screen_height = self.master.winfo_screenheight()\n width, height = img.size\n\n if width>height and width > 0.8*screen_width:\n new_width = 0.8*screen_width\n new_height = new_width * height / width\n elif height > 0.8*screen_height:\n new_height = 0.8*screen_height\n new_width = new_height * width / height\n else:\n new_width = width\n new_height = height\n self.resizefactor = new_width/width\n return height, width, img\n \n self.resizefactor = new_width/width\n #### I think ANTIALIAS keeps the quality of the image. otherwise performance. not sure\n img = img.resize((int(new_width), int(new_height)), Image.ANTIALIAS)\n\n # check if resize is neccesary and return resized PIL Image object\n return new_height, new_width, img\n \n \n def import_(self):\n ##### Get filepath\n filepath = askopenfilename(\n filetypes=[(\"Image Files\", \"*.png\"),\n (\"Image Files\", \"*.jpg\"),\n (\"Image Files\", \"*.jpeg\"),\n (\"All Files\", \"*.*\")]\n )\n if not filepath:\n return\n ##### save reference to filefapth\n self.img_path = filepath\n\n ##### Get rid of old import button.\n self.btn_import.destroy()\n self.master.configure(padx=5, pady=5)\n\n ##### Make tk_img object with resized size and keep its reference\n self.resized_height, self.resized_width, resized_img = self.resize(Image.open(filepath))\n self.tk_img = ImageTk.PhotoImage(resized_img)\n\n ##### Make a canvas with same dimentions as of resizeed image and draw image on it\n self.canvas = tk.Canvas(self.master, width=self.resized_width, height=self.resized_height)\n self.canvas.bind(\"\", self.motion)\n self.canvas.bind(\"\", self.buttonPressed)\n self.canvas.bind(\"\", self.buttonReleased)\n self.canvas.create_image((0, 0), image=self.tk_img, anchor='nw')\n self.canvas.pack()\n ##### End of function\n\n #### Takes a image and exports\n def export(self, cropped):\n filepath = asksaveasfilename(\n defaultextension=\"png\",\n filetypes=[(\"Image Files\", \"*.png\"), (\"All Files\", \"*.*\")],\n )\n if not filepath:\n return\n cropped.save(filepath)\n return filepath\n\n #### Takes a box and updates the canvas\n def updatecanvas(self, x0, y0, x, y):\n #### https://stackoverflow.com/questions/54637795/how-to-make-a-tkinter-canvas-rectangle-transparent/54645103\n\n #### Set canvas background to the image\n self.canvas.create_image((0, 0), image=self.tk_img, anchor='nw')\n \n ##### Depending on state\n #### Set color\n #### Draw line\n #### Make and draw a box\n if self.mode == NRM:\n line_color = \"#2fff00\"\n self.canvas.create_line(x0, y0, x0, y, x, y, x, y0, x0, y0, fill=line_color, width=1)\n temp = Image.new(\"RGBA\", (abs(x-x0), abs(y-y0)), line_color)\n temp.putalpha(50)\n self.temp_img = ImageTk.PhotoImage(temp)\n self.canvas.create_image((min(x,x0), min(y,y0)), image=self.temp_img, anchor='nw')\n else:\n line_color = \"red\"\n \n if self.mode == VRT:\n self.canvas.create_line(x0, 1, x0, self.resized_height-1, x, self.resized_height-1, x, 1, x0, 1, fill=line_color, width=1)\n temp = Image.new(\"RGBA\", (abs(x-x0), int(self.resized_height)), line_color)\n temp.putalpha(50)\n self.temp_img = ImageTk.PhotoImage(temp)\n self.canvas.create_image((min(x,x0), 0), image=self.temp_img, anchor='nw')\n elif self.mode == HOR:\n self.canvas.create_line(1, y0, self.resized_width-1, y0, self.resized_width-1, y, 1, y, 1, y0, fill=line_color, width=1)\n temp = Image.new(\"RGBA\", (int(self.resized_width), abs(y-y0)), line_color)\n temp.putalpha(50)\n self.temp_img = ImageTk.PhotoImage(temp)\n self.canvas.create_image((0, min(y,y0)), image=self.temp_img, anchor='nw')\n\n #### Motion of mouse event handler\n def motion(self, event):\n if self.isPressed:\n self.updatecanvas(self.buttonpressedeventinfo[0], self.buttonpressedeventinfo[1], event.x, event.y)\n\n #### Mouse pressed event handler\n def buttonPressed(self, event):\n self.buttonpressedeventinfo = [event.x, event.y]\n self.isPressed = True\n self.updatecanvas(self.buttonpressedeventinfo[0], self.buttonpressedeventinfo[1], event.x, event.y)\n\n #### Mouse released event handler\n def buttonReleased(self, event):\n self.isPressed = False\n left = min(self.buttonpressedeventinfo[0], event.x)\n right = max(self.buttonpressedeventinfo[0], event.x)\n top = min(self.buttonpressedeventinfo[1], event.y)\n bottom = max(self.buttonpressedeventinfo[1], event.y)\n \n self.box = (left / self.resizefactor, top / self.resizefactor, right / self.resizefactor, bottom / self.resizefactor)\n map(int, self.box)\n del self.buttonpressedeventinfo\n\n #### Enter key event handler\n def keyPressed(self, event):\n try:\n a,b,c,d = self.box\n if a == c or b == d:\n return\n except Exception:\n return\n \n #### The original non resized image\n img = Image.open(self.img_path)\n\n\n #### Normal cropping\n if self.mode == NRM:\n cropped =img.crop((self.box[0], self.box[1], self.box[2], self.box[3]))\n\n \n #### Vertical cropping\n if self.mode == VRT:\n im1 = img.crop((0, 0, self.box[0], img.height))\n im2 = img.crop((self.box[2], 0, img.width, img.height))\n cropped = Image.new(\"RGB\", (im1.width + im2.width, img.height))\n cropped.paste(im1, (0, 0))\n cropped.paste(im2, (im1.width, 0))\n\n \n #### Horizontal cropping\n if self.mode == HOR:\n im1 = img.crop((0, 0, img.width, self.box[1]))\n im2 = img.crop((0, self.box[3], img.width, img.height))\n cropped = Image.new(\"RGB\", (img.width, im1.height + im2.height))\n cropped.paste(im1, (0, 0))\n cropped.paste(im2, (0, im1.height))\n\n #### Export the cropped image\n filepath = self.export(cropped)\n if not filepath == None:\n messagebox.showinfo(\"Success\", \"Your image is successfuly exported to \" + filepath)\n\n #### Called when mode is changed from Toolbar\n def reset(self):\n try:\n del self.box\n self.updatecanvas(0,0,0,0)\n except Exception:\n pass","repo_name":"aditi567/Advance-Image_cropping","sub_path":"playground.py","file_name":"playground.py","file_ext":"py","file_size_in_byte":7749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"37815624611","text":"from openerp import api, fields, models\n\n\nclass RecruitmentSettings(models.TransientModel):\n _name = 'hr.recruitment.config.settings'\n _inherit = ['res.config.settings', 'fetchmail.config.settings']\n\n module_document = fields.Selection(selection=[\n (0, \"Do not manage CVs and motivation letter\"),\n (1, 'Allow the automatic indexation of resumes')\n ], string='Resumes',\n help='Manage your CV\\'s and motivation letter related to all applicants.\\n'\n '-This installs the module document_ftp. This will install the knowledge management module in order to allow you to search using specific keywords through the content of all documents (PDF, .DOCx...)')\n","repo_name":"AwesomeFoodCoops/odoo-production","sub_path":"odoo/addons/hr_recruitment/models/hr_recruitment_config_settings.py","file_name":"hr_recruitment_config_settings.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"12"} +{"seq_id":"40965746508","text":"\"Code retrieved from: https://stackoverflow.com/a/64682734/5647511\"\nfrom typing import Type, Any, TypeVar\n\n\nT = TypeVar(\"T\")\n\n\nclass NoPublicConstructor(type):\n \"\"\"Metaclass that ensures a private constructor\n\n If a class uses this metaclass like this:\n\n class SomeClass(metaclass=NoPublicConstructor):\n pass\n\n If you try to instantiate your class (`SomeClass()`),\n a `TypeError` will be thrown.\n \"\"\"\n\n def __call__(cls, *args, **kwargs):\n raise TypeError(\n f\"{cls.__module__}.{cls.__qualname__} has no public constructor. \"\n f\"Use one of the create methods instead.\"\n )\n\n def _create(cls: Type[T], *args: Any, **kwargs: Any) -> T:\n return super().__call__(*args, **kwargs) # type: ignore\n","repo_name":"SURGroup/UQpy","sub_path":"src/UQpy/utilities/NoPublicConstructor.py","file_name":"NoPublicConstructor.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":216,"dataset":"github-code","pt":"12"} +{"seq_id":"24812318116","text":"from cuckoo import Cuckcoo\nfrom analys.plugins.interfaces import File, URL\n\nclass AnalysPlugin(object):\n def __init__(self, *args, **kwargs):\n super(AnalysPlugin, self).__init__(*args, **kwargs)\n\n def submit(self):\n #remap analys priority to cuckcoo priority\n if \"1\" not in priority:\n if \"low\" in priority:\n priority = \"1\"\n elif \"medium\" in priority:\n priority = \"2\"\n elif \"high\" in priority:\n priority = \"3\" \n \n if isinstance(self.resource, File):\n g = Cuckoo(self.hostname, self.resource.create_temp_file(),\n \"%s.%s\" % (self.resource.md5(),\n self.resource.extension(),), priority)\n\n g.create()\n self.resource.delete_temp_file()\n\n elif isinstance(self.resource, URL):\n g = Cuckoo(self.hostname, self.resource.url(), priority)\n g.create()\n\n results = g.report()\n \n resource_id = self.insert(result)\n #self.emit(task)\n\n \n \n","repo_name":"kevgliss/analys","sub_path":"analys/plugins/behavioral/cuckoo/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"1962653804","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'annotate'\n\nurlpatterns = [\n path('index', views.index, name='index'),\n path('task/', views.task, name='task'),\n path('getimage/', views.getimage, name='getimage'),\n path('tokenlogin', views.tokenlogin, name='tokenlogin'),\n]\n","repo_name":"yuantailing/qrcode-annotate","sub_path":"server/annotate/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"14634993638","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\n\nfrom xml.dom.minidom import parseString\nimport urllib.request, urllib.error, urllib.parse\nimport saldo_util\nimport urllib.request, urllib.parse, urllib.error\nimport re\n\n\ndef sblex(sense):\n senses = \"|\".join([saldo.encode(\"UTF-8\") for saldo in saldo_util.lookup_md1(sense)])\n sblex_address = \"http://demosb.spraakdata.gu.se/ws/lexikon\"\n params = {}\n params[\"lexikon\"] = \"dalin\"\n params[\"saldo\"] = senses\n data = urllib.parse.urlencode(params)\n req = urllib.request.Request(sblex_address, data)\n content = urllib.request.urlopen(req).read()\n dom = parseString(content)\n result = []\n for entry in dom.getElementsByTagName(\"LexicalEntry\"):\n eid = entry.getElementsByTagName(\"eid\")[0].childNodes[0].data\n wfs = set()\n for wf in entry.getElementsByTagName(\"wf\"):\n wfs.add(wf.childNodes[0].data)\n result.append((eid, list(wfs)))\n return result\n","repo_name":"spraakbanken/saldo-dalin-ws","sub_path":"dalin-ws/sblex.py","file_name":"sblex.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"13143011861","text":"import torch\nfrom pathlib import Path\n\nMODEL_NAME = 'range3/textgen'\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nmax_length_src = 30\nmax_length_target = 300\n\nbatch_size_train = 8\nbatch_size_valid = 8\n\nepochs = 1000\npatience = 20\n\nWORKSPACE_ROOT_DIR = Path(__file__).parent.parent \nNOVEL_DATA_PATH = (WORKSPACE_ROOT_DIR / 'data/novels/narou').resolve()\nSENTENCEPIECE_MODEL_DIR = WORKSPACE_ROOT_DIR / 'models/sentencepiece'\n","repo_name":"range3/pytorch-practice","sub_path":"textgen/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"16930480751","text":"from setuptools import setup\n\nwith open(\"README.md\", 'r') as f:\n long_description = f.read()\n\nsetup(\n name='mlutil',\n version='0.1',\n description='Util for ML',\n author='Neil Jie Yan',\n author_email='yanjie@ict.ac.cn, jiey@msr',\n packages=['mlutil'],\n url=\"http://weristdas\",\n install_requires=['numpy', 'pandas', 'pykalman'], #external dependent packages\n)\n","repo_name":"weristdas/mlutil","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"13046080572","text":"import sgtk\nimport pprint\n\nHookBaseClass = sgtk.get_hook_baseclass()\n\n\nclass ShotgunFilters(HookBaseClass):\n \"\"\"\n Controls the filter configuration for the Shotgun Panel.\n\n Via this hook, the data that is retrieved for the Shotgun Panel can be controlled.\n \"\"\"\n\n def get_link_filters(self, sg_location, entity_type, context_project, context_user):\n \"\"\"\n Returns a filter string which links the entity type up to a particular\n location.\n\n :param sg_location: Location object describing the object for\n which associated items should be retrieved.\n :param entity_type: The entity type to link to the location.\n :param context_project: The current context project.\n :param context_user: The current context user.\n\n :returns: Standard SG api3 filters that can be used to retrieve\n associated data\n \"\"\"\n\n link_filters = []\n\n if sg_location.entity_type == \"HumanUser\":\n # the logic for users is different\n # here we want give an overview of their work\n # for the current project\n\n # When the current project is None, the user is in site context and we want to get\n # the requested fields for all user's tasks, notes, versions and publishes.\n if context_project:\n link_filters.append([\"project\", \"is\", context_project])\n\n if entity_type == \"Task\":\n # show tasks i am assigned to\n link_filters.append([\"task_assignees\", \"in\", [sg_location.entity_dict]])\n link_filters.append([\"sg_status_list\", \"is_not\", \"fin\"])\n\n elif (\n entity_type == \"Note\"\n and sg_location.entity_type == context_user.get(\"type\")\n and sg_location.entity_id == context_user.get(\"id\")\n ):\n # not just any user, but this is ME!\n # show notes that are TO me, CC me or on tasks which I have been\n # assigned. Use advanced filters for this one so we can use OR\n #\n # we basically want to show notes that are FOR me.\n\n link_filters.append(\n {\n \"filter_operator\": \"or\",\n \"filters\": [\n [\"created_by\", \"is\", sg_location.entity_dict],\n [\n \"addressings_cc.Group.users\",\n \"in\",\n sg_location.entity_dict,\n ],\n [\n \"addressings_to.Group.users\",\n \"in\",\n sg_location.entity_dict,\n ],\n [\"replies.Reply.user\", \"is\", sg_location.entity_dict],\n [\"addressings_cc\", \"in\", sg_location.entity_dict],\n [\"addressings_to\", \"in\", sg_location.entity_dict],\n [\n \"tasks.Task.task_assignees\",\n \"in\",\n sg_location.entity_dict,\n ],\n ],\n }\n )\n\n elif entity_type == \"Note\":\n # show notes that are created by this user or this user has replied to.\n link_filters.append(\n {\n \"filter_operator\": \"or\",\n \"filters\": [\n [\"created_by\", \"is\", sg_location.entity_dict],\n [\"replies.Reply.user\", \"is\", sg_location.entity_dict],\n ],\n }\n )\n\n else:\n # for other things, show items created by me\n link_filters.append([\"created_by\", \"is\", sg_location.entity_dict])\n\n elif sg_location.entity_type in [\"ClientUser\", \"ApiUser\"]:\n # the logic for users is different\n # here we want give an overview of their work\n # for the current project\n\n if entity_type == \"Note\":\n # show notes that are by this user or where this user has replied\n #\n # we basically want to show items that were generated BY this user.\n link_filters.append(\n {\n \"filter_operator\": \"or\",\n \"filters\": [\n [\"replies.Reply.user\", \"is\", sg_location.entity_dict],\n [\"created_by\", \"is\", sg_location.entity_dict],\n ],\n }\n )\n if context_project:\n # we are in a non-site context. only tasks from this project\n link_filters.append([\"project\", \"is\", context_project])\n\n else:\n link_filters.append([\"created_by\", \"is\", sg_location.entity_dict])\n if context_project:\n # we are in a non-site context. only tasks from this project\n link_filters.append([\"project\", \"is\", context_project])\n\n elif sg_location.entity_type == \"Task\":\n\n # tasks are usually associated via a task field rather than via a link field\n if entity_type == \"Note\":\n link_filters.append([\"tasks\", \"in\", [sg_location.entity_dict]])\n\n elif entity_type == \"Version\":\n link_filters.append([\"sg_task\", \"is\", sg_location.entity_dict])\n\n elif entity_type in [\"PublishedFile\", \"TankPublishedFile\"]:\n link_filters.append([\"task\", \"is\", sg_location.entity_dict])\n\n elif entity_type == \"Task\":\n link_filters.append([\"sibling_tasks\", \"is\", sg_location.entity_dict])\n\n else:\n link_filters.append([\"entity\", \"is\", sg_location.entity_dict])\n\n elif sg_location.entity_type == \"Project\":\n\n # tasks are usually associated via a task field rather than via a link field\n if entity_type == \"Note\":\n link_filters.append([\"project\", \"is\", sg_location.entity_dict])\n\n elif entity_type == \"Version\":\n link_filters.append([\"project\", \"is\", sg_location.entity_dict])\n\n elif entity_type in [\"PublishedFile\", \"TankPublishedFile\"]:\n link_filters.append([\"project\", \"is\", sg_location.entity_dict])\n\n elif entity_type == \"Task\":\n # my tasks tab on project\n if context_user is None:\n raise sgtk.TankError(\n \"Use of the My Tasks tab is not supported when a current SG user \"\n \"cannot be determined. This is most often the case when a script key \"\n \"is used for authentication rather than a user name and password.\"\n )\n\n link_filters.append([\"task_assignees\", \"in\", [context_user]])\n link_filters.append([\"sg_status_list\", \"is_not\", \"fin\"])\n link_filters.append([\"project\", \"is\", sg_location.entity_dict])\n\n else:\n link_filters.append([\"entity\", \"is\", sg_location.entity_dict])\n\n elif sg_location.entity_type == \"Version\":\n\n if entity_type == \"Note\":\n link_filters.append([\"note_links\", \"in\", [sg_location.entity_dict]])\n\n elif entity_type in [\"PublishedFile\", \"TankPublishedFile\"]:\n link_filters.append([\"version\", \"is\", sg_location.entity_dict])\n\n else:\n link_filters.append([\"entity\", \"is\", sg_location.entity_dict])\n\n else:\n if entity_type == \"Note\":\n link_filters.append([\"note_links\", \"in\", [sg_location.entity_dict]])\n else:\n link_filters.append([\"entity\", \"is\", sg_location.entity_dict])\n\n self.logger.debug(\n \"%s Resolved %s into the following sg query:\\n%s\"\n % (self, sg_location, pprint.pformat(link_filters)),\n )\n\n return link_filters\n","repo_name":"shotgunsoftware/tk-multi-shotgunpanel","sub_path":"hooks/shotgun_filters.py","file_name":"shotgun_filters.py","file_ext":"py","file_size_in_byte":8267,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"12"} +{"seq_id":"5453364412","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\n\nfrom SEIR1R2 import SEIR1R2\n\nclass SEIR1R2D(SEIR1R2):\n\t''' SEIR1R2D model, whcih is a combination of N. Bacaer paper and \n\t\t\"Universal masking is urgent in the covid-19 pandemic...\", from Kai, Guy-Philippe Goldstein, et al. ArXiv\n\t'''\n\n\tdef __init__(self, N, dt=1, verbose=1):\n\n\t\tsuper().__init__(N, dt, verbose)\n\n\t\tself.dimState = 6 # The 6 varaibles of the SEIR1R2D model (S, E, ...)\n\t\tself.dimObs = 2 # There is two observed variables R1 and F\n\t\tself.modelName = 'SEIR1R2D' # Long name for the model\n\t\tself.modelShortName = 'SEIR1R2D' # Short name for the model\n\n\t\t# Add suplementary parameters\n\t\tself.mu = 0.001 # taux de mortalité spécifique à la pandémie\n\t\tself.xi = 0.001 # rate of re-susceptibility\n\n\t\tself.setR0() # MAJ de R0\n\n\tdef __str__(self):\n\t\tS = super().__str__()\n\t\tS += '\\n mu=' + str(np.round(self.mu, decimals=3))\n\t\tS += '\\n xi=' + str(np.round(self.xi, decimals=3))\n\t\treturn S\n\n\tdef setR0(self):\n\t\tif self.c+self.mu != 0.:\n\t\t\tself.R0 = (self.a+self.xi)/(self.c+self.mu)\n\t\telse:\n\t\t\tself.R0 = -1.\n\n\tdef getTextParam(self, ROsignificatif=True, Period=1):\n\t\tS = 'Model ' + self.modelShortName + ' - Period ' + str(Period) + ':'\n\t\tS += '\\n' + r' $a=' + str(np.round(self.a, decimals=4)) + r', b=' + str(np.round(self.b, decimals=4)) + '$'\n\t\tS += '\\n' + r' $c=' + str(np.round(self.c, decimals=4)) + r', f=' + str(np.round(self.f, decimals=4)) + '$'\n\t\tS += '\\n' + r' $\\mu=' + str(np.round(self.mu, decimals=5)) + r', \\xi=' + str(np.round(self.xi, decimals=5)) + '$'\n\t\tif self.c!= 0. and ROsignificatif==True:\n\t\t\tS += '\\n' + r' $R_0=' + str(np.round(self.R0, decimals=2)) + '$'\n\t\tif ROsignificatif==False:\n\t\t\tS += '\\n' + r' $R_0$ non significatif'\n\t\treturn S\n\n\tdef setParam(self, N, a, b, c, f, mu, xi):\n\t\tsuper().setParam(N, a, b, c, f)\n\t\tself.mu, self.xi = mu, xi\n\t\tself.setR0() # MAJ de R0\n\t\n\tdef setf(self, mu):\n\t\tself.mu = mu\n\t\tself.setR0() # MAJ de R0\n\tdef setf(self, xi):\n\t\tself.xi = xi\n\t\tself.setR0() # MAJ de R0\n\n\tdef getParam(self):\n\t\treturn (self.N, self.a, self.b, self.c, self.f, self.mu, self.xi)\n\tdef getR0(self):\n\t\treturn self.R0\n\n\t# The SEIR1R2D's differential equations.\n\tdef deriv(self, y, t, N, a, b, c, f, mu, xi):\n\t\tS, E, I, R1, R2, D = y\n\t\tR = R1+R2\n\t\tdSdt = -a * S * I / N + xi * R\n\t\tdEdt = a * S * I / N - b * E\n\t\tdIdt = b * E - c * I - mu * I\n\t\tdR1dt = f * (c * I - xi * R)\n\t\tdR2dt = (1.-f) * (c * I - xi * R)\n\t\tdDdt = mu * I\n\t\treturn dSdt, dEdt, dIdt, dR1dt, dR2dt, dDdt\n\n\tdef getString(self, indice):\n\t\tstring = super().getString(indice)\n\t\tif indice == 5: return r'$D(t)$'\n\t\treturn string\n\n\tdef getColor(self, indice):\n\t\tif indice >= 0 and indice<6: return self.colorCycle[indice]\n\t\tprint('PB getColor - indice =', indice, ' does not exist!')\n\t\texit(1)\n\n\tdef getColorFromString(self, string):\n\t\tcol = super().getColorFromString(string)\n\t\tif string == r'$D(t)$' : return self.colorCycle[5] \n\t\treturn col\n","repo_name":"SDerrode/divoc","sub_path":"SEIR1R2D.py","file_name":"SEIR1R2D.py","file_ext":"py","file_size_in_byte":3095,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"20403241249","text":"import uasyncio\r\nfrom machine import Pin\r\npLED = Pin('LED',Pin.OUT)# RP2 W ou 25 pour RP2\r\n \r\nasync def blink_LED():\r\n while True:\r\n pLED.toggle() #changement état\r\n await uasyncio.sleep_ms(500)\r\n\r\nasync def main():\r\n uasyncio.create_task(blink_LED())\r\n #... \r\n while True :\r\n # ...\r\n await uasyncio.sleep_ms(0)\r\n\r\nuasyncio.run(main())\r\n","repo_name":"christianDUCROS/uasyncio","sub_path":"clignot_led.py","file_name":"clignot_led.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"34341371424","text":"from django.db import DatabaseError, OperationalError\nfrom django.http import HttpResponseServerError\nimport time\n\nclass DatabaseErrorMiddleware:\n def __init__(self, get_response):\n self.get_response = get_response\n\n def __call__(self, request):\n response = self.get_response(request)\n return response\n\n def process_exception(self, request, exception):\n if isinstance(exception, (DatabaseError, OperationalError)):\n retries = 3\n delay = 0.5\n \n while retries > 0:\n try:\n response = self.get_response(request)\n return response\n except (DatabaseError, OperationalError):\n print(\"Database connection error. Retrying...\")\n retries -= 1\n if retries == 0:\n return HttpResponseServerError(\"Database connection error after multiple retries.\")\n time.sleep(delay)\n","repo_name":"arkterra90/giftzilla","sub_path":"giftzillaenv/giftzilla/giftzilla/middleware/data_error_middleware.py","file_name":"data_error_middleware.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"5743793128","text":"import tkinter as tk\r\nfrom tkinter import messagebox\r\nfrom predict import predictor\r\nfrom tkcalendar import Calendar, DateEntry\r\nfrom datetime import datetime\r\nfrom plot_load import plot_load\r\nimport time\r\nimport os\r\nimport sys\r\nimport subprocess\r\nimport pickle\r\n\r\ncurrent_dir = os.path.dirname(__file__)\r\n\r\nclass GUI(tk.Tk):\r\n PRIMARY_COLOR = \"#fff\"\r\n SECONDARY_COLOR = \"#BDBDBD\"\r\n BUTTON_COLOR = \"#FF5733\"\r\n NOW = datetime.now()\r\n DAY= NOW.day\r\n MONTH = NOW.month\r\n YEAR = NOW.year\r\n # PREDICTOR = predictor()\r\n def __init__(self):\r\n self.ignore_warning = False\r\n self.model = os.path.join(current_dir,\"model/m.h5\")\r\n self.graph_process = None\r\n self.predictor_process = None \r\n tk.Tk.__init__(self)\r\n self.resizable(False,False)\r\n self.title(\"Load Predictor\")\r\n num_biomass = 3\r\n num_biogas = 2\r\n num_solar = 2\r\n biomass_pv = 12.35\r\n biogas_pv = 14\r\n # Try to load previous configuration\r\n try:\r\n config_path = os.path.join(current_dir,\"config.pickle\")\r\n with open(config_path,\"rb\") as r:\r\n config = pickle.load(r)\r\n num_biomass = config[\"num_biomass\"]\r\n num_biogas = config[\"num_biogas\"]\r\n num_solar = config[\"num_solar\"]\r\n biomass_pv = config[\"biomass_pv\"]\r\n biogas_pv = config[\"biogas_pv\"]\r\n \r\n except Exception as err:\r\n print(err)\r\n \r\n \r\n self.options = {\r\n \"num_biomass\":tk.IntVar(self,value=num_biomass),\r\n \"num_biogas\":tk.IntVar(self,value=num_biogas),\r\n \"num_solar\":tk.IntVar(self,value=num_solar),\r\n \"biomass_pv\":tk.DoubleVar(self,value=biomass_pv),\r\n \"biogas_pv\":tk.DoubleVar(self,value=biogas_pv),\r\n \"use_gpu\":tk.IntVar(self,value=0),\r\n \"scatter\":tk.IntVar(self,value=0),\r\n \"fill\":tk.IntVar(self,value=1)\r\n }\r\n self.mainframe = tk.Frame(self,bg=GUI.PRIMARY_COLOR)\r\n self.mainframe.config(width=300,height=250)\r\n self.mainframe.pack()\r\n self.add_option() # plot option frame\r\n self.add_calendar() # add calendar \r\n self.add_generator_option() # add generator option frame\r\n self.add_control_button() # add start prediction button\r\n def on_close():\r\n try:\r\n subprocess.Popen.kill(self.graph_process)\r\n except:\r\n pass\r\n try:\r\n self.stop_prediction()\r\n except:\r\n pass\r\n self.destroy() \r\n self.protocol(\"WM_DELETE_WINDOW\", on_close)\r\n self.mainloop()\r\n \r\n def power_error_callback(self,title,message): # handle power failure event\r\n if not self.ignore_warning:\r\n \"\"\"\r\n Yes -> True\r\n No -> False\r\n Cancel -> None\r\n \"\"\" \r\n value = messagebox.askokcancel(title,message)\r\n if value == True:\r\n self.pred.should_terminate = True # force to terminate \r\n try:\r\n subprocess.Popen.kill(self.graph_process)\r\n except:\r\n pass\r\n \r\n def add_option(self):\r\n self.option_frame = tk.Frame(self.mainframe,bg=GUI.PRIMARY_COLOR)\r\n self.option_frame.grid(row=2,column =0,sticky=\"nwe\")\r\n \r\n \r\n tk.Label(self.option_frame,text=\" TF Option\",bg=GUI.PRIMARY_COLOR).pack(anchor=\"nw\")\r\n tk.Checkbutton(self.option_frame, \r\n text=\"Use GPU\",\r\n variable=self.options[\"use_gpu\"],\r\n bg=GUI.PRIMARY_COLOR).pack(anchor=\"nw\")\r\n \r\n tk.Label(self.option_frame,text=\" Plot Option\",bg=GUI.PRIMARY_COLOR).pack(anchor=\"nw\")\r\n tk.Checkbutton(self.option_frame, \r\n text=\"Scatter Plot\",\r\n variable=self.options[\"scatter\"],\r\n # command=lambda : toggle(\"scatter\") ,\r\n bg=GUI.PRIMARY_COLOR).pack(anchor=\"nw\")\r\n\r\n tk.Checkbutton(self.option_frame, \r\n text=\"Fill Plot\",\r\n variable=self.options[\"fill\"],\r\n # command=lambda : toggle(\"scatter\") ,\r\n bg=GUI.PRIMARY_COLOR).pack(anchor=\"nw\")\r\n tk.Label(self.option_frame,text=\" Predict Interval (ms)\",bg=GUI.PRIMARY_COLOR).pack(anchor=\"w\")\r\n self.interval_scaler = tk.Scale(self.option_frame, from_=0, to=2500, orient=tk.HORIZONTAL,bg=GUI.PRIMARY_COLOR)\r\n self.interval_scaler.pack()\r\n self.interval_scaler.set(250) # set default value\r\n \r\n\r\n def add_calendar(self):\r\n tk.Label(self.mainframe,text=\"Select Prediction Date\",bg=GUI.PRIMARY_COLOR).grid(row=1,column=1,columnspan=2,sticky=\"W\")\r\n self.calendar_frame = tk.Frame(self.mainframe,bg=GUI.SECONDARY_COLOR)\r\n self.calendar_frame.grid(row=2,column=1)\r\n self.calendar = Calendar(self.calendar_frame,\r\n font=\"Arial 10\", selectmode='day',\r\n date_pattern=\"y-mm-dd\",\r\n year=GUI.YEAR, month=GUI.MONTH, day=GUI.DAY)\r\n self.calendar.pack(fill=\"both\", expand=True)\r\n \r\n def add_generator_option(self):\r\n self.gen_option_frame = tk.Frame(self.mainframe,bg=GUI.PRIMARY_COLOR)\r\n self.gen_option_frame.grid(row=3,column=1)\r\n # ADD SCALERS COLUMN 0\r\n tk.Label(self.gen_option_frame,text=\"Biomass Generator\",bg=GUI.PRIMARY_COLOR).grid(row=1,column=0)\r\n tk.Scale(self.gen_option_frame, from_=0, to=15, orient=tk.HORIZONTAL,variable=self.options[\"num_biomass\"],bg=GUI.PRIMARY_COLOR).grid(row=2,column=0)\r\n \r\n tk.Label(self.gen_option_frame,text=\"Biogas Generator\",bg=GUI.PRIMARY_COLOR).grid(row=3,column=0)\r\n tk.Scale(self.gen_option_frame, from_=0, to=15, orient=tk.HORIZONTAL,variable=self.options[\"num_biogas\"],bg=GUI.PRIMARY_COLOR).grid(row=4,column=0)\r\n \r\n tk.Label(self.gen_option_frame,text=\"Solar Cell Generator\",bg=GUI.PRIMARY_COLOR).grid(row=5,column=0)\r\n tk.Scale(self.gen_option_frame, from_=0, to=15, orient=tk.HORIZONTAL,variable=self.options[\"num_solar\"],bg=GUI.PRIMARY_COLOR).grid(row=6,column=0)\r\n \r\n \r\n # ADD SCALERS COLUMN 1\r\n tk.Label(self.gen_option_frame,text=\"Biomass Gen. Power (kWH)\",bg=GUI.PRIMARY_COLOR).grid(row=1,column=1)\r\n tk.Scale(self.gen_option_frame, from_=0, to=100,digits=4,resolution = 0.01, orient=tk.HORIZONTAL,variable=self.options[\"biomass_pv\"],bg=GUI.PRIMARY_COLOR).grid(row=2,column=1)\r\n \r\n tk.Label(self.gen_option_frame,text=\"Biogas Gen. Power (kWH)\",bg=GUI.PRIMARY_COLOR).grid(row=3,column=1)\r\n tk.Scale(self.gen_option_frame, from_=0, to=100,digits=4,resolution = 0.01, orient=tk.HORIZONTAL,variable=self.options[\"biogas_pv\"],bg=GUI.PRIMARY_COLOR).grid(row=4,column=1)\r\n \r\n \r\n def stop_prediction(self):\r\n try:\r\n self.pred.should_terminate = True\r\n except:\r\n pass\r\n try:\r\n subprocess.Popen.kill(self.graph_process)\r\n except:\r\n pass\r\n \r\n \r\n def save_config(self):\r\n config = {\r\n \"num_biomass\": self.options[\"num_biomass\"].get(),\r\n \"num_biogas\":self.options[\"num_biogas\"].get(),\r\n \"num_solar\":self.options[\"num_solar\"].get(),\r\n \"biomass_pv\":self.options[\"biomass_pv\"].get(),\r\n \"biogas_pv\":self.options[\"biogas_pv\"].get()\r\n }\r\n with open('config.pickle', 'wb') as f:\r\n pickle.dump(config, f)\r\n messagebox.showinfo(\"Save\",\"Save Configuration Succeeded!\")\r\n \r\n\r\n def add_control_button(self):\r\n self.control_button_frame = tk.Frame(self.mainframe,bg=GUI.PRIMARY_COLOR)\r\n self.control_button_frame.grid(row=2,column=2,sticky=\"NS\")\r\n \r\n self.start_button = tk.Button(self.control_button_frame,text=\"Start Predicting\")\r\n self.start_button.config(command=self.predict)\r\n self.start_button.grid(row=0,column=0,sticky='nesw')\r\n \r\n self.stop_button = tk.Button(self.control_button_frame,text=\"Stop Predicting\")\r\n self.stop_button.config(command=self.stop_prediction)\r\n self.stop_button.grid(row=1,column=0,sticky='nesw')\r\n \r\n self.save_config_button = tk.Button(self.control_button_frame,text=\"Save\\nConfiguration\")\r\n self.save_config_button.config(command=self.save_config)\r\n self.save_config_button.grid(row=2,column=0,sticky='nesw')\r\n \r\n \r\n self.control_button_frame.grid_columnconfigure(0, weight=1, uniform=\"group1\")\r\n # self.control_button_frame.grid_columnconfigure(1, weight=1, uniform=\"group1\")\r\n self.control_button_frame.grid_rowconfigure(0, weight=1)\r\n self.control_button_frame.grid_rowconfigure(1, weight=1)\r\n self.control_button_frame.grid_rowconfigure(2, weight=1)\r\n \r\n def predict(self):\r\n self.ignore_warning = False\r\n try:\r\n self.pred.should_terminate = True\r\n print(\"Successfully terminated predicting thread !\")\r\n except Exception as err:\r\n print(err)\r\n \r\n # KILL EXISTING PROCESS\r\n try:\r\n subprocess.Popen.kill(self.graph_process)\r\n except Exception as err:\r\n print(err)\r\n \r\n date = (self.calendar.get_date())\r\n date = datetime.strptime(date,\"%Y-%m-%d\")\r\n import threading as th\r\n import multiprocessing as mp\r\n daydelta = 0\r\n fill_plot=bool(self.options[\"fill\"].get()) # fill plot\r\n scatter_plot = bool(self.options[\"scatter\"].get()) # use scatter plot\r\n print(\"plot options\",fill_plot,scatter_plot)\r\n use_gpu = bool(self.options[\"use_gpu\"].get()) # use gpu for tensorflow \r\n num_biomass = self.options[\"num_biomass\"].get()\r\n num_biogas = self.options[\"num_biogas\"].get()\r\n num_solar = self.options[\"num_solar\"].get()\r\n biomass_pv = self.options[\"biomass_pv\"].get()\r\n biogas_pv = self.options[\"biogas_pv\"].get()\r\n period = 15\r\n self.pred = predictor(dt=date,model_path=self.model,use_gpu=use_gpu,message_callback=self.power_error_callback) # creat new instance of predictor\r\n self.pred.iteration_delay = self.interval_scaler.get()/1000 # self.predictor loop delay\r\n self.pred.BIOMASS_PV = biomass_pv\r\n self.pred.BIOGAS_PV = biogas_pv\r\n self.pred.num_biomass = num_biomass\r\n self.pred.num_biogas = num_biogas\r\n self.pred.num_solar = num_solar\r\n pred_thread = th.Thread(target=self.pred.run)\r\n pred_thread.start()\r\n self.graph_process = subprocess.Popen([\r\n \"python\",\r\n \"plot_load.py\",\r\n \"--date\",str(date.date()),\r\n \"--scatter-plot\", str(scatter_plot),\r\n \"--fill-plot\", str(fill_plot)\r\n ])\r\n \r\nif __name__ == \"__main__\":\r\n GUI()","repo_name":"phakawatTER/ee-load-prediction","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":11065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"41129978990","text":"#!/usr/bin/env python\n\n# - \"curses\" menu based on https://stackoverflow.com/a/14205494\n\nimport curses,sys,time\nfrom bluetool import Bluetooth\nfrom curses import panel\n\nclass Menu(object):\n def __init__(self, items, stdscreen):\n self.window = stdscreen.subwin(0, 0)\n self.window.keypad(1)\n self.panel = panel.new_panel(self.window)\n self.panel.hide()\n panel.update_panels()\n\n self.position = 0\n self.items = items\n self.items.append((\"Back / Exit\", \"exit\"))\n\n def navigate(self, n):\n self.position += n\n if self.position < 0:\n self.position = 0\n elif self.position >= len(self.items):\n self.position = len(self.items) - 1\n\n def display(self):\n self.panel.top()\n self.panel.show()\n self.window.clear()\n\n while True:\n self.window.refresh()\n curses.doupdate()\n for index, item in enumerate(self.items):\n if index == self.position:\n mode = curses.A_REVERSE\n else:\n mode = curses.A_NORMAL\n\n msg = \"%d. %s\" % (index, item[0])\n self.window.addstr(1 + index, 1, msg, mode)\n\n key = self.window.getch()\n\n if key in [curses.KEY_ENTER, ord(\"\\n\")]:\n if self.position == len(self.items) - 1:\n break\n else:\n self.items[self.position][1]()\n\n elif key == curses.KEY_UP:\n self.navigate(-1)\n\n elif key == curses.KEY_DOWN:\n self.navigate(1)\n\n self.window.clear()\n self.panel.hide()\n panel.update_panels()\n curses.doupdate()\n\nclass MyApp(object):\n def __init__(self, stdscreen):\n self.scan_timeout = 90\n self.bt = Bluetooth()\n self.bt.start_scanning(self.scan_timeout)\n\n self.screen = stdscreen\n curses.curs_set(0)\n mainMenu = [\n ('Rescan devices\\t\\t(scans for {} seconds in background, system bus will be processed every 10 seconds)'.format(self.scan_timeout), self.rescan_devices),\n ('Trust controller\\t\\t(shows only untrusted pairable controllers)', self.trust_controller_menu),\n ('Pair controller\\t\\t(shows only unpaired pairable controllers)', self.pair_controller_menu),\n ('Connect controller\\t\\t(shows only paired and trusted connectable controllers)', self.connect_device_menu),\n ('Disconnect controller\\t(shows only connected controllers)', self.disconnect_device_menu),\n ('Remove controller\\t\\t(shows only trusted, paired OR connected controllers)', self.remove_device_menu),\n ]\n self.make_menu(mainMenu)\n self.menu.display()\n\n def make_menu(self, menulist):\n self.menu = Menu(menulist, self.screen)\n\n def trust_controller_menu(self):\n properties = [\n 'Icon',\n 'RSSI',\n 'Trusted',\n ]\n menu = []\n for device in self.bt.get_available_devices():\n mac_address = device['mac_address']\n for property in properties:\n device[property] = self.bt.get_device_property(mac_address,property)\n if ((device['Icon'] == 'input-gaming') and (device['Trusted'] == 0)):\n menu.append(('{}\\t{}\\tRSSI: {}'.format(device['mac_address'],device['name'],device['RSSI']),self.trust_controller))\n self.make_menu(menu)\n self.menu.display()\n\n def trust_controller(self):\n mac = self.get_selected_device()[0]\n self.bt.trust(mac)\n if self.bt.get_device_property(mac,'Trusted') == 1:\n self.menu.items[self.menu.position] = ('MAC {} ({}) trusted!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back)\n else:\n self.menu.items[self.menu.position] = ('Error trusting MAC {} ({})!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back)\n\n def pair_controller_menu(self):\n properties = [\n 'Icon',\n 'Paired',\n 'RSSI',\n 'Trusted',\n ]\n menu = []\n for device in self.bt.get_devices_to_pair():\n mac_address = device['mac_address']\n for property in properties:\n device[property] = self.bt.get_device_property(mac_address,property)\n if ((device['Icon'] == 'input-gaming') and (device['Trusted'] == 1) and device['Paired'] == 0):\n menu.append(('{}\\t{}\\tRSSI: {}'.format(device['mac_address'],device['name'],device['RSSI']),self.pair_controller))\n self.make_menu(menu)\n self.menu.display()\n\n def pair_controller(self):\n mac = self.get_selected_device()[0]\n self.bt.pair(mac)\n if self.bt.get_device_property(mac,'Paired') == 1:\n self.menu.items[self.menu.position] = ('MAC {} ({}) paired!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back)\n else:\n self.menu.items[self.menu.position] = ('Error paring MAC {} ({})!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back) \n\n def connect_device_menu(self):\n properties = [\n 'Icon',\n 'RSSI',\n 'Connected',\n 'Paired',\n 'Trusted',\n ]\n menu = []\n for device in self.bt.get_available_devices():\n mac_address = device['mac_address']\n for property in properties:\n device[property] = self.bt.get_device_property(mac_address,property)\n if ((device['Icon'] == 'input-gaming') and (device['Paired'] == 1) and (device['Trusted'] == 1) and (device['Connected'] == 0)):\n menu.append(('{}\\t{}\\tRSSI: {}'.format(device['mac_address'],device['name'],device['RSSI']),self.connect_device))\n self.make_menu(menu)\n self.menu.display()\n\n def connect_device(self):\n mac = self.get_selected_device()[0]\n self.bt.connect(mac)\n if self.bt.get_device_property(mac,'Connected') == 1:\n self.menu.items[self.menu.position] = ('MAC {} ({}) connected!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back)\n else:\n self.menu.items[self.menu.position] = ('Error connecting MAC {} ({})!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back) \n\n\n def disconnect_device_menu(self):\n properties = [\n 'Icon',\n 'Connected',\n 'RSSI',\n ]\n menu = []\n for device in self.bt.get_connected_devices():\n mac_address = device['mac_address']\n for property in properties:\n device[property] = self.bt.get_device_property(mac_address,property)\n if ((device['Icon'] == 'input-gaming') and (device['Connected'] == 1)):\n menu.append(('{}\\t{}\\tRSSI: {}'.format(device['mac_address'],device['name'],device['RSSI']),self.disconnect_device))\n self.make_menu(menu)\n self.menu.display()\n\n def disconnect_device(self):\n mac = self.get_selected_device()[0]\n self.bt.disconnect(mac)\n if self.bt.get_device_property(mac,'Connected') == 0:\n self.menu.items[self.menu.position] = ('MAC {} ({}) disconnected!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back)\n else:\n self.menu.items[self.menu.position] = ('Error disconnecting MAC {} ({})!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back) \n\n def remove_device_menu(self):\n properties = [\n 'Icon',\n 'Paired',\n 'Trusted',\n 'RSSI',\n 'Blocked',\n 'Connected',\n ]\n menu = []\n for device in self.bt.get_available_devices():\n mac_address = device['mac_address']\n for property in properties:\n device[property] = self.bt.get_device_property(mac_address,property)\n if ((device['Icon'] == 'input-gaming') and ((device['Paired'] == 1) or (device['Trusted'] == 1) or (device['Blocked'] == 1))):\n menu.append(('{}\\t{}\\tRSSI: {}\\tTrusted: {}\\tPaired: {}\\tConnected: {}\\tBlocked: {}'.format(device['mac_address'],device['name'],device['RSSI'],device['Trusted'],device['Paired'],device['Connected'],device['Blocked']),self.remove_device))\n self.make_menu(menu)\n self.menu.display()\n\n def remove_device(self):\n mac = self.get_selected_device()[0]\n self.bt.remove(mac)\n self.menu.items[self.menu.position] = ('MAC {} ({}) removed!\\n'.format(mac,self.get_selected_device()[1]),self.navigate_to_back)\n\n def rescan_devices(self):\n self.menu.window.addstr(9, 1, 'Scanning for device for {} seconds in background now, please refresh views...'.format(self.scan_timeout), curses.A_NORMAL)\n self.bt.start_scanning(self.scan_timeout)\n\n def get_selected_device(self):\n return(self.menu.items[self.menu.position][0].split('\\t'))\n\n def navigate_to_back(self):\n self.menu.navigate(len(self.menu.items) -1)\n\nif __name__ == \"__main__\":\n if (len(sys.argv) == 1):\n bt = Bluetooth()\n print('Scanning for available devices for 15 seconds, please wait...')\n bt.start_scanning(15)\n time.sleep(15)\n print('Getting pairable devices, please wait...')\n devices = bt.get_devices_to_pair()\n print(devices)\n for device in devices:\n mac = device['mac_address']\n name = device['name']\n print('Found MAC: {}\\tName: {}'.format(mac,name))\n if bt.get_device_property(mac,'Icon') == 'input-gaming':\n print('Found controller {} Name: {}, trusting...'.format(mac,name))\n bt.trust(mac)\n if bt.get_device_property(mac,'Trusted') == 1:\n print('Trusted {}, quick pause, then pairing...'.format(name))\n time.sleep(5)\n bt.pair(mac)\n if bt.get_device_property(mac,'Paired') == 1:\n print('Paired {}, quick pause, then connecting...'.format(name))\n time.sleep(5)\n bt.connect(mac)\n if bt.get_device_property(mac,'Connected') == 1:\n print('Connected {}, exiting...'.format(name))\n else:\n curses.wrapper(MyApp)\n","repo_name":"AmberELEC/AmberELEC","sub_path":"packages/amberelec/config/distribution/scriptmodules/supplementary/bluetoothcontroller.py","file_name":"bluetoothcontroller.py","file_ext":"py","file_size_in_byte":10426,"program_lang":"python","lang":"en","doc_type":"code","stars":971,"dataset":"github-code","pt":"12"} +{"seq_id":"71969897622","text":"import os\nimport importlib\nimport sys\nimport gettext\nimport enum\nimport time\nimport contextlib\nimport io\nimport six\nimport pydoc\nimport collections\n\nfrom freenas.utils.permissions import get_unix_permissions, string_to_int\nfrom freenas.cli import config\nfrom freenas.utils import first_or_default\nfrom freenas.dispatcher import Password\nfrom threading import Lock, Thread\n\n\noutput_lock = Lock()\nt = gettext.translation('freenas-cli', fallback=True)\n_ = t.gettext\n\n\nclass ValueType(enum.Enum):\n STRING = 1\n TEXT_FILE = 2\n NUMBER = 3\n HEXNUMBER = 4\n OCTNUMBER = 5\n BOOLEAN = 6\n SIZE = 7\n TIME = 8\n SET = 9\n DICT = 10\n PERMISSIONS = 11\n ARRAY = 12\n PASSWORD = 13\n DATE = 14\n\n\nclass Object(list):\n class Item(object):\n def __init__(self, descr, name, value, vt=ValueType.STRING, editable=None):\n self.descr = descr\n self.name = name\n self.value = value\n self.vt = vt\n self.editable = editable\n\n def __getstate__(self):\n return {\n 'descr': self.descr,\n 'name': self.name,\n 'value': list(self.value) if hasattr(self.value, '__next__') else self.value,\n 'vt': self.vt.name,\n 'editable': self.editable\n }\n\n def append(self, p_object):\n if not isinstance(p_object, self.Item):\n raise ValueError('Can only add Object.Item instances')\n\n super(Object, self).append(p_object)\n\n def __getitem__(self, item):\n i = first_or_default(lambda x: x.name == item, self)\n if i:\n return i.value\n\n raise KeyError(item)\n\n def __setitem__(self, key, value):\n if not isinstance(value, self.Item):\n raise ValueError('Can only add Object.Item instances')\n\n super(Object, self).__setitem__(key, value)\n\n def __getstate__(self):\n return {\n 'type': self.__class__.__name__,\n 'data': [i.__getstate__() for i in self]\n }\n\n def __init__(self, *args):\n for i in args:\n self.append(i)\n\n\nclass Table(object):\n class Column(object):\n def __init__(self, label, accessor, vt=ValueType.STRING, width=None, name=None):\n self.name = name\n self.label = label\n self.accessor = accessor\n self.vt = vt\n self.width = width\n\n if not self.name and isinstance(accessor, str):\n self.name = accessor\n\n if not self.name:\n self.name = label\n\n def __getstate__(self):\n return {\n 'label': self.label,\n 'vt': self.vt.name,\n 'width': self.width\n }\n\n def __init__(self, data, columns):\n self.data = data\n self.columns = columns\n\n def __len__(self):\n return len(self.data)\n\n def __iter__(self):\n for i in self.data:\n yield {c.name: resolve_cell(i, c.accessor) for c in self.columns}\n\n def __getitem__(self, item):\n return {c.name: resolve_cell(self.data[item], c.accessor) for c in self.columns}\n\n def __getstate__(self):\n return {\n 'type': self.__class__.__name__,\n 'columns': [i.__getstate__() for i in self.columns],\n 'data': [\n [resolve_cell(i, c.accessor) for c in self.columns] for i in self.data\n ]\n }\n\n def pop(self, pop_index):\n return self.data.pop(pop_index)\n\n\nclass Sequence(list):\n def __init__(self, *items):\n super(Sequence, self).__init__(items)\n\n def unwind(self, force=False):\n return self if len(self) > 1 or force else self[0]\n\n def append_flat(self, item):\n if isinstance(item, Sequence):\n self.extend(item)\n return\n\n self.append(item)\n\n def __getstate__(self):\n return {\n 'type': self.__class__.__name__,\n 'data': list(self)\n }\n\n\nclass ProgressBar(object):\n def __init__(self):\n self.message = None\n self.percentage = 0\n self.draw_t = Thread(target=self.draw) if sys.stdout.isatty() else Thread(target=self.draw_static)\n self.finished = False\n sys.stdout.write('\\n')\n self.draw_t.daemon = True\n self.draw_t.start()\n\n def draw(self):\n progress_width = 40\n none_fill = ''.join('#' if i < 8 else '_' for i in range(progress_width))\n\n def get_none_fill(f):\n asc = True\n while True:\n yield f\n if asc:\n f = f[-1] + f[:-1]\n if f[-1] == '#':\n asc = False\n else:\n f = f[1:] + f[0]\n if f[0] == '#':\n asc = True\n\n generator = get_none_fill(none_fill)\n while True:\n if self.percentage is None:\n none_fill = next(generator)\n fill = none_fill\n else:\n filled_width = int(self.percentage * progress_width)\n fill = '#' * filled_width + '_' * (progress_width - filled_width)\n\n sys.stdout.write('\\033[2K\\033[A\\033[2K\\r')\n sys.stdout.write('Status: {}\\n'.format(self.message))\n sys.stdout.write('Total task progress: [{}] '.format(fill) +\n ('' if self.percentage is None else '{:.2%}'.format(self.percentage)))\n\n sys.stdout.flush()\n if self.finished:\n break\n time.sleep(0.5)\n\n def draw_static(self):\n old_message = ''\n\n while True:\n status = ''\n\n if self.percentage is not None:\n if self.message:\n status = 'Status {}. '.format(self.message)\n status += 'Progress {:.2%}\\n'.format(self.percentage)\n elif old_message != self.message:\n old_message = self.message\n status = 'Status {}\\n'.format(self.message)\n\n if status:\n sys.stdout.write(status)\n sys.stdout.flush()\n\n if self.finished:\n break\n\n time.sleep(1)\n\n def update(self, percentage=None, message=None):\n self.percentage = None if percentage is None else float(percentage / 100.0)\n\n if message:\n self.message = message\n\n def finish(self):\n self.percentage = 1\n\n def end(self):\n self.finished = True\n self.draw_t.join()\n sys.stdout.write('\\n')\n\n\ndef get_terminal_size(fd=1):\n \"\"\"\n Returns height and width of current terminal. First tries to get\n size via termios.TIOCGWINSZ, then from environment. Defaults to 25\n lines x 80 columns if both methods fail.\n\n :param fd: file descriptor (default: 1=stdout)\n \"\"\"\n try:\n import fcntl, termios, struct\n hw = struct.unpack('hh', fcntl.ioctl(fd, termios.TIOCGWINSZ, '1234'))\n except:\n try:\n hw = (os.environ['LINES'], os.environ['COLUMNS'])\n except:\n hw = (25, 80)\n\n if hw[0] == 0 or hw[1] == 0:\n hw = (25, 80)\n\n return hw\n\n\ndef resolve_cell(row, spec):\n if type(spec) == str:\n return row.get(spec)\n\n if isinstance(spec, collections.Callable):\n return spec(row)\n\n return ''\n\n\ndef read_value(value, tv=ValueType.STRING):\n if value is None:\n if tv == ValueType.ARRAY:\n return []\n\n if tv == ValueType.DICT:\n return {}\n\n if tv == ValueType.SET:\n return set()\n\n if tv == ValueType.BOOLEAN:\n return False\n\n return value\n\n if tv in (ValueType.STRING, ValueType.TEXT_FILE):\n return str(value)\n\n if tv in (ValueType.NUMBER, ValueType.SIZE):\n return int(value)\n\n if tv == ValueType.BOOLEAN:\n if type(value) is bool:\n return value\n\n if str(value).lower() in ('true', 'yes', 'on', '1'):\n return True\n\n if str(value).lower() in ('false', 'no', 'off', '0'):\n return False\n\n if tv == ValueType.SET:\n if type(value) is list:\n return set(value)\n else:\n return {value}\n\n if tv == ValueType.ARRAY:\n if type(value) is list:\n return value\n else:\n return [value]\n\n if tv == ValueType.DICT:\n if type(value) is dict:\n return value\n\n if tv == ValueType.OCTNUMBER:\n return int(value)\n\n if tv == ValueType.PERMISSIONS:\n if isinstance(value, str):\n value = string_to_int(value)\n else:\n if value > 0o777:\n raise ValueError('Invalid permissions format - use octal notation with maximum value of 0o777')\n\n return get_unix_permissions(value)\n\n if tv == ValueType.PASSWORD:\n return Password(str(value))\n\n raise ValueError(_(\"Invalid value '{0}', expected {1} value\".format(value, str(tv).split('ValueType.')[-1].lower())))\n\n\ndef format_value(value, vt=ValueType.STRING, fmt=None):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).format_value(value, vt)\n\n\ndef output_value(value, fmt=None, **kwargs):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_value(value, **kwargs)\n\n\ndef output_list(data, label=_(\"Items\"), fmt=None, **kwargs):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_list(data, label, **kwargs)\n\n\ndef output_dict(data, key_label=_(\"Key\"), value_label=_(\"Value\"), fmt=None, **kwargs):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_dict(data, key_label, value_label)\n\n\ndef output_table(table, fmt=None, **kwargs):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_table(table, **kwargs)\n\n\ndef output_object(item, **kwargs):\n fmt = kwargs.pop('fmt', None)\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_object(item, **kwargs)\n\n\ndef output_tree(tree, children, label, fmt=None, **kwargs):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_tree(tree, children, label, **kwargs)\n\n\ndef get_formatter(name):\n module = importlib.import_module('freenas.cli.output.' + name)\n return module._formatter()\n\n\ndef output_msg(message, fmt=None, **kwargs):\n fmt = fmt or config.instance.variables.get('output_format')\n return get_formatter(fmt).output_msg(message, **kwargs)\n\n\ndef output_is_ascii():\n return config.instance.variables.get('output_format') == 'ascii'\n\n\n# The following solution to implement `LESS(1)` style output is a combination\n# of snippets taken from the following stackoverflow answers:\n# 1. http://stackoverflow.com/questions/14197009/how-can-i-redirect-print-output-of-a-function-in-python#answer-14197079\n# 2. http://stackoverflow.com/questions/6728661/paging-output-from-python#answer-18234081\n@contextlib.contextmanager\ndef stdout_redirect(where):\n sys.stdout = where\n try:\n yield where\n finally:\n sys.stdout = sys.__stdout__\n\n\nclass StringIO(io.StringIO):\n \"\"\"\n Decode inputs so we can make it work in py2 and py3.\n In py2 the print function automatically encode inputs.\n \"\"\"\n def write(self, value, *args, **kwargs):\n if six.PY2 and isinstance(value, str):\n value = value.decode('utf8')\n return super(StringIO, self).write(value, *args, **kwargs)\n\n\ndef output_less(output_call_list):\n # First check if its either a list or a func (if not then raise TypeError)\n if hasattr(output_call_list, '__call__'):\n # It is a single func so just wrap it in a list and the below code\n # will DTRT\n output_call_list = [output_call_list]\n elif type(output_call_list) is list:\n for x in output_call_list:\n if not hasattr(x, '__call__'):\n raise TypeError('One of the items provided in the ' +\n 'output_call_list was not a function')\n else:\n raise TypeError('Input to `output_less` must either be a function or' +\n ' a list of functions. Instead the following type ' +\n 'was received: {0}'.format(type(output_call_list)))\n\n with stdout_redirect(StringIO()) as new_stdout:\n for output_func_call in output_call_list:\n output_func_call(new_stdout)\n\n new_stdout.seek(0)\n pydoc.pager(new_stdout.read())\n\n\ndef format_output(object, **kwargs):\n if isinstance(object, Object):\n output_object(object, **kwargs)\n\n elif isinstance(object, Table):\n output_table(object, **kwargs)\n\n elif isinstance(object, dict):\n output_dict(object, **kwargs)\n\n elif isinstance(object, Sequence):\n for i in object:\n format_output(i, **kwargs)\n\n elif isinstance(object, list):\n output_list(object, **kwargs)\n\n else:\n output_msg(object, **kwargs)\n\n\ndef refresh_prompt():\n if not config.instance.variables.get('tasks_blocking'):\n config.instance.ml.blank_readline()\n config.instance.ml.restore_readline()\n\n\ndef output_msg_locked(msg):\n output_lock.acquire()\n config.instance.ml.blank_readline()\n output_msg(msg)\n sys.stdout.flush()\n config.instance.ml.restore_readline()\n output_lock.release()\n\n\ndef get_humanized_size(value):\n value = int(value)\n suffixes = [\n 'iB',\n 'KiB',\n 'MiB',\n 'GiB'\n ]\n\n for suffix in suffixes:\n next_step = value / 1024\n if not int(next_step):\n return str(round(value, 2)) + ' ' + suffix\n value = next_step\n\n return str(round(value, 2)) + ' ' + 'TiB'\n","repo_name":"freenas/cli","sub_path":"freenas/cli/output/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":13856,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"12"} +{"seq_id":"5411074921","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import Post\nfrom django.utils import timezone\nfrom .forms import PostForm\nfrom rest_framework.response import Response\nfrom rest_framework import generics\nfrom .serializers import PostSerializer\nfrom django.conf import settings\nfrom django.contrib.auth.models import User\nfrom rest_framework import status\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.views.generic.edit import CreateView\nfrom django.urls import reverse_lazy\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth import login\n\ndef home(request):\n if settings.DEBUG:\n template_name = \"index-dev.html\"\n else:\n template_name = \"index.html\"\n return render(request, template_name)\n\ndef register(request): \n if request.POST == 'POST': \n form = UserCreationForm(request.POST) \n if form.is_valid(): \n user = form.save()\n login(request, user)\n return redirect('home') \n else:\n console.log(\"not valid\")\n else: \n form = UserCreationForm() \n return render(request, 'registration/signup.html', {'form':form} )\n\nclass PostView(generics.RetrieveAPIView):\n queryset = Post.objects.all()\n serializer_class = PostSerializer\n\n #get post by Id or get all posts if there is no Id\n def get(self, request, *args, **kwargs):\n try:\n id = request.query_params[\"id\"]\n if id != None:\n post = Post.objects.get(id=id)\n serializer = PostSerializer(post)\n except:\n queryset = self.get_queryset()\n serializer = PostSerializer(queryset, many=True)\n \n return Response(serializer.data)\n\n #add new post\n @csrf_protect\n def post(self, request, *args, **kwargs):\n new_post_data = request.data\n\n user = User.objects.get(id=new_post_data[\"author\"])\n\n new_post = Post.objects.create(\n author= user,\n title=new_post_data[\"title\"], text=new_post_data[\"text\"],\n created_date=timezone.now(), published_date=timezone.now())\n\n queryset = self.get_queryset()\n serializer = PostSerializer(queryset, many=True)\n \n return Response(serializer.data)\n\n #change post by id\n @csrf_protect\n def put(self, request, *args, **kwargs):\n id = request.query_params[\"id\"]\n \n if id != None:\n post_object = Post.objects.get(id=id) \n data = request.data\n user = User.objects.get(id=data[\"author\"])\n\n post_object.author = user\n post_object.text = data[\"text\"]\n post_object.created_date = data['created_date']\n post_object.published_date = timezone.now()\n post_object.title = data[\"title\"]\n\n post_object.save()\n\n serializer = PostSerializer(post_object)\n \n return Response(serializer.data)\n\n #delete post by id\n @csrf_protect\n def delete(self, request, *args, **kwargs):\n id = request.query_params[\"id\"]\n \n if id != None:\n post_to_delete=Post.objects.get(id=id)\n post_to_delete.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n# from django.contrib.auth import login as auth_login\n# from django.contrib.auth.forms import UserCreationForm\n# from django.shortcuts import render, redirect\n\n# def signup(request):\n# if request.method == 'POST':\n# form = UserCreationForm(request.POST)\n# if form.is_valid():\n# user = form.save()\n# auth_login(request, user)\n# return redirect('home')\n# else:\n# form = UserCreationForm()\n# return render(request, 'signup.html', {'form': form})","repo_name":"Sandreykina/testProjectOnDjango2","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"7655223738","text":"# -*- coding: utf-8 -*-\n# https://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=1166&lang=jp\nimport sys\nimport heapq as hq\n\ninput = sys.stdin.readline\nwhile True:\n w, h = map(int, input().split())\n if w == 0 and h == 0:\n break\n movable_places = [[[] for i in range(w)] for j in range(h)]\n for height in range(h):\n walls_right = list(map(int, input().split()))\n # print(walls_right)\n for width in range(w - 1):\n if not walls_right[width]:\n movable_places[height][width].append((height, width + 1))\n movable_places[height][width + 1].append((height, width))\n if height == h - 1:\n break\n walls_down = list(map(int, input().split()))\n # print(walls_down)\n for width in range(w):\n if not walls_down[width]:\n movable_places[height][width].append((height + 1, width))\n movable_places[height + 1][width].append((height, width))\n search_hq = [] # 距離、x,y\n for x, y in movable_places[0][0]:\n search_hq.append((1, x, y))\n hq.heapify(search_hq)\n min_distance = [[float(\"inf\") for i in range(w)] for i in range(h)]\n min_distance[0][0] = 0\n while search_hq:\n d, x, y = hq.heappop(search_hq)\n if min_distance[x][y] > d:\n min_distance[x][y] = d\n for neighbor in movable_places[x][y]:\n x, y = neighbor\n hq.heappush(search_hq, (d + 1, x, y))\n distance = min_distance[-1][-1] + 1\n if distance < w * h:\n print(distance)\n else:\n print(0)\n","repo_name":"Shinyanogit/procon","sub_path":"unsolved/100問/question32/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"14607302599","text":"import pagoda\nimport pytest\n\n\ndef test_gravity(world):\n assert world.gravity == (0, 0, -9.81)\n world.gravity = 0, 1, 0\n assert world.gravity == (0, 1, 0)\n\n\ndef test_cfm(world):\n world.cfm = 0.1\n assert world.cfm == 0.1\n\n\ndef test_erp(world):\n world.erp = 0.1\n assert world.erp == 0.1\n\n\ndef test_create_body(world):\n assert len(list(world.bodies)) == 0\n s1 = world.create_body('sphere', 'foo', radius=3)\n assert world.get_body('foo') is s1\n assert len(list(world.bodies)) == 1\n s2 = world.create_body('sphere', radius=2)\n assert world.get_body('sphere0') is s2\n assert len(list(world.bodies)) == 2\n\n\ndef test_join(world):\n box = world.create_body('box', lengths=(1, 1, 1))\n cap = world.create_body('cap', length=1, radius=0.1)\n cap.position = 0, 0, 1\n j = world.join('hinge', box, cap, name='foo', anchor=(0, 0, 0))\n assert world.get_joint('foo') is j\n\n\ndef test_body_states(world):\n assert world.get_body_states() == []\n box = world.create_body('box', lengths=(1, 1, 1))\n assert world.get_body_states() == [\n ('box0', (0, 0, 0), (1, 0, 0, 0), (0, 0, 0), (0, 0, 0))]\n BS = pagoda.physics.BodyState\n world.set_body_states([\n BS('box0', (1, 2, 3), (1, 0, 0, 0), (3, -1, 2), (0, 0, 0))])\n assert world.get_body_states() == [\n ('box0', (1, 2, 3), (1, 0, 0, 0), (3, -1, 2), (0, 0, 0))]\n\n\ndef test_are_connected(world):\n box = world.create_body('box', lengths=(1, 1, 1))\n cap = world.create_body('cap', length=1, radius=0.1)\n cap.position = 0, 0, 1\n assert not world.are_connected('box0', 'cap0')\n world.join('hinge', 'box0', 'cap0')\n assert world.are_connected('box0', 'cap0')\n\n\ndef test_on_collision(world):\n box = world.create_body('box', lengths=(1, 1, 1))\n cap = world.create_body('cap', length=1, radius=0.1)\n cap.position = 0, 0, 1\n assert not world.are_connected('box0', 'cap0')\n world.on_collision(None, box.ode_geom, cap.ode_geom)\n assert world.are_connected('box0', 'cap0')\n","repo_name":"EmbodiedCognition/pagoda","sub_path":"test/physics_world_test.py","file_name":"physics_world_test.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"12"} +{"seq_id":"38363545334","text":"from PyQt5.QtWidgets import QApplication, QWidget, QHBoxLayout, QVBoxLayout, QGridLayout, QLabel, QComboBox, \\\n QPushButton, QLineEdit, QListWidget\n\nfrom PyQt5.QtCore import Qt,QTimer\n\nimport sys\nimport serial\nimport serial.tools.list_ports as listport\n\nport=serial.Serial()\n\n\nclass Pencere(QWidget):\n def __init__(self):\n super().__init__()\n self.arayuz()\n self.show()\n\n\n def arayuz(self): # UI design\n self.setWindowTitle(\"Modbus RTU by Python\")\n vboxAna=QVBoxLayout()\n hbox1=QHBoxLayout()\n grid1=QGridLayout()\n labelComport=QLabel(\"COM Port\")\n grid1.addWidget(labelComport,1,1,Qt.AlignLeft)\n self.comboboxComPort = QComboBox()\n grid1.addWidget(self.comboboxComPort,2,1,Qt.AlignLeft)\n labelBaudrate=QLabel(\"Baudrate\")\n grid1.addWidget(labelBaudrate, 1, 2, Qt.AlignLeft)\n self.comboboxBaudrate = QComboBox()\n grid1.addWidget(self.comboboxBaudrate, 2, 2, Qt.AlignLeft)\n labelAyarlar = QLabel(\"Ayarlar\")\n grid1.addWidget(labelAyarlar, 1, 3, Qt.AlignLeft)\n self.comboboxAyarlar = QComboBox()\n grid1.addWidget(self.comboboxAyarlar, 2, 3, Qt.AlignLeft)\n self.pushbuttonBaglan = QPushButton(\"Bağlan\")\n grid1.addWidget(self.pushbuttonBaglan, 1, 4, Qt.AlignLeft)\n self.pushbuttonBaglantiKes = QPushButton(\"Bağlantı Kes\") # Close Connection Button\n grid1.addWidget(self.pushbuttonBaglantiKes, 2, 4, Qt.AlignLeft)\n\n hbox1.addLayout(grid1)\n\n vboxAna.addLayout(hbox1)\n vboxAna.addSpacing(40)\n hbox2=QHBoxLayout()\n grid2= QGridLayout()\n\n labelAdres = QLabel(\"Adres\")\n grid2.addWidget(labelAdres, 1, 1, Qt.AlignLeft)\n self.lineeditAdres = QLineEdit()\n self.lineeditAdres.setText(\"01\")\n self.lineeditAdres.setFixedWidth(40)\n grid2.addWidget(self.lineeditAdres, 2, 1, Qt.AlignLeft)\n\n labelKomut = QLabel(\"Komut ?\")\n labelKomut.setToolTip(\"01- Tek Bobin Durumu Oku \\n\" +\n \"02- Giriş Durumu Oku\\n03- Tutucu Registerleri Oku \\n\" +\n \"04- Giriş Registerleri Oku \\n\" +\n \"05- Sadece Bir bobin durumu değiştir \\n\" +\n \"06- Sadece Bir Register durumunu değiştir \\n\" +\n \"0F- Birden fazla Bobin içeriği değiştir \\n\" +\n \"10- Birden fazla Registere Değer atamak \")\n grid2.addWidget(labelKomut, 1, 2, Qt.AlignLeft)\n self.lineeditKomut = QLineEdit()\n self.lineeditKomut.setText(\"06\")\n self.lineeditKomut.setFixedWidth(40)\n grid2.addWidget(self.lineeditKomut, 2, 2, Qt.AlignLeft)\n labelParametre = QLabel(\"Parametre\")\n grid2.addWidget(labelParametre, 1, 3, Qt.AlignLeft)\n self.lineeditParametre = QLineEdit()\n self.lineeditParametre.setText(\"20010DAC\")\n self.lineeditParametre.setFixedWidth(160)\n grid2.addWidget(self.lineeditParametre, 2, 3, Qt.AlignLeft)\n labelCrc = QLabel(\"CRC\")\n grid2.addWidget(labelCrc, 1, 4, Qt.AlignLeft)\n self.lineeditCrc = QLineEdit()\n self.lineeditCrc.setFixedWidth(40)\n grid2.addWidget(self.lineeditCrc, 2, 4, Qt.AlignLeft)\n hbox2.addLayout(grid2)\n\n vboxAna.addLayout(hbox2)\n vbox1 = QVBoxLayout()\n self.pushbuttonGonder = QPushButton(\"Gönder\")\n self.listCevap = QListWidget()\n labelCevap=QLabel(\"Gelen Cevap\")\n vbox1.addWidget(self.pushbuttonGonder) # Send Data\n vbox1.addWidget(labelCevap)\n vbox1.addWidget(self.listCevap)\n vboxAna.addLayout(vbox1)\n\n self.setLayout(vboxAna)\n self.ilkdurum()\n self.olaylar()\n\n def ilkdurum(self): #initialize\n portlar=listport.comports()\n # Put all serial interfaces in combobox\n for cp in portlar:\n self.comboboxComPort.addItem(str(cp.device))\n ayarliste= [\"8,O,1\",\"8,E,1\",\"8,N,2\"]\n liste=[\"9600\",\"14400\", \"19200\", \"38400\", \"57600\", \"115200\"]\n self.comboboxBaudrate.addItems(liste)\n self.comboboxAyarlar.addItems(ayarliste)\n self.pushbuttonBaglantiKes.setEnabled(False)\n self.pushbuttonGonder.setEnabled(False)\n\n def olaylar(self): #Events\n self.pushbuttonBaglan.clicked.connect(self.baglan) #open serialport\n self.pushbuttonBaglantiKes.clicked.connect(self.baglantikes) #close serialport\n self.pushbuttonGonder.clicked.connect(self.gonder) # send data\n \n\n def baglan(self):\n\n port.baudrate = int(self.comboboxBaudrate.currentText())\n ayar=self.comboboxAyarlar.currentText() # take settings from setting combobox\n\n port.bytesize = serial.EIGHTBITS\n\n if ayar[2] == \"E\":\n port.parity = serial.PARITY_EVEN\n if ayar[2] == \"O\":\n port.parity = serial.PARITY_ODD\n if ayar[2] == \"N\":\n port.parity = serial.PARITY_NONE\n if ayar[4] == \"1\":\n port.stopbits = serial.STOPBITS_ONE\n if ayar[4] == \"2\":\n port.stopbits = serial.STOPBITS_TWO\n port.port = self.comboboxComPort.currentText()\n if not port.is_open:\n port.open()\n if port.is_open:\n self.pushbuttonBaglan.setEnabled(False)\n self.pushbuttonGonder.setEnabled(True)\n self.pushbuttonBaglantiKes.setEnabled(True)\n self.timer=QTimer()\n self.timer.timeout.connect(self.verial)\n self.timer.start(100)\n\n\n\n\n def baglantikes(self): #close connection\n\n if port.is_open:\n port.close()\n if not port.is_open:\n self.pushbuttonBaglan.setEnabled(True)\n self.pushbuttonGonder.setEnabled(False)\n self.pushbuttonBaglantiKes.setEnabled(False)\n self.timer.stop()\n\n def verial(self): #read data from serialport\n veri=\"\"\n if port.is_open:\n gelenVeri = port.read(port.in_waiting)\n\n if not gelenVeri==b'':\n for a in gelenVeri:\n if len(str(hex(a))[2:4].upper())==1:\n veri+=\"0\"+str(hex(a))[2:4].upper()+\"-\"\n\n else:\n veri+= str(hex(a))[2:4].upper()+\"-\"\n\n self.listCevap.insertItem(0, veri)\n\n\n\n def gonder(self): #send data from serialport\n data=self.lineeditAdres.text()+self.lineeditKomut.text()+self.lineeditParametre.text()\n\n data1=[]\n\n for a in range(0,len(data), 2):\n data1.append(int(data[a:a+2],16))\n msbyte, lsbyte =self.crc16(data1)\n self.lineeditCrc.setText(str(hex(msbyte))[2:4].upper()+str(hex(lsbyte))[2:4].upper())\n\n data1.append(msbyte)\n data1.append(lsbyte)\n\n\n port.write(data1)\n\n #calculation of crc16\n def crc16(self,data: bytes, poly=0xA001):\n\n crc = 0xFFFF\n for b in data:\n\n cur_byte = 0xFF & b\n\n for _ in range(0, 8):\n if (crc & 0x0001) ^ (cur_byte & 0x0001):\n crc = (crc >> 1) ^ poly\n else:\n crc >>= 1\n cur_byte >>= 1\n\n crc = (crc << 8) | ((crc >> 8) & 0xFF)\n msbyte = crc >> 8\n lsbyte = crc & 0x00FF\n #returns tupple\n return msbyte & 0xFF, lsbyte & 0xFF\n\n#main\n\nif __name__==\"__main__\":\n app=QApplication(sys.argv)\n pen=Pencere()\n sys.exit(app.exec())","repo_name":"eaglebjkbv/PythonExamples","sub_path":"ModbusPythonQt/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7421,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"12"} +{"seq_id":"14198952614","text":"rock = '''\n _______\n---' ____)\n (_____)\n (_____)\n (____)\n---.__(___)\n'''\n\npaper = '''\n _______\n---' ____)____\n ______)\n _______)\n _______)\n---.__________)\n'''\n\nscissors = '''\n _______\n---' ____)____\n ______)\n __________)\n (____)\n---.__(___)\n'''\n\nimport random\n\n# 잘못 입력했을 때 예외처리\ntry :\n user = int(input('''\n- What did you choose? -\n| 0 for Rock |\n| 1 for Paper |\n| 2 for Scissors |\n>>> '''))\n\n # 이미지 단순하게 불러오기\n image = [rock, paper, scissors]\n print(image[user])\n\n computer = random.randint(0, 2)\n print('Computer chose: \\n')\n print(image[computer])\n\n if user == computer :\n print('Draw!!!\\n')\n elif user == 0:\n if computer == 1 :\n print('You lose..\\n')\n else :\n print('You win!!!\\n')\n elif user == 1 :\n if computer == 2 :\n print('You lose..\\n')\n else :\n print('You win!!!\\n')\n else :\n if computer == 0 :\n print('You lose..\\n')\n else :\n print('You win!!!\\n')\n\nexcept Exception as ex : \n print('''\n| You lose....! |\n| Please insert 0, 1, 2 |\n''')\n","repo_name":"dbal1107/Study_Python","sub_path":"100days/1_beginner/day04_RSP_fin.py","file_name":"day04_RSP_fin.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"43036747833","text":"import pandas as pd\n\nfrom pyspark.sql import functions as F, types as T, SparkSession\n\n# initialize spark session\nspark = SparkSession.builder.getOrCreate()\n\n# read data\nbase_path = \"/home/brett/git/earnings_call_predictor/docs\"\ndf = pd.read_csv(f\"{base_path}/sample_price_data.csv\")\nsdf = spark.read.csv(f\"{base_path}/sample_price_data.csv\",\n header=True)\n\n# spark is a lazy executor, so it doesn't actually run the process until you cache/some other \"trigger\" operation\nsdf.cache().count()\n\n# add column\ndf.loc[:, \"price_vol\"] = df.loc[:, \"close_price\"] + df.loc[:, \"volume\"]\nsdf = sdf.withColumn(\"price_vol\",\n F.col(\"close_price\") + F.col(\"volume\"))\nsdf.cache().count()\n\n# rename column\ndf.rename({\"price_vol\": \"pv\"},\n axis=1,\n inplace=True)\n\nsdf = sdf.withColumnRenamed(\"price_vol\",\n \"pv\")\nsdf.cache().count()\n","repo_name":"brian-nebeker/earnings_call_predictor","sub_path":"docs/pyspark_examples.py","file_name":"pyspark_examples.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"38516921232","text":"# @file WindowsVsToolChain.py\r\n# Plugin to configures paths for the VS2017 and VS2019 tool chain\r\n##\r\n# This plugin works in conjuncture with the tools_def\r\n#\r\n# Copyright (c) Microsoft Corporation\r\n# SPDX-License-Identifier: BSD-2-Clause-Patent\r\n##\r\nimport os\r\nimport logging\r\nfrom edk2toolext.environment.plugintypes.uefi_build_plugin import IUefiBuildPlugin\r\nimport edk2toollib.windows.locate_tools as locate_tools\r\nfrom edk2toollib.windows.locate_tools import FindWithVsWhere\r\nfrom edk2toolext.environment import shell_environment\r\nfrom edk2toolext.environment import version_aggregator\r\nfrom edk2toollib.utility_functions import GetHostInfo\r\n\r\n\r\nclass WindowsVsToolChain(IUefiBuildPlugin):\r\n\r\n def do_post_build(self, thebuilder):\r\n return 0\r\n\r\n def do_pre_build(self, thebuilder):\r\n self.Logger = logging.getLogger(\"WindowsVsToolChain\")\r\n interesting_keys = [\"ExtensionSdkDir\", \"INCLUDE\", \"LIB\", \"LIBPATH\", \"UniversalCRTSdkDir\",\r\n \"UCRTVersion\", \"WindowsLibPath\", \"WindowsSdkBinPath\", \"WindowsSdkDir\", \"WindowsSdkVerBinPath\",\r\n \"WindowsSDKVersion\", \"VCToolsInstallDir\", \"Path\"]\r\n\r\n #\r\n # VS2017 - Follow VS2017 where there is potential for many versions of the tools.\r\n # If a specific version is required then the user must set both env variables:\r\n # VS150INSTALLPATH: base install path on system to VC install dir. Here you will find the VC folder, etc\r\n # VS150TOOLVER: version number for the VC compiler tools\r\n # VS2017_PREFIX: path to MSVC compiler folder with trailing slash (can be used instead of two vars above)\r\n # VS2017_HOST: set the host architecture to use for host tools, and host libs, etc\r\n if thebuilder.env.GetValue(\"TOOL_CHAIN_TAG\") == \"VS2017\":\r\n\r\n # check to see if host is configured\r\n # HostType for VS2017 should be (defined in tools_def):\r\n # x86 == 32bit Intel\r\n # x64 == 64bit Intel\r\n # arm == 32bit Arm\r\n # arm64 == 64bit Arm\r\n #\r\n HostType = shell_environment.GetEnvironment().get_shell_var(\"VS2017_HOST\")\r\n if HostType is not None:\r\n HostType = HostType.lower()\r\n self.Logger.info(\r\n f\"HOST TYPE defined by environment. Host Type is {HostType}\")\r\n else:\r\n HostInfo = GetHostInfo()\r\n if HostInfo.arch == \"x86\":\r\n if HostInfo.bit == \"32\":\r\n HostType = \"x86\"\r\n elif HostInfo.bit == \"64\":\r\n HostType = \"x64\"\r\n else:\r\n raise NotImplementedError()\r\n\r\n # VS2017_HOST options are not exactly the same as QueryVcVariables. This translates.\r\n VC_HOST_ARCH_TRANSLATOR = {\r\n \"x86\": \"x86\", \"x64\": \"AMD64\", \"arm\": \"not supported\", \"arm64\": \"not supported\"}\r\n\r\n # check to see if full path already configured\r\n if shell_environment.GetEnvironment().get_shell_var(\"VS2017_PREFIX\") != None:\r\n self.Logger.info(\"VS2017_PREFIX is already set.\")\r\n\r\n else:\r\n install_path = self._get_vs_install_path(\r\n \"VS2017\".lower(), \"VS150INSTALLPATH\")\r\n vc_ver = self._get_vc_version(install_path, \"VS150TOOLVER\")\r\n\r\n if install_path is None or vc_ver is None:\r\n self.Logger.error(\r\n \"Failed to configure environment for VS2017\")\r\n return -1\r\n\r\n version_aggregator.GetVersionAggregator().ReportVersion(\r\n \"Visual Studio Install Path\", install_path, version_aggregator.VersionTypes.INFO)\r\n version_aggregator.GetVersionAggregator().ReportVersion(\r\n \"VC Version\", vc_ver, version_aggregator.VersionTypes.TOOL)\r\n\r\n # make VS2017_PREFIX to align with tools_def.txt\r\n prefix = os.path.join(install_path, \"VC\",\r\n \"Tools\", \"MSVC\", vc_ver)\r\n prefix = prefix + os.path.sep\r\n shell_environment.GetEnvironment().set_shell_var(\"VS2017_PREFIX\", prefix)\r\n shell_environment.GetEnvironment().set_shell_var(\"VS2017_HOST\", HostType)\r\n\r\n shell_env = shell_environment.GetEnvironment()\r\n # Use the tools lib to determine the correct values for the vars that interest us.\r\n vs_vars = locate_tools.QueryVcVariables(\r\n interesting_keys, VC_HOST_ARCH_TRANSLATOR[HostType], vs_version=\"vs2017\")\r\n for (k, v) in vs_vars.items():\r\n shell_env.set_shell_var(k, v)\r\n\r\n # now confirm it exists\r\n if not os.path.exists(shell_environment.GetEnvironment().get_shell_var(\"VS2017_PREFIX\")):\r\n self.Logger.error(\"Path for VS2017 toolchain is invalid\")\r\n return -2\r\n\r\n #\r\n # VS2019 - Follow VS2019 where there is potential for many versions of the tools.\r\n # If a specific version is required then the user must set both env variables:\r\n # VS160INSTALLPATH: base install path on system to VC install dir. Here you will find the VC folder, etc\r\n # VS160TOOLVER: version number for the VC compiler tools\r\n # VS2019_PREFIX: path to MSVC compiler folder with trailing slash (can be used instead of two vars above)\r\n # VS2017_HOST: set the host architecture to use for host tools, and host libs, etc\r\n elif thebuilder.env.GetValue(\"TOOL_CHAIN_TAG\") == \"VS2019\":\r\n\r\n # check to see if host is configured\r\n # HostType for VS2019 should be (defined in tools_def):\r\n # x86 == 32bit Intel\r\n # x64 == 64bit Intel\r\n # arm == 32bit Arm\r\n # arm64 == 64bit Arm\r\n #\r\n HostType = shell_environment.GetEnvironment().get_shell_var(\"VS2019_HOST\")\r\n if HostType is not None:\r\n HostType = HostType.lower()\r\n self.Logger.info(\r\n f\"HOST TYPE defined by environment. Host Type is {HostType}\")\r\n else:\r\n HostInfo = GetHostInfo()\r\n if HostInfo.arch == \"x86\":\r\n if HostInfo.bit == \"32\":\r\n HostType = \"x86\"\r\n elif HostInfo.bit == \"64\":\r\n HostType = \"x64\"\r\n else:\r\n raise NotImplementedError()\r\n\r\n # VS2019_HOST options are not exactly the same as QueryVcVariables. This translates.\r\n VC_HOST_ARCH_TRANSLATOR = {\r\n \"x86\": \"x86\", \"x64\": \"AMD64\", \"arm\": \"not supported\", \"arm64\": \"not supported\"}\r\n\r\n # check to see if full path already configured\r\n if shell_environment.GetEnvironment().get_shell_var(\"VS2019_PREFIX\") != None:\r\n self.Logger.info(\"VS2019_PREFIX is already set.\")\r\n\r\n else:\r\n install_path = self._get_vs_install_path(\r\n \"VS2019\".lower(), \"VS160INSTALLPATH\")\r\n vc_ver = self._get_vc_version(install_path, \"VS160TOOLVER\")\r\n\r\n if install_path is None or vc_ver is None:\r\n self.Logger.error(\r\n \"Failed to configure environment for VS2019\")\r\n return -1\r\n\r\n version_aggregator.GetVersionAggregator().ReportVersion(\r\n \"Visual Studio Install Path\", install_path, version_aggregator.VersionTypes.INFO)\r\n version_aggregator.GetVersionAggregator().ReportVersion(\r\n \"VC Version\", vc_ver, version_aggregator.VersionTypes.TOOL)\r\n\r\n # make VS2019_PREFIX to align with tools_def.txt\r\n prefix = os.path.join(install_path, \"VC\",\r\n \"Tools\", \"MSVC\", vc_ver)\r\n prefix = prefix + os.path.sep\r\n shell_environment.GetEnvironment().set_shell_var(\"VS2019_PREFIX\", prefix)\r\n shell_environment.GetEnvironment().set_shell_var(\"VS2019_HOST\", HostType)\r\n\r\n shell_env = shell_environment.GetEnvironment()\r\n # Use the tools lib to determine the correct values for the vars that interest us.\r\n vs_vars = locate_tools.QueryVcVariables(\r\n interesting_keys, VC_HOST_ARCH_TRANSLATOR[HostType], vs_version=\"vs2019\")\r\n for (k, v) in vs_vars.items():\r\n shell_env.set_shell_var(k, v)\r\n\r\n # now confirm it exists\r\n if not os.path.exists(shell_environment.GetEnvironment().get_shell_var(\"VS2019_PREFIX\")):\r\n self.Logger.error(\"Path for VS2019 toolchain is invalid\")\r\n return -2\r\n\r\n return 0\r\n\r\n def _get_vs_install_path(self, vs_version, varname):\r\n # check if already specified\r\n path = None\r\n if varname is not None:\r\n path = shell_environment.GetEnvironment().get_shell_var(varname)\r\n\r\n if(path is None):\r\n # Not specified...find latest\r\n try:\r\n path = FindWithVsWhere(vs_version=vs_version)\r\n except (EnvironmentError, ValueError, RuntimeError) as e:\r\n self.Logger.error(str(e))\r\n return None\r\n\r\n if path is not None and os.path.exists(path):\r\n self.Logger.debug(\"Found VS instance for %s\", vs_version)\r\n else:\r\n self.Logger.error(\r\n f\"VsWhere successfully executed, but could not find VS instance for {vs_version}.\")\r\n return path\r\n\r\n def _get_vc_version(self, path, varname):\r\n # check if already specified\r\n vc_ver = shell_environment.GetEnvironment().get_shell_var(varname)\r\n if (path is None):\r\n self.Logger.critical(\r\n \"Failed to find Visual Studio tools. Might need to check for VS install\")\r\n return vc_ver\r\n if(vc_ver is None):\r\n # Not specified...find latest\r\n p2 = os.path.join(path, \"VC\", \"Tools\", \"MSVC\")\r\n if not os.path.isdir(p2):\r\n self.Logger.critical(\r\n \"Failed to find VC tools. Might need to check for VS install\")\r\n return vc_ver\r\n vc_ver = os.listdir(p2)[-1].strip() # get last in list\r\n self.Logger.debug(\"Found VC Tool version is %s\" % vc_ver)\r\n return vc_ver\r\n","repo_name":"tianocore/edk2","sub_path":"BaseTools/Plugin/WindowsVsToolChain/WindowsVsToolChain.py","file_name":"WindowsVsToolChain.py","file_ext":"py","file_size_in_byte":10621,"program_lang":"python","lang":"en","doc_type":"code","stars":3931,"dataset":"github-code","pt":"12"} +{"seq_id":"34377309740","text":"class Image:\r\n def __init__(self, resolution, title, extension):\r\n self.resolution = resolution\r\n self.title = title\r\n self.extension = extension\r\n\r\n def resize(self, new_resolution):\r\n self.resolution = new_resolution\r\n\r\n\r\nimg = Image('1920 x 1080', 'Landscape', '.png')\r\n\r\nprint(img.resolution)\r\n\r\nimg.resize('4000 x 3000')\r\n\r\nprint(img.resolution)\r\n","repo_name":"dmytro-smykov/stashchuk-python","sub_path":"39_objects.py","file_name":"39_objects.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"32158804864","text":"from datetime import datetime, timedelta\nfrom itertools import chain\nfrom django.utils import timezone\nfrom .models import Task, Event, Routine, TimeSlot\n\n\n# We'll need to be able to figure out a concrete date to place routine events on\n# by deriving it from the current date and their assigned weekday\ndef get_date_from_weekday(day):\n today = datetime.today()\n delta = (day - today.weekday()) % 7\n date = datetime.today() + timedelta(days=delta)\n return date\n\n\n# When we run the scheduling algorithm, we'll want to clean out any time slots\n# from last time\ndef clean_time_slots(date):\n TimeSlot.objects.filter(date=date).delete()\n\n\n# Here's the scheduler, it runs based on a given weekday rather than a date.\n# This makes things easier\ndef update_schedule(day):\n # Get the date and clean out time slots\n date = get_date_from_weekday(day)\n clean_time_slots(date)\n\n # Get all events and routine events for today, ordered by start time\n routines = Routine.objects.filter(day=day).order_by(\"start_time\")\n events = Event.objects.filter(date=date).order_by(\"start_time\")\n\n # Sort them together\n all_events = sorted(\n chain(routines, events), key=lambda instance: instance.start_time\n )\n\n # Get all the tasks, ordered by due date, time estimate and descending priority level\n tasks = Task.objects.filter(done=False).order_by(\n \"due_date\", \"time_estimate\", \"-priority\"\n )\n\n # Convert the iterable into a list, this is easier to handle and we can remove tasks\n # from the list once they have been allocated a time slot\n task_list = list(tasks)\n\n # Initialise an empty list for holding the time slots, we'll write them all to the\n # database at the end\n time_slots = []\n\n # Iterate over all the events and routines,\n # creating corresponding time slots\n for item in all_events:\n if isinstance(item, Event):\n ts = TimeSlot(\n date=date,\n start_time=item.get_start(),\n end_time=item.get_end(),\n associated_type=\"E\",\n associated_event=item,\n )\n\n elif isinstance(item, Routine):\n ts = TimeSlot(\n date=date,\n start_time=item.get_start(),\n end_time=item.get_end(),\n associated_type=\"R\",\n associated_routine=item,\n )\n\n # Before adding the timslot to the list,\n # check that it has sensible timings.\n # If it doesn't, we can just discard it.\n if ts.start_time <= ts.end_time:\n time_slots.append(ts)\n\n # We can't use a for loop to iterate through the time slots,\n # because we're going to be changing the length of the list.\n # So we have to use a while loop and a counter to keep track of our position.\n pos = 1\n\n # Iterate through the time slots\n while pos < len(time_slots):\n # Start by assuming that there is at least one task which will fit in this time gap\n is_room = True\n\n # As long as tasks keep getting getting inserted,\n # we need to stay here.\n while is_room:\n # Take a note of where we are\n pos_start_loop = pos\n # Iterate over the tasks which are not yet assigned\n for task in task_list:\n # Get the time gap between this timeslot and the last\n prev = time_slots[pos - 1]\n curr = time_slots[pos]\n tdelta = datetime.combine(date, curr.get_start()) - datetime.combine(\n date, prev.get_end()\n )\n\n # If the gap is large enought,\n # create a time slot corresponding to the task and put it here\n if tdelta > task.time_estimate:\n start = prev.get_end()\n end = (\n datetime.combine(date, prev.get_end()) + task.time_estimate\n ).time()\n time_slots.insert(\n pos,\n TimeSlot(\n date=date,\n start_time=start,\n end_time=end,\n associated_type=\"T\",\n associated_task=task,\n ),\n )\n\n task_list.remove(task)\n\n # Increment the position,\n # unless we've reached the end of the list,\n # in which case there are no more spaces so stop.\n if pos <= len(time_slots):\n pos += 1\n else:\n break\n\n # If we reach the end of the loop and the position is the same,\n # that means there's no more room for tasks here,\n # so flag that there is no room and increment position.\n # Otherwise, we go for another loop.\n if pos == pos_start_loop:\n is_room = False\n pos += 1\n\n # Finally, we save all the time slots to the database\n for item in time_slots:\n item.save()\n","repo_name":"highgateschool/MyTime","sub_path":"mysite/tasks/scheduler.py","file_name":"scheduler.py","file_ext":"py","file_size_in_byte":5171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"22377297658","text":"import gmsh\nimport sys\nimport os\nimport numpy as np\nimport math\n\nclass Point(object):\n def __init__(self, tag, x, y, z):\n self.tag = tag\n self.X = x\n self.Y = y\n self.Z = z\n\n def get_tag(self):\n return self.tag\n \n def get_coords(self):\n return [self.X, self.Y, self.Z]\n\n def getX(self):\n return self.X\n\n def getY(self):\n return self.Y\n\n def getZ(self):\n return self.Z\n\n def distance(self, other):\n dx = self.X - other.X\n dy = self.Y - other.Y\n dz = self.Z - other.Z\n return math.sqrt(dx**2 + dy**2 + dz**2)\n\n def move(self, dx, dy, dz):\n self.X = self.X + dx\n self.Y = self.Y + dy\n self.Z = self.Z + dz\n\n def __str__(self):\n str1 = \"Point \" + str(self.tag) + \" has coords: \" + str(self.X) + \", \" + str(self.Y) + \", \" + str(self.Z) + \"\\n\"\n return str1\n\nclass Triangle:\n def __init__(self, tag, nodes_list):\n self.tag = tag\n self.nodes_list = nodes_list\n\n self.vertex_1 = points[int(nodes_list[0]) - 1]\n self.vertex_2 = points[int(nodes_list[1]) - 1]\n self.vertex_3 = points[int(nodes_list[2]) - 1]\n\n self.nodes_coords = Point.get_coords(self.vertex_1) + Point.get_coords(self.vertex_2) + Point.get_coords(self.vertex_3)\n self.center = (list(map(lambda x, y, z: (x + y + z)/3, Point.get_coords(self.vertex_1) , Point.get_coords(self.vertex_2), Point.get_coords(self.vertex_3))))\n\n def add_neighbours(self):\n neighb = []\n for t in triangles:\n nodes = Triangle.get_nodes(t)\n k = 0\n for i in nodes:\n if i in self.nodes_list:\n k = k+1\n \n if (k == 2):\n neighb.append(Triangle.get_tag(t))\n\n self.neighbours = tuple(neighb)\n\n def get_nodes(self):\n return self.nodes_list\n\n def get_tag(self):\n return self.tag\n\n def get_neighbours(self):\n return self.neighbours\n\n def __str__(self):\n nodeCoords_1 =Point.get_coords(self.vertex_1)\n nodeCoords_2 = Point.get_coords(self.vertex_2)\n nodeCoords_3 = Point.get_coords(self.vertex_3)\n str1 = \"Triangle \" + str(self.tag) + \" has nodes: \" + str(self.nodes_list[0]) + \", \" + str(self.nodes_list[1]) + \", \" + str(self.nodes_list[2]) + \"\\n\"\n str2 = \"Nodes coords are:\" + \"\\n\" + str(nodeCoords_1) + \"\\n\" + str(nodeCoords_2) +\"\\n\" + str(nodeCoords_3) + \"\\n\"\n str3 = \"Center coords are: \" + str(self.center) + \"\\n\"\n str4 = \"Neighbours of this triangle are: \" + str(self.neighbours) + \"\\n\"\n return str1 + str2 + str3 + str4 + \"\\n\"\n\ngmsh.initialize()\n\npath = os.path.dirname(os.path.abspath(__file__))\ngmsh.initialize()\n\ngmsh.model.add(\"Square\")\n\n# Build a square surface:\nlc = 0.7\np1 = gmsh.model.geo.addPoint(0, 0, 0, lc)\np2 = gmsh.model.geo.addPoint(1, 0, 0, lc)\np3 = gmsh.model.geo.addPoint(1, 1, 0, lc)\np4 = gmsh.model.geo.addPoint(0, 1, 0, lc)\n\nl1 = gmsh.model.geo.addLine(p1, p2)\nl2 = gmsh.model.geo.addLine(p2, p3)\nl3 = gmsh.model.geo.addLine(p3, p4)\nl4 = gmsh.model.geo.addLine(p4, p1)\n\ncl1 = gmsh.model.geo.addCurveLoop([l1, l2, l3, l4])\n\npl1 = gmsh.model.geo.addPlaneSurface([cl1])\n\ngmsh.model.geo.synchronize()\n\n# Generate mesh:\ngmsh.model.mesh.generate(2)\ngmsh.option.setNumber(\"Mesh.Format\", 1)\ngmsh.option.setNumber(\"Mesh.NodeLabels\", 1)\n\n# Save mesh:\n# gmsh.write(os.path.join(path, os.curdir, \"Simple_Square.msh\"))\n# gmsh.write(os.path.join(path, os.curdir, \"Simple_Square.geo_unrolled\"))\n\n# Access mesh data:\nelementTags, elementNodeTags = gmsh.model.mesh.getElementsByType(2)\nelemNodeTags = np.array(elementNodeTags) \nNodeTags = np.unique(elemNodeTags) # список всех узлов сетки\n\nN_tetr = len(elementTags)\nN_nodes = len(NodeTags)\n\n# Print data about every triangle:\nprint(\"Model has\", N_tetr, \"triangles\")\nprint(\"Model has\", N_nodes, \"points\")\nprint(\"Number of the first triangle:\", elementTags[0])\n\npoints = []\nfor i in range(1, N_nodes+1):\n nodeCoords = gmsh.model.mesh.getNode(i)[0]\n p = Point(NodeTags[i-1], nodeCoords[0], nodeCoords[1], nodeCoords[2])\n points.append(p)\n\ntriangles = []\nfor i in range(N_tetr):\n p_tetr = int(i)\n p_nodes = 3*p_tetr\n \n tag = elementTags[p_tetr] \n nodes_list = (elementNodeTags[p_nodes], elementNodeTags[p_nodes+1], elementNodeTags[p_nodes+2]) # кортеж, тк должен быть неизменяемым\n\n t = Triangle(tag, nodes_list)\n triangles.append(t)\n\n\nwith open(os.path.join(path, os.curdir, \"out_2D.txt\"), \"w\") as file:\n for t in triangles:\n Triangle.add_neighbours(t)\n file.write(Triangle.__str__(t))\n\nif \"-nopopup\" not in sys.argv:\n gmsh.fltk.initialize()\n while gmsh.fltk.isAvailable():\n gmsh.fltk.wait()\n\n# We can use this to clear all the model data:\ngmsh.clear()\n\ngmsh.finalize()\n","repo_name":"alex-pann/IT_4sem","sub_path":"project/test/Square.py","file_name":"Square.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"10701498007","text":"# #############################################################################\n# Problem: create doubly-linked list that has push front/back\n# remove front/back\n# #############################################################################\n\n\n# 1. Create link class\nclass Link:\n def __init__(self, value):\n self.value = value\n self.prev = None\n self.next = None\n\n def __str__(self):\n return str(self.value)\n\n\n# 2. Create linked list class\nclass LinkedList:\n def __init__(self, list=None):\n self.head = None\n self.tail = None\n self.size = 0\n\n # now iterate over list and create links\n if list is not None:\n for i in list:\n self.push_back(i)\n\n def push_back(self, value):\n link = Link(value)\n if self.tail is not None:\n self.tail.next = link\n link.prev = self.tail\n else:\n self.head = link\n self.tail = link\n self.size += 1\n\n def push_front(self, value):\n link = Link(value)\n if self.head is not None:\n self.head.prev = link\n link.next = self.head\n else:\n self.tail = link\n self.head = link\n self.size += 1\n pass\n\n def pop_front(self):\n if self.size is 0:\n return\n if self.size > 1:\n self.head = self.head.next\n self.head.prev = None\n else:\n self.tail = None\n self.head = None\n self.size -= 1\n\n def pop_back(self):\n if self.size is 0:\n return\n if self.size > 1:\n self.tail = self.tail.prev\n self.tail.next = None\n else:\n self.tail = None\n self.head = None\n self.size -= 1\n\n def __str__(self):\n str = \"Printing List\\n\"\n str += \"size: {}\\n\".format(self.size)\n link = self.head\n count = 0\n while link is not None:\n str += \"LinkedList[{}] = {}\\n\".format(count, link.value)\n link = link.next\n count += 1\n return str\n\n# test code\nif __name__ == \"__main__\":\n\n # Create and print list\n print(\"Creating List\")\n testList = LinkedList([1, 2, 3, 4, 5])\n print(testList)\n\n # test pop front\n print(\"\\nAfter popping front and back:\")\n testList.pop_front()\n testList.pop_back()\n print(testList)\n\n # Check pesky edge cases\n testList.pop_front()\n testList.pop_back()\n testList.pop_back()\n print(testList)\n\n # now adding data back to it\n testList.push_back(1)\n testList.push_back(2)\n testList.push_back(3)\n print(testList)\n","repo_name":"nate-h/algorithm_practice","sub_path":"linkedList.py","file_name":"linkedList.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"7313978403","text":"import numpy as np\nfrom lrscp_utils import *\n\ndef init_lambd(n):\n '''\n A random initialization of Lagrangean multipliers.\n\n Arguments:\n n: the number of rows, also the dimension of lambda\n\n Returns:\n lambd: Lagrangean multipliers, a n by 1 array\n\n '''\n np.random.seed(1)\n return np.random.random((n, 1))\n\n\ndef solve_LRSCP(c, a, lambd):\n '''\n Solve the Lagrangean relaxed set covering problem, which gives a lower bound to the SCP optimal.\n\n Arguments:\n c: an array of cost coefficient in the objective function\n a: an array of coverage information, a[i][j]: 0 or 1, indicating whether jth column can cover ith row\n lambd: Lagrangean multipliers\n\n Returns:\n sol: a list of selected columns of the optimal solution of LRSCP\n LB: the optimal objective value of LRSCP, which gives a lower bound to the SCP optimal\n C: the new cost coefficient of X in the objective function of the LRSCP\n '''\n sol = []\n\n C = c - np.sum(lambd * a, axis = 0) # compute the new cost in the LRSCP\n for i in range(len(C)):\n if C[i] <= 0: # set Xj = 1 if a non-positive C\n sol.append(i)\n\n # compute the corresponding objective value of LRSCP\n LB = sum([C[i] for i in sol]) + float(np.sum(lambd))\n\n return sol, LB, np.array(C)\n\n\ndef find_primal_feasible(m, n, c, cost_coef_SCP, a, M, N, funcType = \"III\"):\n '''\n Find a feasible solution to the primal problem, i.e. the SCP, which gives an upper bound to the SCP optimal.\n\n The algoriothm is detailed in Balas and Ho (1980) Balas, E., & Ho, A. (1980). Set covering algorithms using\n cutting planes, heuristics, and subgradient optimization: a computational study.\n In Combinatorial Optimization (pp. 37-60). Springer, Berlin, Heidelberg.\n\n Arguments:\n m: the number of columns\n n: the number of rows\n c: an array of cost coefficient of X in the objective function of SCP or LRSCP\n cost_coeff_SCP: an array of cost coefficient of X in the objective function of SCP\n a: an array of coverage information, a[i][j]: 0 or 1, indicating whether jth column can cover ith row\n N: a list of coverage set, N[i]: a set of columns that can cover row i\n M: a list of coverage set, M[j]: a set of rows that can be covered by column j\n funcType: f(c, k) function options detailed in Balas and Ho (1980)\n\n Returns:\n sol: a list of selected columns of a primal feasible solution\n UB: a feasible objective value of SCP, which gives an upper bound to the SCP optimal\n '''\n\n # initialize\n R, sol = set(range(n)), [] # a set of uncovered rows, a list of selected columns\n\n # greedy add new rows until each row is covered\n while R:\n K = [len(R.intersection(j)) for j in M] # K[j]: number of uncovered rows that can be covered by column j\n\n temp = {i: len(N[i]) for i in R} # temp dict: key: row i in a, value: number of columns that can cover i\n i_star = min(temp, key = temp.get) # find uncovered i_star that has least amount of columns that can cover\n\n # choose j based on f(c, K)\n pool = set(range(m)).difference(set(sol)) # unselected columns\n pool = pool.intersection(N[i_star]) # unselected columns that can cover i_star\n temp = float(\"Inf\") # minimum f(c, K)\n j_star = float(\"Inf\") # column index for minimum f(c, K)\n for j in pool:\n if f_kc(c[j], K[j], funcType) < temp: # find the minimum f(c, K)\n temp = f_kc(c[j], K[j], funcType)\n j_star = j\n\n # add selected column and update others\n sol.append(j_star)\n R = R.difference(M[j_star])\n\n\n # check if any unnecessary seleted column\n sol_copy = sol.copy()\n for j in sol_copy:\n sol.remove(j) # try removing column j\n if 0 in np.sum(a[:, sol], axis =1): # if sol cannot cover all rows, backtrack\n sol.append(j)\n\n # compute the corresponding objective value of SCP\n UB = sum([cost_coef_SCP[j] for j in sol])\n\n return sol, UB\n\ndef update_lambd(a, LB, best_UB, sol, lambd, alpha):\n '''\n Subgradient procedure to update Lagrangean multipliers.\n\n Arguments:\n a: an array of coverage information, a[i][j]: 0 or 1, indicating whether jth column can cover ith row\n LB: a lower bound of the SCP optimal objective\n best_UB: the best upper of the SCP optimal objective bound so far\n sol: the optimal solution to a LRSCP\n lambd: Lagrangean multipliers\n alpha: a factor to control step length during updating\n\n Returns:\n new_lambd: the updated Lagrangean multipliers\n '''\n\n t = alpha * (best_UB - LB)/(np.sum((1 - np.sum(a[:, sol], axis =1))**2)) # step length\n\n new_lambd = np.maximum(0, lambd + t * (1 - np.sum(a[:, sol], axis =1, keepdims = True))) # update lambda\n\n return new_lambd\n\ndef Lagrangean(m, n, cost_coef, a, M, N, S, alpha, beta, epsilon, maxItr):\n '''\n Compute the solution to a Set Covering Problem (SCP) using Lagrangean relaxation.\n\n Arguments:\n m: the number of columns\n n: the number of rows\n cost_coef: an array of cost coefficient of X in the objective function of SCP\n a: an array of coverage information, a[i][j]: 0 or 1, indicating whether jth column can cover ith row\n N: a list of coverage set, N[i]: a set of columns that can cover row i\n M: a list of coverage set, M[j]: a set of rows that can be covered by column j\n S: the service coverage standard\n alpha: the initial value of alpha, a factor to control step length during updating lambda\n beta: a hyperparameter to update alpha\n epsilon: the tolerance of the gap between lower and upper bounds\n maxItr: the maximum iteration number allowed\n\n Returns:\n LB: the lower bound of the SCP optimal (the objective value of LRSCP)\n sol_LRSCP: the solution of LRSCP, a list of selected columns\n best_UB: the best upper bound of the SCP optimal\n sol_best_UB: the solution corresponding to the best UB found, a list of selected columns\n caches: a dictionary containing itration number, LB, UB, best_UB\n '''\n # initialize\n LB, sol_LRSCP = float(\"Inf\"), [] # the lower bound of the SCP optimal\n UB, sol_feas = float(\"-Inf\"), [] # the upper bound of the SCP optimal\n best_UB, sol_best_UB = float(\"Inf\"), [] # the best upper bound found so far and its solution\n itr = 0 # iteration counter\n caches = {\"Itr\": [], \"LB\": [], \"UB\":[], \"Best_UB\": []}\n\n # initialize Lagrangean multipliers\n lambd = init_lambd(n)\n\n while itr < maxItr: # while the itration counter is less than the maxItr\n\n # solve the LRSCP\n sol_LRSCP, LB, C = solve_LRSCP(cost_coef, a, lambd)\n\n # find a feasible solution to SCP\n sol_feas, UB = find_primal_feasible(m, n, C, cost_coef, a,\n M, N, funcType = \"III\")\n\n # update best_UB if needed\n if UB < best_UB:\n best_UB = UB\n sol_best_UB = sol_feas\n\n # write caches\n caches[\"Itr\"].append(itr)\n caches[\"LB\"].append(LB)\n caches[\"UB\"].append(UB)\n caches[\"Best_UB\"].append(best_UB)\n\n # if the gap between the best known upper bound and lower bound is less than epsilon, stop\n if best_UB - LB < epsilon:\n break\n\n # otherwise, subgradient procedure to update lambda's\n lambd = update_lambd(a, LB, best_UB, sol_LRSCP, lambd, alpha)\n alpha = beta * alpha\n\n itr += 1 # iteration counter\n\n return LB, sol_LRSCP, best_UB, sol_best_UB, caches\n","repo_name":"jxu0410/lrscp","sub_path":"lrscp.py","file_name":"lrscp.py","file_ext":"py","file_size_in_byte":7514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"19792764667","text":"import itertools\nfrom random import randint\n\n_DEFAULT_PRIME = 1073750017\n\n\ndef maximum_matching(edges, mod=_DEFAULT_PRIME):\n \"\"\"\n Returns the maximum cardinality matching of any simple graph (undirected, unweighted, no self-loops)\n Uses a randomized algorithm to compute the rank of the Tutte matrix\n The rank of the Tutte matrix is equal to twice the size of the maximum matching with high probability\n The probability for error is not more than n/mod\n\n Complexity: O(n ^ 3) worst case, O(n * |matching_size|) on average\n\n :param edges: a list of edges, assume nodes can be anything numbered from 0 to max number in edges\n :param mod: optional, a large random prime\n :return: the maximum cardinality matching of the graph\n \"\"\"\n\n n = max(itertools.chain(*edges)) + 1\n matrix = _get_tutte_matrix(n, edges, mod)\n return _gauss(n, matrix, mod) // 2\n\n\ndef _get_tutte_matrix(n, edges, mod):\n matrix = [[0] * n for _ in range(n)]\n\n for u, v in edges:\n val = randint(1, mod - 1)\n matrix[u][v], matrix[v][u] = val, mod - val\n\n return matrix\n\n\ndef _gauss(n, matrix, mod):\n r = 0\n for j in range(n):\n k = r\n while k < n and not matrix[k][j]:\n k += 1\n\n if k == n:\n continue\n\n inv = pow(matrix[k][j], mod - 2, mod)\n for i in range(n):\n matrix[k][i] = inv * matrix[k][i] % mod\n matrix[k], matrix[r] = matrix[r], matrix[k]\n\n for u in range(r + 1, n):\n # reducing indexing costs to gain performance boost for the next loop\n matrix_u, matrix_r = matrix[u], matrix[r]\n if matrix_u[j]:\n for v in range(j + 1, n):\n if matrix_r[v]:\n matrix_u[v] = (matrix_u[v] - matrix_r[v] * matrix_u[j]) % mod\n\n r += 1\n\n return r\n","repo_name":"cheran-senthil/PyRival","sub_path":"pyrival/graphs/maximum_matching.py","file_name":"maximum_matching.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":1039,"dataset":"github-code","pt":"12"} +{"seq_id":"1916409927","text":"#!/usr/bin/python\n\nimport collectd\nfrom ouimeaux.environment import Environment, UnknownDevice\n\nCONFIG=[]\nENV = Environment(with_cache=False)\n\ndef init():\n collectd.info(\"start\")\n ENV.start()\n #collectd.info(\"Discover\")\n ENV.discover()\n\ndef read():\n #collectd.info(\"read\")\n \n for name in CONFIG:\n \n while True:\n #collectd.info(\"querying: \" + name)\n try:\n switch = ENV.get_switch(name)\n\n v1 = collectd.Values(plugin='wemo')\n v1.type = 'power'\n v1.type_instance = 'power'\n v1.plugin_instance = name\n\n power = switch.current_power/1000.0\n\n collectd.info(\"Got power from %s = %fW\" % (name, power))\n\n v1.values = [power]\n v1.dispatch()\n except UnknownDevice:\n collectd.error(\"Unknown device: \" + name)\n except ConnectionError:\n ENV.start()\n ENV.discover()\n continue\n\n break\n\n env = None\n\ndef config_callback(conf):\n name = None\n\n for node in conf.children:\n key = node.key.lower()\n val = node.values[0]\n\n\n if key == 'name':\n #collectd.info(\"found config name = \" + val)\n CONFIG.append(val)\n else:\n collectd.warning('unknown config key: %s' % key)\n continue\n\ncollectd.register_config(config_callback)\ncollectd.register_init(init)\ncollectd.register_read(read)\n","repo_name":"tony-tsang/collectd_wemo","sub_path":"wemo.py","file_name":"wemo.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"34413293786","text":"from odoo import api, fields, models, _\nfrom odoo.tools import pycompat, float_repr\nfrom odoo.exceptions import ValidationError\nfrom odoo.tools.sql import column_exists, create_column\n\nfrom datetime import datetime\nfrom collections import namedtuple\nimport tempfile\nimport zipfile\nimport time\nimport io\nimport re\nimport os\n\nBalanceKey = namedtuple('BalanceKey', ['from_code', 'to_code', 'partner_id', 'tax_id'])\n\n\nclass AccountDatevCompany(models.Model):\n _inherit = 'res.company'\n\n # Adding the fields as company_dependent does not break stable policy\n l10n_de_datev_consultant_number = fields.Char(company_dependent=True)\n l10n_de_datev_client_number = fields.Char(company_dependent=True)\n\n\nclass ResPartner(models.Model):\n _inherit = 'res.partner'\n\n l10n_de_datev_identifier = fields.Integer(string='Datev Identifier',\n copy=False, tracking=True,\n help=\"The Datev identifier is a unique identifier for exchange with the government. \"\n \"If you had previous exports with another identifier, you can put it here. \"\n \"If it is 0, then it will take the database id + the value in the system parameter \"\n \"l10n_de.datev_start_count. \")\n\n @api.constrains('l10n_de_datev_identifier')\n def _check_datev_identifier(self):\n self.flush_model(['l10n_de_datev_identifier'])\n self.env.cr.execute(\"\"\"\n SELECT COUNT(id), l10n_de_datev_identifier FROM res_partner\n WHERE l10n_de_datev_identifier != 0\n GROUP BY l10n_de_datev_identifier\n HAVING COUNT(id) > 1\n \"\"\")\n\n if self.env.cr.dictfetchone():\n raise ValidationError(_('You have already defined a partner with the same Datev identifier. '))\n\n\nclass AccountMoveL10NDe(models.Model):\n _inherit = 'account.move'\n\n l10n_de_datev_main_account_id = fields.Many2one('account.account', compute='_get_datev_account', store=True)\n\n def _auto_init(self):\n if column_exists(self.env.cr, \"account_move\", \"l10n_de_datev_main_account_id\"):\n return super()._auto_init()\n\n cr = self.env.cr\n create_column(cr, \"account_move\", \"l10n_de_datev_main_account_id\", \"int4\")\n # If move has an invoice, return invoice's account_id\n cr.execute(\n \"\"\"\n UPDATE account_move\n SET l10n_de_datev_main_account_id = r.aid\n FROM (\n SELECT l.move_id mid,\n FIRST_VALUE(l.account_id) OVER(PARTITION BY l.move_id ORDER BY l.id DESC) aid\n FROM account_move_line l\n JOIN account_move m\n ON m.id = l.move_id\n JOIN account_account a\n ON a.id = l.account_id\n WHERE m.move_type in ('out_invoice', 'out_refund', 'in_refund', 'in_invoice', 'out_receipt', 'in_receipt')\n AND a.account_type in ('asset_receivable', 'liability_payable')\n ) r\n WHERE id = r.mid\n \"\"\")\n\n # If move belongs to a bank journal, return the journal's account (debit/credit should normally be the same)\n cr.execute(\n \"\"\"\n UPDATE account_move\n SET l10n_de_datev_main_account_id = r.aid\n FROM (\n SELECT m.id mid,\n j.default_account_id aid\n FROM account_move m\n JOIN account_journal j\n ON m.journal_id = j.id\n WHERE j.type = 'bank'\n AND j.default_account_id IS NOT NULL\n ) r\n WHERE id = r.mid\n AND l10n_de_datev_main_account_id IS NULL\n \"\"\")\n\n # If the move is an automatic exchange rate entry, take the gain/loss account set on the exchange journal\n cr.execute(\"\"\"\n UPDATE account_move m\n SET l10n_de_datev_main_account_id = r.aid\n FROM (\n SELECT l.move_id AS mid,\n l.account_id AS aid\n FROM account_move_line l\n JOIN account_move m\n ON l.move_id = m.id\n JOIN account_journal j\n ON m.journal_id = j.id\n JOIN res_company c\n ON c.currency_exchange_journal_id = j.id\n WHERE j.type='general'\n AND l.account_id = j.default_account_id\n GROUP BY l.move_id,\n l.account_id\n HAVING count(*)=1\n ) r\n WHERE id = r.mid\n AND l10n_de_datev_main_account_id IS NULL\n \"\"\")\n\n # Look for an account used a single time in the move, that has no originator tax\n query = \"\"\"\n UPDATE account_move m\n SET l10n_de_datev_main_account_id = r.aid\n FROM (\n SELECT l.move_id AS mid,\n min(l.account_id) AS aid\n FROM account_move_line l\n WHERE {}\n GROUP BY move_id\n HAVING count(*)=1\n ) r\n WHERE id = r.mid\n AND m.l10n_de_datev_main_account_id IS NULL\n \"\"\"\n cr.execute(query.format(\"l.debit > 0\"))\n cr.execute(query.format(\"l.credit > 0\"))\n cr.execute(query.format(\"l.debit > 0 AND l.tax_line_id IS NULL\"))\n cr.execute(query.format(\"l.credit > 0 AND l.tax_line_id IS NULL\"))\n\n return super()._auto_init()\n\n @api.depends('journal_id', 'line_ids', 'journal_id.default_account_id')\n def _get_datev_account(self):\n for move in self:\n move.l10n_de_datev_main_account_id = value = False\n # If move has an invoice, return invoice's account_id\n if move.is_invoice(include_receipts=True):\n payment_term_lines = move.line_ids.filtered(\n lambda line: line.account_id.account_type in ('asset_receivable', 'liability_payable'))\n if payment_term_lines:\n move.l10n_de_datev_main_account_id = payment_term_lines[0].account_id\n continue\n # If move belongs to a bank journal, return the journal's account (debit/credit should normally be the same)\n if move.journal_id.type == 'bank' and move.journal_id.default_account_id:\n move.l10n_de_datev_main_account_id = move.journal_id.default_account_id\n continue\n # If the move is an automatic exchange rate entry, take the gain/loss account set on the exchange journal\n elif move.journal_id.type == 'general' and move.journal_id == self.env.company.currency_exchange_journal_id:\n lines = move.line_ids.filtered(lambda r: r.account_id == move.journal_id.default_account_id)\n\n if len(lines) == 1:\n move.l10n_de_datev_main_account_id = lines.account_id\n continue\n\n # Look for an account used a single time in the move, that has no originator tax\n aml_debit = self.env['account.move.line']\n aml_credit = self.env['account.move.line']\n for aml in move.line_ids:\n if aml.debit > 0:\n aml_debit += aml\n if aml.credit > 0:\n aml_credit += aml\n if len(aml_debit) == 1:\n value = aml_debit[0].account_id\n elif len(aml_credit) == 1:\n value = aml_credit[0].account_id\n else:\n aml_debit_wo_tax = [a for a in aml_debit if not a.tax_line_id]\n aml_credit_wo_tax = [a for a in aml_credit if not a.tax_line_id]\n if len(aml_debit_wo_tax) == 1:\n value = aml_debit_wo_tax[0].account_id\n elif len(aml_credit_wo_tax) == 1:\n value = aml_credit_wo_tax[0].account_id\n move.l10n_de_datev_main_account_id = value\n\n\nclass GeneralLedgerCustomHandler(models.AbstractModel):\n _inherit = 'account.general.ledger.report.handler'\n\n def _custom_options_initializer(self, report, options, previous_options=None):\n \"\"\"\n Add the invoice lines search domain that common for all countries.\n :param dict options: Report options\n :param dict previous_options: Previous report options\n \"\"\"\n super()._custom_options_initializer(report, options, previous_options)\n if self.env.company.country_code == 'DE':\n options.setdefault('buttons', []).extend((\n {\n 'name': _('Datev (zip)'),\n 'sequence': 30,\n 'action': 'export_file',\n 'action_param': 'l10n_de_datev_export_to_zip',\n 'file_export_type': _('Datev zip'),\n },\n {\n 'name': _('Datev + ATCH (zip)'),\n 'sequence': 40,\n 'action': 'export_file',\n 'action_param': 'l10_de_datev_export_to_zip_and_attach',\n 'file_export_type': _('Datev + batch zip'),\n },\n ))\n\n def l10_de_datev_export_to_zip_and_attach(self, options):\n options['add_attachments'] = True\n return self.l10n_de_datev_export_to_zip(options)\n\n def l10n_de_datev_export_to_zip(self, options):\n \"\"\"\n Check ir_attachment for method _get_path\n create a sha and replace 2 first letters by something not hexadecimal\n Return full_path as 2nd args, use it as name for Zipfile\n Don't need to unlink as it will be done automatically by garbage collector\n of attachment cron\n \"\"\"\n report = self.env['account.report'].browse(options['report_id'])\n with tempfile.NamedTemporaryFile(mode='w+b', delete=True) as buf:\n with zipfile.ZipFile(buf, mode=\"w\", compression=zipfile.ZIP_DEFLATED, allowZip64=False) as zf:\n move_line_ids = []\n for line in report._get_lines({**options, 'unfold_all': True}):\n model, model_id = report._get_model_info_from_id(line['id'])\n if model == 'account.move.line':\n move_line_ids.append(model_id)\n\n domain = [\n ('line_ids', 'in', move_line_ids),\n ('company_id', 'in', report.get_report_company_ids(options)),\n ]\n if options.get('all_entries'):\n domain += [('state', '!=', 'cancel')]\n else:\n domain += [('state', '=', 'posted')]\n if options.get('date'):\n domain += [('date', '<=', options['date']['date_to'])]\n # cannot set date_from on move as domain depends on the move line account if \"strict_range\" is False\n domain += report._get_options_journals_domain(options)\n moves = self.env['account.move'].search(domain)\n zf.writestr('EXTF_accounting_entries.csv', self._l10n_de_datev_get_csv(options, moves))\n zf.writestr('EXTF_customer_accounts.csv', self._l10n_de_datev_get_partner_list(options, customer=True))\n zf.writestr('EXTF_vendor_accounts.csv', self._l10n_de_datev_get_partner_list(options, customer=False))\n if options.get('add_attachments'):\n # add all moves attachments in zip file, this is not part of DATEV specs\n slash_re = re.compile('[\\\\/]')\n for move in moves:\n # rename files by move name + sequence number (if more than 1 file)\n # '\\' is not allowed in file name, replace by '-'\n base_name = slash_re.sub('-', move.name)\n if len(move.attachment_ids) > 1:\n name_pattern = f'%(base)s-%(index)0.{len(str(len(move.attachment_ids)))}d%(extension)s'\n else:\n name_pattern = '%(base)s%(extension)s'\n for i, attachment in enumerate(move.attachment_ids.sorted('id'), 1):\n extension = os.path.splitext(attachment.name)[1]\n name = name_pattern % {'base': base_name, 'index': i, 'extension': extension}\n zf.writestr(name, attachment.raw)\n buf.seek(0)\n content = buf.read()\n return {\n 'file_name': report.get_default_report_filename('ZIP'),\n 'file_content': content,\n 'file_type': 'zip'\n }\n\n def _l10n_de_datev_get_client_number(self):\n consultant_number = self.env.company.l10n_de_datev_consultant_number\n client_number = self.env.company.l10n_de_datev_client_number\n if not consultant_number:\n consultant_number = 99999\n if not client_number:\n client_number = 999\n return [consultant_number, client_number]\n\n def _l10n_de_datev_get_partner_list(self, options, customer=True):\n date_to = fields.Date.from_string(options.get('date').get('date_to'))\n fy = self.env.company.compute_fiscalyear_dates(date_to)\n\n fy = datetime.strftime(fy.get('date_from'), '%Y%m%d')\n datev_info = self._l10n_de_datev_get_client_number()\n account_length = self._l10n_de_datev_get_account_length()\n\n output = io.BytesIO()\n writer = pycompat.csv_writer(output, delimiter=';', quotechar='\"', quoting=2)\n preheader = ['EXTF', 510, 16, 'Debitoren/Kreditoren', 4, None, None, '', '', '', datev_info[0], datev_info[1], fy, account_length,\n '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n header = ['Konto', 'Name (AdressatentypUnternehmen)', 'Name (Adressatentypnatürl. Person)', '', '', '', 'Adressatentyp']\n lines = [preheader, header]\n\n move_line_ids = set()\n report = self.env['account.report'].browse(options['report_id'])\n for line in report._get_lines({**options, 'unfold_all': True}):\n model, model_id = report._parse_line_id(line['id'])[-1][-2:]\n if model == 'account.move.line':\n move_line_ids.add(str(model_id))\n\n if len(move_line_ids):\n if customer:\n move_types = ('out_refund', 'out_invoice', 'out_receipt')\n else:\n move_types = ('in_refund', 'in_invoice', 'in_receipt')\n select = \"\"\"SELECT distinct(aml.partner_id)\n FROM account_move_line aml\n LEFT JOIN account_move m\n ON aml.move_id = m.id\n WHERE aml.id IN %s\n AND aml.tax_line_id IS NULL\n AND aml.debit != aml.credit\n AND m.move_type IN %s\n AND aml.account_id != m.l10n_de_datev_main_account_id\"\"\"\n self.env.cr.execute(select, (tuple(move_line_ids), move_types))\n partners = self.env['res.partner'].browse([p.get('partner_id') for p in self.env.cr.dictfetchall()])\n for partner in partners:\n if customer:\n code = self._l10n_de_datev_find_partner_account(partner.property_account_receivable_id, partner)\n else:\n code = self._l10n_de_datev_find_partner_account(partner.property_account_payable_id, partner)\n line_value = {\n 'code': code,\n 'company_name': partner.name if partner.is_company else '',\n 'person_name': '' if partner.is_company else partner.name,\n 'natural': partner.is_company and '2' or '1'\n }\n # Idiotic program needs to have a line with 243 elements ordered in a given fashion as it\n # does not take into account the header and non mandatory fields\n array = ['' for x in range(243)]\n array[0] = line_value.get('code')\n array[1] = line_value.get('company_name')\n array[2] = line_value.get('person_name')\n array[6] = line_value.get('natural')\n lines.append(array)\n writer.writerows(lines)\n return output.getvalue()\n\n def _l10n_de_datev_get_account_length(self):\n param_start = self.env['ir.config_parameter'].sudo().get_param('l10n_de.datev_start_count', \"100000000\")[:9]\n param_start_vendors = self.env['ir.config_parameter'].sudo().get_param('l10n_de.datev_start_count_vendors', \"700000000\")[:9]\n\n # The gegenkonto should be 1 length higher than the account length, so we have to substract 1 to the params length\n return max(param_start.isdigit() and len(param_start) or 9, param_start_vendors.isdigit() and len(param_start_vendors) or 9, 5) - 1\n\n def _l10n_de_datev_find_partner_account(self, account, partner):\n len_param = self._l10n_de_datev_get_account_length() + 1\n if (account.account_type in ('asset_receivable', 'liability_payable') and partner):\n # Check if we have a property as receivable/payable on the partner\n # We use the property because in datev and in germany, partner can be of 2 types\n # important partner which have a specific account number or a virtual partner\n # Which has only a number. To differentiate between the two, if a partner in Odoo\n # explicitely has a receivable/payable account set, we use that account, otherwise\n # we assume it is not an important partner and his datev virtual id will be the\n # l10n_de_datev_identifier set or the id + the start count parameter.\n account = partner.property_account_receivable_id if account.account_type == 'asset_receivable' else partner.property_account_payable_id\n fname = \"property_account_receivable_id\" if account.account_type == 'asset_receivable' else \"property_account_payable_id\"\n prop = self.env['ir.property']._get(fname, \"res.partner\", partner.id)\n if prop == account:\n return str(account.code).ljust(len_param - 1, '0') if account else ''\n return self._l10n_de_datev_get_account_identifier(account, partner)\n return str(account.code).ljust(len_param - 1, '0') if account else ''\n\n def _l10n_de_datev_get_account_identifier(self, account, partner):\n len_param = self._l10n_de_datev_get_account_length() + 1\n if account.account_type == 'asset_receivable':\n param_start = self.env['ir.config_parameter'].sudo().get_param('l10n_de.datev_start_count', \"100000000\")[:9]\n start_count = param_start.isdigit() and int(param_start) or 100000000\n else:\n param_start_vendors = self.env['ir.config_parameter'].sudo().get_param('l10n_de.datev_start_count_vendors', \"700000000\")[:9]\n start_count = param_start_vendors.isdigit() and int(param_start_vendors) or 700000000\n start_count = int(str(start_count).ljust(len_param, '0'))\n return partner.l10n_de_datev_identifier or start_count + partner.id\n\n # Source: http://www.datev.de/dnlexom/client/app/index.html#/document/1036228/D103622800029\n def _l10n_de_datev_get_csv(self, options, moves):\n # last 2 element of preheader should be filled by \"consultant number\" and \"client number\"\n date_from = fields.Date.from_string(options.get('date').get('date_from'))\n date_to = fields.Date.from_string(options.get('date').get('date_to'))\n fy = self.env.company.compute_fiscalyear_dates(date_to)\n\n date_from = datetime.strftime(date_from, '%Y%m%d')\n date_to = datetime.strftime(date_to, '%Y%m%d')\n fy = datetime.strftime(fy.get('date_from'), '%Y%m%d')\n datev_info = self._l10n_de_datev_get_client_number()\n account_length = self._l10n_de_datev_get_account_length()\n\n output = io.BytesIO()\n writer = pycompat.csv_writer(output, delimiter=';', quotechar='\"', quoting=2)\n preheader = ['EXTF', 510, 21, 'Buchungsstapel', 7, '', '', '', '', '', datev_info[0], datev_info[1], fy, account_length,\n date_from, date_to, '', '', '', '', 0, 'EUR', '', '', '', '', '', '', '', '', '']\n header = ['Umsatz (ohne Soll/Haben-Kz)', 'Soll/Haben-Kennzeichen', 'WKZ Umsatz', 'Kurs', 'Basis-Umsatz', 'WKZ Basis-Umsatz', 'Konto', 'Gegenkonto (ohne BU-Schlüssel)', 'BU-Schlüssel', 'Belegdatum', 'Belegfeld 1', 'Belegfeld 2', 'Skonto', 'Buchungstext']\n\n # if we do _get_lines with some unfolded lines, only those will be returned, but we want all of them\n move_line_ids = []\n report = self.env['account.report'].browse(options['report_id'])\n for line in report._get_lines({**options, 'unfold_all': True}):\n model, model_id = report._parse_line_id(line['id'])[-1][-2:]\n if model == 'account.move.line':\n move_line_ids.append(int(model_id))\n\n lines = [preheader, header]\n\n for m in moves:\n line_values = {} # key: BalanceKey\n move_currencies = {}\n payment_account = 0 # Used for non-reconciled payments\n\n for aml in m.line_ids:\n if aml.debit == aml.credit:\n # Ignore debit = credit = 0\n continue\n # If both account and counteraccount are the same, ignore the line\n if aml.account_id == aml.move_id.l10n_de_datev_main_account_id:\n continue\n # If line is a tax ignore it as datev requires single line with gross amount and deduct tax itself based\n # on account or on the control key code\n if aml.tax_line_id:\n continue\n\n aml_taxes = aml.tax_ids.compute_all(aml.balance, aml.company_id.currency_id, partner=aml.partner_id, handle_price_include=False)\n line_amount = aml_taxes['total_included']\n\n code_correction = ''\n if aml.tax_ids:\n codes = set(aml.tax_ids.mapped('l10n_de_datev_code'))\n if len(codes) == 1:\n # there should only be one max, else skip code\n code_correction = codes.pop() or ''\n\n # account and counterpart account\n to_account_code = str(self._l10n_de_datev_find_partner_account(aml.move_id.l10n_de_datev_main_account_id, aml.partner_id))\n account_code = u'{code}'.format(code=self._l10n_de_datev_find_partner_account(aml.account_id, aml.partner_id))\n\n # We don't want to have lines with our outstanding payment/receipt as they don't represent real moves\n # So if payment skip one move line to write, while keeping the account\n # and replace bank account for outstanding payment/receipt for the other line\n\n if aml.payment_id:\n if payment_account == 0:\n payment_account = account_code\n continue\n else:\n to_account_code = payment_account\n\n # group lines by account, to_account & partner\n match_key = BalanceKey(from_code=account_code, to_code=to_account_code, partner_id=aml.partner_id,\n tax_id=code_correction)\n\n if match_key in line_values:\n # values already in line_values\n line_values[match_key]['line_amount'] += line_amount\n line_values[match_key]['line_base_amount'] += aml.price_total\n move_currencies[match_key].add(aml.currency_id)\n continue\n\n # reference\n receipt1 = aml.move_id.name\n if aml.move_id.journal_id.type == 'purchase' and aml.move_id.ref:\n receipt1 = aml.move_id.ref\n\n # on receivable/payable aml of sales/purchases\n receipt2 = ''\n if to_account_code == account_code and aml.date_maturity:\n receipt2 = aml.date\n\n move_currencies[match_key] = set([aml.currency_id])\n currency = aml.company_id.currency_id\n line_values[match_key] = {\n 'waehrung': currency.name,\n 'line_base_amount': aml.price_total,\n 'line_base_currency': aml.currency_id.name,\n 'buschluessel': code_correction,\n 'gegenkonto': to_account_code,\n 'belegfeld1': receipt1[-36:],\n 'belegfeld2': receipt2,\n 'datum': datetime.strftime(aml.move_id.date, '%-d%m'),\n 'konto': account_code,\n 'kurs': str(aml.currency_id.rate).replace('.', ','),\n 'buchungstext': receipt1,\n 'line_amount': line_amount\n }\n\n for match_key, line_value in line_values.items():\n # For DateV, we can't have negative amount on a line, so we need to inverse the amount and inverse the\n # credit/debit symbol.\n line_value['sollhaben'] = 'h' if line_value['line_amount'] < 0 else 's'\n line_value['line_amount'] = abs(line_value['line_amount'])\n # Idiotic program needs to have a line with 116 elements ordered in a given fashion as it\n # does not take into account the header and non mandatory fields\n array = ['' for x in range(116)]\n array[0] = float_repr(line_value['line_amount'], aml.company_id.currency_id.decimal_places).replace('.', ',')\n array[1] = line_value.get('sollhaben')\n array[2] = line_value.get('waehrung')\n if (len(move_currencies[match_key]) == 1) and line_value.get('line_base_currency') != line_value.get('waehrung'):\n array[3] = line_value.get('kurs')\n array[4] = float_repr(line_value['line_base_amount'], aml.currency_id.decimal_places).replace('.', ',')\n array[5] = line_value.get('line_base_currency')\n array[6] = line_value.get('konto')\n array[7] = line_value.get('gegenkonto')\n array[8] = line_value.get('buschluessel')\n array[9] = line_value.get('datum')\n array[10] = line_value.get('belegfeld1')\n array[11] = line_value.get('belegfeld2')\n array[13] = line_value.get('buchungstext')\n lines.append(array)\n\n writer.writerows(lines)\n return output.getvalue()\n","repo_name":"dinar-it/odoo_16_enter","sub_path":"l10n_de_reports/models/datev_export_csv.py","file_name":"datev_export_csv.py","file_ext":"py","file_size_in_byte":26940,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"27982009498","text":"class BinaryTree:\n \"\"\"\n This is the Parent of BinarySearchTree\n \"\"\"\n\n def __init__(self, root=None):\n self.root = root\n self.max_val = self.root\n\n def pre_order(self, data=[]):\n def traverse(node):\n if not node:\n return\n data.append(node.value)\n traverse(node.left)\n traverse(node.right)\n traverse(self.root)\n return data\n\n def in_order(self, data=[]):\n def traverse(node):\n if not node:\n return\n traverse(node.left)\n data.append(node.value)\n traverse(node.right)\n traverse(self.root)\n return data\n\n def post_order(self, data=[]):\n def traverse(node):\n if not node:\n return\n traverse(node.left)\n traverse(node.right)\n data.append(node.value)\n traverse(self.root)\n return data\n\n def find_maximum_value(self):\n def traverse(node):\n if not node:\n return self.max_val.value\n else:\n traverse(node.left)\n if self.max_val.value < node.value:\n self.max_val = node\n return traverse(node.right)\n self.max_val = self.root\n return traverse(self.root)\n\n\nclass Node:\n def __init__(self, value, left=None, right=None):\n self.value = value\n self.left = left\n self.right = right\n\nclass People:\n def __init__(self, value):\n self.age = value\n\n\ndef find_age_range(tree):\n min = tree.root.value.age\n max = tree.root.value.age\n def ages(node, min, max):\n if not node:\n return [min, max]\n else:\n ages(node.left, min, max)\n if max < node.value.age:\n max = node.value.age\n if min > node.value.age:\n min = node.value.age\n return ages(node.right, min, max)\n return ages(tree.root, min, max)\n\n\nif __name__ == '__main__':\n p1 = Node(People(2))\n p2 = Node(People(10))\n p3 = Node(People(20))\n p4 = Node(People(30))\n T = BinaryTree()\n T.root = p1\n T.root.left = p2\n T.root.left.left = p3\n T.root.right = p4\n print(find_age_range(T))\n\n\n\n\n\n\n\n\n\n","repo_name":"jjescandor/data_structures_and_algorithms","sub_path":"python/data_structures/trees/binary_tree.py","file_name":"binary_tree.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"9178785762","text":"import math\n\nimport tensorflow.compat.v1 as tf\nfrom .layers import gumbel_softmax, mse_loss\n\n\n# hacked up recompute grad which handles variable scopes properly and to handle bf16\ndef recompute_grad(f, bf16=False):\n @tf.custom_gradient\n def inner(*args, **kwargs):\n result = tf.stop_gradient(f(*args, **kwargs))\n scope = tf.get_default_graph().get_name_scope()\n\n def grad(dresult, variables=None):\n with tf.GradientTape() as t:\n t.watch(args)\n if variables is not None:\n t.watch(variables)\n # we need to outsmart XLA here to force a control dependency\n zero_with_control_dependency = tf.reduce_mean(dresult[0] * 1e-30)\n new_args = []\n for a in args:\n if a.dtype.is_floating:\n new_args.append(a + tf.cast(zero_with_control_dependency, a.dtype))\n else:\n new_args.append(a)\n\n with tf.control_dependencies([dresult]):\n if bf16:\n with tf.tpu.bfloat16_scope():\n with tf.variable_scope(scope, reuse=True):\n result = f(*new_args, **kwargs)\n else:\n with tf.variable_scope(scope, reuse=True):\n result = f(*new_args, **kwargs)\n kw_vars = []\n if variables is not None:\n kw_vars = list(variables)\n grads = t.gradient(result, list(new_args) + kw_vars, output_gradients=[dresult])\n return grads[:len(new_args)], grads[len(new_args):]\n\n return result, grad\n return inner\n\n\nclass DiscreteVAE:\n def __init__(self,\n num_tokens,\n dimensions,\n convblocks,\n dim=512,\n hidden_dim=64,\n input_channels=3,\n recompute_grad=False,\n use_bf16=False,\n stack_factor=1,\n ):\n self.num_tokens = num_tokens\n self.dim = dim\n self.hdim = hidden_dim\n self.hdim2 = hidden_dim\n self.num_tokens = num_tokens\n self.num_ch = input_channels\n self.H = self.W = dimensions\n self.height_dim = self.H\n self.width_dim = self.W\n self.conv2d = tf.layers.conv2d\n self.conv2dtranspose = tf.layers.conv2d_transpose\n self.activation = tf.nn.relu\n self.dense = tf.layers.dense\n self.norm = tf.layers.batch_normalization\n\n # list of (stacked, channels) with implicit stride 2, conv between groups\n self.convblocks = convblocks\n self.recompute_grad = recompute_grad\n self.bf16 = use_bf16\n\n assert math.log2(stack_factor).is_integer() # maybe you don't actually need this?\n self.stack_factor = stack_factor\n\n def encoder(self, x):\n if self.bf16:\n x = tf.cast(x, tf.bfloat16)\n\n if self.stack_factor > 1:\n x = tf.space_to_depth(x, self.stack_factor)\n\n with tf.variable_scope(\"encoder\"):\n for block, (stack, channels) in enumerate(self.convblocks):\n with tf.variable_scope(f\"block_{block}\"):\n for i in range(stack):\n with tf.variable_scope(f\"layer_{i}\"):\n if i == 0:\n # downsample\n x = self.conv2d(x, channels, (4, 4), (2, 2), padding=\"SAME\", name=f\"conv_downsample\")\n else:\n # normal residual block\n\n def encoder_block(x, channels=channels):\n out = self.conv2d(x, channels, (3, 3), (1, 1), padding=\"SAME\", name=f\"conv_in\")\n # out = self.norm(out, name=f\"bn_in\")\n out = self.activation(out, name=f\"activ\")\n out = self.conv2d(out, channels, (3, 3), (1, 1), padding=\"SAME\", name=f\"conv_out\")\n # out = self.norm(out, name=f\"bn_out\")\n return out\n\n res_out = recompute_grad(encoder_block, self.bf16)(x) if self.recompute_grad else encoder_block(x)\n\n x = x + res_out\n\n with tf.variable_scope(f\"codebook\"):\n self.n_hid = x.shape[-1]\n embedding = tf.get_variable(\"codebook\", shape=[self.n_hid, self.num_tokens], dtype=tf.float32)\n\n if self.bf16:\n x = tf.cast(x, tf.float32)\n\n output = tf.matmul(x, embedding)\n\n return output\n\n\n def decoder(self, x):\n with tf.variable_scope(f\"codebook\", reuse=True):\n embedding = tf.get_variable(\"codebook\", shape=[self.n_hid, self.num_tokens], dtype=tf.float32)\n\n x = tf.matmul(x, embedding, transpose_b=True)\n\n if self.bf16:\n x = tf.cast(x, tf.bfloat16)\n\n with tf.variable_scope(\"decoder\"):\n for block, (stack, channels) in enumerate(reversed(self.convblocks)):\n with tf.variable_scope(f\"block_{block}\"):\n for i in range(stack):\n with tf.variable_scope(f\"layer_{i}\"):\n if i == 0:\n # upsample\n x = self.conv2dtranspose(x, channels, (4, 4), (2, 2), padding=\"SAME\", name=f\"conv_upsample\")\n else:\n # normal residual block\n\n def decoder_block(x, channels=channels):\n out = self.conv2d(x, channels, (3, 3), (1, 1), padding=\"SAME\", name=f\"conv_in\")\n # out = self.norm(out, name=f\"bn_in\")\n out = self.activation(out, name=f\"activ\")\n out = self.conv2d(out, channels, (3, 3), (1, 1), padding=\"SAME\", name=f\"conv_out\")\n # out = self.norm(out, name=f\"bn_out\")\n return out\n\n res_out = recompute_grad(decoder_block, self.bf16)(x) if self.recompute_grad else decoder_block(x)\n\n x = x + res_out\n\n x = self.conv2d(x, self.num_ch * self.stack_factor ** 2, (1, 1), (1, 1))\n\n if self.bf16:\n x = tf.cast(x, tf.float32)\n\n if self.stack_factor > 1:\n x = tf.depth_to_space(x, self.stack_factor)\n\n return x\n\n def forward(self, features, return_recon_loss=False, return_logits=False, hard_gumbel=True, temperature=1.):\n if isinstance(features, dict):\n img = features[\"inputs\"]\n else:\n img = features\n # NHWC\n logits = self.encoder(img)\n\n if return_logits:\n return logits # return logits for getting hard image indices for DALL-E training\n\n soft_one_hot = gumbel_softmax(logits, -1, temperature=temperature, hard=hard_gumbel)\n\n out = self.decoder(soft_one_hot)\n\n if not return_recon_loss:\n return out\n\n loss = mse_loss(tf.cast(img, out.dtype), out)\n return loss, out\n","repo_name":"EleutherAI/DALLE-mtf","sub_path":"src/vae_tf/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":7398,"program_lang":"python","lang":"en","doc_type":"code","stars":435,"dataset":"github-code","pt":"12"} +{"seq_id":"72674527062","text":"def min_number_coins_for_change(n, denoms):\n numbers = [float(\"inf\") for amount in range(n + 1)]\n numbers[0] = 0 # base case\n for denom in denoms:\n for amount in range(len(numbers)):\n if denom <= amount:\n # we compare the current value to the value that we use 1 coin of X\n numbers[amount] = min(numbers[amount], 1 + numbers[amount - denom]) \n return numbers[n] if numbers[n] != float(\"inf\") else -1\n\nprint(min_number_coins_for_change(7, [1,2,5]))","repo_name":"danhhoainam/algo_expert","sub_path":"python/medium/prob_014_min_changes/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"22729171265","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 8 14:53:04 2020\n\n@author: SELICLO1\n\"\"\"\n\ndef LSP(S):\n n = len(S)\n lps = [0] * n\n L = 0\n i = 1\n while (i < n):\n if S[i] == S[L]:\n L +=1\n lps[i] = L\n i += 1\n else:\n if L != 0:\n L = lps[L-1]\n else:\n lps[i] = 0\n i += 1\n \n global result\n result = lps[n-1]\n \n if result > n/2:\n return n//2\n else:\n return result\n \nS = \"CLOVROSELICLO\"\nprint(LSP(S))","repo_name":"lovroselic/Coursera","sub_path":"Capstone/Week1/LPS.py","file_name":"LPS.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"70944001302","text":"from __future__ import annotations\r\nfrom typing import List, Dict, Any, Union\r\nfrom collections import deque\r\n\r\nJSONDict = Dict[str, Any]\r\n\r\nclass Node:\r\n \"\"\"\r\n Representation of one step into a JSON Tree\r\n \"\"\"\r\n\r\n def __init__(self, json_data: Any, tree: 'JsonTree', linked_list: deque = deque([]), prior_keys: List[Union[str, int]] = []) -> None:\r\n self.json_data = json_data\r\n self.tree = tree\r\n self.linked_list = linked_list\r\n self.prior_keys = prior_keys\r\n\r\n self.dtype = type(self.json_data)\r\n\r\n self.nodes = []\r\n\r\n #If the node is a leaf then it has no edges\r\n if self.is_leaf:\r\n self.json_data = {prior_keys[-1]: self.json_data}\r\n self.tree.leaf_nodes.append(self)\r\n\r\n else:\r\n self.get_edges()\r\n\r\n @property\r\n def is_leaf(self):\r\n \"\"\"\r\n If the dtype of self.json_data is not a dict or a list then it must be\r\n a leaf node\r\n \"\"\"\r\n return self.dtype is not list and self.dtype is not dict\r\n\r\n def get_edges(self):\r\n \"\"\"\r\n Get all edges connected to current Node\r\n \"\"\"\r\n iter_arr = zip(range(len(self.json_data)),\r\n self.json_data) if self.dtype is list else self.json_data.items()\r\n\r\n for key, value in iter_arr:\r\n next_linked_list = self.linked_list + deque([self])\r\n next_key = self.prior_keys + [key]\r\n node = Node(value, self.tree, next_linked_list, next_key)\r\n self.nodes.append(node)\r\n\r\n def __repr__(self):\r\n return str(self.json_data)\r\n","repo_name":"chris-greening/json-tree-flattener","sub_path":"python3/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"38131710459","text":"import numpy as np # Numerical library\nfrom std_msgs.msg import Float32MultiArray # Message type\nfrom ROSwrapper.nodecontrol import NodeControl # ROS2 controller\nfrom Problem3_2a import line1 # Line generator\nfrom iknode import IkNode # Derived RosNode\nfrom iknode2 import IkNode2 # Derived RosNode\nimport matplotlib.pyplot as plt # To plot data points\n\n\nclass twolink():\n \"\"\" This class is meant for fk and ik operations around a 2-link\n manipulator. This was updated from problem 10 to allow for \n starting theta values.\n \"\"\"\n\n def __init__(self, length1, length2, path, rate):\n \"\"\" Class initialization \"\"\"\n self.a1 = length1\n self.a2 = length2\n self.x = path[0]\n self.y = path[1]\n self.index = 0\n self.pts = zip(path[0], path[1])\n self.theta = (0.0, 0.0)\n self.plot_data_ik_x = []\n self.plot_data_ik_y = []\n self.plot_data_fk_x = []\n self.plot_data_fk_y = []\n self.showing_plot = False\n self.s_plot = plt.figure()\n\n # ROS init\n self.nc = NodeControl()\n self.nc.addnode(IkNode(name='node_xy',\n obj=self,\n pub_data_type=Float32MultiArray,\n pub_chan='/physData',\n pub_rate=5,\n pub_data=self.pts))\n self.nc.addnode(IkNode(name='node_theta_magic',\n obj=self,\n sub_data_type=Float32MultiArray,\n sub_chan='/physData',\n pub_data_type=Float32MultiArray,\n pub_chan='/thetaData',\n pub_data=self.theta))\n self.nc.addnode(IkNode2(name='node_dual_sub',\n obj=self,\n sub_data_type=Float32MultiArray,\n sub_chan=('/physData', '/thetaData')))\n\n self.nc.run()\n\n def getik(self, xy):\n \"\"\" Calculates the inverse kinematics to determine the theta1\n & theta2 values\n \"\"\"\n x = xy[0]\n y = xy[1]\n theta1 = 0.0\n theta2 = 0.0\n D = (x * x + y * y - self.a1 * self.a1 - self.a2 * self.a2)\\\n / (2 * self.a1 * self.a2)\n theta2 = np.arctan2(np.sqrt(1 - D * D), D)\n gamma = np.arctan2((self.a2 * np.sin(theta2)),\n (self.a1 + self.a2 * np.cos(theta2)))\n theta1 = np.arctan2(y, x) - gamma\n\n return theta1, theta2\n\n def getfk(self, thetas):\n \"\"\" Calculate the forward kinematics to determine the x & y\n values\n \"\"\"\n theta1 = thetas[0]\n theta2 = thetas[1]\n x = self.a2 * np.cos(theta1 + theta2) + \\\n self.a1 * np.cos(theta1)\n y = self.a2 * np.sin(theta1 + theta2) + \\\n self.a1 * np.sin(theta1)\n return x, y\n\n def append_plot_data_ik(self, data):\n if len(self.plot_data_ik_x) < 100:\n self.plot_data_ik_x.append(data[0])\n self.plot_data_ik_y.append(data[1])\n\n def append_plot_data_fk(self, data):\n if len(self.plot_data_fk_x) < 100:\n self.plot_data_fk_x.append(data[0])\n self.plot_data_fk_y.append(data[1])\n elif not self.showing_plot:\n plt.scatter(self.plot_data_ik_x,\n self.plot_data_ik_y,\n c='g',\n label='Workspace Points')\n plt.scatter(self.plot_data_fk_x,\n self.plot_data_fk_y,\n c='b',\n label='Computed Workspace Points')\n self.showing_plot = True\n plt.title('Verifying Workspace Points')\n plt.legend()\n plt.show()\n self.s_plot.savefig('Problem3_2c.pdf',\n format='pdf',\n dpi=1200)\n print('Press \\\"ctrl\\\" + \\\"c\\\" to exit')\n\n\ndef main():\n twolink(10, 10, path=line1(0, 10, 100), rate=5)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"k-macmillan/RoboticsHW","sub_path":"HW2/Problem3_2c.py","file_name":"Problem3_2c.py","file_ext":"py","file_size_in_byte":4266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"21938994443","text":"import hashlib\nimport itertools\nimport json\nimport logging\nimport pickle\nimport urllib2\nimport time\nimport os\nfrom collections import namedtuple\n\nfrom flask_restful import marshal\n\nfrom lewas.exceptions import ConfigError\n\nlogger = logging.getLogger(__name__)\n\ndef mkey(m):\n return (m.station, m.instrument)\n\nauth_attrs = ['password', 'sslcrt', 'sslkey']\n\nAuth = namedtuple('Auth', ' '.join(auth_attrs))\nclass RESTStore():\n def __init__(self, **kwargs):\n self.host = kwargs.get('host') \n self.endpoint = kwargs.get('endpoint')\n self.fields = kwargs.get('fields', None)\n self.saveOnFail = kwargs.get('saveOnFail', True)\n self.storage = kwargs.get('storage', None) \n self.auth = Auth( *[ kwargs.get(label, None) for label in auth_attrs ] )\n #try\n if self.saveOnFail:\n try:\n fn = save_request({ 'test': 'to check for write permission' }, self.storage)\n os.remove(fn)\n except AttributeError:\n raise ConfigError('saveOnFail is set but could not find storage information')\n except IOError:\n raise ConfigError('{}: could not write to storage directory, does it even exist?'.format(self.storage))\n\n def post(self, measurements, **kwargs):\n for g,k in itertools.groupby(sorted(measurements, key=mkey), mkey):\n site_id, instrument_name = g\n url = self.host \\\n + urllib2.quote(self.endpoint.format(site_id=site_id, instrument_name=instrument_name))\n \n logger.log(logging.DEBUG, 'posting to {}'.format(url))\n # marshal measurements into request data\n #for m in k:\n # logger.log(logging.DEBUG, 'm: {}'.format(m))\n d = [ marshal(m, self.fields) for m in k ]\n try:\n request = urllib2.Request(url, json.dumps(d),\n {'Content-Type': 'application/json'})\n logger.log(logging.INFO, 'request of {} measurements\\n'.format(len(d)))\n except TypeError as e:\n print(e)\n logger.log(logging.ERROR, 'message: {}\\nobject: {}'.format(e,d))\n else:\n submitRequest(request, self.auth, storage=self.storage, **kwargs)\n\ndef submitRequest(request, auth, saveOnFail=True, **kwargs):\n #config is ONLY used for authentication\n storage = kwargs.get('storage') if saveOnFail else None\n if auth.password:\n d = json.loads(request.data)\n for m in d:\n m['magicsecret'] = auth.password\n request.data = json.dumps(d)\n\n response = None\n if auth.sslkey and auth.sslcrt:\n opener = urllib2.build_opener(HTTPSClientAuthHandler(\n auth.sslkey, auth.sslcrt)).open\n else:\n opener = urllib2.urlopen\n success = False\n try:\n response = opener(request)\n success = True\n except urllib2.HTTPError as e:\n # import traceback; traceback.print_exc()\n logger.log(logging.ERROR, \"{}\\n\\trequest: {}\".format(e, request.data))\n logger.log(logging.ERROR, \"\\tresponse: {}\".format(e.read()));\n except urllib2.URLError as e:\n logger.log(logging.ERROR, \"{}\\n\\turl: {}\\n\\trequest: {}\".format(e, request.get_full_url(), request.data))\n else:\n logger.log(logging.INFO, \"{}\\t{}\\n\\trequest: {}\".format(\n response.getcode(), request.get_full_url(), request.data))\n\t#TODO: would it be more clear to have the\n\t#if saveOnFail # here?\n finally:\n if response is not None:\n logger.log(logging.INFO, \"\\tresponse: {}\".format(response.read()))\n if saveOnFail and not success:\n save_request(request, storage)\n return success\n\ndef save_request(request, storage):\n p = pickle.dumps(request)\n h = hashlib.sha256()\n h.update(p)\n fn = str(int(time.mktime(time.gmtime())))+h.hexdigest() #todo: include instrument\n fn = os.path.join(storage, h.hexdigest())\n with open(fn, 'w') as f:\n f.write(p)\n return fn\n\nclass HTTPSClientAuthHandler(urllib2.HTTPSHandler):\n def __init__(self, key, cert):\n urllib2.HTTPSHandler.__init__(self)\n self.key = key\n self.cert = cert\n\n def https_open(self, req):\n # Rather than pass in a reference to a connection class, we pass in\n # a reference to a function which, for all intents and purposes,\n # will behave as a constructor\n return self.do_open(self.getConnection, req)\n\n def getConnection(self, host, timeout=300):\n return httplib.HTTPSConnection(host, key_file=self.key, cert_file=self.cert, strict=True)\n","repo_name":"lewas-lab/lewas","sub_path":"lewas/stores/reststore.py","file_name":"reststore.py","file_ext":"py","file_size_in_byte":4644,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"13960847271","text":"from openpyxl import Workbook\n\nfrom data.tables.author import Author\nfrom data.workbooks.works_workbook import WorkTypes\nfrom utilities.global_setup import DATA_PATH\n\nALL_AUTHORS_WOS_FILE_NAME = DATA_PATH + r\"\\people\\authors_all_wos.xlsx\"\nALL_AUTHORS_SCOPUS_FILE_NAME = DATA_PATH + r\"\\people\\authors_all_scopus.xlsx\"\nALL_AUTHORS_SHEET = \"Svi\"\n\nALL_AUTHORS_FILE_NAMES = [ALL_AUTHORS_WOS_FILE_NAME, ALL_AUTHORS_SCOPUS_FILE_NAME]\n\n\nclass AuthorsAllWorkBook:\n def __init__(self, work_book_type: WorkTypes):\n self.work_book = Workbook()\n self.work_book.remove(self.work_book.active)\n self.sheet = self.work_book.create_sheet(ALL_AUTHORS_SHEET)\n self.file_name = ALL_AUTHORS_FILE_NAMES[work_book_type]\n Author.write_headers_to_sheet(self.sheet)\n self.row = 2\n\n def save_author(self, author: Author):\n author.write_to_sheet(self.sheet, self.row)\n self.row += 1\n\n def save(self):\n self.work_book.save(self.file_name)\n","repo_name":"popina1994/university-of-belgrade-faculty-comparison","sub_path":"data/workbooks/authors_all_workbook.py","file_name":"authors_all_workbook.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"23523081812","text":"from qt import *\n\n# Translate titles, etc.\nfrom lilykde.i18n import _\nfrom lilykde.util import romanize\nfrom lilykde.scorewiz import part, nums\nfrom lilykde.lilydom import *\n\n\nclass _SingleVoice(part):\n \"\"\"\n The abstract base class for single voice part types.\n The build function just creates one staff with one voice,\n and uses the .clef, .transpose, .midiInstrument and .instrumentNames\n class (or instance) attributes.\n \"\"\"\n\n # A subclass could set a clef for the staff (e.g. \"bass\")\n clef = None\n\n # The octave for the \\relative command\n octave = 1\n\n # A subclass could set a transposition here.\n transpose = None\n\n # The MIDI instrument to use: see\n # http://lilypond.org/doc/latest/Documentation/user/lilypond/MIDI-instrument-names\n midiInstrument = None\n\n # Should contain a tuple with translated and standard italian\n # instrument names, both long and short, combined with a pipe symbol,\n # to ease the translation (otherwise the short names are not understood.)\n instrumentNames = None\n\n def build(self, braces = False):\n \"\"\"\n Returns both the stub for the music voice, and the newly created\n staff object.\n\n if braces == True the music identifier will be put inside braces\n (needed for addlyrics).\n \"\"\"\n s = self.newStaff()\n self.addPart(s)\n self.setInstrumentNames(s, *self.instrumentNames)\n s1 = braces and Seq(s) or Seqr(s)\n if self.clef:\n Clef(s1, self.clef)\n return self.assignMusic('', s1), s\n\n def assignMusic(self, name, node):\n \"\"\" automatically handles transposing instruments \"\"\"\n return super(_SingleVoice, self).assignMusic(\n name, node, self.octave, self.transpose)\n\n\nclass _KeyboardBase(part):\n \"\"\"\n Base class for keyboard instruments.\n \"\"\"\n def buildStaff(self, name, clef, octave, pdoc, numVoices):\n \"\"\"\n Build a staff with the given number of voices and name.\n \"\"\"\n staff = self.newStaff(pdoc, name)\n c = Seqr(staff)\n if clef:\n Clef(c, clef)\n if numVoices == 1:\n self.assignMusic(name, c, octave)\n else:\n c = Sim(c)\n for i in range(1, numVoices):\n self.assignMusic(name + nums(i), c, octave)\n VoiceSeparator(c)\n self.assignMusic(name + nums(numVoices), c, octave)\n return staff\n\n def build(self):\n \"\"\" setup structure for 2 manuals. \"\"\"\n p = PianoStaff(self.doc)\n self.addPart(p)\n self.setInstrumentNames(p, *self.instrumentNames)\n s = Sim(p, multiline=True)\n # add two staves, with a respective number of voices.\n self.buildStaff('right', '', 1, s, self.rightVoices.value())\n self.buildStaff('left', 'bass', 0, s, self.leftVoices.value())\n\n def widgets(self, p):\n QLabel('

%s (%s)

' % (\n _(\"Adjust how many separate voices you want on each staff.\"),\n _(\"This is primarily useful when you write polyphonic music \"\n \"like a fuge.\")), p)\n h = QHBox(p)\n l = QLabel(_(\"Right hand:\"), h)\n self.rightVoices = QSpinBox(1, 4, 1, h)\n l.setBuddy(self.rightVoices)\n h = QHBox(p)\n l = QLabel(_(\"Left hand:\"), h)\n self.leftVoices = QSpinBox(1, 4, 1, h)\n l.setBuddy(self.leftVoices)\n\n\nclass Organ(_KeyboardBase):\n name = _(\"Organ\")\n instrumentNames = _(\"Organ|Org.\"), \"Organo|Org.\"\n midiInstrument = 'church organ'\n\n def widgets(self, p):\n super(Organ, self).widgets(p)\n h = QHBox(p)\n l = QLabel(_(\"Pedal:\"), h)\n self.pedalVoices = QSpinBox(0, 4, 1, h)\n l.setBuddy(self.pedalVoices)\n self.pedalVoices.setValue(1)\n QToolTip.add(self.pedalVoices, _(\n \"Set to 0 to disable the pedal altogether.\"))\n\n def build(self):\n super(Organ, self).build()\n if self.pedalVoices.value():\n self.addPart(self.buildStaff('pedal', 'bass', -1, self.doc,\n self.pedalVoices.value()))\n\n\nclass Piano(_KeyboardBase):\n name = _(\"Piano\")\n instrumentNames = _(\"Piano|Pno.\"), \"Pianoforte|Pf.\"\n midiInstrument = 'acoustic grand'\n\n\nclass Harpsichord(_KeyboardBase):\n name = _(\"Harpsichord\")\n instrumentNames = _(\"Harpsichord|Hs.\"), \"Cembalo|Cemb.\"\n midiInstrument = 'harpsichord'\n\n\nclass Clavichord(_KeyboardBase):\n name = _(\"Clavichord\")\n instrumentNames = _(\"Clavichord|Clv.\"), \"Clavichord|Clv.\"\n midiInstrument = 'clav'\n\n\nclass Celesta(_KeyboardBase):\n name = _(\"Celesta\")\n instrumentNames = _(\"Celesta|Cel.\"), \"Celesta|Cel.\"\n midiInstrument = 'celesta'\n\n\nclass _SaxBase(_SingleVoice):\n \"\"\"\n All saxophone types.\n \"\"\"\n pass\n\n\nclass SopraninoSax(_SaxBase):\n name = _(\"Sopranino Sax\")\n instrumentNames = _(\"Sopranino Sax|SiSx.\"), \"Sopranino-Sax|Si-Sx.\"\n midiInstrument = 'soprano sax'\n transpose = (0, 2, -1) # es'\n\n\nclass SopranoSax(_SaxBase):\n name = _(\"Soprano Sax\")\n instrumentNames = _(\"Soprano Sax|SoSx.\"), \"Soprano-Sax|So-Sx.\"\n midiInstrument = 'soprano sax'\n transpose = (-1, 6, -1) # bes\n\n\nclass AltoSax(_SaxBase):\n name = _(\"Alto Sax\")\n instrumentNames = _(\"Alto Sax|ASx.\"), \"Alto-Sax|A-Sx.\"\n midiInstrument = 'alto sax'\n transpose = (-1, 2, -1) # es\n\n\nclass TenorSax(_SaxBase):\n name = _(\"Tenor Sax\")\n instrumentNames = _(\"Tenor Sax|TSx.\"), \"Tenor-Sax|T-Sx.\"\n midiInstrument = 'tenor sax'\n transpose = (-2, 6, -1) # bes,\n\n\nclass BaritoneSax(_SaxBase):\n name = _(\"Baritone Sax\")\n instrumentNames = _(\"Baritone Sax|BSx.\"), \"Bariton-Sax|B-Sx.\"\n midiInstrument = 'baritone sax'\n transpose = (-2, 2, -1) # es,\n\n\nclass BassSax(_SaxBase):\n name = _(\"Bass Sax\")\n instrumentNames = _(\"Bass Sax|BsSx.\"), \"Basso-Sax|Bs-Sx.\"\n midiInstrument = 'baritone sax'\n transpose = (-3, 6, -1) # bes,,\n\n\nclass _StringBase(_SingleVoice):\n \"\"\"\n All string instruments\n \"\"\"\n pass\n\n\nclass Violin(_StringBase):\n name = _(\"Violin\")\n instrumentNames = _(\"Violin|Vl.\"), \"Violino|Vl.\"\n midiInstrument = 'violin'\n\n\nclass Viola(_StringBase):\n name = _(\"Viola\")\n instrumentNames = _(\"Viola|Vla.\"), \"Viola|Vla.\"\n midiInstrument = 'viola'\n clef = 'alto'\n octave = 0\n\n\nclass Cello(_StringBase):\n name = _(\"Cello\")\n instrumentNames = _(\"Cello|Cl.\"), \"Violoncello|Vcl.\"\n midiInstrument = 'cello'\n clef = 'bass'\n octave = -1\n\n\nclass Contrabass(_StringBase):\n name = _(\"Contrabass\")\n instrumentNames = _(\"Contrabass|Cb.\"), \"Contrabasso|Cb.\"\n midiInstrument = 'contrabass'\n clef = 'bass'\n octave = -1\n\n\nclass BassoContinuo(Cello):\n name = _(\"Basso continuo\")\n instrumentNames = _(\"Basso Continuo|B.c.\"), \"Basso Continuo|B.c.\"\n def build(self):\n s = self.newStaff()\n self.addPart(s)\n self.setInstrumentNames(s, *self.instrumentNames)\n s = Sim(s)\n if self.clef:\n Clef(s, self.clef)\n self.assignMusic('bcMusic', s)\n b = FigureMode(self.doc)\n Identifier(b, 'global')\n Newline(b)\n Text(b,\n \"\\\\override Staff.BassFigureAlignmentPositioning \"\n \"#'direction = #DOWN\\n\")\n Comment(b, ' ' + _(\"Figures follow here.\"))\n Newline(b)\n self.assignGeneric('bcFigures', s, b)\n\n\nclass _WoodWindBase(_SingleVoice):\n \"\"\" All woodwind instruments \"\"\"\n pass\n\n\nclass Flute(_WoodWindBase):\n name = _(\"Flute\")\n instrumentNames = _(\"Flute|Fl.\"), \"Flauto|Fl.\"\n midiInstrument = 'flute'\n\n\nclass Piccolo(_WoodWindBase):\n name = _(\"Piccolo\")\n instrumentNames = _(\"Piccolo|Pic.\"), \"Flauto piccolo|Fl.pic.\"\n midiInstrument = 'piccolo'\n transpose = (1, 0, 0)\n\n\nclass BassFlute(_WoodWindBase):\n name = _(\"Bass flute\")\n instrumentNames = _(\"Bass flute|Bfl.\"), \"Flautone|Fln.\"\n midiInstrument = 'flute'\n transpose = (-1, 4, 0)\n\n\nclass Oboe(_WoodWindBase):\n name = _(\"Oboe\")\n instrumentNames = _(\"Oboe|Ob.\"), \"Oboe|Ob.\"\n midiInstrument = 'oboe'\n\n\nclass OboeDAmore(_WoodWindBase):\n name = _(\"Oboe d'Amore\")\n instrumentNames = _(\"Oboe d'amore|Ob.d'am.\"), \"Oboe d'amore|Ob.d'am.\"\n midiInstrument = 'oboe'\n transpose = (-1, 5, 0)\n\n\nclass EnglishHorn(_WoodWindBase):\n name = _(\"English Horn\")\n instrumentNames = _(\"English horn|Eng.h.\"), \"Corno Inglese|C.Ingl.\"\n midiInstrument = 'english horn'\n transpose = (-1, 3, 0)\n\n\nclass Bassoon(_WoodWindBase):\n name = _(\"Bassoon\")\n instrumentNames = _(\"Bassoon|Bn.\"), \"Fagotto|Fg.\"\n midiInstrument = 'bassoon'\n clef = 'bass'\n octave = -1\n\n\nclass ContraBassoon(_WoodWindBase):\n name = _(\"Contrabassoon\")\n instrumentNames = _(\"Contrabassoon|C.Bn.\"), \"Contra fagotto|C.Fg.\"\n midiInstrument = 'bassoon'\n transpose = (-1, 0, 0)\n clef = 'bass'\n octave = -1\n\n\nclass Clarinet(_WoodWindBase):\n name = _(\"Clarinet\")\n instrumentNames = _(\"Clarinet|Cl.\"), \"Clarinetto|Cl.\"\n midiInstrument = 'clarinet'\n transpose = (-1, 6, -1)\n\n\nclass SopranoRecorder(_WoodWindBase):\n name = _(\"Soprano recorder\")\n instrumentNames = _(\"Soprano recorder|S.rec.\"), \"Flauto dolce soprano|Fl.d.s.\"\n midiInstrument = 'recorder'\n transpose = (1, 0, 0)\n\n\nclass AltoRecorder(_WoodWindBase):\n name = _(\"Alto recorder\")\n instrumentNames = _(\"Alto recorder|A.rec.\"), \"Flauto dolce alto|Fl.d.a.\"\n midiInstrument = 'recorder'\n\n\nclass TenorRecorder(_WoodWindBase):\n name = _(\"Tenor recorder\")\n instrumentNames = _(\"Tenor recorder|T.rec.\"), \"Flauto dolce tenore|Fl.d.t.\"\n midiInstrument = 'recorder'\n\n\nclass BassRecorder(_WoodWindBase):\n name = _(\"Bass recorder\")\n instrumentNames = _(\"Bass recorder|B.rec.\"), \"Flauto dolce basso|Fl.d.b.\"\n midiInstrument = 'recorder'\n clef = 'bass'\n octave = -1\n\n\nclass _BrassBase(_SingleVoice):\n \"\"\"\n All brass instruments.\n \"\"\"\n pass\n\n\nclass HornF(_BrassBase):\n name = _(\"Horn in F\")\n instrumentNames = _(\"Horn in F|Hn.F.\"), \"Corno|Cor.\"\n midiInstrument = 'french horn'\n transpose = (-1, 3, 0)\n\n\nclass TrumpetC(_BrassBase):\n name = _(\"Trumpet in C\")\n instrumentNames = _(\"Trumpet in C|Tr.C\"), \"Tromba Do|Tr.Do\"\n midiInstrument = 'trumpet'\n\n\nclass TrumpetBb(TrumpetC):\n name = _(\"Trumpet in Bb\")\n instrumentNames = _(\"Trumpet in Bb|Tr.Bb\"), \"Tromba Si-bemolle|Tr.Sib\"\n transpose = (-1, 6, -1)\n\n\nclass Trombone(_BrassBase):\n name = _(\"Trombone\")\n instrumentNames = _(\"Trombone|Trb.\"), \"Trombone|Trb.\"\n midiInstrument = 'trombone'\n clef = 'bass'\n octave = -1\n\n\nclass Tuba(_BrassBase):\n name = _(\"Tuba\")\n instrumentNames = _(\"Tuba|Tb.\"), \"Tuba|Tb.\"\n midiInstrument = 'tuba'\n transpose = (-2, 6, -1)\n\n\nclass BassTuba(_BrassBase):\n name = _(\"Bass Tuba\")\n instrumentNames = _(\"Bass Tuba|B.Tb.\"), \"Tuba bassa|Tb.b.\"\n midiInstrument = 'tuba'\n transpose = (-2, 0, 0)\n clef = 'bass'\n octave = -1\n\n\nclass _TablatureBase(_SingleVoice):\n \"\"\"\n A class for instruments that support TabStaffs.\n \"\"\"\n octave = 0\n tunings = () # may contain a list of tunings.\n tabFormat = '' # can contain a tablatureFormat value.\n\n def widgets(self, p):\n h = QHBox(p)\n l = QLabel(_(\"Staff type:\"), h)\n self.staffType = QComboBox(False, h)\n l.setBuddy(self.staffType)\n for i in (\n _(\"Normal staff\"),\n _(\"Tablature\"),\n _(\"Both\"),\n ):\n self.staffType.insertItem(i)\n if self.tunings:\n QObject.connect(self.staffType, SIGNAL(\"activated(int)\"),\n self.slotTabEnable)\n self.widgetsTuning(p)\n self.slotTabEnable(0)\n\n def widgetsTuning(self, p):\n \"\"\" Implement widgets related to tuning \"\"\"\n h = QHBox(p)\n l = QLabel(_(\"Tuning:\"), h)\n self.tuningSel = QComboBox(False, h)\n l.setBuddy(self.tuningSel)\n self.tuningSel.insertItem(_(\"Default\"))\n for t in self.tunings:\n self.tuningSel.insertItem(t[0])\n\n def slotTabEnable(self, enable):\n \"\"\"\n Called when the user changes the staff type.\n Non-zero if the user wants a TabStaff.\n \"\"\"\n self.tuningSel.setEnabled(bool(enable))\n\n def newTabStaff(self, node = None, name = None, midiInstrument = None):\n \"\"\"\n Create a new TabStaff object and set it's MIDI instrument if desired.\n \"\"\"\n s = TabStaff(node or self.doc, name)\n if self._midi:\n midi = midiInstrument or self.midiInstrument\n if midi:\n s.getWith()['midiInstrument'] = midi\n if self.tabFormat:\n Scheme(Assignment(s.getWith(), 'tablatureFormat'), self.tabFormat)\n return s\n\n def build(self):\n t = self.staffType.currentItem()\n if t == 0:\n # normal staff\n super(_TablatureBase, self).build()\n return\n\n # make a tabstaff\n tab = self.newTabStaff()\n s = Seqr(tab)\n self.assignMusic('', s)\n # Tunings?\n self.setTunings(tab)\n # both?\n p = tab\n if t == 2:\n s = StaffGroup(self.doc)\n if self._instr:\n Text(s.getWith(), '\\\\consists \"Instrument_name_engraver\"\\n')\n s1 = Sim(s, multiline=True)\n s1.append(tab)\n p = s\n s = Seqr(self.newStaff(s1))\n if self.clef:\n Clef(s, self.clef)\n self.assignMusic('', s)\n self.setInstrumentNames(p, *self.instrumentNames)\n self.addPart(p)\n\n def setTunings(self, tab):\n \"\"\" set tunings \"\"\"\n if self.tunings and self.tuningSel.currentItem() > 0:\n tuning = self.tunings[self.tuningSel.currentItem() - 1][1]\n Scheme(Assignment(tab.getWith(), 'stringTunings'), tuning)\n\n\n\nclass Mandolin(_TablatureBase):\n name = _(\"Mandolin\")\n instrumentNames = _(\"Mandolin|Mdl.\"), \"Mandolino|Mdl.\"\n midiInstrument = 'acoustic guitar (steel)'\n tunings = (\n (_(\"Mandolin tuning\"), 'mandolin-tuning'),\n )\n\n\nclass Banjo(_TablatureBase):\n name = _(\"Banjo\")\n instrumentNames = _(\"Banjo|Bj.\"), \"Banjo|Bj.\"\n midiInstrument = 'banjo'\n tabFormat = 'fret-number-tablature-format-banjo'\n tunings = (\n (_(\"Open G-tuning (aDGBD)\"), 'banjo-open-g-tuning'),\n (_(\"C-tuning (gCGBD)\"), 'banjo-c-tuning'),\n (_(\"Modal tuning (gDGCD)\"), 'banjo-modal-tuning'),\n (_(\"Open D-tuning (aDF#AD)\"), 'banjo-open-d-tuning'),\n (_(\"Open Dm-tuning (aDFAD)\"), 'banjo-open-dm-tuning'),\n )\n def widgetsTuning(self, p):\n super(Banjo, self).widgetsTuning(p)\n self.fourStrings = QCheckBox(_(\"Four strings (instead of five)\"), p)\n\n def slotTabEnable(self, enable):\n super(Banjo, self).slotTabEnable(enable)\n self.fourStrings.setEnabled(bool(enable))\n\n def setTunings(self, tab):\n if not self.fourStrings.isChecked():\n super(Banjo, self).setTunings(tab)\n else:\n Scheme(Assignment(tab.getWith(), 'stringTunings'),\n '(four-string-banjo %s)' %\n self.tunings[self.tuningSel.currentItem()][1])\n\n\nclass ClassicalGuitar(_TablatureBase):\n name = _(\"Classical guitar\")\n instrumentNames = _(\"Guitar|Gt.\"), \"Chitarra|Chit.\"\n midiInstrument = 'acoustic guitar (nylon)'\n transpose = (-1, 0, 0)\n tunings = (\n (_(\"Guitar tuning\"), 'guitar-tuning'),\n (_(\"Open G-tuning\"), 'guitar-open-g-tuning'),\n )\n\n\nclass JazzGuitar(ClassicalGuitar):\n name = _(\"Jazz guitar\")\n instrumentNames = _(\"Jazz guitar|J.Gt.\"), \"Jazz Chitarra|J.Chit.\" #FIXME\n midiInstrument = 'electric guitar (jazz)'\n\n\nclass Bass(_TablatureBase):\n name = _(\"Bass\")\n instrumentNames = _(\"Bass|Bs.\"), \"Bass|B.\" #FIXME\n midiInstrument = 'acoustic bass'\n transpose = (-1, 0, 0)\n clef = 'bass'\n octave = -1\n tunings = (\n (_(\"Bass tuning\"), 'bass-tuning'),\n )\n\n\nclass ElectricBass(Bass):\n name = _(\"Electric bass\")\n instrumentNames = _(\"Electric bass|E.Bs.\"), \"Electric bass|E.B.\" #FIXME\n midiInstrument = 'electric bass (finger)'\n\n\nclass Harp(_KeyboardBase):\n name = _(\"Harp\")\n instrumentNames = _(\"Harp|Hp.\"), \"Arpa|Ar.\"\n midiInstrument = 'harp'\n def build(self):\n \"\"\" setup structure for 2 manuals. \"\"\"\n p = PianoStaff(self.doc)\n self.addPart(p)\n self.setInstrumentNames(p, *self.instrumentNames)\n s = Sim(p, multiline=True)\n # add two staves, with a respective number of voices.\n self.buildStaff('upper', '', 1, s, 1)\n self.buildStaff('lower', 'bass', 0, s, 1)\n\n def widgets(self, p):\n part.widgets(self, p)\n\n\nclass _PitchedPercussionBase(_SingleVoice):\n \"\"\"\n All pitched percussion instruments.\n \"\"\"\n pass\n\n\nclass Timpani(_PitchedPercussionBase):\n name = _(\"Timpani\")\n instrumentNames = _(\"Timpani|Tmp.\"), \"Timpani|Tmp.\"\n midiInstrument = 'timpani'\n clef = 'bass'\n octave = -1\n\n\nclass Xylophone(_PitchedPercussionBase):\n name = _(\"Xylophone\")\n instrumentNames = _(\"Xylophone|Xyl.\"), \"Silofono|Sil.\"\n midiInstrument = 'xylophone'\n\n\nclass Marimba(_PitchedPercussionBase):\n name = _(\"Marimba\")\n instrumentNames = _(\"Marimba|Mar.\"), \"Marimba|Mar.\"\n midiInstrument = 'marimba'\n\n\nclass Vibraphone(_PitchedPercussionBase):\n name = _(\"Vibraphone\")\n instrumentNames = _(\"Vibraphone|Vib.\"), \"Vibrafono|Vib.\"\n midiInstrument = 'vibraphone'\n\n\nclass TubularBells(_PitchedPercussionBase):\n name = _(\"Tubular bells\")\n instrumentNames = _(\"Tubular bells|Tub.\"), \"Campana tubolare|Cmp.t.\"\n midiInstrument = 'tubular bells'\n\n\nclass Glockenspiel(_PitchedPercussionBase):\n name = _(\"Glockenspiel\")\n instrumentNames = _(\"Glockenspiel|Gls.\"), \"Campanelli|Camp.\"\n midiInstrument = 'glockenspiel'\n\n\nclass Drums(part):\n name = _(\"Drums\")\n instrumentNames = _(\"Drums|Dr.\"), \"Tamburo|Tamb.\"\n\n def assignDrums(self, name, node):\n s = DrumMode(self.doc)\n Identifier(s, 'global')\n Newline(s)\n Comment(s, ' '+_(\"Drums follow here.\"))\n Newline(s)\n self.assignGeneric(name, node, s)\n\n def build(self):\n p = DrumStaff(self.doc)\n s = Simr(p, multiline = True)\n\n if self.drumVoices.value() > 1:\n for i in range(1, self.drumVoices.value()+1):\n q = Seq(DrumVoice(s))\n Text(q, '\\\\voice%s' % nums(i))\n self.assignDrums('drum%s' % nums(i), q)\n else:\n self.assignDrums('drum', s)\n self.addPart(p)\n self.setInstrumentNames(p, *self.instrumentNames)\n i = self.drumStyle.currentItem()\n if i > 0:\n v = ('drums', 'timbales', 'congas', 'bongos', 'percussion')[i]\n p.getWith()['drumStyleTable'] = Scheme(self.doc, '%s-style' % v)\n v = (5, 2, 2, 2, 1)[i]\n Text(p.getWith(), \"\\\\override StaffSymbol #'line-count = #%i\\n\" % v)\n if self.drumStems.isChecked():\n Text(p.getWith(), \"\\\\override Stem #'stencil = ##f\\n\")\n Text(p.getWith(), \"\\\\override Stem #'length = #3 %% %s\"\n % _(\"keep some distance.\"))\n\n def widgets(self, p):\n h = QHBox(p)\n l = QLabel(_(\"Voices:\"), h)\n self.drumVoices = QSpinBox(1, 4, 1, h)\n l.setBuddy(self.drumVoices)\n QToolTip.add(h, _(\"How many drum voices to put in this staff.\"))\n h = QHBox(p)\n l = QLabel(_(\"Style:\"), h)\n self.drumStyle = QComboBox(False, h)\n l.setBuddy(self.drumStyle)\n for i in (\n _(\"Drums (5 lines, default)\"),\n _(\"Timbales-style (2 lines)\"),\n _(\"Congas-style (2 lines)\"),\n _(\"Bongos-style (2 lines)\"),\n _(\"Percussion-style (1 line)\"),\n ):\n self.drumStyle.insertItem(i)\n self.drumStems = QCheckBox(_(\"Remove stems\"), p)\n QToolTip.add(self.drumStems, _(\"Remove the stems from the drum notes.\"))\n\n\nclass Chords(part):\n name = _(\"Chord names\")\n def build(self):\n p = ChordNames(self.doc)\n s = ChordMode(self.doc)\n Identifier(s, 'global')\n Newline(s)\n i = self.chordStyle.currentItem()\n if i > 0:\n Identifier(s, '%sChords' %\n ('german', 'semiGerman', 'italian', 'french')[i-1])\n Newline(s)\n Comment(s, ' ' + _(\"Chords follow here.\"))\n Newline(s)\n self.assignGeneric('chordNames', p, s)\n self.addPart(p)\n\n def widgets(self, p):\n h = QHBox(p)\n l = QLabel(_(\"Chord style:\"), h)\n self.chordStyle = QComboBox(False, h)\n l.setBuddy(self.chordStyle)\n for i in (\n _(\"Default\"),\n _(\"German\"),\n _(\"Semi-German\"),\n _(\"Italian\"),\n _(\"French\"),\n ):\n self.chordStyle.insertItem(i)\n\n\nclass BassFigures(part):\n name = _(\"Figured Bass\")\n def build(self):\n p = FiguredBass(self.doc)\n s = FigureMode(self.doc)\n Identifier(s, 'global')\n Newline(s)\n Comment(s, ' ' + _(\"Figures follow here.\"))\n Newline(s)\n self.assignGeneric('figBass', p, s)\n self.addPart(p)\n if self.useExtenderLines.isChecked():\n p.getWith()['useBassFigureExtenders'] = Scheme(self.doc, '#t')\n\n def widgets(self, p):\n self.useExtenderLines = QCheckBox(_(\"Use extender lines\"), p)\n\n\nclass _VocalBase(part):\n \"\"\"\n Base class for vocal stuff.\n \"\"\"\n midiInstrument = 'choir aahs'\n\n def assignLyrics(self, name, node, verse = 0):\n l = LyricMode(self.doc)\n if verse:\n name = name + nums(verse)\n Text(l, '\\\\set stanza = \"%d.\"\\n' % verse)\n Comment(l, ' ' + _(\"Lyrics follow here.\"))\n Newline(l)\n self.assignGeneric(name, node, l)\n\n def widgets(self, p):\n self.stanzaWidget(p)\n self.ambitusWidget(p)\n\n def stanzaWidget(self, p):\n h = QHBox(p)\n l = QLabel(_(\"Stanzas:\"), h)\n self.stanzas = QSpinBox(1, 10, 1, h)\n l.setBuddy(self.stanzas)\n QToolTip.add(h, _(\"The number of stanzas.\"))\n\n def ambitusWidget(self, p):\n self.ambitus = QCheckBox(_(\"Ambitus\"), p)\n QToolTip.add(self.ambitus, _(\n \"Show the pitch range of the voice at the beginning of the staff.\"))\n\n def addStanzas(self, node, name = '', count = 0):\n r\"\"\"\n Add stanzas in count (or self.stanzas.value()) to the (Voice) node\n using \\addlyrics.\n \"\"\"\n name = name or 'verse'\n count = count or self.stanzas.value()\n if count == 1:\n self.assignLyrics(name, AddLyrics(node))\n else:\n for i in range(count):\n Newline(node)\n self.assignLyrics(name, AddLyrics(node), i + 1)\n\n\nclass _VocalSolo(_VocalBase, _SingleVoice):\n \"\"\"\n Base class for solo voices\n \"\"\"\n def build(self):\n stub, staff = _SingleVoice.build(self, True)\n stub[1].insert(stub[1][-2], Text(self.doc, '\\\\dynamicUp\\n'))\n self.addStanzas(staff)\n if self.ambitus.isChecked():\n Text(staff.getWith(), '\\\\consists \"Ambitus_engraver\"\\n')\n\nclass SopranoVoice(_VocalSolo):\n name = _(\"Soprano\")\n instrumentNames = _(\"Soprano|S.\"), \"Soprano|S.\"\n\n\nclass MezzoSopranoVoice(_VocalSolo):\n name = _(\"Mezzo soprano\")\n instrumentNames = _(\"Mezzo-soprano|Ms.\"), \"Mezzosoprano|Ms.\"\n\n\nclass AltoVoice(_VocalSolo):\n name = _(\"Alto\")\n instrumentNames = _(\"Alto|A.\"), \"Alto|A.\"\n octave = 0\n\n\nclass TenorVoice(_VocalSolo):\n name = _(\"Tenor\")\n instrumentNames = _(\"Tenor|T.\"), \"Tenore|T.\"\n octave = 0\n clef = 'treble_8'\n\n\nclass BassVoice(_VocalSolo):\n name = _(\"Bass\")\n instrumentNames = _(\"Bass|B.\"), \"Basso|B.\"\n octave = -1\n clef = 'bass'\n\n\nclass LeadSheet(_VocalBase, Chords):\n name = _(\"Lead sheet\")\n\n def build(self):\n \"\"\"\n Create chord names, song and lyrics.\n Optional a second staff with a piano accompaniment.\n \"\"\"\n Chords.build(self)\n if self.accomp.isChecked():\n p = ChoirStaff(self.doc)\n #TODO: instrument mames ?\n s = Sim(p, multiline = True)\n mel = Sim(Staff(s), multiline = True)\n v1 = Voice(mel)\n s1 = Seq(v1, multiline = True)\n Text(s1, '\\\\voiceOne\\n')\n self.assignMusic('melody', s1, 1)\n s2 = Seq(Voice(mel), multiline = True)\n Text(s2, '\\\\voiceTwo\\n')\n self.assignMusic('accRight', s2, 0)\n acc = Seqr(Staff(s))\n Clef(acc, 'bass')\n self.assignMusic('accLeft', acc, -1)\n if self.ambitus.isChecked():\n # We can't use \\addlyrics when the voice has a \\with {}\n # section, because it creates a nested Voice context.\n # So if the ambitus engraver should be added to the Voice,\n # we don't use \\addlyrics but create a new Lyrics context.\n # So in that case we don't use addStanzas, but insert the\n # Lyrics contexts manually inside our ChoirStaff.\n v1.cid = 'melody'\n Text(v1.getWith(), '\\\\consists \"Ambitus_engraver\"\\n')\n count = self.stanzas.value() # number of stanzas\n if count == 1:\n l = Lyrics(self.doc)\n s.insert(acc.parent, l)\n self.assignLyrics('verse', LyricsTo(l, v1.cid))\n else:\n for i in range(count):\n l = Lyrics(self.doc)\n s.insert(acc.parent, l)\n self.assignLyrics('verse', LyricsTo(l, v1.cid), i + 1)\n else:\n self.addStanzas(v1)\n else:\n p = Staff(self.doc)\n self.assignMusic('melody', Seq(p), 1)\n self.addStanzas(p)\n if self.ambitus.isChecked():\n Text(p.getWith(), '\\\\consists \"Ambitus_engraver\"\\n')\n self.addPart(p)\n\n def widgets(self, p):\n QLabel('

%s

' % _(\n \"The Lead Sheet provides a staff with chord names above \"\n \"and lyrics below it. A second staff is optional.\"), p)\n self.accomp = QCheckBox(_(\"Add accompaniment staff\"), p)\n QToolTip.add(self.accomp, _(\n \"Adds an accompaniment staff and also puts an accompaniment \"\n \"voice in the upper staff.\"))\n Chords.widgets(self, p)\n _VocalBase.widgets(self, p)\n\n\nclass Choir(_VocalBase):\n name = _(\"Choir\")\n\n def widgets(self, p):\n QLabel('

%s

(%s)

' % (\n _(\"Please select the voices for the choir. \"\n \"Use the letters S, A, T, or B. A hyphen denotes a new staff.\"),\n _(\"Tip: For a double choir you can use two choir parts.\")), p)\n h = QHBox(p)\n l = QLabel(_(\"Voicing:\"), h)\n self.voicing = QComboBox(True, h)\n l.setBuddy(self.voicing)\n for i in (\n 'SA-TB', 'S-A-T-B',\n 'SA', 'S-A', 'SS-A',\n 'TB', 'T-B', 'TT-B',\n 'SS-A-T-B', 'SS-A-TT-B',\n 'S-S-A-T-T-B', 'S-S-A-A-T-T-B-B'\n ):\n self.voicing.insertItem(i)\n b = QVButtonGroup(_(\"Lyrics\"), p)\n self.lyrAllSame = QRadioButton(_(\"All voices same lyrics\"), b)\n self.lyrAllSame.setChecked(True)\n QToolTip.add(self.lyrAllSame, _(\n \"One set of the same lyrics is placed between all staves.\"))\n self.lyrEachSame = QRadioButton(_(\"Every voice same lyrics\"), b)\n QToolTip.add(self.lyrEachSame, _(\n \"Every voice gets its own lyrics, using the same text as the \"\n \"other voices.\"))\n self.lyrEachDiff = QRadioButton(_(\"Every voice different lyrics\"), b)\n QToolTip.add(self.lyrEachDiff, _(\n \"Every voice gets a different set of lyrics.\"))\n self.stanzaWidget(b)\n self.ambitusWidget(p)\n\n partInfo = {\n 'S': ('soprano', 1, SopranoVoice.instrumentNames),\n 'A': ('alto', 0, AltoVoice.instrumentNames),\n 'T': ('tenor', 0, TenorVoice.instrumentNames),\n 'B': ('bass', -1, BassVoice.instrumentNames),\n }\n\n def build(self):\n # normalize voicing\n staffs = unicode(self.voicing.currentText()).upper()\n # remove unwanted characters\n staffs = re.sub(r'[^SATB-]+', '', staffs)\n # remove double hyphens, and from begin and end\n staffs = re.sub('-+', '-', staffs).strip('-')\n splitStaffs = staffs.split('-')\n p = ChoirStaff(self.doc)\n choir = Sim(p, multiline = True)\n self.addPart(p)\n # print main instrumentName if there are more choirs, and we\n # have more than one staff.\n if self._instr and '-' in staffs and self.num:\n self.setInstrumentNames(p, _(\"Choir|Ch.\"), \"Coro|C.\")\n Text(p.getWith(), '\\\\consists \"Instrument_name_engraver\"\\n')\n\n count = dict.fromkeys('SATB', 0) # dict with count of parts.\n toGo = len(splitStaffs)\n maxLen = max(map(len, splitStaffs))\n lyr, staffNames = [], []\n for staff in splitStaffs:\n toGo -= 1\n # sort the letters in order SATB\n staff = ''.join(i * staff.count(i) for i in 'SATB')\n # Create the staff for the voices\n s = self.newStaff(choir)\n # Build lists of the voices and their instrument names\n instrNames, voices = [], []\n for part in staff:\n if staffs.count(part) > 1:\n count[part] += 1\n name, octave, (translated, italian) = self.partInfo[part]\n instrNames.append(\n self.buildInstrumentNames(translated, italian, count[part]))\n voices.append((name, count[part], octave))\n if len(staff) == 1:\n # There is only one voice in the staff. Just set the instrument\n # name directly in the staff.\n s.instrName(*instrNames[0])\n # if *all* staves have only one voice, addlyrics is used.\n # In that case, don't remove the braces.\n mus = maxLen == 1 and Seq(s) or Seqr(s)\n else:\n # There are more instrument names for the staff, stack them in\n # a markup column.\n def mkup(names):\n # return a markup object with names stacked vertically\n if max(names):\n n = Markup(self.doc)\n # from 2.11.57 and above LilyPond uses center-column\n from lilykde.version import version\n if version and version >= (2, 11, 57):\n m = MarkupEncl(n, 'center-column', multiline=True)\n else:\n m = MarkupEncl(n, 'center-align', multiline=True)\n for i in names:\n QuotedString(m, i)\n return n\n s.instrName(*map(mkup, zip(*instrNames)))\n mus = Simr(s, multiline = True)\n # Set the clef for this staff:\n if 'B' in staff:\n Clef(mus, 'bass')\n elif 'T' in staff:\n Clef(mus, 'treble_8')\n\n stanzas = self.stanzas.value()\n stanzas = stanzas == 1 and [0] or range(1, stanzas + 1)\n\n # Add the voices\n if len(staff) == 1:\n name, num, octave = voices[0]\n mname = name + (num and nums(num) or '')\n if self.lyrEachDiff.isChecked():\n lyrName = mname + 'Verse'\n else:\n lyrName = 'verse'\n if maxLen == 1:\n # if all staves have only one voice, use \\addlyrics...\n self.assignMusic(mname, mus, octave)\n if not (self.lyrAllSame.isChecked() and not toGo):\n for verse in stanzas:\n Newline(s)\n lyr.append((lyrName, AddLyrics(s), verse))\n else:\n # otherwise create explicit Voice and Lyrics contexts.\n vname = name + str(num or '')\n v = Seqr(Voice(mus, vname))\n self.assignMusic(mname, v, octave)\n if not (self.lyrAllSame.isChecked() and not toGo):\n for verse in stanzas:\n lyr.append(\n (lyrName, LyricsTo(Lyrics(choir), vname), verse))\n\n if self.ambitus.isChecked():\n Text(s.getWith(), '\\\\consists \"Ambitus_engraver\"\\n')\n else:\n # There is more than one voice in the staff.\n # Determine their order (\\voiceOne, \\voiceTwo etc.)\n if len(staff) == 2:\n order = 1, 2\n elif staff in ('SSA', 'TTB'):\n order = 1, 3, 2\n elif staff in ('SAA', 'TBB'):\n order = 1, 2, 4\n elif staff in ('SSAA', 'TTBB'):\n order = 1, 3, 2, 4\n else:\n order = range(1, len(staff) + 1)\n # What name would the staff get if we need to refer to it?\n staffName, snum = staff, 1\n # if a name (like 's' or 'sa') is already in use in this part,\n # just add a number ('ss2' or 'sa2', etc.)\n while staffName in staffNames:\n snum += 1\n staffName = staff + str(snum)\n staffNames.append(staffName)\n # We want the staff name (actually context-id) in lower case.\n staffName = staffName.lower()\n # Create the voices and their lyrics.\n for (name, num, octave), vnum in zip(voices, order):\n mname = name + (num and nums(num) or '')\n vname = name + str(num or '')\n v = Voice(mus, vname)\n # Add ambitus to voice, move to the right if necessary\n if self.ambitus.isChecked():\n Text(v.getWith(), '\\\\consists \"Ambitus_engraver\"\\n')\n if vnum > 1:\n Text(v.getWith(),\n \"\\\\override Ambitus #'X-offset = #%s\\n\" %\n ((vnum - 1) * 2.0))\n v = Seqr(v)\n Text(v, '\\\\voice' + nums(vnum))\n self.assignMusic(mname, v, octave)\n if self.lyrAllSame.isChecked() and toGo and vnum == 1:\n lyrName = 'verse'\n above = False\n elif self.lyrEachSame.isChecked():\n lyrName = 'verse'\n above = vnum & 1\n elif self.lyrEachDiff.isChecked():\n lyrName = mname + 'Verse'\n above = vnum & 1\n else:\n continue\n # Create the lyrics. If they should be above the staff,\n # give the staff a suitable name, and use alignAboveContext\n # to align the Lyrics above the staff.\n if above:\n s.cid = staffName\n for verse in stanzas:\n l = Lyrics(choir)\n if above:\n l.getWith()['alignAboveContext'] = staffName\n lyr.append((lyrName, LyricsTo(l, vname), verse))\n\n # Assign the lyrics, so their definitions come after the note defs.\n for name, node, verse in lyr:\n self.assignLyrics(name, node, verse)\n\n\n\n\n\n# The structure of the overview\ncategories = (\n (_(\"Strings\"), (\n Violin,\n Viola,\n Cello,\n Contrabass,\n BassoContinuo,\n )),\n (_(\"Plucked strings\"), (\n Mandolin,\n Banjo,\n ClassicalGuitar,\n JazzGuitar,\n Bass,\n ElectricBass,\n Harp,\n )),\n (_(\"Woodwinds\"), (\n Flute,\n Piccolo,\n BassFlute,\n Oboe,\n OboeDAmore,\n EnglishHorn,\n Bassoon,\n ContraBassoon,\n Clarinet,\n SopraninoSax,\n SopranoSax,\n AltoSax,\n TenorSax,\n BaritoneSax,\n BassSax,\n SopranoRecorder,\n AltoRecorder,\n TenorRecorder,\n BassRecorder,\n )),\n (_(\"Brass\"), (\n HornF,\n TrumpetC,\n TrumpetBb,\n Trombone,\n Tuba,\n BassTuba,\n )),\n (_(\"Vocal\"), (\n LeadSheet,\n SopranoVoice,\n MezzoSopranoVoice,\n AltoVoice,\n TenorVoice,\n BassVoice,\n Choir,\n )),\n (_(\"Keyboard instruments\"), (\n Piano,\n Harpsichord,\n Clavichord,\n Organ,\n Celesta,\n )),\n (_(\"Percussion\"), (\n Timpani,\n Xylophone,\n Marimba,\n Vibraphone,\n TubularBells,\n Glockenspiel,\n Drums,\n )),\n (_(\"Special\"), (\n Chords,\n BassFigures,\n )),\n)\n\n\n","repo_name":"wbsoft/lilykde","sub_path":"lilykde/py/lilykde/parts.py","file_name":"parts.py","file_ext":"py","file_size_in_byte":37664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"39394130057","text":"#Exercise 1:\n#Using in-place algorithm?\nwords = ['this' , 'is', 'a', 'sentence', '.']\ndef swap(lst1, w,x,y,z):\n lst1[w], lst1[x], lst1[y], lst1[z] = lst1[z], lst1[y], lst1[x], lst1[w]\n return lst1\nswap(words,0,1,3,4) \n\n#list comprehension - reverses string too\ndef swap2(lst1):\n return [x[::-1] for x in lst1[::-1]]\n\n#standard reverse\nwordscopy = words[::-1]\nprint(wordscopy)\n\n#Exercise 2:\na_text = 'In computing, a hash table hash map is a data structure which implements an associative array abstract data type, a structure that can map keys to values. A hash table uses a hash function to compute an index into an array of buckets or slots from which the desired value can be found'\n\ndef countwords(string):\n string = string.lower()\n stringlist = string.split()\n dict1 = {}\n for x in stringlist:\n count = 0\n for item in stringlist:\n if x == item:\n count+=1\n dict1[x] = count\n return dict1\ncountwords(a_text)\n\n#Exercise 3:\ndef linearsearch(list1, num):\n for i in range(len(list1)):\n if list1[i] == num: \n return f'{num} is at index {i}' \n\n\ndef linearsearch2(list1,num):\n count = 0 \n while count < len(list1):\n if list1[count] == num:\n return f'{num} is at index {count}'\n else:\n count+=1\nlinearsearch2([1,2,3,4,5,6,7,8],8)","repo_name":"iCornYu/Week-3-Day-2-","sub_path":"Homework.py","file_name":"Homework.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"15848772574","text":"import datetime\nimport hashlib\n\nfrom PIL import ImageFont,ImageDraw,Image\nfrom random import randint\n\nclass VerifyCode:\n\tdef __init__(self,width=100,height=40,size=4):\n\t\t\"\"\"\n\n\t\t:param width: 验证码的宽度\n\t\t:param height: 验证码的高度\n\t\t:param size: 验证码的长度\n\t\t\"\"\"\n\t\tself.width = width if width > 0 else 100\n\t\tself.height = height if height > 0 else 40\n\t\tself.size = size if size > 0 else 4\n\t\tself.pen = None # 画笔\n\t\tself.code = \"\" # 保存验证码字符串\n\n\t# @property\n\t# def code(self):\n\t# \treturn self.__code\n\t# @code.setter\n\t# def code(self,code):\n\t# \tself.__code = code\n\n\tdef generate(self):\n\t\t# 1.生成画布 # 越靠近255的颜色越浅\n\t\tim = Image.new(\"RGB\",(self.width,self.height),self.randColor(160,255))\n\t\t# 2.生成画笔\n\t\tself.pen = ImageDraw.Draw(im)\n\t\t# 3.生成随机字符串\n\t\tself.randString()\n\t\t# 4.画字符串\n\t\tself.__drawCode()\n\t\t# 5.画干扰点\n\t\tself.__drawPoint()\n\t\t# 6.画干扰线\n\t\tself.__drawLine()\n\t\t# 7.保存图片\n\t\tim.save(\"vc.jpg\")\n\tdef __drawLine(self):\n\t\t\"\"\"\n\t\t画干扰线\n\t\t:return:\n\t\t\"\"\"\n\t\tfor i in range(6):\n\t\t\tstart = (randint(1,self.width-1),randint(1,self.height-1))\n\t\t\tend = (randint(1,self.width-1),randint(1,self.height-1))\n\t\t\tself.pen.line([start,end],fill=self.randColor(50,150),width = 1)\n\n\tdef __drawPoint(self):\n\t\t\"\"\"\n\t\t画干扰点\n\t\t:return:\n\t\t\"\"\"\n\t\tfor i in range(200):\n\t\t\tx = randint(1,self.width-1)\n\t\t\ty = randint(1,self.height-1)\n\t\t\tself.pen.point((x,y),fill= self.randColor(30,100))\n\tdef __drawCode(self):\n\t\t\"\"\"\n\t\t画字符串\n\t\t:return:\n\t\t\"\"\"\n\t\tmyFont = ImageFont.truetype(\"MSYH.TTF\",size=20,encoding=\"UTF-8\")\n\t\tfor i in range(self.size):\n\t\t\tx = 15 + i*(self.width - 20)/self.size # 为每个字符均匀分配位置\n\t\t\ty = randint(5,10) # 随机高度\n\t\t\tself.pen.text((x,y),self.code[i],fill = self.randColor(0,60),font = myFont)\n\n\tdef randString(self):\n\t\t\"\"\"\n\t\t产生随机整数字符串\n\t\t:return:\n\t\t\"\"\"\n\t\tresult = \"\"\n\t\tfor i in range(self.size):\n\t\t\tresult += str(randint(0,9))\n\t\tself.code = result\n\n\tdef randColor(self,low,high): # 随机背景颜色\n\t\treturn randint(low,high),randint(low,high),randint(low,high)\n\n# class StrCode(VerifyCode):\n# \tdef randString(self):\n# \t\ts1 =hashlib.md5(b\"2314\").hexdigest()\n# \t\tprint(s1)\n# \t\tself.code = s1[:self.size]\nif __name__ == \"__main__\":\n\tvc = VerifyCode()\n\t# vc = StrCode()\n\tvc.generate()\n\tprint(vc.code)\n","repo_name":"zaoyuaner/Learning-materials","sub_path":"python1812/python_1/17_测试_收发邮件_二维码/代码/04_验证码生成器.py","file_name":"04_验证码生成器.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"18830263837","text":"import os\nimport modules.file_utils as file_utils\nfrom ..base_service import BaseService\n\nclass GiabEvaluator(BaseService):\n def transcriptome_regions_path(self, alignment_path, parameters):\n transcriptome_regions_path = alignment_path + \"aligned_coverage_regions.bed\"\n if not os.path.exists(transcriptome_regions_path):\n bam_path = alignment_path + \"Out.bam\"\n coverage_path = alignment_path + \"Out.base_coverage\"\n min_coverage = 2\n\n # Create coverage file\n command = \"bedtools genomecov -d -ibam /{}\".format(bam_path)\n output_parameters = {\n \"log_is_output\": True,\n \"out_file_path\": coverage_path,\n \"log_file_path\": parameters[\"destination\"] + \"Coverage.log\"\n }\n self.run_docker(command, parameters, output_parameters)\n file_utils.validate_file_content(coverage_path)\n\n # Create BED from coverage file\n command = \"python base_coverage_to_bed.py /{} {} /{}\".format(\n coverage_path,\n str(min_coverage),\n transcriptome_regions_path\n )\n self.run_docker(command, parameters, log_file_name=\"CoverageToBed.log\")\n file_utils.validate_file_content(transcriptome_regions_path)\n\n return transcriptome_regions_path\n\n def bedtools(self, function, a_file_path, b_file_path, out_file_path, parameters, options=\"\"):\n destination = parameters[\"destination\"]\n log_file_path = destination + function.capitalize() + \".log\"\n command = \"bedtools {} \" \\\n \"-a /{} \" \\\n \"-b /{} {}\".format(function, a_file_path, b_file_path, options)\n output_parameters = {\n \"log_is_output\": True,\n \"out_file_path\": out_file_path,\n \"log_file_path\": log_file_path\n }\n self.run_docker(command, parameters, output_parameters)\n\n def run(self, parameters):\n experiment = parameters[\"experiment\"]\n reference_id = experiment.get(\"reference\")\n destination = parameters[\"destination\"]\n vcf_file_path = destination + \"Out.vcf\"\n alignment_path = experiment.get(\"pipeline\")[\"alignment\"][\"directory\"]\n confidence_regions_path = alignment_path + \"confidence_calls.bed\".format(reference_id)\n\n # Intersect confidence regions with transcriptome regions if not already done\n if not os.path.exists(confidence_regions_path):\n confidence_genome_regions_path = \"data/giab/{}/confidence_calls.bed\".format(reference_id)\n transcriptome_regions_path = self.transcriptome_regions_path(alignment_path, parameters)\n self.bedtools(\n \"intersect\",\n confidence_genome_regions_path,\n transcriptome_regions_path,\n confidence_regions_path,\n parameters\n )\n file_utils.validate_file_content(confidence_regions_path)\n\n\n # Filter data if necessary\n action_handler = parameters[\"action_handler\"]\n additional_commands = \"\"\n if hasattr(action_handler, \"chromosomes\"):\n # Escape spaces for bash\n space_escape = \"%%\"\n additional_commands = \"--location{}{}\".format(\n space_escape,\n \",\".join(action_handler.chromosomes)\n )\n\n command = \"./hap.py /data/giab/{0}/confidence_calls.vcf /{1}Out.vcf \" \\\n \"-f /{2} \" \\\n \"-o /{1}Evaluation \" \\\n \"-r /data/references/{0}.fa \" \\\n \"--location {3}\".format(\n reference_id,\n destination,\n confidence_regions_path,\n additional_commands\n )\n output_parameters = { \"log_file_path\": destination + \"Evaluation.log\" }\n self.run_docker(command, parameters, output_parameters)\n\n for file_name in os.listdir(destination):\n if file_name.startswith(\"Evaluation\"):\n file_path = destination + file_name\n if not file_utils.file_has_content(file_path):\n file_utils.delete(file_path)\n","repo_name":"tamslo/koala","sub_path":"services/giab/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"7656557800","text":"from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\r\nfrom sklearn.model_selection import cross_val_score\r\nimport pandas as pd\r\nimport plot\r\n\r\n\r\ndef get_clf_eval(y_test, pred):\r\n confusion = confusion_matrix(y_test, pred)\r\n accuracy = accuracy_score(y_test, pred)\r\n precision = precision_score(y_test, pred)\r\n recall = recall_score(y_test, pred)\r\n f1 = f1_score(y_test, pred)\r\n roc_score = roc_auc_score(y_test, pred)\r\n\r\n print(\"오차행렬\")\r\n print(confusion)\r\n print(\r\n \"정확도: {0:.4f}\\n정밀도: {1:.4f}\\n재현율: {2:.4f}\\nF1: {3:.4f}\\nROC AUC 값 :{4:.4f}\".format(accuracy, precision, recall,\r\n f1, roc_score))\r\n\r\n\r\ndef scoring(model, x_val, y_val):\r\n pred = model.predict(x_val)\r\n pred_prob = model.predict_proba(x_val)[:, 1]\r\n\r\n get_clf_eval(y_val, pred)\r\n plot.make_important_plot(model)\r\n plot.roc_plot(y_val, pred_prob)\r\n\r\n\r\ndef train_scoring(model, x_train, x_val, y_train, y_val):\r\n score = cross_val_score(model, x_train, y_train, scoring='accuracy', cv=5)\r\n print(\"cross_val_score: {0:.4f}\".format(score.mean()))\r\n\r\n scoring(model, x_val, y_val)\r\n\r\n\r\ndef search_param(model):\r\n score_df = pd.DataFrame(model.cv_results_)\r\n print(score_df[['params', 'mean_test_score', 'rank_test_score']])\r\n print(model.best_params_)\r\n","repo_name":"kjyju3955/titanic","sub_path":"clf_eval.py","file_name":"clf_eval.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"26922068804","text":"import json\nimport pandas\nimport os\nimport re\n\nrgi_antibiotics = snakemake.input[\"all_rgi\"]\n\nnr_drug_list = []\n\n\n#ARO\tName\tSoftware\tGene\tResistance Type\tVariant\tAntibiotic resistance prediction\tARO_antibiotic\tClass\tMechanism\tAMR family\n#ARO:3004628\tAAC(2')-IIa\trgi\tAAC(2')-IIa\thomology model\t\taminoglycoside\tARO:0000016\taminoglycoside \tantibiotic inactivation\tAAC(2')\n\nsample2drug2variant = {}\n\nfor rgi_result in rgi_antibiotics:\n result_table = pandas.read_csv(rgi_result, sep=\"\\t\", index_col=\"ARO\")\n sample = rgi_result.split(\"/\")[1]\n sample2drug2variant[sample] = {}\n for n, row in result_table.iterrows():\n mechanism = row[\"Resistance Mechanism\"].split(\", \")\n model_type = row[\"Model_type\"]\n snps = row[\"SNPs_in_Best_Hit_ARO\"]\n \n aro_name = row[\"Best_Hit_ARO\"]\n drug_list = [re.sub(\" antibiotic\",\"\", i) for i in row[\"Drug Class\"].lower().split(\"; \")]\n \n # resistance due to SNPs\n if isinstance(snps, str):\n snps = snps.split(\", \")\n for drug in drug_list:\n if \"antibiotic efflux\" in mechanism:\n continue\n if drug not in sample2drug2variant[sample]:\n sample2drug2variant[sample][drug] = []\n for snp in snps:\n var_label = \"%s##%s##%s\" % (aro_name, model_type, snp)\n sample2drug2variant[sample][drug].append(var_label)\n \n # resistance NOT due to SNPs\n else:\n for drug in drug_list:\n if \"antibiotic efflux\" in mechanism:\n continue\n if drug not in sample2drug2variant[sample]:\n sample2drug2variant[sample][drug] = []\n\n var_label = \"%s##%s##%s\" % (aro_name, model_type, \"nosnp\")\n sample2drug2variant[sample][drug].append(var_label)\n\nwith open(snakemake.output[0], 'w') as f:\n for sample in sample2drug2variant:\n for drug in sample2drug2variant[sample]:\n for variant in sample2drug2variant[sample][drug]:\n gene, model, change = variant.split(\"##\")\n regex = '[A-Za-z]+([\\-\\d]+)[A-Za-z]+'\n if change != \"nosnp\":\n s = re.search(regex, change)\n position = s.group(1)\n vartype = \"SNP\"\n else:\n position = None\n vartype = \"nosnp\"\n f.write(f\"rgi\\t{sample}\\t{drug}\\t{gene}\\t{position}\\t{change}\\t{vartype}\\n\")\n","repo_name":"metagenlab/diag_pipelines","sub_path":"rules/benchmark/resistance/scripts/frequency_rgi.py","file_name":"frequency_rgi.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"12"} +{"seq_id":"35664141262","text":"def swapFileData():\r\n \r\n f1=input('data from') \r\n with open(f1,'r') as a1:\r\n data_a=a1.read()\r\n \r\n f2=input('data to') \r\n with open(f2,'r') as a2:\r\n data_b=a2.read()\r\n\r\n with open(f1,'w') as a1:\r\n a1.write(data_b)\r\n \r\n with open(f2,'w') as a2:\r\n a2.write(data_a)\r\n \r\n print('done')\r\n\r\nswapFileData()","repo_name":"nisargCR7/switchtext","sub_path":"swappingFile.py","file_name":"swappingFile.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"14324024259","text":"from keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, SpatialDropout1D, Conv1D, MaxPooling1D, Activation, Embedding, Flatten, GlobalMaxPooling1D, LSTM\r\nfrom keras import regularizers, callbacks, optimizers\r\nfrom keras.models import load_model\r\nfrom keras.utils import plot_model\r\nimport argparse\r\nimport os\r\nimport logging\r\nfrom data_loaders import TextsLoader, TokenizerLoader, WordVectorsLoader, TextSequencesLoader\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom sklearn import metrics\r\nimport tensorflow as tf\r\n\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\nconv_version = 5\r\nlstm_version = 1\r\nconv_lstm_version = 1\r\n\r\nsem_eval_path = ''\r\nseq_len = 800 # 5000 # 2500 # Inferred from checking the sequences length distributions\r\nwords_count = 1207#438\r\nembedding_mode = 0\r\ncrowdsourced = False\r\nalgorithm = 0\r\nfinal_model_name = ''\r\n\r\nimport pandas as pd\r\n\r\ndef load_embedding_layer(tokenizer):\r\n # Get vocabulary size\r\n vocab_size = len(tokenizer.word_index) + 1\r\n logging.info('Vocab size: {}'.format(vocab_size))\r\n logging.info(\"tokenizer word_index\", tokenizer.word_index)\r\n\r\n # Load word vectors\r\n word_vectors_loader = WordVectorsLoader(sem_eval_path, crowdsourced, embedding_mode)\r\n word_vectors_loader.load()\r\n weights_matrix = word_vectors_loader.create_embedding_weights_matrix(tokenizer.word_index)\r\n \r\n return Embedding(input_dim=vocab_size, \r\n output_dim=weights_matrix.shape[1], \r\n weights=[weights_matrix],\r\n input_length=seq_len,\r\n trainable=False\r\n )\r\n\r\ndef define_conv_model(tokenizer, filters=64, kernel_size=4, hidden_dims=256):\r\n model = Sequential()\r\n\r\n embedding_layer = load_embedding_layer(tokenizer)\r\n # embedding_layer = Embedding(words_count, embedding_size=100, input_length=seq_len)\r\n model.add(embedding_layer)\r\n model.add(SpatialDropout1D(0.6))\r\n\r\n model.add(Conv1D(filters,\r\n kernel_size,\r\n activation='relu'))\r\n model.add(Dropout(0.9))\r\n \r\n model.add(MaxPooling1D(pool_size=4))\r\n\r\n # model.add(Conv1D(filters,\r\n # kernel_size,\r\n # activation='relu'))\r\n # model.add(Dropout(0.5))\r\n # model.add(MaxPooling1D(pool_size=4))\r\n\r\n model.add(GlobalMaxPooling1D())\r\n # model.add(Flatten())\r\n\r\n model.add(Dense(hidden_dims, \r\n activation='relu', \r\n kernel_regularizer=regularizers.l2(0.1)\r\n ))\r\n model.add(Dropout(0.9))\r\n\r\n model.add(Dense(1, activation='sigmoid'))\r\n\r\n return model\r\n\r\ndef define_lstm_model(tokenizer, units=128, embedding_size=128):\r\n model = Sequential()\r\n\r\n logging.info('Building LSTM v2...')\r\n logging.info('words_count: {}'.format(words_count))\r\n logging.info('seq_len: {}'.format(seq_len))\r\n logging.info('embedding_size: {}'.format(embedding_size))\r\n\r\n model.add(load_embedding_layer(tokenizer))\r\n # model.add(Embedding(words_count, embedding_size, input_length=seq_len))\r\n model.add(SpatialDropout1D(0.2))\r\n\r\n model.add(LSTM(units, dropout=0.2, recurrent_dropout=0.2))\r\n model.add(Dense(1, activation='sigmoid'))\r\n\r\n return model\r\n\r\ndef define_conv_lstm_model(tokenizer, units=128, filters=64, kernel_size=4): # farkl olarak senteces input'unu alıyor\r\n model = Sequential()\r\n\r\n embedding_layer = load_embedding_layer(tokenizer)\r\n model.add(embedding_layer)\r\n\r\n model.add(Conv1D(filters,\r\n kernel_size,\r\n activation='relu'))\r\n model.add(Dropout(0.3))\r\n model.add(MaxPooling1D(pool_size=4))\r\n\r\n model.add(Conv1D(filters,\r\n kernel_size,\r\n activation='relu'))\r\n model.add(Dropout(0.3))\r\n model.add(MaxPooling1D(pool_size=2))\r\n \r\n model.add(LSTM(units, dropout=0.2, recurrent_dropout=0.2))\r\n\r\n model.add(Dense(1, activation='sigmoid'))\r\n\r\n return model\r\n\r\ndef generate_new_model_name():\r\n alg = ''\r\n version = 1\r\n if algorithm == 0:\r\n alg = 'conv'\r\n version = conv_version\r\n elif algorithm == 1:\r\n alg = 'conv_lstm'\r\n version = conv_lstm_version\r\n elif algorithm == 2:\r\n alg = 'lstm'\r\n version = lstm_version\r\n else:\r\n raise Exception('Unknown algorithm')\r\n return 'words_{}_model_w{}_v{}'.format(alg, embedding_mode, version)\r\n\r\ndef load_pretrained(model, model_name, model_weights_location):\r\n model_file = os.path.join(sem_eval_path, 'models', \"{}.h5\".format(model_name))#\"/homedtic/hkavas/SemEval/models/words_conv_lstm_model_w1_v1.h5\"#\r\n print(\"model LocatioN:\", model_file)\r\n if os.path.isfile(model_file) and os.path.isfile(model_weights_location):\r\n model_file_time = os.path.getmtime(model_file)\r\n weights_file_time = os.path.getmtime(model_weights_location)\r\n if weights_file_time > model_file_time:\r\n logging.info('Loading the weights (latest modified).')\r\n model.load_weights(model_weights_location)\r\n else:\r\n model = load_model(model_file)\r\n logging.info('Loading the model (latest modified)')\r\n elif os.path.isfile(model_weights_location):\r\n model.load_weights(model_weights_location)\r\n logging.info('Loading the weights')\r\n elif os.path.isfile(model_file):\r\n model = load_model(model_file)\r\n logging.info('Loading the model')\r\n else:\r\n raise Exception(\"Neither model nor weights file exists\")\r\n return model\r\n\r\ndef plot_model_history(history, model_name):\r\n #plt.plot(history.history['val_accuracy'])\r\n #plt.plot(history.history['val_loss'])\r\n plt.title('validation accuracy and loss')\r\n plt.ylabel('accuracy')\r\n plt.xlabel('epoch')\r\n plt.savefig(os.path.join(sem_eval_path, 'models', '{}_history.png'.format(model_name)))\r\n\r\ndef evaluate_model(model, X_val, y_val):\r\n y_predict = (np.asarray(model.predict(X_val))).round()\r\n\r\n acc = metrics.accuracy_score(y_val, y_predict)\r\n logging.info('Accuracy: {}'.format(acc))\r\n print('Accuracy: {}'.format(acc))\r\n # let's see\r\n logging.info('y_val: {}'.format(y_val))\r\n logging.info('y_predict: {}'.format(y_predict))\r\n \r\n conf_matrix = metrics.confusion_matrix(y_val, y_predict)\r\n logging.info('Confusion matrix: {}'.format(conf_matrix))\r\n\r\n precision = metrics.precision_score(y_val, y_predict)\r\n logging.info('Precision score: {}'.format(precision))\r\n\r\n recall = metrics.recall_score(y_val, y_predict)\r\n logging.info('Recall score: {}'.format(recall))\r\n\r\n val_f1 = metrics.f1_score(y_val, y_predict)\r\n logging.info('F1 score: {}'.format(val_f1))\r\n\r\n model_plot_file = os.path.join(sem_eval_path, 'models', '{}.png'.format(final_model_name))\r\n plot_model(model, to_file=model_plot_file, show_shapes=True, show_layer_names=True)\r\n \r\n\r\ndef transferLearning(model):\r\n print(\"Transfer learning is on!\")\r\n model.add(Dense(1, activation='sigmoid'))\r\n \r\n return model\r\n \r\n\r\ndef main(): \r\n tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"--path\",'-p', default=\"/home/agon/Files/SemEval\",\r\n help=\"Use this argument to change the SemEval directory path (the default path is: '/home/ashwath/Files/SemEval')\")\r\n parser.add_argument(\"--crowdsourced\", '-c', action='store_true', default=\"False\",\r\n help=\"Use this argument to work with the crowdsourced file\")\r\n parser.add_argument(\"--model\", '-m', default=\"\", #\"words_conv_lstm_model_w1_v1\"\r\n help=\"Use this argument to continue training a stored model\")\r\n parser.add_argument(\"--word_vectors\", '-w', default=\"0\", # 2 for BERT\r\n help=\"Use this argument to set the word vectors to use: 0: Google's Word2vec, 1: GloVe, 2: Fasttext, 3: Custom pretrained word2vec, 4: Custom pretrained Fasttext, 5: Custom pretrained news word2vec. Default: 0\")\r\n parser.add_argument(\"--algorithm\", '-a', default=\"0\", # 1 used''!\r\n help=\"Use this argument to set the algorithm to use: 0: CNN, 1: CNN + LSTM, 2: LSTM. Default: 0\")\r\n parser.add_argument(\"--learning_rate\", '-l', default=\"0.001\",\r\n help=\"Use this argument to set the learning rate to use. Default: 0.001\")\r\n parser.add_argument(\"--evaluate\", '-e', action='store_true', default=\"False\", # True\r\n help=\"Use this argument to set run on evaluation mode\")\r\n args = parser.parse_args()\r\n \r\n global sem_eval_path\r\n sem_eval_path = args.path\r\n\r\n global embedding_mode\r\n embedding_mode = int(args.word_vectors)\r\n\r\n global algorithm\r\n algorithm = int(args.algorithm)\r\n\r\n evaluate_mode = args.evaluate\r\n\r\n global seq_len\r\n sentences = False\r\n if algorithm == 0:\r\n seq_len = 500 #700 #5000\r\n elif algorithm == 1:\r\n seq_len = 800 #2064\r\n elif algorithm == 2:\r\n seq_len = 800#100\r\n sentences = True\r\n else:\r\n raise Exception('Unknown algorithm')\r\n\r\n model_name = args.model\r\n model_dir = os.path.join(sem_eval_path, 'models')\r\n new_model_name = generate_new_model_name()\r\n model_location = os.path.join(model_dir, '{}.h5'.format(new_model_name))\r\n model_weights_location = os.path.join(model_dir, '{}_weights.h5'.format(new_model_name))\r\n print(\"location:\", model_location)\r\n\r\n # ---LOGS---\r\n logs_path = os.path.join(sem_eval_path, 'logs_new', '{}_log.log'.format(model_name if model_name else new_model_name))\r\n logging.basicConfig(filename=logs_path, filemode='w', \r\n format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\r\n logging.info('model_location: {}'.format(model_location))\r\n \r\n global crowdsourced\r\n crowdsourced = args.crowdsourced\r\n\r\n learning_rate = float(args.learning_rate)\r\n batch_size = 16#32 # default\r\n\r\n # Get data (252-270 değiştirildi)\r\n texts_loader = TextsLoader(sem_eval_path, crowdsourced, logs_path)\r\n train_texts, y_train = texts_loader.load(sentences=sentences)\r\n \r\n logging.info('Train shape: {}'.format(train_texts.shape))\r\n logging.info('Number of biased samples: {}'.format(len(y_train[y_train == 1])))\r\n logging.info('Number of non-biased samples: {}'.format(len(y_train[y_train == 0])))\r\n \r\n \r\n # Get Test data\r\n #df = pd.read_csv('allsides_train.csv', engine='python')\r\n #print(\"1 is well\", df.head())\r\n #df_allsides = df[~df.text.str.isnumeric()]\r\n #print(\"all is well\", df_allsides.head())\r\n \r\n #train_texts_tl = df_allsides['text']\r\n #y_train_tl = df_allsides['predicted_hyperpartisan']\r\n \r\n # Get Tweets data\r\n df_ = pd.read_csv(\"84k_pol.csv\", encoding= 'unicode_escape')\r\n df_['Tweet'] = df_['Tweet'].astype('str')\r\n df_t = df_[(~df_.Tweet.fillna('').str.isnumeric())]\r\n \r\n train_texts_tl = df_t['Tweet']\r\n y_train_tl = df_t['Party']\r\n \r\n \r\n logging.info('Train shape(TL): {}'.format(train_texts_tl.shape))\r\n logging.info('Number of biased samples(TL): {}'.format(len(y_train_tl[y_train_tl == 1])))\r\n logging.info('Number of non-biased samples(TL): {}'.format(len(y_train_tl[y_train_tl == 0])))\r\n\r\n val_texts, y_val = texts_loader.load(sentences=sentences, validation=True)\r\n logging.info('Validation shape: {}'.format(val_texts.shape))\r\n logging.info('Number of biased samples (burası çokomelli!!! maine texts_loader.load(sentences[] ile çağrılıyor: {}'.format(len(y_val[y_val == 1])))\r\n logging.info('Number of non-biased samples: {}'.format(len(y_val[y_val == 0])))\r\n logging.info(train_texts[:20])\r\n logging.info('-----------------------------------------------------------------------------------------------------')\r\n\r\n tokenizer = TokenizerLoader(train_texts, sem_eval_path, logs_path, most_common_count=words_count).load()\r\n\r\n sequences_loader = TextSequencesLoader(tokenizer, seq_len, sem_eval_path=sem_eval_path)\r\n X_train = sequences_loader.load(train_texts, truncate_sequences=(algorithm == 2))\r\n X_train_tl = sequences_loader.load(train_texts_tl, truncate_sequences=(algorithm == 2))\r\n \r\n zeroes = []\r\n for seq in X_train:\r\n seq_zeroes = 0\r\n for item in seq:\r\n if item == 0:\r\n seq_zeroes += 1\r\n zeroes.append(seq_zeroes)\r\n zeroes = np.array(zeroes)\r\n logging.info('Min. number of zeroes: {}'.format(zeroes.min()))\r\n logging.info('Avg. number of zeroes: {}'.format(zeroes.mean()))\r\n logging.info('Std. number of zeroes: {}'.format(zeroes.std()))\r\n logging.info('Max. number of zeroes: {}'.format(zeroes.max()))\r\n logging.info('Training sequences: ')\r\n logging.info(X_train[:20])\r\n logging.info('-----------------------------------------------------------------------------------------------------')\r\n \r\n if len(sequences_loader.indices_to_remove) > 0:\r\n logging.info('Removing train {} sequences'.format(len(sequences_loader.indices_to_remove)))\r\n logging.info('X_train pre shape: {}'.format(X_train.shape))\r\n X_train = np.delete(X_train, sequences_loader.indices_to_remove, axis=0)\r\n logging.info('X_train post shape: {}'.format(X_train.shape))\r\n logging.info('y_train pre shape: {}'.format(y_train.shape))\r\n y_train.drop(y_train.index[sequences_loader.indices_to_remove], inplace=True)\r\n logging.info('y_train post shape: {}'.format(y_train.shape))\r\n\r\n # sequences_loader.indices_to_remove\r\n \r\n X_val = sequences_loader.load(val_texts)\r\n if len(sequences_loader.indices_to_remove) > 0:\r\n logging.info('Removing validation {} sequences'.format(len(sequences_loader.indices_to_remove)))\r\n logging.info('X_val pre shape: {}'.format(X_val.shape))\r\n X_val = np.delete(X_val, sequences_loader.indices_to_remove, axis=0)\r\n logging.info('X_val post shape: {}'.format(X_val.shape))\r\n logging.info('y_val pre shape: {}'.format(y_val.shape))\r\n y_val.drop(y_val.index[sequences_loader.indices_to_remove], inplace=True)\r\n logging.info('y_val post shape: {}'.format(y_val.shape))\r\n \r\n #seq_len = sequences_loader.seq_len\r\n\r\n if algorithm == 0:\r\n model = define_conv_model(tokenizer)\r\n elif algorithm == 1:\r\n model = define_conv_lstm_model(tokenizer)\r\n elif algorithm == 2:\r\n model = define_lstm_model(tokenizer)\r\n else:\r\n raise Exception('Unknown algorithm')\r\n\r\n if model_name:\r\n model = load_pretrained(model, model_name, model_weights_location)\r\n\r\n global final_model_name\r\n final_model_name = model_name if model_name else new_model_name\r\n \r\n logging.info(model.summary())\r\n\r\n if evaluate_mode is True: # MAKE EVALUATE MODE ON\r\n evaluate_model(model, X_val, y_val)\r\n else:\r\n # Implement Early Stopping\r\n \r\n \r\n \r\n early_stopping_callback = callbacks.EarlyStopping(monitor='val_loss',\r\n min_delta=0,\r\n patience=5,\r\n verbose=1)\r\n # restore_best_weights=True)\r\n save_best_model = callbacks.ModelCheckpoint(model_weights_location, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')\r\n \r\n \r\n \r\n adam = optimizers.Adam(lr=learning_rate)\r\n model.compile(loss='binary_crossentropy',\r\n optimizer=adam,\r\n metrics=['accuracy'])\r\n\r\n history = model.fit(X_train, y_train,\r\n batch_size=batch_size,\r\n epochs=20,\r\n verbose=2,\r\n validation_data=(X_val, y_val),\r\n callbacks=[early_stopping_callback, save_best_model])\r\n \r\n #reload best weights\r\n model.load_weights(model_weights_location)\r\n \r\n model = transferLearning(model)\r\n print(\"SUCCESS!\")\r\n \r\n history = model.fit(X_train_tl, np.array(y_train_tl),\r\n batch_size=batch_size,\r\n epochs=10,\r\n verbose=2,\r\n validation_data=(X_val, y_val),\r\n callbacks=[early_stopping_callback])\r\n \r\n \r\n \r\n \r\n\r\n plot_model_history(history, final_model_name)\r\n\r\n logging.info('Model trained. Storing model on disk.')\r\n model.save(\"/homedtic/hkavas/SemEval/models_new/allsidesTL-3.h5\")\r\n #model.save(model_location) \r\n logging.info('Model stored on disk.')\r\n\r\n \r\nif __name__ == \"__main__\":\r\n main()","repo_name":"hmtkvs/NLP-Political-Bias-Detection","sub_path":"Train/train_words_dl_model.py","file_name":"train_words_dl_model.py","file_ext":"py","file_size_in_byte":16893,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"20612803099","text":"import os\nimport abc\nimport typing\nimport datetime\nimport dataclasses\n\nfrom selenium import webdriver\nfrom selenium.webdriver.remote.webelement import WebElement\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.support import expected_conditions as EC\n\nfrom controllers.core.recaptcha import ReCaptcha\nfrom controllers.functionalities.tools import selecionar_contas\n\ndef custom_to_float(_str: typing.AnyStr) -> float:\n _ = _str.replace('.', '')\n _ = _.replace(',', '.')\n\n try: \n return float(_)\n\n except ValueError:\n return float()\n\n@dataclasses.dataclass\nclass GrabOnPage(abc.ABC):\n driver: webdriver.Chrome = dataclasses.field(repr=False)\n\n def grab_text(self, by: typing.AnyStr, value: typing.AnyStr, default = None) -> WebElement:\n try:\n text = self.driver.find_element(by, value).text\n\n return text if text else default\n\n except NoSuchElementException:\n return default\n\n def has_elmt(self, by: typing.AnyStr, value: typing.AnyStr) -> bool:\n return bool(len(self.driver.find_elements(by, value)))\n\n def extract_float_from_table(self, elmt: WebElement) -> typing.Dict:\n _pre_dict = {}\n\n def _(m: WebElement) -> typing.Dict:\n td = m.find_elements(By.TAG_NAME, \"td\")\n _r = {}\n\n _t = td[4].text.replace('.', '')\n _t = _t.replace(',', '.')\n\n try:\n _r[td[0].text] = float(_t)\n except ValueError:\n _r[td[0].text] = 0.00\n\n return _r\n\n try:\n for i in elmt.find_elements(By.TAG_NAME, \"tr\"):\n \n _pre_dict = {**_pre_dict, **_(i)}\n except:\n pass\n\n return _pre_dict\n\n def extract_float_from_table_ipva(self, elmt: WebElement) -> typing.Dict:\n _pre_dict = {}\n\n def _(m: WebElement) -> typing.Dict:\n td = m.find_elements(By.TAG_NAME, \"td\")\n _r = {}\n\n try:\n\n _t = td[4].text.replace('.', '')\n _t = _t.replace(',', '.')\n \n except:\n _t = 0\n\n\n try:\n _r[td[0].text] = float(_t)\n except ValueError:\n _r[td[0].text] = 0.00\n\n return _r\n\n try:\n for i in elmt.find_elements(By.TAG_NAME, \"tr\"):\n _pre_dict = {**_pre_dict, **_(i)}\n except Exception as err:\n print(err, \"<- erro Ignorado\")\n\n return _pre_dict\n\n@dataclasses.dataclass\nclass Licenciamento(GrabOnPage):\n total_licenciamento: float = dataclasses.field(default_factory=float)\n\n def __post_init__(self):\n year_actual = datetime.date.today().year\n year_prev = datetime.date.today().year + 1\n\n _ = self.extract_float_from_table(self.driver.find_element(By.ID, \"conteudoPaginaPlaceHolder_tbTaxasDetalhe\"))\n keys = _.keys()\n\n if f'Licenciamento {year_prev}' in keys:\n for i in keys:\n if \"Licenciamento\" in i:\n if str(year_prev) in i:\n continue\n\n self.total_licenciamento += _[i]\n else:\n for i in keys:\n if \"Licenciamento\" in i:\n if str(year_actual) in i:\n continue\n\n self.total_licenciamento += _[i]\n\n@dataclasses.dataclass\nclass Ipva(GrabOnPage):\n\n ipva: float = dataclasses.field(default_factory=float)\n\n def __post_init__(self):\n default = 0.00\n year_actual = datetime.date.today().year\n\n _ = self.extract_float_from_table_ipva(self.driver.find_element(By.ID, \"conteudoPaginaPlaceHolder_tbIpvaPend\"))\n\n for y in range(2000, year_actual + 1):\n self.ipva += round(_.get(str(y), default), ndigits=2)\n\n@dataclasses.dataclass\nclass Multas(GrabOnPage):\n\n detran: float = dataclasses.field(default_factory=float)\n renainf: float = dataclasses.field(default_factory=float)\n outras_multas: float = dataclasses.field(default_factory=float)\n\n\n def __post_init__(self):\n default = 0.00\n _ = self.extract_float_from_table(self.driver.find_element(By.ID, \"conteudoPaginaPlaceHolder_tbMultaResumo\"))\n \n self.detran = _.get('DETRAN', default)\n self.renainf= _.get('RENAINF', default)\n \n municipal = _.get('MUNICIPAL', default)\n convenio = _.get('CONVENIO', default)\n der = _.get('D.E.R.', default)\n\n self.outras_multas = round(municipal + convenio + der, ndigits=2)\n\n@dataclasses.dataclass\nclass MultasDetalhadas(GrabOnPage):\n\n\n detran: float = dataclasses.field(default_factory=float)\n renainf: float = dataclasses.field(default_factory=float)\n outras_multas: float = dataclasses.field(default_factory=float)\n detalhamento: typing.List[typing.Dict] = dataclasses.field(default_factory=list)\n\n\n def __post_init__(self):\n default = 0.00\n _ = self.extract_float_from_table(self.driver.find_element(By.ID, \"conteudoPaginaPlaceHolder_tbMultaResumo\"))\n \n self.detran = _.get('DETRAN', default)\n self.renainf= _.get('RENAINF', default)\n \n municipal = _.get('MUNICIPAL', default)\n convenio = _.get('CONVENIO', default)\n der = _.get('D.E.R.', default)\n\n self.outras_multas = round(municipal + convenio + der, ndigits=2)\n\n if self.has_elmt(By.ID, \"conteudoPaginaPlaceHolder_btnDetalharMultas\"):\n\n # Vai a sessão de multas detalhadas\n self.driver.find_element(By.ID, \"conteudoPaginaPlaceHolder_btnDetalharMultas\").click()\n\n # Captura as multas uma a uma\n self.detalhamento = self.grab_details_debt()\n\n # Retorna a pagina principal dos dados\n self.driver.back()\n\n else: self.detalhamento = []\n\n \n\n def grab_details_debt(self) -> typing.List[typing.Dict]:\n _final = []\n\n target_child = self.driver.find_element(By.ID, \"conteudoPaginaPlaceHolder_trMultaCab\")\n target = target_child.find_element(By.XPATH, \"..\")\n\n rows = target.find_elements(By.TAG_NAME, \"tr\")\n del rows[-1]\n\n for i in range(0, len(rows), 5):\n\n second_tr = rows[i+2]\n third_tr = rows[i+3]\n fourth_tr = rows[i+4]\n\n value = custom_to_float(\n third_tr.find_elements(By.TAG_NAME, \"td\")[5].text.replace('R$','')\n )\n\n _final.append(\n {\n 'name': fourth_tr.find_elements(By.TAG_NAME, \"td\")[3].text,\n 'guia': third_tr.find_elements(By.TAG_NAME, \"td\")[3].text,\n 'ait': second_tr.find_elements(By.TAG_NAME, \"td\")[3].text,\n 'value': value\n }\n )\n \n return _final\n\n\n@dataclasses.dataclass\nclass SFPDividas(GrabOnPage):\n anti_captcha_key: str\n\n balance: float\n lote: typing.AnyStr\n\n is_valid: bool = False\n\n renavam: typing.AnyStr = None # OK\n placa: typing.AnyStr = None # Ok\n ipva: typing.AnyStr = None # OK\n divida_ativa: typing.AnyStr = None # OK\n multas_renainf: typing.AnyStr = None # OK\n multas_detran: typing.AnyStr = None # Ok\n outras_multas_sp: typing.AnyStr = None # OK\n dpvat: typing.AnyStr = None # OK\n taxa_licenciamento: typing.AnyStr = None # OK\n\n data: typing.List[dict] = dataclasses.field(default_factory=list)\n\n multas: Multas = None\n\n def __post_init__(self):\n\n self.is_valid = self.has_elmt(By.ID, \"tituloPaginaPlaceHolder_txtDataConsulta\")\n\n if self.is_valid:\n self.placa = self.grab_text(By.ID, \"conteudoPaginaPlaceHolder_txtPlaca\")\n self.renavam = self.grab_text(By.ID, \"conteudoPaginaPlaceHolder_txtRenavam\")\n self.dpvat = self.grab_text(By.XPATH, '/html/body/form/table[3]/tbody/tr/td[2]/table/tbody/tr/td[2]/div/table[22]/tbody/tr/td/table/tbody/tr[2]/td[5]', default=0.00)\n\n # = Ipva ===\n ipva = \\\n Ipva(self.driver)\n\n self.ipva = ipva.ipva\n\n # = Licenciamento ===\n licenciamento = \\\n Licenciamento(self.driver)\n\n self.taxa_licenciamento = licenciamento.total_licenciamento\n\n # = Multas ===\n self.multas = \\\n MultasDetalhadas(self.driver)\n \n self.multas_detran = self.multas.detran\n self.outras_multas_sp = self.multas.outras_multas\n self.multas_renainf = self.multas.renainf\n\n # = Divida ativa ===\n divida_ativa = \\\n DividaAtiva(self.driver, self.renavam, anti_captcha_key=self.anti_captcha_key)\n \n self.divida_ativa = divida_ativa.total\n\n self.process_debt()\n\n def process_debt(self) -> typing.NoReturn:\n order = [\n # { 'name' : 'outras_multas', 'value' : self.outras_multas_sp },\n { 'name' : 'ipva', 'value' : self.ipva },\n { 'name' : 'divida_ativa', 'value' : self.divida_ativa },\n\n { 'name' : 'multas_detran', 'value' : self.multas_detran },\n { 'name' : 'renainf', 'value' : self.multas_renainf },\n { 'name' : 'outras_multas', 'value' : self.outras_multas_sp },\n { 'name' : 'taxa_licenciamento', 'value' : self.taxa_licenciamento },\n ]\n\n\n for index, seq in enumerate(order):\n ait = \"RENAVAM\"\n guia = \"RENAVAM\"\n pay_type = seq['name'].upper()\n\n\n # se a divida estiver zerada pula o mesmo\n if seq['value'] <= 0:\n continue \n\n if (self.balance - seq['value']) < 0:\n \n # Paga tudo ou nada\n if seq['name'] in ('renainf', 'ipva', 'taxa_licenciamento'):\n break\n \n elif seq['name'] in ('divida_ativa'):\n \n # subtrai o saldo\n self.balance -= seq['value']\n pay_type = \"DIVIDA ATIVA\"\n\n # Paga com total ou parcial\n seq['value'] += self.balance\n\n # break\n\n elif(seq['name'] == \"multas_detran\" and seq['value'] > self.balance):\n\n detran = selecionar_contas(self.balance, \\\n list(filter(lambda x: x['name']==\"DETRAN\", self.multas.detalhamento))\n )\n \n for d in detran:\n self.balance -= d['value']\n\n # self.data.append([\n # self.lote,\n # self.placa,\n # self.renavam,\n # d['value'],\n # d['ait'],\n # d['guia'],\n # \"MULTA DETRAN\",\n # ])\n\n self.data.append({\n \"lote\": self.lote,\n \"placa\": self.placa,\n \"renavam\": self.renavam,\n \"valor\": d['value'],\n \"ait\": d['ait'],\n \"guia\": d['guia'],\n \"tipo_debito\": \"MULTA DETRAN\",\n })\n \n\n\n # break\n\n elif(seq['name'] == \"outras_multas\" and seq['value'] > self.balance):\n\n der = selecionar_contas(self.balance, \\\n list(filter(lambda x: x['name']==\"D.E.R.\", self.multas.detalhamento))\n )\n\n for d in der:\n self.balance -= d['value']\n\n # self.data.append([\n # self.lote,\n # self.placa,\n # self.renavam,\n # d['value'],\n # d['ait'],\n # d['guia'],\n # \"D.E.R.\",\n # ])\n\n \n self.data.append({\n \"lote\": self.lote,\n \"placa\": self.placa,\n \"renavam\": self.renavam,\n \"valor\": d['value'],\n \"ait\": d['ait'],\n \"guia\": d['guia'],\n \"tipo_debito\": \"D.E.R.\",\n })\n\n municipal = selecionar_contas(self.balance, \\\n list(filter(lambda x: x['name']==\"MUNICIPAL\", self.multas.detalhamento))\n )\n \n for m in municipal:\n self.balance -= m['value']\n\n # self.data.append([\n # self.lote,\n # self.placa,\n # self.renavam,\n # m['value'],\n # m['ait'],\n # m['guia'],\n # \"MUNICIPAL\",\n # ])\n\n self.data.append({\n \"lote\": self.lote,\n \"placa\": self.placa,\n \"renavam\": self.renavam,\n \"valor\": m['value'],\n \"ait\": m['ait'],\n \"guia\": m['guia'],\n \"tipo_debito\": \"MUNICIPAL\",\n })\n\n \n\n convenio = selecionar_contas(self.balance, \\\n list(filter(lambda x: x['name']==\"CONVENIO\", self.multas.detalhamento))\n )\n\n for c in convenio:\n self.balance -= c['value']\n\n # self.data.append([\n # self.lote,\n # self.placa,\n # self.renavam,\n # c['value'],\n # c['ait'],\n # c['guia'],\n # \"CONVENIO\",\n # ])\n\n self.data.append({\n \"lote\": self.lote,\n \"placa\": self.placa,\n \"renavam\": self.renavam,\n \"valor\": c['value'],\n \"ait\": c['ait'],\n \"guia\": c['guia'],\n \"tipo_debito\": \"CONVENIO\",\n })\n \n\n\n # break\n\n # caso nao tenha saldo pula a sequencia\n if not seq['name'] in ('divida_ativa'):\n # self.data.append([\n # self.lote,\n # self.placa,\n # self.renavam,\n # '-',\n # '-',\n # '-',\n # '-1',\n # ])\n\n self.data.append({\n \"lote\": self.lote,\n \"placa\": self.placa,\n \"renavam\": self.renavam,\n \"valor\": '0.00',\n \"ait\": '-',\n \"guia\": '-',\n \"tipo_debito\": '-1',\n })\n continue\n else:\n self.balance-=seq['value']\n\n # renomeando\n if seq['name'] == 'divida_ativa':\n ait = \"BOLETO\"\n guia = \"BOLETO\"\n\n elif seq['name'] == 'multas_detran':\n pay_type = \"MULTA DETRAN (PAGAR TUDO)\"\n\n elif seq['name'] == 'outras_multas':\n pay_type = \"OUTRAS MULTAS SP (PAGAR TUDO)\"\n \n elif seq['name'] == 'taxa_licenciamento':\n pay_type = \"TAXA DE LICENCIAMENTO\"\n\n # self.data.append([\n # self.lote,\n # self.placa,\n # self.renavam,\n # seq['value'],\n # ait,\n # guia,\n # pay_type,\n # ])\n\n self.data.append({\n \"lote\": self.lote,\n \"placa\": self.placa,\n \"renavam\": self.renavam,\n \"valor\": seq['value'],\n \"ait\": ait,\n \"guia\": guia,\n \"tipo_debito\": pay_type,\n })\n \n def __iter__(self) -> typing.List:\n \n return iter(self.data)\n\n@dataclasses.dataclass\nclass SFP(GrabOnPage):\n anti_captcha_key: str\n\n is_valid: bool = False\n\n renavam: typing.AnyStr = None # OK\n placa: typing.AnyStr = None # Ok\n ipva: typing.AnyStr = None # OK\n divida_ativa: typing.AnyStr = None # OK\n multas_renainf: typing.AnyStr = None # OK\n multas_detran: typing.AnyStr = None # Ok\n outras_multas_sp: typing.AnyStr = None # OK\n dpvat: typing.AnyStr = None # OK\n taxa_licenciamento: typing.AnyStr = None # OK\n\n\n def __post_init__(self):\n\n self.is_valid = self.has_elmt(By.ID, \"tituloPaginaPlaceHolder_txtDataConsulta\")\n\n if self.is_valid:\n self.placa = self.grab_text(By.ID, \"conteudoPaginaPlaceHolder_txtPlaca\")\n self.renavam = self.grab_text(By.ID, \"conteudoPaginaPlaceHolder_txtRenavam\")\n self.dpvat = self.grab_text(By.XPATH, '/html/body/form/table[3]/tbody/tr/td[2]/table/tbody/tr/td[2]/div/table[22]/tbody/tr/td/table/tbody/tr[2]/td[5]', default=0.00)\n\n # = Ipva ===\n ipva = \\\n Ipva(self.driver)\n\n self.ipva = ipva.ipva\n\n # = Multas ===\n multas = \\\n Multas(self.driver)\n \n self.multas_detran = multas.detran\n self.outras_multas_sp = multas.outras_multas\n self.multas_renainf = multas.renainf\n\n # = Licenciamento ===\n licenciamento = \\\n Licenciamento(self.driver)\n\n self.taxa_licenciamento = licenciamento.total_licenciamento\n\n # = Divida ativa ===\n divida_ativa = \\\n DividaAtiva(self.driver, self.renavam, anti_captcha_key=self.anti_captcha_key)\n \n self.divida_ativa = divida_ativa.total\n\n@dataclasses.dataclass\nclass DividaAtiva(GrabOnPage):\n renavam: typing.AnyStr\n anti_captcha_key: typing.AnyStr\n\n is_valid: bool = False\n total: float = dataclasses.field(default_factory=float)\n\n def __post_init__(self):\n\n ignore_divida_ativa = False\n key = self.anti_captcha_key\n\n if \"NADA CONSTA\" not in self.grab_text(By.ID, \"conteudoPaginaPlaceHolder_txtExisteDividaAtiva\"):\n\n if not ignore_divida_ativa:\n self.driver.get(\"https://www.dividaativa.pge.sp.gov.br/sc/pages/consultas/consultarDebito.jsf\")\n WebDriverWait(self.driver, 10).until(\n EC.presence_of_element_located((By.ID, \"consultaDebitoForm:consulta\"))\n )\n self.is_valid = self.has_elmt(By.ID, \"consultaDebitoForm:consulta\")\n \n else:\n self.total = \"COM\"\n\n if self.is_valid:\n\n \n \n # ------ Codigo feio\n \n sl = WebDriverWait(self.driver, 20).until(\n EC.presence_of_element_located((By.ID, \"consultaDebitoForm:decLblTipoConsulta:opcoesPesquisa\"))\n )\n \n sl = Select(sl)\n\n # sl = Select(self.driver.find_element(By.ID,\"consultaDebitoForm:decLblTipoConsulta:opcoesPesquisa\"))\n sl.select_by_value('RENAVAM')\n \n element = WebDriverWait(self.driver, 20).until(\n EC.presence_of_element_located((By.ID, \"consultaDebitoForm:decTxtTipoConsulta:cdaEtiqueta\"))\n )\n \n element.send_keys(self.renavam)\n \n \n for i in range(1):\n if ReCaptcha(self.driver, key, recaptcha_data_site_key_ID=\"recaptcha\").solve(recaptcha_res=\"g-recaptcha-response\"):\n break\n \n \n\n self.driver.find_element(By.NAME, \"consultaDebitoForm:j_id104\").click()\n \n # ------- fim de codigo feio\n \n \n sl = WebDriverWait(self.driver, 10).until(\n EC.presence_of_element_located((By.ID, \"consultaDebitoForm:decLblTipoConsulta:opcoesPesquisa\"))\n )\n \n \n # sl = Select(sl)\n # sl.select_by_value('RENAVAM')\n \n element = WebDriverWait(self.driver, 10).until(\n EC.presence_of_element_located((By.ID, \"consultaDebitoForm:decTxtTipoConsulta:renavam\"))\n )\n\n element.send_keys(self.renavam)\n \n for i in range(1):\n if ReCaptcha(self.driver, key, recaptcha_data_site_key_ID=\"recaptcha\").solve(recaptcha_res=\"g-recaptcha-response\"):\n break\n \n self.driver.find_element(By.NAME, \"consultaDebitoForm:j_id104\").click()\n\n if self.has_elmt(By.ID, \"consultaDebitoForm:dataTable:j_id164\"):\n _ = self.grab_text(By.ID, \"consultaDebitoForm:dataTable:j_id164\")\n\n self.total = custom_to_float(_)\n","repo_name":"codexfast/sysma","sub_path":"sysma/controllers/core/grabonpage.py","file_name":"grabonpage.py","file_ext":"py","file_size_in_byte":21910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"2378202230","text":"#!/bin/env python\n\n'''Demonstrate the logging module.'''\n\nimport logging\nimport logging.config\nimport logging.handlers\n\nlogging.config.fileConfig('loggingDemo.conf')\n\nlogger = logging.getLogger('demo')\n\ndef doWork():\n 'Log some messages.'\n \n logger.debug('A debug message')\n logger.info('An info message')\n logger.warning('A warning message')\n logger.error('An error message')\n logger.critical('A critical message')\n\nif __name__ == '__main__':\n \n doWork()\n \n","repo_name":"PrincetonPy/TenThingsIUseAllTheTime","sub_path":"loggingDemo.py","file_name":"loggingDemo.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"603173534","text":"week_hours = 40 # Стандартна работна седмица.\nhours = float(input(\"Изработени часове: \"))\nhour_rate = float(input(\"Заплащане на час: \"))\n\n\ndef func_hours(h, r):\n h_r = h * r\n return h_r\n\n\nif hours <= week_hours:\n salary = func_hours(hours, hour_rate)\nelse:\n over_hours = hours - week_hours\n over_rate = hour_rate + (0.5 * hour_rate)\n over_salary = func_hours(over_hours, over_rate)\n salary = func_hours(week_hours, hour_rate) + over_salary\n\nprint(f\"Седмичната заплата е: {salary:.2f}\")\n","repo_name":"MAtanasova/Phyton-1-MA","sub_path":"1. Начинаещи/8. Функции/Simple functions/HW/payment.py","file_name":"payment.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"36481390076","text":"import unittest\n\nimport ai_flow as af\nfrom ai_flow.api.ai_flow_context import ENGINE_NAME\nfrom ai_flow.common import path_util, json_utils\nfrom ai_flow.graph.graph import _default_ai_graph\nfrom ai_flow.test import test_util\nfrom ai_flow.workflow.workflow_config import WorkFlowConfig\n\n\nclass ContextTests(unittest.TestCase):\n\n def test_context(self):\n global_config = af.BaseJobConfig(platform='a', engine='b', properties={'c': 'c'})\n job_config = af.BaseJobConfig(platform='aa', engine='bb', properties={'cc': 'cc'})\n with af.global_config(global_config):\n with af.config(job_config):\n af.user_define_operation(executor=None)\n node_list = list(_default_ai_graph.nodes.values())\n self.assertEqual('bb', node_list[0].properties[ENGINE_NAME])\n self.assertEqual('cc', node_list[0].config.properties[\"cc\"])\n self.assertEqual('c', node_list[0].config.properties[\"c\"])\n self.assertEqual('bb', node_list[0].config.engine)\n self.assertEqual('aa', node_list[0].config.platform)\n\n def test_context_with_file(self):\n config_file = path_util.get_file_dir(__file__) + \"/workflow_config.json\"\n\n def generate_workflow_config():\n workflow_config = WorkFlowConfig()\n workflow_config.add_job_config(config_key=\"global_config_key\",\n job_config=af.BaseJobConfig(platform=\"local\", engine=\"python\",\n properties={\"common_key\": \"common_value\"}))\n workflow_config.add_job_config(config_key=\"test_job\",\n job_config=af.BaseJobConfig(platform=None, engine=None,\n properties={\"job_key\": \"job_value\"}))\n workflow_config.add_job_config(config_key=\"test_job_1\",\n job_config=af.BaseJobConfig(platform='kubernetes', engine='flink',\n properties={\"job_key_1\": \"job_value_1\"}))\n with open(config_file, 'w') as f:\n f.write(json_utils.dumps(workflow_config))\n\n generate_workflow_config()\n\n with af.global_config_file(config_path=config_file):\n with af.config(config=\"test_job\") as cc:\n cc.properties['aa'] = 'aa'\n af.user_define_operation(executor=None)\n node_list = list(_default_ai_graph.nodes.values())\n self.assertEqual('python', node_list[len(node_list) - 1].properties[ENGINE_NAME])\n self.assertEqual('common_value', node_list[len(node_list) - 1].config.properties[\"common_key\"])\n self.assertEqual('job_value', node_list[len(node_list) - 1].config.properties[\"job_key\"])\n self.assertEqual('aa', node_list[len(node_list) - 1].config.properties[\"aa\"])\n\n self.assertEqual('python', node_list[len(node_list) - 1].config.engine)\n self.assertEqual('local', node_list[len(node_list) - 1].config.platform)\n with af.config(config=\"test_job_1\"):\n af.user_define_operation(executor=None)\n node_list = list(_default_ai_graph.nodes.values())\n self.assertEqual('flink', node_list[len(node_list) - 1].properties[ENGINE_NAME])\n self.assertEqual('common_value', node_list[len(node_list) - 1].config.properties[\"common_key\"])\n self.assertEqual('job_value_1', node_list[len(node_list) - 1].config.properties[\"job_key_1\"])\n self.assertEqual('flink', node_list[len(node_list) - 1].config.engine)\n self.assertEqual('kubernetes', node_list[len(node_list) - 1].config.platform)\n\n\nif __name__ == '__main__':\n test_util.set_project_config(__file__)\n unittest.main()\n","repo_name":"jxxmskulong/flink-ai-extended","sub_path":"flink-ai-flow/ai_flow/test/api/test_af_context.py","file_name":"test_af_context.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"12"} +{"seq_id":"72726940180","text":"from tars import *\n\nfrom utils.data_management import *\nfrom evaluation.evaluation_measures import *\nfrom evaluation.calculate_aggregate_statistics import calculate_aggregate\n\n\nclass TBP:\n\n def __init__(self):\n self.__state = 'initialized'\n self.tars_tree = None\n self.tars = None\n self.rs_intervals_support = None\n\n def get_state(self):\n return self.__state\n\n def build_model(self, baskets):\n\n self.tars_tree = TARSTree(baskets, root_value=None, root_count=None, root_timeseries=None)\n self.tars = self.tars_tree.mine_patterns(max_rec_dept=0, patterns_subset=None, nbr_patterns=None,\n get_items_in_order_of_occurrences=True)\n self.nbr_patterns = len(self.tars)\n self.rs_intervals_support = calculate_intervals_support(self.tars, self.tars_tree)\n self.__state = 'built'\n\n return self\n\n def update_model(self, new_baskets):\n return self.build_model(new_baskets)\n\n def predict(self, customer_data, day_of_next_purchase, nbr_patterns, pred_length=5, queue=None):\n if self.__state != 'built':\n raise Exception('Model not built, prediction not available')\n\n if nbr_patterns is not None and nbr_patterns > 0:\n rs_purchases, rs_day_of_last_purchase = calcualte_active_rp(customer_data, self.rs_intervals_support,\n day_of_next_purchase)\n\n self.tars = self.tars_tree.mine_patterns(max_rec_dept=1, patterns_subset=rs_purchases,\n nbr_patterns=nbr_patterns,\n get_items_in_order_of_occurrences=False)\n self.rs_intervals_support = calculate_intervals_support(self.tars, self.tars_tree)\n\n rs_purchases, rs_day_of_last_purchase = calcualte_active_rp(customer_data, self.rs_intervals_support,\n day_of_next_purchase)\n\n self.nbr_active_patterns = len(rs_purchases)\n\n item_score = calcualte_item_score(self.tars_tree, rs_purchases, self.rs_intervals_support)\n\n max_nbr_item = min(pred_length, len(item_score))\n pred_basket = sorted(item_score, key=item_score.get, reverse=True)[:max_nbr_item]\n\n if queue is not None:\n queue.put(pred_basket)\n\n return pred_basket\n","repo_name":"GiulioRossetti/tbp-next-basket","sub_path":"tbp/tbp.py","file_name":"tbp.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"12"} +{"seq_id":"26556387598","text":"#!/usr/bin/python3\n\n## Tech case for Cybel Angel\n##\n## Write a Python 3 function that takes 2 strings as arguments, and returns\n## true if they are anagrams of each other, and false otherwise.\n## Use only the language features and standard library.\n##\n## Author: Mariya Rychkova\n\nimport sys\n\ndef is_anagram(s1, s2):\n\n '''\n Trims whitespaces and spaces between the words.\n Then, forces a string to lowercase format.\n '''\n s1 = s1.replace(\" \", \"\").lower()\n s2 = s2.replace(\" \", \"\").lower()\n\n '''\n Checks if the strings have the same size, as otherwise they\n cannot be anagrams.\n '''\n if len(s1) != len(s2):\n return False\n\n '''\n Initializes dict hash table.\n Iterates over each string.\n '''\n hashtable = dict()\n for i in s1:\n '''\n For each new occurence of letter in the string, it increments\n it by one in a hashtable, if new letter it initializes it at 1\n '''\n if i in hashtable:\n hashtable[i] += 1\n else:\n hashtable[i] = 1\n\n for i in s2:\n '''\n For each new occurence of letter in the string, it decrements\n it by one in a hashtable, if new letter it initializes it at 1\n '''\n if i in hashtable:\n hashtable[i] -= 1\n else:\n hashtable[i] = 1\n\n '''\n If hashtable is empty, then each letter has the exact same number of\n occurences in each string, so they are anagrams.\n '''\n for i in hashtable:\n if hashtable[i] != 0:\n return False\n return True\n\n## Small main test : python3 is_anagram.py s1 s2\nif __name__ == '__main__':\n if len(sys.argv) == 3:\n print(is_anagram(str(sys.argv[1]), str(sys.argv[2])))\n else:\n print ('Please make sure you have entered at least 2 strings')\n","repo_name":"Turandotte1/CybelAngel","sub_path":"is_anagram/is_anagram.py","file_name":"is_anagram.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"32977441804","text":"import PyPDF2 as p2\nimport nltk\nimport fitz \nnltk.download('punkt')\nfrom pprint import pprint\n\nclass word_extract():\n content = []\n def __init__(self) -> None:\n pass\n \n def extract_from_pdf(self, file):\n pdfread = p2.PdfFileReader(file)\n # Extract single page\n\n if pdfread.getIsEncrypted() :\n return False\n \n # Extract entire pdf\n \n for i in range(0, pdfread.getNumPages()):\n pageinfo = pdfread.getPage(i)\n self.content.extend(pageinfo.extractText().split())\n return self.content\n \n def preprocess(self, content):\n def extractDigits(lst):\n return [[el.strip('\"\"')] for el in lst]\n a_list = nltk.tokenize.sent_tokenize(content) \n tmp = []\n list_list=extractDigits(a_list)\n \n for i in list_list:\n a = nltk.word_tokenize(i[0])\n tmp.append(a)\n\n for i in tmp:\n for n in i:\n if \"@\" in n:\n sentencenum = tmp.index(i)\n x = i.index(n)\n tmp[sentencenum][x-1 : x+2] = [''.join(tmp[sentencenum][x-1 : x+2])]\n return tmp\n \n def pdf_to_dict(self, file_bytes):\n text = \"\"\n with fitz.Document(stream=file_bytes, filetype='pdf') as doc:\n \n for page in doc:\n text += page.get_text()\n # print(res)\n list_list = self.preprocess(text)\n return list_list\n \n def extract_from_txt(self, file_bytes):\n text = file_bytes.rstrip()\n # print(text)\n list_list = self.preprocess(text)\n # print(res)\n return list_list\n \n\n \n \n ","repo_name":"PII-detection/server","sub_path":"upload/utils/word_extract.py","file_name":"word_extract.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"42371801430","text":"# %%\nfrom time import perf_counter, sleep\nfrom random import randint\nfrom concurrent.futures import ThreadPoolExecutor\n# %%\n\n\ndef do_something(x):\n print(f'Sleeping {x} second...', end='')\n sleep(x)\n return 'end well...'\n\n\n# %%\n\nstart = perf_counter()\n\nwith ThreadPoolExecutor() as executer:\n r = randint(1, 9)\n f1 = executer.submit(do_something, r)\n \n print(f1.result())\n\nfinish = perf_counter()\n\nprint(f'Finishes in {round(finish - start, 2):0.2f} seconds...')\n","repo_name":"ed9bh/AnotacoesEstudosBackPythonLSP","sub_path":"2019_Py_LSP/Py/Referencia/Threading/001_4.py","file_name":"001_4.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"14"} +{"seq_id":"39923570461","text":"import os\nimport sys\nimport traceback\nimport subprocess\nimport platform\nimport imp\nfrom pathlib import Path\nfrom PrismUtils.Decorators import err_catcher_plugin as err_catcher\n\ntry:\n from PySide2.QtCore import *\n from PySide2.QtGui import *\n from PySide2.QtWidgets import *\n\n psVersion = 2\nexcept Exception:\n from PySide.QtCore import *\n from PySide.QtGui import *\n\n psVersion = 1\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"UserInterfaces\"))\nif psVersion == 1:\n import FtrackPublish_ui\nelse:\n import FtrackPublish_ui_ps2 as FtrackPublish_ui\n\ntry:\n import CreateItem\n\nexcept Exception:\n modPath = imp.query_module(\"CreateItem\")[1]\n if modPath.endswith(\".pyc\") and os.path.exists(modPath[:-1]):\n os.remove(modPath)\n# 20221216 - change by Danko\n# path = r'D:\\dev\\GitHub\\Prism-CXPlugin\\Scripts'\npath = r'C:\\Prism\\Plugins\\Custom\\CXPlugin\\Scripts'\n\nsys.path.append(path)\nimport Prism_CXPlugin_Functions\n\n\nclass ftrackPublish(QDialog, FtrackPublish_ui.Ui_dlg_ftrackPublish):\n def __init__(self, core, origin, ptype, shotName, task, version, sources, startFrame, endFrame):\n QDialog.__init__(self)\n self.setupUi(self)\n\n self.core = core\n self.core.parentWindow(self)\n self.ptype = ptype\n self.shotName = shotName\n self.taskVersion = version\n self.fileSources = sources\n self.startFrame = startFrame\n self.endFrame = endFrame\n self.shotList = {}\n self.task = task\n\n ftrackData = origin.connectToFtrack()\n\n if ftrackData[0] is None or ftrackData[1] is None:\n return\n\n self.session, self.ftrackProjectName, self.ftrackUser = ftrackData\n\n self.core.appPlugin.ftrackPublish_startup(self)\n\n # for i in range(7):\n # self.cb_playlist.addItem(\n # \"DAILIES_%s\" % (datetime.date.today() + datetime.timedelta(days=i))\n # )\n\n if self.ptype == \"Asset Build\":\n self.rb_asset.setChecked(True)\n else:\n self.rb_shot.setChecked(True)\n\n self.updateShots()\n try:\n self.navigateToCurrent(self.shotName, self.task)\n except Exception:\n return\n\n if self.core.appPlugin.pluginName == \"Houdini\" and hasattr(\n self.core.appPlugin, \"fixStyleSheet\"\n ):\n self.core.appPlugin.fixStyleSheet(self.gb_playlist)\n\n self.connectEvents()\n\n @err_catcher(name=__name__)\n def connectEvents(self):\n self.rb_asset.pressed.connect(self.updateShots)\n self.rb_shot.pressed.connect(self.updateShots)\n # self.b_addTask.clicked.connect(self.createTask)\n self.b_addTask.setVisible(False)\n self.cb_shot.activated.connect(self.updateTasks)\n self.cb_task.activated.connect(self.updateTasks)\n self.b_ftrackPublish.clicked.connect(self.publish)\n\n @err_catcher(name=__name__)\n def updateShots(self):\n if self.rb_asset.isDown():\n self.ptype = 'Asset Build'\n elif self.rb_shot.isDown():\n self.ptype = 'Shot'\n\n ftrackTasks, self.ftrackDict = Prism_CXPlugin_Functions.Prism_CXPlugin_Functions.getFtrackEntityData(self, self.ptype)\n\n self.cb_shot.clear()\n\n for x in self.ftrackDict.keys():\n if self.ptype == 'Shot':\n name = \"%s%s%s\" % (\n x['parent']['name'],\n self.core.sequenceSeparator,\n x['name']\n )\n self.shotList[name] = x['name']\n else:\n name = x['name']\n localHierarchy = os.path.join(x['_link'][2:][0]['name'], name)\n self.shotList[name] = localHierarchy\n\n self.cb_shot.addItems(sorted(self.shotList.keys(), key=lambda s: s.lower()))\n self.updateTasks()\n\n @err_catcher(name=__name__)\n def updateTasks(self, idx=None):\n self.cb_task.clear()\n self.ftrackTasks = []\n # shotName is also assetName, seqName is also parentName\n shotName, seqName = self.core.entities.splitShotname(self.shotName)\n\n for i in self.ftrackDict:\n if i['name'] == shotName and (i['parent']['name'] == seqName or seqName == 'no sequence'):\n self.ftrackTasks = self.ftrackDict[i]\n self.curShot = i\n\n success = False\n for x in self.ftrackTasks:\n if x['name'] == self.task:\n self.curTask = x\n success = True\n\n if success is False:\n self.curTask = None\n QMessageBox.warning(self.core.messageParent, \"Ftrack Publish\", \"That %s has not been assignt to you.\" % self.ptype,)\n return\n\n if len(self.ftrackTasks) == 0:\n QMessageBox.warning(self.core.messageParent, \"Ftrack Publish\", \"That %s has not been assignt to you.\" % self.ptype,)\n return\n\n ftrackTaskNames = [x['name'] for x in self.ftrackTasks]\n ftrackTaskNames = list(set(ftrackTaskNames))\n\n self.cb_task.addItems(ftrackTaskNames)\n\n checklist = ['Animation', 'Compositing']\n\n if self.curTask['type']['name'] in checklist:\n self.chb_proxyVid.setChecked(True)\n else:\n self.chb_proxyVid.setChecked(False)\n\n @err_catcher(name=__name__)\n def navigateToCurrent(self, shotName, task):\n idx = self.cb_shot.findText(shotName)\n if idx != -1:\n self.cb_shot.setCurrentIndex(idx)\n\n self.updateTasks()\n\n idx = self.cb_task.findText(task)\n if idx != -1:\n self.cb_task.setCurrentIndex(idx)\n\n @err_catcher(name=__name__)\n def enterEvent(self, event):\n QApplication.restoreOverrideCursor()\n\n @err_catcher(name=__name__)\n def publish(self):\n if self.cb_shot.currentText() == \"\":\n QMessageBox.warning(\n self.core.messageParent,\n \"Ftrack Publish\",\n \"No %s exists in the Ftrack project. Publish canceled\" % self.ptype,\n )\n return\n\n if self.cb_task.currentText() == \"\":\n QMessageBox.warning(\n self.core.messageParent,\n \"Ftrack Publish\",\n \"No task is selected. Publish canceled.\",\n )\n return\n\n curShot = self.curShot\n curTask = self.curTask\n\n def frames_to_TC(frames):\n h = int(frames / 180000)\n m = int(frames / 3000) % 60\n s = (frames % 3000) / 50\n return (\"%02d:%02d:%2.1f\" % (h, m, s))\n\n pubVersions = []\n for source in self.fileSources:\n versionInfoPath = os.path.join(os.path.dirname(source[0]), \"versioninfo.yml\")\n if not os.path.exists(versionInfoPath):\n versionInfoPath = os.path.join(\n os.path.dirname(os.path.dirname(source[0])), \"versioninfo.yml\"\n )\n\n if not os.path.exists(versionInfoPath):\n QMessageBox.warning(self.core.messageParent, \"Error\", 'Could not find the versionInfo file.')\n return\n\n localScenefile = self.core.getConfig(\"information\", \"Source scene\", configPath=versionInfoPath)\n scenefile = str(source[0].rpartition('03_Workflow')[0]) + '03_Workflow' + str(localScenefile.rpartition('03_Workflow')[2])\n scenefile = self.core.fixPath(scenefile)\n\n versionName = \"%s_%s_%s\" % (\n self.cb_shot.currentText(),\n self.cb_task.currentText(),\n self.taskVersion,\n )\n\n if len(self.fileSources) > 1:\n versionName += \"_%s\" % os.path.splitext(os.path.basename(source[0]))[0]\n\n baseName, extension = os.path.splitext(source[0])\n videoInput = extension in [\".mp4\", \".mov\"]\n\n if videoInput:\n sequenceName = source[0]\n else:\n try:\n sequenceName = baseName[:-self.core.framePadding] + \"#\" * self.core.framePadding + extension\n except Exception:\n sequenceName = source[0]\n\n tmpFiles = []\n\n ffmpegIsInstalled = False\n if platform.system() == \"Windows\":\n ffmpegPath = os.path.join(\n self.core.prismLibs, \"Tools\", \"FFmpeg\", \"bin\", \"ffmpeg.exe\"\n )\n if os.path.exists(ffmpegPath):\n ffmpegIsInstalled = True\n elif platform.system() == \"Linux\":\n ffmpegPath = \"ffmpeg\"\n try:\n subprocess.Popen([ffmpegPath])\n ffmpegIsInstalled = True\n except Exception:\n pass\n elif platform.system() == \"Darwin\":\n ffmpegPath = os.path.join(self.core.prismLibs, \"Tools\", \"ffmpeg\")\n if os.path.exists(ffmpegPath):\n ffmpegIsInstalled = True\n\n imgPath = source[0]\n\n if extension in [\".exr\", \".mp4\", \".mov\"]:\n inputpath = self.core.fixPath(source[0])\n outputpath = os.path.splitext(inputpath)[0] + \".jpg\"\n\n if ffmpegIsInstalled:\n if videoInput:\n nProc = subprocess.Popen(\n [\n ffmpegPath,\n \"-apply_trc\",\n \"iec61966_2_1\",\n \"-i\",\n inputpath,\n \"-pix_fmt\",\n \"yuv420p\",\n \"-vf\",\n \"select=gte(n\\,%s)\" % source[1],\n \"-frames\",\n \"1\",\n outputpath,\n \"-y\",\n ]\n )\n else:\n nProc = subprocess.Popen(\n [\n ffmpegPath,\n \"-apply_trc\",\n \"iec61966_2_1\",\n \"-i\",\n inputpath,\n \"-pix_fmt\",\n \"yuv420p\",\n outputpath,\n \"-y\",\n ]\n )\n result = nProc.communicate()\n imgPath = outputpath\n tmpFiles.append(imgPath)\n else:\n QMessageBox.warning(self.core.messageParent, \"FFmpeg Error\", 'No FFmpeg Instalation found!')\n\n asset_parent = curShot\n asset_name = curTask['name']\n asset = self.session.query('Asset where name is \"{0}\" and parent.id is \"{1}\"'.format(asset_name, curShot['id'])).first()\n asset_type = self.session.query('AssetType where name is \"{0}\"'.format('Upload')).one() # Undedingt Ändern!!!\n # status = self.session.query('Status where name is \"{0}\"'.format('Awaiting Approval CX')).one()\n status = self.session.query('Status where name is \"{0}\"'.format('Awaiting Client Approval')).one()\n version = self.taskVersion[1:5]\n local_location = self.session.query('Location where name is \"ftrack.unmanaged\"').one()\n server_location = self.session.query('Location where name is \"ftrack.server\"').one()\n\n data = {}\n\n if asset is None:\n data = {\n 'name': asset_name,\n 'type': asset_type,\n 'parent': asset_parent\n }\n try:\n asset = self.session.create('Asset', data)\n self.session.commit()\n\n except Exception:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n erStr = \"ERROR:\\n%s\" % traceback.format_exc()\n QMessageBox.warning(self.core.messageParent, \"Ftrack Publish\", erStr)\n return\n\n # QMessageBox.warning(self.core.messageParent, \"asset parent\", asset['parent']['parent']['name'] + '-' + asset['parent']['name'])\n # QMessageBox.warning(self.core.messageParent, \"curTask parent\", curTask['parent']['parent']['name'] + '-' + curTask['parent']['name'])\n\n data = {}\n data = {\n 'comment': self.te_description.toPlainText(),\n 'asset': asset,\n 'task': curTask,\n 'version': version,\n 'is_published': False\n }\n\n try:\n createdVersion = self.session.create(\"AssetVersion\", data)\n self.session.commit()\n\n user = self.session.query('User where username is \"{0}\"'.format(self.ftrackUser)).first()\n note = self.session.create('Note', {\n 'content': self.te_description.toPlainText(),\n 'author': user\n })\n createdVersion['notes'].append(note)\n curTask['status'] = status\n # self.session.commit()\n\n if self.chb_proxyVid.isChecked() and ffmpegIsInstalled:\n createdVersion['custom_attributes']['clientReview'] = True\n # self.session.commit()\n\n ftrackPrj = self.session.query('Project where name is \"{0}\"'.format(self.ftrackProjectName)).first()\n pre = ftrackPrj['root']\n project = self.core.getConfig('globals', 'project_name', configPath=self.core.prismIni)\n sequenceName = os.path.normpath(pre + sequenceName.rpartition(project)[2])\n scenefile = os.path.normpath(pre + scenefile.rpartition(project)[2])\n\n createdVersion.create_component(sequenceName, {'name': 'Global SequencePath'}, location=local_location)\n createdVersion.create_component(scenefile, {'name': 'Global SceneFilePath'}, location=local_location)\n\n # exportFilePath = scenefile.split('.')[0] + 'versionInfo.yml'\n # exportFile = self.core.getConfig(\"information\", \"export-path\", configPath=exportFilePath)\n\n # if exportFile is None:\n # QMessageBox.warning(self.core.messageParent, \"Warning\", 'No Exportfile has been created with this Version.')\n # else:\n # exportFileList = exportFile.split(', ')\n # exportFileList.pop()\n # exportNewFileList = []\n\n # for i in exportFileList:\n # exportNewFileList.append(os.path.normpath(pre + i.rpartition(project)[2]))\n\n # exportFile = ', '.join(exportNewFileList)\n # # exportFile = os.path.normpath(pre + exportFile.rpartition(project)[2])\n # # createdVersion.create_component(exportFile, {'name': 'Global ExportFilePath'}, location=local_location)\n\n if os.path.exists(imgPath):\n thumbnail_component = self.session.create_component(imgPath, dict(name='thumbnail'), location=server_location)\n createdVersion['thumbnail'] = thumbnail_component\n\n createdVersion['is_published'] = True\n self.session.commit()\n\n except Exception:\n QMessageBox.warning(self.core.messageParent, \"Debug\", 'Version already published',)\n for i in tmpFiles:\n os.remove(i)\n exc_type, exc_obj, exc_tb = sys.exc_info()\n erStr = \"ERROR:\\n%s\" % traceback.format_exc()\n QMessageBox.warning(self.core.messageParent, \"Ftrack Publish\", erStr)\n return\n\n if self.chb_proxyVid.isChecked() and ffmpegIsInstalled:\n proxyPath = \"\"\n inputpath = self.core.fixPath(source[0])\n soundfilePath = os.path.normpath(str(Path(os.path.dirname(inputpath)).parents[2]) + os.path.sep + \"Incoming\" + os.path.sep + \"03_VR-Storyboard\")\n soundfilePath = self.core.convertPath(soundfilePath, 'global')\n try:\n with open(os.path.join(soundfilePath, 'AudioStart.txt')) as f:\n lines = int(f.readlines()[0])\n delay = frames_to_TC(self.startFrame - lines)\n except Exception:\n delay = frames_to_TC(self.startFrame - 1000)\n \n fullAudioFilePath = ''\n\n if os.path.exists(soundfilePath):\n for file in os.listdir(soundfilePath):\n if file.endswith(\".mp4\"):\n filename = file.split('.')[0] + '.mp4'\n fullAudioFilePath = os.path.join(soundfilePath, filename)\n\n mp4File = (\n os.path.join(\n os.path.dirname(inputpath) + \"(mp4)\",\n os.path.basename(inputpath),\n )[:-9] + \".mp4\"\n )\n\n pwidth = 0\n pheight = 0\n\n if os.path.exists(mp4File):\n proxyPath = mp4File\n else:\n isSequence = False\n\n if len(os.listdir(os.path.dirname(inputpath))) > 2:\n if not videoInput:\n isSequence = True\n else:\n pass\n\n if os.path.splitext(inputpath)[1] in [\n \".jpg\",\n \".jpeg\",\n \".JPG\",\n \".png\",\n \".tif\",\n \".tiff\",\n ]:\n size = QImage(inputpath).size()\n pwidth = size.width()\n pheight = size.height()\n elif os.path.splitext(inputpath)[1] in [\".exr\"]:\n oiio = self.core.media.getOIIO()\n\n if oiio:\n imgSpecs = oiio.ImageBuf(str(inputpath)).spec()\n pwidth = imgSpecs.full_width\n pheight = imgSpecs.full_height\n\n elif os.path.splitext(inputpath)[1] in [\".mp4\", \".mov\"]:\n try:\n import imageio\n except Exception:\n pass\n vidReader = imageio.get_reader(inputpath, \"ffmpeg\")\n\n pwidth = vidReader._meta[\"size\"][0]\n pheight = vidReader._meta[\"size\"][1]\n\n if int(pwidth) % 2 == 1 or int(pheight) % 2 == 1:\n QMessageBox.warning(\n self.core.messageParent,\n \"Media conversion\",\n \"Media with odd resolution can't be converted to mp4. No proxy video could be generated.\",\n )\n else:\n if isSequence or videoInput:\n if isSequence:\n inputpath = os.path.splitext(inputpath)[0][:-(self.core.framePadding)] + \"%04d\".replace(\"4\", str(self.core.framePadding)) + os.path.splitext(inputpath)[1]\n outputpath = os.path.splitext(inputpath)[0][:-(self.core.framePadding + 1)] + \".mp4\"\n\n if platform.system() == \"Windows\":\n overlay = \"\"\"[in]\n drawbox=y=ih-24:color=black@0.4:width=iw:height=24:t=fill,\n drawbox=y=0:color=black@0.4:width=iw:height=24:t=fill,\n drawtext='fontfile=c\\:/Windows/Fonts/l_10646.ttf:text=Modul\\: xModul Task\\: xTaskName':start_number=1:x=(w-tw)/2: y=(lh/2):fontcolor=white:fontsize=15:,\n drawtext='fontfile=c\\:/Windows/Fonts/l_10646.ttf:text=MayaFrame\\: %{frame_num}':start_number=xSnum:x=(w-tw-300)/2:y=h-(lh+lh/2-2):fontcolor=white:fontsize=15:,\n drawtext='fontfile=c\\:/Windows/Fonts/l_10646.ttf:text=VideoFrame\\: %{eif\\:n\\:d\\:4} / xFanz':start_number=1:x=(w-tw+300)/2:y=h-(lh+lh/2):fontcolor=white:fontsize=15: \n [OUT]\"\"\"\n elif platform.system() == \"Linux\":\n overlay = \"\"\"[in]\n drawbox=y=ih-24:color=black@0.4:width=iw:height=24:t=fill,\n drawbox=y=0:color=black@0.4:width=iw:height=24:t=fill,\n drawtext='font=adobe-source-code-pro:text=Modul\\: xModul Task\\: xTaskName':start_number=1:x=(w-tw)/2: y=(lh/2):fontcolor=white:fontsize=15:,\n drawtext='font=adobe-source-code-pro:text=MayaFrame\\: %{frame_num}':start_number=xSnum:x=(w-tw-300)/2:y=h-(lh+lh/2-2):fontcolor=white:fontsize=15:,\n drawtext='font=adobe-source-code-pro:text=VideoFrame\\: %{eif\\:n\\:d\\:4} / xFanz':start_number=1:x=(w-tw+300)/2:y=h-(lh+lh/2):fontcolor=white:fontsize=15: \n [OUT]\"\"\"\n elif platform.system() == \"Darwin\":\n overlay = \"\"\"[in]\n drawbox=y=ih-24:color=black@0.4:width=iw:height=24:t=fill,\n drawbox=y=0:color=black@0.4:width=iw:height=24:t=fill,\n drawtext='font=adobe-source-code-pro:text=Modul\\: xModul Task\\: xTaskName':start_number=1:x=(w-tw)/2: y=(lh/2):fontcolor=white:fontsize=15:,\n drawtext='font=adobe-source-code-pro:text=MayaFrame\\: %{frame_num}':start_number=xSnum:x=(w-tw-300)/2:y=h-(lh+lh/2-2):fontcolor=white:fontsize=15:,\n drawtext='font=adobe-source-code-pro:text=VideoFrame\\: %{eif\\:n\\:d\\:4} / xFanz':start_number=1:x=(w-tw+300)/2:y=h-(lh+lh/2):fontcolor=white:fontsize=15: \n [OUT]\"\"\"\n\n overlay = overlay.replace('xModul', self.cb_shot.currentText())\n overlay = overlay.replace('xTaskName', curTask['name'])\n overlay = overlay.replace('xSnum', str(self.startFrame))\n overlay = overlay.replace('xFanz', str(self.endFrame - self.startFrame).zfill(4))\n\n fnameData = self.core.getScenefileData(scenefile)\n step = fnameData[\"step\"]\n\n if step == 'srf':\n fps = str(12)\n else:\n fps = str(curShot['custom_attributes']['fps'])\n\n # QMessageBox.information(self.core.messageParent, 'Debug', str(delay))\n # QMessageBox.information(self.core.messageParent, 'Debug', fullAudioFilePath)\n # QMessageBox.information(self.core.messageParent, 'startFrame', str(self.startFrame))\n # QMessageBox.information(self.core.messageParent, 'fps', fps)\n # QMessageBox.information(self.core.messageParent, 'inputpath', inputpath)\n # # QMessageBox.information(self.core.messageParent, 'Debug', overlay)\n # QMessageBox.information(self.core.messageParent, 'outputpath', outputpath)\n\n if step == 'anm':\n nProc = subprocess.Popen(\n [\n ffmpegPath,\n \"-ss\",\n str(delay),\n \"-i\",\n fullAudioFilePath,\n \"-start_number\",\n str(self.startFrame),\n \"-framerate\",\n fps,\n \"-apply_trc\",\n \"iec61966_2_1\",\n \"-i\",\n inputpath,\n \"-map\",\n \"0:a\",\n \"-map\",\n \"1:v\",\n \"-vf\",\n overlay,\n \"-pix_fmt\",\n \"yuv420p\",\n \"-start_number\",\n str(self.startFrame),\n \"-shortest\",\n outputpath,\n \"-y\",\n ]\n )\n else:\n nProc = subprocess.Popen(\n [\n ffmpegPath,\n \"-start_number\",\n str(self.startFrame),\n \"-framerate\",\n fps,\n \"-apply_trc\",\n \"iec61966_2_1\",\n \"-i\",\n inputpath,\n \"-pix_fmt\",\n \"yuv420p\",\n \"-start_number\",\n str(self.startFrame),\n outputpath,\n \"-y\",\n ]\n )\n\n else:\n outputpath = os.path.splitext(inputpath)[0][:-(self.core.framePadding + 1)] + \"(proxy).mp4\"\n\n nProc = subprocess.Popen(\n [\n ffmpegPath,\n \"-apply_trc\",\n \"iec61966_2_1\",\n \"-i\",\n inputpath,\n \"-pix_fmt\",\n \"yuv420p\",\n \"-start_number\",\n str(self.startFrame),\n outputpath,\n \"-y\",\n ]\n )\n mp4Result = nProc.communicate()\n proxyPath = outputpath\n tmpFiles.append(proxyPath)\n\n else:\n try:\n import json\n component = createdVersion.create_component(\n path=inputpath,\n data={\n 'name': 'ftrackreview-image'\n },\n location=server_location\n )\n\n # Meta data needs to contain *format*.\n component['metadata']['ftr_meta'] = json.dumps({\n 'format': 'image',\n })\n\n component.session.commit()\n\n except Exception as e:\n QMessageBox.warning(\n self.core.messageParent,\n \"Warning\",\n \"Uploading image failed:\\n\\n%s\" % str(e),\n )\n\n if (proxyPath != \"\" and os.path.exists(proxyPath) and os.stat(proxyPath).st_size != 0):\n try:\n # Retrieve or create version.\n import json\n component = createdVersion.create_component(\n path=proxyPath,\n data={\n 'name': 'ftrackreview-mp4'\n },\n location=server_location\n )\n\n component['metadata']['ftr_meta'] = json.dumps({\n 'frameIn': self.startFrame,\n 'frameOut': self.endFrame,\n 'frameRate': curShot['custom_attributes']['fps'],\n 'height': pheight,\n 'width': pwidth\n })\n component.session.commit()\n\n except Exception as e:\n QMessageBox.warning(\n self.core.messageParent,\n \"Warning\",\n \"Uploading proxy failed:\\n\\n%s\" % str(e),\n )\n\n pubVersions.append(versionName)\n\n for i in tmpFiles:\n os.remove(i)\n\n ftrackSite = self.core.getConfig(\"ftrack\", \"site\", configPath=self.core.prismIni)\n ftrackPrj = self.session.query('Project where name is \"{0}\"'.format(self.ftrackProjectName)).first()\n ftrackPrjId = ftrackPrj['id']\n user_security_roles = self.session.query('UserSecurityRole where user.username is \"{0}\"'.format(self.session.api_user)).all()\n\n for i in user_security_roles:\n userRole = i['security_role']['type']\n\n if userRole == 'PROJECT':\n ftrackSite += \"/#slideEntityId=\" + str(createdVersion[\"id\"]) + \"&slideEntityType=assetversion&view=tasks&itemId=projects&entityId=\" + str(ftrackPrjId) + \"&entityType=show\"\n elif userRole == 'ASSIGNED':\n ftrackSite += '/#slideEntityId=' + str(createdVersion[\"id\"]) + '&slideEntityType=assetversion&itemId=home'\n\n versionInfoPath = os.path.join(os.path.dirname(source[0]), \"versioninfo.yml\")\n if not os.path.exists(versionInfoPath):\n versionInfoPath = os.path.join(os.path.dirname(os.path.dirname(source[0])), \"versioninfo.yml\")\n\n self.core.setConfig(\"information\", \"ftrack-url\", ftrackSite, configPath=versionInfoPath)\n\n msgStr = \"Successfully published:\"\n for i in pubVersions:\n msgStr += \"\\n%s\" % i\n\n msg = QMessageBox(QMessageBox.Information, \"Ftrack Publish\", msgStr, parent=self.core.messageParent,)\n msg.addButton(\"Open version in Ftrack\", QMessageBox.YesRole)\n msg.addButton(\"Close\", QMessageBox.YesRole)\n msg.setFocus()\n action = msg.exec_()\n\n if action == 0:\n import webbrowser\n\n webbrowser.open(ftrackSite)\n\n self.accept()\n","repo_name":"Cine-Chromatix/Prism","sub_path":"Prism/Plugins/ProjectManagers/Ftrack/Scripts/FtrackPublish.py","file_name":"FtrackPublish.py","file_ext":"py","file_size_in_byte":32104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"70302272974","text":"import torch\nimport os\nimport random\nimport numpy as np\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nfrom torch import nn\nimport torchvision.transforms as T\nimport torch.nn.functional as F\nimport kornia.augmentation as K\nimport kornia\nimport torchvision\nimport argparse\nfrom wrappers.dataset_selector import DatasetSelector\nfrom vit_pytorch import ViT\nfrom vit_pytorch.cross_vit import CrossViT\nfrom contrastive_framework.byol import BYOL\n\nfrom torchvision import models\n\nfrom sklearn.metrics import auc, roc_curve, recall_score, precision_score\nfrom sklearn.covariance import EmpiricalCovariance, LedoitWolf, ShrunkCovariance\nfrom utils.train_utils import AverageMeter\nfrom models.unet import UNet\nimport pdb\nfrom PIL import Image\nimport optuna\nfrom vit_pytorch.nest import NesT\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nimport torch.multiprocessing\ntorch.multiprocessing.set_sharing_strategy('file_system')\n\nclass RandomApply(nn.Module):\n def __init__(self, fn, p):\n super().__init__()\n self.fn = fn\n self.p = p\n def forward(self, x):\n if random.random() > self.p:\n return x\n return self.fn(x)\n\ndef load_image(img_path):\n with open(img_path, 'rb') as f:\n img = Image.open(f)\n return img.convert('RGB')\n\ndef train(args, learner, optimizer, loader, epoch, lr_scheduler=None):\n\n losses = AverageMeter(f\"Epoch {epoch +1}\")\n learner = learner.train()\n \n local_progress= tqdm(loader, desc=f'Epoch {epoch+1}/{args.epochs}')\n for idx, (x, _) in enumerate(local_progress):\n optimizer.zero_grad()\n x = x.to(device)\n\n loss = learner(x)\n\n loss.backward()\n optimizer.step()\n if lr_scheduler:\n lr_scheduler.step()\n losses.update(loss.item(), x.size(0))\n\n data_dict = {\"avg loss\": losses.avg}\n local_progress.set_postfix(data_dict)\n\n return losses.avg\n\ndef get_features(model, dataloader):\n extracted_features, labels = [], []\n with torch.no_grad():\n # extract features\n for x, y in dataloader:\n x = T.Resize(args.image_size)(x)\n x = x.to(device)\n \n _, features = model(x, return_embedding=True)\n\n extracted_features += list(features)\n labels += list(y)\n\n labels = np.array(labels)\n \n \n out_dim = extracted_features[0].size(-1)\n return torch.stack(extracted_features).reshape(-1, out_dim).to(device), labels\n\n\ndef val(args, model, train_dataloader, val_dataloader, epoch):\n\n group_lasso = LedoitWolf(assume_centered=False)\n\n model = model.eval()\n\n train_features, _ = get_features(model, train_dataloader)\n val_features, labels = get_features(model, val_dataloader)\n\n train_features = F.normalize(train_features, dim=-1, p=2)\n val_features = F.normalize(val_features, dim=-1, p=2)\n cov = group_lasso.fit(train_features.cpu().numpy())\n # pdb.set_trace()\n scores = cov.mahalanobis(val_features.cpu().numpy())\n fpr, tpr, threshold = roc_curve(labels, scores)\n auc_score = auc(fpr, tpr)\n\n return auc_score\n\ndef train_model(args, model, train_dataloader, val_dataloader, trial=None):\n model = model.to(device)\n \n print(args)\n\n # if args.optname in [\"SGD\"]:\n # optimizer = getattr(torch.optim, args.optname)(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)\n # else:\n optimizer = getattr(torch.optim, args.optname)(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, amsgrad=args.amsgrad)\n # lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n # optimizer, args.epochs * len(train_dataloader), 1e-4\n # )\n # lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(\n # optimizer, milestones=[5], gamma=0.1\n # )\n\n best_auc = 0\n for epoch in range(args.epochs):\n avg_loss = train(args, model, optimizer, train_dataloader, epoch)\n auc_score = val(args, model, train_dataloader, val_dataloader, epoch)\n\n if trial:\n trial.report(auc_score, epoch+1)\n\n if trial.should_prune():\n raise optuna.exceptions.TrialPruned()\n\n print(f'auc: {auc_score:.6f}')\n if auc_score > best_auc:\n best_auc = auc_score\n print(f'Saving Model AUC: {best_auc:.6f}')\n model_path = os.path.join(args.model_path)\n torch.save(model.state_dict(), model_path)\n\n return best_auc \n\ndef run(args, trial=None):\n from utils.kornia_utils import GaussianBlur\n kornia_transforms = nn.Sequential(\n K.ColorJitter(0.8, 0.8, 0.8, 0.2, p = 0.3),\n K.RandomGrayscale(p=0.2),\n K.RandomHorizontalFlip(p=.5),\n GaussianBlur((3, 3), (1.0, 2.0), p=0.2),\n K.RandomResizedCrop((args.image_size, args.image_size), p=.5),\n K.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225])) # )\n )\n\n # transform = torch.nn.Sequential(\n # T.RandomHorizontalFlip(),\n # RandomApply(\n # T.GaussianBlur((3, 3), (1.0, 2.0)),\n # p = 0.2\n # ),\n # RandomApply(\n # T.RandomResizedCrop((args.image_size // 2, args.image_size // 2)),\n # p = 0.5\n # ),\n # )\n\n in_channels = 3 if args.dataset == 'cifar-10' or args.dataset == 'mvtech-ad' else 1\n\n # model = ViT(\n # image_size = args.image_size,\n # patch_size = 16,\n # num_classes = 10,\n # dim = 512, # 512\n # depth = 6,\n # heads = 16,\n # mlp_dim = 1024, # 1024\n # dropout = 0.5,\n # emb_dropout = 0.1,\n # channels = in_channels\n # )\n\n model = NesT(\n image_size = args.image_size,\n patch_size = 4,\n dim = 96,\n heads = 3,\n num_hierarchies = 3, # number of hierarchies\n block_repeats = (8, 4, 1), # the number of transformer blocks at each heirarchy, starting from the bottom\n num_classes = 512\n )\n\n\n # model = models.resnet50(pretrained=False)\n # model.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\n learner = BYOL(\n model,\n augment_fn=kornia_transforms,\n image_size = args.image_size,\n hidden_layer='mlp_head', #hidden_layer= 'to_latent',\n use_momentum = False # turn off momentum in the target encoder\n )\n\n train_dataloader, val_dataloader, _ = DatasetSelector.select_dataset(args)\n\n best_auc = train_model(args, learner, train_dataloader, val_dataloader, trial)\n return best_auc\n\ndef objective(args):\n\n def final(trial):\n lr = trial.suggest_float(\"lr\", 1e-5, 1e-1, log=True)\n weight_decay = trial.suggest_float(\"weight_decay\", 0, 0.9)\n momentum = trial.suggest_float(\"momentum\", 0, 0.9)\n amsgrad = trial.suggest_categorical(\"amsgrad\", [True, False])\n args.lr = lr\n # args.optname = optname\n args.amsgrad = amsgrad\n args.weight_decay = weight_decay\n args.momentum = momentum\n return run(args, trial)\n\n return final\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='RIAD anomaly detection')\n parser.add_argument('--pdata', type=float, default=1.0, help='learning rate of Adam')\n parser.add_argument('--obj', type=str, default='screw')\n parser.add_argument('--model_path', default='saved_models/contrastive/best_model_resnet_mvtech', type=str)\n parser.add_argument('--eval', default=False, type=bool)\n parser.add_argument('--dataset', type=str, default='mvtech-ad') #kaggle_pneumonia\n parser.add_argument('--epochs', type=int, default=20, help='maximum training epochs')\n parser.add_argument('--batch_size', type=int, default=12) # 12\n parser.add_argument('--test_batch_size', type=int, default=1)\n parser.add_argument('--val_batch_size', type=int, default=1)\n parser.add_argument('--image_size', type=int, default=256) # 256\n parser.add_argument('--alpha', type=float, default=1.0)\n parser.add_argument('--belta', type=float, default=1.0)\n parser.add_argument('--gamma', type=float, default=1.0)\n parser.add_argument('--lr', type=float, default=0.0006949058882671142, help='learning rate of Adam') #0.0006949058882671142\n parser.add_argument('--num_workers', type=int, default=2)\n parser.add_argument('--in_cls', default=0, type=int)\n parser.add_argument('--seed', default=123, type=int)\n parser.add_argument('--optname', default='Adam', type=str)\n parser.add_argument('--weight-decay', default=0, type=float)\n parser.add_argument('--momentum', default=0, type=float)\n parser.add_argument('--amsgrad', default=False, type=bool)\n\n args = parser.parse_args()\n\n seed = args.seed\n os.environ[\"PYTHONHASHSEED\"] = str(seed)\n random.seed(seed)\n np.random.seed(seed)\n\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n \n # study = optuna.create_study(direction=\"maximize\", storage=\"sqlite:///mvtech_experiments.db\", study_name=\"mvtech_cable_vit_adam\", load_if_exists=True)\n # study.optimize(objective(args), n_trials=100)\n\n # # pruned_trials = study.get_trials(deepcopy=False, states=[TrialState.PRUNED])\n # complete_trials = study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])\n\n # print(\"Study statistics: \")\n # print(\" Number of finished trials: \", len(study.trials))\n # # print(\" Number of pruned trials: \", len(pruned_trials))\n # print(\" Number of complete trials: \", len(complete_trials))\n\n # print(\"Best trial:\")\n # trial = study.best_trial\n\n # print(\" Value: \", trial.value)\n\n # print(\" Params: \")\n # for key, value in trial.params.items():\n # print(\" {}: {}\".format(key, value))\n auc = run(args)\n\n with open(f'contrastive_results_{args.seed}.txt', 'a') as fl:\n print(f'obj={args.obj} auc: {auc:.3f}', file=fl)\n","repo_name":"esdrascosta/anomaly-detection","sub_path":"contrastive_train.py","file_name":"contrastive_train.py","file_ext":"py","file_size_in_byte":10045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"12314136947","text":"from pandas import Series, DataFrame\nimport pandas as pd\n\ndf = pd.DataFrame(\n [[4,5,6],\n [5,8,11],\n [6,9,12]],\n index = [1,2,3],\n columns = ['a','b','c']\n)\nprint(df)","repo_name":"cguxxxxx/sample-repository","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"29248165674","text":"import pymongo\nimport json\n\nclient = pymongo.MongoClient(\"mongodb+srv://shawn:shawn@cluster0.uebyo.mongodb.net/plannerbee?retryWrites=true&w=majority\")\ndb = client[\"plannerbee\"]\ncol = db[\"transactions_users\"]\n\ndef initiateTransactions():\n #raw data\n usertrans = {\n \"_id\": \"16cecd11-2f83-4864-bf6b-f270f4be88cb\",\n \"local_currency_code\": \"SGD\",\n \"transactions\": {\n \"250773972570868310\": {\n \"base_currency_amount\": -1.3,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -1.3,\n \"transacted_at\": \"2020-04-03T00:00:00Z\",\n \"description\": \"COLD STORAGE-BJ SINGAPORE SG\",\n \"category\": \"shopping\"\n },\n \"250773972570868311\": {\n \"base_currency_amount\": -2.62,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -2.62,\n \"transacted_at\": \"2020-04-03T00:00:00Z\",\n \"description\": \"BUS/MRT 33803686 SINGAPORE SG\",\n \"category\": \"transfers\"\n },\n \"250773972570868312\": {\n \"base_currency_amount\": -11.8,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -11.8,\n \"transacted_at\": \"2020-04-03T00:00:00Z\",\n \"description\": \"UNIQLO BUGIS+ SINGAPORE SG\",\n \"category\": \"shopping\"\n },\n \"250773972570868313\": {\n \"base_currency_amount\": -4.32,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -4.32,\n \"transacted_at\": \"2020-04-05T00:00:00Z\",\n \"description\": \"POPULAR BOOK COMPANY-M SINGAPORE SG\",\n \"category\": \"education\"\n },\n \"250773972570868314\": {\n \"base_currency_amount\": -50.29,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -50.29,\n \"transacted_at\": \"2020-05-05T00:00:00Z\",\n \"description\": \"SWENSEN'S-PWP SINGAPORE SG\",\n \"category\": \"shopping\"\n },\n \"250773972570868315\": {\n \"base_currency_amount\": 271.86,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": 271.86,\n \"transacted_at\": \"2020-05-07T00:00:00Z\",\n \"description\": \"GIRO PAYMENT\",\n \"category\": \"transfers\"\n },\n \"250773972570868316\": {\n \"base_currency_amount\": -138.0,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -138.0,\n \"transacted_at\": \"2020-05-09T00:00:00Z\",\n \"description\": \"EU YAN SANG SINGAPORE SINGAPORE SG\",\n \"category\": \"personal_care\"\n },\n \"250773972579256925\": {\n \"base_currency_amount\": -1.5,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -1.5,\n \"transacted_at\": \"2020-05-11T00:00:00Z\",\n \"description\": \"HAO MART - MANDARIN GA SINGAPORE SG\",\n \"category\": \"groceries\"\n },\n \"250773972579256926\": {\n \"base_currency_amount\": 9.36,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": 9.36,\n \"transacted_at\": \"2020-06-16T00:00:00Z\",\n \"description\": \"30CASHBACK\",\n \"category\": \"transfers\"\n },\n \"250773972579256927\": {\n \"base_currency_amount\": -17.19,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -17.19,\n \"transacted_at\": \"2020-06-20T00:00:00Z\",\n \"description\": \"DELIVEROO SINGAPORE SG\",\n \"category\": \"shopping\"\n },\n \"250773972579256928\": {\n \"base_currency_amount\": 614.87,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": 614.87,\n \"transacted_at\": \"2020-06-21T00:00:00Z\",\n \"description\": \"PAYMENT - THANK YOU\",\n \"category\": \"income\"\n },\n \"250773972579256929\": {\n \"base_currency_amount\": -46.53,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -46.53,\n \"transacted_at\": \"2020-06-07T00:00:00Z\",\n \"description\": \"FAIRPRICE FINEST-MARIN SINGAPORE SG\",\n \"category\": \"groceries\"\n },\n \"250773972579256930\": {\n \"base_currency_amount\": 63.72,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": 63.72,\n \"transacted_at\": \"2020-07-05T00:00:00Z\",\n \"description\": \"PAYMENT - THANK YOU\",\n \"category\": \"transfers\"\n },\n \"250773972579256931\": {\n \"base_currency_amount\": -33.89,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -33.89,\n \"transacted_at\": \"2020-07-05T00:00:00Z\",\n \"description\": \"DELIVEROO SINGAPORE SG\",\n \"category\": \"shopping\"\n },\n \"250773972579256932\": {\n \"base_currency_amount\": -55.27,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -55.27,\n \"transacted_at\": \"2020-07-05T00:00:00Z\",\n \"description\": \"DELIVEROO SINGAPORE SG\",\n \"category\": \"shopping\"\n },\n \"250773972579256933\": {\n \"base_currency_amount\": 33.89,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": 33.89,\n \"transacted_at\": \"2020-07-05T00:00:00Z\",\n \"description\": \"PAYMENT - THANK YOU\",\n \"category\": \"transfers\"\n },\n \"250773972587645542\": {\n \"base_currency_amount\": -13.65,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -13.65,\n \"transacted_at\": \"2020-07-06T00:00:00Z\",\n \"description\": \"NTUC FP-BEDOK B SINGAPORE SG\",\n \"category\": \"groceries\"\n },\n \"250773972587645543\": {\n \"base_currency_amount\": 2.5,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": 2.5,\n \"transacted_at\": \"2020-07-06T00:00:00Z\",\n \"description\": \"30CASHBACK\",\n \"category\": \"transfers\"\n },\n \"250773972587645544\": {\n \"base_currency_amount\": -10.5,\n \"base_currency_code\": \"SGD\",\n \"local_currency_amount\": -10.5,\n \"transacted_at\": \"2020-07-06T00:00:00Z\",\n \"description\": \"HOMEGROUND COFFEE ROAS SINGAPORE SG\",\n \"category\": \"shopping\"\n }\n }\n }\n #insert or update data to mongodb\n x = col.update_one(\n {\"_id\": \"16cecd11-2f83-4864-bf6b-f270f4be88cb\"},\n {\"$setOnInsert\":usertrans},\n upsert = True\n )\n \n return x.modified_count","repo_name":"ShawnWon/MyFirstFastAPI","sub_path":"project/functions/transactions.py","file_name":"transactions.py","file_ext":"py","file_size_in_byte":7224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"23239620367","text":"import torch\nimport numpy as np\nimport torch.optim as optim\nimport torch.nn as nn\nfrom torch.nn import init\nfrom model.Linear import Linear\nfrom dataset.Linear_dataset import getdata\n\n\nnum_input = 2\nnet = nn.Sequential()\n# linear = Linear(num_input)\n# net.add_module(\"linear\", linear)\nnet.add_module(\"linear2\", nn.Linear(num_input, 1))\n\ndataset = getdata()\n\n#初始化模型参数\nprint(net)\ninit.normal_(net[0].weight, mean=0, std=0.01)\ninit.constant_(net[0].bias, val=0)\n# print(net[0])\n\n#初始化损失函数\nloss = nn.MSELoss()\n\n#定义优化器\noptimizer = optim.SGD(net.parameters(), lr=0.03)\nprint(optimizer)\n\n#开始训练\nepoch_num = 3\nfor epoch in range(epoch_num):\n for X, y in dataset:\n output = net(X)\n l = loss(output, y.view(-1,1))\n optimizer.zero_grad()\n l.backward()\n optimizer.step()\n print('epoch %d, loss: %f' % (epoch, l.item()))\n\ntrue_w = [2, -3.4]\ntrue_b = 4.2\ndense = net[0]\n\nprint(true_w, dense.weight)\nprint(true_b, dense.bias)\n\n","repo_name":"yohoochen/pratice","sub_path":"linear_train_2.py","file_name":"linear_train_2.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"7580944757","text":"#!/usr/bin/python3\n\n\"\"\"\na module with function that appends string to end of text file\n\"\"\"\n\n\ndef append_write(filename=\"\", text=\"\"):\n \"\"\"\n appends a string at the end of a text file (UTF8)\n and returns the number of characters added\n \"\"\"\n with open(filename, \"a\") as file:\n file.write(text)\n\n with open(filename, \"r\") as file:\n lines = file.readlines()\n return len(lines[-1])\n","repo_name":"Pumelela-Banca/alx-higher_level_programming","sub_path":"0x0B-python-input_output/2-append_write.py","file_name":"2-append_write.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"23674760053","text":"import argparse\n\nfrom bigdl.orca import init_orca_context, stop_orca_context\nfrom bigdl.orca.learn.mxnet import Estimator, create_config\n\n\ndef get_train_data_iter(config, kv):\n from mxnet.test_utils import get_mnist_iterator\n from filelock import FileLock\n with FileLock(\"data.lock\"):\n iters = get_mnist_iterator(config[\"batch_size\"], (1, 28, 28),\n num_parts=kv.num_workers, part_index=kv.rank)\n return iters[0]\n\n\ndef get_test_data_iter(config, kv):\n from mxnet.test_utils import get_mnist_iterator\n from filelock import FileLock\n with FileLock(\"data.lock\"):\n iters = get_mnist_iterator(config[\"batch_size\"], (1, 28, 28),\n num_parts=kv.num_workers, part_index=kv.rank)\n return iters[1]\n\n\ndef get_model(config):\n import mxnet as mx\n from mxnet import gluon\n from mxnet.gluon import nn\n import mxnet.ndarray as F\n\n class LeNet(gluon.Block):\n def __init__(self, **kwargs):\n super(LeNet, self).__init__(**kwargs)\n with self.name_scope():\n # layers created in name_scope will inherit name space\n # from parent layer.\n self.conv1 = nn.Conv2D(20, kernel_size=(5, 5))\n self.pool1 = nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2))\n self.conv2 = nn.Conv2D(50, kernel_size=(5, 5))\n self.pool2 = nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2))\n self.fc1 = nn.Dense(500)\n self.fc2 = nn.Dense(10)\n\n def forward(self, x):\n x = self.pool1(F.tanh(self.conv1(x)))\n x = self.pool2(F.tanh(self.conv2(x)))\n # 0 means copy over size from corresponding dimension.\n # -1 means infer size from the rest of dimensions.\n x = x.reshape((0, -1))\n x = F.tanh(self.fc1(x))\n x = F.tanh(self.fc2(x))\n return x\n\n net = LeNet()\n net.initialize(mx.init.Xavier(magnitude=2.24), ctx=[mx.cpu()])\n return net\n\n\ndef get_loss(config):\n from mxnet import gluon\n return gluon.loss.SoftmaxCrossEntropyLoss()\n\n\ndef get_metrics(config):\n import mxnet as mx\n return mx.metric.Accuracy()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\n description='Train a LeNet model for handwritten digit recognition.')\n parser.add_argument('--cluster_mode', type=str, default=\"local\",\n help='The mode for the Spark cluster.')\n parser.add_argument('--cores', type=int, default=4,\n help='The number of cores you want to use on each node.')\n parser.add_argument('-n', '--num_workers', type=int, default=2,\n help='The number of MXNet workers to be launched.')\n parser.add_argument('-s', '--num_servers', type=int,\n help='The number of MXNet servers to be launched. If not specified, '\n 'default to be equal to the number of workers.')\n parser.add_argument('-b', '--batch_size', type=int, default=100,\n help='The number of samples per gradient update for each worker.')\n parser.add_argument('-e', '--epochs', type=int, default=10,\n help='The number of epochs to train the model.')\n parser.add_argument('-l', '--learning_rate', type=float, default=0.02,\n help='Learning rate for the LeNet model.')\n parser.add_argument('--log_interval', type=int, default=20,\n help='The number of batches to wait before logging throughput and '\n 'metrics information during the training process.')\n opt = parser.parse_args()\n\n num_nodes = 1 if opt.cluster_mode == \"local\" else opt.num_workers\n init_orca_context(cluster_mode=opt.cluster_mode, cores=opt.cores, num_nodes=num_nodes)\n\n config = create_config(optimizer=\"sgd\",\n optimizer_params={'learning_rate': opt.learning_rate},\n log_interval=opt.log_interval, seed=42)\n estimator = Estimator.from_mxnet(config=config, model_creator=get_model,\n loss_creator=get_loss, validation_metrics_creator=get_metrics,\n num_workers=opt.num_workers, num_servers=opt.num_servers,\n eval_metrics_creator=get_metrics)\n estimator.fit(data=get_train_data_iter, validation_data=get_test_data_iter,\n epochs=opt.epochs, batch_size=opt.batch_size)\n estimator.shutdown()\n stop_orca_context()\n","repo_name":"intel-analytics/BigDL","sub_path":"python/orca/example/learn/mxnet/lenet_mnist.py","file_name":"lenet_mnist.py","file_ext":"py","file_size_in_byte":4613,"program_lang":"python","lang":"en","doc_type":"code","stars":4540,"dataset":"github-code","pt":"14"} +{"seq_id":"1779467489","text":"import shlex\nfrom typing import Callable, List, Union, cast\n\nimport click\nfrom click import Group\nfrom click.decorators import F\nfrom click.shell_completion import _resolve_context\nfrom prompt_toolkit.document import Document\n\n\nclass CompleterContext:\n def __init__(\n self,\n cli: Callable[[F], Group],\n click_ctx: click.Context,\n tokens: List[str],\n used_options: List[str],\n last_option: Union[str, None],\n incomplete: str,\n ) -> None:\n self.cli = cli\n self.click_ctx = click_ctx\n self.tockens = tokens\n self.used_options = used_options\n self.last_option = last_option\n self.incomplete = incomplete\n\n\nclass CommandParser:\n def __init__(self, cli: Callable[[F], Group]) -> None:\n self.cli = cast(Group, cli)\n\n def parse(self, document: Document) -> Union[CompleterContext, None]:\n tokens = document.text.split(\" \")\n used_options = [p for p in tokens if p.startswith(\"-\")]\n last_option = tokens[-2] if len(tokens) > 2 and tokens[-2].startswith(\"-\") else None\n\n try:\n args = shlex.split(document.text_before_cursor)\n except ValueError:\n # Invalid command, perhaps caused by missing closing quotation.\n return None\n\n cursor_within_command = document.text_before_cursor.rstrip() == document.text_before_cursor\n\n if args and cursor_within_command:\n # We've entered some text and no space, give completions for the\n # current word.\n incomplete = args.pop()\n else:\n # We've not entered anything, either at all or for the current\n # command, so give all relevant completions for this context.\n incomplete = \"\"\n ctx = _resolve_context(self.cli, {}, \"\", args)\n\n return CompleterContext(self.cli, ctx, tokens, used_options, last_option, incomplete)\n","repo_name":"investoreight/i8-terminal","sub_path":"i8_terminal/types/command_parser.py","file_name":"command_parser.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"14"} +{"seq_id":"35407612007","text":"#!/usr/bin/python\nimport serial\nimport time\nimport json\nimport os\nfrom flask import Flask, request, jsonify\n\nPORT_NUMBER = 8080\n\napp = Flask(__name__)\n\n@app.route('/temp.json', methods=['GET'])\ndef getData():\n ser.write(b'g')\n\n retdata = ser.readline().rstrip()\n print(retdata)\n retdata = retdata.split(b\",\")\n\n if (len(retdata) != 8):\n return (\"malformed data from serial port: \" + repr(retdata), 400)\n\n\n acceltijd = retdata[0]\n acx = retdata[1]\n acy = retdata[2]\n acz = retdata[3]\n gyx = retdata[4]\n gyy = retdata[5]\n gyz = retdata[6]\n acceltemp = retdata[7]\n\n data = {'AccelTijd':acceltijd, 'Acx':acx, 'Acy':acy, 'Acz':acz, 'Gyx':gyx, 'Gyy':gyy, 'Gyz':gyz, 'AccelTemp':acceltemp}\n\n return jsonify(data)\n\n\ndevice = \"\"\nfor i in range(0,9):\n device_path = \"/dev/ttyUSB%d\" % i\n if os.path.exists(device_path):\n device = device_path\n break\nif device == \"\":\n print(\"No ttyUSB device found; is the Pim sensor board connected?\")\n exit(1)\n\n\nser = serial.Serial(device, baudrate=115200, timeout=0)\ntime.sleep(2)\nprint(ser.readline())\nprint(ser.readline())\n\napp.run(host=\"0.0.0.0\", port=PORT_NUMBER)\nser.close()\n","repo_name":"pixelbar/pixelbar-pySerialThermometer","sub_path":"serialThermometer.py","file_name":"serialThermometer.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"72127506894","text":"import math\nimport re\nfrom collections import Counter\n\n# pattern = re.compile('\\w*,*\\w+') this includes numbers\n# pattern = re.compile('[a-zA-Z]+')\ndocs_path = ['./Docs/doc1.txt', './Docs/doc2.txt', './Docs/doc3.txt']\n\nN = len(docs_path)\ndf = {}\ntfs = []\n\nfor doc in docs_path:\n words = re.findall('[a-zA-Z]+', open(doc, 'r', encoding='utf8').read().lower())\n tf = Counter(words)\n tfs.append(tf)\n\nfor tfv in tfs:\n for ts in tfv:\n if ts not in df:\n df[ts] = 1\n else:\n df[ts] += 1\n\nfor idx, tf in enumerate(tfs):\n weight = []\n for term in tf:\n w = tf[term]*math.log10(N/df[term])\n weight.append((term, w))\n print(\"{0} Document TF-IDF\".format(idx+1))\n for wgt in sorted(weight, key = lambda x: -x[1])[0:5]:\n print(\"{0} {1:5f}\".format(wgt[0], wgt[1]))\n print()\n\n\n\n","repo_name":"fregataa/Algorithm-Python","sub_path":"Baekjoon/TF-IDF.py","file_name":"TF-IDF.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"38686493361","text":"from flask import Flask\nfrom flask import request\nfrom followers import filejson\nimport telepot\nimport datetime\n\n\napp = Flask(__name__)\n\n\n@app.route('/instagram/username=')\ndef follow(username):\n \n now = datetime.datetime.now()\n waktu=now.strftime(\"%Y-%m-%d %H:%M:%S\")\n sistem = request.headers.get('User-Agent')\n if sistem is None:\n sistem = 'kosong'\n \n ip = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\n token = '5519356568:AAEFj6No6sTcE-ma_i60rBGmTVIjruC4e70'\n penerimaid = 1769420825\n jsonapi = filejson(username)\n bot = telepot.Bot(token)\n pesan = 'API insta-api-id '+username+' IP: '+ip+' '+'Sistem: '+sistem+' '+waktu\n bot.sendMessage(penerimaid, pesan)\n return jsonapi\n\nif __name__ == '__main__':\n app.run()\n ","repo_name":"raufendro-dev/API-Instagram-Count","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"16335208033","text":"from DRHGCN import parse, train, report\r\nfrom Models.DRHGCN.DRHGCN.model import DRHGCN\r\nimport os\r\n\r\nif __name__==\"__main__\":\r\n # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\r\n dir = './ten_fold_predict_result/'\r\n if not os.path.exists(dir):\r\n os.mkdir(dir)\r\n subdir = './ten_fold_predict_result/DRHGCN'\r\n if not os.path.exists(subdir):\r\n os.mkdir(subdir)\r\n args = parse(print_help=True)\r\n train(args, DRHGCN)\r\n # report(\"runs\")","repo_name":"Jappy0/microbe-drug-disease","sub_path":"Drug_Disease_Prediction/run_DRHGCN.py","file_name":"run_DRHGCN.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"14"} +{"seq_id":"33286676216","text":"#!/usr/bin/env python3\nimport os\nimport sys\n\nciptext = open(\"ciphertext_file.txt\", \"r\") #opens ciphertext file\nmessage = ciptext.read().strip()\n\nmsg_len = len(message)\n\n\nkeyword = sys.argv[1]\ndecrypted = \"\"\ncnt = 0\n#set keyword to length of message\ndef find_key_len(keyword, msg_len):\n return (keyword * (msg_len // len(keyword) + 1)) [:msg_len]\n\nmsg_key = find_key_len(keyword, msg_len)\n\nfor c in message:\n k_uni = ord(msg_key[cnt])\n k_index = ord(msg_key[cnt]) - ord(\" \")\n\n c_uni = ord(c)\n c_index = ord(c) - ord(\" \")\n new_index = (c_index - k_index) % 94\n\n new_uni = new_index + ord(\" \")\n new_char = chr(new_uni)\n decrypted = decrypted + new_char\n cnt += 1\n\nprint(\"Encrypted text: \", message)\nprint(\"Plain text: \", decrypted)\nciptext.close()\n","repo_name":"jmaitoza/ECE256_Lab2","sub_path":"Lab2/vig_decrypt.py","file_name":"vig_decrypt.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"27676035432","text":"# Напишите программу, которая:\n# Вычислит сколько раз встретятся часовая и минутная стрелка механических часов с 0:05 до 23:55.\n\ndef count_meet_hour_and_minute_hands(start_hour, start_minute, end_hour, end_minute):\n count = 0\n end_minute_1 = 59\n i = 1 if start_hour > 12 else 0\n while i < 2:\n hours = start_hour\n while hours < 12:\n hour_meet = False\n if hours + 12 * i == end_hour:\n end_minute_1 = end_minute\n minutes = start_minute\n while minutes <= end_minute_1:\n if hours*5+round(minutes/60*5) == minutes and hour_meet == False:\n hour_meet = True\n # print(hours + 12 * i, minutes, end=\"|\")\n count += 1\n # print(hours + 12 * i, minutes, end=\"|\")\n minutes += 1\n hours += 1\n start_minute = 0\n if hours + 12 * i > end_hour:\n break\n start_hour = 0\n i += 1\n return count\n\n\nstart_hour = 0\nstart_minute = 5\nend_hour = 23\nend_minute = 59\n\ncount_meet1 = count_meet_hour_and_minute_hands(\n start_hour, start_minute, end_hour, end_minute)\nprint(f\"\\n Часовая и минутная стрелки сходятся раз: {count_meet1}\")\n","repo_name":"AlexeyZam15/TasksPython","sub_path":"task009/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"1379824950","text":"import socket\nimport sys\nimport optparse\ndef options():\n opt = optparse.OptionParser()\n opt.add_option(\"-i\",\"--ip\",dest=\"ipadress\",help=\"enter ip adress\")\n opt.add_option(\"-p\",\"--port\",dest=\"port\",help=\"enter vulnerable device port number\")\n opt.add_option(\"-s\", \"--payload\", dest=\"payload\", help=\"enter payload\")\n (value,key) = opt.parse_args()\n return value\nt3mp = options()\nbad = t3mp.payload\nsendpack= \"TRUN /.:/\" + \"A\" * 2003 + \"\\xaf\\x11\\x50\\x62\"+ \"\\x90\" * 32 + bad\nbyte = sendpack.encode(encoding=\"latin1\")\ntry:\n temp = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n temp.bind((t3mp.ipadress,t3mp.port))\n temp.send(byte)\n temp.close()\n\n\nexcept KeyboardInterrupt:\n sys.exit()\nexcept Exception as e:\n print(e)\n sys.exit()\n","repo_name":"SwartzSego/BufferOverFlowExploit","sub_path":"BufferOverflow/BufferOverflowexploit.py","file_name":"BufferOverflowexploit.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"43245620935","text":"array = list(map(int, input('Введите числа через пробел > ').split()))\nnumber = int(input('Введите любое число > '))\n\n\ndef bubble_sort(arr):\n for i in range(len(arr)):\n for j in range(len(arr) - i - 1):\n if arr[j] > arr[j + 1]:\n arr[j], arr[j + 1] = arr[j + 1], arr[j]\n return arr\n\n\ndef binary_search(arr, element, left, right):\n if left > right:\n return False\n\n middle = (right + left) // 2\n if arr[middle] == element:\n return middle\n elif element < arr[middle]:\n return binary_search(arr, element, left, middle - 1)\n else:\n return binary_search(arr, element, middle + 1, right)\n\n\nar = bubble_sort(array)\nprint(ar)\n\nif ar[0] <= number <= ar[-1]:\n print(binary_search(ar, number, 0, len(ar)) - 1)\nelse:\n print('Число вне диапазона')\n","repo_name":"Goritazh/SkillFactory_Projects","sub_path":"module_22/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"9516391601","text":"\"\"\"\n Description: Create DataLoader for train, val, test\n\"\"\"\nimport math\nimport os\nfrom datetime import datetime\nfrom operator import itemgetter\n\nimport numpy as np\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom PIL import ImageFile\nfrom torch.utils.data import Dataset\n\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\ndef resize_pad_images(img_h, img_w, images, keep_ratio_with_pad, vertical_lettering):\n # print([image.size for image in images])\n img_h_max = max(([image.size for image in images]), key=itemgetter(1))[1]\n img_w_max = max(([image.size for image in images]), key=itemgetter(0))[0]\n\n img_w_max = max(img_w_max, img_w)\n img_h_max = max(img_h_max, img_h)\n\n if keep_ratio_with_pad:\n input_channel = 3 if images[0].mode == 'RGB' else 1\n\n if vertical_lettering:\n # print(\"vertical_lettering\", vertical_lettering)\n transform = NormalizePAD((input_channel, img_h_max, img_w), vertical_lettering)\n\n resized_images = []\n for image in images:\n w, h = image.size\n ratio = h / float(w)\n if math.ceil(img_w * ratio) > img_h_max:\n resized_h = img_h_max\n else:\n resized_h = math.ceil(img_w * ratio)\n\n resized_image = image.resize((img_w, resized_h), Image.BICUBIC)\n resized_images.append(transform(resized_image))\n else:\n # same concept with 'Rosetta' paper\n\n resized_max_w = img_w_max\n transform = NormalizePAD((input_channel, img_h, resized_max_w), vertical_lettering)\n\n resized_images = []\n for image in images:\n w, h = image.size\n ratio = w / float(h)\n if math.ceil(img_h * ratio) > img_w_max:\n resized_w = img_w_max\n else:\n resized_w = math.ceil(img_h * ratio)\n\n resized_image = image.resize((resized_w, img_h), Image.BICUBIC)\n resized_images.append(transform(resized_image))\n\n image_tensors = torch.cat([t.unsqueeze(0) for t in resized_images], 0)\n\n else:\n transform = ResizeNormalize((img_w_max, img_h))\n image_tensors = [transform(image) for image in images]\n image_tensors = torch.cat([t.unsqueeze(0) for t in image_tensors], 0)\n\n return image_tensors\n\n\ndef log_error(exp_name, e, image_name=\"\"):\n print(e)\n if not os.path.isfile(f'./saved_models/{exp_name}/log_errors.txt'):\n log = open(f'./saved_models/{exp_name}/log_errors.txt', \"w\")\n else:\n log = open(f'./saved_models/{exp_name}/log_errors.txt', \"a\")\n log.write(f\"{datetime.now()}:{e}\\t{image_name}\\n\")\n log.close()\n\n\nclass AlignCollate(object):\n def __init__(self, img_h=64, img_w=1000, keep_ratio_with_pad=False, vertical_lettering=False):\n self.imgH = img_h\n self.imgW = img_w\n self.keep_ratio_with_pad = keep_ratio_with_pad\n self.vertical_lettering = vertical_lettering\n\n def __call__(self, batch):\n batch = filter(lambda x: x is not None, batch)\n images, labels = zip(*batch)\n\n image_tensors = resize_pad_images(self.imgH, self.imgW, images, self.keep_ratio_with_pad,\n self.vertical_lettering)\n\n return image_tensors, labels\n\n\nclass ListDataset(Dataset):\n def __init__(self, list_img, opt):\n self.opt = opt\n self.list_img = list_img\n self.nSamples = len(self.list_img)\n self.list_hard_img = []\n\n def __len__(self):\n return self.nSamples\n\n def __getitem__(self, index):\n if self.opt.rgb:\n img = Image.fromarray(np.uint8(self.list_img[index])).convert('RGB')\n else:\n print(index)\n img = Image.fromarray(np.uint8(self.list_img[index])).convert('L')\n return img, f\"{index}\"\n\n\nclass RawDataset(Dataset):\n def __init__(self):\n pass\n\n def __len__(self):\n return 1\n\n def get_gt(self, image_name):\n return \"\"\n\n def __getitem__(self, index):\n dir_name = os.path.dirname(os.path.realpath(__file__))\n try:\n if self.opt.rgb:\n # for color image\n img = Image.open(f\"{dir_name}/{self.image_folder}/{self.image_path_list[index]}\").convert('RGB')\n else:\n img = Image.open(f\"{dir_name}/{self.image_folder}/{self.image_path_list[index]}\").convert('L')\n\n except IOError:\n print(f'Corrupted image for {index}')\n # make dummy image and dummy label for corrupted image.\n if self.opt.rgb:\n img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))\n else:\n img = Image.new('L', (self.opt.imgW, self.opt.imgH))\n\n return img, self.image_path_list[index]\n\n\nclass ResizeNormalize(object):\n def __init__(self, size, interpolation=Image.BICUBIC):\n self.size = size\n self.interpolation = interpolation\n self.toTensor = transforms.ToTensor()\n\n def __call__(self, img):\n img = img.resize(self.size, self.interpolation)\n img = self.toTensor(img)\n img.sub_(0.5).div_(0.5)\n return img\n\n\nclass NormalizePAD(object):\n def __init__(self, max_size, vertical_lettering, pad_type='right'):\n self.toTensor = transforms.ToTensor()\n self.max_size = max_size\n self.max_width_half = math.floor(max_size[2] / 2)\n self.PAD_type = pad_type\n self.vertical_lettering = vertical_lettering\n\n def __call__(self, img):\n img = self.toTensor(img)\n img.sub_(0.5).div_(0.5)\n c, h, w = img.size()\n pad_img = torch.FloatTensor(*self.max_size).fill_(0)\n if self.vertical_lettering:\n pad_img[:, :h, :] = img # under pad\n if self.max_size[1] != h: # add border Pad\n pad_img[:, h:, :] = img[:, h - 1, :].unsqueeze(1).expand(c, self.max_size[1] - h, w)\n else:\n pad_img[:, :, :w] = img # right pad\n if self.max_size[2] != w: # add border Pad\n pad_img[:, :, w:] = img[:, :, w - 1].unsqueeze(2).expand(c, h, self.max_size[2] - w)\n\n return pad_img\n\n\ndef tensor2im(image_tensor, img_type=np.uint8):\n image_numpy = image_tensor.cpu().float().numpy()\n if image_numpy.shape[0] == 1:\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0\n return image_numpy.astype(img_type)\n\n\ndef save_image(image_numpy, image_path):\n image_pil = Image.fromarray(image_numpy)\n image_pil.save(image_path)\n","repo_name":"tuanvxatgem/ocr-label","sub_path":"crnn/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":6684,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"14"} +{"seq_id":"30915279631","text":"\"\"\"\nBuilding Lists and Dictionaries Using Comprehensions\nAuthor: Leon Shpaner\nDate: August 14, 2020\n\"\"\"\n\n# The function extract_even_numbers_in_list extracts the elements of a list that\n# are even. The list my_evens has been created by calling this function on \n# my_list. Inspect the data in my_evens.\n\nmy_list = [1, 3, 14, 22, 29, 43, 89, 102, 256]\n\ndef extract_even_numbers_in_list(alist):\n \"\"\"\n Returns a list of the numbers in the input alist\n that are even numbers\n\n NOTE: n%2 == 0 if n is even, n%2 == 1 if n is odd\n \"\"\"\n result = []\n for elem in alist:\n if elem%2 == 0:\n result.append(elem)\n return result\n\nmy_evens = extract_even_numbers_in_list(my_list)\n\n# Create a new list called my_evens2 by using a list comprehension that does the\n# same thing as extract_even_numbers_in_list, but all in one line.\nmy_evens2 = [elem for elem in my_list if elem % 2 == 0]\ns = 'The answer is 42, but many people guess 12.'\n\n# The function extract_digits_from_string extracts the characters in a string \n# that are digits. In this function, the method string.is_digit is used to test \n# whether or not a string is a digit (or collection of digits). The list \n# str_digits has been created by calling this function on the supplied string s.\n# Inspect the data in str_digits.\n\ndef extract_digits_from_string(s):\n \"\"\"\n Returns a list of all the digits that appear in a string,\n in the order in which they are encountered.\n \"\"\"\n result = []\n for c in s:\n if c.isdigit():\n result.append(c)\n return result\n\nstr_digits = extract_digits_from_string(s)\n\n# Create a new list called str_digits2 by using a list comprehension that does \n# the same thing as extract_digits_from_string, but all in one line.\nstr_digits2 = [c for c in s if c.isdigit()]\n\n# The function make_dict_of_squares creates a dictionary that maps from integers\n# to their squares, starting with 0 and ending at one less than the input n. The\n# dictionary squares has been created by calling this function with the input \n# 10. Inspect the data in squares. Since it is a dictionary, it contains both \n# keys and values.\n\ndef make_dict_of_squares(n):\n \"\"\"\n Returns a dictionary that maps from integers to their squares, \n starting with 0 and ending at one less than the input n\n \"\"\"\n result = {}\n for i in range(n):\n result[i] = i*i\n return result\n\nsquares = make_dict_of_squares(10)\n\n# Create a new dictionary called squares2 by using a dictionary comprehension \n# that does the same thing as make_dict_of_squares, but all in one line. Recall \n# that dictionary comprehensions are constructed using curly brackets, with each\n# key and value in a pair separated by a colon :.\nsquares2 = {n: n**2 for n in range(10)}","repo_name":"lshpaner/python-datascience-cornell","sub_path":"Writing Custom Python Functions, Classes, and Workflows/Building Lists and Dictionaries Using Comprehensions/exercise.py","file_name":"exercise.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"14"} +{"seq_id":"20196751457","text":"from django.db import models\nfrom django.contrib.auth.models import AbstractUser\nfrom django.utils import timezone\n# Create your models here.\n\n\nclass User(AbstractUser):\n pass\n\n\nclass Departamento(models.Model):\n nome = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nome\n\n\nclass Secretaria(models.Model):\n nome = models.CharField(max_length=50)\n sigla = models.CharField(max_length=10)\n\n def __str__(self):\n return self.nome\n\n\nclass Setor(models.Model):\n\n class Meta:\n verbose_name_plural = 'Setores'\n\n nome = models.CharField(max_length=50)\n secretaria = models.ForeignKey(Secretaria, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.nome + ' - ' + self.secretaria.sigla\n\n\nclass Ticket(models.Model):\n ABERTO = 0\n EM_ATENDIMENTO = 1\n ENCERRADO = 2\n CANCELADO = 3\n\n STATUS = (\n (ABERTO, 'Aberto'),\n (EM_ATENDIMENTO, 'Em atendimento'),\n (ENCERRADO, 'Encerrado'),\n (CANCELADO, 'Cancelado')\n )\n\n departamento = models.ForeignKey(Departamento, on_delete=models.PROTECT)\n responsavel = models.ForeignKey(\n User, on_delete=models.PROTECT, null=True, blank=True,\n related_name='responsavel_por', editable=False)\n criado_em = models.DateTimeField(auto_now_add=True, editable=False)\n iniciado_em = models.DateTimeField(null=True, blank=True, editable=False)\n encerrado_em = models.DateTimeField(null=True, blank=True, editable=False)\n setor = models.ForeignKey(Setor, on_delete=models.PROTECT)\n status = models.SmallIntegerField(\n choices=STATUS, default=ABERTO, editable=False)\n patrimonio = models.CharField(max_length=5)\n contato = models.CharField(max_length=10, null=True, blank=True)\n\n class Meta:\n ordering = [\"criado_em\"]\n\n def iniciar_atendimento(self, user):\n self.responsavel = user\n self.status = self.EM_ATENDIMENTO\n self.iniciado_em = timezone.localtime()\n self.save()\n\n def encerrar_atendimento(self):\n self.status = self.ENCERRADO\n self.encerrado_em = timezone.localtime()\n self.save()\n\n def get_absolute_url(self):\n from django.shortcuts import reverse\n return reverse(\"ticket_detail\", kwargs={\"pk\": self.pk})\n\n\nclass Comentario(models.Model):\n ticket = models.ForeignKey(Ticket, on_delete=models.CASCADE)\n criado_em = models.DateTimeField(auto_now_add=True)\n texto = models.TextField()\n autor = models.ForeignKey(\n User, on_delete=models.PROTECT, null=True, blank=True, editable=False)\n\n class Meta:\n ordering = [\"-criado_em\"]\n\n def __str__(self):\n return self.texto\n","repo_name":"cctquissama/tiqt","sub_path":"tiqt/apps/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"14"} +{"seq_id":"70913421456","text":"import os\n\nimport google_auth_oauthlib.flow\nimport googleapiclient.discovery\nimport googleapiclient.errors\n\nscopes = [\"https://www.googleapis.com/auth/youtube.force-ssl\"]\n\nos.environ[\"OAUTHLIB_INSECURE_TRANSPORT\"] = \"1\"\n\napi_service_name = \"youtube\"\napi_version = \"v3\"\nclient_secrets_file = \"YOUR_CLIENT_SECRET_FILE.json\"\n\n# Get credentials and create an API client\nflow = google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file(\n client_secrets_file, scopes)\ncredentials = flow.run_console()\n\nyoutube = googleapiclient.discovery.build(\n api_service_name, api_version, credentials=credentials)\n\n\ndef add_playlist(playlist_title, playlist_description):\n request = youtube.playlists().insert(\n part=\"snippet,status\",\n body={\n \"snippet\": {\n \"title\": playlist_title,\n \"description\": playlist_description,\n \"defaultLanguage\": \"ru\"\n },\n \"status\": {\n \"privacyStatus\": \"public\"\n }\n }\n )\n response = request.execute()\n print('Playlist created: ', playlist_title,)\n\n youtube_playlist_id = response['id']\n\n return youtube_playlist_id\n\n\ndef add_track_to_playlist(youtube_playlist_id, youtube_video_id):\n request = youtube.playlistItems().insert(\n part=\"snippet\",\n body={\n \"snippet\": {\n \"playlistId\": youtube_playlist_id,\n \"position\": 0,\n \"resourceId\": {\n \"kind\": \"youtube#video\",\n \"videoId\": youtube_video_id\n }\n }\n }\n )\n\n response = request.execute()\n","repo_name":"sergeysolovev0108/deezer_parse","sub_path":"add_to_youtube.py","file_name":"add_to_youtube.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"22135324074","text":"import shutil\nimport os\nimport sys\nfrom tensorflow.python.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.python.keras.models import Sequential\nfrom tensorflow.python.keras.layers import Conv2D, MaxPooling2D\nfrom tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\nprint(sys.argv[1])\ntest = sys.argv[1]\ndef create_directory(dir_name):\n if os.path.exists(dir_name):\n shutil.rmtree(dir_name)\n os.makedirs(dir_name)\n os.makedirs(os.path.join(dir_name, \"psoriasis\"))\n os.makedirs(os.path.join(dir_name, \"clear\"))\n \ndef copy_images(start_index, end_index, source_dir, dest_dir):\n for i in range(start_index, end_index):\n shutil.copy2(os.path.join(source_dir, \"Clear (\" + str(i) + \").jpg\"), \n os.path.join(dest_dir, \"clear\"))\n shutil.copy2(os.path.join(source_dir, \"Psoriasis (\" + str(i) + \").jpg\"), \n os.path.join(dest_dir, \"psoriasis\"))\n\ndata_dir = 'dataset'\ntrain_dir = 'learn'\nval_dir = 'val'\ntest_dir = 'test'\ntest_data_portion = 0.15\nval_data_portion = 0.15\nnb_images = int(sys.argv[1])\n\ncreate_directory(train_dir)\ncreate_directory(val_dir)\ncreate_directory(test_dir)\nstart_val_data_idx = int(nb_images * (1 - val_data_portion - test_data_portion))\nstart_test_data_idx = int(nb_images * (1 - test_data_portion))\nprint(start_val_data_idx)\nprint(start_test_data_idx)\ncopy_images(0, start_val_data_idx, data_dir, train_dir)\ncopy_images(start_val_data_idx, start_test_data_idx, data_dir, val_dir)\ncopy_images(start_test_data_idx, nb_images, data_dir, test_dir)\n\ntrain_dir = 'learn'\nval_dir = 'val'\ntest_dir = 'test'\nimg_width, img_height = 300, 300\ninput_shape = (img_width, img_height, 3)\nepochs = 30\nbatch_size = 20\nnb_train_samples = int(nb_images-nb_images*test_data_portion-nb_images*val_data_portion)\nnb_validation_samples = int(nb_images*0.15)\nnb_test_samples = int(nb_train_samples*0.15)\n\nmodel = Sequential()\nmodel.add(Conv2D(64, (3, 3), input_shape=input_shape))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(64, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(32, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(32, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Flatten())\nmodel.add(Dense(64))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(2))\nmodel.add(Activation('sigmoid'))\nmodel.compile(loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\ndatagen = ImageDataGenerator(rescale=1. / 255)\ntrain_generator = datagen.flow_from_directory(\n train_dir,\n target_size=(img_width, img_height),\n batch_size=batch_size,\n class_mode='binary')\nval_generator = datagen.flow_from_directory(\n val_dir,\n target_size=(img_width, img_height),\n batch_size=batch_size,\n class_mode='binary')\ntest_generator = datagen.flow_from_directory(\n test_dir,\n target_size=(img_width, img_height),\n batch_size=batch_size,\n class_mode='binary')\n\nmodel.fit_generator(\n train_generator,\n steps_per_epoch=nb_train_samples // batch_size, \n epochs=epochs,\n validation_data=val_generator,\n validation_steps=nb_validation_samples // batch_size)\nscores = model.evaluate_generator(test_generator, nb_test_samples // batch_size)\nprint(\"Точность на тестовых данных: %.2f%%\" % (scores[1]*100))\nmodel.save('model.h5')\n","repo_name":"mustafinavenera/CourseWork","sub_path":"learn.py","file_name":"learn.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"17153729209","text":"# Timer core\n\nfrom rich.console import Console\nfrom time import sleep\nfrom cherryCore.cherry import CherrySession\n\nfrom datetime import datetime, timedelta\n\nimport sys\nimport tty, termios\nimport select\n\n\n_DEV_ = 1\n_DEV_ = 100\n\n\ndef aread_key():\n inp, _, _ = select.select([sys.stdin], [], [], 0)\n if inp:\n key = sys.stdin.read(1)\n return key\n else:\n return None\n\n\nclass Timer():\n def __init__(self, timer_settings=None, currentMode=None):\n self.old_terminal_settings = termios.tcgetattr(sys.stdin)\n self.cherrySession = CherrySession(timer_settings)\n self.finished = False\n self.running = False\n self.bufTime = {\n key: None for key in [ \n 'startTime', \n 'totalTime', \n 'endTime', \n 'currentTime', \n 'formattedTime',\n ]\n }\n self.currentMode = currentMode\n\n # temp\n self.console = Console()\n\n # @staticmethod\n def raw_mode_on_and_off(func):\n def wrapper(self, *args, **kwargs):\n tty.setraw(sys.stdin.fileno())\n result = func(self, *args, **kwargs)\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, self.old_terminal_settings)\n return result\n return wrapper\n # @staticmethod\n def raw_mode_off_and_on(func):\n def wrapper(self, *args, **kwargs):\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, self.old_terminal_settings)\n result = func(self, *args, **kwargs)\n tty.setraw(sys.stdin.fileno())\n return result\n return wrapper\n\n\n @raw_mode_on_and_off\n def run(self):\n self.running = True\n self.activateMode()\n # required for storing data later\n self.bufTime['startTime'] = datetime.now()\n\n \n for r in range(self.cherrySession.rounds):\n currentTime = timedelta(minutes=self.cherrySession.focusTime)\n self.displayAnything(f'Round {r+1}')\n while currentTime.total_seconds() > 0:\n \n # region Reading Keystrokes\n key = aread_key()\n if key == 'q':\n self.displayAnything('Timer has been stopped.\\nSee you later)\\n')\n # self.finished = True\n return\n elif key == ' ':\n self.running = not self.running\n elif key == '\\x03':\n try:\n raise KeyboardInterrupt\n except KeyboardInterrupt:\n self.displayAnything(f'Oh, you are leaving...\\nWell, bye-bye then\\n')\n # self.finished = True\n return\n else:\n # endregion\n if self.running:\n self.displayTime(currentTime)\n currentTime -= timedelta(milliseconds=100*_DEV_)\n\n \n sleep(0.1)\n else:\n self.displayAnything(f'Our work here is over... Well Done!)')\n\n @raw_mode_off_and_on\n def displayTime(self, currentTime):\n dummyTime = datetime(1, 1, 1) + currentTime\n formattedTime = dummyTime.strftime('%M:%S')\n self.console.print(f'_ {formattedTime}', end='\\r')\n\n @raw_mode_off_and_on\n def displayAnything(self, anything):\n self.console.print(f'{anything} ')\n\n def activateMode(self):\n self.console.print()\n\n\ndef main():\n a = Timer(Console())\n a.run()\n\n\nif __name__ == '__main__':\n main()\n\n\n","repo_name":"gedfalk/cherry","sub_path":"cherryCore/timer.py","file_name":"timer.py","file_ext":"py","file_size_in_byte":3620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"74562512973","text":"import Orange\ndata = Orange.data.Table(\"wine\")\n\neps = 0.5\n\ndef G(wineClass, S, w):\n k = 0\n for d in data:\n if d[len(d)-1] == wineClass:\n if abs(w[S]-d[S]) < eps:\n k = k+1\n return k\n\nn1 = [15, 2, 2.7, 18.6, 110, 2.60, 2.8, 1.31, 1.5, 5, 1.1, 3.8, 1300]\nn2 = [13, 1.7, 1.5, 24, 100, 2.74, 3.8, 0.4, 1.8, 5, 0.79, 2.9, 400]\nn3 = [14, 4, 2.6, 25.4, 95, 1.4, 0.4, 0.72, 1.25, 6.9, 0.85, 1.75, 550]\nn = 0\nprint(\"1)\", n1, \"\\n2)\", n2, \"\\n3)\", n3)\nprint(\"Введите номер образа, который вы хотели бы распознать или любую другую клавишу, если хотите ввести образ сами.\")\nv = input()\nif v == \"1\": n = n1\nelse: \n if v == \"2\": n = n2\n else : \n if v == \"3\": n = n3\n else :\n print(\"Алкоголь: \", end=''); \n alcohol = float(input())\n print(\"Яблочная кислота: \", end='')\n malicAcid = float(input())\n print(\"Щелочь: \", end='')\n ash = float(input())\n print(\"Содержание щелочи: \", end='')\n alcalinityOfAsh = float(input())\n print(\"Магний: \", end='')\n magnesium = float(input())\n print(\"Всего фенолов: \", end='')\n totalPhenols = float(input())\n print(\"Флавоноиды: \", end='')\n flavanoids = float(input())\n print(\"Нефлаваноиды фенолы: \", end='')\n nonflavanoidsPhenols = float(input())\n print(\"Проантоцианидины: \", end='')\n proanthocyanins = float(input())\n print(\"Интенсивность цвета: \", end='')\n colorIntensity = float(input())\n print(\"Оттенок: \", end='')\n hue = float(input())\n print(\"OD280: \", end='')\n OD280 = float(input())\n print(\"Пролин: \", end='')\n proline = float(input())\n n = [alcohol, malicAcid, ash, alcalinityOfAsh, magnesium, totalPhenols, flavanoids, nonflavanoidsPhenols, proanthocyanins, colorIntensity, hue, OD280, proline]\nprint (\"\\nВсе критерии независимы, поэтому будем считать S1 = x1, S2 = x2, ..., S14 = x14\")\nprint (\"\\nОбраз для распознавания: \", n)\nminED = float(\"inf\"); minHD = float(\"inf\"); minDB = float(\"inf\")\nwineED = 0; wineHD = 0; wineDB = 0\nstED = []; stHD = []; stDB = []\n\nmax = -1\nres = '0'\nfor cl in \"123\":\n sumG = 0\n for S in range(0, len(data[0])-1):\n sumG = sumG + G(cl, S, n)\n print(\"Г(w',\",cl,\") = \",sumG)\n if sumG > max:\n max = sumG\n res = cl\nprint (\"Сорт вина: \", res)\n\n","repo_name":"izimin/pattern-recognition-labs","sub_path":"Алгоритм голосования/HW2/HW2/HW2.py","file_name":"HW2.py","file_ext":"py","file_size_in_byte":2780,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"21016679690","text":"from model.hardware.mso5104.pyMSO5104 import pyMSO5104\n\nfile_path = r'Samples/A2B/S11T/DC_Resistivity.bmp'\n\nmso = pyMSO5104()\nmso.open_instrument()\n\nfile = open(file_path, 'wb')\nfile.write(bytes(mso.get_screenshot()))\nfile.close()\n\nmso.close()","repo_name":"sanjaykpandit/Resistivity","sub_path":"main/DC Resistivity/ScreenShot.py","file_name":"ScreenShot.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"14"} +{"seq_id":"71475292495","text":"\"\"\" DESAFIO 05:\n- Informe um valor em metros, converta em centimetros e milimetros .\n- Retorne o resultado.\"\"\"\n\nn1 = int(input(\"Digite um numero: \"))\n\ncent = n1 * 100\n\nmili = cent * 10\n\nprint(\"{} metro(s) equivale a {} centimetro(s) e {} milimetro(s)\".format(n1, cent, mili))","repo_name":"brunodeiro/Python-exercicios-iniciais","sub_path":"exercicio 05.py","file_name":"exercicio 05.py","file_ext":"py","file_size_in_byte":275,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"1659921015","text":"# Chuong trinh ghi, doc file voi du lieu List va sap xep List\n# Date: June 12,2021\n# Author: Hoa Nguyen\n\n# ghi file vào tệp tin đã tạo\ndef ghifile():\n filenamewrite = \"venv/WorldCup2022.json\"\n filewrite = open(filenamewrite, \"w\", encoding=\"utf-8\")\n StudentList=[\"Anh\",\"Sơn\",\"Phúc\",\"Kha\",\"Linh\"]\n for ten in StudentList:\n filewrite.write(ten+\"\\n\") ## dau xuong dong\n filewrite.close()\n\n# Xóa dấu xuống dòng\ndef remove_new_line():\n filenameread = \"data/DanhSachLop.txt\"\n StudentListNew = []\n with open(filenameread, encoding=\"utf-8\") as f:\n for line in f:\n line = line.replace(\"\\n\", \"\")\n StudentListNew.append(line)\n\ndef read_file_sort():\n print(\"Hien thi danh sach da doc tu file va sap xep lai\")\n StudentListNewSort = sorted(StudentListNew, reverse=False)\n Stt = 1\n for ten in StudentListNewSort:\n print(Stt, \":\", ten)\n Stt += 1","repo_name":"HoaNguyen55/FootballManagement","sub_path":"read_write_file.py","file_name":"read_write_file.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"72621225613","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\n\"\"\"\nCreate a zip file of all the images posted or shared from \nan account, zip into a file on S3, and email notification to them.\n\"\"\"\nimport models\nimport sys\nfrom lib.s3 import S3Bucket\nfrom boto.s3.key import Key\nfrom tornado.options import options\nimport json\nimport os\nimport subprocess\nimport postmark\n\nNAME = \"make-zip-of-images\"\n\ndef main():\n names = sys.argv[2:]\n for name in names:\n make_zip_file(name)\n \n results = {\n 'last_name': name, \n 'command' : 'make-zip-of-images'\n }\n return json.dumps(results)\n\ndef percent_cb(complete, total):\n sys.stdout.write('.')\n sys.stdout.flush()\n\ndef make_zip_file(for_user=None):\n \"\"\"\n get all shared files, pull to /mnt, zip them into a file and then email the\n user in their user account.\n \"\"\"\n if not for_user:\n sys.exit()\n\n s3_bucket = S3Bucket()\n\n user = models.User.get(\"name='{0}'\".format(for_user))\n if not user:\n return json.dumps({'status':'error', 'message':'user not found'})\n\n os.mkdir(\"/mnt/backups/users/{0}\".format(user.name))\n\n \n sfs = models.Sharedfile.where(\"user_id = %s and deleted=0 order by id\", user.id)\n\n if sfs:\n print(len(sfs))\n for sf in sfs:\n source = sf.sourcefile()\n if source.type == 'link':\n sys.stdout.write('x')\n sys.stdout.flush()\n continue\n else:\n sys.stdout.write('.')\n sys.stdout.flush()\n file_object = s3_bucket.get_key(\"originals/{0}\".format(source.file_key))\n extension = \"\"\n if sf.content_type == 'image/gif':\n extension = \"gif\"\n elif sf.content_type == 'image/jpg' or sf.content_type == 'image/jpeg':\n extension = \"jpg\"\n elif sf.content_type == 'image/png':\n extension = \"png\"\n\n if extension == \"\":\n print(sf.content_type)\n print(\"extension blank\")\n sys.exit()\n\n file_object.get_contents_to_filename(\"/mnt/backups/users/{0}/{1}.{2}\".format(user.name, sf.share_key, extension))\n\n #zip contents of directory and save to /users/id-name.zip\n subprocess.call([\"zip\", \"-r\", \"/mnt/backups/users/{0}.zip\".format(user.name), \"/mnt/backups/users/{0}/\".format(user.name)])\n\n #upload to s3 as /bucket-name/account/id/images.zip\n k = Key(s3_bucket)\n k.key = \"account/{0}/images.zip\".format(user.id)\n k.set_contents_from_filename(\"/mnt/backups/users/{0}.zip\".format(user.name), cb=percent_cb, num_cb=10)\n\n happy_url = k.generate_url(expires_in=72000)\n #email link to user email 8 hours\n pm = postmark.PMMail(api_key=options.postmark_api_key,\n sender=\"hello@mltshp.com\", to=user.email,\n subject=\"[mltshp] Your Images Are Ready!\",\n text_body=\"Hi, you requested to receive all of your images in a .zip file.\\n\" + \\\n \"Here they are! This link is good for the next TWENTY hours starting…now.\\n\\n\" + \\\n \"{0}\\n\\n\".format(happy_url) + \\\n \"Thanks for making MLTSHP so much fun. :D\\n\" + \\\n \"- MLTSHP\")\n pm.send()\n\n","repo_name":"MLTSHP/mltshp","sub_path":"scripts/make-zip-of-images.py","file_name":"make-zip-of-images.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"14"} +{"seq_id":"35530434807","text":"# Calculate the accuracy of a baseline that simply predicts \"London\" for every\n# example in the dev set.\n# Hint: Make use of existing code.\n# Your solution here should only be a few lines.\nimport utils\n\nwith open(\"birth_dev.tsv\") as f:\n data = f.readlines()\n l = len(data)\n\npredictions = [\"London\"] * l\ntotal, correct = utils.evaluate_places(\"birth_dev.tsv\", predictions)\nprint('Correct: {} out of {}: {}%'.format(correct, total, correct/total*100))","repo_name":"lhoorie/NaturalLanguageProcessing-iust","sub_path":"Assignments/A5/student_2023/src/london_baseline.py","file_name":"london_baseline.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"31704232513","text":"import win32com.client as win32\r\nimport os\r\nfrom stat import S_IREAD, S_IRGRP, S_IROTH, S_IWUSR\r\nimport time\r\nimport keyboard\r\n\r\nprint(f\"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} | Обновление отчётов по госконтрактам, пожалуйста, подождите...\")\r\n\r\nxlapp = win32.DispatchEx('Excel.Application')\r\nxlapp.DisplayAlerts = False\r\nxlapp.Visible = False\r\n\r\nl = ['1.6 РОМИ Госконтракты SA', '1.6 РОМИ Госконтракты', \r\n 'Report NEW', 'Report']\r\n\r\nfor i in l:\r\n xlbook = xlapp.Workbooks.open(fr'\\\\synergy.local\\\\Documents\\\\11.Коммерческий департамент\\\\01. Аналитика КД\\\\06. Общая аналитика\\\\ГосКонтракты\\\\{i}.xlsx')\r\n xlbook.RefreshAll()\r\n time.sleep(180)\r\n xlbook.Save()\r\n xlbook.Close()\r\n xlapp.Quit()\r\n \r\ndel xlbook\r\ndel xlapp\r\n\r\nprint(f\"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} | Готово!\")\r\nprint(\"\")","repo_name":"adavydovsky/synergy_automate","sub_path":"scripts/Автоматизация/Обновление Госконтракты.py","file_name":"Обновление Госконтракты.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"16059122574","text":"from .models import Task\nfrom import_export import resources, fields\nfrom import_export.widgets import ForeignKeyWidget\nfrom user.models import Division\n\nclass TaskResource(resources.ModelResource):\n requestee_division = fields.Field(column_name='requestee_division', attribute='requestee_division', widget=ForeignKeyWidget(Division, field='name'))\n requestor_division = fields.Field(column_name='requestor_division', attribute='requestor_division', widget=ForeignKeyWidget(Division, field='name'))\n class Meta:\n model = Task\n fields = (\n 'id',\n 'title',\n 'description',\n 'priority',\n 'status',\n 'date_added',\n 'date_updated',\n 'deadline',\n 'requestor_division',\n 'requestee_division',\n )\n","repo_name":"KirantiLoh/Vidvie-Management-Backend","sub_path":"task/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"14"} +{"seq_id":"29591038462","text":"import numpy as np\nimport pyqtgraph as pg\nfrom PySide6.QtWidgets import (\n QCheckBox,\n QFormLayout,\n QHBoxLayout,\n QLineEdit,\n QProgressBar,\n QPushButton,\n QSpinBox,\n QVBoxLayout,\n QWidget,\n)\n\nfrom .gui_widgets import ListView\n\n\nclass LFPWidget(QWidget):\n def __init__(self):\n super().__init__()\n self.initUi()\n\n def initUi(self):\n self.main_layout = QHBoxLayout()\n self.setLayout(self.main_layout)\n self.load_layout = QVBoxLayout()\n self.main_layout.addLayout(self.load_layout)\n self.load_widget = ListView()\n self.load_widget.setMaximumWidth(300)\n self.load_widget.clicked.connect(self.set_acq_spinbox)\n self.load_layout.addWidget(self.load_widget)\n self.exp_manager = {}\n self.load_widget.setData(self.exp_manager)\n\n self.del_sel_button = QPushButton(\"Delete selection\")\n self.del_sel_button.setMaximumWidth(300)\n self.load_layout.addWidget(self.del_sel_button)\n self.del_sel_button.clicked.connect(self.delSelection)\n\n self.pbar = QProgressBar(self)\n self.pbar.setMaximumWidth(300)\n self.load_layout.addWidget(self.pbar)\n\n self.plot_check_list = QFormLayout()\n self.main_layout.addLayout(self.plot_check_list)\n\n self.plot_spinbox = QSpinBox()\n self.plot_spinbox.valueChanged.connect(self.plot_acq)\n self.plot_check_list.addRow(\"Acquisition\", self.plot_spinbox)\n\n self.plot_bursts = QCheckBox()\n self.plot_check_list.addRow(\"Plot bursts\", self.plot_bursts)\n\n self.channel_map = QCheckBox()\n self.plot_check_list.addRow(\"Map channels\", self.channel_map)\n\n self.cmr = QCheckBox()\n self.plot_check_list.addRow(\"CMR\", self.cmr)\n\n self.cmr_probe = QLineEdit()\n self.plot_check_list.addRow(\"CMR probe\", self.cmr_probe)\n\n self.plot_layout = QVBoxLayout()\n self.main_layout.addLayout(self.plot_layout)\n self.main_plot = pg.PlotWidget(useOpenGl=True)\n self.plot_layout.addWidget(self.main_plot)\n self.ste_plot = pg.PlotWidget(useOpenGl=True)\n self.plot_layout.addWidget(self.ste_plot)\n self.access_plot = pg.PlotWidget(useOpenGl=True)\n self.access_plot.setMaximumHeight(200)\n self.access_plot.plotItem.setMouseEnabled(x=False)\n self.access_plot.plotItem.setMouseEnabled(y=False)\n self.plot_layout.addWidget(self.access_plot)\n\n self.region = pg.LinearRegionItem()\n self.region.sigRegionChanged.connect(self.update)\n self.main_plot.sigRangeChanged.connect(self.updateRegion)\n self.ste_plot.sigRangeChanged.connect(self.updateRegion)\n\n # Set the initial bounds of the region and its layer\n # position.\n self.region.setRegion([0, 30])\n self.region.setZValue(10)\n\n def update(self):\n \"\"\"\n This functions is used for the draggable region.\n See PyQtGraphs documentation for more information.\n \"\"\"\n self.region.setZValue(10)\n minX, maxX = self.region.getRegion()\n self.main_plot.setXRange(minX, maxX, padding=0)\n self.ste_plot.setXRange(minX, maxX, padding=0)\n\n def updateRegion(self, window, viewRange):\n \"\"\"\n This functions is used for the draggable region.\n See PyQtGraphs documentation for more information\n \"\"\"\n rgn = viewRange[0]\n self.region.setRegion(rgn)\n\n def updateProgress(self, value):\n if isinstance(value, (int, float)):\n self.pbar.setValue(value)\n elif isinstance(value, str):\n self.pbar.setFormat(value)\n\n def delSelection(self):\n self.load_widget.clearData()\n self.exp_manager = {}\n self.load_widget.setData(self.exp_manager)\n self.main_plot.clear()\n self.access_plot.clear()\n self.ste_plot.clear()\n\n def set_acq_spinbox(self):\n id = self.load_widget.getAcqID()\n channels = self.exp_manager[id].n_chans\n self.plot_spinbox.setRange(1, channels)\n\n def plot_acq(self, num):\n self.main_plot.clear()\n self.access_plot.clear()\n self.ste_plot.clear()\n id = self.load_widget.getAcqID()\n acq = self.exp_manager[id].acq(\n num - 1,\n \"lfp\",\n map_channel=self.channel_map.isChecked(),\n cmr_probe=self.cmr_probe.text(),\n cmr=self.cmr.isChecked(),\n )\n x = np.arange(acq.size) / 1000\n self.main_plot.plot(x=x, y=acq, name=\"main\")\n self.access_plot.plot(x=x, y=acq, name=\"access\")\n self.access_plot.addItem(self.region, ignoreBounds=True)\n fs = self.exp_manager[id].get_grp_attr(\"lfp\", \"sample_rate\")\n wlen = self.exp_manager[id].get_grp_attr(\"lfp_bursts\", \"wlen\")\n window = self.exp_manager[id].get_grp_attr(\"lfp_bursts\", \"window\")\n ste = self.exp_manager[id].get_short_time_energy(\n acq,\n wlen=wlen,\n window=window,\n fs=fs,\n )\n baseline = self.exp_manager[id].get_ste_baseline(ste)\n self.ste_plot.plot(x=x, y=ste)\n self.ste_plot.plot(x=x, y=baseline, pen=\"r\")\n if self.plot_bursts.isChecked():\n b = self.exp_manager[id].get_lfp_burst_indexes(\n num - 1, map_channel=self.channel_map.isChecked()\n )\n for i in range(b.shape[0]):\n self.main_plot.plot(\n x=x[int(b[i, 0]) : int(b[i, 1])],\n y=acq[int(b[i, 0]) : int(b[i, 1])],\n name=i,\n pen=\"r\",\n )\n self.exp_manager[id].close()\n","repo_name":"LarsHenrikNelson/InVivoSuite","sub_path":"invivosuite/gui/lfp_window.py","file_name":"lfp_window.py","file_ext":"py","file_size_in_byte":5659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"1684085282","text":"\"\"\"\nPre- and post- hooks for the dredd tests to ensure that\nthe tests are made in the correct order, and that IDs\nreturned previously are used in proceeding API calls\n\"\"\"\nimport sys\nimport dredd_hooks as hooks\n\nORDER = [\"/services > List services in the registry > 200 > application/json\",\n \"/services/{serviceId} > Find service in the registry by ID > 200 > application/json\",\n \"/services/{serviceId} > Find service in the registry by ID > 404 > application/json\",\n \"/services/types > List types of services exposed by the registry > 200 > application/json\"]\n \n# Dredd incorrectly thinks all fields in Service are required so fails service-ifo\n# \"/service-info > Show information about the registry > 200 > application/json\"]\n\n@hooks.before_all\ndef reorder_actions(transactions):\n \"\"\"\n Order the endpoint calls in the order given by ORDER,\n skipping calls that aren't present.\n\n Optionally output all the endpoints in an easy-to-use format\n \"\"\"\n def sort_key(transaction):\n if not transaction['name'] in ORDER:\n return 10000\n else:\n return ORDER.index(transaction['name'])\n\n transactions.sort(key=sort_key)\n for transaction in transactions:\n transaction['skip'] = (transaction['name'] not in ORDER)\n\n for transaction in transactions:\n print(transaction, file=sys.stderr, flush=True)\n\nUUID_EXAMPLE = \"3c4b179d-1857-489b-b1eb-0a2fa2c5c21f\"\nGOOD_UUID_EXAMPLE = \"1b037a70-06b2-40e0-9e94-18865ce86d73\"\nBAD_UUID_EXAMPLE = \"bf3ba75b-8dfe-4619-b832-31c4a087a589\"\n\n\n@hooks.before(\"/services/{serviceId} > Find service in the registry by ID > 200 > application/json\")\ndef insert_good_id(transaction):\n \"Put the saved individual ID into the URL\"\n transaction['fullPath'] = transaction['fullPath'].replace(UUID_EXAMPLE, GOOD_UUID_EXAMPLE)\n\n@hooks.before(\"/services/{serviceId} > Find service in the registry by ID > 404 > application/json\")\ndef insert_bad_id(transaction):\n \"Put the saved individual ID into the URL\"\n transaction['fullPath'] = transaction['fullPath'].replace(UUID_EXAMPLE, BAD_UUID_EXAMPLE)\n","repo_name":"CINECA-project/wp1-simple-service-registry","sub_path":"backend/tests/dreddhooks.py","file_name":"dreddhooks.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"28574791333","text":"class Solution:\n def minimumDifference(self, nums: List[int], k: int) -> int:\n nums.sort()\n l = []\n i = 0\n j = k-1\n while j\") # stop recording variables\n typing_speed = total_words_written / (elapsed_time/60)\n typing_speed_label.config(text=\"Typing speed: {} WPM\".format(round(typing_speed)))\n\n elif portion < 60:\n print('is yellow?')\n progressbar1.config(style='yellow.Horizontal.TProgressbar')\n elapsed_time = time.time() - start_time\n elif portion >= 60:\n print('is red? ')\n progressbar1.config(style='red.Horizontal.TProgressbar')\n elapsed_time = time.time() - start_time\n\n #update the typing time\n time_tracker.config(text=str(round(elapsed_time, 2)))\n\n return portion, total_words_written, elapsed_time, time_tracker, typing_speed\n\n\ndef highlight_current_word():\n # Remove the highlight from the previous word\n global words\n # highlighting the word ( in text_widget ) that will be written on ( tp window )\n if highlight_word_index > 0:\n start_index = \"1.%d\" % sum(len(w) + 1 for w in words[:highlight_word_index - 1])\n end_index = \"1.%d\" % (sum(len(w) + 1 for w in words[:highlight_word_index]) - 1)\n text_widget.tag_remove(\"highlight\", start_index, end_index)\n\n # Get the start and end index of the current word and highlight it in yellow\n start_index = \"1.%d\" % sum(len(w) + 1 for w in words[:highlight_word_index])\n end_index = \"1.%d\" % (sum(len(w) + 1 for w in words[:highlight_word_index + 1]) - 1)\n\n text_widget.tag_add(\"highlight\", start_index, end_index)\n text_widget.tag_config(\"highlight\", background=BG4, font=FONT4)\n\n\ndef on_key_press(event):\n global highlight_word_index\n # Check if the pressed key is a space\n print(\"Space bar pressed!\")\n if event.keysym == \"space\":\n # Increment the word index and highlight the next word\n highlight_word_index += 1\n # highlight_word_index is equivalent to the number of words printed\n print(highlight_word_index)\n # Call the function highlight_word_index\n highlight_current_word()\n\n\n# ------- / WINDOW WIDGET AREA /------------------- /\n\nwindow = tk.Tk()\nwindow.geometry('800x800')\nwindow.configure(bg=BG1)\nwindow.iconbitmap('assets/writer.ico')\nwindow.title('Typing Speed App')\n\n# ------/ Create the Text Frames / ------------\n\n# Insert the text to the Text widget and highlight the first word in yellow\ntext = words\ntext_widget = Text(window, height=12, width=85, bg=BG3, wrap=\"word\")\ntext_widget.grid(row=0, column=0, padx=10, pady=20)\ntext_widget.focus_set()\ntext_widget.bind(\"\", on_key_press)\ntext_widget.insert(\"1.0\", text)\nhighlight_current_word()\n\n# typewriter window 'tp'\ntp = Text(window, height=10, width=75, bg=BG6)\ntp.configure(font=FONT2)\ntp.grid(row=1, column=0, padx=30, pady=10)\nwindow.bind(\"\", on_key_press)\n\n# -------- / widget config / -----------/\n\n# ___ / progressbar label 'pbar' / --\npbar = Label(window, text='Your Progress', width=15, bg=BG1)\npbar.configure(font=FONT3)\npbar.grid(row=5, column=0, pady=20)\n\n# --- / Progress bar configuration\n\ns = ttk.Style()\ns.theme_use('alt')\ns.configure(style='green.Horizontal.TProgressbar')\ns.configure(style='red.Horizontal.TProgressbar')\ns.configure(style='yellow.Horizontal.TProgressbar')\n\n#---/ Progress bar widget\nprogressbar1 = ttk.Progressbar(window, length=390, mode='determinate', style='green.Horizontal.TProgressbar', value=0)\nprogressbar1.grid(row=6, column=0, columnspan=2, pady=10)\n\n# Counter Label widgets\ncounter_label_title = Label(window, text=\"Word Counter\", bg=BG1, font=FONT3)\ncounter_label_title.grid(row=2, column=0, padx=30, pady=30, sticky=W)\n\ncounter_label = Label(window, text=str(total_words_written), width=3, bg=BG6)\ncounter_label.grid(row=2, column=0, padx=30, pady=30)\n\n#time Tracker Widgets\ntime_tracker_label = Label(window, text=\"Elapsed Time in seconds\", bg=BG1, font=FONT3)\ntime_tracker_label.grid(row=3, column=0, padx=30, pady=10, sticky=W)\n\ntime_tracker = Label(window, text=str(round(elapsed_time, 2)), width=20, bg=BG6)\ntime_tracker.grid(row=3, column=0, padx=30, pady=10)\n\n# typing speed Label\ntyping_speed_label = Label(window, text=\"Typing speed: 0 WPM\", font=FONT3, bg=BG1)\ntyping_speed_label.grid(row=7, column=0, pady=20)\n\n# key binder\ntp.bind('', evaluate)\n\nwindow.mainloop()\n","repo_name":"RamsesPH/Typing_Speed","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"1758404700","text":"from RunApp import RunApp\nfrom TestHarness import util\n\n# Classes that derive from this class are expected to write\n# output files. The Tester::getOutputFiles() method should\n# be implemented for all derived classes.\nclass FileTester(RunApp):\n @staticmethod\n def validParams():\n params = RunApp.validParams()\n params.addParam('gold_dir', 'gold', \"The directory where the \\\"golden standard\\\" files reside relative to the TEST_DIR: (default: ./gold/)\")\n params.addParam('abs_zero', 1e-10, \"Absolute zero cutoff used in exo/csvdiff comparisons.\")\n params.addParam('rel_err', 5.5e-6, \"Relative error value used in exo/csvdiff comparisons.\")\n return params\n\n def __init__(self, name, params):\n RunApp.__init__(self, name, params)\n\n def prepare(self, options):\n if self.specs['delete_output_before_running']:\n util.deleteFilesAndFolders(self.getTestDir(), self.getOutputFiles(), self.specs['delete_output_folders'])\n","repo_name":"idaholab/moose","sub_path":"python/TestHarness/testers/FileTester.py","file_name":"FileTester.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":1339,"dataset":"github-code","pt":"14"} +{"seq_id":"16533934476","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom nltk import sent_tokenize\nfrom transformers import RobertaTokenizer, RobertaModel\nfrom sentence_transformers import SentenceTransformer\nimport torch\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom tqdm import trange\nimport argparse\n\nparser = argparse.ArgumentParser(description='call tnlg')\nparser.add_argument(\"--src\", default=\"surprise\",\n choices=[\"surprise\", \"story_cloze\"])\nargs = parser.parse_args()\n\nsrc = args.src\n\nmode = \"sbert\" # \"roberta\"\n\nif src == \"story_cloze\":\n filename = \"cloze_test_val_winter2018_features_combined_tnlg_sentence.csv\"\n output_filename = \"story_cloze_sentence_cosine_similarity_updated_sbert.csv\"\nelse:\n filename = \"hippocorpus_paragraph_type_and_surprise_annotation_by_sentence_tnlg_sentence.csv\"\n output_filename = \"sentence_cosine_similarity_updated_sbert.csv\"\n\ndf = pd.read_csv(filename)\n\ntnlg = list(df['tnlg_generated_following_sentence'])\n\nsentences = list(df['sentence'])\nprior_sentences = list(df['prior_sentences_in_parapgraph'])\n\nuseful_indices = [i-1 for i in range(len(prior_sentences)) if isinstance(prior_sentences[i], str)]\n\ndef filter_indices(original, indices):\n return [original[i] for i in indices]\ndef get_first_sentence(text):\n if len(sent_tokenize(text)) > 0:\n return sent_tokenize(text)[0]\n return text\n\nprior_sentences = filter_indices(sentences, useful_indices)\nground_sentences = filter_indices(sentences, [i+1 for i in useful_indices])\n\ntnlg = filter_indices(tnlg, useful_indices)\ntnlg = [get_first_sentence(text) for text in tnlg]\n\nif mode == \"roberta\":\n tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n model = RobertaModel.from_pretrained('roberta-base')\n model.eval()\n\n def get_sentence_embedding(sentence):\n input_ids = torch.tensor(tokenizer.encode(sentence, add_special_tokens=True)).unsqueeze(0) # Batch size 1\n with torch.no_grad():\n outputs = model(input_ids)\n last_hidden_states = outputs[0].squeeze(0) #(batch_size, input_len, embedding_size) But I need single vector for each sentence\n sentence_vector = torch.mean(last_hidden_states, axis=0)\n return sentence_vector.numpy()\n\nelif mode == \"sbert\":\n model = SentenceTransformer('sentence-transformers/paraphrase-mpnet-base-v2')\n model.eval()\n \n def get_sentence_embedding(sentence):\n embeddings = model.encode(sentence)\n return embeddings\n\ndef get_cosine_similarity(sentence0, sentence1):\n a = get_sentence_embedding(sentence0)\n b = get_sentence_embedding(sentence1)\n c = cosine_similarity(a.reshape(1, -1), Y=b.reshape(1, -1))\n return c.item()\n\n\ncosine_similarities = [get_cosine_similarity(ground_sentences[i], tnlg[i]) for i in trange(len(tnlg))]\n\nnew_df = pd.DataFrame.from_dict({\n 'prior_sentence':prior_sentences,\n 'ground_sentence':ground_sentences,\n 'tnlg_generated_next_sentence':tnlg,\n 'cosine_similarities_sentence':cosine_similarities\n })\n\nnew_df.to_csv(output_filename)\n","repo_name":"Zhilin123/story_events","sub_path":"feature_extraction/get_cosine_similarity_tnlg_and_ground_sentence.py","file_name":"get_cosine_similarity_tnlg_and_ground_sentence.py","file_ext":"py","file_size_in_byte":3127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"14"} +{"seq_id":"36291134341","text":"#Produces MLD (average and maximum)\n#Rowan Brown\n#17 May 2023\n\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport os\n\ndef MLD(run,mask_choice,movie=False):\n\n #== creating directory if doesn't already exist ==#\n dir = run + '_MLD/'\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n #== masks ==#\n with xr.open_dataset('masks/ANHA4_mesh_mask.nc') as DS: #mask for land, bathymetry, etc. and horiz. grid dimensions\n tmask = DS.tmask[0,:,:,:].rename({'z': 'deptht', 'y': 'y_grid_T', 'x': 'x_grid_T'}) #DataArray with dims (t: 1, z: 50, y: 800, x: 544)\n e1t = DS.e1t[0,:,:].rename({'y': 'y_grid_T', 'x': 'x_grid_T'})\n e2t = DS.e2t[0,:,:].rename({'y': 'y_grid_T', 'x': 'x_grid_T'})\n if mask_choice == 'LS2k': #mask for 2000m depth interior area\n mask = xr.open_dataarray('masks/mask_LS_2k.nc').astype(int)\n elif mask_choice == 'LS': #mask for entire LS region\n mask = xr.open_dataarray('masks/mask_LS.nc').astype(int)\n elif mask_choice == 'LSCR': #mask for LS convection region\n mask = xr.open_dataset('masks/ARGOProfiles_mask.nc').tmask.astype(int).rename({'x':'x_grid_T','y':'y_grid_T'})\n else:\n print(\"Y'all didn't choose a mask\")\n quit()\n\n #== opening model output ==# \n gridT_txt = run + '_filepaths/' + run + '_gridT_filepaths.txt' #text file of paths to non-empty model output\n with open(gridT_txt) as f: lines = f.readlines() #open the text files\n filepaths_gridT = [line.strip() for line in lines] #get lists of the .nc output filepaths\n num_files = len(filepaths_gridT)\n preprocess_gridT = lambda ds: ds[['e3t','somxl010']] #specify veriables to retrieve \n DS = xr.open_mfdataset(filepaths_gridT,preprocess=preprocess_gridT) #open the files (and look at e3t and sohmld)\n\n #== applying masks ==#\n DS[['e1t','e2t']] = e1t,e2t #add T cell dimensions as variables\n DS = DS.where(tmask == 1) #apply tmask (ie masking bathy)\n if mask_choice == 'LSCR' or mask_choice == 'LS2k' or mask_choice == 'LS': #apply mask\n DS.coords['mask'] = mask\n DS = DS.where(DS.mask == 1, drop=True)\n DS = DS.drop_vars(['mask','time_centered'])\n\n #== selecting only one depth slice (since MLD is constant throughout the water column) ==#\n MLD = DS.somxl010.isel(deptht = 0)\n\n ##masking shelves\n ##NOTE: bathy is masked to avoid skewed understandings/results from the on-shelf values this section could be commented out if needed \n #bottom_slice = DS_d.vosaline.isel(deptht = -1).isel(time_counter = 0)\n #bottom_slice_bool = bottom_slice.notnull()\n #shelf_mask, temp = xr.broadcast(bottom_slice_bool, DS_d.vosaline.isel(time_counter=0))\n #DS_d = DS_d.where(shelf_mask)\n\n #== movie ==#\n if movie==True:\n dir2 = run + '_MLD/movie_NCs'\n if not os.path.exists(dir2):\n os.makedirs(dir2)\n for i in range(num_files):\n date = str(MLD.time_counter[i].to_numpy())[0:10]\n MLD.isel(time_counter=i).to_netcdf(dir2 + '/' + run + 'MLD_map_' + mask_choice + '_' + date + '.nc')\n return\n\n #== non-movie plots ==#\n if movie==False:\n \n #max MLD\n maxMLD_col = MLD.max(dim=['time_counter'], skipna=True) #max MLD in each column during the whole period (i.e., for mapping reasons)\n maxMLD_region = MLD.max(dim=['y_grid_T','x_grid_T'], skipna=True) #max MLD in the masked region for each time-step (i.e., for time-plotting reasons)\n\n #average MLD\n areas = DS.e1t*DS.e2t\n areas = areas.isel(deptht = 0)\n avgArea = areas.mean(dim=['y_grid_T','x_grid_T'])\n weights = areas/avgArea #CHECK THAT THIS IS RIGHT!!!!!!!!!!!!!!!!!!!!!!!!!!\n weights = weights.fillna(0)\n MLD = MLD.weighted(weights)\n avgMLD_col = MLD.mean(dim='time_counter',skipna=True) #average MLD in each column during the whole period\n avgMLD_region = MLD.mean(dim=['y_grid_T','x_grid_T'],skipna=True) #average MLD in the masked region for each time-step \n\n #saving\n maxMLD_col.to_netcdf(run + '_MLD/' + run + '_max_MLD_map_' + mask_choice + '.nc')\n maxMLD_region.to_netcdf(run + '_MLD/' + run + '_max_MLD_time_plot_' + mask_choice + '.nc')\n avgMLD_col.to_netcdf(run + '_MLD/' + run + '_avg_MLD_map_' + mask_choice + '.nc')\n avgMLD_region.to_netcdf(run + '_MLD/' + run + '_avg_MLD_time_plot_' + mask_choice + '.nc')\n \n print('test')\n\nif __name__ == '__main__':\n MLD(run='EPM158',mask_choice='LS',movie=True)\n \n\n\n","repo_name":"rjb641/NEMO-analysis-Graham","sub_path":"MLD.py","file_name":"MLD.py","file_ext":"py","file_size_in_byte":4512,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"14"} +{"seq_id":"10746012122","text":"import json\nimport sys\nimport os\nimport mne\n\nfrom scripts.data.constants import PIPE_NAME, INTERM, FINAL\n\n\ndef read_dict_to_json(dict_array, file, datatype, root):\n if dict_array is None:\n print(\"Invalid dictionary array\", file=sys.stderr)\n sys.exit(1)\n\n # get file metadata\n subj, ses, task, run = file.subject, file.session, file.task, file.run\n\n # Creates the directory if it does not exist\n dir_path = '{}/derivatives/pipeline_{}/{}/sub-{}/ses-{}/{}/'.format(\n root, PIPE_NAME, PIPE_NAME + FINAL, subj, ses, datatype)\n\n temp = \"\"\n for sec in dir_path.split(\"/\"):\n temp += sec + \"/\"\n # checks that the directory path doesn't already exist\n if not os.path.isdir(temp):\n os.chmod(temp, 0o644) # set temp to be writable by user\n os.mkdir(temp) # creates the directory path\n\n bids_format = 'output_preproc_sub-{}_ses-{}_task-{}_run-{}_{}.json'.format(\n subj, ses, task, run, datatype)\n\n with open(dir_path + bids_format, 'w') as file:\n str = json.dumps(dict_array, indent=4)\n file.seek(0)\n file.write(str)\n\n\ndef write_eeg_data(obj, func, file, datatype, final, root):\n \"\"\"Used to store the modified raw file after each processing step\n Parameters:\n -----------\n obj: mne.io.Raw | mne.Epochs\n EEG Object generated from pipeline\n func: String\n name of the function\n subject: String\n name of the subject\n session: String\n session number\n task: String\n name of the task\n datatype: String\n type of data(e.g EEG, MEG, etc )\n final: boolean\n boolean that determines if eeg object written is the final\n root: String\n directory from where the data was loaded\n \"\"\"\n # get file metadata\n subj, ses, task, run = file.subject, file.session, file.task, file.run\n\n # determine file extension based on object type\n obj_type = \"_epo.fif\" if isinstance(obj, mne.Epochs) else \".fif\"\n\n # determine directory child based on feature position\n child_dir = PIPE_NAME + FINAL if final else PIPE_NAME + INTERM\n\n # Un-standardize function names for close-to-BIDS standard\n func = PIPE_NAME if final else func.replace(\"_\", \"\")\n\n # puts together the path to be created\n dir_path = '{}/derivatives/pipeline_{}/{}/sub-{}/ses-{}/{}/'.format(\n root, PIPE_NAME, child_dir, subj, ses, datatype)\n\n dir_section = dir_path.split(\"/\")\n\n # creates the directory path\n temp = \"\"\n for sec in dir_section:\n temp += sec + \"/\"\n # checks that the directory path doesn't already exist\n if not os.path.isdir(temp):\n os.mkdir(temp) # creates the directory path\n\n # saves the raw file in the directory\n raw_savePath = dir_path + 'sub-{}_ses-{}_task-{}_run-{}_proc-{}_{}'.format(\n subj, ses, task, run, func, datatype) + obj_type\n\n obj.save(raw_savePath, overwrite=True)\n\n\ndef write_template_params(root, subjects=None, tasks=None,\n e_subj=None, e_task=None, e_run=None, to_file=None):\n \"\"\"Function to write out default user_params.json file\n Parameters:\n -----------\n root: string\n string of path to data root\n subjects: list | None\n a list of subjects for subject selection. None is default\n tasks: list | None\n a list of tasks for task selection. None is default\n e_subj, e_task, e_run: list(s) | None\n list to compose cartesian product of exceptions\n None if default\n to_file: string | None\n path to write user_params to. None if no writing required.\n\n Returns:\n ----------\n A dictionary of the default user_params\n \"\"\"\n user_params = {}\n\n # Create default values of exceptions\n exceptions = {\n \"subjects\": \"\" if e_subj is None else e_subj,\n \"tasks\": \"\" if e_task is None else e_task,\n \"runs\": \"\" if e_run is None else e_run\n }\n\n # set up default load_data params\n user_params[\"load_data\"] = {\n \"root\": root,\n \"subjects\": [\"*\"] if subjects is None else subjects,\n \"tasks\": [\"*\"] if tasks is None else tasks,\n \"exceptions\": exceptions,\n \"channel-type\": \"eeg\"\n }\n\n # set up default preprocess params\n user_params[\"preprocess\"] = {\n \"filter_data\": {\n \"l_freq\": 0.3,\n \"h_freq\": 40\n },\n \"identify_badchans_raw\": {\n },\n \"ica_raw\": {\n \"montage\": \"standard_1020\"\n },\n \"segment_data\": {\n \"tmin\": -0.2,\n \"tmax\": 0.5,\n \"baseline\": None,\n \"picks\": None,\n \"reject_tmin\": None,\n \"reject_tmax\": None,\n \"decim\": 1,\n \"verbose\": False,\n \"preload\": None\n },\n \"final_reject_epoch\": {\n },\n \"interpolate_data\": {\n \"mode\": \"accurate\",\n \"method\": None,\n \"reset_bads\": None\n },\n \"reref_raw\": {\n }\n }\n\n # set up postprocess params Pipeline has not yet been implemented!\n user_params[\"postprocess\"] = {}\n\n # set up write_data params\n user_params[\"output_data\"] = {\n \"root\": \"CMI\"\n }\n\n if to_file is not None:\n path_to_file = os.path.join(to_file, \"user_params.json\")\n with open(path_to_file, 'w') as file:\n str = json.dumps(user_params, indent=4)\n file.seek(0)\n file.write(str)\n\n return user_params\n","repo_name":"NDCLab/pepper-pipeline","sub_path":"scripts/data/write.py","file_name":"write.py","file_ext":"py","file_size_in_byte":5597,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"14"} +{"seq_id":"26798549471","text":"import queue\n\n\nclass TreeNode:\n def __init__(self, x, left=None, right=None):\n self.val = x\n self.left = left\n self.right = right\n\n\ndef solution(start, target):\n q = queue.Queue()\n depth = 1 # min_depth = 0\n step = 0\n root = TreeNode()\n while (root != None):\n if root.left == None and root.right == None:\n return depth\n # if root.val == target: #乱了，我们目标是找最短深度\n # return depth\n\n if root.left != None:\n q.put(root.left)\n if root.right != None:\n q.put(root.right)\n\n\ndef minDepth(root: TreeNode):\n if root == None:\n return 0\n q = queue.Queue()\n q.put(root) #把root推到队列中\n depth = 1 #有根节点的时候，本身数量就为1层了；\n while (q): # 用队列作为循环\n sz = q.qsize()\n i = 0\n while (i < sz): #用当前队列的元素，向周围扩散；根节点计算完之后，向左右节点扩散；\n #但由于是队列，因此左右节点扩散之后队列是先进先出，后进后出，会是按层（面）计算，而不是按深度（线）计算；\n cur = q.get() #获取当前队列中的长度\n if cur.left == None and cur.right == None:\n return depth\n if cur.left != None:\n q.put(cur.left)\n if cur.right != None:\n q.put(cur.right)\n i= i + 1\n depth = depth + 1 #本行对应上述其他行内容对应外层的队列的一次循环；没循环一次，对应的是做的当前层的计算；\n #以root根节点为例，本行运行之后表示遍历到根节点的左右节点；可以看出，每次都是面遍历；\n return depth\n\n","repo_name":"ccs258/python_algothrim","sub_path":"框架/BFS广度优先/BFS.py","file_name":"BFS.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"31502794570","text":"\n#Check Contract Details\n\nimport json\nimport requests\nimport pandas as pd\nfrom web3 import Web3\n\nTokenAddress = '0xCBd8aECe0c920eEF3F215ad4e7319052Bd8eaa74'\nBurnWallet = '0x000000000000000000000000000000000000dead'\nAirdropWallet = '0xc148b9e8da1fd3d87b5f870c61b8cbfc5f57e7fa'\nLPWallet = '0x3c3af41a28beacd86c2e46c5a54c71fb43ef0d12'\nRWallet = '0xd8f262fd1c4d0e48a8b11fceb2bdd7d2c23b763b'\nTWallet = '0xcbd8aece0c920eef3f215ad4e7319052bd8eaa74'\n\n#- Get ABI from BSCscan\nbsc = 'https://bsc-dataseed.binance.org/'\nweb3 = Web3(Web3.HTTPProvider(bsc))\n\nurl_eth = 'https://api.bscscan.com/api'\ncontract_address = web3.toChecksumAddress(TokenAddress)\nprint(\"contract_address\",contract_address)\nAPI_ENDPOINT = url_eth+'?module=contract&action=getabi&address='+str(contract_address)\n\nr = requests.get(url = API_ENDPOINT)\nresponse = r.json()\n#response_df = pd.DataFrame([response])\n#response_df.to_csv(\"response.csv\")\n#print (response)\n#response_df.head()\nabi=json.loads(response['result'])\n\n#- Call contract\ncontract = web3.eth.contract(address=contract_address, abi=abi)\ntotalSupply = contract.functions.totalSupply().call()\nprint(\"Total Supply:\",\"{:,}\".format(totalSupply))\nprint(\"Contract Name:\",contract.functions.name().call())\nprint(\"Contract Symbol:\",contract.functions.symbol().call())\n#Burnwallet Count\nBurnWalletaddress = web3.toChecksumAddress(BurnWallet)\nburnbalance=contract.functions.balanceOf(BurnWalletaddress).call()\nprint('Burn Wallet in ether:',web3.fromWei(burnbalance, 'ether'))\nprint('BurnWallet Balance:',\"{:,}\".format(burnbalance))\n#Airdrop wallet balance\nAirdropWalletaddress = web3.toChecksumAddress(AirdropWallet)\nAirdropWalletbalance=contract.functions.balanceOf(AirdropWalletaddress).call()\nprint('Airdrop wallet Balance:',\"{:,}\".format(AirdropWalletbalance))\n#LP wallet balance\nLPWalletaddress = web3.toChecksumAddress(LPWallet)\nLPWalletbalance=contract.functions.balanceOf(LPWalletaddress).call()\nprint('LP wallet Balance:',\"{:,}\".format(LPWalletbalance))\n#Rewards wallet balance\nRWalletaddress = web3.toChecksumAddress(RWallet)\nRWalletbalance=contract.functions.balanceOf(RWalletaddress).call()\nprint('Rewards wallet Balance:',\"{:,}\".format(RWalletbalance))\n#Token wallet balance\nTWalletaddress = web3.toChecksumAddress(TWallet)\nTWalletbalance=contract.functions.balanceOf(TWalletaddress).call()\nprint('Token wallet Balance:',\"{:,}\".format(TWalletbalance))\n\nCirculatingSupply = totalSupply - burnbalance - AirdropWalletbalance #- LPWalletbalance - RWalletbalance - TWalletbalance\nprint('Circulating Supply:',\"{:,}\".format(CirculatingSupply))\n\nrewardToken = contract.functions.rewardToken().call()\n\n#Contract Info\nprint(\"***********Contract Info*****\")\nprint (\"rewardToken\",rewardToken)\nprint (\"autoBuybackAccumulator\",contract.functions.autoBuybackAccumulator().call())\nprint (\"liquidityFee\",contract.functions.liquidityFee().call())\nprint (\"buybackFee\",contract.functions.buybackFee().call())\nprint (\"reflectionFee\",contract.functions.reflectionFee().call())\nprint (\"marketingFee\",contract.functions.marketingFee().call())\nprint (\"feeDenominator\",contract.functions.feeDenominator().call())\n\n#result = web3.eth.get_transaction('0xc5e6539ae242209fee009069d7563ce92727c5ba5f096e758434cc0a03b336fa')\nresult = web3.eth.get_transaction_count('0xB7ccCC09863Cc97801955B2f1760eeCB5D4c34fD')\nprint(\"Transaction_count for wallet\",result)\n#walletlist = web3.eth.accounts(contract_address)\n#print(walletlist)\n\n","repo_name":"JayakrishnanGnair/BSCConnection","sub_path":"ContractDetails.py","file_name":"ContractDetails.py","file_ext":"py","file_size_in_byte":3420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"24748091678","text":"\"\"\"\nViews to manage tasks and task categories and submitted solutions.\n\n\"\"\"\n\nimport re\nfrom os.path import join\nfrom urllib.parse import unquote\n\nfrom django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import Http404, HttpRequest, HttpResponse\nfrom django.shortcuts import get_object_or_404\nfrom django.template.response import TemplateResponse\n\nfrom constance import config\n\nfrom inloop.common.sendfile import sendfile\nfrom inloop.tasks.models import Category, Task\n\n\n@login_required\ndef index(request: HttpRequest) -> HttpResponse:\n exam_category_slug = config.EXAM_CATEGORY_SLUG\n if exam_category_slug:\n return category(request, exam_category_slug)\n return TemplateResponse(\n request,\n \"tasks/index.html\",\n {\n \"categories\": Category.objects.order_by(\"display_order\", \"name\"),\n },\n )\n\n\n@login_required\ndef category(request: HttpRequest, slug: str) -> HttpResponse:\n category = get_object_or_404(Category, slug=slug)\n tasks = category.task_set.visible_by(user=request.user).completed_by_values(\n request.user, order_by=\"pubdate\"\n )\n have_deadlines = any(task.deadline for task in tasks)\n return TemplateResponse(\n request,\n \"tasks/category.html\",\n {\n \"category\": category,\n \"tasks\": tasks,\n \"have_deadlines\": have_deadlines,\n \"show_progress\": config.IMMEDIATE_FEEDBACK,\n },\n )\n\n\n@login_required\ndef serve_attachment(request: HttpRequest, slug: str, path: str) -> HttpResponse:\n \"\"\"\n Serve static files from a task subdirectory, but only for published tasks\n and for tasks the user has permission to view. Otherwise, return status 404.\n\n Access is granted exclusively to whitelisted subdirectories.\n \"\"\"\n if re.search(\"^(images|attachments)/\", path) is None:\n raise Http404\n\n if \"..\" in unquote(path):\n raise Http404\n\n task = get_object_or_404(Task.objects.published().visible_by(user=request.user), slug=slug)\n filesystem_path = join(task.system_name, path)\n\n return sendfile(request, filesystem_path, settings.REPOSITORY_ROOT)\n","repo_name":"st-tu-dresden/inloop","sub_path":"inloop/tasks/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2180,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"12"} +{"seq_id":"72697213460","text":"import numpy as np\r\nimport pandas as pd\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\ndf = pd.read_csv(\"UPST.csv\")\r\n\r\nreturns = np.log(1 + df['Adj Close'].pct_change())\r\n\r\nmu, sigma = returns.mean(), returns.std()\r\n\r\n# print(mu, sigma)\r\n# mu = 0.01\r\n# sigma = 0.1\r\n\r\nsim_rets = np.random.normal(mu, sigma, 5)\r\n\r\nstart_price = df['Adj Close'].iloc[-1]\r\n\r\nprices = start_price * (1 + sim_rets).cumprod()\r\n\r\nprint(prices)\r\n\r\ndiff = np.diff(prices)\r\nprint(diff)\r\n\r\npos = 0\r\ngain = []\r\nbalance = 0.0\r\n\r\nfor i, d in enumerate(diff):\r\n if d > 0 and pos == 0: # buy\r\n pos = 1\r\n balance -= prices[i]\r\n elif d < 0 and pos == 1: # sell\r\n pos = 0\r\n balance += prices[i]\r\n gain.append(balance)\r\n balance = 0.0\r\n elif d >= 0 and pos == 1: # hold\r\n pass\r\n\r\nif pos == 1:\r\n pos = 0\r\n balance += prices[-1]\r\n gain.append(balance)\r\n balance = 0.0\r\n\r\nprint(gain)\r\n\r\nprint(sorted(gain)[-1])\r\n\r\nprint(prices.max(), prices.min(), prices.max() - prices.min())\r\n\r\n# 2 transactions\r\ndef maxProfit(price, n):\r\n profit = [0] * n\r\n\r\n max_price = price[n-1]\r\n for i in range(n-2, 0, -1):\r\n if price[i] > max_price:\r\n max_price = price[i]\r\n profit[i] = max(profit[i+1], max_price - price[i])\r\n\r\n print(profit)\r\n\r\n min_price = price[0]\r\n\r\n for i in range(1, n):\r\n if price[i] < min_price:\r\n min_price = price[i]\r\n profit[i] = max(profit[i-1], profit[i] + (price[i] - min_price))\r\n\r\n print(profit)\r\n return profit[n-1]\r\n\r\n# price = [2, 30, 15, 10, 8, 25, 80]\r\n# print(maxProfit(price, len(price)))\r\n\r\n\r\n","repo_name":"bryanlie/Python","sub_path":"practice/stockSimulation.py","file_name":"stockSimulation.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"10590656644","text":"def numVowels(text):\n \n vowelCount = 0\n #listOfVowels = [\"a\",\"e\",\"i\",\"o\",\"u\"]\n vowels = \"aeiou\"\n for letter in text:\n if letter in vowels:\n #if letter in listOfVowels:\n #if letter == \"a\" or letter == \"e\" or letter == \"i\" or letter == \"o\" or letter == \"u\":\n vowelCount = vowelCount + 1 \n return vowelCount\n\n# 847091\ndef numEvenDigits(number):\n digitCount = 0\n if number == 0:\n return 1\n if number < 0:\n number = number * -1 \n\n while number != 0:\n lastDigit = number % 10\n if number %2 == 0:\n digitCount += 1\n number = number // 10\n\n \"\"\"\n number = str(number)\n for digit in number:\n digit = int(digit)\n if digit % 2 == 0:\n digitCount += 1\n \"\"\"\n return digitCount\n\n# 345 514 914\ndef isArmstrongNumber(number):\n ones = number % 10\n tens = (number % 100) // 10\n huns = number // 100 \n\n return (ones**3 + tens**3 + huns**3) == number \n\n#5432\ndef riddler():\n for number in range(1001,10000,2):\n ones = number % 10\n tens = (number % 100) // 10\n huns = (number % 1000) // 100\n thos = number // 1000\n if len(set([ones,tens,huns,thos])) == 4:\n if thos == tens * 3:\n if ones + tens + huns + thos == 27:\n print(number)\n\n \n\nriddler()\n\n\n","repo_name":"abrosen/classroom","sub_path":"itp/spring2022/intermediate.py","file_name":"intermediate.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"12"} +{"seq_id":"23989758985","text":"import unittest\nfrom bed import utils\n\nclass GetExtensionTests(unittest.TestCase):\n def test_get_browser(self):\n opera = \"https://addons.opera.com/en/extensions/details/ublock/\"\n firefox = \"https://addons.mozilla.org/en-US/firefox/addon/2048-webextension/\"\n chrome = \"https://chrome.google.com/webstore/detail/aha-music-music-identifie/dpacanjfikmhoddligfbehkpomnbgblf\"\n\n self.assertEqual(utils.get_browser(opera), \"opera\")\n self.assertEqual(utils.get_browser(firefox), \"firefox\")\n self.assertEqual(utils.get_browser(chrome), \"chrome\")\n\n\n def test_get_chrome_extension(self):\n data = utils.get_chrome_extension(\"https://chrome.google.com/webstore/detail/aha-music-music-identifie/dpacanjfikmhoddligfbehkpomnbgblf\")\n\n file_url = data[0]\n extension_name = data[1]\n extension_version = data[2]\n\n self.assertEqual(file_url, \"https://clients2.google.com/service/update2/crx?response=redirect&prodversion=49.0&x=id%3Ddpacanjfikmhoddligfbehkpomnbgblf%26installsource%3Dondemand%26uc\")\n self.assertEqual(extension_name, \"aha-music-music-identifie\")\n self.assertEqual(extension_version, \"0.3.1\")\n\n\n def test_get_firefox_extension(self):\n data = utils.get_firefox_extension(\"https://addons.mozilla.org/en-US/firefox/addon/2048-webextension/\")\n\n file_url = data[0]\n extension_name = data[1]\n extension_version = data[2]\n\n self.assertEqual(file_url, \"https://addons.mozilla.org/firefox/downloads/file/631008/2048_webextension-1.0-an+fx-mac.xpi?src=dp-btn-primary\")\n self.assertEqual(extension_name, \"2048-webextension\")\n self.assertEqual(extension_version, \"1.0\")\n\n\n def test_get_opera_extension(self):\n data = utils.get_opera_extension(\"https://addons.opera.com/en/extensions/details/ublock/\")\n\n file_url = data[0]\n extension_name = data[1]\n extension_version = data[2]\n\n self.assertEqual(file_url, \"https://addons.opera.com/extensions/download/ublock/\")\n self.assertEqual(extension_name, \"ublock\")\n self.assertEqual(extension_version, \"1.26.0\")\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"sdushantha/bed","sub_path":"tests/all.py","file_name":"all.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"12"} +{"seq_id":"38580399769","text":"'''\n[中等|难]\n给出二叉 搜索 树的根节点，该树的节点值各不相同，请你将其转换为累加树（Greater Sum Tree），使每个节点 node 的新值等于原树中大于或等于 node.val 的值之和。\n\n提醒一下，二叉搜索树满足下列约束条件：\n\n节点的左子树仅包含键 小于 节点键的节点。\n节点的右子树仅包含键 大于 节点键的节点。\n左右子树也必须是二叉搜索树。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/convert-bst-to-greater-tree\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。”\n\n【解题】\n1. 用反先序遍历。\n2. 设计一个累加和，边遍历边计算和。并修改节点。\n【难点】\n1. 左孩子遍历时，没想到怎么把和传递过去。设置一个累加和就行了。\n2. python3 可用nonlocal 传递全局变量，python2 得用参数了\n2. 树的变量还是不清楚啊。一拳超人学习资料：\nhttps://leetcode-cn.com/problems/convert-bst-to-greater-tree/solution/yi-tao-quan-fa-shua-diao-nge-bian-li-shu-de-wen-5/\n\n\n'''\n\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = \n# self.left = None\n# self.right = None\n\nclass Solution(object):\n def convertBST(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: TreeNode\n \"\"\"\n #遍历得到\n # self.prePost(root)\n total = 0\n\n def sumRight(total,root): \n if root is not None:\n total = sumRight(total,root.right)\n total = total + root.val\n root.val = total\n total = sumRight(total,root.left)\n return total\n sumRight(total,root)\n \n # 替换\n return root\n\n \n\n def prePost(self,root):\n if root is None:\n return\n self.prePost(root.right)\n print(root.val)\n self.prePost(root.left)\n","repo_name":"gaozhichang/LeetCode","sub_path":"538-middle.py","file_name":"538-middle.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"13223523739","text":"import sys\nimport math\nfrom checkFuel import checkNeededFuel\n\n\n\ndef main():\n sum = 0\n for i in range(100000):\n temp = sys.stdin.readline()\n if temp == '':\n print(\"here\")\n break\n value = checkNeededFuel(temp)\n sum += value\n print(sum)\nmain()\n","repo_name":"alli959/adventOfCode2019","sub_path":"Day 1/python/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"8188917049","text":"#!/usr/bin/env python3\n\nimport rospy\nfrom geometry_msgs.msg import Twist\nimport sys\n \n \ndef turtle_circle(radius):\n\trospy.init_node('turtlesim', anonymous=True)\n\tpub = rospy.Publisher('/turtle1/cmd_vel',Twist, queue_size=10)\n\trate = rospy.Rate(30)\n\tvel = Twist()\n\n\tt0 = rospy.Time.now().to_sec()\n\n\tdist = 0\n\tfin_d = 3.142\n\tprint(rate)\n\n\twhile dist <= fin_d:\n\t\tvel.linear.x = radius\n\t\tvel.linear.y = 0\n\t\tvel.linear.z = 0\n\t\tvel.angular.x = 0\n\t\tvel.angular.y = 0\n\t\tvel.angular.z = 1\n\t\trospy.loginfo(\"Radius = %f\",radius)\n\t\tpub.publish(vel)\n\t\trate.sleep()\n\n\t\tt1 = rospy.Time.now().to_sec();\n\n\t\tdist = 0.93 * (t1 - t0)\n\n\trot = 0\n\twhile rot <= 22.55:\n\t\tvel.linear.x = 0\n\t\tvel.linear.y = 0\n\t\tvel.linear.z = 0\n\t\tvel.angular.x = 0\n\t\tvel.angular.y = 0\n\t\tvel.angular.z = 1\n\t\t\n\t\tpub.publish(vel)\n\t\trate.sleep()\n\t\t\n\t\trot += 0.5\n\n\tdist = 0\n\twhile dist <= fin_d:\n\t\tvel.linear.x = radius\n\t\tvel.linear.y = 0\n\t\tvel.linear.z = 0\n\t\tvel.angular.x = 0\n\t\tvel.angular.y = 0\n\t\tvel.angular.z = 0\n\t\trospy.loginfo(\"Radius = %f\",radius)\n\t\tpub.publish(vel)\n\t\trate.sleep()\n\n\t\tt1 = rospy.Time.now().to_sec();\n\n\t\tdist = 0.51 * (t1 - t0)\n \nif __name__ == '__main__':\n try:\n turtle_circle(1)\n except rospy.ROSInterruptException:\n pass\n","repo_name":"nitinp45/ROS","sub_path":"src/pkg_ros_basics/scripts/circle_turtle.py","file_name":"circle_turtle.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"21236210505","text":"from tkinter import *\nfrom PIL import ImageTk, Image\nclass Hero():\n def __init__(self, root, canvas):\n self.root = root\n # spawn in random position??\n self.x = 0\n self.y = 0\n self.inplay = True\n \n self.speed = 5\n self.canvas = canvas\n\n self.PILimg1 = Image.open(\"media/cat.png\")\n self.PILimg1 = self.PILimg1.resize((32, 28))\n self.tkimg = ImageTk.PhotoImage(self.PILimg1)\n\n self.heroimg = self.canvas.create_image((25,25),image=self.tkimg)\n\n self.width = int(self.canvas.cget(\"width\"))\n self.height = int(self.canvas.cget(\"height\"))\n self.movement()\n\n \n def movement(self):\n # super hardcoding here for current board size\n if self.inplay == True:\n coords = self.canvas.coords(self.heroimg)\n if coords[0] < 25 and self.x < 0:\n self.x = 0\n elif coords[0] > (self.width - 25) and self.x > 0:\n self.x = 0\n if coords[1] < 25 and self.y < 0:\n self.y =0\n elif coords[1] > (self.height - 30) and self.y > 0:\n self.y = 0\n\n self.canvas.move(self.heroimg, self.x, self.y)\n self.canvas.after(5, self.movement)\n\n def left(self, event):\n self.x = -5\n self.y = 0\n \n # for motion in positive x direction\n def right(self, event):\n self.x = 5\n self.y = 0\n \n # for motion in positive y direction\n def up(self, event):\n self.x = 0\n self.y = -5\n \n # for motion in negative y direction\n def down(self, event):\n self.x = 0\n self.y = 5\n\n # this feature has been removed to make game more challenging\n def stop(self, event):\n self.x = 0\n self.y = 0\n\n def getSprite(self):\n return self.heroimg\n\n def setInPlay(self, inplay):\n self.inplay = inplay\n\n","repo_name":"bsande6/halloween-game-project","sub_path":"hero.py","file_name":"hero.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"22164233525","text":"# Given a two integer numbers return their product \n# and if the product is greater than 1000, \n# then return their sum\n\nprint (\"Enter the 1st number\")\nnumber_1 = int(input ())\nprint (\"Enter the 2nd number\")\nnumber_2 = int (input ())\n\nproduct = number_1 * number_2\nsum_of_numbers = number_1 + number_2\nif (product > 1000):\n print (\"The result is sum ={}\" .format(sum_of_numbers))\nelse:\n print (\"product = {}\".format(product))\n","repo_name":"ChristyRachel/Python","sub_path":"check_product_problem.py","file_name":"check_product_problem.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"73198157140","text":"\"\"\"\n多单swap-12.09与空单swap6.527取自2019年2月某刻数据\n\"\"\"\nimport sys\n\nsys.path.append(\"E:/TheSecondElement/Program/TheSecondElement001/\")\nfrom Strategy import *\nfrom Transactions import *\nimport pandas as pd\n\nfor j in range(19, 21):\n result = pd.DataFrame()\n s = []\n t = []\n j = j * 0.1\n for i in range(30):\n print(round(j, 1), ':', i + 1)\n s.append(\n Strategy(pd.read_csv('E:/TheSecondElement/History/XAUUSD60.csv'), 30, 100, -12.09, 6.527,\n 300000))\n s[i].setStrategyAugment(j, 12, 26, 24, i + 1)\n transactions = s[i].getTransactions()\n t.append(Transactions(transactions))\n t[i].caculateDetails()\n t[i].caculateAboutDrawDown()\n openTradesHistory = s[i].openTradesHistory\n days = t[i].days\n drawDown = t[i].drawDown\n result.at[i, 'lossLimit'] = i + 1\n result.at[i, '年收益'] = t[i].netProfitPerYear\n result.at[i, '最大衰落金额'] = drawDown.describe().loc['max']['drawDown']\n result.at[i, 'MAR'] = result.at[i, '年收益'] / result.at[i, '最大衰落金额']\n result.at[i, '期望'] = t[i].expectedPayOff\n result.at[i, '最大衰落期'] = drawDown.describe().loc['max']['drawDownPeriod']\n result.at[i, '胜率'] = t[i].profitTradesRate\n result.at[i, '盈利月占比'] = len(t[i].profitPerMonth[t[i].profitPerMonth.profitPerMonth > 0]) / len(\n t[i].profitPerMonth)\n result.at[i, '盈利单均盈利'] = t[i].profitTradesAverageProfit\n result.at[i, '亏损单均亏损'] = t[i].lossTradesAverageLoss\n result.at[i, '月均交易'] = t[i].tradesPerMonth\n result.at[i, '多单数'] = t[i].longTrades\n result.at[i, '多单胜率'] = t[i].longTradesWinRate\n result.at[i, '空单数'] = t[i].shortTrades\n result.at[i, '空单胜率'] = t[i].shortTradesWinRate\n result.at[i, '获利因子'] = t[i].profitFactor\n\n result.at[i, '平均同时开仓数'] = openTradesHistory.describe().loc['mean']['openTradesHistory']\n result.at[i, '25%同时开仓数'] = openTradesHistory.describe().loc['25%']['openTradesHistory']\n result.at[i, '50%同时开仓数'] = openTradesHistory.describe().loc['50%']['openTradesHistory']\n result.at[i, '75%同时开仓数'] = openTradesHistory.describe().loc['75%']['openTradesHistory']\n result.at[i, '最大同时开仓数'] = openTradesHistory.describe().loc['max']['openTradesHistory']\n\n result.at[i, '平均持仓天数'] = days.describe().loc['mean']['days']\n result.at[i, '25%持仓天数'] = days.describe().loc['25%']['days']\n result.at[i, '50%持仓天数'] = days.describe().loc['50%']['days']\n result.at[i, '75%持仓天数'] = days.describe().loc['75%']['days']\n result.at[i, '最大持仓天数'] = days.describe().loc['max']['days']\n\n result.at[i, '平均衰落金额'] = drawDown.describe().loc['mean']['drawDown']\n result.at[i, '25%衰落金额'] = drawDown.describe().loc['25%']['drawDown']\n result.at[i, '50%衰落金额'] = drawDown.describe().loc['50%']['drawDown']\n result.at[i, '75%衰落金额'] = drawDown.describe().loc['75%']['drawDown']\n result.at[i, '最大衰落金额(副本)'] = drawDown.describe().loc['max']['drawDown']\n\n result.at[i, '平均衰落率'] = drawDown.describe().loc['mean']['drawDownRate']\n result.at[i, '25%衰落率'] = drawDown.describe().loc['25%']['drawDownRate']\n result.at[i, '50%衰落率'] = drawDown.describe().loc['50%']['drawDownRate']\n result.at[i, '75%衰落率'] = drawDown.describe().loc['75%']['drawDownRate']\n result.at[i, '最大衰落率'] = drawDown.describe().loc['max']['drawDownRate']\n\n result.at[i, '平均衰落期'] = drawDown.describe().loc['mean']['drawDownPeriod']\n result.at[i, '25%衰落期'] = drawDown.describe().loc['25%']['drawDownPeriod']\n result.at[i, '50%衰落期'] = drawDown.describe().loc['50%']['drawDownPeriod']\n result.at[i, '75%衰落期'] = drawDown.describe().loc['75%']['drawDownPeriod']\n result.at[i, '最大衰落期(副本)'] = drawDown.describe().loc['max']['drawDownPeriod']\n\n result.at[i, '平均每单获利'] = transactions.describe().loc['mean']['profit']\n result.at[i, '25%每单获利'] = transactions.describe().loc['25%']['profit']\n result.at[i, '50%每单获利'] = transactions.describe().loc['50%']['profit']\n result.at[i, '75%每单获利'] = transactions.describe().loc['75%']['profit']\n result.at[i, '最小每单获利'] = transactions.describe().loc['min']['profit']\n result.at[i, '最大每单获利'] = transactions.describe().loc['max']['profit']\n\n if os.path.exists('./result%s.csv' % str(round(j, 1))):\n os.remove('./result%s.csv' % str(round(j, 1)))\n result.to_csv('./result%s.csv' % str(round(j, 1)))\n","repo_name":"geekavan/Zero","sub_path":"TheSecondElement/Program/TheSecondElement003/TheSecondElement.py","file_name":"TheSecondElement.py","file_ext":"py","file_size_in_byte":4939,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"12"} +{"seq_id":"42068517382","text":"from torchvision import transforms\nfrom torchvision.datasets import ImageFolder\nfrom torchvision.transforms.functional import Image\n\nTRAIN_TRANSFORMS = transforms.Compose([\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\nTEST_TRANSFORMS = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\n\ndef load_image(file):\n image = Image.open(file)\n return TEST_TRANSFORMS(image)\n\n\ndef load_dataset(data_folder, split='train'):\n if split == 'train':\n transform = TRAIN_TRANSFORMS\n else:\n transform = TEST_TRANSFORMS\n\n return ImageFolder(data_folder, transform=transform)\n","repo_name":"cumason123/bhacks2019","sub_path":"classifier/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"12"} +{"seq_id":"15284526327","text":"import numpy as np\n\ndata = [line.strip(\"\\n\").split(\" -> \") for line in open('05.txt', 'r').readlines()]\ndata = [(int(coord.split(\",\")[0]), int(coord.split(\",\")[1])) for line in data for coord in line]\ndata = [[data[i], data[i+1]] for i in range(len(data)) if i % 2 == 0]\n\ndef check_if_usable(data):\n new_data = []\n \n for line in data:\n xa, ya = line[0]\n xb, yb = line[1]\n \n if (xa == xb) or (ya == yb):\n new_data.append(line)\n return new_data\n\ndef generate_points_from_line(data):\n new_data = []\n x_diag = []\n y_diag = []\n\n for line in data:\n xa, ya = line[0]\n xb, yb = line[1]\n\n if xa == xb:\n for y_coord in range(min(ya, yb), max(ya, yb)+1):\n new_data.append((xa, y_coord))\n if ya == yb:\n for x_coord in range(min(xa, xb), max(xa, xb)+1):\n new_data.append((x_coord, ya))\n return new_data\n\ndef generate_points_from_coords(data):\n new_data = []\n x_coords = []\n y_coords = []\n \n\n for line in data:\n xa, ya = line[0]\n xb, yb = line[1]\n diags = []\n\n if abs(xa-xb) == abs(ya-yb):\n for y_coord in range(min(ya, yb), max(ya, yb)+1):\n y_coords.append(y_coord)\n if ya > yb:\n y_coords = y_coords[::-1]\n print(y_coords)\n \n for x_coord in range(min(xa, xb), max(xa, xb)+1):\n x_coords.append(x_coord)\n if xa > xb:\n x_coords = x_coords[::-1]\n print(x_coords)\n diags = [(x_coords[i], y_coords[i]) for i in range(len(x_coords))] \n return diags\n\n\n\nfield = np.zeros((999, 999))\ndata = check_if_usable(data)\n# print(data)\n# x_data = [[(9, 7), (7,9)], [(1,1), (3,3)]]\n# x_data = generate_points_from_coords(x_data)\n# print(x_data)\n\nline_data = generate_points_from_line(data)\ndiag_data = generate_points_from_coords(data)\n\nfor coord in line_data:\n field[coord] += 1\n\nfor coord in diag_data:\n print(coord)\n field[coord] += 1\n\nx, y = np.where(field > 1)\nprint(len(x))\n","repo_name":"StijnMatsHendriks/aoc_2021","sub_path":"05/05.py","file_name":"05.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"12"} +{"seq_id":"29475806936","text":"\"\"\" Script for preparing directories and files for WRF simulation. This creates a seperate directory \r\nfor each WRF simulation e.g. each initialsation time. These simulation directories are self-contained, \r\nwith all inputs, outputs, and excutables either copied or linked into them. \r\nThis enables job-level parallelisation when running large reanalysis or reforecasting cases.\r\n\r\nConfig comes from a file, specified as --config argument. Some configuration options (listed below) \r\ncan also be given at the command line, where they will override the configuration file. \r\nSee example/forecast.yaml for a full list of configuration options. \r\n\r\nUsage:\r\n prepare.py [--config=] [options]\r\n \r\nOptions:\r\n --config= yaml/json file specificying any of the options below\r\n --start=